2019 Ingenium: Undergraduate Research at the Swanson School of Engineering

Undergraduate Research at the Swanson School of Engineering

University of Pittsburgh Swanson School of Engineering Undergraduate Research Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA 15261 USA Spring 2019

The image on the cover shows three dimensional, graphic reconstructions of neuronal connections within the striatum, thalamus, and cortex.

The colors represent the orientation of neurons in 3-D space (See page 47 by Lauren Grice, Department of Bioengineering).

Please note that neither Ingenium nor the Swanson School of Engineering retains any copyright of the original work produced in this issue.

However, the Swanson School does retain the right to nonexclusive use in print and electronic formats of all papers as published in Ingenium.

The University of Pittsburgh is an affirmative action, equal opportunity institution.

Published in cooperation with the Office of University Communications. 111728-0319

Ingenium 2019

Table of Contents A Message from the Associate Dean for Research............................................4 A Message from the Co-Editors-in-Chief...........................................................5 Graduate Student Review Board – Ingenium 2019............................................6 Deep learning for hyperspectral image classification on embedded platforms Siddharth Balakrishnan, David Langerman, Evan Gretok, and Alan D. George Department of Electrical and Computer Engineering, NSF Center for Space, High-performance, and Resilient Computing (SHREC).............................................7 Smarter riversheds: Real-time water sensors Kathleen Beaudoin, Christian Ference, Angela Chung, Joseph Zappitelli, Emily Elliot, and David Sanchez Department of Civil and Environmental Engineering, Department of Geology and Environmental Science.................................................................................12 Modal analysis of human brain dynamics after head impact Ryan T. Black, Kaveh Laksari, and Hessam Babaee Department of Mechanical Engineering and Materials Science, Department of Biomedical Engineering, University of Arizona.................................17 Evaluating occlusion success of Esophocclude prototypes in comparison to diameter and radial force Gordon Bryson, Youngjae Chun, and Phillip Carullo Department of Bioengineering, Department of Industrial Engineering, McGowan Institute for Regenerative Medicine, Department of Anesthesiology........21 Investigating the influence of carbon nanomaterials on the mechanical properties of concrete Nathanial Buettner, Steven Sachs, and Leanne Gilbertson Department of Civil and Environmental Engineering..............................................25 Vision field testing with virtual reality Ava Chong, Tang Kok Zuea, and Murat Akcakaya Department of Electrical & Computer Engineering; National University of Singapore..........................................................................29 Myocardin-related transcription factor’s role in cell migration Aidan Dadey, Dave Gau, and Partha Roy Cell Migration Laboratory, Department of Bioengineering......................................33 Design of a wearable upper limb exoskeleton Zach Egolf and Nitin Sharma Sharma Lab: Neuromuscular Control and Robotics Laboratory, Department of Mechanical Engineering and Materials Science..............................37 In vivo dopamine sensors for basic neuroscience and biomedical research: A review (Invited review) Noah Freedman and X. Tracy Cui Neural Tissue Engineering Lab, Department of Bioengineering..............................42 Diffusion tensor image analysis of stroke damaged brains treated with combined neural stem cell and physical therapy Lauren Grice, Harman Ghuman, Franziska Nitzsche, Madeline Gerwig, Jeffrey Moorhead, Nikhita Perry, Alex Poplawsky, Brendon Wahlberg, Fabrisia Ambrosio, and Michel Modo McGowan Institute for Regenerative Medicine, Department of Radiology, Department of Bioengineering, Department of Neuroscience, Department of Physical Medicine and Rehabilitation.............................................46 Delamination of soft thin films from dynamic wrinkling substrates Joseph Hamm and Sachin Velankar Department of Chemical Engineering..................................................................51

Adventitial delivery of therapeutic cells for localization in porcine aortas Trevor M. Kickliter, Timothy K. Chung, Aneesh K. Ramaswamy, Justin S. Weinbaum, and David A. Vorp Departments of Mechanical Engineering and Materials Science, Bioengineering, Pathology, Surgery, Cardiothoracic Surgery, and Chemical and Petroleum Engineering; McGowan Institute for Regenerative Medicine; and Center for Vascular Remodeling and Regeneration...............................................................55 The influence of nitrogen doping on electrocatalytic activity of FeN4 embedded graphene (Editors’ choice) Lydia Kuebler, Boyang Li, and Guofeng Wang Laboratory of Dr. Guofeng Wang, Department of Mechanical Engineering and Materials Science........................................................................................59 Combined neural stem cells and physical therapy improve somatosensory cortex activity after stroke Nikhita Perry, Harman Ghuman, Franziska Nitzsche, Madeline Gerwig, Jeffrey Moorhead, Lauren Grice, Alex Poplawsky, Brendon Wahlberg, Fabrisia Ambrosio, and Michel Modo McGowan Institute for Regenerative Medicine, Department of Radiology, Department of Bioengineering, Department of Physical Medicine & Rehabilitation, Department of Neuroscience..............................................................................65 Angiogenic response to abdominal and vaginal polypropylene mesh implants in a rabbit model McKenzie Sicke, Aimon Iftikhar, Alexis Nolfi, Hannah Geisler, and Bryan Brown McGowan Institute for Regenerative Medicine, Department of Bioengineering........69 The effect of pivot-bearing surface roughness on thrombus formation: An in-vitro study Katherine Stevenson, Alexandra May, Ryan Orizondo, Sang-Ho Ye, Brian Frankowski, William R. Wagner, and William J. Federspiel Medical Devices Lab, Cardiovascular Engineering Lab, Department of Bioengineering, Department of Chemical Engineering.....................72 Infant feed thickening characterization at UPMC Children’s Hospital of Pittsburgh Kelsey Toplak, Kimberly Kubistek, Kelly Fill, Sheryl Rosen, and Mark Gartner Department of Bioengineering; Department of Occupational Therapy, Children’s Hospital of Pittsburgh.........................................................................76 Analyzing right ventricular response to Sacubitril/Valsartan in pulmonary hypertension Claire Tushak, Danial Sharifi Kia, Evan Benza, Kang Kim, and Marc Simon Department of Bioengineering; Heart and Vascular Institute, University of Pittsburgh Medical Center (UPMC)...................................................80 Thermal resistance and stiffness of iSIM-90 support blades for CASPR camera system Michael Ullman, Theodore Schwarz, Kevin Glunt, and Alan George National Science Foundation Center for Space, High-performance, and Resilient Computing (SHREC)........................................................................84 Modeling and energy calculations of perovskite methylammonium lead iodide grain boundaries Philip A. Williamson and Wissam A. Saidi Department of Mechanical Engineering & Materials Science.................................89 Mitochondrial amidoxime reducing component 2 (mARC2) knockout mice are partly protected from diet-induced obesity Jimmy Zhang, Bin Sun, Mark T. Gladwin, and Courtney E. Sparacino-Watkins Vascular Medicine Institute, Department of Medicine............................................93 Student and Mentor Bios.................................................................................97 Index................................................................................................................99

Category Definitions Computational research—using computational techniques to address a scientific question Device design—focusing on the development of a product or device Experimental research—using laboratory methods to achieve a novel overarching experimental aim Review—summarizes the current state of knowledge on a particular topic

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A Message from the Associate Dean for Research English: engine, engineer; French: moteur, ingénieur; German: motor, ingenieur; Greek: κινητήρας, μηχανικός; Italian: motore, ingegnere; Polish: silnik, inżynier; Portuguese: motor, engenheiro; Spanish: motor, ingeniero; Vietnamese: động cơ, kỹ sư; Chinese: 发动机 (fādòngjī), 工程师 (gōngchéngshī) The Merriam-Webster dictionary defines engine as “something used to effect a purpose,” “something that produces a particular and usually desirable result,” “a mechanical tool,” “a machine for converting any of various forms of energy into mechanical force and motion,” “a railroad locomotive” or “computer software that performs a fundamental function especially of a larger program.” The word engineer is derived from the Latin words ingeniare (“to create, generate, contrive, devise”) and ingenium (“cleverness”).

On behalf of the University of Pittsburgh Swanson School of Engineering and U.S. Steel Dean of Engineering James R. Martin II, I proudly present the fifth edition of Ingenium: Undergraduate Research at the Swanson David A. Vorp, PhD School of Engineering, a compilation of articles representing the achievements of selected Swanson School undergraduate students who demonstrated excellence in our 2018 summer research program.

With each edition of Ingenium we have produced, it has been exciting to witness the growth of our undergraduate students when presented with the opportunity to directly engage in scientific research. Applying what is learned in the classroom is only the beginning of an engineer’s career. The application of this knowledge and information presents countless opportunities to create, build, and encourage the students of today to create the prospects of tomorrow.

Engaging in such research—“to create, generate, contrive, devise”—secures the legacy of past innovators while giving rise to the next generation of creative minds. The world lost some prolific engineers and scientists in 2018, including the following: • Raye Montague, the inspiration behind the movie Hidden Figures who developed the U.S. Navy's first computer-generated draft for a warship design • Stephen Hawking, the famed theoretical physicist and author and recipient of the Presidential Medal of Freedom, (the highest civilian honor in the United States) • Evelyn Berezin, a computer designer who is known for designing the first-ever word processor and for developing the first computer systems for banking and airline reservations • Alan Bean, an aeronautical engineer and Apollo 12 astronaut who was the fourth person to walk on the moon • Paul Allen, co-founder of Microsoft and microcomputer revolution starter These ingenious women and men did not leave a void to be filled, but rather left opportunities for engineering students to exercise their knowledge, build it, share it with others, and continue the legacy of those who came before them.

The student authors of the articles contained within this edition of Ingenium studied mostly under the tutelage of faculty mentors in the Swanson School of Engineering. In some cases, the research took place within other schools or even at other institutions. At the conclusion of the program, students were asked to submit an abstract summarizing the results of their research, which were reviewed by the Ingenium Editorial Board made up of Swanson School graduate student volunteers. Students with the highest-ranking abstracts were invited to submit full manuscripts for consideration for inclusion in Ingenium and those submitted manuscripts were peer reviewed by the Editorial Board. Therefore, Ingenium serves as more than a record of our undergraduate student excellence in research. It also serves as a practical experience for our undergraduate students in scientific writing and in the author’s perspective of the peer-review process. It also provides graduate students with an opportunity to experience the editorial review process and the reviewer’s perspective of peer review.

I would like to acknowledge the hard work and dedication of the Co-Editors-in-Chief of this edition of Ingenium, Michelle Heusser and Lisa Stabryla, as well as the production assistance of Melissa Penkrot and the Office of University Communications. This issue also would not have been possible without the hard work of the graduate student volunteers who constituted the Ingenium Editorial Board and who are listed by name in this edition. It is also altogether fitting to thank the faculty mentors and other co-authors of each of the reports included in this edition.

I hope that you enjoy reading this fifth edition of Ingenium and that the many talents of our students inspire the engineers of the future!

David A. Vorp, PhD Associate Dean for Research, University of Pittsburgh Swanson School of Engineering

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

A Message from the Co-Editors-in-Chief

Michelle R. Heusser

Lisa M. Stabryla

Greetings! We are excited to present the fifth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering. This edition of Ingenium contains 21 articles, showcasing a selection of the diverse research projects conducted by undergraduate students and their mentors across the various departments of the Swanson School of Engineering (SSoE) during the summer of 2018. This year, we incorporated a few novel features intended to refresh Ingenium for its fifth anniversary—some of which were modifications to the peer-review process that happened behind the scenes and others of which you can observe within the publication itself.

Ingenium is a peer-reviewed publication, and as such, all submitted manuscripts were evaluated using a two-step single-blind review process. The editorial board, consisting of graduate student volunteers across all departments of the SSoE, thoroughly reviewed both extended abstracts and full manuscripts. The most notable modification to the peer-review process took the form of response-to-reviewer documents. We asked the students to respond to their reviewers’ comments in a rigorous point-by-point fashion, as this more closely mimics the peer-review process many journals employ.

This year, for the first time, we would like to recognize an outstanding piece of student work both in terms of providing scientific enhancement to her field as well as her presentation of it, as nominated by the editorial board and selected by the Co-Editors-in-Chief as an Editors’ Choice article. Additionally, we would like to highlight our first Invited Review article, which went through the same peer-review process as described above.

We also wanted to provide the reader with a more viewer-friendly experience. Articles fall into one of four main categories: Experimental Research, Computational Research, Device Design, and Review.

To find papers organized by article type, an index has been provided. At the beginning of each article, you will find headshots of the student and mentor as well as a statement of significance. This statement briefly summarizes how the work enhances or elucidates our understanding of a particular challenge in the student’s field of study, and helps to convey the relevance of the article to the reader. At the end of the journal are bios of each student and mentor—a chance for you, the reader, to get to know the people behind the work. We encourage readers to thoughtfully consider the content of these included articles. If you have an insightful comment or refreshing perspective on one or more of the manuscripts, you are invited to send a message to ingenium@pitt.edu. With permission, these “Letters to the Editor” may be published in next year’s edition of Ingenium.

This publication would not have been possible without the hard work of 1) the graduate student review board—we appreciate how thorough and insightful they were in offering comments regarding the students’ scientific work, and 2) the Ingenium team comprised of Dr. David Vorp, Melissa Penkrot, and the team at the Office of the University Communications—we thank them for being so open to the inclusion of new features and bringing our vision to light. We are also most impressed with this cohort of students, particularly with the professionalism they demonstrated in their responses to reviewers. We would also like to thank the faculty advisors for their mentorship and for being actively involved in all stages of this process—from project conception to publication.

It was very rewarding to us as Co-Editors-in-Chief to participate in this process and we hope that whether you are a student, reviewer, mentor, or reader, you will enjoy this work in print.

Michelle R. Heusser Lisa M. Stabryla Co-Editor-in-Chief Co-Editor-in-Chief

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Graduate Student Review Board – Ingenium 2019 Name Department Agrawal, Ankur................................................................. Mechanical Engineering and Materials Science Akinbade, Yusuf................................................................ Civil and Environmental Engineering Allen, Abigail..................................................................... Bioengineering Apte, Akanksha................................................................. Chemical and Petroleum Engineering Chen, Zhao....................................................................... Civil and Environmental Engineering DeLozier, Jenna................................................................ Electrical and Computer Engineering Dhamotharan, Vishaal....................................................... Bioengineering Dong, Chaosheng............................................................. Industrial Engineering Gade, Piyusha................................................................... Bioengineering Gardner, Haley.................................................................. Civil and Environmental Engineering Grigsby, Erinn................................................................... Bioengineering Haghanifar, Sajad.............................................................. Industrial Engineering Hemler, Sarah................................................................... Bioengineering Heusser, Michelle*............................................................ Bioengineering Hughes, Christopher.......................................................... Bioengineering Jian, Jianan...................................................................... Electrical and Computer Engineering Kovalchuk, Matthew.......................................................... Mechanical Engineering and Materials Science Kumar, Ritesh................................................................... Bioengineering Li, Haoran......................................................................... Civil and Environmental Engineering Liu, Monica....................................................................... Bioengineering Maldonado, Alexander....................................................... Chemical and Petroleum Engineering Manimaran, Nithil Harris.................................................... Chemical and Petroleum Engineering McClain, Nicole................................................................. Bioengineering Menallo, Giorgio................................................................ Bioengineering Patil, Rituja....................................................................... Chemical and Petroleum Engineering Pliner, Erika...................................................................... Bioengineering Pressly, Michelle............................................................... Chemical and Petroleum Engineering Rickenbacker, Harold......................................................... Civil and Environmental Engineering Rodriguez, Simone............................................................ Mechanical Engineering and Materials Science Rooney, Stephen............................................................... Mechanical Engineering and Materials Science Sanatkhani, Soroosh......................................................... Bioengineering Sezginel, Kutay................................................................. Chemical and Petroleum Engineering Sharifi Kia, Danial.............................................................. Bioengineering Stabryla, Lisa*.................................................................. Civil and Environmental Engineering Stevens, Erica................................................................... Mechanical Engineering and Materials Science Taylor, Michael.................................................................. Chemical and Petroleum Engineering Vishnubhotla, Sai Bharadwaj.............................................. Mechanical Engineering and Materials Science Wang, Muying................................................................... Chemical and Petroleum Engineering Wang, Yan........................................................................ Civil and Environmental Engineering Wiltman, Stephanie........................................................... Bioengineering Zhai, Xuetong................................................................... Bioengineering *Co-Editors-in-Chief: Michelle Heusser and Lisa Stabryla

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Deep learning for hyperspectral image classification on embedded platforms Siddharth Balakrishnan, David Langerman, Evan Gretok, and mentor Alan D. George Department of Electrical and Computer Engineering, University of Pittsburgh, Room 1238D, Benedum Hall NSF Center for Space, High-performance, and Resilient Computing (SHREC) Pittsburgh, PA 15261

Significance Statement:

Downlinking image sets from space using radiation hardened platforms is not efficient. Conducting preliminary analysis on these platforms can help identify a subset of relevant images. The feasibility of using deep learning models to conduct such analysis on computation-constrained platforms is assessed using accuracy, run-time and memory benchmarks.

Balakrishnan

Abstract

George Hyperspectral image analysis refers to the process used to identify objects photographed using equipment that can image photons from a broad range of the electromagnetic spectrum. Downlinking such large images from space on radiation-resistant platforms with limited computing power takes a large amount of time, memory, and other missioncritical resources. Performing preliminary analysis in space before downlinking all images will save such resources by enabling a subset of images of interest to be downloaded rather than the entire set. The goal of this study is to benchmark and evaluate HSI-classification methods which incorporate deep learning on embedded platforms with limited computing resources. The models used in this study include: SVM, MLP, and CNN. The models were executed on a desktop PC, the ODROID-C2 and the Raspberry Pi 3B. Accuracy, run-time, and memory benchmarks determined the optimal model for each platform. Based on results, CNN model is recommended for the desktop PC due to its high accuracy of 97%, and MLP model for embedded platforms, as it showcased the shortest run-time and second-highest accuracy.

Category: Computational research

Keywords: Hyperspectral image analysis, deep learning, embedded platforms, performance benchmarking Abbreviations: Hyperspectral Image (HSI), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN)

1. Introduction

Numerous images of Earth are taken from satellites and other spacecraft. These images are usually very high in resolution and bit-depth. As a result, downloading every single image for analysis on earth is inefficient in terms of communication time and processing power in an already computation-constrained environment [1]. The primary goal of this research is to benchmark and evaluate deep-learning apps for hyperspectral image (HSI) classification on embedded platforms. From this study, the optimal HSI-classification method for platforms with limited computing capabilities can be determined. This study also serves as a framework for conducting such analysis on-board a spacecraft, which could allow a subset of images of interest to be downloaded—saving time, memory, and other mission-critical resources.

Hyperspectral images are taken with a hyperspectral camera that collects amplitude readings from a subset of spectral bands (various wavelengths in the electromagnetic spectrum) for each pixel in the image [2]. Patterns can be extracted from these amplitudes in order to classify each pixel in the image. By doing so, the HSI data of interest can be recognized for further investigation, depending upon the context in which HSI is being used. HSI imagery is used in a wide variety of applications such as astronomy and space surveillance. For example, [3] uses an adaptive opticscompensated telescope to acquire HSI. Three classification methods were used for HSI analysis in this study: Support Vector Machine (SVM); Multi-Layer Perceptron (MLP); and Convolutional Neural Network (CNN). SVM is a machine-learning model that creates a margin in a transformed input space, splitting the input data into two classes using hyperplane in multidimensional space [4]. SVMs are inherently a two-class classifier. As a result, in order to conduct predictions for a multi-class dataset, a one-versus-all method is used for each class. A MLP is a deep-learning model that is capable of modeling nonlinear functions. MLPs consist of “fully connected layers” in which every node is connected with respective weights determined when a model is trained [5]. Similar to MLPs, CNNs are a deep-learning model, but they contain convolutional layers, where each layer transforms one set of feature maps. The last few layers of a typical CNN are normally “fully connected layers” that mirror an MLP in functionality. However, the convolutional layers in CNNs generally increase their accuracy over MLPs, because these convolutional layers tend to extract relevant features from the image and discard noise and extraneous information [5] [6].

The classification models were all trained on the desktop PC and the final prediction was executed on desktop PC, ODROID-C2, and Raspberry Pi 3B.

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2. Methods

This section discusses the HSI dataset, models, and methods used to train, test, and make final predictions. The parameters and architectures of the models are also detailed.

Figure 1: Ground Truth Matrix Visualization

A. Dataset The Indian Pines HSI dataset was leveraged to train and test the classification algorithms under study. This dataset is widely used by HSI researchers in testing proposed classification algorithms. The dataset represents an image of farmland in northwestern Indiana and consists of 224 spectral bands with 16 different agricultural classes [9]. The ground truth of the dataset identifies different types of land cover, ranging from buildings to a variety of vegetation.

Table 1. Parameters used for SVM, MLP and CNN (a) SVM

(b) MLP

(c) CNN

Gamma:2-8

Patch Size:1

Patch Size:27

C:2

Batch Size:200

Batch Size:200

Tolerance:1e-14

Learning Rate:0.01

Learning rate:0.01

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B. Models The Scikit-learn Python library was leveraged to develop the SVM [10]. The parameter values for the SVM model are summarized in Table I(a). A radial basis function (which uses squared Euclidean distance between feature vectors) was used as kernel to develop the SVM. The value of C in Table I(a) indicates the relative weight coefficient. It should be noted that the tolerance value may differ on other platforms. The values for each parameter were chosen using a five-fold cross-validation technique, a resampling procedure in which the shuffled dataset split into five groups and each group is used as the test set while the model is trained on the rest of the dataset. For each instance that the model is trained, parameters are tuned over the specified range of values, and the model with the optimal parameters is selected to maximize accuracy. The TensorFlow framework was used to construct and train the MLP and CNN deep-learning models in this study. These models were based on land-cover classification models developed by the Satellite Application Center from the Indian Space Research Organization [5] [11]. The MLP model can be described by a weighted, directed acyclic graph. The output of each nodal layer is a function of the sum of inputs modified by a nonlinear transfer

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Undergraduate Research at the Swanson School of Engineering

(a) MLP Architecture (b) CNN Figure 2: Model Architectures

function such as a sigmoid, which was the activation function used in [9]. The architecture of the MLP model was set so that each patch of the image could be used as input to the model with the architecture shown in Fig. 2a. The parameters used to construct the model are summarized in Table I (b). The model was trained for 50,000 epochs. Similarly, each patch of the image was used as an input to the CNN with the architecture shown in Fig. 2b. Typical CNNs contain alternating layers of convolutional filters and maxpooling layers. The final layers of most CNNs are fully connected layers, used for classification [12].

C. Training, Testing, and Prediction The raw dataset was pre-processed to include a border to avoid loss of data at the output. The pixels in the image were divided into 80% training and 20% testing sets. These sets were used to develop and validate the SVM, MLP, and CNN models on the desktop PC before porting the trained models to the ODROID-C2 and Raspberry Pi 3B platforms. For the final prediction, each pixel from the data was fed into the trained models and the predicted outputs were used to reconstruct an image as shown in Fig. 3. The labels predicted for each pixel were compared with the pre-defined labels in the ground-truth image to determine the accuracy of the prediction. During prediction, run-time and memory benchmarks were calculated. These predictions and calculations were executed on the desktop PC, ODROID-C2, and Raspberry Pi 3B platforms. After the models were trained and tested, the entire data set was used to conduct the final predictions. The accuracies run-time and memory benchmarks were averaged over ten trials.

Ingenium 2019

3. Experimental Results

Table 3. Inter-class accuracies for each model

Accuracy, run-time, and memory benchmarks on the desktop PC, ODROID-C2, and Raspberry Pi 3B are detailed in this section. The overall accuracy of each model tested in this study is compared to the accuracy of other models from the literature.

Table 2. Accuracy of models Ours

Others

SVM

62%

68% [8]

MLP

85%

82% [5]

CNN

97%

96% [5]

A. Accuracy Benchmarks The accuracy results shown in Fig. 4a were determined through comparison of the model’s pixel-wise prediction with the preset pixel classifications from the ground-truth matrix. The results include accuracies for the SVM, MLP, and CNN models on the desktop PC, ODROID-C2, and Raspberry Pi 3B. The output images for each model are shown in Fig. 3. Comparing the output images in Fig. 3 with the ground-truth image in Fig. 1 reflects the accuracies of these models, which are summarized in Table II. Out of the 16 agricultural land classes identified in the ground-truth matrix, inter-class accuracies (percent of pixels that were correctly identified within the class) were also calculated (Table 3).

1. Support Vector Machines The SVM model in this study is performed with an accuracy of 62% on the desktop PC with an Intel i7-6700 vPro quad-core processor, and an accuracy of 61% on the embedded platforms with ARM Cortex-A53 quad-core processors. The model from [8] achieved an accuracy of 68% on a server with the Nvidia GeForce GTX 1080 and the Tesla K40c. The higher accuracy achieved by [8] is likely due to training the model on a GPU. The disparity between the accuracy of the SVM are due to random variations of accuracies within the ten trials.

2. Multi-Layer Perceptron The MLP model used in this study achieved an accuracy of 85% on the desktop PC and the embedded platforms, while the model from [5] achieved an accuracy of 82% on a desktop PC with dual Intel XeonE5-2630 v2 processors and an Nvidia Tesla K20c GPU. The greater accuracy in this study, despite the use of a GPU in [5], is due to the usage of the latest version of Adagrad (parameter update algorithm used), as the study was conducted in 2016 [13]. a. SVM

Classes

SVM

MLP

CNN

1. Stone/Steel/Tower

2.2%

95.7%

100%

2. Build/Grass/Tree/Drives

15.1%

76.3%

81.2%

3. Wood

2.7%

82.3%

63.2%

4. Wheat

8.9%

92.8%

76.7%

5. Soybean-Clean

75.2%

95.0%

78.5%

6. Soybean-Min

79.6%

97.3%

99.6%

7. Soybean-Notill

7.1%

96.4%

100%

8. Oats

93.5%

98.1%

71.2%

9. Hay/Windrowed

5.0%

100%

100%

10. Grass/Pasture Mowed

79.8%

90.1%

93.7%

11. Grass/Trees

89.0%

75.4%

90.1%

12. Grass/Pasture

15.2%

89.0%

70.5%

13. Corn

81.5%

99.5%

73.1%

14. Corn-Min

89.4%

91.0%

66.1%

15. Corn-Notill

61.7%

83.2%

90.6%

16. Alfalfa

100%

100%

60.8%

3. Convolutional Neural Network Lastly, the CNN model in this study performed with an accuracy of 97% on the desktop PC and the embedded platforms.

The model from [5] had an accuracy of 96% on a desktop PC with dual Intel Xeon E5-2630 v2 processors and an Nvidia Tesla K20c GPU. Similar to the MLP, the discrepancies are likely due to the version differences of Adagrad. Comparing the accuracies of the models reveal that the SVM model provided the lowest accuracy, while the CNN model offered the best overall accuracy when identifying the pixels. The inter-class accuracies (same on all platforms) of the SVM model also had a large range (97.8%) and standard deviation (38.3%). The massive spread of the SVMs interclass accuracies can be attributed to the SVM being over-trained on the classes with a higher number of samples and therefore not able to identify classes with relatively fewer number of samples as accurately. By contrast, the deep-learning models had better interclass accuracies with a smaller range and standard deviation than the SVM. The range and standard deviation are 24.6% and 7.9%, respectively, for the MLP, and 39.2% and 13.7%, respectively, for the CNN. b. MLP

c. CNN

Legend

Figure 3: Image Outputs

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(a) Accuracy Benchmarks (b) Run-Time Benchmarks (c) Memory Benchmarks Figure 4: Accuracy, Run-Time, and Memory Benchmarks of Each Algorithm on All Tested Platforms

B. Run-Time Benchmarks Run-time benchmarks were recorded for each classification model and averaged over ten trials. The CNN was consistently the slowest on all platforms with a run-time of 205 seconds on the desktop PC, 1543 seconds on the ODROID-C2, and 3032 seconds on the Raspberry Pi 3B. The SVM was fastest on the desktop PC at 42 seconds, while the MLP was fastest on the ODROID-C2 and Raspberry Pi 3B at 390 seconds and 529 seconds, respectively (Figure 4b).

1. Desktop PC vs. Embedded Platforms An overview of the run-time benchmarks of the three models reveals that all three models ran faster on the desktop PC than the embedded platforms by at least a factor of ten due to the abundance of computational capacity available on a desktop PC compared to embedded platforms. The Intel i7-6700 vPro quadcore processor on the desktop PC uses hyperthreading technology that enables the processor to run two threads in each core at once, while the ARM Cortex-A53 quad-core processor in the embedded platforms does not [14]. Apart from hyperthreading, it should also be noted that the desktop PC is clocked 2.3 times the ODROID-C2 and 2.8 times the Raspberry Pi 3B. Higher clock speed, larger cache size and improved memory management technology in the desktop PC, all further contribute to better performance on the PC. The CNN was consistently the slowest algorithm on all platforms due to the computational cost of the convolutional layers in the architecture of the CNN model and the use of the spectral bands of a 27×27-pixel input patch to predict the class for each pixel.

The SVM and MLP models only used the spectral bands of the pixel being predicted. It should also be noted that the SVM model ran faster than the MLP model on the desktop PC but slower than the MLP model on the embedded platforms. When conducting the final predictions, SVMs are inherently slower than MLPs since 16 one-versus-all SVMs have to be executed for prediction of each pixel in this dataset, whereas MLP only has to be executed once since the single model in Fig. 2a can act as a multi-class classifier.

The larger cache on the desktop PC enables better execution of 16 one-versus-all SVMs during prediction, while the embedded platforms have frequent calls to memory. The constant referencing to memory in embedded platforms contributes to a jump in runtime for SVM, resulting in prediction taking longer than MLP [14].

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Undergraduate Research at the Swanson School of Engineering

2. ODROID-C2 vs. Raspberry Pi 3B While the ODROID-C2 and the Raspberry Pi 3B share the same architecture, the compatibility issues of the Tensorflow wheels with the operating systems must be noted. The armv7l kernel on the Raspberry Pi 3B is incompatible with the 64-bit architecture of the ARM Cortex-A53 quad-core processor. As a result, the 32-bit version of the Tensorflow wheel was used on the Raspberry Pi 3B, and the 64-bit version was used on the ODROID-C2 (with aarch64 kernel), causing run-time to be greater on the Raspberry Pi 3B than the ODROID-C2 [15].

C. Memory Benchmarks On average, the MLP model consumed the least amount of memory on all platforms tested, and the SVM model had the second highest benchmarks. Lastly, the CNN model had the highest memory usage on all platforms. The memory benchmarks on the desktop PC and the ODROID-C2 differed by less than 6% from the desktop PC, however, the difference between the memory benchmarks on the desktop PC and the Raspberry Pi 3B were 14% greater than the desktop PC benchmark. The greater percent difference between the desktop PC and the embedded platforms can be attributed to the ODROID-C2 having nearly twice as much memory bandwidth as the Raspberry Pi 3B (4000 and 2000 MB/s, respectively) [15]. The SVM’s one-vs-all classification method used more memory than the MLP, despite the complexity of the MLP model [16].

D. Discussion The abundance of processing power, memory capacity (RAM), and memory bandwidth on the desktop PC compared to the embedded platforms means that the accuracy of these HSI classification algorithms should be maximized using the CNN (achieved 7.5 times faster than the fastest embedded platform— ODROID-C2). Despite the two embedded platforms having the same architecture, the differences in memory management of the processors, kernels, clock speeds, and contribute to the performances of the models on these platforms being different.

The greater memory bandwidth on the ODROID-C2 than the Raspberry Pi 3B contributes to the better benchmarks on the ODROID-C2. The 32-bit TensorFlow wheel used on the Raspberry Pi 3B due to compatibility issues with the kernel contributed to

Ingenium 2019

longer run-times. Lastly, the older DDR2 RAM on the Raspberry Pi 3B also contributed to models running relatively slowly on the platform. The accuracy-per-time and accuracy-per-memory metrics were maximized to select the best algorithm for each embedded platform. The MLP model was determined to be the best algorithm for the embedded platforms, as it showcased the shortest runtimes (390 and 529 seconds, respectively) and the second-highest accuracy benchmark of 85%. The 12% increase in accuracy, in the authors’ opinion, does not justify the use of CNN on embedded platforms due to run-time increase on the ODROID-C2 and Raspberry Pi 3B by 400% and 570%, respectively.

4. Conclusions

Hyperspectral Imaging in space can reveal useful information about our world. HSI analysis techniques have been developed and are often executed on computationally tractable environments on Earth. However, conducting these analyses in computationconstrained environments on-board a spacecraft would be extremely beneficial, enabling users to intelligently downlink a subset of data rather than the entirety. Benchmarking different machine-learning algorithms for HSI analysis on different platforms with varying performance capabilities allowed the authors to determine the best algorithms to run on embedded platforms.

SVM, MLP, and CNN models were benchmarked on a popularly used Indian Pines HSI dataset. Accuracy, run-time, and memory benchmarks were collected on the desktop PC, ODROID-C2, and Raspberry Pi 3B platforms for the final prediction of pixel classifications. The more powerful processor on the desktop PC than the embedded platforms contributes to the best accuracy and run-time benchmarks, as a result, the CNN was chosen for the desktop PC. However, the MLP was chosen over the CNN for embedded platforms due to the minimal increase in accuracy compared to the massive run-time increases.

Acknowledgments

This research was funded by NSF SHREC Center (c/o IUCRC grant CNS-1738783), Swanson School of Engineering, and Office of Provost at the University of Pittsburgh.

References

6. K. Makantasis, K. Karantzalos, A. Doulamis and N. Doulamis, “Deep Supervised earning for Hyperspectral Data Classification through Convolutional Neural Networks,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959-4962, 2015.

7. H. Petersson, D. Gustafsson and D. Bergstrom, “Hyperspectral Image Analysis Using Deep Learning - A Review,” 2016 Sixth International Conference on Image Processing Theory, Tools, and Applications (IPTA), 2016.

8. X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu and J. Paisley, “Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network,” IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2354-2367, 2018.

9. M. Baumgardner, L. Beihl and D. Landgrebe , “220 Band AVRIS Hyperspectral Image Data Set,” Purdue University Research Repo, 2015.

10. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel and e. al., “SciKit Learn: Machine Learning in Python,” JMLR, vol. 12, pp.

2825-2830, 2011.

11. M. Abadi, A. Agarwal, P. Barham, E. Brevdo and e. al., “TensorFlow: A System for Large-Scale Machine Learning,” 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265-283, 2016.

12. W. Hu, Y. Huang, L. Wei, F. Zhang and H. Li, “Deep Convolutional Neural Networks for Hyperspectral Image Classification,” Journal of Sensors, pp. 1-12, 2015.

13. S. Ruder, “An Overview of Gradient Descent Optimization Algorithms,” axXiv, vol. 1609, 2017.

14. VERSUS, “ARM Cortex-A53 vs Intel Core i7-6700 | Mobile chipset comparison,” 2018. [Online]. Available: https://versus.com/ en/arm-cortex-a53-vs-intel-core-i7-6700. [Accessed 22 09 2018].

15. M. Plauth and A. Polze, “Are Low-Power SoCs Feasible for Heterogenous HPC Workloads?,” in Euro-Par 2016: Parallel Processing Workshops Lecture Notes in Computer Science, Grenoble, France, Springer, 2017, pp. 763-774.

16. E. Mizutani and E. S. Dreyfus, “On Complexity Analysis of Supervised MLP-learning for Algorithmic Comparisons,” International Joint Conference on Neural Networks (IJCNN’01), vol. 1, pp. 347-352, 2001.

1. A. J. Pellish, Radiation 101: Effects on Hardware and Robotic Systems, MD: NASA Goddard, 2015.

2. HySpex, Hyperspectral Imaging, Oslo, Norway: Norsk Elektro Optikk, 2016.

3. K. Hege, D. O’Connell, W. Johnson, S. Basty and E. Dereniak, “Hyperspectral imaging for astronomy and space surveillance,” Proceedings of SPIE - The Internation Society for Optical Engineering, vol. 5159, pp. 380-391, 2004.

4. A. Shmilovici, “Support Vector Machines,” In Data mining and knowledge discovery handbook, pp. 231-247, 2009.

5. A. Santara, K. Mani, P. Hatwar, A. Singh, A. Garg, P. Kirti and P. Mitra, “BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification,” arXiv, vol. 1612, 2016.

11

Smarter riversheds: Real-time water sensors

1. Introduction

Kathleen Beaudoin , Christian Ference , Angela Chungb, Joseph Zappitellia, Emily Elliotb, and mentor David Sancheza a

a

Department of Civil and Environmental Engineering, Swanson School of Engineering b Department of Geology and Environmental Science, Kenneth P. Dietrich School of Arts and Sciences University of Pittsburgh, Pittsburgh, USA a

Significance Statement:

Combined sewer overflow is detrimental to water quality in Pittsburgh and approximately 770 other US cities with combined sewer systems. The present study investigates the use of automatic real-time water quality sensors to assess CSO’s impact on water quality and to validate the efficacy of improvements such as green infrastructure.

Beaudoin

Abstract

Pittsburgh has combined sewer Sanchez overflow (CSO) volumes of 9 billion gallons annually, but sporadic water quality monitoring makes it difficult to assess CSO’s impact on the health of the rivers or validate engineering improvements (Fischbach et al.). An inexpensive real-time sensor was developed to measure temperature, pH, dissolved oxygen, oxygen reduction potential, and conductivity. The goal was to triangulate real-time data with historical and grab sample data. Grab samples were taken from 35 sampling locations to supplement sensor data over two trips, one during a CSO alert (6/21/18) and one not (6/15/18). One sample was taken directly in front of an overflowing CSO. Samples were analyzed for total organic carbon, total nitrogen, suspended solids, nitrates, nitrites, and heavy metals. Correlations between sensor and grab sample data were examined to identify robust surrogates. Conductivity was positively correlated with total carbon (R2 = 0.91) for data collected 6/15/18. The CSO adjacent sample had high levels (z > 4σ) of total organic carbon and total nitrogen, but nitrites and nitrates were not elevated, suggesting non-point runoff contributed more than CSOs to their concentrations. The limitations of the identified surrogates suggest that direct monitoring of CSO and green infrastructure effluent may provide more useful data.

Category: Experimental research

Keywords: Combined sewer overflow, real-time sensor, water quality, water quality monitoring

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Undergraduate Research at the Swanson School of Engineering

The majority of the city of Pittsburgh uses a combined sewer system. Because there is no separation between the storm and sanitary sewers, rainfall of as little as a tenth of an inch can overwhelm the sewage treatment plant and cause combined sewer overflows. The Allegheny County Sanitary Authority (ALCOSAN) estimates that each year, there is 9 billion gallons of combined sewer overflow (CSO) and 600 million gallons of sanitary sewer overflow (SSO), which represents over 10% of the volume treated at the Woods Run treatment plant [6]. While the impact of CSO discharges on Pittsburgh’s water quality has not been well quantified, a study by Phillips et al. in Burlington, Vermont found that between 40-90% of annual load of hormones and wastewater micropollutants with high removal efficiencies was attributable to CSOs [1]. Furthermore, a study by Mascher et al. of the Mur River in Austria found that CSO discharges, which comprised 4.3% of the annual flow load volume led to an increase of annual fecal indicator bacteria loads of between 30-37 times [7]. This significant detrimental impact makes CSO mitigation an important goal for improving water quality.

An EPA-issued consent decree gave ALCOSAN a deadline of 2026 to make significant reductions to CSO volumes and eliminate SSOs completely [10]. The first plan developed would have increased treatment plant capacity and built pipes for additional waste and stormwater storage to reduce CSO volumes by an estimated 92%. However, its 3.6 billion dollar cost was judged an unreasonable burden on ratepayers [6]. A less costly alternative, which at 2 billion dollars, would have used a mixture of gray and green infrastructure, was rejected by the EPA as insufficient to “result in compliance with all of the requirements of the consent decree” [10].

Adding additional complication to the planning is the large number of sources of uncertainty. Historical data concerning overflow volumes and Pittsburgh river water quality is sparse. A pilot study of Pittsburgh stormwater management found that past overflow volumes may have been significantly underestimated.

Furthermore, historical rainfall data is a decreasingly reliable predictor of future weather patterns. As a result of climate change, more frequent and more intense storms are expected in the northeastern United States, but these expected increases are difficult to quantify. Additionally, ALCOSAN’s current plan makes extensive use of green infrastructure (such as bioswales, permeable pavements, and rain gardens) but there have not been extensive studies regarding the performance of these systems [6].

More data concerning the rivers’ water quality is clearly necessary to evaluate the performance of the CSO reduction plans. Traditional grab sampling provides one option but gives only discrete data points and is time consuming and expensive.

Automated sensors provide another potential source of information.

The benefit of sensors over grab samples is that they provide continuous, remote monitoring at reduced costs. However, only certain water quality parameters can be measured by sensor; others require the more advanced equipment of a laboratory.

Automated high frequency monitoring has been used with some

Ingenium 2019

Figure 1: The sensor probe

success in water quality monitoring in lakes [7], as well as in the Mississippi River to supplement grab sampling for nitrate analysis [8]. The goal of this research is to test the ability of a real-time water quality sensor to monitor the impact of CSO on Pittsburgh’s rivers, and to validate engineering improvements such as green infrastructure.

2. Methods

A sensor probe unit capable of wirelessly transmitting sensor measurements in real time was developed with probes to measure temperature, pH, dissolved oxygen, oxygen reduction potential, and conductivity, as seen in Figure 1.

Thirty-five sampling locations in Pittsburgh’s rivers were selected, as seen in Figure 2. Five were on the Allegheny, twenty on the Monongahela, and ten were on the Ohio. The locations were selected for their location downstream of CSO outfalls. Samples were collected by boat on June 15 and 21, 2018. The latter date was during an CSO alert while the former was not.

At each location, a 1250 mL sample was taken at two-thirds of the depth of the river while the sensor probes were dropped into the water. On June 15, pH, total dissolved solids, and conductivity were also measured from the grab sample at the time of sampling.

Samples were stored on ice while in transit to the laboratory and

Figure 2: The locations at which samples were taken

were then refrigerated at 4°C until analysis, which was completed in the two weeks following sampling. In addition to the planned sampling locations, an additional sample was taken on June 21 approximately 3 meters from the mouth of an actively overflowing CSO outfall.

For heavy metal analysis, 15 mL of each sample was filtered through a 0.45 µm filter. So that the metal ion concentrations would be expected to fall within the equipment calibration range, a 1:10 dilution was prepared before being acidified to 5% nitric acid. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to perform analysis of aluminum, arsenic, barium, cadmium, cobalt, chromium, copper, mercury, potassium, manganese, molybdenum, nickel, lead, selenium, strontium, and zinc concentrations.

For total organic carbon and total nitrogen analysis, 40 mL of each sample was filtered through a 0.45 µm filter and analyzed for total carbon, inorganic carbon, and total nitrogen by non-dispersive infrared analysis (NDIR). Analysis of total suspended solids was performed for the samples collected June 21 by filtering 300 mL of each sample through a 1.5 µm filter and measuring the change in the mass of the filter after drying. Table 1 provides additional information on the measured parameters’ relationship to water quality.

Table 1: Parameters measured and their significance as indicators of water quality Parameter

Method

Impact on Water Quality

Heavy metals

ICP-OES

Potential toxicity to humans

Total organic carbon

NDIR

Indicator of amount of decaying organic matter

Total nitrogen

NDIR

In excess causes eutrophication

Total suspended solids

Filtration

Measure of cloudiness of water

pH

Sensor

pH values far from neutral indicate poor water quality

Dissolved oxygen

Sensor

Low DO values may indicate polluted water

Oxygen reduction potential

Sensor

Measure of a solution’s tendency to be an oxidizing or reducing agent

Temperature

Sensor

Impacts other water quality parameters

Conductivity

Sensor

Measure of dissolved ions 13

Table 2: Mean values of measured parameters between the two sampling dates, and significance testing between the two means.

Comparison of Parameter Averages Parameter

Units

June 15 Mean

June 21 Mean

p-value

Total organic carbon

mg/L

3.11

2.92

0.005

Total nitrogen

mg/L

0.85

0.82

0.210

Dissolved oxygen

mg/L

12.8

12.3

0.157

Oxygen reduction potential

mV

pH Conductivity

µS/cm

3. Results

Average values of each measured parameter were taken for both days and compared, as seen in Table 2. Statistically significant differences were found between the averages from the two sampling trips for several parameters. Total organic carbon levels were found to be lower on the CSO sampling trip, 2.92 mg/L compared to 3.11 mg/L. Oxygen reduction potential was also lower on the CSO sampling trip, 207 mV compared to 253 mV. Both of these differences were statistically significant difference at the α = 0.01 level. Conductivity, however, was higher on the CSO sampling trip, averaging 3120 µS/cm compared to 2360 µS/ cm. This difference was significant at the α = 0.05 level. Heavy metal analysis yielded little useful information, as few metals were present in concentrations detectable by the ICP-OES used.

253

207

0.021

7.38

7.30

0.262

2360

3120

0.000

4. Discussion

The strength of correlations between measured parameters was analyzed in search of robust surrogates. Surrogates, if identified, would allow the sensors to indirectly monitor water quality parameters beyond those which they directly record. The values of these correlations can be seen in Table 3. The strongest correlation is between inorganic carbon and total carbon, but this is insignificant. The next strongest correlation is between total suspended solids and conductivity. For the sake of brevity, this discussion will focus on the correlations of total carbon with conductivity.

The overall correlation between total carbon and conductivity is weak, with an R2 value of 0.14. However, separating the data from the two sampling runs, a distinct pattern emerges, as seen in

Table 3: A table of R2 values for the correlations between measured parameters. Darker green indicates a stronger correlation.

The parameters are temperature, turbidity, total suspended solids, total carbon, inorganic carbon, total organic carbon, total nitrogen, pH, total dissolved solids, conductivity, dissolved oxygen, and oxygen reduction potential.

Temp Turb TSS TC IC TOC TN pH TDS Cond DO ORP Temp Turb TSS TC IC TOC TN pH TDS Cond DO ORP

14

Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Total Carbon vs. Conductivity

Total Carbon vs. Conductivity CSO

20

Total Carbon (mg/L)

Total Carbon (mg/L)

25

20

NonCSO

15 10 5 0 1500

2000

2500 3000 3500 Conductivity µS/cm

15 10 5 0 2100

4000

2300

2500

2700

Conductivity µS/cm

Figure 3: Conductivity vs. total carbon values

Figure 4: Conductivity vs. total carbon values, restricted to conductivities between 2000 and 2700 µS/cm

Figures 3 and 4. Samples taken during the CSO alert had higher conductivities across the board, with a median conductivity of 3092 µS/cm compared to 2387 µS/cm. When focusing on the non-CSO data and restricting the range to conductivities between 2000 and 2700 µS/cm, a strong correlation (R2 = 0.91) emerges.

Another observation emerges from the sample taken from directly in front of an active CSO outfall. When compared to other samples taken on the same date, values for total carbon, inorganic carbon, total organic carbon, and total nitrogen are extremely high, all more than 4 standard deviations above the mean, as seen in Table 4. However, neither concentrations of nitrates nor of nitrites were elevated. This suggests that nonpoint sources of runoff contribute more significantly to their concentration in Pittsburgh’s rivers than CSOs. Because of this, efforts to reduce CSO volumes will likely not decrease nitrate and nitrite concentrations.

The difficulty in identifying robust surrogates suggests that there may be too much noise in the data obtained from river water to make the pursuit of surrogates a useful endeavor. The extremeness of the measured parameters in the sample taken next to the CSO suggests that more direct monitoring of CSO outfalls may provide more useful data. This type of monitoring would more effectively identify and measure pollutants for which CSO is responsible. Further research should focus on monitoring CSO discharge and green infrastructure effluent more directly.

5. Conclusions

References

Acknowledgments

This project would not be possible without funding from the Mascaro Center for Sustainable Innovation and Charles A. and Linda E. Sorber. This project was done with guidance from David Sanchez and Christian Ference.

Conductivity shows promise as a potential surrogate for total carbon and total nitrogen. It is possible that the correlation observed in the non-CSO samples may be representative of other days without active CSOs. More data is needed to define the conditions, if any exist, under which the observed correlation hold.

If further validated, this surrogate would allow for more information to be gained from real-time sensors. This, in turn, would provide those implementing CSO mitigation measures with a more complete picture of the efficacy of various strategies.

1. Fischbach, J. R.; Siler-Evans, K.; Tierney, D.; Wilson, M. T.; Cook, L. M.; May, L. W., Robust Stormwater Management in the Pittsburgh Region: A Pilot Study. RAND Corporation: Santa Monica, CA. 2. Herrig, I. M.; Böer, S. I.; Brennholt, N.; Manz, W., Development of multiple linear regression models as predictive tools for fecal indicator concentrations in a stretch of the lower Lahn River, Germany. Water Research 2015, 85, 148-157

Table 4: Values of total carbon, inorganic carbon, total organic carbon, total nitrogen, nitrates, and nitrites in the sample taken directly in front of an active CSO, compared to the day’s averages.

TC (mg/L)

IC (mg/L)

TOC (mg/L)

TN (mg/L)

Nitrate (mg/L)

Nitrite (mg/L)

Average

15.21

12.26

2.95

0.85

2.78

2.42

Std. dev

1.59

1.38

0.31

0.19

0.61

0.52

CSO value

22.42

18.16

4.26

1.79

1.88

1.59

Z-score

4.54

4.27

4.28

5.07

-1.48

-1.58

15

3. Hofer, T.; Montserrat, A.; Gruber, G.; Gamerith, V.; Corominas, L.; Muschalla, D., A robust and accurate surrogate method for monitoring the frequency and duration of combined sewer overflows. Environmental Monitoring and Assessment 2018, 190 (4), 209.

4. Hopey, D., EPA calls Alcosan’s $2 billion sewer system proposal deficient. Pittsburgh Post-Gazette 2014.

5. Hopey, D., Pittsburgh’s sewage overflow season starts.

Pittsburgh Post-Gazette 2017.

6. Marcé, R.; George, G.; Buscarinu, P.; Deidda, M.; Dunalska, J.; de Eyto, E.; Flaim, G.; Grossart, H.-P.; Istvanovics, V.; Lenhardt, M.; et al., Automatic High Frequency Monitoring for Improved Lake and Reservoir Management. Environmental Science & Technology 2016, 50 (20), 10780-10794.

7. Mascher, F.; Mascher, W.; Pichler-Semmelrock, F.; Reinthaler, F. F.; Zarfel, E. G.; Kittinger, C., Impact of Combined Sewer Overflow on Wastewater Treatment and Microbiological Quality of Rivers for Recreation. Water 2017, 9 (11).

8. Pellerin, B. A.; Bergamaschi, B. A.; Gilliom, R. J.; Crawford, C. G.; Saraceno, J.; Frederick, C. P.; Downing, B. D.; Murphy, J. C., Mississippi River Nitrate Loads from High Frequency Sensor Measurements and Regression-Based Load Estimation.

Environmental Science & Technology 2014, 48 (21), 1261212619.

9. Phillips, P. J.; Chalmers, A. T.; Gray, J. L.; Kolpin, D. W.; Foreman, W. T.; Wall, G. R., Combined Sewer Overflows: An Environmental Source of Hormones and Wastewater Micropollutants. Environmental Science & Technology 2012, 46 (10), 5336-5343.

10. Wang, R.; Eckelman, M. J.; Zimmerman, J. B., Consequential Environmental and Economic Life Cycle Assessment of Green and Gray Stormwater Infrastructures for Combined Sewer Systems. Environmental Science & Technology 2013, 47 (19), 11189-11198.

16

Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Modal analysis of human brain dynamics after head impact Ryan T. Black , Kaveh Laksari , and mentor Hessam Babaeea a

b

Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh PA, USA b Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA a

Significance Statement:

Knowledge of underlying low dimensional structures in human brain impact dynamics could be used to improve clinical assessment of mild traumatic brain injury, a prevalent injury that is difficult to diagnosis.

Black

Abstract

Each year in the U.S., approximately 1.4 million people suffer mild traumatic brain injury (MTBI). However, diagnosing MTBI is challenging due to the acute nature of symptoms that resolve quickly, lack of injury evidence in brain imaging, Babaee and the absence of a universally accepted definition of MTBI. To better understand the mechanisms behind MTBI to prevent brain injury and improve diagnostic accuracy, we investigated the spatiotemporal characteristics of human brain impact dynamics through modal analysis. Using relative brain tissue deformation data from 183 Finite Element (FE) simulations, we extracted the modal behavior of the nodal relative brain displacement using a novel data-driven low-rank approximation technique, that can extract the underlying low dimensional structure of the FE simulations.

It was determined that over 90% of the modal energy relative to reduction order (RO) 6 can be captured using a RO of 3. The results suggest that human brain impact dynamics have low dimensional structures, which could be utilized to build reduced order models (ROM) of brain tissue deformations. Access to rapid simulations of brain dynamics following head trauma using ROM would provide clinicians with critical information for brain injury assessment.

Category: Computational research

Keywords: Dynamic Basis (DB), Mild Traumatic Brain Injury (MTBI), Modal Analysis

1. Introduction

Each year in the U.S., approximately 1.4 million people suffer mild traumatic brain injury (MTBI) [1,2]. However, diagnosing MTBI is challenging due to the acute nature of symptoms that resolve quickly, lack of injury evidence in brain imaging, and the absence of a universally accepted definition of MTBI [3].

As a result, this leads to undiagnosed cases of MTBI, which is especially problematic for at-risk groups, such as contact-sport athletes, because chronic concussions can lead to increased risk of neurocognitive impairments, poor mental health, and neurodegenerative diseases [4]. With the prevalence of MTBI and the long-term effects of multiple concussions, there is a need to better understand the mechanisms behind MTBI to prevent brain injury and improve diagnostic accuracy.

Currently, the widely accepted hypothesis for the injury mechanism of MTBI is excessive axonal stretching in certain regions of the brain caused by rapid accelerations of the tissue following impact [5]. This hypothesis has been supported by several studies that have investigated the relationship between indicators of brain injury, such as large tissue deformations and brain acceleration [5,6]. In addition, due to the transient nature of brain impact dynamics, some studies have been performed to investigate the temporal aspects of brain injury [7,8,9]. However, the temporal characteristics of brain motion after head impact are largely unknown [8].

In this study, we investigate the spatiotemporal characteristics of human brain impact dynamics through modal analysis, using a new data-driven low-rank approximation technique to extract modal behavior [10,11,12]. This reduction utilizes a new optimality condition built to capture transient phenomena in stochastic data [10,11,12]. We hypothesize that the new reduction technique will be able to identify low dimensional structures in brain impact dynamics, i.e. high brain tissue displacement in regions similar to those found in previous studies [8]. However, the propagation of these low dimensional structures in time may exhibit different behavior than results from previous studies due to the nature of the novel reduction technique, possibly providing new insights into brain impact dynamics [8].

2. Methods 2.1 Dynamic Basis For this study, we used relative (to the skull) brain tissue deformation data from 183 Finite Element (FE) simulations from a previously published study by Laksari et al. [8]. The FE simulations were performed using measured head impact kinematics as inputs to an FE brain model to determine the resulting brain tissue deformation for each case, as described in Laksari et al. [8]. We extracted the spatiotemporal characteristics of the nodal relative brain displacement data using a novel data-driven low-rank approximation technique that can capture the transient behavior of time developed stochastic data, Dynamic Basis (DB) [10,11,12].

17

DB is a linear reduction of time developed stochastic data of the form: (1)

where T(t) (n x s) is a snapshot data matrix (expected value of the matrix E[T(t)] = 0), whose columns are observations of the stochastic system at time t, U(t) (n x r) is a low rank subspace, whose columns ui are modes, Y(t) (s x r) are stochastic coefficients whose columns are yi, ℇ is the reduction error, and PT is the transpose of the matrix P. To capture transient behavior of the data, the following variational principle is utilized, with an orthonormality condition for the modes (â&#x;¨ui, uj â&#x;Š = δij ) imposed using Lagrange multipliers (Îťij (t) ): (2) where Ď•ij (t) is defined as â&#x;¨ui, uj â&#x;Š, ‖∙‖ is the Euclidean norm (2-norm), ( ) is the time derivative, and r represents the reduction order (RO). The goal of this variational principle is to build an optimally time dependent basis that minimizes the difference between the dimensionality-reduced nodal derivatives and the actual nodal derivatives. Through minimization of the above variational principle (2), evolution equations for U and Y are derived: (3)

2.2 Dynamic Basis Workflow To perform modal analysis using the DB algorithm, we first assembled the zero mean snapshot data matrix T(t) whose columns are the relative nodal brain displacements for all three principal directions for all 183 cases:

(5)

(6)

18

(7)

where đ??Ť is the truncation error.

To determine the initial conditions of the evolution equations, we performed a Karhunen-LoĂŠve decomposition (KL) of T(0): (8)

where the initial condition for the modes and stochastic coefficients are given by ui0 = vi and yi0 = √ Îťi zi respectively.

Finally, we evolved the evolution equations to determine U(t) and Y(t) using a fourth-order Runge-Kutta scheme and use the r dominant KL modes ([u1(t) ‌ ur(t)]n x r ) to form the low-rank subspace.

For analysis of the DB reduction, we plot the eigenvalues (Îťi (t) ) of the covariance matrix (modal energy) versus time, relative modal energy versus time, the two highest energy modes, u1(t) and u2(t), and reduction error computed at each snapshot in time using the following equation:

(4)

where P—1 is the inverse of the matrix P, and C = E[YTY] is the covariance matrix.

where T Ěƒis the snapshot data matrix with E[T Ěƒ] ≠0, n = number of nodes x 3 degrees of freedom, and s = number of snapshots in time. Removal of the mean at each snapshot ensures only brain dynamics relative to the skull are captured in the reduction, excluding large translations of the brain and skull during injury. Next, we computed Ṫ(t) using a second order finite difference approximation of the form:

Undergraduate Research at the Swanson School of Engineering

(9)

where RE(t) is reduction error and ‖∙‖ is the Euclidean norm (2-norm). These plots provide insights into whether this data has low dimensional structures, and the ability of the DB algorithm to extract any underlying structures existing in the relative nodal brain displacement data.

Ingenium 2019

Figure 1: Plot of modal energies (eigenvalues of the covariance matrix) versus time for the first six ROs. Each color represents a RO and all eigenvalues are plotted for that RO.

Figure 2: Percent of energy captured by the sum of the eigenvalues for a given RO relative to the sum of the eigenvalues for RO 6.

3. Results

The eigenvalues of the covariance matrix C versus time are plotted for the first 6 ROs in Figure 1. For each RO, the modes have similar energy for the duration of the simulation regardless of the RO, with high modal energies immediately following head impact that decrease as brain motion decays. In addition, there seems to be two distinct groupings of eigenvalues, with the higher energy grouping around 10-10 and the lower energy grouping around 10-12.

Furthermore, over 90% of the modal energy relative to RO 6 can be captured using a RO of 3 as seen in Figure 2.

To visualize the effectiveness of the DB algorithm in capturing low dimensional structures in the highly transient brain impact dynamics data, the two highest energy modes (u1(t) and u2(t)) for RO 3 are plotted versus time along with the relative brain displacement data for Case 1 in Figure 3. RO 3 was selected for the plot in Figure 3 because of the high percentage of modal energy captured by this RO. The modes are plotted in Figure 3 with their corresponding amplitudes (√ Îťi(t) * ui(t)) since the modes themselves (u1(t) and u2(t)) are orthonormal. As the brain displaces following impact in Case 1 (Figure 3a), the highest energy mode

Figure 3: (a) Relative brain displacement for Case 1 FE simulation (b) Mode 1 plotted with its amplitude for RO 3 (c) Mode 2 plotted with its amplitude for RO 3.

(Figure 3b) indicates the highest displacements in a similar region as Case 1, while the second highest energy mode (Figure 3c) captures high displacements in nearby regions as well.

The reduction error of the DB algorithm is plotted in Figure 4 for each RO. Overall, the reduction error increases for the first 0.1 seconds as impact excites brain motion, followed by a decrease in reduction error for the last 0.1 seconds as brain motion decays. In addition, there is minimal difference between the reduction errors for each RO as seen in Figure 4.

Figure 4: Reduction error versus time (seconds) plotted for the first six ROs.

19

4. Discussion

With the prevalence of MTBI, knowledge of the spatiotemporal characteristics of brain motion is essential to better understand the mechanisms behind MTBI and prevention of brain injury. Through modal analysis of brain impact dynamics using a new data-driven low-rank approximation technique, low dimensional structures were identified in the complex dynamics of head trauma that can be utilized for analysis of the system through reduced order modeling.

These low dimensional structures were recognized as the distinct groupings of modal energies separated by approximately two orders of magnitude and large relative energies captured by small ROs, which suggests that brain motion after head impact could be represented by a few dominant modes. When comparing the relative brain displacement data to the modes of RO 3, the reduction appears to accurately reconstruct relative brain displacement data as shown in Figure 3. In particular, the reduction error is low for the duration of the simulation and the difference in reduction error is minimal for ROs greater than 3, indicating that DB accurately captured underlying structures in the data.

Furthermore, the optimally time dependent modes identified using DB for this dataset appeared to have similar spatial characteristics to the time independent dynamic mode decomposition (DMD) modes identified by Laksari et al. for the same dataset [8].

Specifically, both techniques identified spatial characteristics such as high brain displacements in the top and sides of the cerebrum and localized areas of high displacement in the anterior and posterior regions of the brain, which suggests these common spatial structures are important for brain impact dynamics [8].

The main limitation in this study is the relative brain displacements were produced from data collected for football head impacts only [8]. In the future, data for dynamics of both lesser and greater head trauma will be incorporated to generalize the brain impact dynamics analyzed. Furthermore, to assess the effectiveness of DB reduction for this highly-transient data, a comparison to other reduction techniques will be performed for this dataset.

5. Conclusions

Overall, exploration of brain impact dynamics using DB identified dominant spatial structures in the motion and how these spatial structures varied in time to reconstruct brain tissue deformation. Furthermore, the results of this analysis suggest that human brain impact dynamics have low dimensional structures in the motion, which could be utilized to build reduced order models of brain tissue deformations. Access to rapid simulations of brain dynamics following head trauma using a reduced order model would provide clinicians with critical information for brain injury assessment [8].

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Undergraduate Research at the Swanson School of Engineering

Acknowledgments

Support from the University of Pittsburgh Department of Mechanical Engineering and Materials Science is gratefully acknowledged.

References

1. M. Faul, L. Xu, M. M. Wald, et al. Traumatic brain injury in the United States: national estimates of prevalence and incidence, 2002–2006. Injury Prevention 16 (2010).

2. J. D. Cassidy, L. J. Carroll, P. M. Peloso, et al. Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 43 (2004) 28-60.

3. R. M. Ruff, G. L. Iverson, J. T. Barth, et al.

Recommendations for Diagnosing a Mild Traumatic Brain Injury: A National Academy of Neuropsychology Education Paper. Arch Clinical Neuropsychol 24 (2009) 3-10.

4. Sports-Related Concussions in Youth: Improving the Science, Changing the Culture. R. Graham, F. P. Rivara, M. A. Ford, et al. (Eds.). The National Academies Press, Washington, DC, 2014.

5. D. F. Meany and D. H. Smith. Biomechanics of Concussion.

Clin Sports Med 30 (2011) 19-31, vii.

6. J. J. Bazarian, T. Zhu, J. Zhong, et al. Persistent, Long-term Cerebral White Matter Changes after Sports-Related Repetitive Head Impacts. PloS One 9 (2014).

7. K. Laksari, L. C. Wu, M. Kurt, et al. Resonance of human brain under head acceleration. J. R. Soc. Interface 12 (2015).

8. K. Laksari, M. Kurt, H. Babaee, et al. Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis. Phys Rev Lett 120 (2018) 138101-138107.

9. K. K. Darvish and J. R. Crandall. Nonlinear viscoelastic effects in oscillatory shear deformation of brain tissue. Med. Engr.

Phys. 23 (2001) 633-645.

10. H. Babaee, M. Choi, T. P. Sapsis, et al. A robust biorthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems. J Comput Phys. 344 (2017) 303-319.

11. H. Babaee and T. Sapsis. A minimization principle for the description of time-dependent modes associated with transient instabilities. Proc. R. Soc. A. 472 (2016).

12. H. Babaee, A Data-Driven Low-Rank Approximation for Time-Dependent Stochastic Problems, Technical Report, University of Pittsburgh, 2018.

Ingenium 2019

Evaluating occlusion success of Esophocclude prototypes in comparison to diameter and radial force Gordon Bryson1, mentor Youngjae Chun1,2,3, and Phillip Carullo4 Department of Bioengineering, University of Pittsburgh; Department of Industrial Engineering, University of Pittsburgh; 3McGowan Institute for Regenerative Medicine, University of Pittsburgh; 4Department of Anesthesiology, University of Pittsburgh School of Medicine 1 2

Significance Statement:

This device aims to prevent pulmonary aspiration during rapid sequence intubation by deploying a swallowed stent-like device to occlude the esophagus. While studying the relationship between device dimensions and occlusion success, this study illuminated potential design elements that could improve device function in future iterations.

Bryson

Chun

Abstract

Emergency surgeries see patients who have not fasted before the procedure and who are therefore at a higher risk for pulmonary aspiration, a potentially life-threatening complication. The Esophocclude is a device that can be swallowed by the conscious patient and deployed in the esophagus to block gastric contents from flowing up the esophagus. The purpose of this study is to determine an effect of varying radial force and diameter of the deployable balloon on the occlusion of an esophagus model. Five prototype devices were tested, each with different radial forces and diameters.

To simulate the passive pressure of fluids from the stomach, a balloon was filled with water and attached to silicone tubes to the esophagus model containing the device. The results of the study did not show a strict relationship between diameter and occlusion success, nor between radial force and occlusion success. In fact, only two of the devices were able to reduce liquid flow through the esophagus model to a meaningful extent. The study did show that the more successful devices filled with liquid due to perforations in their surface. This design flaw that turned out to be advantageous will be incorporated into the next iteration of prototypes.

Category: Device design

Keywords: Pulmonary aspiration, esophageal stent, nitinol

1. Introduction 1.1 Background An advantage of planning surgeries in advance is that the patient will have fasted before the surgery takes place. This is directed by the physicians because it ensures that there are no contents in the stomach at the time of surgery, lowering the risk of aspiration, a complication that occurs most often when physicians place the breathing tube before surgery [1]. Aspiration is the involuntary movement of gastric contents up the esophagus and into the lungs, and it can have disastrous complications [2].

Food particles obstructing the lung tissue can result in increased heart and breathing rates, low blood oxygen levels, and eventually pneumonia [3]. Acid from the stomach contents can damage the lung tissue, and bacteria from the esophagus and stomach can more easily infect the lung through these areas.

It is difficult to put a number on the rate of aspiration for several reasons. One reason is that physicians fail to see gastric fluids entering the lungs, yet they believe they must witness the aspiration event to diagnose it. Another reason is that physicians attribute the complications of aspiration to other causes [3].

Nevertheless, it is estimated that 1 in 2000-3000 surgeries results in aspiration [4]. A patient who has aspirated is not guaranteed to develop symptoms. Numerous tests are performed on patients who do appear to have aspirated, at the cost of the hospital and the patient. An estimated 8-10% of patients who have aspirated will die of complications attributable to aspiration [5].

1.2 Problem Statement Efforts to avoid patient aspiration are uncomfortable and ineffective. For many years, the preferred method was cricoid pressure, which is when a physician applies an external pressure to the trachea to temporarily close the esophagus while the breathing tube is being placed [6]. Recent studies have shown that this does not in fact reduce the chances of aspiration occurring and may even cause harm [7]. In addition, the process of cricoid pressure is very difficult to standardize among physicians [8]. Instead, physicians may now employ a nasogastric tube (NGT) or gastric emptying drugs. Both methods are flawed. The nasogastric tube is forced through the patient’s nostril, down the esophagus, and into the stomach. An external pump draws gastric contents through the tube so there will be less contents to potentially enter the lung later [9]. One problem with NGT is that it is often guided into the lung instead of the esophagus [10]. Gastric emptying drugs are given intravenously to the patient. They help the stomach move contents into the small intestines more quickly. Both NGT and gastric emptying drugs cause discomfort to the patient and have been known to cause vomiting [11]. Neither prevents aspiration; they only reduce the contents that could enter the lung when aspiration occurs.

1.3 Our Approach The Esophocclude is a device that can fully prevent aspiration.

The device is comprised of an encapsulated self-expanding balloon on a guidewire that is swallowed by a conscious patient and then deployed in the lower part of the esophagus. The balloon expands 21

to fully block stomach contents from ascending the esophagus into the lungs. Once the balloon is expanded, normal breathing tube placement can occur. One end of the guidewire remains external to the patient, so the physician can control deployment and retrieval of the device. As shown in Fig. 1, current Esophocclude prototypes use a super-elastic nitinol frame to achieve the self-expanding property. This frame is covered with an electrospun membrane of polytetrafluoroethylene (PTFE). Since the device uses the natural swallowing physiology of the patient, there will be very little chance the device is accidently directed into the trachea, which is an improvement over the NGT. The device can act instantaneously once swallowed, unlike gastric emptying drugs. The Esophocclude addresses the problem of pulmonary aspiration without the inconvenient consequences of current methods.

exposed to pressure. It was also important to simulate the ability of the esophagus to expand as a result of internal pressure. It was decided that surgical glove material was the best approximation as it is strong, elastic, and smooth. Thus, three esophagus models were constructed of increasing circumference: 5cm, 6cm, and 7cm, with a normal adult being best approximated by the 6cm circumference. This array of model sizes was made to illuminate ways the device may act in individuals of different sizes.

2.2 Experimental Setup The setup seeks to measure the flow rate of fluid past the device and the pressure the device can withstand, as these results show the device’s ability to occlude the esophagus successfully.

Most aspiration cases are caused by the elastic recoil of the stomach rather than forced stomach contractions, so a commercial latex balloon is used to model a pressure source. This balloon is connected via silicone tubing to a syringe which allows for filling the balloon. Another tube links the balloon with the esophagus model, and this tube passes fluid through a pressure meter (PendoTECH PressureMAT DPG). The esophagus model is open on one end to allow the Esophocclude device to be inserted and to allow fluid to flow out. Fluid flowing through the esophagus model is collected in a volumetric beaker. An image of the setup is shown in Fig. 2.

Figure 1: The structure of the current design of the Esophocclude device.

The nitinol wire frame is covered by an electrospun PTFE membrane.

1.4 Device Refinement The current prototype device needs to be scaled down to a size that can fit in a delivery capsule. As such, the diameter should be as small as possible without inhibiting its ability to block the entire esophagus cross-sectional area. Furthermore, the device needs to have a strong enough radial force that it blocks gastric contents from passing, yet weak enough that the balloon can be deflated upon retrieval. The radial force can be altered to an optimal strength by changing the size of nitinol wire. The purpose of this study is to identify relationships between diameter and occlusion success as well as radial force and occlusion success.

In addition, the study should provide qualitative feedback about the device’s behavior in response to flow. The relationships and qualitative data will be considered when designing the next iteration of the device.

2. Methods 2.1 Esophagus Laboratory Model Since porcine esophagus tissue was not available for testing the device prototype, a synthesized esophagus model was used.

It was important to preserve the correct texture of endothelial tissue to study how the Esophocclude devices would migrate when

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Undergraduate Research at the Swanson School of Engineering

Figure 2: The setup of the test. Fluid flows from the passive pressure balloon on the left through the pressure meter to the esophagus model on the right.

2.3 Procedure The balloon is filled via the syringe with 350ml of water before each test. It was determined that this volume of water allowed the balloon to exert consistent pressure for 100ml of flow. The device of interest is inserted into the esophagus model at least 5cm from the connection of the model to the tubing. The device expands on its own as much as the elastic esophagus will allow. At the start of the test, water flows through the tubing. The highest pressure during the first 100ml of flow is read from the pressure meter. The time for 100ml of fluid to exit the esophagus model past the device is measured, at which point the pressure meter is noted again to give the minimum pressure the device experiences. Five different devices are tested in each of the three esophagus models, and these devices vary in their radial force and diameters.

Ingenium 2019

3. Results 3.1 Flow Rate Results The results of the 15 flow rate tests are shown in Fig. 3.

Each bar color represents a different esophagus model size. The devices are numbered arbitrarily, but along the x-axis they are ordered by increasing diameter. To compare radial forces of the device, the order of increasing radial force would be 4, 5, 3, 1, 2, as shown in Table 1. The plot shows that devices 1 and 5 were the most successful at reducing flow. In addition, the plot shows that the three esophagus sizes achieve similar results for each single device.

3.2 Pressure Results Fig. 4 shows the results of pressure measurement. As above, the devices are ordered along the x-axis by increasing diameter.

The blue and orange bars correspond with the maximum and minimum pressures occurring during the measurement period. The plot shows that devices 1 and 5 withstood the highest pressure, but each device showed improvement over the control pressure measure of 0.18 psi.

Figure 4: Results of the pressure measurement. Legend depicts the maximum or minimum pressure. Device numbers are ordered by increasing diameter.

Figure 3: Results of the flow rate measurement. Legend depicts the esophagus model circumference. Device numbers are ordered by increasing diameter.

Table 1. The diameter and radial force required to depress device by 20% are shown. Comparisons between devices were used to identify trends in the device parameters and their occlusion success.

Device

1

2

3

4

5

Diameter (mm)

24.4

25.5

27.6

22.1

26.8

Radial Force (N)

3.48

3.77

1.22

0.67

1.14

3.3 Qualitative Observation During the flow period, device 5 was pushed by the fluid toward the end exit of the esophagus model. Devices 1 and 5 were filled with water once they were retrieved, and this was not observed with the other devices. Upon closer inspection, holes were observed in the front face of devices 1 and 5 which allowed fluid to enter.

4. Discussion

Considering the order of increasing diameter force of the five devices, no relationship was shown between diameter and occlusion success. With no a device in the esophagus model, the flow rate measured 455 ml/min. Devices 2, 3, and 4 were only slightly successful in reducing the flow rate of fluid from this control value. Devices 1 and 5 were successful in reducing flow rate, making them better candidates for a successful design. In addition, their ability to withstand higher pressure as shown from Fig. 3 represents success because they could potentially withstand a more forceful stomach efflux. This success could be a result of the perforations in their front face. Factoring in the order of increasing radial force, no relationship was shown between radial force and occlusion success. Devices 1 and 5 were the more successful devices, yet device 3 showed little success, even though its radial force is between the values of devices 5 and 1.

23

5. Conclusions

This study was unsuccessful in illuminating the relationships it set out to find, but it did reveal improvements for the device design and experimental setup for future iterations. First, the set of devices available to test were not conducive to showing a relationship between diameter and occlusion success nor radial force and occlusion success. The devices were a mix of radial forces and diameters, so these variables were not changed independently from each other. Future tests should involve a new set of devices for which only one variable is changed at a time, such as three devices of the same diameter with three different radial forces.

Second, the test offered an improvement on the structure of the device to facilitate its function, namely the perforations in the membrane. Additional studies will seek to verify that this design in fact improves the device’s ability to occlude the esophagus model.

Finally, the migration of one of the devices in response to higher pressure suggests that the esophagus model may not be ideal for testing these devices. Even slight differences between the textures of surgical glove material and esophagus tissue could create different device migration patterns. To guarantee future studies show the most accurate and useful information, porcine esophagus tissue should be used.

Acknowledgments

I would like to acknowledge Dr. Youngjae Chun, the Swanson School of Engineering, and the Office of the Provost for jointly funding this project. I would also like to acknowledge Yanfei Chen for his guidance in the experimental setup and materials selection.

References

1. Trethewy, C. E., Burrows, J. M., Clausen, D., & Doherty, S. R., Effectiveness of cricoid pressure in preventing gastric aspiration during rapid sequence intubation in the emergency department: study protocol for a randomised controlled trial, Trials 13 (2012) 17.

2. Marik, P. E., Aspiration Pneumontitis and Aspiration Pneumonia, New England Journal of Medicine 344 (2001), 9.

3. Nason, K. S., Acute Intraoperative Pulmonary Aspiration, Thorac. Surg. Clin. 25(3) (2015) 301-307.

4. Bernardini, A., Natalini, G., Risk of pulmonary aspiration with laryngeal mask airway and tracheal tube: analysis on 65 712 procedures with positive pressure ventilation, Anaesthesia 64(12) (2009) 1289-1294.

5. Landreau, B., Odin, I., & Nathan, N., Pulmonary aspiration: epidemiology and risk factors, Ann. Fr. Anesth. Reanim. 28(3) (2009) 206-210.

6. Beckford, L., Holly, C., & Kirkley, R., Systematic Review and Meta-Analysis of Cricoid Pressure Training and Education Efficacy, AORN J 107(6) (2018) 716-725.

7. Algie, C. M., Mahar, R. K., Tan, H. B., Wilson, G., Mahar, P. D., & Wasiak, J., Effectiveness and risks of cricoid pressure during rapid sequence induction for endotracheal intubation, Cochrane Database Syst. Rev. (11) (2015).

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Undergraduate Research at the Swanson School of Engineering

8. Beavers, R. A., Moos, D. D., & Cuddeford, J. D., Analysis of the application of cricoid pressure: implications for the clinician, J. Perianesth. Nurs. 24(2) (2009) 92-102.

9. Demaret, C., David, J. S., & Piriou, V., When should a nasogastric tube be inserted before a rapid sequence induction?

Look at the x-rays!, Can. J. Anaesth. 58(7) (2011) 662-663.

10. Fattal, M., Suiter, D. M., Warner, H. L., & Leder, S. B., Effect of presence/absence of a nasogastric tube in the same person on incidence of aspiration, Otolaryngol. Head Neck Surg.

145(5) (2011) 796-800.

11. Czarnetzki, C., Elia, N., Frossard, J. L., Giostra, E., Spahr, L., Waeber, J. L., . . . Tramer, M. R., Erythromycin for Gastric Emptying in Patients Undergoing General Anesthesia for Emergency Surgery: A Randomized Clinical Trial, JAMA Surg. 150(8) (2015) 730-737.

Investigating the influence of carbon nanomaterials on the mechanical properties of concrete Nathanial Buettner and mentors Steven Sachs and Leanne Gilbertson Department of Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Significance Statement:

Carbon nanomaterials (CNMs), specifically carbon nanotubes (CNTs) and graphene oxide (GO), are candidates to extend the lifetimes of concrete pavements and therefore reduce the economic burden of bridge rehabilitation and replacement for transportation departments.

Abstract

Buettner

Transportation departments exhaust numerous resources in repairing and replacing concrete pavements deteriorated by weathering and loading. Carbon nanomaterials (CNMs) (i.e. carbon nanotubes (CNTs) and graphene oxide (GO)) are among the materials that can extend the lifetimes of concrete pavements. CNTs Sachs and GO are ideal concrete reinforcement candidates due to excellent mechanical, physical, and chemical properties. GO disperses well in the cementitious matrix and is more effective than CNTs in enhancing the mechanical properties of concrete, but CNT-reinforced concrete is more commonly developed as the material Gilbertson cost of GO is significantly higher.

There are questions remaining on how to optimize the performance (through enhanced dispersion) of CNT-reinforced concrete. The use of superplasticizers and functionalization of the CNTs are the two current leading methods. Results from this study suggest there are performance benefits resulting from mixing CNTs in GO before dispersion in the cementitious matrix. In this experimental investigation, four pavement concrete mixes were designed with variable functionalized CNM and superplasticizer concentrations. The concrete mixes were cast into concrete cylinders for compressive strength and elastic modulus testing. The results of these tests demonstrate how GO and the superplasticizer influence the reinforcing effects of CNTreinforced pavement concrete. Concrete cylinders with 0.1 wt. % (weight by cement) CNT and 0.05 wt. % GO exhibited a significant 15% greater compressive strength than that of concrete cylinders with no CNM addition.

Ingenium 2019

1. Introduction

The Federal Highway Administration estimates that over 18 billion dollars were devoted to rehabilitating National Highway System bridges in 2017 [1]. Bridge pavements experience extensive cracking and loss of strength caused by a combination of weather and use effects. Transportation departments are motivated to extend the lifetimes of these critical transportation materials as a way to reduce the economic burden caused by consistent maintenance of the concrete pavements. Carbon nanomaterials (CNMs), specifically carbon nanotubes (CNTs) and graphene oxide (GO), are candidates to extend the lifetime of concrete by enhancing its mechanical properties. The physical, mechanical, electrical, and thermal properties along with the manipulatable surface of both CNMs have garnered significant attention for concrete reinforcement.

To use CNMs as reinforcing agents for concrete, they must be effectively dispersed in a liquid suspension or solvent and then mixed with the cement. Preliminary results of enhanced concrete and cement paste mechanical properties (i.e. compressive strength, flexural strength, elastic modulus) caused by CNT addition are detailed in the literature [2,3]. However, CNT reinforcement benefits are not maximized as the CNTs tend to bundle together in the cementitious matrix because of large aspect ratio and intermolecular forces [4]. Surface functionalization and mixing the CNTs with superplasticizer are among possible methods to improve CNM dispersion. Some studies have observed the positive effects that GO has on the mechanical properties of concrete and cement paste [5,6], but GO is expensive and may not be economically viable to use alone as reinforcement in concrete pavements. The utilization of CNTs and GO together as reinforcement can be critical to developing a CNM-reinforced concrete mix for commercial use.

There are results in the literature that suggest possible synergistic effects between CNTs and GO on the mechanical properties of cement paste, as GO can enhance the dispersion of CNTs in the cementitious matrix [7].

The aim of this project is to inform the design of CNMreinforced pavement concrete by comparing the mechanical properties of concrete mixes with variable functionalized CNM and superplasticizer addition. Compressive strength and elastic modulus are the two mechanical properties selected for this study, as both quantities provide insight to the concrete’s performance when a load is applied. Unlike previous studies, this study provides compressive strength and elastic modulus data on pavement concrete reinforced with both CNTs and GO. The results demonstrate how small amounts of GO and superplasticizer alter the reinforcing effects of CNT-reinforced pavement concrete. It is hypothesized that GO and the superplasticizer are significant factors in optimizing the mechanical performance of CNTs in the cementitious matrix.

Category: Experimental research

Keywords: concrete, carbon nanotubes, graphene oxide, compressive strength, elastic modulus Abbreviations: carbon nanomaterials (CNMs), carbon nanotubes (CNTs), graphene oxide (GO)

25

2. Methods 2.1 Carbon Nanomaterial Properties Table 1 displays the properties of the CNMs (Cheap Tubes, Inc., VT) used in this study.

Table 1. Summary of CNM Properties Carbon Nanotube Properties Diameter (nm)

10-20

Size (nm)

300-800

3-5

Thickness (nm)

1.4-4.8

Length (µm) Purity Functionalization

Graphene Oxide Properties

95%

Purity

99%

1.8% COOH groups

% Oxygen

45-55%

CNM properties were determined from a literature review. The CNTs are multi-walled instead of single-walled for durability and economic reasons, and the CNT surfaces are functionalized with carboxyl groups.

2.2 Concrete Mix Design Table 2 details the concrete mix proportions used for each concrete mix.

The mix proportions of coarse aggregate, fine aggregate, cement, and water were designed using the Absolute Volume Method from the Design and Control of Concrete Mixtures, 14th edition [8]. The mix proportions of CNMs and superplasticizer were determined from a literature review. The superplasticizer, Sikament SPMN (Sika Corporation U.S.), was not included in the third and fourth mixes to examine the reinforcing effects on the concrete individually from the CNMs. 2.3 Concrete Cylinder Preparation The functionalized multi-walled CNTs were added in equal proportions to two flasks of 450 mL distilled water. These flasks were placed in an ultrasonication bath and sonicated for 60 minutes to disperse the CNTs. For the superplasticizer mix, Sikament SPMN was also sonicated with the CNTs and water. For the GO mix, GO was dispersed using the same process for 45 minutes, and then the CNTs were sonicated in the GO solution for 60 minutes. After ultrasonication, each CNM mixture was added to the remaining concrete ingredients. Concrete mixes of 0.5 ft3 were hand-mixed in accordance to American Society of Testing and Materials (ASTM) C192 and portioned into three 4-inch by 8-inch plastic cylinder molds [9]. The cylinders were demolded after 24 hours and placed in the curing room for 28 days.

Table 2: Concrete mix proportions using a 28-day design strength of 5 ksi. “wt. %” is defined as weight by cement.

Material

Mix 2

Mix 3

Mix 4

Coarse Aggregate (lb/yd )

2055

2055

2055

2055

829

829

829

829

Cement (lb/yd )

680

680

680

680

Water (lb/yd )

299

299

299

299

Superplasticizer (wt. %)

3

3

Mix 1

Fine Aggregate (lb/yd3)

3

0.8

0.8

0

0

Functionalized Multi-Walled CNT (wt.%)

0

0.1

0.1

0.1

Graphene Oxide (wt. %)

0

0

0

0.05

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Figure 1: Example concrete cylinder fracture surfaces (from left to right: Mix 1, Mix 2, Mix 3, Mix 4)

2.4 Compressive Strength and Elastic Modulus Testing After curing, the cylinders were removed from the curing room and placed in a constant temperature bath until testing. After sulfur capping the cylinders, one cylinder from each mix was loaded to compressive failure. This served as the companion specimen for the elastic modulus testing of the other two cylinders. During elastic modulus testing, a harness equipped with a longitudinal strain gauge was placed on the cylinder. Strain measurements were measured while the cylinder was loaded to 40% of the companion specimen’s compressive strength. The harness was then removed from the cylinder before undergoing compressive strength testing. Compressive strength and elastic modulus testing were completed in accordance with ASTM C39 and ASTM C469 respectively [9]. The compressive loads were applied at a constant rate to the cylinders using an Instron 600DX Universal Testing Machine. Partner Materials Testing Software collected stress data from the compression strength testing. Using the longitudinal strain gauge attached to the elastic modulus harness, the elastic modulus is calculated based on the following formula: E = (S2 – S1) / (ε2 - 0.000050) (1) where: E = chord modulus of elasticity, MPa (psi) S2 = stress corresponding to 40% of ultimate load S1 = stress corresponding to a longitudinal strain, ε1, of 50 millionths, MPa (psi) ε2 = longitudinal strain produced by stress S2

Mix 1 and Mix 2 cylinders seem to have a cone fracture surface, while Mix 3 and Mix 4 cylinders clearly show a cone and shear fracture surface. Cone fracture surfaces and cone and shear fracture surfaces indicate proper concrete mix preparation. The results from the compressive strength and elastic modulus testing are shown in Figure 2 and Figure 3 respectively.

Figure 2: ASTM C39 compressive strength results for the four concrete mixes.

An asterisk denotes a statistically significant difference from Mix 1.

The error bars represent the sample standard deviations.

3. Results

Example fracture surfaces for each of the four concrete mixes are displayed in Figure 1.

Figure 3: ASTM C469 elastic modulus results for the four concrete mixes.

The error bars represent the sample standard deviations.

27

For each mix, compressive strength measurements were collected from all three cylinders. Elastic modulus measurements were collected from two of those cylinders as the third cylinder served as the companion specimen.

4. Discussion

The average compressive strength of Mix 4 is approximately 15% greater than that of Mix 1. An addition of just 0.1 wt. % CNT and 0.05 wt. % GO significantly increased the compressive strength of pavement concrete as hypothesized. Surprisingly, the CNTs performed better as compressive strength reinforcement in Mix 3 (9% increase) than Mix 2 (negligible increase). This could be a result of a negative chemical interaction between the superplasticizer and the CNTs. Superplasticizers can be used to enhance the dispersion of CNTs in liquids, but Sikament SPMN was not effective in this study.

The elastic modulus results are positive but not statistically significant. The addition of 0.05 wt. % GO did not increase the elastic modulus of concrete cylinders reinforced with 0.1 wt. % CNT. The elastic moduli of Mix 3 and Mix 4 were approximately 4% greater than that of Mix 1. This shows while the CNMs may have been able to bridge cracks during compressive loading, they did not significantly alter the concrete’s resistance to longitudinal deformation. The lack of longitudinal deformation resistance may be attributed to an unequal CNM distribution in the cementitious matrix. Low CNM concentrations are sometimes difficult to disperse uniformly, and the ultrasonication bath may not have supplied enough power to equally distribute the CNMs in the cementitious matrix.

5. Conclusions

The results of this comparative study demonstrate that incorporating small percentages of GO together with CNTs significantly improves the compressive strength of pavement concrete. While elastic modulus results were not statistically significant, experimental parameters can be altered to potentially achieve greater gains. Considerations should be made regarding the chemical interaction between the superplasticizer and the CNMs when designing CNM-reinforced concrete. Future efforts should be dedicated to finding the optimal superplasticizer, dispersion method, and combination of CNMs to make an economically viable and high strength mix. Beyond optimizing these parameters, there remains a critical need to comprehensively evaluate the performance, costs, and environmental impacts of this concrete in a life-cycle perspective to ensure there is a net benefit of using CNM-reinforced concrete as a commercial product.

Acknowledgments

This summer research fellowship award was funded by the Mindlin Foundation Undergraduate Mentored Research Program and supported by the Swanson School of Engineering. I express my sincere gratitude to Dr. Leanne Gilbertson and Dr. Steven Sachs for mentoring me and Charles Hager for helping me in the laboratory.

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Undergraduate Research at the Swanson School of Engineering

References

1. https://www.fhwa.dot.gov/bridge/nbi/sd2017.cfm 2. Lu et al. “Mechanical Properties and Durability of Ultra High Strength Concrete Incorporating Multi-Walled Carbon Nanotubes”.

Materials 9, 419, 2016.

3. Rhee et al. “Properties of normal-strength concrete and mortar with multi-walled carbon nanotubes”. Magazine of Concrete Research 65, 951-961, 2013.

4. Hilding, J., et al. “Dispersion of Carbon Nanotubes in Liquids”. Journal of Dispersion Science and Technology 24(1), 1-41.

5. Lu, L. and Ouyang, D. “Properties of Cement Mortar and Ultra-High Strength Concrete Incorporating Graphene Oxide Nanosheets”. Nanomaterials 7, 187, 2017.

6. Yang, H. et al. “Experimental study of the effects of graphene oxide on microstructure and properties of cement paste composite”. Composites: Part A 103, 263-272, 2017.

7. Zhou et al. “Enhanced mechanical properties of cement paste by hybrid graphene oxide/carbon nanotubes”. Construction and Building Materials 134, 336-345, 2017.

8. S. Kosmatka et al. Design and Control of Concrete Mixtures, 14th Edition. Portland Cement Association, Skokie, IL, 2002.

9. ASTM C39, C192, C469. ASTM International, West Conshohocken, PA, USA. 2018.

Ingenium 2019

Vision field testing with virtual reality Ava Chonga, Tang Kok Zueab, and mentor Murat Akcakayaa Department of Electrical & Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA b National University of Singapore a

Abstract

This report details the design and integration of comprehensive eye tests in order to develop an intelligent eye examination system through virtual reality.

The existing devices used to conduct eye examinations are time consuming and Chong costly. They are unable to carry out more than one test and require a 1-to-1 specialist to patient ratio throughout the duration of the test from an ophthalmologist or nurse. This project aims to integrate iVESA (Intelligent Virtual Eye Screening Automation) with an existing virtual reality-based prototype from I3 Precision.

Akcakaya A working, autonomous prototype of a glaucoma test that is ready for clinical trials is the ultimate goal from this project. The design on the new tests is guided from specialists, to ensure compliance with existing protocols that serve as the golden standard of physical tests. Implementation for the Oculus Rift VR device is done using Visual Studio Code and Unity, after which the tests are conducted in a pre-clinical setting on volunteer test subjects in conjunction with SNEC (Singapore National Eye Center). The scope of this study concludes upon successful testing and integration of the system. The decision to proceed with formal clinical trials will be left to the discretion of I3 Precision.

Category: Device design

Keywords: Intelligent Eye Examination System, Virtual Reality, Oculus Rift

1. Introduction 1.1 Setting Context Early detection, through regular and complete eye exams, is key to protecting vision and treating damage as soon as possible.

Vision test and eye examination are traditionally practiced in hospitals and other health facilities to diagnose certain diseases such as color blindness, glaucoma, optic neuritis and brain damage. Such eye examinations are overseen by an optometrist or nurse and required 1 on 1 patient to supervisor attention.

The implementation of various test such as the ones discussed in this report (Wilkins, Dry Eye and Glaucoma) are often performed on specialized instruments that cannot be reused easily for other tests. Many of these tests also take a substantial amount of space causing for a need for large testing areas. This makes tests not as easily accessible to patients and the 1 on 1 supervision causes for slow patient turnover rate.

Glaucoma is a group of diseases that cause nerve damage within the eye’s optic nerve and results in vision loss and blindness. Regular glaucoma eye tests are performed in multiple distinct ways. Tonometry Eye drops are used to numb the eye followed by a warm puff of air. Normal eye pressure ranges from 12-22mm Hg with causes of glaucoma having an eye pressure exceeding 20 mm Hg. Ophthalmoscopy looks for nerve damage of the eye using specialized eye drops to dilate the pupil to examine the shape and color of the optic nerve. The perimetry test uses vision field testing the map out the patient’s complete field on vision. During this test, the patient’s response to various flashes of light in different sections of their field of vision are recorded and used to create a representation of the patient’s field of vision. [1].

Simplifying this test and extracting it in such manner as to allow multiple tests to be run at the same time through the nurse station is the goal of this project.

1.2 Research Questions Traditional methods of testing for glaucoma require high precision from the optometrist and therefore take an enormous amount of 1 on 1 time with the patient. Diagnosing glaucoma is not easy and requires careful examination. In this report we will investigate the algorithms and methods used to implement a glaucoma test in virtual reality.

In this report, we will also investigate saving time during eye tests by optimizing the nurse. Instead of having 1 on 1 attention per patient, we will investigate a solution to how multiple patients can be seen at once.

1.3 Conceptual Framework In this study we will attempt to research and develop the following using previous methods and studies. Some framework has already been laid out from previous projects so we will aim to debug and further develop the current project.

The eye tests will be able to be taken by the patient without 1 on 1 supervision from a nurse or doctor. The eye tests will be located within the Oculus rift. There will be visual cues within the eye tests that will take the patient through the entire test. For some tests, voice recognition will be used. The voice recognition software 29

Figure 1: GTX 1080 in the Dell computer

Figure 2: GTX 1080 connected to external fan

will compare the patient’s findings to the correct answers and then output a score for the test for the nurse.

A nurse station will be implemented in order to toggle between multiple patients and keep track of each individual’s personal data. The nurse will be able to monitor up to four patients both visually and numerically.

The computers were connected to the iVESA nurse station with crossfire wires. The nurse station interface allows for monitoring of multiple eye tests. This is shown in Figures 3, 4, 5 and 6.

2. Methods 2.1 Design of the study With the help from Dr. Tang Kok Zuea, the study was broken down into three separate parts.

1. Port the Wilkins Eye Test on the Oculus Rift VR platform a. Implement voice recognition using UWP

2. Set up nurse station a. Connect up to 4 computers implementing the eye tests

3. Implement and develop eye tests in WebVR 2.2 Methods First, an Asus ROG gaming desktop (i7-770 processor, 32 GB DDR4, GTX 1080 graphics) was used to port the Wilkins test. The Oculus Rift was setup with this computer to test the Wilkins test.

Three Dell desktops in the lab were upgraded with Nvidia GeForce GTX 1080 Windforce graphics cards and an external fan. In total, 4 computers were able to run the eye tests at optimal resolution.

The Oculus rift was configured to work on all computers.

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Undergraduate Research at the Swanson School of Engineering

Figure 3: Diagram of nurse station implementation followed

Ingenium 2019

Figure 4: iVESA nurse station with crossfire wire

Figure 5: iVESA nurse station interface

In Figure 4, an example of how the nurse station monitors another computer is shown. In practice, the nurse should be able to see all four computers and results will be recorded as they happen.

Figure 7: Unity application with low graphical requirements

Figure 6: Example iVESA nurse station setup

Visual Studio Code was used to first develop the source code need for the tests. C# is the primary language used to implement the base code. The code is then further developed in Unity to create the graphics and user interface (UI).

Figure 8: Contrast test GUI implementation

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3. Results

During this study, two tests were further developed. The Wilkin’s test consists of a paragraph of words that did not make sense strung together in black font on a white background. The patient is instructed to read the paragraph out loud and speak into a microphone which will record the speech.

The second test was the vision field test. The patient is instructed to look at the center of the screen which consists of a red dot. A white dot will flash across the screen in various areas and the patient is instructed to notify the program whenever a white dot is seen.

Both tests are full implemented in the Oculus rift and completely immerse the patient within the eye exam. The benefits include a controlled setting for the eye exam to take place along within minimizing the amount of space and equipment needed to take the test. Multiple eye exams can be developed for the Oculus and can be taken simultaneously.

4. Data analysis and discussion

The nurse station developed to oversee multiple patient stations was able to capture the patients’ visuals and data from patient tests. However, the original iVESA nurse station only has one crossfire input and therefore can only handle one patient station at once. The quality of the Wilkins reading test was also degraded through the Oculus. Users have noted that the paragraph is blurry and hard to read even with good vision.

The voice recognition software was only able to pick up pronounced and loud feedback. This was troublesome for some trials in which the test patient spoke quietly. The software failed to understand line breaks in the paragraph and multiple words as one single word at some points. If the user read one word wrong, it would subsequently mark all other words after it as wrong.

5. Conclusion

After investigating the current setup, a few conclusions have been reached. The nurse station was not able to handle all computers. An introduction of a router to hook up all the computer will be needed. The router will have four cross fire cables and connect all the computers a central point. The router will convey the computers data to the iVESA nurse station.

Conclusive results as to whether or not the glaucoma test could accurately detect glaucoma could not be reached and there are still some quality and technical issues that need to be resolved within the eye exams. However, the tests made during this study show promising results. With further development and clinical trials, I believe an eye examination system through virtual reality is a feasible reality.

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Acknowledgments

This project was an extension of the tremendous work already done by many theses at the National University of Singapore through the Innovation and Design Center Program. Much of this project was tying loose ends found in previous projects.

I would like to extend the warmest thanks to Dr. Tang Kok Zuea for his support and input as the principle supervisor. His valuable and constructive suggestions directed this project. Our project collaborator, i3 Precision, also contributed much need constructive feedback in bringing the project to the consumerready phase. Specifically, Mr. Lim Teck Sin helped with providing feedback and aiding the project.

Special thanks to the doctors and clinicians at the Singapore National Eye Center (SNEC) who helped by conduction the project’s experimental study with patients. A handful of local high school students were also able to help test the vision test and voice recognition software throughout the lab.

Lastly, I would like to thank the SERIUS program and my home university, the University of Pittsburgh Swanson School of Engineering, for making this opportunity possible for me. It is not without the gracious support from my family that I would be anywhere I am today.

References

1. National Eye Institute (2015), “Facts About Glaucoma,” National Eye Institute [Online]. Available: https://nei.nih.gov/health/ glaucoma/glaucoma_facts. [Accessed 2 July 2018].

2. Lim Keng, Zhi (2017), “Development and Feasibility Study of Digitized Wilkins Rate of Ready Test”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme.

3. Cheng, Shan (2017), “Intelligent Medical Assistant for Comprehensive Eye Examination”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme.

4. Goh Chung, Sern (2018), “Development of a Cloud-Based and Scalable Virtual Reality Platform with Data Analytics”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme.

Ingenium 2019

Myocardin-related transcription factor’s role in cell migration Aidan Dadey, Dave Gau, and mentor Partha Roy Cell Migration Laboratory, Department of Bioengineering, University of Pittsburgh, PA, USA

Significance Statement:

Breast cancer is widespread, as well as a deadly disease from the metastases of the cancer. Due to tumor heterogeneity, breast cancer proves to be difficult to treat.

This study concluded that MyocardinRelated Transcription Factor (MRTF) may be a new molecular target for breast cancer treatment.

Abstract

Dadey

Triple-negative breast cancer (TNBC) has a higher propensity to metastasize and reoccur post-treatment. Treatment of TNBC is difficult because of the tumor heterogeneity and lack of well-defined molecular targets.

Our team conducted various assays Roy on MDA-231 TNBC cells under various testing conditions to determine the role of myocardin-related transcription factor (MRTF). TNBC cells were transiently transfected with a wild-type (WT) form of MRTF, GFP served as a control, for an overexpression random motility assay where the WT treatment group moved more than 50% faster than the control (p<0.01). TNBC cells also had MRTF knocked down (three treatment groups: MRTF-A knockdown (KD), MRTF-B KD, and a double knockdown group) and put through a random motility assay where all three groups showed significant reduction in cell speed, ~50% (p<0.05). The double knockdown group was also used to conduct an actin intensity assay as well as a chemotactic migration assay, both reduced by about 40% (p<0.05). Overall, we concluded that MRTF does play a role in TNBC motility, as well as showing promise for future treatment options.

Category: Experimental research

Keywords: cancer biology, protein expression, genetic knockdown

1. Introduction

Breast cancer (BC) is the second leading cause of cancer deaths in the United States for women, much of which is due to metastasis of the cancer [1]. TNBC accounts for nearly 15% of all BC. Recurrence of metastatic tumors arises from a switch from “dormant” state single cells or non-growing micro-metastasis into an active proliferative state. As such, keeping BC cells in a dormant state and/or slowing the metastatic growth of BC cells should prolong the survival of TNBC patients. The overall goal of this study is to identify new molecular targets for metastatic TNBC. Recent studies have highlighted the importance of actin cytoskeleton regulatory proteins and their upstream regulators in promoting metastatic outgrowth of disseminated BC cells. In response to actin polymerization, myocardin-related transcription factor (MRTF) promotes the activation of serum-response factor (SRF), and in turn SRF-mediated transcription initiates for a wide range of actin cytoskeletal and adhesion regulatory proteins in addition to SRF itself [2]. MRTF has two distinct forms, MRTF-A and MRTF-B, that both interact with SRF in a pathway that we look to highlight in future experiments. Loss of function of either MRTF or SRF dramatically impairs the development of pulmonary metastases from extravasated TNBC cells suggesting critical importance of MRTF/SRF signaling for metastatic colonization of TNBC cells. However, the mechanism by which MRTF regulates BC metastasis and whether MRTF depends on SRF function is still unknown. It is known that MRTF can also promote gene transcription in an SRF-independent manner utilizing its SAPdomain (a putative DNA-binding region of MRTF) function [2].

There is also emerging evidence of the ability of MRTF to influence cancer cell migration and proliferation through its SAPdomain function. Utilizing a proliferation assay that is known to successfully predict the post-extravasation pulmonary metastatic outgrowth competency of BC cells, we have interestingly found that depletion of MRTF but not SRF significantly retards the outgrowth of isolated TNBC cells suggesting that MRTF may also have SRFindependent function. Recently, we reported MRTF plays a key role in positively regulating the expression of actin-binding protein Profilin-1 (Pfn1—an important regulator of actin cytoskeleton dynamics) through an indirect mechanism in an SRF-independent manner that in fact is linked to its SAP-domain function [3].

We hypothesize that MRTF has both SRF-dependent and SRFindependent (SAP-domain dependent) functions promoting different aspects of metastatic colonization such as cell migration, and that Pfn1 is one of the important downstream mediators of SAP-domain-directed MRTF function on migration, unfortunately this study only covers MRTF’s role on migration and not proposed mechanisms. I will perform motility studies to examine the role of MRTF (wild-type) on MDA-231 triple negative breast cancer cell migration [4].

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2. Methods

SiRNA was used to achieve the knockdown of MRTF. MRTFA/B siRNA (Thermo Fisher/Santa Cruz) or control siRNA (Thermo Fisher) was transfected using RNAiMAX (Invitrogen) at 25 nM each for assays, confirmed via western blot, (Figure 1).

Random cell migration assay was performed by time-lapse imaging of MDA-231 cells at one frame a minute for 120 minutes and manually tracking the centroid of the cells through ImageJ. Serum-induced chemotactic migration of MDA-231 cells was assessed in a transwell assay 6 hours after seeding of the cells, (Figures 6&7).

MRTF-A

MRTF-B

Tubulin siRNA

MRTF-A

MRTF-B

MRTF-A&B

Figure 1: siRNA treatment of MDA-231 with either control, MRTF-A, MRTF-B, or both MRTF-A&B siRNA. Tubulin was used as a loading control.

In complementary experiments, MRTF-A overexpression was performed by transiently transfecting cells with flag-tagged MRTF-A constructs (Figures 2&3).

Figure 2: Phase picture of transiently transfected MDA-231 cells.

This is the final frame of the 2-hour random migration movie before fixing and immunostaining.

Figure 6: Crystal violet stained MDA-231 cells transfected with a scrambled control siRNA after chemotactic-directed migration.

Cells were allowed 6 hours to migrate through the transwell, where serum was used as the chemoattractant.

Figure 7: Crystal violet stained MDA-231 cells transfected with MRTF A&B siRNA after chemotactic-directed migration.

Actin cytoskeleton assay was performed by seeding cells on a collagen coated cover slip then later fixing and staining with Rhodamine Phalloidin (Invitrogen), (Figure 9). Western blot was performed to confirm the knockdown of MRTF where siRNA was used.

Figure 3: Immunofluorescence picture of transiently transfected FLAGtagged MRTF-WT MDA-231 cells. Cells that glow indicate successful transient transfection.

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Undergraduate Research at the Swanson School of Engineering

2.1 DATA PROCESSING For the random cell migration assay the distance travelled by the centroid of the cell from frame to frame was tracked and obtained through ImageJ. Speed was determined by taking the distance travelled by dividing it by the time between frames using Excel (Microsoft). Statistical significance was determined with SPSS (IBM) for all assays through one-way ANOVA, and post-hoc analysis was conducted with the Dunnett Test.

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3. Results

The knockdown of MRTF-A and MRTF-B was successful as shown by Figure 1. Overall, the knockdown of each isoform or the double knockdown of both isoforms of MRTF reduced random migration (Figure 5) by: 37% (MRTF-A), 33% (MRTF-B), 50% (MRTF-A&B) TNBC cells also showed reduced chemotactic migration (Figure 8).

Figure 10: siRNA treatment of MDA-231 cells showed about a 40% reduction in actin intensity. Average intensity was then multiplied by cell area to determine total actin fluorescence to reduce error based on larger cells, * denotes p-value<0.05 (0.024).

Conversely the overexpression of MRTF-A caused an increase, 64%, in cell speed (Figure 4).

Figure 5: siRNA treatment of MDA-231 with either control, MRTF-A, MRTF-B, or both MRTF-A&B siRNA showed that MRTF knockdown reduces migration of MDA by almost 50%, *** p-value of <0.001 (0.00057).

Figure 4: Transient transfection of MRTF-A constructs, wild-type (MKL WT) in MDA-231 cells showed that MRTF overexpression increases migration, * denotes p-value of <0.05 (0.0015).

4. Discussion Figure 8: siRNA knockdown of MRTF A&B reduced chemotactic motility by almost 50%, * denotes p<0.05 (0.015).

Actin cytoskeleton changes were brought on through the same treatment (Figures 9&10).

Figure 9: (Left) Rhodamine phalloidin stained MDA-231 cell after transfection with scrambled control siRNA. (Right) MDA-231 cell after transfection with MRTF A&B siRNA. Overall reduction in actin filaments which leads to decreased actin intensity.

Knockdown of MRTF showed to have a statistically significant decrease in random motility, as well as decreases in chemotactic motility 52% and actin intensity 43%. While conclusive data is not available to determine if the changes in actin cytoskeleton account for the observed phenotypes, all the observed phenotypes relate to cellular motility which is directly influenced by the actin cytoskeleton.

The goal was to determine MRTFâ&#x20AC;&#x2122;s role in TNBC migration, then determine if regulation of MRTF can revert TNBC to a less pervasive state. We showed that overexpressed MRTF in MDA231 caused an increase in cellular speed. This increased cellular speed would compare to a more aggressive form of TNBC. Since the overexpression of MRTF showed a more aggressive phenotype, an increase of 64%, we wanted to determine the phenotype of TNBC with MRTF KD. While knocked down levels of MRTF lead to a slower phenotype, both single knockdowns showed similar, 37% (MRTF-A KD), 33% (MRTF-B KD), decreases in cellular speed while the double knockdown showed a 50% reduction in cellular speed.

These slowed TNBC cells displayed a comparative phenotype to a less aggressive form of TNBC. The knockdowns, double and single, show promise as a possible target for therapeutic treatment.

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As for the shortcomings of the project, we had hoped to obtain evidence for the proposed mechanisms for MRTF’s SRF dependent and independent functions and were unable to do so due to time constraints.

In summary, our findings provide an initial proof-of-concept of MRTF inhibition as a potential novel strategy to inhibit growth and migration of TNBC cells.

5. Conclusions

Overall, MRTF’s role in cell motility is still being studied, but we found that overexpression leads to a more motile cell as well as a knockdown of MRTF leads to a more quiescent phenotype, comparable to the data found by Dr. Gau when treating with CCG1423, a drug that targets MRTF [4]. MRTF may be a viable route for TNBC treatment to achieve a more dormant cancer state.

Looking to the future: I plan to explore the differing phenotype between the double knockdown and both single knockdowns shown in Figure 5, as well as gather more information on MRTF motility pathway by comparing different MRTF-A mutants in similar motility assays.

Acknowledgments

The author would like to acknowledge the support from the University of Pittsburgh Swanson School of Engineering, University of Pittsburgh Office of the Provost, and Dr. Partha Roy.

References

1. “Breast Cancer.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 12 June 2018, www.cdc.gov/cancer/breast/statistics/index.htm.

2. Gau, David, and Partha Roy. “SRF’ing and SAP’ing – the Role of MRTF Proteins in Cell Migration.” Journal of Cell Science, The Company of Biologists Ltd, 1 Oct. 2018, jcs.biologists.org/ content/131/19/jcs218222.

3. Joy, Marion, et al. The Journal of Biological Chemistry, American Society for Biochemistry and Molecular Biology, 14 July 2017, www.ncbi.nlm.nih.gov/pmc/articles/PMC5512072/.

4. Gau, David, et al. “Pharmacological Intervention of MKL/SRF Signaling by CCG-1423 Impedes Endothelial Cell Migration and Angiogenesis.” Angiogenesis., U.S. National Library of Medicine, Nov. 2017, www.ncbi.nlm.nih.gov/pubmed/28638990.

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Design of a wearable upper limb exoskeleton Zach Egolf and mentor Nitin Sharma Sharma Lab: Neuromuscular Control and Robotics Laboratory, Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Significance Statement:

Current exoskeletons developed to aid in the rehabilitation of individuals with limited upper extremity function are bulky machines. A hybrid wearable exoskeleton, that incorporates functional electrical stimulation and electric motors, reduces the bulkiness by using smaller and lighter motors. Taking this design improvement into consideration, we developed a wearable exoskeleton.

Egolf

Abstract

Neurological disorders such as stroke, traumatic brain injury, or spinal cord injury can limit upper extremity function, Sharma limiting the performance of Activities of Daily Living (ADL). Many robotic exoskeletons have been developed recently to assist individuals in both ADL and rehabilitation. These exoskeletons are not ideal as they are generally large stationary machines which are only available in clinical settings. In contrast, self-contained, portable, and wearable exoskeletons have the potential to lower costs for treatment. In this study, a single Degree of Freedom (DOF) exoskeleton weighing approximately 1.42 kg was developed for elbow flexion and extension. The prototype was controlled via laptop in one test that simulated a step response to measure the Range of Motion (ROM). The device successfully extended and flexed the arm of two “able-bodied” participants, achieving an ROM of 11° to 103°. In a second test, communication between the exoskeleton and the Simulink real-time environment were evaluated. The exoskeleton was successfully controlled by the Simulink model in real-time. We expect in later tests, with the addition of a Functional Electrical Stimulation (FES) system, people with neurological disorders could potentially regain upper extremity function and mobility.

Category: Device design

Keywords: Activities of Daily Living (ADL), Functional Electrical Stimulation (FES), Series Elastic Actuator (SEA), Wearable Exoskeleton

1. Introduction

Neurological disorders (i.e. stroke, traumatic brain injury, spinal cord injury, etc.) limit or result in the loss of upper extremity function. Current treatment plans consist of physical therapy to manually extend and flex a person’s limbs. Not only can this put a strain on the individuals, family members, and institutions involved, but these traditional regimens are more expensive than newer alternatives as shown by Lo et al. [1]. In their study, Lo et al. compared a robot-assisted therapy (using MIT-Manus) with an intensive human-assisted therapy, and with a common practice therapy, showing that the robot-assisted therapy cost less.

While a plethora of exoskeletons exist, the previously developed exoskeletons are usually bulky machines that confine the person to the location of the machines. One survey of multiple systems showed that the common actuation methods include electric motors, pneumatic actuators, and hydraulic actuators [2].

Each of these actuation methods has design considerations that are expanded upon in Section 2.2. None of the systems reviewed made use of FES, which is an ideal actuation solution as it uses muscles as actuators. However, a system solely comprised of FES actuation will cause a rapid onset of muscle fatigue [3]. Another design consideration is the method of power transmission between the motor/actuator and the joint(s) actuated which is explained later in Section 2.2. Therefore, the development of a wearable exoskeleton utilizing hybrid actuation methods could help lower therapy costs as well as address challenges embedded within existing solutions.

The objective of this project was the initial development of a single DOF exoskeleton for controlling elbow flexion and extension.

We hypothesized that the device would achieve an acceptable ROM from 11° to 103°. If achieved, the development of this device will be considered viable for future rehabilitation studies involving people with limited upper extremity function and the implementation of FES to create a hybrid exoskeleton.

2. Materials and Methods 2.1 Brace Design The brace used for the exoskeleton was a Bregg Telescoping Brace which was modified so that the electric motor could be directly mounted to the brace’s elbow joint. While the ROM of the human elbow is considered to be 0° to 150° [4], it was determined that an acceptable ROM would be 11° to 103°. This decision was based on the brace’s ROM of -10° to 110° and the fact that the brace is currently used in the rehabilitation field. Additionally, the brace has a built-in mechanism to adjust the allowable ROM. This feature is beneficial in the ongoing stages of treatment.

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Motor

Aluminum bracket

Aluminum support

User’s arm

Aluminum bracket

3D printed spacer Breg Brace

Figure 1: A person wearing the wearable upper limb exoskeleton (Left: Front view, Right: Side view). The exoskeleton weighed 1.42 kg.

The brackets used to mount the motor to the brace were made of Aluminum 6061 T6. Equations to determine flexural stress and deflection along with torsional stress and deflection were used to evaluate various bracket designs1. The final prototype can be seen in Figure 1.

2.2 Actuation As stated in section 1, common actuation methods include the use of electric motors, pneumatic actuators, and hydraulic actuators. Any of these three systems could be integrated with an FES system to create a hybrid exoskeleton, however, we decided that electric motors were an ideal choice. Unlike hydraulic and pneumatic actuators, electric motors do not rely on a fluid. This eliminates the need to design for fluid leakage, making the system less complex. In addition, hydraulic and pneumatic actuators require a reservoir, as well as a pump or compressor, which can produce substantial noise and increases the weight of the exoskeleton making them less than desirable for a lightweight exoskeleton. While electric motors are rigid compared to hydraulic or pneumatic actuators, multiple methods of making a more compliant motor have been developed.

Electric motors also offer a wide range of power transmission methods, such as linkage, direct drive, and cable/pulley driven systems. Out of these systems, the exoskeleton uses a direct drive

1. The equations used are available from the authors upon request.

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Undergraduate Research at the Swanson School of Engineering

system, as this is the least complex transmission system since the motor’s and elbow’s centers of rotation may then be aligned.

The electric motor used is a HEBI X8-16 (HEBI Robotics, Pittsburgh, USA). This motor was chosen because it only weighs 500g, it produces up to 16 Nm of torque, and contains the motor, gearbox, and motor drivers. Further, this motor has a built-in Series Elastic Actuator (SEA) which has a spring on the motor output.

SEAs have been used to increase the compliance of the system [5]. In addition to the brace’s mechanism described in section 2.1, ROM limits were set for the motor in the software. The ROM was limited from 11° to 103°. The motor can provide feedback such as torque/force, deflection of the spring, angular position, angular velocity, angular acceleration, current, and voltage.

2.3 Programming and Testing Two tests were developed to initially determine if the exoskeleton was a viable device for future research. The first test (ROM Test) focused on giving the motor a position command in the form of a step signal while an “able-bodied” participant wore the exoskeleton. The participant was asked to relax his arm and not assist the exoskeleton. This test was conducted a second time on a different participant. The data collection focused on the ROM achieved as well as the motor’s generated output torque. The exoskeleton was powered via power supply plugged into a wall outlet and connected via Ethernet to a laptop running the MATLAB script as shown in Figure 2. Initially, the participant’s elbow joint angle was 11° (rest angle). The MATLAB script then sent a position command of 103° to the motor. After a few seconds, the script

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then sent a position command of 11°. The ROM along with the input signal, and output torque generated were then graphed versus time on two separate plots.

Figure 2: In the first test, the laptop simulated a step signal using a MATLAB script. The generated signal is then sent to the motor via an Ethernet connection. The motor provided position and torque feedback to the script.

The objective of the second test (Simulink Command Test) was to see if the motor could be controlled by a Simulink model in real-time. Simulink, a simulation tool packaged with MATLAB, uses blocks containing MATLAB scripts to simulate signals and response. Thus, it has become a common tool for research groups in developing controls. The Simulink model, as shown in Figure 3, consisted of a simple sine wave which was then compiled on a Speedgoat Real-Time Target machine. The Speedgoat then passed the output signal as an analog voltage signal from the I/O board to an ADS115 Analog to Digital Converter (ADC) which had a precision of 16-bits. The digital signals were then sent to a Python script running on a Raspberry Pi 2 Model B (a small form factor computer), and then relayed to the motor as position commands.

The motor and Raspberry Pi communicated via Ethernet over a standard Wi-Fi router. An oscilloscope was connected between the Raspberry Pi and the ADC to visually track the signal sent to the Raspberry Pi. The response was visually observed to confirm that it tracked the input signal in real-time as feedback wasn’t available.

3. Results 3.1 Project Results We expected that the exoskeleton, using a HEBI X8-16 actuator (an SEA), would be able to achieve an ROM of 11° to 103° when worn by an “able-bodied” person. As the graphs in Figure 4 show, the ROM of the exoskeleton was 11° (~0.19 rad) to 103° (~1.80 rad). As shown in Figure 4, the max motor output torque for Participant 1 was +/- 1 Nm and about 6 Nm for Participant 2. The variance in the output torques could be attributed to the different weights of each participant’s arm. Further, it was visually observed that the exoskeleton responded to the input signal in real-time during the Simulink Command Test. Additionally, the exoskeleton weighed 1.42 kg.

Figure 4: ROM Test Results. The exoskeleton achieved an acceptable ROM from 11° to 103°. The max generated output torque was 6 Nm, well below the motor’s max torque of 16 Nm. The orange line is the input command signal, and the blue lines are the exoskeleton’s responses.

Both participants were adult males in their early 20s with no limited upper extremity function.

Figure 3: A Simulink model was created to simulate a sine wave signal on a desktop computer. The model was then compiled on a Speedgoat and sent to an ADS1115 as an analog voltage signal. The signal was then sent to a Raspberry Pi running a Python script. The commands were sent to the motor as positions.

Future work will include adding an MCP4725 to send feedback to the Simulink model. Dashed lines represent future wireless connections.

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Table 1: Comparison of Exoskeleton Results. The exoskeleton we developed (Sharma Lab Exoskeleton) has a smaller ROM. We hope in increase the ROM in the future.

Exoskeleton Achieved ROM Max Motor Output Torque [Nm] Number of DOF

4. Discussion 4.1 Discussion of Results Table 1 summarizes the ROM, motor output torque, and DOF of the exoskeleton in comparison to previously developed exoskeletons by other research groups [4, 6]. While the ROM of the exoskeleton we developed is smaller compared to larger stationary machines, our exoskeleton is more portable. We anticipate that future iterations with additional DOF will still achieve the goal of a light weight and portable platform. The minor disturbances shown in Figure 4 are believed to be caused by the impedance of the system and the compliant nature of the series elastic member.

4.2 Exoskeleton Limitations Although the exoskeleton was deemed viable for future research, it still faces some key limitations. Despite the device being wearable and allowing for a limited range of mobility, it is still not fully portable. It relies on an Ethernet connection to a desktop or laptop computer as well as a power supply plugged into a wall outlet. Additionally, the max potential ROM is restricted from -10° to 110° because of the design of the Bregg Telescoping elbow brace.

As a further matter, there were roadblocks associated with the motor. Currently, feedback cannot be transmitted from the motor to Simulink, which is important in the development of a safe closedloop controller. Participants also stated that the exoskeleton was slightly uncomfortable due to the weight at the elbow joint. This might not matter in a scenario where the individual has someone to assist them in therapy or is resting their arm on another object, however, it defeats the purpose of the exoskeleton allowing the individual to continue treatment independently and comfortably.

Furthermore, the motor was hard to back drive meaning that the exoskeleton didn’t respond to user input as well as anticipated.

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Undergraduate Research at the Swanson School of Engineering

Our Exoskeleton

CADEN-7 [4]

Kiguchi, K. et al. [6]

11° - 103°

0° - 150°

0° - 120°

+/- 6

Not Reported

Not Reported

1

7

1

4.3 Future Work Future research into the development of a hybrid actuated exoskeleton will focus on addressing the issues outlined in section 3.2, incorporating an FES system, increasing the DOF, and will need to move towards experiments involving people with limited, or loss of, upper extremity function. In order to transmit feedback to Simulink, we will add an MCP4725 Digital-to-Analog converter between the Raspberry Pi and the I/O board of the Speedgoat Real-time target machine, as shown in red in Figure 3. Feedback characterizing the fatigue of the individual’s muscles will be collected in future experiments. We will achieve this by implementing an ultrasound sleeve, as the electrical signals from FES may interfere with EMG sensors. Regarding the mobility of the user while wearing the device, we will work to establish Wi-Fi communication between the motor and Raspberry Pi in addition to installing a rechargeable battery pack to the exoskeleton. The exoskeleton can be made more comfortable by eliminating the metal brace and by changing power transmission methods. Instead of using direct drive, a cable pulley transmission will be used, as this will allow for the motor’s weight to be relocated to a different area of the body (i.e. the user’s back). Further, the motor, onboard computer, Wi-Fi router, and battery pack could be housed inside a backpack or on a belt.

5. Conclusion

A single DOF upper limb exoskeleton, comprised of an SEA, was prototyped and tested to see if it could obtain an ROM for use in rehabilitation therapies. Alongside the ability to achieve a viable range of motion, the exoskeleton was observed to track a signal generated in Simulink in real-time. Future work will focus on creating a portable, compliant hybrid exoskeleton utilizing an SEA and FES. An impedance controller will be developed and future studies will evaluate the exoskeleton’s performance when used in therapies for people with limited, or loss of, upper extremity function.

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Acknowledgments

I would like to thank the Swanson School of Engineering, the Office of the Provost, and Kennametal for funding the project.

Additionally, I would like to specially thank Dr. Sharma and his lab for their support and guidance.

References

1. Lo, A.C., et al., Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke. New England Journal of Medicine, 2010. 362(19): p. 1772-1738.

2. Gopura, R.A.R.C., et al., Developments in hardware systems of active upper-limb exoskeleton robots: A review.

Robotics and Autonomous Systems, 2016. 75: p. 203-220.

3. Sharma, N., et al., A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation. Frontiers in Robotics and AI, 2017. 4: p. 68.

4. Perry, J.C., J. Rosen, and S. Burns, Upper-Limb Powered Exoskeleton Design. IEEE/ASME Transactions on Mechatronics, 2007. 12(4): p. 408-417.

5. Ragonesi, D., et al. Series elastic actuator control of a powered exoskeleton. in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011.

6. Kiguchi, K., et al., An exoskeletal robot for human elbow motion support-sensor fusion, adaptation, and control.

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2001. 31(3): p. 353-361.

41

In vivo dopamine sensors for basic neuroscience and biomedical research: A review

Abstract

Dopamine (DA) is a neurotransmitter of the central nervous system (CNS) involved in a multitude of essential cognitive functions and for which dysregulation is implicated in numerous neurological Cui disease processes. Therefore, the study of CNS DA systems is required if we hope to fully characterize the biochemical bases of relevant neural functions and disease pathologies. Measuring the complex, multi-scale temporal profiles of CNS DA signaling with reasonable spatial resolution requires sophisticated instrumentation. Multiple techniques have been developed for measuring DA signaling in vivo, but it may be unclear which technique is most appropriate for a given experimental paradigm due to their specific capabilities and limitations. Here, we cover leading methods for in vivo DA-sensing to provide guidance to neurochemical investigators.

Dopamine (DA) is a monoamine neurotransmitter of the central nervous system (CNS) which undergoes vesicular release and active transporter reuptake [1]. The importance of DA signaling in the CNS has been established for important functions including pleasure and reward motivated behaviors, motor control, and hormonal regulation among others [2-4]. In mediating these functions, DA signaling occurs in two primary modes. Phasic signaling comprises sub-second fluctuations in extracellular (EC) DA and is implicated in learning, controlling conditioned stimuli, drug abuse and appetitive behaviors [2,5,6]. In contrast, tonic signaling arises from modulations in the basal DA-neuron firing rate which maintains local EC DA concentrations that fluctuate over minutes to hours. Tonic signaling is important for mediating phasic signaling effects, motor control and higher order cognitive functions [7,8]. Therefore, it is essential to consider both phasic and tonic signaling for full characterization of a given DA system.

Dysregulation of phasic and tonic DA signaling is implicated in multiple high-impact diseases including major depressive disorder, schizophrenia, substance abuse disorder, attention-deficit/ hyperactivity disorder and Parkinsonâ&#x20AC;&#x2122;s disease [9-12]. Thus, the neurochemical study of CNS DA systems within the context of phasic and tonic signaling holds promise for uncovering the neural basis of natural human functions and disease pathologies.

A direct approach for characterizing in vivo DA systems generally comprises measuring the EC concentration of DA in a local cerebral region over time such that tonic and phasic signaling profiles may be quantified and interpreted. Mice and rats are most commonly used as animal models of the human DA systems, but fish, fly, non-human primate, and human studies have been conducted as well [13-16]. A multitude of techniques have been developed for the measurement of in vivo DA signaling, each possessing unique advantages and limitations. In the following sections we present a thorough discussion of the leading in vivo neurotransmitter sensing technologies for phasic and tonic DA quantification.

Category: Invited review

2. Methods

Noah Freedman and mentor X. Tracy Cui Neural Tissue Engineering Lab, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

Significance Statement:

As new in vivo dopamine measurement technologies are continuously devised and published, determining appropriate techniques for basic science and clinical application as well as avenues for sensor improvements becomes more difficult. This work guides neurochemical investigators through a discussion of leading DA-sensing tools to facilitate these processes.

Freedman

Keywords: dopamine, microdialysis, electrochemical, transgenesis

1. Introduction

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Undergraduate Research at the Swanson School of Engineering

2.1 Microdialysis Microdialysis was introduced in the 70s by Delgado, Ungerstedt and Pycock [17,18] as a minimally invasive tool for sampling extracellular analytes in the CNS. The system remains relatively unchanged and comprises three components: the microdialysis probe, the perfusate, and the ex vivo analysis technique. Microdialysis probes are generally 4 mm in length and 220 Îźm in outer diameter, possessing an inlet channel through the center of the probe, a semipermeable membrane covering most of the outer surface area, and an outlet channel which collects perfusate that has passed along the inner wall of the membrane.

Once implanted into the CNS, the probe is perfused with artificial cerebrospinal fluid at a rate of 0.3-3 Îźl/min and the outlet perfusate is collected every 1-20 min [19,20]. During perfusion, analytes adjacent the dialysis membrane diffuse into the perfusate

Ingenium 2019

driven by concentration gradient. Once the outlet perfusate is collected, compositional analysis may be performed to determine DA concentration by any one of many established techniques including liquid chromatography, microchip electrophoresis, UV analysis, fluorescence, electrochemical detection, mass spectroscopy, and nuclear magnetic resonance [21-25]. Diffusion based collection and flexibility in the choice of the perfusate quantification method has enabled sampling of multiple EC species simultaneously. Numerous studies have reported simultaneous analyte detection in addition to DA including dopac (DO) (a metabolite of DA), 5-hydroxyindoleacetic acid, homovanillic acid, serotonin, glutamate, and aspartate [23,26].

At present, microdialysis sample quantifications may be performed with a maximum temporal resolution of 1 min, thus preventing phasic DA monitoring. Tonic DA signaling may still be observed, but due to difficulties in accounting for diffusion differences at the membrane interface between in vitro and in vivo environments, quantifications are typically not absolute and instead reported as differentials from an initially measured baseline [28,29]. Furthermore, large probe dimensions prevent high spatial resolution monitoring and cause significant tissue damage upon implantation which is exacerbated by the relatively small CNS blood vessel spacing of 60 μm [29,30]. Substantial acute tissue injury confounds DA measurement by perturbing the local neural tissue. It has been demonstrated that the implantation injury of microdialysis is significantly larger than other DA quantification techniques which utilize smaller probes [30]. Additionally, the tissue response to implantation injury limits chronic studies to 10 days, rendering the technique unsuitable for monitoring long-term neurochemical changes throughout disease progression [27,28].

and is limited to differential phasic DA detection [33]. Despite the presence of multiple electrochemically active molecules in the EC space of the CNS, FSCV has demonstrated selective DA detection in the dorsal striatum and nucleus accumbens via DA-selective adsorption to the CFE surface and specialized signal processing methods [32,35,36].

FSCV provides spatial resolution and biocompatibility advantages over microdialysis due to the relatively small size of CFEs, minimal electrode degradation and lower immune response over long-term implantation periods [36,37]. Of significance, a specialized methodology has been devised to enable chronic recording of phasic DA signaling in freely moving animals, allowing measurements for long-term behavioral and disease progression studies [36]. Furthermore, recent efforts have enabled multi-site phasic DA characterizations using microelectrode arrays (MEAs) composed of carbon fibers [38]. Additionally, DA-sensitive FSCV has been demonstrated on gold (Au) substrates, suggesting a potential future application of FSCV using multi-site silicon-based Au MEAs for high spatial resolution phasic DA characterizations [39-40].

A significant limitation of FSCV and other electrochemicallybased sensors is the inability to remove DA signal interference arising from molecules with similar redox profiles. Two such molecules are norepinephrine (NE) and DO, which both possess redox potentials and profiles indistinguishable from those of DA [40]. Thus, application of FSCV for DA detection is limited to neural regions with relatively low concentrations of NE and DO such as the dorsal striatum and nucleus accumbens [34]. Despite these limitations, FSCV at CFEs represents the most popular tool at present for measuring phasic DA signaling in vivo.

2.2 Fast-scan cyclic voltammetry Fast-scan cyclic voltammetry (FSCV) is an electrochemical quantification method designed to measured phasic DA signaling and has been used in neurochemical analysis since the late 80’s when introduced by Wightman [31]. DA is an electrochemically active molecule which undergoes reversible oxidization to dopamine-o-quinone if subjected to sufficiently high and low voltages in sequence, in the process exchanging a pair of electrons with the oxidizing and reducing substrate. FSCV exploits this phenomenon through implanting a 50 μm length and 7 μm diameter carbon-fiber microelectrode (CFE) into the brain followed by repeated triangle waveform voltage application at 10 Hz while the CFE current is measured [32]. CFEs possess high affinity for DA adsorption, and as the voltage scan passes DA oxidation and reduction (redox) potentials each surface-adsorbed DA molecule exchanges 2 electrons with the CFE as it undergoes redox reactions. These redox currents are masked by simultaneously induced background capacitive currents as the CFE surface repeatedly charges and discharges with ionic species. Therefore, to isolate DA redox signals for quantification, it is necessary to subtract a reference scan with a similar capacitive current profile collected shortly before the scan of interest [31]. Due to the unstable nature of background capacitive currents over time, traditional FSCV may not be applied for tonic DA characterization

2.3 Fast-scan controlled adsorption voltammetry In vitro fast-scan controlled adsorption voltammetry (FSCAV) was first introduced in 2013 by Heien [41] as a modification of the traditional FSCV waveform to enable absolute DA quantification at CFEs. Measurements begin with a brief period of 100 Hz FSCV application to minimize DA adsorption at the CFE surface. This is followed by a prolonged period of -0.4 V static potential application which induces DA-adsorption surface in a controlled manner such that adsorption equilibrium is reached with a final surface content proportional to the bulk concentration. Finally, a second round of 100 Hz FSCV is performed to monitor redox activity as DA diffuses away from the surface until a new FSCV equilibrium is reached distinct from the static potential equilibrium. To quantify the local DA concentration, DA redox currents are isolated throughout the FSCV diffusion period using reference scans from the initial FSCV DA minimization period. Redox signals are then integrated across all scans to obtain a final charge value which correlates linearly with local DA concentration in vitro. This procedure may be repeated every 20 s, achieving final temporal and spatial resolutions for absolute tonic DA signaling quantification much greater than those of microdialysis [41]. Successful in vivo FSCAV applications have since been performed for basal DA quantification in the mouse nucleus accumbens [42].

43

As FSCAV depends extensively on the DA adsorption properties of carbon, it is unclear if this method may be adopted for a non-carbon electrode such as MEAs with metal electrode sites for enhanced spatial information. Although carbon fiber based MEAs may be employed, lithographically fabricated MEAs provide a significant advantage with regards to fabrication ease and spatial resolution [38,39]. Thus, FSCAV is presently limited in its ability to resolve tonic signals with high spatial resolution and range. Additionally, FSCAV suffers the same electrochemical interference issues of NE and DO experienced with FSCV, thereby limiting application to brain regions with low concentrations of these molecules.

3. Conclusion

2.4 Genetically encoded fluorescent indicators Recently, an alternative approach for in vivo DA quantification was introduced comprising transgenesis of fluorescent indicators.

Sun et al. [13] have demonstrated a novel G-protein-coupled receptor (GPCR) fluorescent indicator by covalently linking a circular-permutated enhanced green fluorescent protein (cpEGFP) with the DA GPCR D2 receptor (D2R) expressed on the post-synaptic membrane. Upon DA binding following vesicular release from the pre-synaptic membrane, D2R undergoes a conformational change, in turn conferring greater fluorescence to the cpEGFP by means of altered arrangement. After optimization, the GPCR-activationbased-DA (GRABDA) sensor was able to detect DA fluctuations via associated fluorescence with nanomolar specificity and selectivity over NE. Response times were found to be approximately 100 ms, representing sensor kinetics slower than those of FSCV yet still fast enough to record phasic signaling events. GRABDA may be integrated into animal models via transgenesis, and successful in vivo measurements were demonstrated in Drosophilia via 2-photon imaging, zebrafish via fluorescence imaging and in the dorsal striatum of transgenic mice via imaging with an implanted optical fiber of approximately 100 μm diameter. Zebrafish and mouse measurements were performed in freely moving animals, allowing real-time monitoring of phasic DA events during complex behaviors [13].

GRABDA sensors are not capable of detecting actual dopamine concentrations, but rather apparent concentration changes in the form of altered fluorescence relative to a baseline value. Therefore, only apparent differential phasic DA detection is possible.

Additionally, measurements in non-transparent organisms such as the mouse were only possible through implanting optical probes near the size of typical microdialysis probes. Thus, optical probe implantation injury is a concern and chronic application remains in question [13,30].

1. Girault, J. et al., The Neurobiology of Dopamine Signaling.

Arch Neurol. 2004;61(5):641–644.

2. Brooks, 2001. Functional imaging studies on dopamine and motor control. J. Neural Transm. 108(11), 1283-1298.

3. Liu, X. et al., Dopamine Regulation of GonadotropinReleasing Hormone Neuron Excitability in Male and Female Mice, Endocrinology, Volume 154, Issue 1, 1 January 2013, Pages 340–350.

4. Urban, N.L. et al., 2012. Imaging human reward processing with positron emission tomography and functional magnetic resonance imaging. Psychopharmacology (Berl.) 221(1), 67-77.

5. Caprioli, Daniele et al. “Loss of phasic dopamine: a new addiction marker?” Nature neuroscience vol. 17,5 (2014): 644-6.

6. Wanat, Matthew J et al. “Phasic dopamine release in appetitive behaviors and drug addiction” Current drug abuse reviews vol. 2,2 (2009): 195-213.

7. Schultz, W., 1998. Predictive Reward Signal of Dopamine Neurons. J. Neurophysiol. 80 1-27.

8. Schultz, W., 2007. Multiple Dopamine Functions at Different Time Courses. Annu. Rev. Neurosci. 30(1), 259-288.

9. Belujon, Pauline and Anthony A Grace. “Dopamine System Dysregulation in Major Depressive Disorders” international journal of neuropsychopharmacology vol. 20,12 (2017): 1036-1046.

10. Cook, E H et al. “Association of attention-deficit disorder and the dopamine transporter gene” American journal of human genetics vol. 56,4 (1995): 993-8.

11. Damier, P et al., The substantia nigra of the human brain: II. Patterns of loss of dopamine-containing neurons in Parkinson’s disease, Brain, Volume 122, Issue 8, 1 August 1999.

12. Grace, A.A., 2016. Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nature reviews. Neuroscience vol. 17,8 (2016): 524-32.

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Perfect in vivo DA sensing technologies which enable simultaneous phasic and tonic DA measurements with high spatial and temporal resolution have yet to be developed. Nonetheless, numerous sensing methods are available for phasic and tonic DA system characterization. Although each possess limitations, it is possible to select sensing methods appropriate to the experimental design without sacrificing substantial information loss. Efforts to improve in vivo DA sensing, along with detection of other functionally relevant neurotransmitters, is ongoing.

References

Ingenium 2019

13. Sun, F. et al., “A Genetically Encoded Flourescent Sensor Enables Rapid and Specific Detection of Dopamine in Flies, Fish, and Mice” Cell. Vol. 174, I2. P481-496.E19 Jul 12, 2018 14. Bickel, Stephan and Daniel C Javitt. “Neurophysiological and neurochemical animal models of schizophrenia: focus on glutamate” Behavioural brain research vol. 204,2 (2009): 352-62.

15. Perona, M.T.G et al. “Animal models of depression in dopamine, serotonin, and norepinephrine transporter knockout mice: prominent effects of dopamine transporter deletions” Behavioural pharmacology vol. 19,5-6 (2008): 566-74.

16. Volkow, N.D. et al., “Imaging endogenous dopamine competition with [11C]raclopride in the human brain” Synapse.

Vol. 16 Issue 4. April 1994. Pages 255-262.

17. Delgado JMR, DeFeudis FV, Roth RH, Ryugo DK, Mitruka BK. Dialytrode for long term intracerebral perfusion in awake monkeys. Arch Int Pharmacodyn. 1972;198:9–21.

18. Ungerstedt U, Pycock C. Functional correlates of dopamine neurotransmission. Bull Schweitz Akad Med Wiss.

1974;1278:1–13.

19. Chefer, Vladimir I et al. “Overview of brain microdialysis” Current protocols in neuroscience vol. Chapter 7 (2009): Unit7.1.

20. Gu, H. et al., 2015. In Vivo Monitoring of Dopamine by Microdialysis with 1 min Temporal Resolution Using Online Capillary Liquid Chromatography with Electrochemical Detection. Anal.

Chem. 87(12), 6088-6094.

21. Cheng, G.W. et al., On-line Microdialysis Coupled with Liquid Chromatography for Biomedical Analysis, Journal of Chromatographic Science, Vol 47 (8), 1 Sept. 2009, 624–630.

22. Eeckhaut A.V. et al. “The absolute quantification of endogenous levels of brain neuropeptides in vivo using LC-MS/MS” Bioanalysis vol. 3 NO. 11, 7 June 2011.

23. Kehr, J. “Determination of glutamate and aspartate in microdialysis samples…” J. Chromatogr. B Biomed. Sci. Appl., 708 (1998), pp. 27-38.

24. Mabrouk, O.S. et al. “Microdialysis and mass spectrometric monitoring of dopamine and enkephalins in the globus pallidus…” Journal of neurochemistry vol. 118,1 (2011): 24-33.

25. Saylor, R.A. et al., “A review of microdialysis coupled to microchip electrophoresis for monitoring biological events” Journal of chromatography. A vol. 1382 (2015): 48-64.

26. Chaurasia, CS et al., (1999) “In vivo on-line HPLCmicrodialysis: simultaneous detection of monoamines and their metabolites in awake freely-moving rats” J. Pharm. and Bio. Anal.

Vol 19.

27. Bassareo, et al., 2015. Monitoring dopamine transmission in the rat nucleus accumbens shell and core during acquisition of nose-poking for sucrose. Behav. Brain Res. 287, 200-206.

28. Ngo, Khanh T et al. “Monitoring Dopamine Responses to Potassium Ion and Nomifensine by in Vivo Microdialysis…” ACS chemical neuroscience vol. 8,2 (2017): 329-338.

29. Nesbitt, Kathryn M et al. “Pharmacological mitigation of tissue damage during brain microdialysis” Analytical chemistry vol. 85,17 (2013): 8173-9.

30. Kozai, Takashi D Y et al. “Brain tissue responses to neural implants impact signal sensitivity and intervention strategies” ACS chemical neuroscience vol. 6,1 (2014): 48-67.

31. Wightman, R.M., May, L.J., Michael, A.C., 1988.

Detection of Dopamine Dynamics in the Brain. Anal. Chem. 60(13), 769A–779A. 32. John CE, et al. “Fast Scan Cyclic Voltammetry of Dopamine and Serotonin in Mouse Brain Slices” EChem. Methods for Neuro. Boca Raton (FL): CRC Press/Taylor & Francis; 2007.

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33. Oh, Y. et al. (2016) “Monitoring In Vivo Changes in Tonic Extracellular Dopamine Level by Charge-Balancing Multiple Waveform Fast-Scan Cyclic Voltammetry” Anal. Chem.

34. Heien, M.L.A.V. et al. “Resolving Neurotransmitters Detected by Fast-Scan Cyclic Voltammetry” Anal. Chem., 2004, 76 (19), pp 5697–5704. DOI: 10.1021/ac0491509 35. Robinson, D.L., Venton, B.J., Heien, M.L.A.V., Wightman, R.M., 2003. Detecting Subsecond Dopamine Release with FastScan Cyclic Voltammetry in Vivo. Clin. Chem. 49(10), 1763-1773.

36. Clark, J. J., et al. (2010) “Chronic microsensors for longitudinal, subsecond dopamine detection in behaving animals” Nat. Methods 7, 126−129.

37. Patel, Paras R et al. “Chronic in vivo stability assessment of carbon fiber microelectrode arrays” Journal of neural engineering vol. 13,6 (2016): 066002.

38. Zachek, Matthew K et al. “Microfabricated FSCVcompatible microelectrode array for real-time monitoring of heterogeneous dopamine release” Analyst vol. 135,7 (2010): 1556-63.

39. Kovacs, G. et al., “Silicon-substrate microelectrode arrays for parallel recording of neural activity in peripheral and cranial nerves”, IEEE Trans. Biomed. Eng., vol. 41, pp. 567-577, 1994.

40. Zachek, Mathew K et al. “Electrochemical dopamine detection: Comparing gold and carbon fiber microelectrodes using backgrounds substracted fast scan cyclic voltammetry” Journal of Electroanalytical Chemistry, Volume 613, Issues 1-2, 15 March 2008, Pages 113-120.

41. Atcherley, et al., 2013. “Fast-Scan Controlled-Adsorption Voltammetry for the Quantification of Absolute Concentrations and Adsorption Dynamics” Langmuir 29(48), 14885-14892.

42. Atcherley, C.W. et al., 2015b. “The coaction of tonic and phasic dopamine dynamics” Chem. Commun. 51(12), 2235-2238.

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Diffusion tensor image analysis of stroke damaged brains treated with combined neural stem cell and physical therapy Lauren Grice1,3, Harman Ghuman1,3, Franziska Nitzsche1,2, Madeline Gerwig1,4, Jeffrey Moorhead1,2, Nikhita Perry1,3, Alex Poplawsky1,2, Brendon Wahlberg1,2, Fabrisia Ambrosio1,5, and mentor Michel Modo1,2,3 McGowan Institute for Regenerative Medicine, Department of Radiology, 3Department of Bioengineering, 4Department of Neuroscience, 5 Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA 1 2

Grice

Significance Statement:

Stroke is a major cause of disability and is treated with physical therapy, which provides insufficient functional recovery.

This study seeks to understand the role neural stem cells could serve in future treatment. Importantly, it is shown the effects of physical and stem cell therapy on tissue microstructure are sub-additive.

Modo

Abstract

Ischemic stroke due to middle cerebral artery occlusion (MCAo) causes long-lasting functional impairments. Physical therapy (PT) in the form of aerobic exercise has shown to improve behavioral function of stroke patients and is currently the only treatment option used. As PT is limited in its curative ability, Neural Stem Cell (NSC) therapy is a promising future treatment that could support tissue repair and remodeling.

As a hypothesis, the combination of human NSC injection and physical therapy in a rat model of stroke may improve therapeutic efficacy compared to either treatment alone. In this study, adult male Sprague-Dawley rats underwent transient MCAo then received either no treatment or treatment of NSCs, exercise, or a combination of the two. Diffusion tensor magnetic resonance imaging (DTI) scans were acquired immediately after stroke and 11 weeks after treatment. DTI showed tissue microstructure improved in the Cell and Exercise treatment groups, but not in the combined treatment group. These results suggest a sub-additive therapeutic effect of cell and physical therapy.

Category: Experimental research

Keywords: ischemic stroke, physical therapy, neural stem cells, diffusion tensor imaging

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1. Introduction

Stroke is the leading cause of adult disability in the United States and results in the physical and cognitive impairment of more than 795,000 Americans annually [1]. Currently, physical therapy (PT) is the only approved intervention after stroke and is used to help patients re-learn behavioral functions. It has been found that PT in the form of aerobic exercise (AE) induces a cascade of events that upregulates neurotrophic growth factors in the brain that in turn increase neuroplasticity, neurogenesis, and angiogenesis. In fact, previous studies have shown cellular changes induced by AE are responsible for the improvement of motor skill acquisition in humans [2]. Despite its benefits, however, AE cannot fully replace cells and tissue lost from stroke and is therefore limited in its ability to provide full functional recovery [3].

Advances in regenerative medicine have turned to the use of human neural stem cells (NSCs) because of their potential to replace damaged neurons, integrate with host tissue, provide trophic support, and regulate immune responses [4]. Because of these qualities, NSCs may encourage tissue remodeling through functions of dendritic plasticity and axonal rewiring [5]. Supporting experiments confirmed that transplantation of NSCs reduced the infarcted area and promoted the recovery of neurological function in animal models with ischemic stroke [6]. The exact mode of action of NSCs remains elusive, however, and many questions arise regarding the possible interaction between stem cell and physical therapy. Therefore, this project serves to combine aerobic exercise and NSC therapy to assess their concomitant anatomical effects. It is hypothesized that AE will create an environment in the brain that promotes NSC survival and the formation of new synapses between implanted cells and host tissue. Therefore, the combination of human NSC injection and AE in a rat model of ischemic stroke may yield a more efficacious therapy than either treatment alone.

In this study, T2-weighted, diffusion tensor imaging (DTI) was used to quantify changes in neuronal connectivity in the brains of rats. DTI is a specialized form of magnetic resonance imaging that is sensitive to the magnitude and orientation of water movement [7]. Software can be used to detect the water diffusion vectors in an image to calculate the fractional anisotropy (FA), or the directional dependence of diffusion in tissue. Values for FA range from 0 to 1, with higher values suggesting greater microstructural organization of tissue [8]. Furthermore, the vectors in a DT image can be weighted by FA and â&#x20AC;&#x153;tracedâ&#x20AC;? to create streamlines, or three-dimensional representations of bundles of neuronal connections.

2. Methods 2.1 Experimental Design Throughout the entirety of the study, adult male Sprague Dawley rats were kept on a 12-hour light/dark schedule with constant access to food and water. Upon arrival, the rats (n=100) underwent one week of acclimatization to avoid stressors from transport. The next week, rats were randomly allocated to act as

Ingenium 2019

Week

Animal Arrival

MCAo Stroke

DTI

NSC Injection (Treatment Start)

DTI, Perfusion

-3

-2

-1 pre time point

0

11 post time point

Figure 1: Experimental Design. Timeline of experimental procedures including when DTIs were taken. Week 0 indicates the start of all treatments.

healthy controls or to undergo middle cerebral artery occlusion (MCAo). Success of MCAo was determined one week after stroke surgery by DTI. Next, animals that received MCAo were randomly divided into four experimental treatment groups: no treatment (MCAo only), NSC treatment (Cells), aerobic exercise (Exercise), and combined NSC and exercise treatment (Combined). Eleven weeks after treatment began, animals underwent T2-weighted DTI before they were sacrificed via overdose of FatalPlus.

2.2 Stroke Model: Middle cerebral artery occlusion Healthy control rats (n=9) underwent sham surgery and received a neck incision to ensure the blinding of researchers.

The rest of the animals (n=91) underwent transient intraluminal right middle cerebral artery occlusion. Rats subjected to MCAo were anesthetized using isoflurane and incised on the ventral side of the neck to expose the common carotid artery. Then, a 5-0 silicone rubber-coated monofilament was inserted and advanced to the ostium of the middle cerebral artery (MCA) in the circle of Willis where it remained for 70 minutes. Then, the filament was drawn back to the carotid bifurcation, wounds were closed, and animals recovered from anesthesia. Animals that did not recover within 5 days after surgery or exhibit signs of MCAo damage during post-operative care were not included in the study (n=25).

The remaining rats underwent T2-weighted DTI 10 days post MCAo surgery to verify success of ischemic stroke. Animals with hemorrhagic stroke or lesions <10 mm3 were excluded (n=27).

The animals that remained and had equivalent lesion volumes (n=39) were randomly assigned to one of the following treatment groups: MCAo only (n=13), Cells (n=8), Exercise (n=9), Combined (n=9).

2.3 Cell Therapy: NSC Implantation Surgery CTX0E03 clinical grade human neural stem cells were used in this study. Cells were cultured and once they had reached 80%

confluence, were dissociated from the flasks and formulated in a 50,000 cells/μL concentration of Hypothermisol with >86% cell viability. Then, while anesthetized with isoflurane, the Cells and Combined groups received a 4.5 μL cell suspension in the periinfarct area at a rate of 1 μL/minute (450,000 cells per rat).

2.4 Aerobic Exercise: Means of Physical Therapy Aerobic exercise on a treadmill was administered to the Exercise and Combined groups. Treadmill running was used because it is representative of current clinical practices. To account for differing athletic abilities, each rat ran at 80% of its maximum capacity (determined using the Bruce protocol) for 30 minutes, 5 days/week.

2.5 Magnetic Resonance Imaging At the pre- and post-treatment time points, rats underwent T2-weighted, magnetic resonance imaging (TurboSpin Echo sequence, TR=5891ms, TE=40ms, 10 Averages, 60 slices, 0.3mm slice thickness, FOV=30x30, Matrix=192x192, Acquisition Time=25 min, on a 9.4T Bruker microimaging system).

Additionally, diffusion tensor imaging (DTI, Spin Echo Sequence; TR=2500ms; TE=19ms; 4 Averages; number of directions=6; 60 slices, slice thickness=0.3mm, FOV=30x30; Matrix=96x96; Acquisition Time =85 min) was performed.

2.6 Data Processing Using DSI Studio, a tractography software, three-dimensional maps of anatomical regions of interest (ROIs) were manually drawn on the MR images to delineate the motor cortex (MC), somatosensory cortex (SMC), thalamus, and striatum (STR).

The ROIs were drawn by selecting appropriate voxels on each slice of the DT image. Then, the selected voxels for each ROI were compiled to create a 3-D representation from which a corresponding FA value and streamline count were extracted, as shown in figure 2.

Figure 2: Regions of Interest and Streamlines. The left image shows 3-D ROIs on the coronal plane as viewed from a posterior perspective, with the stroke shown in the right hemisphere. The middle image shows streamlines calculated from each ROI. The image on the right shows streamlines without overlaid ROIs. The color of a streamline indicates its direction in a 3-D coordinate system.

47

Table 1: Tractography Parameters. The above parameters were input into DSI Studio’s tractogaphy algorithm to trace streamlines starting in one region and ending in another at a specified step size. R and L indicate ‘right’ and ‘left’ hemispheres. Seed density describes the factor multiplied to the volume of the starting region; one streamline is generated from each seed. The FA termination threshold serves to prevent software from tracing fibers in voxels where robustness is not assured. The angular threshold and min/max length parameters indicate the max desired dimensions of the streamlines.

Starting Region

Ending Region

Seed Density

Angular Threshold

Min Length (mm)

Max Length (mm)

Step Size (mm)

Termination Threshold

R STR

R MC

x100

60

2.2

10

0.15

FA = 0.02

R STR

R SMC

x100

60

2.2

10

0.15

FA = 0.02

L MC

R MC

x100

60

2.7

12

0.15

FA = 0.02

Then, tractography was performed to trace streamlines starting in the ipsilateral, or right, striatum and ending in the ipsilateral motor cortex/somatosensory cortex. Streamlines starting in the contralateral, or left, MC and ending in the ipsilateral MC were also calculated. Table 1 below describes the parameters used for each tractography procedure.

2.7 Statistics For each measure in each ROI, an ordinary one-way ANOVA was performed followed by a Tukey’s post hoc test to determine what experimental groups were significantly different. A significance of p<0.05 was used.

3. Results 3.1 Measures of FA The results show Exercise alone significantly increased FA in the MC, SMC, and striatum (p<0.05). Conversely, NSCs alone increased FA in MC, SMC, and thalamus (p<0.05). As shown in figure 3, the Combined group had no FA increase in any of the ROIs.

Figure 3: Measures of FA and number of streamlines in each ROI. Graphs show percent change from 1 week after stroke to 11 weeks after with standard error of the mean. Only ROIs from the ipsilateral hemisphere were considered.

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Figure 4: Number of streamlines between two brain regions. Graphs show percent change form 1 week after stroke to 11 weeks after with standard error of the mean.

(A) Streamlines calculated start in the first region and end in the second (i.e. STR-MC represents the number of streamlines starting in the STR and ending in the MC).

STR, MC, and SMC ROIs are from the ipsilateral hemisphere. (B) Streamlines calculated start in the left MC and end in the right MC.

3.2 Streamline Data The streamline data in figure 3 show that the transplantation of cells increased the streamline count in the MC (39.4%), while exercise increased streamlines in the thalamus (37.0%) and SMC (61.7%). Figure 4A shows exercise resulted in a significant increase of streamlines between the striatum and MC and the striatum and SMC (p<0.05). The cells group showed an increase of neuronal connections between the left and right MC, as shown in figure 4B.

4. Discussion

Since FA has been shown to be sensitive to the diameter, density, distribution, and directional coherence of axons, an increase of FA in this study was considered an indication of increased anatomical organization [9]. Therefore, the results show exercise and NSCs have varying effects on tissue microstructure in different brain regions. NSCs appear to have the greatest influence in the MC, as both the FA and number of streamlines increased after treatment. Using the same reasoning, exercise appears to be most effective in the SMC. Furthermore, exercise therapy also appears to increase interregional connectivity between the striatum and cortex. Since most of the ipsilateral cortex is damaged from MCAo, the striatum may be the region responsible for reformation of connections with the stroke lesion during exercise treatment.

The graph of interregional connections between the left and right MC (figure 4B) shows cells again had an effect in the MC. In a previous study, it was found that stem cells appear to increase fiber-density between the contralesional motor cortex and white matter regions like the caudate and putamen, which make up the dorsal striatum [10].

Therefore, it is possible in our study the striatum was involved in the formation of connections between the left and right motor cortex post stem cell transplant.

Importantly, compared to either NSC or exercise treatment alone, combined therapy has little effect on FA and streamline count in each ROI, as well as interregional connections. Therefore, it appears exercise and NSCs do not have a greater therapeutic

efficacy when combined, but rather their effects are sub-additive in regard to microstructural change. This apparent ineffectiveness may imply structural recovery occurs through a Hebbian mechanism, meaning the coincident presynaptic and postsynaptic activity contributes to the gain of functioning synapses [11]. In other words, although the environment created by aerobic exercise promotes neuroregeneration and axonal rewiring, it is not enough to form new connections between host tissue and implanted cells in a combined therapy. It is possible that each session of aerobic exercise must be followed by a repetitive behavioral exercise for combined treatment to form and maintain new circuits. This concept has been supported by physical therapy research involving humans. Exercise programs for individuals with chronic stroke that combined aerobic and resistance training performed at moderate ratings of perceived exertion improved patients executive function and memory [12,13].

5. Conclusions

DTI used in this study served to gain an accurate, comprehensive, and longitudinal characterization of the functional changes in tissue within the brains of treated and untreated animals post stroke. It is important to note that the data acquired from DTI does not provide conclusive insights about the biological/ cellular mechanisms of structural change, but simply quantifies the magnitude of anatomical change in correlation with type of treatment [6]. From the results of this study then, it can be concluded that NSCs and PT sub-additively improve microstructural integrity and interregional connectivity. Stem cells appear to improve microstructure in the MC and exercise in the SMC, while combined treatment demonstrates no greater therapeutic effect.

Furthermore, the results suggest that the striatum plays an integral role in improving interregional connectivity between the striatum and cortex post stroke and may be the structure most affected by transplanted NSCs. Therefore, the findings of this study show novel characteristics of NSC function in brain with stroke injury and should be accounted for when considering combined NSC and physical therapy.

49

Acknowledgments

This study received funding from the Alliance for Regenerative Rehabilitation Research & Training (AR3T), which is supported by the Eunice Kennedy Shiver National institute of Child Health and Human Development (NICHD). Funding was under award number P2CHD086843. Lauren Grice was supported by the University of Pittsburgh Swanson School of Engineering and Office of the Provost.

References

1. E.J. Benjamin, M.J. Blaha, S.E. Chiuve, et al, Heart disease and stroke statistics, Circulation 135 (2017) 146-203.

2. C. Mang, K.L. Campbell, C.J.D. Ross, L.A. Boyd, Promoting Neuroplasticity for Motor Rehabilitation After Stroke: Considering the Effects of Aerobic Exercise and Genetic Variation on BrainDerived Neurotrophic Factor, Physical Therapy (2013) 1707-1716.

3. M. Endres, K. Gertz, U. Lindauer, et al, Mechanisms of stroke protection by physical activity, Ann Neurol 54 (2003) 582590.

4. X. Zhang, F. Du, D. Yang, C. Yu, et al, Transplanted bone marrow stem cells relocate to infarct penumbra and co-express endogenous proliferative and immature neuronal markers in a mouse model of ischemic cerebral stroke, BMC Neuroscience 11 (2010) 1-9.

5. R.H. Andres, N. Horie, W. Slikker, et al, Human neural stem cells enhance structural plasticity and axonal transport in the ischemic brain, Brain 134 (2011), 1777-17789.

6. D. Kalladka, K. Muir, Brain repair: cell therapy in stroke, Stem Cells Cloning 7 (2014) 31- 44.

7. S. Mori, J. Zhang, Principles of diffusion tensor imaging and its applications to basic neuroscience research Neuron 51 (2006), 527-539.

8. C.H. Sotak. The role of diffusion tensor imaging in the evaluation of ischemic brain injury â&#x20AC;&#x201C; a review, NMR Biomed 15 (2002) 561-569.

9. A.L. Alexander, J.E. Lee, M. Lazar, A.S. Field, Diffusion tensor imaging of the brain, Neurotherapeutics 4 (2007), 316-329.

10. C. Green, A. Minassian, S. Vogel, et al Sensorimotor Functional and Structural Networks after Intracerebral Stem Cell Grafts in the Ischemic Mouse Brain. JNeurosci 38 (2018), 16481661.

11. Lisman J. Glutamatergic synapses are structurally and biochemically complex because of multiple plasticity processes: long-term potentiation, long-term depression, short-term potentiation and scaling. Phil. Trans. R. Soc. 2017. B 372, 12. P.M.Kluding, B.Y. Tseng, S.A. Ballinger, Exercise and Executive Function in Individuals With Chronic Stroke: A Pilot Study, J Neurol Phys Ther 35 (2011) 11-17.

13. D. Rand, J.J. Eng, T. Liu-Ambrose, A.E. Tawashy, Feasibility of a 6-Month Exercise and Recreation Program to Improve Executive Functioning and Memory in Individuals with Chronic Stroke, Neurorehabilitation and Neurorepair 24 (2010) 722-729.

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Undergraduate Research at the Swanson School of Engineering

Ingenium 2019

Delamination of soft thin films from dynamic wrinkling substrates Joseph Hamm and mentor Sachin Velankar Department of Chemical Engineering, University of Pittsburgh, PA, USA

Significance Statement:

Fouling poses costly penalties on numerous industries. Self-renewing surfaces offer an effective and sustainable alternative to current solutions. Actuated topography offers a technique for foulant delamination but relations between topographic size and delamination mechanism have yet to be accurately defined. This project outlines a procedure to do so.

Hamm

Abstract

Fouling reduces process or device efficiency in multiple industries, including food processing, maritime surfaces, medicine, membrane-based separations, and fluid processing. Surfaces with dynamic wrinkling topography actuated by Velankar mechanical stress, have been proposed to exhibit anti-fouling properties to delaminate surface-adhered material (foulant). Data from delamination experiments, replicating a soft, pliant foulant film attached to a wrinkling substrate, was to be used to validate mechanisms of delamination predicted by Finite Element Analysis (FEA) simulations. The critical strain, applied strain necessary for delamination, was dependent on the proportionality between the thickness of the surface-adhered foulant and the topographic wavelength (t/λ). The foulant thickness (t) is the distance from the interfacial surface to the surface of the foulant, while the wavelength (λ) describes the uniaxially patterned wrinkles that form on the substrates surface. This relation was predicted to be interdependent when the foulant thickness was relatively large (t/λ>1) and transition to be inversely dependent on the ratio when the thickness became relatively small compared to the wrinkle wavelength. Although an experimental method was established, procedural issues arose from compromising between uniform foulant adhesion and foulant rigidity which hindered data collection. The data processing of a single experiment was explored, which was hoped to be conducted with a series of experiments.

Category: Experimental research

Keywords: Antifouling, thin film buckling, wrinkling, actuated topography

1. Introduction 1.1 Motivation A variety of industries are negatively impacted by the ubiquitous process of fouling. Medical devices, maritime surfaces, filtration membranes and heat exchangers are just several objects whose integrity and efficiency are stifled by foulant deposition. Fouling typically appears in the forms of bacterial biofilms, particulates, insoluble salts and organic matter, but vary depending on industry. Solutions currently used to combat fouling have various consequences such as process down time, shortlived device replacement and the use of hazardous chemicals.

Self-renewing surfaces employ purely mechanical strategies to inhibit fouling which can be used continually, thus avoiding many drawbacks of conventional cleaning practices.

1.2 Theoretical Background Multiple examples found in nature use unique surface topography to exhibit fouling resistance, which offers novel insight to the development of effective and sustainable technologies. The Velankar lab has exploited actuating topographical deformation to inhibit fouling and create surface renewing materials. Surfaces which dynamically transition from flat to wrinkled states are capable of topographic-driven delamination of externally adhered matter.

Bilayers composed of a stiff membrane and relatively thick elastic substrate, wrinkle at the surface when compressed by an applied strain[1]. The wrinkle wavelength is roughly λ~h(Em/Es )1/3.

The elastic moduli of the membrane and substrate are defined by Em and Es , and h is the membrane thickness[2]. Undulations of the substrate surfaceforces bound foulant films to conform to changes to interfacial curvature. This forced deformation of the foulant film causes elastic stress to build within the foulant material, eventually driving foulant film delamination. Interfacial separation initiates when the applied strain of the foulant film and bilayer reaches the critical strain, εc.

Figure 1: Computational (C, D) tests of delamination of a soft foulant film induced by topographic changes of the underlying substrate.

Left corresponds to the foulant film that is thin compared to the wavelength of the topography, whereas right is a thick film.

Figure kindly provided by Luka Pocivavsek.

Previously, FEA was used to understand the dependency between the critical strain the ratio between the foulant film thickness (t) and wrinkle wavelength (λ)[2]. Simulation predictions in Fig. 1 accentuate the existence of separate delamination mechanisms that is determined by the relation between foulant film thickness and penetration depth, d, the depth which significant 51

elastic energy is stored within the foulant material. The penetration depth of thin foulant films is associated as the foulant thickness, since elastic strain builds across the entire thickness of the film.

Since elastic energy concentrates near the material interface, the penetration depth of foulants sufficiently thick is fixed by an upper limit, whereas the patch surface remains undeformed[2].

Penetration depth is proportional to the material’s capacity to store elastic strain. Critical strain is determined by εc~λ2γ/Epd3+ε, where the adhesion energy is γ, the foulant patch modulus is Ep and εc is the strain at which the membrane-substrate bilayer shows buckles[2]. The critical strain εc has a theoretically-predicted dependence on ratio of patch thickness to wrinkle wavelength (t/λ) given by Fig. 2.

uniaxially pre-stretched to approximately 40% strain. A stiff membrane of uniform thickness was created by dragging the edge of a glass microscope slide (being used as a doctor blade) over super glue resting between a channel created by two 37.6-micron spacer films. The glue was then allowed to cure for a period of 10 minutes. Relief of the mechanical tension allowed the rubber substrate to relax, while placing the thin film under compression.

The bilayer responded to the compressive load by surface wrinkling perpendicular to the direction of compression.

Figure 3: Production process of wrinkling substrates. A) A rubber strip is uniaxially stretched. B) A thin film of superglue is coated on the surface and allowed to dry while the rubber remains under stress.

C) After the glue has cured, the tension is released the surface buckles.

Figure 2: Schematic of relationship between critical strain and ratio of foulant thickness to wrinkle wavelength. The amount of critical strain approaches the lower limit when a foulant film is sufficiently thick but develops a steep inverse relationship as it thins.

The relationship transitions from being independent when the foulant is thick compared to wrinkle wavelength (t/λ >1), to being inverse when the foulant was relatively thin in comparison to wavelength (t/λ <1) .

Validating the simulated relationship between critical strain and the (t/λ) ratio required experimental data for comparison.

Previous lab work suggested that the simulated predictions were accurate but those experiments were conducted over a limited range of the length scale ratio. The central focus of the current project was to extend the range of the length scale ratio used in experiments for prediction validation. Unforeseen experimental limitations however, confined the usable range of the foulant thickness to wrinkle wavelength ratio. Observing delamination of the thin patch limit was difficult to establish, thus the following results illustrate the data processing procedure which would be used to equate critical strain for a given ratio.

2. Methods 2.1 Procedure Thin film wrinkling depends on a large elastic moduli mismatch between the substrate and membrane material. N-butyl rubber and cyanoacrylate, commonly referred to as superglue, were used in conjuncture.. Cyanoacrylate was chosen to constitute the stiff membrane because of its economic cost and easy manipulation to be spread, while the N-butyl rubber was economic and was capable of bonding with the glue. 0.64 cm thick rubber in 2.5 x 20 cm strips were fastened at both ends in two clamps of a mechanical-jig. Once fixed within the clamps, the strip was

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Undergraduate Research at the Swanson School of Engineering

The entire construction process can be visually exemplified

by Fig. 3. An aerial view of a wrinkled sample can be seen in Fig. 4, overlaid by a ruler to provide a sense of wavelength size. Samples were not reusable since the superglue film either cracked or debonded from severe compression. Although samples were not durable, super glue offered the convenience of quick production compared to silicone-cast samples used in the previous experiments, which took at least 24 hours to cure and exhibited high rates of failure.

Figure 4: Top view of the wrinkling sample used for the presented results. The wrinkle wavelength is smaller compared to those found near the edge. This difference was due to rubber strip curling while it was prestretched, resulting in a non-flat surface. When the glue was then spread, regions near the edge had thicker films of glue, giving larger wavelength.

GI-245 (Silicones Inc.) polydimethylsiloxane (PDMS) was chosen to represent soft fouling films. Patches of material were cut in 1 cm x 4 cm pieces, which were then placed on top of the superglue film of the bilayer. GI-245 sheets were cast with varying thickness to manipulate the ratio of the foulant thickness to wrinkle wavelength for individual trials. Silicone sheets were spread between 500 – 1750 micron although cured products thinned to between ~135 – 814 microns due to stratification while curing.

Ingenium 2019

2.2 Data Processing Individual experimental trials , visually recorded using two camcorders positioned aerially and laterally.. Digital imaging correlation (DIC) techniques combined the motion tracking software, Blender, and MATLAB to define the local strain of the experimental sample. A high contrast white gel-marker dotted the wrinkling substrate, whose markings were used for tracking. The pixel coordinates of selected markers were cataloged by Blender at each frame. Archived data was then processed within MATLAB which translated the marker’s temporal pixel displacement into local strain.

3. Results

The local strain of the wrinkling bilayer was recorded over time and plotted as seen in Fig. 5. Critical strain was realizable by qualitatively noting the moment of initial delamination from the recordings and then referring to the plot to correlate the strain to a time.

Figure 6: Time lapse of silicone “foulant” film delamination.

1) Silicone patch rests on substrate surface stress free.

2)The interfacial separation of the mock silicone foulant propagates from the patch edge. 3) Initial blisters produce buckles as delamination progresses. The local strain of at the three frames is shown sequentially in Fig. 5 as open circles.

Figure 5: Local strain of the ‘fouled’ bilayer during the recording.

The compression on the sample was manually released at an approximately constant rate. The ‘X’ corresponds to the moment where interfacial separation was first noted. The three open ‘O’ correspond to the time which the three frames below were taken.

A snapshot progression of the entire delaminating process was captured in Fig. 6 which depicts the system towards the start, the near end and an intermediary point. The proliferation of patch delamination was qualitatively visible by noting the transparent to opaque transition of the patch as air pervaded the interfacial void created by delamination.

Interfacial separation of the silicone patch and bilayer was witnessed after ~89.1 seconds. The critical strain was approximated as 4.57%. This moment was indicated in as an ‘X’ in Fig. 5. . Each of frame of Fig. 6 is denoted by open circles in the plot. Detachment of the silicone patch initiated along its edge, propagating inward as the wrinkles amplified. This suggests that patch delamination was driven by a surface buckling mechanism.

The derived critical strain was not influenced by pre-existing bonding defects, since interfacial separation did not nucleate at pre-existing blisters located between the width of the foulant film.

53

The aspect ratio between patch thickness and wavelength (t/λ) was found to vary between 0.84 – 1.22. The patch thickness was measured using two separate methods involving lengthcalibrated photo-microscopy and measurement of the mass, m, and area, A, to calculate thickness with the material specified density, ρ, as t≈mA/ρ. Micrscopy analysis found thickness to be ~800 microns, whereas density-based calculations suggested thickness to ~760 microns. The wavelength of the wrinkled experimental substratewavelengtht is pictured in Fig. 4, where the greatest values appeared at the edges and the smallest towards the center. Central wavelengths had minimum values of ~656 microns and reached upwards of ~908 microns near the sample’s edge which undesirably varied the effective wavelength and thus the thickness to wavelength ratio.

4. Discussion

The original goal of the project was to test the delamination over a range of the ratio between the patch thickness and wrinkle wavelength (t/λ). The ratio value was to be manipulated by varying the thickness of the PDMS mock foulant and keeping the wavelength fixed. The recorded trial had a near 1:1 aspect ratio where the critical strain was 4.57%, which suggests that the mock foulant delaminated via the thick patch limit (t/λ >1), which is characteristic of low values of critical stress. Although foulant films were capable of delaminating entirely, parameter variability was hindered by experimental limitations.

Mock foulant films exhibited little to no delamination when experiments fell under the thin patch limit (t/λ <1). Surfaces of stiff superglue membranes were unexpectedly rough (as cast and cured) which generated defects in the interfacial bond between the patch and wrinkling surface. These defects presented inconsistencies between experimental trails and sites for premature interfacial crack growth. Poor interface bonding due to surface roughness was compensated by using softer materials as the mock foulants. Despite maintaining uniform bonding, reducing the moduli of the fouling patch to improve uniform bonding increased the adhesion strength and decreased the bending stiffness, which raised the critical strain necessitated for delamination beyond the experimental capability. Another defect caused by the rubber strips curling perpendicularly to the uniaxial stretch leading to disproportionate stiff film thickness when applying the superglue, affecting the wavelength relative to the edges and middle of the substrates.

Only one experiment was fully processed amongst numerous experiments that were conducted. The exemplary trial serves to outline the general procedure for correlating the critical strain to the patch thickness to wavelength ratio which would have been iterated for a series of successful experiments spanning a range of the ratio of interest. The data processing of recordings was well established since local strain and initial delamination were realizable however the material selection of the experimental methods require revaluation to reach the experimental goals.

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Undergraduate Research at the Swanson School of Engineering

5. Conclusions

The methodology and material selection only allowed for patch delamination within the thick patch regime; when the foulant thickness was near unity or sufficiently larger relative to the wrinkle wavelength. Without being able to correspond critical strain to trials conditioned to the thin patch regime, the entire span on the relationship between critical strain and t/λ cannot be appropriately verified. The single case presented however, outlines the necessary steps which would be used to correlate the two related variables.

Further work will need to refine current or develop new methods to mimic the foulant delamination such that a larger range of length scale ratio may be analyzed.

Acknowledgments

My research involvement this summer would not have been possible without the financial support of both Covestro and the Swanson School of Engineering of the University of Pittsburgh.

I thank them for their generous contribution. I would also like to thank my adviser, Dr. Sachin Velankar, for his invaluable expertise and guidance while working in his lab.

References

1. Ned Bowden, S.B., Anthony G. Evans, John W. Hutchinson and George M. Whitesides Spontaneous formation of ordered structures in thin films of metals supported on an elastomeric polymer. Nature, 1998. 393.

2. Pocivavsek, L., et al., Topography-driven surface renewal.

Nature Physics, 2018. 14(9): p. 948-953.

Ingenium 2019

Adventitial delivery of therapeutic cells for localization in porcine aortas Trevor M. Kicklitera, Timothy K. Chungb, Aneesh K. Ramaswamyb, Justin S. Weinbaumb,c,g, and mentor David A. Vorpb,d,e,f,g,h Departments of Mechanical Engineering and Materials Science, Bioengineering, cPathology, dSurgery, e Cardiothoracic Surgery, and fChemical and Petroleum Engineering; gMcGowan Institute for Regenerative Medicine; and hCenter for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh PA a

b

Significance Statement:

Abdominal aortic aneurysm (AAA) is a common cause of death, for which there are no strategies for early intervention.

Stem cell-based therapies present an effective alternative, but lack an effective mechanism for localized delivery. Our work introduces and tests a novel method to localize such therapies in large animals.

Kickliter

Vorp

Abstract

Rupture of an abdominal aortic aneurysm (AAA) is a leading cause of death in the United States. Due to inadequate diagnostic markers, surgical intervention for this disease is often contraindicated for those in need of care or performed on patients who don’t need surgery, subjecting them to unnecessary risks. Our lab has previously investigated the use of adipose-derived mesenchymal stem cells (ADMSCs) in therapies for AAA, but a method to effectively target ADMSCs to the aorta has yet to be developed or tested in large animals.

Therefore, the primary goal of this study was to design and create a method for localizing ADMSCs in large animal aortas.

Aortas were harvested from 10 adult pigs, implanted with a diametric magnet to allow for ADMSC guidance, and treated with iron nanoparticle-loaded ADMSCs under pulsatile loading and flow to mimic physiologic conditions. The aorta was then harvested, cryosectioned, and mounted on slides for fluorescence microscopy. A significantly greater concentration of ADMSCs was observed around the aortic adventitia in the group where ADMSCs were magnetically guided. These results suggest that our method could be used to improve localization of stem cell-based vascular therapies in other large animals, including humans.

Category: Experimental research

Keywords: aortic aneurysm, stem cell therapy, fluorescence microscopy, magnetic targeting Abbreviations: Abdominal aortic aneurysm (AAA), adipose derived mesenchymal stem cell (ADMSC)

1. Introduction

Rupture of an abdominal aortic aneurysm (AAA) is a devastating medical phenomenon, ranking as the 14th leading cause of death in the United States [1]. Defined as a balloon-like dilatation of the abdominal aorta, this disease propagates due to weakening of the aortic wall and degradation of the extracellular matrix of the native tissue. If left untreated, this disease culminates in rupture of the aortic wall, resulting in a mortality rate of 90% [2].

To prevent this outcome, patients typically undergo elective repair once their AAA grows beyond 5.0-5.5 cm in diameter. However, elective repair presents a mortality rate as high as 30.9% after 28 days [3]. Moreover, 60% of AAAs with diameters above 5.5 cm fail to rupture, while 13% of AAAs rupture before their diameter reaches 5.0 cm [4]. Combining these facts, elective repair fails to protect many patients in need of care and subjects many others to unnecessary risks. This necessitates new therapies for AAA to overcome these shortcomings.

Adipose-derived mesenchymal stem cells (ADMSCs) offer a promising alternative to traditional surgical intervention. The properties of ADMSCs make them well-suited for therapeutic applications: they secrete pro-angiogenic growth factors—such as vascular endothelial growth factor and platelet-derived growth factor—that promote tissue revascularization [5], suppress inflammatory mechanisms [2], and can differentiate into a broad range of cells. Moreover, these cells are widely abundant, being present in body fat, and can be obtained via minimally-invasive liposuction. Our group has previously demonstrated the ability of ADMSCs to halt AAA growth and ECM degradation in a murine elastin perfusion model of AAA [6]. Together, these developments suggest that ADMSCs can be used to prevent the progression of AAA early on, avoiding the need for intervention late in the disease’s progression.

Implementing an MSC-based therapy for AAA involves developing an optimal delivery mechanism. Previous strategies for stem cell delivery have employed direct injection into the bloodstream, thereby delivering stem cells to the aortic lumen.

However, this leads to delivery to irrelevant systemic targets.

Moreover, systemic delivery is impractical due to the presence of intraluminal thrombus (ILT), a dense mass of various cells and ECM components present in 70-80% of AAAs [7]. This acts as a physical barrier preventing the delivery of ADMSCs to the lumen, necessitating delivery to the adventitia. Other groups have employed periadventitial delivery mechanisms for stem cells [8,9], but these methods were limited to small animals or small vessels.

Moreover, they lacked a method to retain ADMSCs to the aorta without direct injection into the aortic wall, which is difficult to guide in vivo.

To overcome these issues, ADMSCs can be loaded with iron nanoparticles, thereby allowing them to be guided by a magnetic field. Iron-oxide nanoparticles are used in a wide variety of clinical applications, including enhanced contrast and targeting small biologics [10]. These cells can also be loaded in a hydrogel, which subsequently cures and binds them to the aortic adventitia.

Preliminary, unpublished work in our lab has successfully deployed 55

this concept in a rat elastase perfusion model of AAA. However, this work had several limitations, most notably the size of the animal, which allowed for strategies that would be impractical for use in humans. Therefore, the goal of this work was to implement and test this concept in large animal aortas, whose size is more representative of human aortas.

In this work, we subjected adult pig aortas to physiologic conditions (loading via pulsatile flow), administered ADMSCs to the aortic adventitia in the presence or absence of an internal magnet, and used fluorescence microscopy to evaluate any increase in ADMSC localization. We found that our technique significantly improved ADMSC localization in vitro.

2. Methods 2.1 Aorta Harvest and Perfusion: Healthy aortas were harvested from ten adult pigs and subsequently frozen in Hank’s Balanced Salt Solution. The average aortic diameter was 1.9 ± 0.1 cm. The aortas were prepared by removing connective tissue and ligating peripheral branch arteries to allow for perfusion. Aortas were mounted in a custom-built perfusion system, complete with pressure transducers to record the pressure to which the aortas were perfused. To allow for cell guidance, either a ¼-inch diameter diametric magnet (K&J magnetics) or a de-magnetized rod of the same dimensions was fixed within the aortic lumen. The interior was then lined with compliant latex to prevent leakage and subjected to pulsatile loading at 0.5 Hz and systolic/diastolic pressures of approximately 120/80 mmHg. No appreciable pre-stretch was applied beyond making sure the vessel was axially taut. The experimental system is shown in Fig. 1.

2.2 Cell Culture ADMSCs were isolated from deidentified human adipose tissue provided to us by Dr. J. Peter Rubin, Department of Plastic Surgery, UPMC, using an established protocol [11]. Cells were then cultured in ADMSC growth media (10% fetal bovine serum, 1% penicillin-streptomycin, 1% fungizone, and 88% Dulbecco’s Modified Eagle Medium and Nutrient Mixture F-12, ThermoFisher Scientific) at 37ºC and 5% CO2. ADMSCs were incubated with 14µL 200 nm iron nanoparticles (Chemicell) per mL cell media 1 day prior to usage to allow for attraction to the internal magnet. 1 hour before treatment was administered, ADMSCs were incubated with CellTrackerTM Red CMTPX Dye (ThermoFisher Scientific) to allow for visualization after delivery.

2.3 In Vitro Delivery and Evaluation For delivery, ADMSCs were suspended in culture media and mixed with fibrin gel precursors to produce a gel with 3.3 mg/mL fibrin, 8.3 mg/mL thrombin, and 1.6 ± 0.5 million cells/mL. Three gels were prepared, and all gel parameters were kept constant between the experimental (magnetic guidance) and control (no magnet) groups. To ensure retention of the fibrin gel, a custom fixture was developed in SolidWorks (Dassault Systèmes) and 3D printed using a Formlabs 2 SLA printer (Formlabs). The fixture was designed to expose a 1.25 cm square region of interest on the adventitia, as shown in Fig. 2.

Figure 2: A fixture to isolate a region of interest on the adventitia.

A custom fixture was modeled in SolidWorks (a), 3D printed using an SLA printer, and fixed to the aorta (b).

Figure 1: A custom perfusion system to induce pulsatile loading. A custom perfusion system, complete with pressure transducers to measure the internal pressure of the aorta, was used to create physiologic loading conditions.

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Undergraduate Research at the Swanson School of Engineering

3mL of the fibrin gel was injected onto the region of interest during pulsatile loading and left undisturbed for 15-20 minutes.

Aortas with cell-loaded fibrin gel were incubated at 37ºC for 30-45 minutes for gel curing and fixed overnight in 0.37 wt% formaldehyde to prepare the tissue for sectioning. A crosssection of the region of interest was subsequently harvested, cryosectioned, and mounted on slides for fluorescence microscopy.

Images were acquired using a Nikon Eclipse 90i fluorescent microscope with NIS Elements software and processed using ImageJ software.

Ingenium 2019

Figure 3: Effect of an internal magnet on ADMSC localization. ADMSCs (CellTrackerTM red dye) appear to be more localized around the aorta (green autofluorescence) in the presence of an internal magnet than in its absence. The aortic adventitia is denoted by the white arrows.

One of the samples in Pair 5 was lost due to leakage of the hydrogel.

3. Results

The fibrin gel surrounding the aorta appeared to be much more densely populated with ADMSCs in the group in which an internal magnet was present, as shown in Fig. 3â&#x20AC;&#x201D;this trend was clearly observable across all samples.

Moreover, ADMSCs appeared to populate the adventitial surface in the group where the internal magnet was present, as indicated by the presence of red and yellow bands along the adventitia.

4. Discussion

Fluorescence microscopy imaging reveals an increase in ADMSC density in the fibrin gel surrounding the aorta in the presence of an internal magnet (Fig. 3). This suggests that our method improved the localization of ADMSCs around the aorta.

These images also reveal the presence of red and yellow bands within the adventitia in the group where an internal magnet was deployed. These bands may indicate the presence of ADMSCs embedded in the adventitia itself, suggesting that ADMSCs may have been recruited directly to the tissue. However, these bands may also be artifacts of our imaging method, as it is difficult to identify individual cells within these bands.

Improved localized cell delivery is beneficial for numerous reasons. Firstly, our group has shown that periadventitial ADMSC delivery can slow degradation of elastin and prevent expansion of AAAs, effectively halting the progression of this disease [6]. This may be due to the therapeutic nature of ADMSC secreted factors, which could be administered in much greater quantity were the density of ADMSCs increased. Hence, improved ADMSC density in the fibrin gel surrounding the aorta could greatly improve the effectiveness of ADMSC treatment. Secondly, ADMSCs have the ability to differentiate into smooth muscle cells, potentially allowing

them to replace damaged smooth muscle cells in the diseased aorta, although this effect requires further investigation. As such, the observed embedding of ADMSCs in the adventitia could lead to even greater therapeutic benefits. Therefore, our method has significant potential for improving both the specificity and effectiveness of therapeutic, cell-based treatments for AAAs. It is also worth mentioning that, due to the nature of this method, it can potentially be used to guide stem cell-based therapies for various vascular diseases in other large animals, including humans.

Our study builds upon previous work investigating the use of ADMSCs as a therapy for AAA, overcoming previous limitations associated with their deployment. Firstly, previous works were limited to small animals or small veins [6,8,9], whereas our strategy can be successfully deployed in large aortas, which are closer to human scale. While Turnbull et al. deployed ADMSCs in porcine aortas [12], their group was limited to direct injection into the aortic wall, a technique previously deployed by our group [6].

The use of an internal magnet is preferable because it requires less guidance in vivo, removing the need to expose the aorta or puncture it with needles. However, additional work is needed to ensure that our method is viable for use in live animals.

This work has some key limitations. Firstly, the effectiveness of our method is limited by our inability to ensure that only iron nanoparticle-loaded ADMSCs were administered, meaning that not all of the delivered ADMSCs could be guided. This also meant that there was no way to confirm whether all of the cells in the experimental group were guided by the internal magnet. Secondly, the in vitro model employed in this work lacked any of the organs and other anatomical features surrounding the aorta, which would greatly complicate the deployment of our localization method.

Moreover, the use of fluorescence microscopy limited us to a qualitative assessment of our methodâ&#x20AC;&#x2122;s effectiveness, although we 57

felt a quantitative assessment of these images to be unnecessary.

Finally, our method may not be as effective for aneurysmal aortas, as the magnetic field will be weaker for larger vessels. Future work will involve in vivo studies in large, aneurysmal aortas and investigate strategies for deployment of this method in vivo. Such strategies will include methods for catheterizing our diametric magnet and guiding it into the target site. Additional studies will be conducted to confirm the therapeutic effects of ADMSCs and the mechanisms responsible.

5. Conclusions

Our method improves retention of ADMSCs to adult pig aortas under physiologic conditions (i.e. aortic pulsation). This proofof-concept suggests that this method can be used to improve localization of stem cell-based vascular therapies in other large animals, including humans.

Acknowledgments

Funding for this project was provided by the Swanson School of Engineering and the Office of the Provost (to TMK) and the National Institutes of Health (R21 HL129066 to DAV). We thank Dr. Rubin and his team for the ADMSCs used in this study.

References

1. S. Aggarwal, A. Qamar, V. Shrama, A. Sharma, Abdominal aortic aneurysm: A comprehensive review, Exp Clin Cardiol. 16.1 (2011) 11-15.

2. W.H. Pearce, C. K. Zarins, J. M. Bacharach, Atherosclerotic Peripheral Vascular Disease Symposium II: Controversies in Abdominal Aortic Aneurysm Repair, Circulation. 118 (2008) 2860-63.

3. F.J. Schlösser, I. Vaartjes, G.J. van der Heijden, F.L. Moll, H.J. Verhagen, B.E. Muhs, G.J. de Borst, A.T. Tiel Groenestege, J.W. Kardaun, A. de Bruin, Mortality after elective abdominal aortic aneurysm repair, Ann Surg. 262.1 (2010) 158-64.

4. K. Kurosawa, J.S. Matsumuru, D. Yamanouchi, Current Status of Medical Treatment for Adominal Aortic Aneurysm, Circ J. 77 (2013) 2860-66.

5. J. Rehman, D. Traktuev, J. Li, S. Merfeld-Clauss, C.J. Temm-Grove, J.E. Bovenkerk, Secretion of angiogenic and antiapoptotic factors by human adipose stromal cells, Circulation.

109.10 (2009) 1292–8.

6. K.J. Blose, T.L. Ennis, A. Batool, J.S. Weinbaum, J.A. Curci, D.A. Vorp, Periadventitial adipose-derived stem cell treatment halts elastase-induced abdominal aortic aneurysm progression, Regen Med. 9.6 (2014) 733-41.

7. A. Piechota-Polanczyk, A. Jozkowicz, W. Nowak, W. Eilenberg, C. Neumayer, T. Malinski, I. Huk, C. Brostjan, The Abdominal Aortic Aneurysm and Intraluminal Thrombus: Current Concepts of Development and Treatment, Front Cardiovasc Med. 2.19 (2015). DOI: 10.3389/fcvm.2015.00019.

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8. R. Katare, F. Riu, J. Rowlinson, A. Lewis, R. Holden, M. Meloni, C. Reni, C. Wallrapp, C. Emanueli, P. Madeddu, Perivascular Delivery of Encapsulated Mesenchymal Stem Cells Improves Postischemic Angiogenesis Via Paracrine Activation of VEGF-A, Arterioscler Thromb Vasc Biol. 33 (2013) 1872-80.

9. W. Huang, G.B. Newby, A.L. Lewis, P.W. Stratford, C.A. Rogers, A.C. Newby, G.J. Murphy, Periadventitial Human Stem Cell Treatment Reduces Vein Graft Intimal Thickening in Pig Vein-IntoArtery Interposition Grafts, J Surg Res. 183.1 (2013) 33-39.

10. A. C. Anselmo, S. Mitragotri, Nanoparticles in the clinic, AIChE Bioengineering and Translational Medicine. 1 (2016) 10-29.

11. J.T. Krawiec, J.S. Weinbaum, H. Liao, A.K. Ramaswamy, D.J. Pezzone, A.D. Josowitz, A. D’Amore, J.P. Rubin, W.R. Wagner, D.A. Vorp, In Vivo Functional Evaluation of Tissue-Engineered Vascular Grafts Fabricated Using Human Adipose-Derived Stem Cells from High Cardiovascular Risk Populations, Tissue Eng Part A. 22.9-10 (2016) 765-75.

12. I.C. Turnbull, L. Hadri, L. Rapti, M. Sadek, L. Liang, H.J. Shin, K.D. Costa, M.L. Marin, R.J. Hajjar, P.L. Faries, Aortic Implantation of Mesenchymal Stem Cells after Aneurysm Injury in a Porcine Model, J Surg Res. 170.1 (2100) 179-88.

Ingenium 2019

The influence of nitrogen doping on electrocatalytic activity of FeN4 embedded graphene* Lydia Kuebler, Boyang Li, and mentor Guofeng Wang Laboratory of Dr. Guofeng Wang, Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, PA, USA

Significance Statement:

Hydrogen fuel cells are currently not a viable energy alternative due to their inefficient electrocatalytic performance during the oxygen reduction reaction. Here, the effects of nitrogen dopants on the FeN4 catalytic mechanism are explored, specifically how the doping concentration and distance from the active site of the catalyst influences performance.

Kuebler

Abstract

Hydrogen fuel cells are one of the most promising technologies to address current energy shortages and Wang environmental pollutants, especially in the transportation industry. However, the wide commercialization of fuel cells is largely limited by its requirement of platinum-group metals (PGM) as the electrocatalyst for the oxygen reduction reaction (ORR).

Therefore, non-PGM replacements have been widely explored and carbon-based supports have gained attention due to their low costs and favorable properties. This study investigates the effect of nitrogen doping on FeN4 embedded graphene, in terms of the surface adsorption energies, free energy evolution of ORR, and the limiting potential of the fuel cell based off first principle density functional theory (DFT) calculations. Two main factors were explored, namely the distance between Fe and N dopant and the overall nitrogen concentration. The optimal doping distance and concentration were predicted to be 4.98 Å and 4.2 at % N, respectively. The total cell performance is quantified by changes in adsorption energies due to electronic interactions between N and the active FeN4 complex. It was concluded that the best limiting potential is 0.73 eV, approaching that of Pt.

Category: Computational research

Keywords: oxygen reduction reaction, density functional theory, electrocatalysts, nitrogen doping *Editors’ choice

1. Introduction

Hydrogen fuel cells have become one of the most promising renewable energy technologies, converting hydrogen fuel to electricity and water via electrochemical reactions. However, the commercialization of fuel cells is largely limited by their cost, stability, and reaction kinetics. To achieve desirable fuel cell performance, efficient catalysts are often used to increase the rate of the electrochemical reaction. The oxygen reduction reaction (ORR) is known to be five times slower than the hydrogen oxidation reaction [1]. Thus, efforts have mainly focused on manipulating the ORR reaction to increase fuel cell efficiencies.

Currently, platinum group metal (PGM) based catalysts have continued to dominate the market due to their superior performance. However, due to the high cost of PGMs, earth abundant metals have been considered as viable replacements.

Non-precious metal catalysts (i.e. PGM-free) such as iron and cobalt show promise as low-cost alternatives, but are still hindered by instability [2]. Despite this, iron shows selectivity for the 4-electron reduction reaction and binds favorably to ORR intermediates, which provides motivation for its study here.

Active sites are the region on a substrate where the chemical reaction occurs. For this study, the active site complex FeN4 was chosen because nitrogen has been experimentally and computationally determined to play a role in catalytic activity [3,4,5,6]. This complex has favorable weak binding to the main product, H2O, and strong binding to reactant O2, which allows for the active site to successfully initiate and complete the ORR reaction [7]. A previous study also revealed that the FeN4 complex could promote the desired four-electron ORR reaction following an OOH dissociation pathway and its activity can be tuned by varying the local carbon structure around the active sites [7].

Additionally, this specific structure was found to have the lowest formation energy compared to other nitrogen complexes, due to its planar arrangement [8]. Graphene is utilized as the support, a necessity for proper catalyst function, due to its high surface area and conductivity. In addition, its molecular structure prevents contamination of the active site, as the sheets of graphene create a protective layer between the metal and the acidic medium where the reaction is typically carried out [2].

Although it is established that FeN4 embedded graphene has the potential to replace Pt as an effective electrocatalyst, the knowledge of how the local chemical environment affects the ORR activity of the FeN4 site is currently lacking. To our best knowledge, the research following this avenue has not been done before.

Introducing non-metal dopants (such as N) onto the graphene could enhance the chemical reactivity of the catalyst by modifying its electronic structure. This is due to the partially charged nature of the carbon and nitrogen bond because of nitrogen’s higher electronegativity. As the valence electrons of nitrogen become delocalized from sp2 bonding with neighboring carbon atoms, n-type doping occurs, characteristic of semiconductor materials [2]. Based on Sabatier’s principle, which states that the interaction between the catalyst and the substrate must be neither too strong nor too weak, the amount of nitrogen dopants must be carefully 59

controlled in order for the reaction to successfully take place [9].

If the interaction is too strong, as suggested by too many nitrogen dopants, the product will fail to leave the active site, whereas if it is too weak, as in the case of too few dopants, the reaction will not be enhanced. Therefore, the amount of nitrogen dopants introduced into the graphene will be fundamental in finding an optimum range for catalytic activity enhancement. Recent experiments found that 4-5 at % N led to the highest performance, [10,4] in contrast, others suggested 7.7 at % to 9.05 at % N to be optimal [6, 3].

This project explores the effects of both the doping concentration and the configuration of nitrogen in the lattice of FeN4 embedded graphene on its electrocatalytic activity.

Computational methods will be used to find the optimal surface structure. The adsorption energies of ORR intermediates will be calculated and used to predict reaction feasibility via free energy diagrams. This work will assess if the catalyst’s limiting potential, the smallest energy difference between two steps in the ORR, is comparable to Pt thus quantifying its catalytic performance.

2. Methods

Density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package (VASP) software [11,12]. The generalized gradient approximation (GGA) method functional developed by Perdew, Burke, and Ernzerhof (PBE) performed the first principle calculations of the surface of the active site complex [13]. A 3 x 3 x 1 Monkhorst-Pack k point sampling was used for the graphene super cell. The first principle calculations run reached energy convergence of 1x10-6 eV and forces convergence of -0.01 eV. Energy calculations factored in both zero-point energy (ZPE) and entropy corrections. The molecular free energy of the intermediates was calculated based off the PAW pseudo-potentials database [14,15]. The following energies were defined as: Ead = Etot – (Eslab + Emolecule ) (1) Here, Etot is the total energy of the molecule adsorbed on the surface, Eslab is the clean surface, and Emolecule is the energy of the gas phase molecule. To increase the rate of the ORR reaction, the adsorption energy, Ead , should be lower for oxygen (O2) and higher for water (H2O) as compared to the undoped slab baseline.

2.1 ORR Mechanism Overall reaction: Associative path:

60

O2 + 4H++ 4e – = 2H2O (acid) O2 + H++ e – =* OOH + – * OOH + H + e =* O + H2O + – * O + H + e =* OH + – * OH + H + e = H2O

Undergraduate Research at the Swanson School of Engineering

Figure 1: FeN4 Undoped Graphene Slab composed of C (brown), N (light gray), and Fe (yellow)

2.2 Models/Structures All structures were built and visualized using the Visualization for Electronic and Structural Analysis (VESTA) software. The undoped slab is composed of 90 carbon atoms, 4 nitrogen atoms, and 1 iron atom in a hexagonal lattice viewed from the c unit cell vector vertical to the surface plane, as shown in Figure 1. This creates a two-dimensional (2-D) monolayer of graphene with the FeN4 active site fully embedded into the surface.

Based on previous studies on transition metal nitrides (TM-Nx), the five intermediates of ORR (O*, OH*, OOH*, O2, H2O) preferentially adsorb to the central metal atom [16, 17]. Thus, all intermediates were arranged to have the most stable bond with Fe as determined by previous studies optimizing bond length and angle [8].

Nitrogen doping consists of replacing carbon in the graphene structure based on previously reported stable positions [2]. The first doping case consisted of adding two nitrogen atoms and was incrementally increased by 2 until there was 10 keeping doping effects symmetric within the graphene lattice as this is more stable than unsymmetrical cases. The five doping cases (2N, 4N, 6N, 8N, 10N) were then additionally tested to find the most stable slab for that doping concentration.

Ingenium 2019

Figure 3: 2N Case: Influence of Doping Distance on Total Energy

Figure 2: Free Energy Diagram of Undoped Slab

Once this was found, all intermediates of the ORR were added to calculate adsorption energies. These were then used to construct free energy diagrams for each system and determine the limiting step of the reaction. The free energy diagrams were based off the standard hydrogen electrode with a baseline potential of 0 V. The free energy change of O2 was obtained from the overall reaction mechanism of the ORR and was 4.92 eV. The limiting potential of the electrocatalyst was computed to be 0.695 eV based off the smallest step of the reaction, the O* and OH* intermediate step, seen in Figure 2.

3. Results 3.1 Undoped Slab Computations To establish a basis for performance, the undoped slab adsorption energies were computed, resulting in an Ead = -0.90 eV for O2 and Ead = -0.14 eV for H2O. These energies will give an accurate estimation of the effectiveness of the catalyst by showing the interaction between the active site, the main reactant, and product. Based off Sabatierâ&#x20AC;&#x2122;s principle, it is expected that there will an ideal value showing strong adsorption to O2 and weak adsorption to H2O. In addition, the adsorption energies of the reaction intermediates were computed with Ead = -4.12 eV for O*, Ead = -2.65 eV for OH*, and Ead = -1.68 eV for OOH*.

3.2 Doping Distance Then the slab was doped with two additional nitrogen atoms (2N) to determine the distal influence of dopant placement into the graphene lattice. Similar to doping concentration, there is an optimal distance between the dopant and the active atom in the catalyst (in this case Fe). This length should give the most stable, lowest energy, slab configuration. A total of six configurations were created, beginning with the closest possible substitutional atom without creating a N-N bond with the FeN4 complex. This was the maximum number of configurations possible if replacing the neighboring carbon atoms up to the end of the slab symmetrically.

The most stable configuration occurs when the Fe-N distance is 4.98 Ă&#x2026;. This is illustrated in Figure 3 where the total energy of the slab is decreased by 0.15 eV, contrary to any of the other configurations which increased the total energy.

3.3 Doping Concentration Once this was established, an average of 12 different configurations were created for each doping concentration case (2N, 4N, 6N, 8N, 10N). Each concentration higher than the 2N case was based on the previously lower doped case that was most stable. For instance, the 10N case would have 4 configurations based on the most stable 2N, 4N, 6N, and 8N case with additional dopants added. The additional dopants added were arranged in as many different configurations as possible, which was a trial and error process based on the results received and other possibilities were eliminated once there was a trend of increasing instability.

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Figure 4: Best 4N Slab

Figure 6: ORR Performance with Increasing N doping

4. Discussion 4.1 Electronic Effect The total nitrogen content, or doping concentration, creates an electronic effect on the surface. While there was a decrease in O2 adsorption barriers, seen in the adsorption energy of the 4N case versus the undoped slab, there was also an increase in adsorption energies for the ORR reaction intermediates. This is because of the electron withdrawing nature of nitrogen. Thus, as more nitrogen atoms are introduced onto the surface, negatively charged species such as O or OH are less likely to adsorb. This was confirmed by comparing the Ead of O and OH of the undoped slab with the 2N and 4N case seen in Table 1. This decreasing adsorption trend of intermediates continued for increasing nitrogen content, explaining the unfavorable performance of the 6, 8, and 10N cases.

Table 1. Trend in Adsorption Energies Figure 5: 4N Doped Free Energy Diagram

The carbon atoms at the edges of the slab were eliminated as an option as this distance is too far away from the active site to influence the reaction. This is equivalent to a doping concentration of 2.1 at % N up to 10. 4 at %. Overall, the 4N case showed the best performance with 4.2 at % N, somewhat lower than typical literature values, seen in Figure 4. There was a decrease in energy of -0.04 eV for O2 and an increase in energy of 0.05 eV for H2O which confirms predictions that there would be an intermediate adsorption energy value ideal for catalytic performance. The limiting potential was 0.73 eV for the 4N case which is an increase of 0.03 eV compared to the undoped slab, seen in Figure 5. In addition to the 4N case, there was an increase in limiting potential for the 2N case and the 6N case, with a potential of 0.715 eV and 0.710 eV, and a decrease in limiting potential for the 8N and 10N case, with a potential of 0.692 eV and 0.680 eV seen in Figure 6.

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Undergraduate Research at the Swanson School of Engineering

Ead (eV)

Undoped Slab

2N Case

4N case

O*

-4.122

-4.087

-3.984

OH*

-2.654

-2.640

-2.554

The distance between Fe and the nitrogen dopants, creates a different electronic effect on the surface. The shortest distance possible between them was 3.959 Å, demonstrated in the best 2N case (Figure 3). Other minimum distances were 4.209 Å for the 6N case and 4.977 Å for the 2N case. As seen in Figure 3, the adsorption energies of intermediates correlate with the minimum distances. For example, the adsorption energy of the OH* intermediate for the shortest dopant distance is -2.630 eV (6N case) and is -2.640 eV (best 2N case). This demonstrates that if the nitrogen is too close, the adsorption of intermediates is too strong, but if too far, it is too weak.

Ingenium 2019

4.2 Overall Performance The overall performance of the FeN4 electrocatalyst is illustrated in Figure 5. Since the limiting potential for all structures occurs at the same step in the ORR reaction, the reduction of O* to OH*, it can be concluded that it is the rate determining step for these models. This is inferred from the thermodynamics of the process and the database of standard calculated O and OH adsorption energies rather than directly calculated rates [18]. An increase in O* adsorption energy also resulted in an increase in OH* adsorption which is mainly influenced by the distance between iron and nitrogen dopants. The total performance then is based off these changes in adsorption energies resulting from the electronic interactions between nitrogen and the active FeN4 complex embedded in graphene. Though this rate determining step is different than some studies, solvation effects were not considered, and different functionals may explain this. Additionally, the O* chemisorption has been found to be more complicated than that of OH* and OOH*, since it can either form single bonding with the adsorption center on graphene, or form epoxy-type bonding, which may explain the lack of a linear scaling relationship [19].

4.3 Comparison with Pt Electrocatalyst Lastly, to demonstrate the enhancement of electrocatalytic performance via nitrogen doping, the adsorption energies of the 4N case were compared with Pt (111) surface based on theoretical calculations [20], seen in Table 2. The O2 adsorption for the 4N case is well within the Pt range and demonstrates acceptable adsorption performance. Pt weakly binds to OOH and strongly to O and OH. Conversely, the 4N case shows higher adsorption energy to OOH than Pt by 0.41 eV. Though the OH adsorption energy is relatively close to Pt, the O adsorption energy is higher than Pt, explaining why the limiting potential performance is only a 0.03 eV increase compared to the undoped slab. Thus, 4N does not perform as well as Pt (limiting potential of 0.80 eV) despite favorable weak binding of H2O. This stems from the inability to break the scaling relationships between the O*, OH*, and OOH* intermediates.

Table 2. Adsorption Energies of ORR Species for FeN4 and Pt Electrocatalyst [13] Ead (eV)

4N Case

Pt (111)

O2

-0.94

-0.41 to -1.04

O

-3.98

-3.28

0H

-2.55

-2.26 to -2.45

0OH

-1.57

-1.06 to -1.16

H 2O

-0.09

-0.22 to -0.60

Admittedly, due to the specificity of the model structures in this project, it is impossible to directly compare to other work with different DFT parameters, energy considerations, or baseline models. Therefore, this comparison is strictly an estimation of the potential of this catalyst in a best-case scenario.

5. Conclusions

In this study, the first principles DFT calculations explored the influence of nitrogen doping concentration and configuration on the electrocatalytic activity of FeN4 embedded graphene. A global effect is created on the graphene surface based on total doping concentration of nitrogen, which repels ORR adsorbates as the concentration increases. Therefore, the best doping concentration was found to be 4. 2 at % N. A local effect is also created on the surface from the configuration of nitrogen in the lattice, which modifies the electronic structure of Fe and adsorbates based on Fe-N distance. The best doping distance was found to be 4.98 Å. Based off these two effects, the highest performing surface structure was 4N with a limiting potential of 0.73 eV which is still lower than Pt. This study demonstrated that there are definite relations between electrocatalytic activity and doping distance and concentration, and that DFT calculations can improve the understanding of the mechanisms driving catalytic performance in ORR reactions.

Acknowledgments

Special thanks to the Swanson School of Engineering, Office of the Provost, and Kennametal for providing the funding necessary to carry out this research. Additional thanks to the Center for Research Computing for providing computational resources and to Boyang Li and Guofeng Wang for providing insightful collaboration and help on this project.

References

1. Larminie, James, and Andrew Dicks. Fuel Cell Systems Explained. Second Edition. John Wiley & Sons. 2003.

2. Lu, Yu-Fen, et al. “Nitrogen-Doped Graphene Sheets Grown

by Chemical Vapor Deposition: Synthesis and Influence of Nitrogen Impurities on Carrier Transport,” ACS Nano, Vol. 7. July 2013. Pg. 6522-6532.

3. Liu, Yisi, et al. “Exploring the nitrogen species of nitrogen doped graphene as electrocatalysts for oxygen reduction reaction in Al-air batteries,” Int. J. Hydrogen Energy, Vol. 41. 2016. Pg. 10354-10365.

4. Videla, Alessandro, et al. “Non-noble Fe-Nx electrocatalysts supported on the reduced graphene oxide for oxygen reduction reaction,” Carbon, Vol. 76. September 2014. Pg. 386-400.

5. Chen, Minghua, et al. “Nitrogen-doped GrapheneSupported Transition-metals Carbide Electrocatalysts for Oxygen Reduction Reaction,” Scientific Reports, Vol. 5. July 2015.

6. Shao, Minhua, et al. “Recent Advances in Electrocatalysts for Oxygen Reduction Reaction,” Chem. Reviews, Vol. 116.

February 2016.

7. Liu, Kexi, et al. “Role of Local Carbon Structure Surrounding FeN4 Sites in Boosting the Catalytic Activity for Oxygen Reduction,” J. Phys. Chem., Vol. 121, 2017, pg. 11319-11324.

8. Liu, Kexi, et al. “Electrochemical and Computational Study of Oxygen Reduction Reaction on Nonprecious Transition Metal/ Nitrogen Doped Carbon Nanofibers in Acid Medium,” J. Phys.

Chem., Vol. 120. January 2016. Pg. 1586-1596.

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9. Rothenberg, Gadi. Catalysis: Concepts and Green Applications. Wiley-VCH. 2008. p. 65.

10. Stacy, John, et al. “The recent progress and future of oxygen reduction reaction catalysis: a review,” Renewable and Sustainable Energy Reviews, Vol. 69. March 2017. Pg. 401-414.

11. Kresse, G.; Hafner, J. Ab Initio Molecular Dynamics for Liquid Metals. Phys. Rev. B: Condens. Matter Mater. Phys. 1993, 47, 558−561.

12. Kresse, G.; Furthmuller, J. Efficiency of Ab-Initio Total Energy Calculations for Metals and Semiconductors Using a PlaneWave Basis Set. Comput. Mater. Sci. 1996, 6, 15−50.

13. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 3865−3868.

14. Blochl, P. E. Projector Augmented-Wave Method. Phys.

Rev. B: Condens. Matter Mater. Phys. 1994, 50, 17953−17979.

15. Kresse, G.; Joubert, D. From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method. Phys. Rev. B: Condens.

Matter Mater. Phys. 1999, 59, 1758−1775.

16. Liu Wengang, et al. “Discriminating Catalytically Active FeNx Species of Atomically Dispersed Fe-N-C Catalyst for Selective Oxidation of the C-H Bond,” J. ACS, Vol. 31, 2017, pg. 1079010798.

17. Zitolo, Andrea, et al. “Identification of catalytic sites for oxygen reduction in iron- and nitrogen-doped graphene materials,” Nature Materials, vol. 14, 2015, pg. 937–942.

18. Norskov, J.K., et al. “Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode,” J. Phys. Chem., Vol. 108, 2004, pg. 17886-17892.

19. Krishnamurthy, Dilip, et al. “Maximal Predictability Approach for identifying the right descriptors for electrocatalytic reactions,” J. Phys. Chem. Lett. Vol. 9, 2018, pg. 588-595.

20. Jacob, Timo, et al. “Chemisorption of Atomic Oxygen on Pt (111) from DFT Studies of Pt Clusters,” J. Phys. Chem., Vol. 107, 2003, pg. 9465–9476.

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Undergraduate Research at the Swanson School of Engineering

Combined neural stem cells and physical therapy improve somatosensory cortex activity after stroke Nikhita Perry1,3, Harman Ghuman1,3, Franziska Nitzsche1,2, Madeline Gerwig1,5, Jeffrey Moorhead1,4, Lauren Grice1,3, Alex Poplawsky2, Brendon Wahlberg2, Fabrisia Ambrosio1,4, and mentor Michel Modo1,2,3 McGowan Institute for Regenerative Medicine, 2Department of Radiology, 3Department of Bioengineering, 4 Department of Physical Medicine & Rehabilitation, 5Department of Neuroscience, University of Pittsburgh, PA, USA 1

Significance Statement:

Stroke is the leading cause of adult disability, and one in every six people are affected worldwide. Neural stem cells and physical therapy have proven to facilitate functional recovery from stroke, but little is known about the biological effects physical therapy has on neural stem cells in a combined therapy.

Perry

Abstract

Modo Stroke caused by the occlusion of a cerebral artery can lead to death or severe, long-lasting functional impairments. Treatment options, however, are extremely limited, extensive, and cost-intensive without the guarantee to restore lost functions. Physical therapy is the only treatment used to improve chronic behavioral impairments. Emerging treatments, such as stem cell based therapies, can support tissue restoration and functional recovery by integration in the peri-infarct tissue. Still, behavioral deficits remain, and a combined therapy could improve the efficacy of each treatment compared to the therapies alone.

Each therapy was tested both individually and combined in a rodent model of stroke by transient middle cerebral artery occlusion (MCAO), alongside a sham-operated control and MCAO only group. The neurological effects of the treatments were quantified through cerebral blood volume (CBV) magnetic resonance imaging and functional magnetic resonance imaging (fMRI). Results showed that none of the three therapies had a significant effect on cerebral blood volume. However, fMRI revealed that the combined group had a significantly higher number of active voxels than the other groups, although the strength of the signal was not significantly different. A combined therapy could promote neuroplasticity, and cause microstructural changes that affect brain activity after stroke.

Category: Experimental research

Keywords: contrast-enhanced MRI, neuroplasticity Abbreviations: MCAO: Middle Cerebral Artery Occlusion; CBV: Cerebral Blood Volume; fMRI: functional magnetic resonance imaging; PT: Physical Therapy; NSC: Neural Stem Cell; SMC: Somatosensory Cortex

Ingenium 2019

1. Introduction

Stroke caused by middle cerebral artery occlusion (MCAO) can lead to long-lasting behavioral and functional impairment.

Treatment options for stroke are limited, but two of the leading options are physical and stem cell therapy. Preclinical studies on the use of stem cells for the treatment of stroke have shown that the administration of various types and sources of stem cells can reduce neurological deficits by about 55% and in some cases, significantly reduce the size of the infarct by approximately 50% [1]. Large numbers of active stem cells can implement the repair of nervous tissue in the brain, and activate cells around the suffering brain tissue to catalyze rapid healing and to improve brain function [1]. Recent studies have demonstrated that stem cell transplantation can induce the reinstatement of functional connectivity, as determined by functional magnetic resonance imaging (fMRI) [1]. Other studies have shown that cell therapy results in the upregulation of certain growth factors that are responsible for the inhibition of inflammatory processes [2]. PT promotes neuroplasticity, and allows the restoration of functional connectivity after damage from stroke [3]. PT has shown a range of small to large effect sizes for task-oriented exercise after stroke, especially when applied intensively and soon after stroke onset.

However, effects are mainly restricted to tasks directly trained [3].

Physical therapy allows the stroke patient to repeatedly “use” the synaptic connections in their brain, which promotes the restoration of functional connectivity. Both stem cell therapy and physical therapy have proven to be effective independently. However, a combination of the two therapies could produce combined therapeutic effects, greater than each treatment on its own.

The aim of this study was to establish how a combined therapy of PT and NSCs facilitates functional recovery after stroke, and to quantify the effect of each therapy independently.

One region thought to experience a change in blood flow and activity with these therapies is the somatosensory cortex (SMC). fMRI measures brain activity by detecting changes in blood flow.

Cerebral blood volume (CBV) weighted MRI is a physiological indicator of tissue viability and vascularization. Both fMRI and CBV were used to measure the effect of each therapy, and establish how cells promote neuroplasticity in the SMC.

2. Methods 2.1 Middle Cerebral Artery Occlusion (MCAO) All animal procedures complied with the U.S. Animal Welfare Act (2010) and were approved by the University of Pittsburgh Institutional Animal Care and Use Committee (IACUC). Adult, male, Sprague-Dawley rats (260 ± 15 g) underwent transient MCAO via insertion of a 5-0 silicon rubber-coated monofilament (diameter 0.12 mm, length 30 mm) in the MCA while under isoflurane for the duration of an hour. After recovery from anesthesia, the animals were assessed for forelimb flexion and contralateral circling with daily post-operative care until they recovered preoperative weight.

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2.2 Assessment of Stroke and Administration of Treatment A hyperintense signal on T2-weighted MRI determined success of MCAO [4], and animals were randomly assigned to the following groups: Controls, MCAO, MCAO+NSCs, MCAO+PT, and MCAO+combined. The animals that underwent NSC transplantation received a perilesional NSC graft (450,000 cells) 2 weeks poststroke [5]. The PT consisted of daily treadmill running at 80% capacity in both groups receiving PT [6]. T2, fMRI, and CBV MRI scans were collected using a horizontal bore 9.4 T Varian scanner at the 10-week time point.

2.3 Cerebral blood volume magnetic resonance imaging acquisition For CBV acquisition, each animal was scanned for 5 minutes before intra-arterial 15 mg/kg monocrystalline iron oxide nanoparticle (MION) injection to provide a baseline intensity, and then 5 minutes after injection (TR= 2500ms; TE= 19ms; 4 Averages; number of directions= 6; 12 slices, slice thickness 0.3mm, FOV 30x30 mm; Matrix 96x96). ROIs for the striatum, thalamus, motor cortex (MC), and SMC were drawn on both the pre- and post-MION scans in order to determine blood volume in each region [7].

2.4 Functional magnetic resonance imaging acquisition Immediately after CBV acquisition, an electrode was inserted subcutaneously in each forepaw for contrast-enhanced fMRI acquisition. Each paw was stimulated alternately for 5 minutes with a current of 1 mA over the duration of an hour, with short periods of no stimulation to provide a resting-state intensity [8].

Figure 1: CBV for regions in ipsilateral hemisphere.

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2.5 Imaging processing The signal change between scans is directly related to the proton relaxation rate induced by MION (ΔR*2;agent(s-1)).This rate can be found from the intensities of the pre and post MION CBV via the equation: ΔR*2;agent(s-1) = ln(Spre/Spost)/TE where Spre and Spost are the signal intensities before and after MION injection and TE is echo time. For the condition in which static dephasing is dominant, the change in apparent transverse relaxation rate in tissue caused by MION, and in the absence of stimulation can be described as: fCBV= [ΔR*2;agent• (3/4π)] / [(1 – Hct) • Δχagent• γB0] where fCBV is whole blood volume fraction (mL blood/mL brain), which includes plasma and blood cells, γ is the gyromagnetic ratio, which is 2.675 x 108rad/(sT), and B0 is the applied magnetic field. Assuming a normal cortical hematocrit (Hct) of 0.40 and susceptibility difference (Δχagent) of 0.29 ppm, fCBV can be calculated from ΔR*2;agent. fCBV is then converted to baseline CBV (mL/g) by dividing by the blood density of 1.06 g/mL [7].

3. Results 3.1 Cerebral blood volume A one-way ANOVA test of CBV indicated that there was no significant difference in blood volume across groups (including control) in the ipsilateral striatum, MC, and SMC (p>0.05) (Fig. 1).

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Figure 2: fMRI signal map of affected and unaffected hemisphere with a threshold of 0.05.

Figure 3: Number of active voxels, amplitude of signal, and time course of a single stimulation in the ipsilateral and contralateral hemisphere.

In the thalamus, a Tukeyâ&#x20AC;&#x2122;s multiple comparison test indicated that the MCAO group had a significantly higher CBV than the controlsham group.

3.2 fMRI The location of the fMRI signal was in the SMC for all treatment groups (Fig. 2). A Tukeyâ&#x20AC;&#x2122;s multiple comparison test of fMRI revealed that the combined therapy group had a significantly higher number of active voxels in the ipsilateral hemisphere than the other groups. However, there was no significant difference in the amplitude of the signal in that hemisphere, as well as the number of voxels and amplitude of signal in the contralateral hemisphere (p>0.05) (Fig. 3).

4. Discussion

CBV indicated that neither NSCs nor exercise had an effect on tissue perfusion in the stroke-affected regions (Striatum and SMC) in the ipsilateral hemisphere. Past studies have shown a reduction in signal change during the passage of a contrast agent in the stroke cavity, therefore CBV is reduced in the infarction compared to the control-sham group [9]. In the striatum and the SMC, the MCAO group had a lower mean CBV than the controlsham group, although this difference is not significant. This could be due to small sample sizes or the time of CBV MRI acquisition.

Because the scans were not collected until three months poststroke, adequate time was allowed for tissue regeneration while the study referenced above performed CBV acquisition within 6 hours of stroke onset. Alternatively, The MC shows almost no difference between groups while the thalamus showed a significantly higher

CBV in the MCAO group compared to the control group, but no other treatment group was significantly different than another. This may be due to the fact that the stroke lesion was largely contained in the striatum and SMC, and rarely affected the thalamus and MC. The difference between the Control and MCAO group could be due to small sample sizes.

Previous studies also demonstrated that stem cell transplantation promoted angiogenesis/neovascularization after stroke because of trophic factors secreted by stem cells [10], and PT induced angiogenesis and neurogenesis through several proposed mechanisms including the induction of VEGF [11]. Here however, there was no significant increase in CBV after the three treatments. Immunohistochemical analysis is currently being performed to further analyze the neurobiological basis for changes observed on CBV maps. Cross-sections of the brain of each subject are being stained with an anti-endothelial cell antibody (RECA-1) to further analyze the effect of each treatment on angiogenesis. fMRI revealed that a larger area of the brain was active in the combined group. However, the strength of this signal was not significantly different between treatment groups in the ipsilateral hemisphere. This suggests that the combined therapy caused a larger volume of the brain to respond to the electrical stimulus than the group that received the transplanted cells without exercise. An explanation for this could be that more tissue was recruited to meet the threshold in order to respond to the stimulus. Past studies have shown that NSC implantation results in an enhanced fMRI signal change in the treatment group in comparison to the control group [12]. fMRI has been used previously to monitor brain activity during a motor learning task [13], but little is known about the effects of a combined NSC and physical therapy. The results here propose that NSCs and PT have a sub-additive effect on brain activity after stroke.

5. Conclusions

NSCs, PT, and a combined therapy had no effect on tissue perfusion after stroke as indicated by CBV MRI. Histology is currently being performed to confirm this. Previous studies showed a reduced CBV in MCAO-affected subjects 6 hours after occlusion [5], however because the MRI acquisition was three months post-stroke, neurorestorative angiogenesis could have occurred naturally around the infarct [14]. A combined therapy facilitates functional recovery after stroke, more so than NSCs and PT alone, as demonstrated by fMRI. Previous studies used fMRI to evaluate the effects of NSCs and PT individually and found increased signal [9,10], but the combined therapy had a significantly higher signal in comparison. A combined therapy greatly promotes neuroplasticity, therefore potentially offering a new combined treatment option for stroke patients. In addition, this study increased the understanding of the biological effects of NSCs and PT on stroke patients. In further studies, the PT could be refined to a more task-specific protocol, which is typical of clinical rehabilitation paradigms. With this, a time course of recovery could be created, and the rate at which each therapy promotes healing after stroke could be studied.

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Acknowledgments

The summer research fellowship award funded by the Swanson School of Engineering and the Office of the Provost supported Nikhita Perry during the course of this research.

This study received funding from the Alliance for Regenerative Rehabilitation Research & Training (AR3T, which is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under award number P2CHD086843.

References

1. D. Kalladka, K. Muir, Brain repair: cell therapy in stroke, Stem Cells Cloning 7 (2014) 31-44.

2. V. Misra, M. Ritchie, L. Stone, W. Low, V. Janardhan, Stem cell therapy in ischemic stroke: Role of IV and intra-arterial therapy.

Neurology 79 (2012) 207-212.

3. R. Peppen, G. Kwakkel, S. Dauphinee, H. Hendriks, P. Van der Wees, J. Dekker, The impact of physical therapy on functional outcomes after stroke: whatâ&#x20AC;&#x2122;s the evidence?, Clinical Rehabilitation 18 (2004) 833-862.

4. M. Stille, E. Smith, W. Crum, M. Modo, 3D reconstruction of 2D fluorescence histology images and registration with in vivo MR images: Application in a rodent stroke model, Journal of Neuroscience Methods 219 (2013) 27-40.

5. E. Smith, R. Stroemer, N. Gorenkova, M. Nakajima, W. Crum, E. Tang, L. Stevanato, J. Sinden, M. Modo, Implantation site and lesion topology determine efficacy of human neural stem cell line in a rat model of chronic stroke, Stem Cells 30 (2012) 785-96.

6. G. Kim, E. Kim, Effects of treadmill training on limb motor function and acetylcholinesterase activity in rats with stroke, Journal of Physical Therapy Science 25 (2013) 1227-1230.

7. K. Seong-Gi, N. Harel, T. Jin, T. Kim, P. Lee, F. Zhao, Cerebral blood volume MRI with intravascular superparamagnetic iron oxide nanoparticles, NMR in Biomedicine (2012).

8. A. Van de Linden, N. Van Camp, P. Ramos-Cabrer, M. Hoehn, Current status of functional MRI on small animals: application to physiology, pathophysiology, and cognition, NMR Biomed 20 (2007) 522-545.

9. J. Hatazawa, E. Shimosegawa, H. Toyoshima, B. Ardekani, A. Suzuki, T. Okudera, Y. Miura, Cerebral blood volume in acute brain infarction, Stroke 30 (1999) 800-806.

10. N. Horie, T. Hiu, I. Nagata, Stem cell transplantation enhances endogenous brain repair after experimental stroke, Neurol. Med. Chir. 55 (2015) 107-112.

11. A. Ergul, A. Alhusban, S. Fagan, Angiogenesis: a harmonized target for recovery after stroke, Stroke 43 (2012) 2270-2274.

12. B. Duffy, A. Weitz, J.H. Lee, In vivo imaging of transplanted stem cells in the central nervous system, Department of Health and Human Services 28 (2014) 83-88.

13. B. MacIntosh, R. Mraz, W. Mcllroy, S. Graham, Brain activity during a motor learning task: an fMRI and skin conductance study, Human Brain Mapping 28 (2007) 1359-1367.

14. J. Liu, Post stroke angiogenesis, Stroke 46 (2015) 105-106.

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Ingenium 2019

Angiogenic response to abdominal and vaginal polypropylene mesh implants in a rabbit model McKenzie Sickeb, Aimon Iftikhara,b, Alexis Nolfia,b, Hannah Geislerb, and mentor Bryan Browna,b McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA, bDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA a

Significance Statement:

Polypropylene mesh (PPM) implants are widely used in medicine. However, there are complications observed at times.

This project aims to better understand physiological response to PPM implants in a large animal model to then identify and generalize clinical modifications that may improve PPM implant success.

Sicke

Abstract

While polypropylene mesh is often implanted abdominally and vaginally to address a variety of structural medical Brown issues, complication rates are higher than is desirable for such a commonly used medical solution (10-20% for vaginal implants) [1]. Implantation of any foreign object triggers an immune response in the host, and this immune response can contribute some of the complications observed. Macrophages are the primary initiators of the host response after initial neutrophil recruitment, and the nature of this response is often determined by the polarity of the macrophages present at implant site. Generally macrophages have been modeled as existing on a polarity spectrum between M1 (generally pro-inflammatory) and M2 (generally pro-remodeling). It has been observed that slightly shifting macrophage polarity profile towards an M2 profile can improve implant outcome. Based on this observation, methods to introduce interleukin-4 (IL-4), an immunomodulatory cytokine that encourages an M2 macrophage phenotype, to the implant site have been developed. One indicator of positive outcome is angiogenesis, the formation of new blood vessels that can provide nutrients to an implant site. While IL-4 controlled release and its effect on angiogenic response has been studied in mice, effects observed in a larger animal model are more clinically relevant. Because of this, analogous studies in a rabbit model will be conducted by this lab. It is important to identify angiogenic response to unmodified PPM implants so that changes in angiogenesis from modified PPM implants can be attributed to said modifications. This study focuses on developing a method for identifying differing angiogenic responses that exist for abdominal and vaginal PPM implants in rabbits.

1. Introduction

Over a million women in the United States each year are affected by pelvic organ prolapse, a condition characterized by the prolonged weakening of the pelvic floor muscles [2]. Reinforcement can be implemented via a surgical reconstruction using polypropylene mesh (PPM). However synthetic mesh can often cause complications such as fibrous tissue encapsulation, erosion, or mesh degradation due to foreign body reaction [3]. The overall host response to the PPM is the sum of several factors, including the polarization of macrophages present, cellularity of the tissue at the wound site, and whether the tissue being remodeled is healthy tissue or fibrous encapsulation. The polarized macrophages that mediate this response can be categorized broadly into subtypes M1 (proinflammatory) or M2 (proremodeling) [4]. Mesh-tissue response can be improved with a limited M2 predominant response, but excess M2 can cause complications such as fibrous encapsulation as well [4].

Modulating the inflammatory response to implanted surgical mesh can improve the long-term outcome of reconstructive treatment for those with pelvic organ prolapse. Encouraging the M2 phenotype in macrophages in the implant environment can be achieved with release of interleukin-4 (IL-4), an immunomodulatory cytokine. Current methods in this lab incorporate IL-4 into a nanoscale coating to encourage a shift to the M2 profile in the macrophage population at the host-implant interface during the early immune response to implanted mesh. This is achieved by using an adapted radio frequency glow discharge method to establish a negative charge on the mesh, then adding polycationic and polyanionic polymers to create the stable coating.

A key component of tissue remodeling is the angiogenic response in the developing tissue because increased vasculature can provide oxygen and nutrients to the implant site. These factors could improve the growth of remodeled tissue rather than fibrotic tissue that can potentially cause complications, and identifying trends in angiogenesis could aid the development of better implant methods. This lab has conducted IL-4 controlled release studies in mice using the coating method previously mentioned and have analyzed the angiogenic response at the host-implant interface.

It has been initially concluded that IL-4 controlled release does increase angiogenic response at 14 days post-implantation. In this phase of study, the angiogenic response will be analyzed in a larger, more relevant animal model (New Zealand white rabbit).

Initial focus is to identify the differences this model may present and achieve a better understanding of the host response to unaltered PPM in the new model. PPM is also used in abdominal hernia repair, and the anatomical differences between abdominal tissue and vaginal tissue may lead to differing host responses.

The first step of this study is to identify the angiogenic response at the host-implant interface of abdominal and vaginal unaltered PPM implants in the rabbit model, with the long-term goal of incorporating the controlled release of IL-4 into the study.

Therefore, changes in angiogenesis found later can be attributed to the presence of the modified PPM.

Category: Experimental research

Keywords: Angiogenesis, polypropylene mesh, implantation

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Figure 1: A representative image is shown for the immunolabeling of the CD31 marker for endothelial cells to identify blood vessels in a 20x field of the hostimplant interface in a vaginal tissue sample (scale bar =100μm).

Figure 2: An H&E stain of the host-implant interface of the same vaginal tissue sample as Figure 1 is shown at a 20x objective. These images were used for qualitative observation of the differences in cell infiltration (scale bar = 100μm).

2. Methods

2.1 Data processing The blood vessels in the region of interest were hand counted by a trained investigator with the aid of an ImageJ color deconvolution macro to identify vessels. An example image of counted vessels is shown in Figure 3. A Student’s independentsamples t-test was used to compare the average vessel density between vaginal and abdominal tissue.

Prior to this phase of the study, a New Zealand white rabbit model was used for PPM implants in both a subcutaneous abdominal implant and a vaginal implant that mimics the abdominal sacrocolpopexy procedure, the current gold standard treatment for prolapse repair. The PPM used was Gynemesh manufactured by Ethicon. At 14 days post-implantation the rabbits were sacrificed, and samples of the tissue-mesh complex were extracted for histological analysis in this study. The tissue was mounted in paraffin and sectioned at 7 µm. The slides were immunolabeled using peroxidase staining of the CD31 marker for endothelial cells using a 4% diaminobenzidine (DAB) substrate solution and a Hematoxylin QS counterstain (Figure 1). A CD31 monoclonal antibody (JC/70a) manufactured by Invitrogen was used as the primary, and a biotinylated anti-mouse IgG antibody manufactured by Vector was used for the secondary. The slides were also stained using hematoxylin and eosin (H&E) for qualitative comparison of the tissues (Figure 2). Images for blood vessel analysis were taken at a 20x objective of the host-implant interface centered on a mesh fiber.

Figure 3: The yellow markers indicate the blood vessels counted in this image of the host-implant interface for a vaginal tissue sample. This image was also stained using immunolabeling of the CD31 marker and is shown at a 20x objective (scale bar =100μm).

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3. Results

Figure 1 demonstrates positive immunolabeling of the endothelial cells that line blood vessels, identifying them for analysis. Additionally, the purple nuclei stained in the H&E images such as Figure 2 provide information on the infiltration of cells during the immune response. As stated in section 2.1, an example image noting the blood vessels counted for analysis is displayed in Figure 3. Represented in Figure 4 are the average blood vessel counts normalized to area with standard deviation.

The average number of blood vessels normalized to area found was 148.9 vessels/mm2 with a standard deviation of 28.03 vessels/mm2 for vaginal implants, and 179.5 vessels/mm2 with a standard deviation of 41.28 vessels/mm2. It was found that there is no significant difference between the angiogenic response in abdominal and vaginal mesh implants at 14 days (p > 0.05).

5. Conclusions

It is found that there is no significant difference between the angiogenic response to PPM implantation vaginally and abdominally 14 days post-implantation in a rabbit model. For future experiments, it can be assumed that differences in angiogenic response due to implantation of altered mesh both abdominally and vaginally are not solely due to the location of implantation. This will greatly aid the validation of any conclusions found in the future studies mentioned in section 4.

Acknowledgments

Studies conducted at the McGowan Institute for Regenerative Medicine under Dr. Bryan Brown. Funding provided by the Swanson School of Engineering.

References

1. Dällenbach P. To mesh or not to mesh: a review of pelvic organ reconstructive surgery. Int J Womens Health. 2015;7:33143. Published 2015 Apr 1. doi:10.2147/IJWH.S71236 2. Iftikhar, A., Nolfi, A., Moalli, P., Brown, B., A Clinically Relevant Rabbit Surgical Model of Pelvic Reconstruction to Evaluate the Immune Response to Novel Surgical Materials, 2018.

3. Udpa, N., Iyer, S., Rajoria, R., Breyer, K., Valentine, H., Singh, B., McDonough, S., Brown, B., Bonassar, L., Gao, Y., Effects of Chitosan Coatings on Polypropylene Mesh for Implantation in a Rat Abdominal Wall Model, 2013, vol. 19: no. 23 and 24.

4. Nolfi, A., Brown, B., Liang, R., Palcsey, S., Bonidie, M., Abramowitch, S., Moalli, P., Host response to synthetic mesh in women with mesh complications, AJOG 2016.

Figure 4: The number of blood vessels normalized to the area of the region of interest at the host-implant interface is plotted for both vaginal and abdominal implants with standard deviation. There is no significant difference found.

4. Discussion

These results regarding angiogenesis may suggest that the early host response is similar at the two sites considered in the rabbit model. This may also suggest that the macrophage polarity ratio is similar at the interface of abdominal and vaginal implants, however it is not certain that angiogenesis is directly affected by this. Going forward we will be analyzing multiple time points under different mesh conditions including a group with the nanoscale coating, and a group with the coating and loaded IL-4. Since the relationship between IL-4 elution and shifted macrophage polarity has already been established, this experiment has the ability to demonstrate differences in macrophage profile at the two implant sites. This further study will give a better picture of the relationship between macrophages and angiogenesis during the early immune response as well as allow an analysis of the effects of angiogenesis upon long-term success of implantable mesh.

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The effect of pivot-bearing surface roughness on thrombus formation: An in-vitro study Katherine Stevensona,c, Alexandra Maya,d, Ryan Orizondoa,c, Sang-Ho Yeb,c, Brian Frankowskia, William R. Wagnerb,c,d, and mentor William J. Federspiela,c,d Medical Devices Lab, bCardiovascular Engineering Lab, Department of Bioengineering, dDepartment of Chemical Engineering

a c

Significance Statement:

Material roughness affects the thrombogenicity of blood contacting surfaces. Thrombosis detrimentally affects the performance and longevity of medical devices. In this in vitro work, two pivot materials for a novel pump-lung were evaluated. The smoother, zirconium pivot showed reduced thrombus formation. This pivot will be utilized during in-vivo studies.

Stevenson

1. Introduction

Patients with end stage chronic lung disease or acute respiratory distress syndrome are commonly treated with extracorporeal membrane oxygenation (ECMO) [1]. A primary shortcoming of ECMO is that patients are typically bedridden in the ICU. This leads to muscle degeneration and poorer patient outcomes [2]. The medical devices lab is developing the Paracorporeal Ambulatory Assist Lung (PAAL) as a compact, wearable pump-lung to provide ambulatory respiratory support to lung failure patients. The device is intended to bridge patients to transplant or recovery [1]. The PAAL is currently being evaluated in 30-day animal studies. One device related challenge, however, is the development of thrombus at the bottom pivot of the impeller (Figure 1) during these long-term animal studies. This has caused increased blood damage as well as a detrimental increase in motor torque.

Abstract

Blood-contacting surfaces with higher Federspiel roughness are more prone to thrombus formation. This in vitro work evaluates the effect of pivot material surface roughness on thrombus formation caused by protein and platelet adherence at the bottom pivot of the impeller of a pump-lung device. This pumplung device is designed to provide ambulatory respiratory support to lung failure patients. A ceramic pivot (CPI) and zirconium pivot (ZPI) were evaluated. Surface roughness measurements showed the CPI to be 68.8 times rougher than the ZPI (n=9). ZPI and CPI modified devices were operated in parallel in otherwise identical blood-filled circuits for up to 10 hours. Blood flow was generated by the integrated pump. The thrombi surface area on the bottom of the CPI (1.32±0.7 cm2) was significantly greater than that of the ZPI (0.35±0.4 cm2) (p = 0.012, n=4). The CPI thrombus weight below the impeller (8.29±6.9 mg) was greater than that of the ZPI (2.78±2.1 mg) although not statistically different (p = 0.055, n=4). The total thrombus weight on the impeller (top and bottom) was significantly larger on the CPI (65.64±26.72 mg) than the ZPI (12.21±11.43 mg) (p = 0.018, n=4). The average plasma-free hemoglobin increase in the first 6 hours for the CPIs (61.0±15.3 mg/dl) vs. the ZPIs (19.4±8.0 mg/ dl) showed significantly greater hemolysis in the CPI device (p = 0.018, n=4). Thus, the smoother surface of the ZPI reduces thrombosis. Future devices will utilize ZPIs.

Category: Device design

Keywords: Thrombosis, Artificial Extracorporeal Lung, Surface Roughness, Integrated Pump

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Figure 1: Device Impeller - Pivot (red) in device impeller, seen from a side view (left) and top view (right). Thrombus often forms underneath impeller around pivot.

Thrombus formation is a common problem in bloodcontacting medical devices [3,4,5]. As proteins adhere to the artificial surface of the device, platelets, leukocytes, and red blood cells are able to attach. This results in thrombus formation on the blood contacting surface of the device. Current research efforts in the field are focused on preventing, or decreasing, thrombus formation through improved biocompatible surfaces, local anticoagulation, and anti-thrombogenic coatings [6,7]. The effect of surface roughness on platelet adhesion has been established in the literature. Rougher surfaces correlate to an increase in platelet adhesion and protein adsorption [8]. Thus, smoother materials are often selected for blood contacting surfaces to reduce thrombosis.

This in vitro work evaluates the effect of pivot material surface roughness on thrombus formation at the bottom pivot of the PAAL. Specifically, the surface roughness of zirconium and ceramic pivots were measured. Different impellers were created with the two pivot materials and the effect of each pivot material on thrombus formation was evaluated at a blood flow rate of 0.5 L/ min. Thrombi were quantitatively evaluated based on mass, surface area and location. It is hypothesized that thrombus formation will be decreased on the impeller with a smoother surface.

Ingenium 2019

2. Methods 2.1 Device Set-up The PAAL device consists of an integrated centrifugal pump which uses an impeller to pump blood through a channel and then through a gas exchanging fiber bundle. A ceramic pivot impeller (CPI) and zirconium pivot impeller (ZPI) were evaluated in four repeated trials. The same impellers were used in each experiment. The resistance of the fiber bundle and the intended use 15.5 Fr Hemolung catheter were replicated by a 1/8" diameter circular conduit and 16 ft of 3/16" diameter tubing, respectively.

The resistance of the fiber bundle was calculated using the Blake-Kozeny equation, k = (ℇ3 Dp2) / (150 * (1- ℇ)2), where k = Darcy permeability (m2), ℇ= bundle porosity, and Dp= effective fiber diameter (m) [9, 10]. The pressure drop of the tubing was calculated using the Hagan-Poiseuille equation, ∆P = (8 LQ) / (πR4), where ∆P = pressure drop (Pa), L = length (m), = dynamic viscosity (Pa*s), Q = volumetric flow rate (m3/s), and R = radius (m). The resistance of the 15.5 Fr Hemolung catheter is previously reported as 0.32 mmHg/ml/min [11].

2.2 Surface Roughness An Alpha-Step IQ Surface Profiler (KLA Tencor) was used to measure pivot surface roughness of each pivot material. Nine pivots were measured for each pivot material. These pivots were identical to the ones used in our test impellers. The surface roughness measurement was taken along the long axis of the pivot, the vertical direction as shown previously in Figure 1.

2.3 In-Vitro Procedure Two loops (one with a ZPI impeller, and one with a CPI impeller) were run in parallel. Each loop contained 750 mL of ACD-A ovine blood (Lampire Biological Lab, Pipersville, PA).

Heparin (3.5 U/mL, Fresenius Kabi, Lake Zurich, IL), CaCl2 (3 g/L, Sigma Aldrich), L-glutamine (0.292 g/L, Sigma Aldrich) and gentamicin (2.5 mL/L, Fresenius Kabi, Lake Zurich, IL) were added to the blood. Blood flow rate was set to 0.5 L/min and driven by the integrated pump. Flow rate was recorded every 30 minutes while motor torque was acquired via LabView at 2 Hz. Hemoglobin (Hb) (Siemens RAPIDPoint 405 Blood Gas System), plasma free hemoglobin (pfHb), and activated clotting time (Medtronic ACT II) measurements were made every 1.5 hours. Experiments were terminated after 10 hours or if the flow rate in one of the circuits dropped to 0.2 L/min due to thrombus formation in the blood reservoir. Photographs were taken of thrombi to document size and location on the impeller. Thrombi were removed from the top and bottom of the impeller and dried for 24 hours before weighing separately.

2.4 Platelet Activation Platelet activation was measured in three of the experiments.

Blood samples (3 mL) were taken from each loop and immediately placed in a citrate tube (S-Monovette 3 mL 9NC) every 30 minutes for the first 3 hours of the experiment. The samples were analyzed using flow cytometry to measure the percentage of activated platelets by the amount of expressed CD62P. Platelet activating factor was used as the agonist.

2.5 Data Processing Torque data collected using the DAQ Device (USB-6002) was processed using MATLAB (version 9.0.0.341360, R2016A) to find the average torque per hour for each loop. Digital photographs of thrombi were processed using ImageJ (version 1.51j8) and MATLAB to find surface area, radial location on impeller, and to create heat maps. Photographs were taken using a camera with consistent zoom settings placed in a stand to ensure the same depth, magnification, and position between all images. Images were then cropped into squares around the impeller and scaled to the same size, between 390 and 394 pixels/cm2. Using ImageJ, the red thrombus was manually color thresholded from the blue background using the hue to pass only the red areas of the photo.

Once the thrombus was selected, the surface area could be found, and the image was converted into a binary image such that only the thrombus had pigment. These images were then converted into a stack and imported into MATLAB where each pixel of the stack was analyzed to determine the number of samples that had thrombus covering each pixel. This number was averaged with the other pixels in concentric circles with a radius starting at 62.23 pixels (the radius of the pivot) and increasing radially outward by increments of 30 pixels. This average number was converted into a percentage of the total number of samples and corresponds to a color in the heat map. Statistical comparisons for size, weight, platelet activation, and pfHb increase were made using the one-tailed paired two sample t-test. Differences were deemed significant for p < 0.05.

3. Results 3.1 Surface Roughness, Blood and Device Parameters The average surface roughness of the CPI (639.6 nm) was 68.8 times rougher than the ZPI (9.3 nm) (n = 9). Torque did not increase by more than 0.1 mN-m, which is within typical variation under normal operation. The average pfHb increase in the first 6 hours for the CPIs (61.0±15.3 mg/dl) vs. the ZPIs (19.4±8.0 mg/dl) showed significantly greater hemolysis in the CPI device (p = 0.018, n = 4). There was no significant difference in the increase of platelet activation over the experiment between the ZPI (24.81±16.65 %) and CPI (23.55±12.23 %) (p = 0.41 n = 3).

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Figure 2: Thrombi - Representative composition and thrombus size on ZPI’s and CPI’s.

Figure 3: Heat Maps – These represent the combined data from all 4 experiments, showing the average radial size and location of thrombi on the bottom of the circular impeller. For the coordinate system, the change in radius between each white circle is 90 pixels, or 0.23 cm.

3.2 Thrombus Formation The thrombi surface area on the bottom of the CPI (1.32±0.7 cm2) was significantly greater than that of the ZPI (0.35±0.4 cm2) (p = 0.012, n=4). The surface area as well as the radial location of the thrombus on the impeller is shown in digital photographs (Figure 2), as well as heat maps (Figure 3). The CPI thrombus weight below the impeller (8.29±6.9 mg) was greater that of the ZPI (2.78±2.1 mg). Due to the variability between blood samples, this data was not statistically significant (p=0.055). However, the weight of the thrombus on the entire impeller (bottom and top) was significantly greater on the CPI (65.64±26.72 mg) than the ZPI (12.21±11.43 mg) (p = 0.018, n=4).

The dynamic and complex nature of the coagulation cascade ultimately requires in vivo evaluation of anti-thrombogenic designs.

This results in limitations to the extent that an in vitro experiment can replicate in vivo data. The increased motor torque seen in in-vivo studies was not replicated in these in-vitro studies due to the smaller thrombus size in the in-vitro studies. This is likely due to the accelerated time of these studies relative to 30-day in-vivo studies. Reduced anti-coagulation is necessary in the in-vitro studies for thrombus to form at the impeller in a 6-10 hour period, but as a result thrombus also forms in the blood reservoir, reducing the blood flow and forcing an end to the study before a significant increase in motor torque occurs. However, in vivo studies are both time consuming and expensive when multiple design iterations need to be evaluated. In addition, the use of in-vitro experiments to replicate thrombus seen in vivo has previously been done with success. For example, a research group at Georgia Institute of Technology were able to use an in-vitro experiment to create similar thrombus to those seen in centrifugal pumps of ECMO devices [3]. Therefore, despite the limitations of this in-vitro study, it has been previously shown that in-vitro results correlate with in-vivo results. This demonstrates that the results of this study can be used to influence changes to the PAAL device.

The results of the present study demonstrated sufficient reduction in thrombus formation and hemolysis with the ZPI device to propose a change to the pivot in the PAAL device. Long-term in vivo studies utilizing the ZPI device are on-going and will be used to validate these in vitro results. Future work will evaluate the effect of other design modifications, such as impeller washout holes, on thrombus formation.

4. Discussion

Thrombus formation within artificial lung devices can lead to detrimental changes in device performance. During 30-day studies of the PAAL device, thrombus formation at the bottom pivot of the impeller has resulted in high hemolysis and motor torque. This manuscript details the in vitro evaluation of the effect of surface roughness of two pivot materials, zirconium and ceramic, on thrombus formation at the pivot. This was accomplished using a benchtop experiment to replicate the thrombus formation observed during in-vivo studies. The findings show a reduction in thrombus surface area and weight on the ZPI and indicate that the smoother zirconium surface of the pivot decreases thrombosis. No significant difference in platelet activation was expected since the shear rates within the two devices are similar. In addition, the literature reports no correlation between surface roughness and platelet activation [8].

Thrombosis causes a large issue in artificial pump-lung devices. For example, in trials of the HeartWare device, 8.1 percent of patients had a pump thrombus event, and 52 percent of those patients underwent pump exchange [4]. Thrombus formation in these devices is a complex issue and is not always triggered by the same cause. For this reason, multiple avenues are being studied to reduce thrombus formation [6]. This study is focused on the effects of surface roughness in areas of the device known to be prone to thrombosis.

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5. Conclusions

Thrombus formation within blood pumps has a detrimental effect on device performance. This study evaluated the effect of pivot material surface roughness on thrombus formation. Smoother zirconium pivot impellers had less hemolysis and smaller thrombus formation compared to ceramic pivot impellers. Future work will involve evaluating the zirconium pivot impeller in 30-day PAAL animal studies.

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Acknowledgments

Supported by NIH grant R01HL117637 and R01HL135482, the University of Pittsburgh Office of the Provost, and the Swanson School of Engineering.

References

1. Madhani et al, In vitro and in vivo evaluation of a novel integrated wearable artificial lung, J. Heart Lung Transplant. 36.9 (2017) 806-811.

2. Maury et al, Skeletal muscle force and functional exercise tolerance before and after lung transplantation: a cohort study, AM J Transplant. 8 (2008) 1275-1281.

3. Shea et al, Thrombosis in centrifugal pumps: Location and composition in clinical and in vitro circuits, IJAO. 39:4 (2016) 200-204.

4. Najjar et al, An analysis of pump thrombus events in patients in the HeartWare ADVANCE bridget to transplant and continued access protocol trial, J. Heart Lung Transplant. 33:1 (2014) 23-34.

5. Fauchier et al, Device-Related Thrombosis After Percutaneous Left Atrial Appendage Occulusion for Atrial Fibrillation, J. Am. Coll. Cardiol. 71:14 (2018) 1528-1536.

6. Jaffer et al, Medical device-induced thrombosis: what causes it and how can we prevent it?, J. Thromb. Haemost.

13 (2015).

7. Malkin et al, Development of zwitterionic sulfobetaine block copolymer conjugation strategies for reduced platelet deposition in respiratory assist devices, J. Biomed. Mater. Res. B. 106 (2018).

8. Linneweber et al, The effect of surface roughness on activation of the coagulation system and platelet adhesion in rotary blood pumps, Artif Organs. 31 (2007) 345-351.

9. Madhani et al, Darcy Permeability of Hollow Fiber Membrance Bundles Made from Membrana Polymethulpentene Fibers Used in Respiratory Assist Devices, ASAIO J. 62 (2016) 329-331.

10. Pacella et al, Darcy Permeability of Hollow Fiber Bundles Used in Blood Oxygenation Devices, J Memb Sci. 382 (2011) 238-242.

11. May et al, Extracorporeal CO2 removal by hemodialysis: in vitro model and feasibility, ICMx 5:20 (2017).

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Infant feed thickening characterization at UPMC Children’s Hospital of Pittsburgh Kelsey Toplaka, Kimberly Kubistekb, Kelly Fillb, Sheryl Rosenb, and Mark Gartnera Department of Bioengineering, University of Pittsburgh, Department of Occupational Therapy, Children’s Hospital

a

b

Significance Statement:

To reduce aspiration in dysphagic infants under 12 months of age, formula of known and accurate viscosity must be prescribed. Infants receiving formula of a viscosity different from what they were prescribed increases risk of aspiration.

This study aims to assess possible risks to patients by obtaining baseline values of the viscosity of the formula they are fed.

Toplak

Abstract

UPMC Children’s Hospital of Pittsburgh currently uses oatmeal flakes to thicken formula for dysphagic infants age zero to 12 months to reduce risk of aspiration and subsequent oxygen depletion. Formula is Gartner thickened to either a 1:1 or 1:2 oatmeal-toformula ratio based on results from a barium swallow test. This work both quantified the baseline viscosity of several thickened formulas and assessed changes in viscosity over time with the goal of determining the optimal viscosity and feeding period length to reduce risks of aspiration. Secondary goals of this work included investigating a potential difference in viscosity in the thickened formula used in treatment and the thickened diluted Varibar (Bracco Imaging, Milan, Italy) used in diagnosis.

A significant discrepancy between these two viscosities could pose a threat to patient safety in the form of a higher risk of aspiration due to the feeding of a thinner formula than was diagnosed. The clinicians at Children’s Hospital were in need of a methodology to ensure their dysphagic patients were receiving the formula at a safe viscosity such that they are able to decrease the chance of aspiration. By analyzing the viscosities of formula, thickened formula, diluted Varibar, and thickened Varibar over time, the behavior of these substances were able to be quantified, showing that a discrepancy exists between the viscosity of the Varibar used in diagnosis and the corresponding thickened formula used in feeding such that the Varibar is significantly more viscous and that 1:2 and 1:2 thickened formula will behave differently over time, with 1:1 increasing in viscosity and 1:2 decreasing in viscosity until it reaches a steady state. The clinicians can use this information to determine “best practices” when feeding dysphagic infants age 0-12 months.

Category: Experimental Research

Keywords: Viscosity, Thickened Formula, Pediatrics

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1. Introduction 1.1 Dysphagia in infants under 12 months Dysphagia is the inability to swallow or difficulty swallowing and greatly increases risk of aspiration. Dysphagia is most commonly diagnosed in elderly patients and in infants [1]. The Occupational Therapy Department at Children’s Hospital of Pittsburgh diagnoses and treats infants of all ages with dysphagia.

However, infants aged 0-12 months face the greatest risk of aspiration while feeding due to an inability to communicate verbally or physically. When an infant under the age of 1 aspirates, the primary indicator of a lack of oxygen is the sounding of the alarm on their monitor or the infants skin changing to a blue or purple color [2]. By this point, typically enough oxygen has been depleted to cause serious harm to the infant—or death.

A Barium Swallow Test is used to diagnose the severity of the dysphagia and in turn to prescribe the proper feeding method for each patient. Varibar is a viscous liquid used by speech therapists and radiologists to visualize the swallowing motion of patients under an x-ray. Infants thought to have dysphagia will simultaneously be fed Varibar via a bottle and examined via x-ray.

The Varibar contains radioactive barium which will fluoresce under the x-ray to allow for visualization of the swallowing motion. Given the radioactivity of x-rays and Varibar, infants are exposed to these conditions for a maximum of 5 minutes. In that time, the radiologist and speech therapist(s) will first feed the infant with diluted Varibar and continue to change the nipple type and thickness of Varibar until cues indicating aspiration are not detected on the x-ray[2].

The Varibar can be used as diluted Varibar or as 1:2 thickened Varibar or 1:1 thickened Varibar. 1:2 thickened Varibar is made up of a ratio of 1 scoop of Gerber Iron Infused oatmeal flakes (Nestle Infant Nutrition, Florham Park, NJ) for every 2 ounces of diluted Varibar, where 1:1 thickened Varibar is 1 scoop of oatmeal flakes for every one ounce of diluted Varibar[2]. Varibar, or any substance being fed to an infant is thickened to reduce the chance of that substance entering the airway. When thicker, the substance can move as a whole mass past the airway opening and down into the stomach.

Based on the recommendation of the speech pathologist from the Barium Swallow Test the infant patient will be fed Similak formula (Abbott Nutrition, Chicago, IL) as just formula, 1:2 thickened formula or 1:1 thickened formula, via a specific nipple type. An initial observation was made that the thickened formula settles into various layers while sitting in the bottle over a certain period of time. This prompted the first goal of the study: to observe the change (or lack thereof) in viscosity of thickened formula over time to ascertain baseline viscosity values and potentially a recommended feeding time.

A further observation was made upon visual inspection of the Varibar used to diagnose dysphagia and the Similak formula, that it appeared that the Varibar could be more viscous than the formula, which poses a threat to the infant. If the infant passes the Barium Swallow Test with 1:2 thickened Varibar and then 1:2 thickened formula is prescribed as the desired feeding viscosity, the infant

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could actually be receiving a substance much less viscous than the 1:2 thickened Varibar that was used to prescribe them. Consuming the less viscous substance increases risk of aspiration. As such, determining if there is a significant difference in viscosity of the Varibar and formula was necessary.

The purpose of this study is to first, observe the viscosity and properties of thickened formula over time to obtain a recommended feeding viscosity and time frame. The second goal is to determine any difference in viscosity between the Varibar used in diagnosing dysphagia and the formula used to feed infants clinically.

2. Methods

The first step in the study was to ensure calibration of the DV2T Brookfield viscometer (Brookfield Engineering, Middleboro, MA) using manufacturer provided instructions and a standardized fluid of a known viscosity [3]. After ensuring the viscometer was calibrated, the first phase of testing began. Phase I centered around thickened formula. Phase II examined the viscosity of diluted Varibar and 1:2 and 1:1 thickened Varibar.

2.1 Assessing viscosity of formula To obtain a baseline viscosity of Similak formula, the viscosity of 16 oz of formula was measured. Formula was made according to the manufacturer’s instructions. The manufacturer provided “scooper” holds 8.6g of formula. To make 16 oz of formula, 16 oz of water and 8 scoops (68.8 g) of formula mix were used. Viscosity was measured by changing both the spindle size and revolutions per minute (RPMs) on a trial and error basis until a consistently valid torque, falling between 10% and 90% (according to the manufacturer’s instructions) was recorded [3]. Spindles ranged in number from 61 -64 in a manufacturer provided numbering system [3]. Spindle diameter size decreased as the number of the spindle increased. As spindle size decreases, it becomes better suited to measure more viscous fluids. The viscosity of 16 oz of Similak formula was measured over 10 seconds, 10 times at 150 RPM and again, over 10 seconds, 10 times at 200 RPM using Spindle 61.

Spindle 61 was use after previously assessing that it provided the most consistent results for this mixture.

2.2 Assessing viscosity of thickened formula In the first part of Phase I, 1:2 thickened formula was made with 16 oz of Similak formula and 8 tablespoons of Gerber Oatmeal flakes. The Similak formula was made according to manufacturer instructions and put in a 16 oz bottle. 8 tablespoons of oatmeal flakes were measured out and added to the 16 oz bottle. The mixture was shaken for 30 seconds and poured into a 500 mL beaker. The viscosity of this mixture was collected with Spindle 61 at 100 RPM over the course of 1 hour. With in that hour, the viscosity was taken every 5 minutes, and each time the viscosity was taken, it was averaged over 30 seconds of data collection.

This procedure was repeated 2 more times for the 1:2 thickened formula for a total of 3 trials. New thickened formula was made for each trial.

Second, the 1:1 thickened formula was created using 16 oz of Similak formula and 16 tablespoons of Gerber Oatmeal

flakes. Once again, the Similak formula was made according to the manufacturer’s instructions and added to a 16 oz bottle. 16 tablespoons of oatmeal were added to the formula and shaken for 30 seconds. The ratios of formula to oatmeal for both 1:2 and 1:1 thickened Varibar are described for easy comparison in Table 1.

The mixture was transferred to a 500 mL beaker and the viscosity was measured at 100 RPM using Spindle 63(a thinner diameter spindle, better suited to more viscous substances). In each trial, the viscosity was measured over a total time of 1 hour. Readings were taken every 5 minutes and each reading took the average viscosity over 30 seconds of data collection. A total of 3 complete trials were conducted, and new thickened formula was created each time. A fourth trial was conducted but was not included in the results as it lasted only for a total of 40 minutes due to time restraints on the day of testing.

2.3 Varibar Varibar is the main substance used in Barium Swallow Tests to diagnose infants under 12 months with dysphagia. As previously discussed, there is thought to be a discrepancy in Varibar viscosity and the corresponding thickened formula used in feeding. The first step in assessing this possibility was to create Varibar as it is used in testing and measure the viscosity. First Varibar was made according to the manufacturer’s instructions. Then Varibar was diluted such that there were 2 parts Varibar to 1-part water, as is standard at Children’s Hospital of Pittsburgh. In this case, this mixture, known as diluted Varibar, was made of 4 oz Varibar and 2 oz of water. Due to the scarcity of Varibar available for testing, the viscosity was measured using a small sample adapter (SPC-47) on the viscometer and 4 mL of diluted Varibar. After trial and error testing, 200 RPM was selected at the optimum rate to measure the viscosity of Varibar. Data was collected for 30 seconds and averaged, and this was done every 5 minutes for 1 hour at 200 RPM. This test was conducted one time due to limited supply.

Table 1 Oatmeal: Formula Ratio

Formula Oatmeal

Spindle

1:2

68.8g

8 tbsp

61

1:1

68.8g

16 tbsp

63

2.4 Thickened Varibar To create 1:2 thickened Varibar, 14.25 oz of diluted Varibar was combined with 7.125 tablespoons of oatmeal. The diluted Varibar was made by combining 9.5 oz of Varibar made according to manufacturer instructions with 4.75 oz of water in 16 oz bottle.

The 7.125 tablespoons of oatmeal were added to the bottle and shaken until the mixture was completely combined (or for about 1 minute). Viscosity data was collected at 50 RPM using spindle 62 for 30 seconds and averaged. This measurement was taken every 5 minutes for 1 hour. The small sample adapter could not produce data for this substance, therefore a traditional spindle had to be used and a greater volume of the substance was needed.

The 1:1 diluted Varibar was the last session of data collection conducted, as both the remaining amount of Varibar and time 77

until expiration were running low. As such, the 1:1 diluted Varibar testing was conducted over 20 minutes. To create the 1:1 mixture, 14.25 oz of diluted Varibar were combined with 14.25 tablespoons of oatmeal. The ratios of 1:2 thickened diluted Varibar and 1:1 thickened diluted Varibar are described in Table 2 for easy comparison. Viscosity was measured at 150 RPM using the spindle with the smallest diameter (#64) as this was the most viscous substance tested thus far. Data was collected for 30 seconds and averaged every 5 minutes for 20 minutes.

Table 2 Varibar: Formula Ratio

Diluted Varibar

Oatmeal

Spindle

1:2

14.5 oz

7.125 tbsp

64

1:1

1.0oz

1 tbsp

63

3.3 Diluted and thickened Varibar The diluted Varibar had an initial viscosity of 26.72 cP and a final viscosity of 27.66 cP. As such, the diluted Varibar maintained a steady state viscosity over the one-hour test period. This data serves as a reference point across all other tests.

The viscosity of the 1:2 oatmeal-to-diluted Varibar decreased from 344.8 cP to 171.6 cP over one hour (Table 3). A steady asymptotic viscosity of approximately 170 cP was reached for the 1:2 ratio at approximately 40 minutes after mixing (Figure 1). The viscosity of the 1:1 oatmeal-to-diluted Varibar decreased from 968 cP to 860 cP over 20 minutes (Figure 2). Due to the decreased time of this trial, it cannot be concluded if a steady state viscosity was achieved.

3. Results 3.1 Standard Similak formula The viscosity of Similak formula at room temperature was found to be 5.764 cP at 150 RPM and 6.642 cP at 200 RPM. 3.2 1:2 and 1:2 thickened Similak formula The 1:1 thickened formula increased from an initial viscosity of 358.4 cP to 405.2 cP over 60 minutes (Figure 1). The 1:2 thickened formula decreased from an initial viscosity of 24.8 cp to 10.6 cP over the same time period (Figure 2). A steady asymptotic viscosity of approximately 10 cP was reached for the 1:2 ratio at approximately 40 minutes after mixing. The maximum percent change in viscosity in the 1:1 mixture was 15.3% where the maximum percent change in the 1:2 mixture was 59.3%. These results suggest that the thicker formula oatmeal mixture has more consistent viscosity and behavior over the test period. The change in viscosity of all mixtures from the initial time to the final time are described in Table 3.

Figure 1: The Average viscosities of the diluted Varibar, 1:2 thickened Varibar and 1:2 thickened formula display a large difference in viscosity between the thickened Varibar and formula. The thickened oatmeal and unthickened Varibar are very similar in viscosity.

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Figure 2: The average viscosities of the diluted Varibar, 1:1 thickened Varibar, and 1:1 thickened formula display a large difference in viscosity between the thickened Varibar and formula. The diluted Varibar is different from both thickened Varibar and formula, which was to be expected.

The average viscosity of 1:2 oatmeal-to-diluted Varibar over 60 minutes was 192.5 cP. The average viscosity of 1:2 oatmealto-formula over 60 minutes was 16.7 cP. A 330% difference in viscosity between the minimum oatmeal-to-Varibar and maximum oatmeal-to-formula was found.

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Table 3 Ratio Oatmeal:Material

Formula/Varibar

Viscosity at t0

Viscosity at tf

Percent Change

tf - t0

1:2

Formula

24.8 cP

10.6 cP

57.3%

60 min

1:1

Formula

358.4 cP

405.2 cP

13.1%

60 min

1:2

Varibar

344.8 cP

171.6 cP

50.2%

60 min

1:1

Varibar

968 cP

860 cP

12.6%

20 min

The average viscosity of 1:1 oatmeal-to-diluted Varibar over 20 minutes was 896.8 cP (Figure 2). The average viscosity of 1:1 oatmeal-to-formula over 60 minutes was 380.4 cP.

4. Discussion

The primary goal of this study was to quantify two oatmeal flake thickened formula prescriptions used to feed dysphagic infants under 12 months at Children’s Hospital of Pittsburgh. The 1:1 ratio demonstrated a smaller percent change in viscosity after mixing, suggesting more flexible feeding time periods may be possible. Infants may be able to be fed for a longer period of time with a more consistent viscosity. In contrast, the 1:2 formula-tooatmeal mixture demonstrated shear thinning. The mixture reached an asymptotic viscosity after 40 minutes, suggesting that there may be a finite time in which feeding should occur after mixing. In the future, characterization of thickened formula may be extended to barium-based swallow test formulas to improve the consistency between testing conditions and clinical feeding treatments.

The secondary goal of this study was to assess a potentially significant difference between the thickened formula used to treat infants and the thickened Varibar used to diagnose them. The 1:2 thickened Varibar was on average 11.5 times more viscous than the 1:2 thickened formula, where the 1:1 thickened Varibar was on average 2.4 times more viscous than the 1:1 thickened formula.

While more Varibar trials would help further support these results, it is apparent that there exists a notable difference in viscosity between the thickened Varibar and the thickened formula.

Because the Varibar mixtures are more viscous than the thickened formula being fed to the patient there is an increased chance of aspiration with the thinner fluid (formula).

5. Conclusions

Phase I of the study uncovered a difference in behavior between 1:1 and 1:2 thickened formula. 1:2 thickened formula will decrease in viscosity over time until it reaches steady state.

Given the limited number of trials, a certain time point at which the mixture reached steady state cannot be confirmed, but based on the results, this appears to happen at about 25 minutes after initial mixing. The recommendation to clinicians is to determine if the steady state viscosity is the ideal viscosity. If so, protocols should be created in which the nurse waits 25-30 minutes to feed infants.

If it is not the case, the window of time in which dysphagic infants can be fed decreases to less than 25 minutes.

Phase II revealed that the viscosity of the Varibar does not align with the viscosity of the thickened formula. Based on the

results, diluted Varibar is most similar in viscosity to 1:1 thickened formula. The 1:1 Varibar is not similar to the 1:1 thickened formula, and 1:2 Varibar and 1:2 thickened formula do not directly relate either. This is a red flag that protocol during diagnosis must be changed to more accurately prescribe the correct viscosity to the patient to reduce the risk of aspiration.

In the future, the Varibar testing should be completed at least an additional two times to gain more accurate and reliable data.

Also, testing the effect of shaking the bottle immediately before feeding (after the contents have settled into various layers) to see if the original viscosity is regained, would be helpful to determine if a simple change to the feeding protocol, such as “shake every 5 minutes while feeding,” could be used to reduce chance of aspiration. Another study to be conducted is to determine if there is a significant difference in viscosity of thickened formula when the thickened formula is made inaccurately. Many infants with dysphagia will return home and still require thickened formula.

Without hospital staff providing the regulated feeding, it is possible that the ratio of formula to oatmeal will vary. It would be helpful to know how much variance is “too much” and begins to present a threat to the health of the infant due to a lower viscosity of the homemade thickened formula. Regardless, due to the limited data currently available on viscosity of formula and infants with dysphagia, any future research in this field will ultimately help provide better care for this section of the population.

Acknowledgments

This research was conducted in conjunction with Occupational Therapy Department of the Children’s Hospital of Pittsburgh.

Kelsey received funding from the Swanson School of Engineering at the University of Pittsburgh and from Dr. Mark Gartner in the Department of Bioengineering.

References

1. Logemann, J. (1983) Evaluation and treatment of swallowing disorders. San Diego, CA: College Hill Press.

2. Kubistek, K., Fill, K., Rosen, S. Personal Interview. 10 May 2018.

3. “DV2T Touch Screen Viscometer.” Brookfield Engineering, https://www.brookfieldengineering.com/ products/viscometers/laboratory-viscometers/dv2ttouch-screen-viscometer?gclid=Cj0KCQiAj4biBRCARIsAA4WaFh06FAGouAaoJub-bMD08ntMRxayvXt_Qt8INKwpdcwY-LX9RxKpYaAnRYEALw_wcB, Accessed June 2018.

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Analyzing right ventricular response to Sacubitril/Valsartan in pulmonary hypertension Claire Tushak , Danial Sharifi Kia , Evan Benza , Kang Kima,b, and mentor Marc Simona,b a,b

a

b

Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, bHeart and Vascular Institute, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA a

Significance Statement:

Our research is aimed to use stressstrain analysis of cardiovascular tissue to analyze the extent of tissue remodeling in pulmonary arterial hypertension.

Treatment with the dual-acting drug Sacubitril/Valsartan is evaluated using this analysis method and suggests a promising improvement in adverse tissue remodeling in our ongoing study.

Tushak

Abstract

Pulmonary arterial hypertension (PAH) Simon is a condition in which constriction or dysfunction of the pulmonary arteries results in increased blood pressure causing dyspnea and functional impairment. Dysfunction of the right ventricle (RV) has been closely linked to death in patients with PAH, so an improved understanding of the RVâ&#x20AC;&#x2122;s response to PAH is necessary to develop treatments aimed to restore normal RV function. The increased pressure load on the right ventricular free wall (RVFW) in PAH causes tissue hypertrophy and there is some evidence to suggest collagen fiber remodeling in the longitudinal direction associated with myocardial stiffness. We hypothesize that treatment with Sacubitril/Valsartan (Sac/Val) in an experimental PAH model induced by pulmonary artery (PA) banding will prevent RV remodeling. Hemodynamic data is obtained with pressure-volume loops, which indicate the afterload faced by the RV. Biaxial biomechanical testing is used to analyze the stress-strain response of RVFW tissue, which provides an understanding of tissue stiffening and remodeling.

While our study is still ongoing, we have found promising results from tissues treated with Sac/Val when compared to control and PA banded samples. Sac/Val appears to lower the end systolic pressure substantially as well as prevent tissue stiffening and decrease adverse RV remodeling.

Category: Experimental Research

Keywords: Tissue biomechanics, pulmonary arterial hypertension, right ventricle, stress-strain analysis

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1. Introduction 1.1 Pulmonary Arterial Hypertension PAH is a condition in which constriction or dysfunction of the pulmonary arteries results in increased blood pressure causing dyspnea and functional impairment. RV dysfunction has been found to be closely linked to death in patients with PAH [1]. As a result, an improved understanding of how the RV responds to high blood pressure and eventually fails in PAH is necessary to develop treatments aimed to restore normal RV function.

The increased pressure load on the RVFW resulting from PAH is thought to cause both myofibers and collagen fibers to become reoriented and realigned in the longitudinal direction [2]. Myofiber hypertrophy and realignment are associated with increased tissue stiffness and anisotropy, particularly in the longitudinal direction [2]. Increased fiber stiffness is thought to be caused from intrinsic myofiber stiffening alone, due to the observations of a greater than two-fold increase in myofiber stiffness and a slight decrease in intrinsic collagen fiber stiffness [2]. Additional sequelae of fiber remodeling have been found to occur in the midwall region including an increase in the use of collagen fibers, and an increase in the interactions of myo- and collagen fibers [1]. Adverse RV remodeling results in RV failure and death, therefore a better understanding of the RV remodeling process and treatments to prevent this are needed.

1.2 Sacubitril/Valsartan Sac/Val is a dual-acting drug that is used to treat systolic left ventricular heart failure [3]. This drug combines an angiotensin receptor blocker Valsartan with Sacubitril, a specific inhibitor of neprilysin [4]. Sac/Val works in two ways. The Valsartan component of the drug blocks overactive neurohormonal pathways, which left unchecked lead to an increased systemic vascular resistance and hypertrophy [3]. In addition, Sacubitril increases regulatory hormones in the natriuretic peptide system by inhibiting the degradation by the catalyst neprilysin. These mechanisms of action result in regulation of blood pressure and fluid homeostasis [3]. We hypothesize that treatment with Sac/Val in an experimental PAH model induced by PA banding will prevent remodeling as well as tissue stiffening and therefore prevent progressive RV dysfunction in PAH [1].

2. Methods

Under a protocol approved by the University of Pittsburgh Institutional Animal Care and Use Committee (Protocol #15127354), three experimental groups of rats are used in this study: control animals administered water, PA banded animals administered water, and PA banded animals treated with Sac/Val. The animals used are male Sprague-Dawley rats.

2.1 Initial Pulmonary Arterial Hypertension Induction Procedure The animals rest for one week after delivery before beginning experimental procedures. The rats, excluding those in the control group, undergo PA banding to induce an experimental model of PAH and RV remodeling. Animals are anesthetized with 5%

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isoflurane. The chest is entered laterally, and the PA is isolated and exposed. A surgical clip with a radius such that a uniform RV pressure of 45-50 mmHg is produced is placed around the PA to induce a model of pressure overload. The animalsâ&#x20AC;&#x2122; chests are closed and sutured. Sensorcaine is administered immediately following the procedure. Two pain medications, ketoprofen and buprenorphine, are administered twice daily for three days after the PA banding procedure. [2] 2.2 Animal Care and Drug Administration Oral medication or water is administered daily via gavage to all animals. Medication or water is administered beginning the first day after the PA banding procedure, lasting for three weeks. In our experience, three weeks is sufficient to produce elevated RV endsystolic pressures and tissue remodeling [2].

2.3 Tissue Harvest and Testing Pressure-volume loops are obtained in situ before harvesting the heart to characterize end systolic pressure as well as contractility of the RV. Multiple pressure-volume loops are generated by standard methods using vena cava occlusion [2].

Volume calibration is performed. First, a saline volume calibration is performed in which the catheter rests for a minimum of 30 minutes in saline. Then, a 2-point calibration is completed to obtain a relative volume unit using the MPVS-400 transducer box (Millar Instruments, Houston, TX) [2]. Animals are anesthetized, and the above hemodynamic data is obtained. Animals are then euthanized by harvesting the heart. The RV is placed in cardioplegic solution to maintain tissue viability while preparing the sample for biaxial testing. The RVFW is cut into a square sample, indicated in Figure 1. Initial measurements of the sampleâ&#x20AC;&#x2122;s mass, length, width, and thickness are collected. Colored beads are placed on the tissue to maintain orientation of the apex to outflow tract. Four visual tracking markers shown in Figure 1 are affixed to the epicardium using glue for deformation measurements.

After initial measurements, the sample is hooked onto the biaxial testing device (CellScale Biomaterials Testing BioTester 5000, Waterloo, Ontario, Canada) using four metal hooks, shown in Figure 1, and two pulleys on each of its four sides and placed in modified Krebs solution [5] with oxygen, as previously reported [1, 2, 5]. Figure 1 illustrates both the region of the heart taken as a sample as well as a fully prepared tissue sample with correctly placed tracking markers and stringed hooks. Figure 2 provides an example of a tissue sample hooked onto the biaxial testing device. The biaxial testing protocols include a pre-conditioning step followed by multi-protocol displacement-controlled testing to investigate the tissue behavior under a wide range of possible strains (1:1, 1:2, 1:4, 1:6 displacements). The data reproducibility is confirmed by comparing the equibiaxial testing results performed after the pre-conditioning step and repeated at the end of the biaxial testing protocol.

Figure 2: An Example of a Tissue Sample Loaded onto the Biaxial Testing Machine.

2.4 Data Processing Marker displacements under multi-protocol biaxial loading are recorded using a CCD camera. Isoparametric mapping and finite element interpolations are used to obtain the strain levels experienced by the tissue using the recorded marker displacements [6]. Stress-strain plots are created using an inhouse Mathcad (PTC, Needham, MA) code.

Figure 1: Biaxial Testing Device, Animal Heart Representation with the Region of Testing and Directions Indicated, and Tissue Sample Prepared for Biaxial Testing.

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3. Results 3.1 End Systolic Pressure The end systolic pressure is an indication of the afterload faced by the RV. As shown in Figure 3, the control group consisting of nine rats had an end systolic pressure of 25 ± 3.3 mmHg. The PA banded group with four rats had a mean RV end systolic pressure of 72.5 ± 21 mmHg. The Sac/Val group with two rats had pressures of 50 mmHg and 40 mmHg.

Figure 3: Mean End Systolic Pressure (mmHg) of Each Group of Animals Studied.

Figure 5 shows that PAH resulted in an increased overall RVFW stiffness compared to the control group. There was increased tissue anisotropy with a much greater increase in stiffness in the preferred (longitudinal) direction as compared to the cross-preferred (circumferential) direction, also illustrated by Figure 5. This was most evident in the early portion of the stressstrain relationship. Animals treated with Sac/Val represented nearhealthy levels of the RVFW stress-strain curves. These results are demonstrated in Figure 5.

Figure 5: A Representative Equibiaxial Displacement-Controlled Stress-Strain Relationship from the Control Group, PAH Group, and Group Treated with Sac/Val.

3.2 Stress-Strain Curves Multi-protocol testing provided insight into regional behavior of the RVFW at a wide range of physiologically possible strains, as shown in Figure 4 from a representative data set.

Figure 4: A Control Sample’s Representative Stress-Strain Curves of Multi-Protocol Displacement-Controlled Biaxial Biomechanical Testing.

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4. Discussion

Initial results suggest the Sac/Val therapy may prevent PAHinduced RV biomechanical remodeling compared to untreated animals with induced PAH. The study is ongoing and these are preliminary results. The number of samples in each group will increase as data accrual continues.

4.1 End Systolic Pressure From the end systolic pressures collected and reported in Section 3.1 and illustrated in Figure 3, Sac/Val seems to show effectiveness by lowering the end systolic pressure substantially compared to untreated animals with PAH. The end systolic pressure is an indication of the afterload faced by the RV. The effectiveness of Sac/Val in lowering the end systolic pressure is a promising indication of the medicationâ&#x20AC;&#x2122;s potential effectiveness in treating PAH. 4.2 Stress-Strain Curves Investigating tissue behavior under a wide range of displacements, illustrated in Figure 4, provides an improved understanding of the tissueâ&#x20AC;&#x2122;s response under physiologic conditions. RVFW control tissues represent an overall nonlinear behavior with significant anisotropy in the preferred (apex to outflow tract) direction.

When comparing equibiaxial displacements, PAH caused the tissue to become significantly stiffer than the control samples.

Treatment with Sac/Val seems to decrease myocardial stiffness and deformability to near-healthy levels comparable to those of the control samples. This data suggests that Sac/Val may be a treatment for PAH capable of reducing RV afterload and preventing adverse RV remodeling, which is felt to be a precursor to RV failure.

The pronounced difference in stress-strain curves in the preferred (longitudinal) and cross-preferred (circumferential) directions of the untreated diseased tissue leads it to be classified as anisotropic.

PAH tissue exhibits anisotropy most evident in the early portion of the stress-strain curve, which represents myofiber stiffness as shown in a previous study [2]. While there is a small degree of anisotropy, control samples demonstrate a largely isotropic behavior. PAH was associated with an increased amount of tissue anisotropy. Sac/Val treatment resulted in some attenuation of the anisotropic behavior of the RV myocardium. These findings are illustrated by Figure 5.

There are some limitations to this study. Since the tracking markers are placed on the surface of the tissue, strain data is not obtained transmurally. Transmural variation in fiber orientation that further changes with remodeling due to PAH prevents exact alignment of specimens with respect to fiber orientations which may introduce variability in measured results. To limit variability, care is taken during specimen preparation to maintain consistent

anatomical tissue orientation. Also, displacement-controlled testing does not control force or strain, allowing these quantities to vary in samples. This limitation can be addressed by using testing devices capable of performing strain- or force-controlled testing.

The current sample size is limited and prevents drawing firm conclusions. This data is considered to be preliminary, as data accrual is continuing.

5. Conclusions

While our study is still ongoing, initial results are encouraging regarding the effectiveness of Sac/Val in treating PAH and preventing RV remodeling. If confirmed, these results could provide evidence in support of a clinical trial of Sac/Val to treat PAH and prevent or ameliorate adverse RV remodeling and failure. Our lab will continue to analyze this data in the future with constitutive modeling.

Acknowledgments

We gratefully acknowledge research funding from Novartis Pharmaceutical Corporation for this IIT (LCZ696BUSNC20T). We also gratefully acknowledge the support for this Summer Research Internship from the Simon Lab, the Swanson School of Engineering, and the Office of the Provost.

References

1. R. Avazmohammadi, M. Hill, M. Simon, M. Sacks, Transmural remodeling of right ventricular myocardium in response to pulmonary arterial hypertension, APL Bioengineering. 1 (2017) 016105.

2. M. R. Hill, M. A. Simon, D. Valdez-Jasso, W. Zhang, H. C. Champion, M. S. Sacks, Structural and mechanical adaptations of right ventricle free wall myocardium to pressure overload, Annals of Biomedical Engineering. 42 (2014) 2451-2465.

3. J. T. Menendez, The mechanism of action of LCZ696, Cardiac Failure Review. 2 (2016) 40-46.

4. M. B. Andersen, U. Simonsen, M. Wehland, J. Pietsch, D. Grimm, LCZ696 (Sacubitril/Valsartan) â&#x20AC;&#x201C; A possible new treatment for hypertension and heart failure, Basic & Clinical Pharmacology & Toxicology. 118 (2016) 14-22.

5. D. Valdez-Jasso, M. A. Simon, H. C. Champion, M. S. Sacks, A murine experimental model for the mechanical behaviour of viable right-ventricular myocardium, The Journal of Physiology. 590 (2012) 4571-4584.

6. W. Zhang, Y. Feng, C. Lee, K. L. Billiar, M. S. Sacks, A generalized method for the analysis of planar biaxial mechanical data using tethered testing configurations, Journal of Biomechanical Engineering. 137 (2015) 064501.

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Thermal resistance and stiffness of iSIM-90 support blades for CASPR camera system Michael Ullman, Theodore Schwarz, Kevin Glunt, and mentor Alan George National Science Foundation Center for Space, High-performance, and Resilient Computing (SHREC), University of Pittsburgh, PA, USA

Significance Statement:

SHREC researchers are developing space systems with the goal of increasing performance while reducing cost. By enhancing the thermal functionality of the CASPR system’s support blades, this research advances the development of innovative, cost-effective space imaging components that will increase the accessibility, feasibility, and desirability of future space research.

Ullman

Abstract

The advancement of satellite imagers necessitates the development of higher George resolution cameras with greater on-board computational capabilities. Despite efforts taken to increase computational efficiency and decrease power usage, operation of these imager systems inevitably generates heat, which must be sufficiently dissipated to ensure that the systems remain below their maximum operating temperatures. Adequate dissipation of heat can be achieved, in part, by ensuring that pertinent components have sufficiently low thermal resistance to conduction—the primary mode of heat transfer between the imager components.

This research utilizes ANSYS simulation software to examine the thermal resistance and stiffness of the support blades for the integrated Standard Imager for Microsatellites (iSIM) designed by SATLANTIS. Analyses are conducted to redesign the iSIM blades with drastically lower thermal resistance to conduction and minimal change to the six principle stiffness values. The final iteration of the redesigned iSIM blade achieves these goals within the prescribed design parameters, while also exhibiting a significant reduction in weight. Pending satisfactory performance in future simulations, the new blade can be integrated into the new imaging system being developed by the Center for Space, High-performance, and Resilient Computing (SHREC).

1. Introduction

In March 2017, the National Science Foundation Center for High-performance Reconfigurable Computing (CHREC) began operations for its first computing system aboard the International Space Station. This system, known as the CHREC Space Processor, or CSP, was developed and implemented with help from NASA and the United States Air Force Research Lab (AFRL) as part of the Space Test Program – Houston 5 (STP-H5) mission [1,2]. The goal of this project was to determine the efficacy and potential uses of hybrid reconfigurable computing systems, which integrate commercial-off-the-shelf (COTS) components and radiationhardened components to create a system that achieves maximum computational performance while remaining resistant to the harmful radiation of space environments [1]. As a result of the success of the CSP project, CHREC—now known as the NSF Center for Space, High-performance, and Resilient Computing (SHREC)—was contracted to develop another hybrid space system for the STP-H6 mission. This system, called the Spacecraft Supercomputer for Image and Video Processing (SSIVP), consists of five interconnected CSP units and two high-resolution cameras to be used for studying and surveilling Earth’s surface, as well as conducting experiments in software applications and sensor processing [2]. SSIVP was delivered to NASA Johnson Space Center in February 2018, and is scheduled to be launched to the ISS in April 2019 [2].

In May 2018, SHREC proposed its third space system for the International Space Station, called CASPR (Configurable and Autonomous Sensor Processing Research) [2]. The system is intended for the STP-H7 program and will consist of a cluster of CSP units, various computational accelerators, a neuromorphic event-based camera, and a high-resolution binocular imager [2]. This binocular imager is a version of the integrated Standard Imager for Microsatellites (iSIM) designed by SATLANTIS, an independent satellite imaging company. To secure the imager to its pallet on the ISS, the iSIM utilizes three blade supports attached to its bottom faces. A depiction of the iSIM-90—the iSIM model chosen to be used in the CASPR system—is shown in Figure 1.

Category: Device design

Keywords: Thermal resistance, stiffness, satellite imaging, support blades

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Figure 1: SolidWorks model of iSIM-90 system. The three blade supports, shown at the bottom, connect the imager to its pallet [3].

Ingenium 2019

The iSIM-90 blades must have sufficiently low thermal resistance to conduction so that heat dissipated by the binocular imager can be effectively transferred to the STP-H7 pallet, which is assumed to act as a heat sink in this project. The purpose of this project is to characterize the thermal and mechanical behavior of the original iSIM-90 blade, and redesign it to have a thermal resistance to conduction of less than 10 ºC/W. This will ensure proper heat dissipation from the CASPR system, and consequently prevent it from exceeding its maximum operating temperature of 30 ºC. To ensure compatibility with the CASPR system, the new blade must have the same bolt pattern on its top face as the original. Also, the height of the new blade must be within ±20% of the original blade to ensure that the camera system remains within its designated viewing field.

The original iSIM-90 blades were designed to allow for some deflection from applied forces and moments, as well as from the thermal expansion of the STP-H7 pallet. These permissible deflections allow the CASPR cameras to remain focused on their intended targets. Consequently, the redesigned iSIM-90 blades must behave similarly to the original design in terms of linear and rotational stiffness, while also exhibiting drastically lower thermal resistance to conduction.

2. Methods 2.1 Thermal Resistance of the Original iSIM-90 Blade To provide a basis for the redesigned iSIM-90 blades, the pertinent characteristics of the original blade were evaluated and recorded. The first of these analyses examined the blade’s thermal resistance to conduction. All thermal analyses were performed using steady-state thermal simulations in ANSYS Workbench.

Because thermal resistance is intrinsic to a given object, the boundary conditions of the simulations were arbitrarily chosen to simplify subsequent calculations. Thus, a fixed temperature of 0 ºC was applied to the bottom faces of the blade, and a constant heat flux of 100 W/m2 was applied to the top face. This heat flux was directed downward along the blade’s y-axis to simulate heat transfer from the CASPR system to its pallet. After computation, a simulation temperature probe was used to determine the temperature of the top face. Then, the thermal resistance of the blade was calculated using the following formula:

2.2 Stiffness of the Original iSIM-90 Blade The original iSIM-90 blades were also characterized in terms of mechanical stiffness. All stiffness analyses were conducted using static structural simulations in ANSYS Workbench. In this project, only the principle stiffness values were examined—the linear deflection along the x-axis from a force along the x-axis, the rotational deflection about the y-axis from a moment about the y-axis, etc. Thus, six stiffness values were considered—three linear and three rotational. The stiffness values of the new design were constrained to within ±10% of the original design. Similar to the simulations of thermal resistance, the values of the forces and moments applied in these simulations were arbitrarily chosen to simplify subsequent calculations.

In the simulations of linear stiffness, a force of 1 N was applied to the top face of the blade, and a directional displacement probe was used to find the deflection of the top face in the direction of interest. To capture how the deflection of the blade’s top face would affect the entire camera system, the deflection at the center of the top face was used to determine each linear stiffness. In the case of the x-direction, the linear stiffness is given by kxx = Fx /x; where kxx is the principle linear stiffness in the x-direction, Fx is the force applied along the x-axis, and x is the displacement of the top face along the x-axis.

In the simulations of rotational stiffness, a moment of 1 N•m was applied to the top face of the blade, and directional displacement probes were used to determine the angle to which the top face deflected. For rotation about the y-axis, x- and z-displacement probes were used to find the angle to which a given corner of the top face was deflected relative to its original position. For rotations about the x- and z- axes, y-displacement probes were used to find the slope of the top face, from which the angle of deflection could be determined. The rotational stiffness about the x-axis is thus given by krx = Mx /⍬x; where krx is the rotational stiffness about the x-axis, Mx is the moment applied about the x-axis, and ⍬x is the angular deflection about the x-axis.

2.3 Choosing Materials The original iSIM-90 blades were made of Titanium Ti-6Al-4V, which has a thermal conductivity of 6.7 W/m•ºC and an elastic modulus of 113.8 GPa [3,4]. In order to dramatically reduce the thermal resistance of the blades, a highly conductive material was desired. Aluminum 4032-T6 was thus chosen for its high thermal conductivity (138 W/m•ºC), its high machinability, and its high elastic modulus (78.6 GPa) relative to other aluminum alloys [5].

Because Al-4032-T6 has an elastic modulus much lower than Ti6Al-4V, the new blade design needed to include geometry changes to bring its stiffness values within the acceptable ranges.

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Table 1: Effects of 10% increase in value of select geometrical aspects.

Variable

Thermal Resistance

Linear X-Stiffness (kxx)

Linear Y-Stiffness (kyy)

Linear Z-Stiffness (kzz)

Rotational X-Stiffness (krx)

Rotational Y-Stiffness (kry)

Rotational Z-Stiffness (krz)

Leg Radius

-0.06%

+0.01%

+0.05%

+0.05%

+0.09%

+0.34%

+0.32%

Neck Width

-4.44%

+12.44%

+10.39%

+3.41%

+5.20%

+8.68%

+12.97%

Neck Height

+2.14%

-7.20%

-2.04%

-5.66%

-2.60%

-4.01%

-2.31%

Leg Thickness

-8.42%

+9.57%

+12.37%

+31.82%

+31.81%

+30.85%

+6.63%

Notch Height

+0.84%

-0.35%

-1.82%

-1.08%

-0.90%

-0.61%

-0.81%

Foot Width

-3.84%

+5.24%

+4.11%

+6.87%

4.73%

4.61%

3.37%

Leg Height

+3.17%

-8.63%

+1.21%

-15.13%

-4.23%

-7.96%

+0.60%

Foot Spread

+3.32%

+0.94%

-10.32%

-4.29%

-2.70%

-1.00%

-3.23%

Leg Angle

+4.22%

-0.73%

-5.19%

-3.62%

-4.55%

-5.28%

7.31%

2.4 Determining Effects of Geometry Changes To determine the necessary geometry changes, nine different geometrical aspects of the original blade design were isolated and examined. For each aspect, its value was individually increased by 10%. The effects of this change on the thermal resistance and stiffness values were then simulated and tabulated. The results of these simulations can be seen in Table 1.

After determining the effects of individual geometry changes, all of the dimensions of interest could be efficiently fine-tuned to yield acceptable thermal resistance and stiffness values. In addition to modifying the dimensions of these aspects, additional features were added to alter the stiffness values and alleviate stress concentrations. These modifications include a tapered neck thickness and filleted edges where the neck meets the legs.

3. Results

Acceptable results for all six stiffness values, which were initially defined as within Âą10% of the original blade design, were obtained in the new design. Significant reductions in thermal resistance and mass were also achieved. Figure 2 shows the new blade design, and Table 2 lists the relevant values for both the original and new designs.

Figure 2: SolidWorks model of the redesigned iSIM-90 blade, with geometry changes labeled. Leg radius and leg angle remained unchanged in the final blade design.

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Ingenium 2019

Table 2: Comparison of original iSIM-90 design and redesign.

Design

Thermal Resistance [°C/W]

Linear X-Stiffness (kxx) [N/m]

Linear Y-Stiffness (kyy) [N/m]

Linear Z-Stiffness (kzz) [N/m]

Rotational X-Stiffness (krx) [N·m/rad]

Rotational Y-Stiffness (kry) [N·m/rad]

Rotational Z-Stiffness (krz) [N·m/rad]

Mass [kg]

Original

186.997

1.93·107

9.86·107

3.23·104

20.543

51.759

3433.79

0.0278

Redesign

7.142

1.81·107

9.60·107

3.03·104

21.208

56.668

3261.75

0.0188

Difference

-96.18%

-6.47%

-2.68%

-6.40%

+3.24%

+9.49%

-5.01%

-32.4%

4. Discussion

The reduction in thermal resistance achieved in the new iSIM-90 blade significantly reduces the temperature gradient required to achieve steady-state heat transfer through the blades and away from the CASPR binocular imager. If the imager releases approximately 10 Watts of heat—corresponding to an estimated 2 Watts being transferred through the front blade and 4 Watts being transferred through each of the two rear blades—then in a case of one-dimensional, steady-state conduction through one of the rear blades, a temperature difference of 747.99 ºC must exist between the top and bottom faces of the original iSIM-90 blade. This follows from Eq. 1, where thermal resistance is held constant and the product of heat flux and area is equal to 4 Watts. The redesigned iSIM-90 blade, on the other hand, requires a temperature difference of 28.57 ºC in the same scenario. This does not provide a full account of the heat transfer from the CASPR system and its blade supports, as some heat will be expelled via radiation into ambient space. Nevertheless, this approximation shows that implementation of the new iSIM-90 blades will greatly reduce the risk of the CASPR imager exceeding its maximum operating temperature of 30 ºC. The primary objective of this project was to reduce the blade’s thermal resistance to conduction—an innate property of the design that can be determined through steady-state thermal analyses alone. However, it follows that the lower thermal resistance exhibited by the new iSIM-90 blade will also allow for more substantial heat transfer in transient conduction processes.

Analyses of these processes will be critical for future studies, as cyclical exposure to solar radiation will cause the temperatures of CASPR and its pallet to fluctuate.

As required by the design parameters, the new blade design exhibits principle stiffness values within ±10% of the original blade. This means that for a given force or moment applied to the top face of the blade, the deflection of the top face of the new blade in the direction of the applied force or moment will be within ±10% of the deflection shown in the original blade. This is an elementary basis for reasoning that the new design will generally exhibit elastic behavior similar to the original, and will thus maintain the original functionality of compensating for applied forces and thermal expansions. However, further simulations and studies outside of the scope of this project would need to be performed to ensure the validity of the new design. These studies could simulate forces and moments applied to different locations on the blade, such as to the bottom faces of the bottom bolt interfaces. Also,

because of conservation of mass, deflections are inherently threedimensional—a stretch along the x-axis will cause a contraction along the y- and z- axes. Therefore, examination of non-principle stiffness values will provide further insight into how the elastic behavior of the new iSIM-90 blade differs from that of the original blade.

While the new blade design has been shown to exhibit elastic behavior similar to the original design, the results of this project do not indicate whether the new design will exhibit similar yielding behavior. Aluminum 4032-T6 has a yield strength of 317 MPa, which is considerably lower than that of Titanium Ti-6Al4V (880 MPa) [4,5]. Thus, the new blade design is theoretically much more likely to fail due to yielding. Further simulations and calculations, including both static and dynamic stress analyses, will be conducted by the SHREC team to determine whether the new design can withstand the forces expected in a rocket launch.

In addition to achieving the intended goals of the project, the new blade design offers a mass reduction that would financially benefit the CASPR system. According to the Marshall Space Flight Center, the cost of sending a pound of payload into orbit around the Earth is approximately $10,000 [6]. The new iSIM-90 blade design has a mass of 0.0188 kg, which is 0.009 kg less than the mass of the original blade. Consequently, each new blade will cost approximately $198.42 less to send into orbit, making the binocular imager $595.26 cheaper to deliver to the ISS. This difference is small when compared to the total cost of a mission like STP-H7, but nevertheless coincides with SHREC’s goal of developing innovative, yet cost-effective space technologies.

5. Conclusion

The new iSIM-90 blade exhibits a significantly reduced thermal resistance to conduction—smaller than the target value of 10 ºC/W. It also adheres to the design requirement for its six principle stiffness values, all of which were within ±10% of those shown in the original design. These attributes of the new blade, along with its reduced mass, will potentially improve the functionality and economic feasibility of the CASPR system. Future analyses, which will utilize the data gathered in this project, will examine the yield and ultimate tensile strengths of the new blades and their resulting ability to survive the forces of a rocket launch.

Pending success in these tests, the new iSIM-90 blade design will serve as a critical component in the CASPR system by ensuring sufficient heat dissipation and desirable elastic behavior.

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Acknowledgments

I would like to thank Dr. Alan George for his support in this project and the opportunity to work with SHREC. Also, thank you to Theo Schwarz and Kevin Glunt for their guidance on this project and their commitment to teaching the Mechanical/Mission Simulation team.

References

1. A. George, V. Escobedo, (2018) STP-H5-Center for Highperformance REconfigurable Computing (CHREC) Space Processor (STP-H5 CSP), International Space Station Program Science, NASA, https://www.nasa.gov/mission_pages/station/research/ experiments/1990.html.

2. A. George, (2018) CASPR: Configurable & Autonomous Sensor Processing Research.

3. SATLANTIS, (2018, June 7) CAD Model of iSIM90 Optomechanical Structure. SATLANTIS. 4. Titanium Ti-6Al-4V (Grade 5), Annealed, Aerospace Specification Metals, Inc., MatWeb, http://asm.matweb.com/ search/SpecificMaterial.asp?bassnum=mtp641.

5. Aluminum 4032-T6, MatWeb: Material Property Data, http://www.matweb.com/search/datasheet.aspx?matguid=357076 60584d4f7caef591324e592396&ckck=1.

6. Advanced Space Transportation Program: Paving the Highway to Space, Marshall Space Flight Center, NASA, https:// www.nasa.gov/centers/marshall/news/background/facts/astp.html.

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Modeling and energy calculations of perovskite methylammonium lead iodide grain boundaries Philip A. Williamson and mentor Wissam A. Saidi Department of Mechanical Engineering & Materials Science, University of Pittsburgh, PA, USA

Significance Statement:

Methylammonium lead iodide (MAPbI3) has recently attracted increased attention for its photovoltaic and optical applications.

Herein, we computationally investigate grain boundaries (GBs) in MAPbI3, which can have a negative impact on photovoltaic performance. Our results show there is little correlation between GB energy and GB angle or sigma number (Σ) in MAPbI3. This procedure can be utilized to study GBs in other crystalline materials.

Williamson

Abstract

Hybrid perovskite photovoltaic materials such as methylammonium lead Saidi iodide perovskite (MAPbI3) have garnered much attention thanks to their high-power conversion efficiency. Grain boundary (GB) defects are known to have a deleterious impact on overall photovoltaic performance; however, their overall atomic structure is still unknown. To better understand these defects, we modeled MAPbI3 GBs and calculated their relative energies with respect to the bulk. GB models were produced from coincident-site lattices (CSL) based on the MAPbI3 cubic structure with varying densities of coincident-sites, indicated by the sigma number (Σ).

We hypothesize that higher sigma models will have higher GB energies as these are expected to have more broken/strained bonds. Energy calculations were carried out using the LAMMPS software package employing energy minimizations in tandem with molecular dynamics. The expected correlation of GB energy vs. sigma number was not observed as expected. This result may indicate that the GBs present in hybrid perovskites may be equally stable despite sigma number and GB angle.

Category: Computational research

Keywords: perovskite, MAPbI3, grain boundaries, computational materials science, LAMMPS

1. Introduction

Photovoltaic hybrid perovskite materials, such as methylammonium lead halides (MAPbX3, X = Cl, Br, I), have garnered much attention as over the past decade as their power conversion efficiency has increased from approximately 2% to over 23%. This efficiency is commensurate with industrial solar materials such as silicon or CdSe. The preferred fabrication method for solar cells incorporating these hybrid perovskites is solutionbased such as a spin coating procedure. However, as the material is synthesized from solution, the crystal growth and orientation is difficult to control and the final product is inevitably polycrystalline [1]. Crystalline interfaces, i.e. grain boundaries (GBs), are known to have a negative impact on overall cell performance. Thus, there is an incentive to study GB structure in order to understand how they impact performance [2]. To better understand the structure of polycrystalline hybrid perovskites, our group has begun a computational study of MAPbI3 GBs via atomistic modeling and simulations. Our goal is to use these methods to determine the likely structure of MAPbI3 GB interfaces based on relative GB energies and stability.

Previous research on several materials has shown that there is a correlation between the energy of a GB and its occurrence in a polycrystalline material. The energy of a GB can be attributed to broken or strained bonds that occur when there is a mismatch of atomic positions across the GB interface [5]. This mismatch is also correlated with a parameter known as the sigma number (Σ) that serves as an indicator of how close crystal lattice points are to one another along the GB interface. Kim et al. and Saylor et al. demonstrated that these energy and population trends occur in brass and aluminum respectively and that the most common GBs were either low angle or low sigma [3,4]. Saylor et al. also demonstrated the same correlations in polycrystalline SrTiO3, an inorganic perovskite [6]. While the GB population of inorganic perovskites has been well studied, there has been little investigation to determine if the same trends occur in hybrid perovskites, such as MAPbI3.

A GB is defined as a region in a bulk material where two misoriented crystal grains contact each other at an interface. The GB is defined by an angle ⍬, rotation axis o denoted by direction vector [uvw], and GB interface plane n denoted by miller indices (hkl) [7]. GBs can be classified into pure tilt GBs, with o and n being perpendicular, pure twist GBs, with o and n being parallel, and mixed GBs that have tilt and twist character. Pure tilt GBs can be further classified into symmetric and asymmetric tilt GBs. In our study, we limit our pool of GB models to symmetric tilt GBs such as that shown in Figure 1. Symmetric, pure tilt GBs are chosen as these are easily modeled compared to asymmetric GBs. To construct these virtual models, we employ a method using the coincident-site lattice (CSL) of the MAPbI3 cubic unit cell. CSLs can be geometrically constructed by superimposing two generating lattices and rotating them relative to each other, as is illustrated in Figure 2 below. At certain angles, points from each lattice coincide, creating a super lattice of points from each generating lattice. This 89

Figure 1: Model of symmetric tilt GB defined by angle ⍬, rotation axis o, and GB interface plane n.

Figure 2: Construction of a GB from a coincident site lattice.

super-lattice is known as the coincident-site lattice. Points from the CSL can be used to construct a GB model by defining the bounds of the model and the interface between the grains [7].

The CSL-based GB model is defined by the sigma number (Σ) followed by miller indices (h k l) to specify the GB interface plane or the family of the GB interface plane. CSL-based GB models ensure a periodic structure across the boundaries perpendicular to the GB plane. Another advantage of CSL-based GB models is the corresponding Σ, which is the ratio of the volume of a CSL unit cell to the volume of a generating lattice unit cell. Thus, the higher Σ is the further apart the CSL lattice points will be from one another.

This leads to lattice points along the GB interface of a CSL-based GB model being further from one another [8]. For example, Figure 3 below shows two periodic GB models based on CSLs. Each of these models is one CSL unit cell across. However, the Σ97 model is much wider than the Σ3 model, indicating that the lattice points from the CSL along the interface are further apart.

The sigma number is a geometric property that is associated with the idealized CSL-based GB, but it is also used in empirical studies, such as those of Kim et al. and Saylor et al., to describe certain observed CSL-like GBs. Based on the results of the experimental studies found in the literature, we hypothesize that CSL-based GB models of MAPbI3 with lower Σ will have a comparatively lower GB energy, indicating that these are most stable in MAPbI3.

Figure 3: Σ91 and Σ3 GB models. The Σ91 GB has 48.71 Å while the Σ3 model only has 17.69 Å between CSL lattice points along the interface

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Ingenium 2019

Figure 4: Profile view of MAPbI3 Σ3 {2 1 1} GB. The GB plane is perpendicular to the blue arrow while the axis of rotation, specified by position vector in brackets, is parallel to the green arrow (out of page).

2. Methods 2.1 Model Construction and Energy Calculations Using software provided by Ogawa [8], CSL-based symmetric tilt GB models and corresponding bulk models were constructed with Σ ranging from 3 to 37 using a cubic generating lattice. Figure 4 shows the Σ3{211} MAPbI3 model produced from this software as an example. The lattice constant for the cubic generating lattice was optimized using a bulk crystal model with energy minimization algorithms and set at 6.25 Å. This is in good agreement with the experimental values reported ranging from 6.27 Å to 6.33 Å [2].

Figure 5 shows the data used to determine this lattice constant.

To determine the optimized structure of each bulk crystal and GB model, we use the interatomic potential parameters provided

by Caddeo et al. [9]. Energy minimization and molecular dynamics (MD) simulations were performed using the LAMMPS software package [10]. This procedure included an initial damped dynamics (FIRE) energy minimization followed by 10 MD runs, each with 30,000 timesteps using 1.0 fs increments. Each MD run was followed by another damped dynamics energy minimization. After structure optimization, the final potential energy of each model was used to calculate the GB energies.

2.2 Data Processing For each GB type, we calculate the GB energy by subtracting the total potential energy of the corresponding bulk model from the total potential energy of the GB model and divide by the total GB interface area in accordance with equation (1):

Figure 5: Unit Cell Energy vs. Lattice Parameter. The optimum lattice constant minimizes the energy of the system, which is found to be 6.25 Å compared to the literature value ranging from 6.27 Å – 6.33 Å (Ono et al.). We used the optimum lattice constant to construct all models used in this study.

The energy difference between the total potential energy of the GB model, EGB,Model, and the total potential energy of the bulk model, EBulk,Model, arises from the irregular bonds that occur along the GB interface. Figure 6 below shows the Σ5{210} GB model side by side with the corresponding bulk model to illustrate how their structures differ.

Figure 6: GB and corresponding bulk model used for the calculation of the Σ5{210} GB energy.

(1)

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3. Results

Table 1 below shows the calculated GB energies of our selected models. Observing the data, we find that the values are rather close to one another. Between all the investigated GBs the average GB energy was 0.011 eV/Å2 with a standard deviation of 0.004 eV/Å2.

Table 1: GB Energy Data, GB energy in units of eV/Å

2

Sigma Number (GB Plane)

GB Angle (Degrees)

MD GB Energy

DFT CsPbI3 GB Energy

Σ3 {2 1 1} Σ3 {1 1 1} Σ5 {2 1 0} Σ7 {3 2 1} Σ13 {4 3 1} Σ19 {5 3 2} Σ21 {5 4 1} Σ37 {7 4 3}

60.0

0.018

0.001

70.5

0.009

0.002

36.9

0.010

0.008

38.2

0.010

-

148

0.009

-

167

0.010

-

142

0.013

-

50.6

0.004

-

4. Discussion

These results suggest that sigma number and GB angle have little impact on the GB energy. There is some similarity between the GB energies calculated here for MAPbI3 and those of other perovskites. In our group’s previous work [11], density functional theory (DFT) calculations were used to calculate the GB energies of Σ3 and Σ5 GBs in the inorganic CsPbI3 perovskite structure, show in Table 1. While both methods predict that a Σ3 GB will be lower in energy than Σ5, there is disagreement as to which Σ3 boundary.

The MD method predicts that the Σ3 (211) boundary will actually have a higher energy than the Σ5 (210) boundary while the Σ3 (111) is predicted to have a lower energy than both. Several factors could explain these differences, such as the different formula units of each perovskite, and a DFT study of the MAPbI3 GB can be used to verify the present results produced by MD. Overall, the results from this present study indicate that there is little correlation between the GB energy and sigma number or GB angle in MAPbI3. The results also indicate that some of these GBs may be equally stable as their energies are relatively close to one another, despite Σ and angle. This could be due to each having similar bond irregularities along the GB interfaces.

5. Conclusion

In contrast to our hypothesis and results of previous studies on different systems that have found a correlation between sigma number and GB stability in other inorganic perovskites, our computational study found that there was no clear significant energy difference between high and low Σ GBs in the hybrid perovskite MAPbI3, suggesting that these are equally stable. To verify these results, our group plans on continuing investigation into the structural similarities and differences between high and low

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Σ MAPbI3 GBs. Furthermore, our group will seek to improve upon

the interatomic force-field parameters used in MD calculations to ensure they yield accurate results for GB models.

Acknowledgments

This research was made possible thanks to funding from the University of Pittsburgh’s Swanson School of Engineering and the Office of the Provost. Calculations were carried out using resources provided by the Center for Research Computing at the University of Pittsburgh.

References

1. Q. Chen, N. De Marco, Y. Yang, T. Bin Song, C.C. Chen, H. Zhao, Z. Hong, H. Zhou, Y. Yang, Under the spotlight: The organic-inorganic hybrid halide perovskite for optoelectronic applications, Nano Today. 10 (2015) 355–396. doi:10.1016/j. nantod.2015.04.009.

2. L.K. Ono, Y. Qi, Surface and Interface Aspects of Organometal Halide Perovskite Materials and Solar Cells, J. Phys. Chem. Lett. 7 (2016) 4764–4794. doi:10.1021/acs. jpclett.6b01951.

3. D.M. Saylor, B.S. El Dasher, A.D. Rollett, G.S. Rohrer, Distribution of grain boundaries in aluminum as a function of five macroscopic parameters, Acta Mater. 52 (2004) 3649–3655. doi:10.1016/j.actamat.2004.04.018.

4. C.S. Kim, Y. Hu, G.S. Rohrer, V. Randle, Five-parameter grain boundary distribution in grain boundary engineered brass, Scr. Mater. 52 (2005) 633–637. doi:10.1016/j. scriptamat.2004.11.025.

5. D.A. Porter, K.E. Easterling, M.Y. Sherif, Phase Transformations in Metals and Alloys, CRC Press, 2016.

6. D.M. Saylor, B.S. El Dasher, A.D. Rollett, G.S. Rohrer, Distribution of grain boundaries in SrTiO3 as a function of five macroscopic parameters, J. Am. Ceram. Soc. 87 (2004) 670–676.

7. P. Lejček, Grain Boundary Segregation in Metals, 2010. doi:10.1007/978-3-642-12505-8.

8. H. Ogawa, GBstudio: A Builder Software on Periodic Models of CSL Boundaries for Molecular Simulation, Mater. Trans. 47 (2006) 2706–2710. doi:10.2320/matertrans.47.2706.

9. C. Caddeo, M.I. Saba, S. Meloni, A. Filippetti, A. Mattoni, Collective Molecular Mechanisms in the CH 3 NH 3 PbI 3 Dissolution by Liquid Water, ACS Nano. 11 (2017) 9183–9190. doi:10.1021/acsnano.7b04116.

10. S. Plimpton, T. Aidan, S. Moore, A. Kohlmeyer, LAMMPS Molecular Dynamics Simulator, (n.d.). https://lammps.sandia.gov/.

11. Y. Guo, Q. Wang, W.A. Saidi, Structural stabilities and electronic properties of high-angle grain boundaries in perovskite cesium lead halides, J. Phys. Chem. C. 121 (2017) 1715–1722. doi:10.1021/acs.jpcc.6b11434.

Ingenium 2019

Mitochondrial amidoxime reducing component 2 (mARC2) knockout mice are partly protected from diet-induced obesity Jimmy Zhang, Bin Sun, Mark T. Gladwin, and mentor Courtney E. Sparacino-Watkins Vascular Medicine Institute, Department of Medicine, University of Pittsburgh, PA, USA

Significance Statement:

Mitochondrial amidoxime reducing component 2 (mARC2) is a molybdenumdependent enzyme which has an unclear role in lipid metabolism and weight gain.

Understanding the biological function of mARC2 may reveal novel strategies to combat the obesity epidemic.

Zhang

Abstract

Obesity is a common and preventable disease that has reached epidemic proportions in the United States.

Mitochondrial amidoxime reducing Sparacino-Watkins component 2 (mARC2) is a molybdenumdependent oxidoreductase with unclear function that has been proposed to regulate lipid metabolism.

This study was conducted to determine if deleting mARC2 in mice repressed diet-induced obesity. Nine week old male mice were feed normal chow (low fat diet, LFD) or a high fat chow (high fat diet, HFD) for twenty weeks. The data herein demonstrates that mARC2 KO mice weigh less than WT littermates. Interestingly, deletion of mARC2 protected the mice from the high fat diet (HFD) model of diet-induced obesity (DIO) during the initial exponential weight gain phase (0-11 weeks old), but not the latter plateau phase (12-20 weeks).

The mARC2 KO mice continued to gain weight throughout experiment, eventually reaching a similar body weight as the WT control mice feed HFD chow.

Category: Experimental research

Keywords: obesity, hepatocytes, molybdenum, mouse models Abbreviations: diet-induced obesity (DIO), high fat diet (HFD), low fat diet (LFD), mitochondrial amidoxime reducing component 2 (mARC2). Type 2 diabetes (T2D), knock out (KO), wild type (WT)

1. Introduction

Obesity is a common and preventable disease that has reached epidemic proportions in the United States. According to the U.S. Department of Health and Human Services, approximately 40% of adults and 18% of children are obese [1]. Obesity is one of the most prevalent causes of preventable death in the US [2] and obesity-related conditions (i.e., type 2 diabetes (T2D), heart disease, and some cancers) are among the top 10 leading causes of mortality [3]. Despite substantial efforts to combat this growing epidemic, obesity remains a major public problem [4] and novel strategies to treat obesity are desperately needed.

Mitochondrial amidoxime reducing component 2 (mARC2) is a molybdenum-dependent oxidoreductase enzyme that may function in lipid metabolism. Humans encode two mARC gene paralogues, mARC1 and mARC2 [5], which exhibit overlapping catalytic functionality. The mARC enzymes functions as part of a threeenzyme metabolon, with the heme enzyme cytochrome b5 (CYB5) and NADH-dependent flavin enzyme cytochrome b5 reductase (CYB5R), to transform a range of oxidized substrates (e.g., N(Ď&#x2030;)hydroxy-L-arginine [6], nitrite[7], sulfonamides [8]; N-hydroxylated DNA base analogues [9, 10], trimethylamine N-oxide (TMAO) [11]).

The proteins are not segregated to any specific tissue, although adipose tissue contains more mARC1 and the liver is rich in mARC2 [12-14].

Nutritional stimuli can alter mARC2 levels. Glucose stimulates mARC2 expression in cultured renal cells [12] and mARC2 (and mARC1) protein levels hepatocytes [15]. High fat diet (HFD) feeding increased liver mARC2 (and mARC1) protein levels, but not transcript levels [15]; while fasting lowers mARC2 (not mARC1) protein levels in the livers of fasting humans, rats, and mice [14, 15]. Additionally, differentiated adipocytes contain more mARC2 protein than precursor cells and knocking-down mARC2 expression in differentiated adipocytes alters intracellular lipid content [16].

Still, the physiological and pathological functions of mARC1 and mARC2 are unknown, though proposed functions include NO-signaling, xenobiotic metabolism, detoxification, and lipid metabolism[5].

Recently, the first mARC2 knockout (KO) mouse was developed and phenotyped by the international mouse phenotyping consortium (IMPC). This global mARC2 KO mouse, which doesnâ&#x20AC;&#x2122;t express mARC2 in any tissue, provides insight in to the physiological function of mARC2. According to the IMPC phenotyping data, the mARC2 KO mice have decreased body weight, increased startle reflex, and decreased pre-pulse inhibition compared to wild type (WT) controls (data accessed on 1/1/2019) [17]. The latter two phenotypes suggest that mARC2 may function in regulating behavior or neurological function, but this has not been tested experimentally. The decreased body weight phenotype observed in the mARAC2 KO is reinforced by in vitro studies, suggesting that mARC2 may have a function in lipid metabolism and is a novel target to treat or prevent obesity.

This study was designed to explore the function of mARC2 in lipid homeostasis and obesity using the mARC2 KO mice. First, the effect of high fat diet (HFD) on hepatic mARC2 RNA transcript 93

and protein levels was tested in two obesity prone mouse strains, C57BL6/J and AKR. Next, the effect of mARC2 on weight gain was tested in HFD model of diet-induced obesity (DIO) using global mARC2 KO mice.

2. Methods 2.1 Western Blot Analysis and Polymerase Chain Reaction (PCR) Protein and transcript were extracted from C57BL/6J or AKR mice livers to measure the effect of HFD feeding on mARC2 levels. Mouse liver tissue from male C57BL/6J and AKR mice feed either HFD or LFD were provided by a collaborator, Dr. Yen-Chun Lai. A total of 12 animals were tested: C57BL/6J-LFD (N=3), C57BL/6J-HFD (N=3), AKR-LFD (N=3), and AKR-LFD (N=3) were tested. For protein analysis, frozen tissue was homogenized in the commercially available protein extraction reagent with Halt protease inhibitor cocktail (ThermoFisher). Protein concentration was measured using a BCA protein assay (Thermo-fisher).

Denaturing electrophoresis (SDS-PAGE) was used to separate protein electrophoresis prior to western blot (WB) analysis. An anti-tubulin antibody was used as a loading control and an antimARC2 antibody (Sigma, Human Protein Atlas) specific for mARC2 (i.e., no reactivity to mARC1). Tubulin and mARC2 expression were visualized using chemiluminescence (ThermoFisher) and a digital chemiluminescence detector (BioRad). RNA extraction from frozen liver tissue was accomplished using the RNeasy Plus Mini Kit quick-start protocol (Qiagen). Spectrophotometry was used to measure RNA content prior to quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Commercially available Taqmen primers specific for mouse mARC2 and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were used. GAPDH was used as an internal control and a means of normalization. All statistical and graphical analysis was accomplished in GraphPad Prism 7.

2.2 Mouse model of diet-induced obesity (DIO): Twenty-four male C57BL/6N wild-type (WT) and mARC2 knockout (KO) mice (mARC2tm2a(EUCOMM)Hmgu) were generated from filial heterozygote breeding. At 9 weeks old, WT and KO littermates were randomly selected to receive a high-fat diet (HFD) or a lowfat diet (LFD). Chow was purchased from Research Diets, LFD (D12450J) contained 10% fat, 70% carbohydrate, 20% protein) and HFD (D12492) contained 60% fat, 20% carbohydrate, 20% protein). Of the 24 mice, 14 mice (6 WT, 8 KO) were fed a HFD and 10 mice (6 WT, 4 KO) were fed a LFD. Body weights were recorded weekly. All animal experiments were conducted in accordance with institutional animal care procedures at the University of Pittsburgh.

Statistical significance determined using the Holm-Sidak method, with alpha = 0.05. Each row was analyzed individually, without assuming a consistent SD.

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3. Results

First, the effect of HFD feeding on mARC2 transcript and protein levels was measured in the livers of WT mice using qRTPCR and western blot, respectively. HFD has been reported to upregulate mARC2 protein levels in the liver of C57BL/6W mouse feed HFD for 16 weeks [15]. Each mouse strain can respond differently to HFD feeding[19], therefore mARC2 transcript and protein levels were measured in two obesity-prone mouse strains, C57BL/6J and AKR [20]. No significant differences in mARC-2 transcript (Figure 1A) or protein (Figure 1B) levels among HFD and LFD feed mice was found. However, the AKR mouse strain had significantly higher mARC2 transcript (Figure 1A) and protein (Figure 1B) levels, independent of diet, compared to the C57BL/6J mouse livers.

Figure 1: Liver mARC-2 levels are different in mouse strains (BL6 and AKR).

A. mARC-2 transcript levels measured by qRT-PCR. B. Quantification of relative mARC-2 protein levels measured by western blot. C. Raw western blot data quantified in B. positive controls for each western blot were isolated recombinant human mARC2 (rM2) and a Bio-Rad standard protein molecular weight (Std.).

Values measured from each mouse are represented by blue squares. Low fat diet (LFD), open squares; High fat diet (HFD), solid squares. Group mean and standard deviation values are represented by lines and error bars, respectively.

Statistical significance was measured by the studentâ&#x20AC;&#x2122;s t-test with Graph Pad Prism software.

Ingenium 2019

Next, the effect of mARC2 deletion on the HFD mouse model of DOI was tested using male mARC2 KO mice. The IMPC phenotyping data reports that the young (8- 15 weeks old) male and female mARC2 KO mice have lower body weights than age matched WT controls [17]. The decreased body weight in the mARC2 KO mice is maintained with age, mice maintained on normal chow for up to one year of age weigh less the WT littermates (Figure 2).

Figure 2: Photograph illustrating the body weight differences among 12-month old male mARC2 KO and WT mice.

While the qualitative body weight differences are clear in the photo (Figure 2), the effect of mARC2 KO on weight gain from DIO has not been formally tested. Therefore, this experiment was designed to test the effect of mARC2 KO on weight gain in mice feed a LFD and HFD. Age- matched male mARC2 KO and WT littermates were generated by heterozygous filial breeding. At nine-weeks old the mice were randomly separated into two groups (LFD and HFD) and body weights were recorded weekly. In the LFD feeding group, mARC2 KO mice maintained lower BW than WT littermates over the 20-week experiment (Figure 3). comparison of HFD feed mice revealed that mARC2 KO had lower BW than WT mice during the initial phase of the experiment, but the mARC2 KO body weight was not maintained over the entire 20-week experiment (Figure 3). Significant differences (p-value < 0.01) in body weight among the WT and KO mice were observed between 1 and 11 weeks of HFD exposure, but not during the 12-20 weeks.

Figure 3: mARC2 deletion inhibits HFD induced weight gain in male mice. Male wild type (WT, blue squares) and knockout (KO, red squares) littermates were randomly provided either HFD (solid squares) or LFD (empty squares). Special diet was started at 9 weeks old. Body weight was recorded weekly. Statistical analysis was performed at each time point to compare the KO and WT mice on HFD. **, p â&#x2030;¤ 0.001; *, p â&#x2030;¤ 0.01

4. Discussion

Previous studies have shown that mARC2 levels are differentially regulated by nutrient intake (e.g., glucose and fat) [12, 14-16]. To confirm these observations, the effect of HFD on hepatic mARC2 transcript and protein levels was measured in C57BL/6J and AKR mice (Figure 1). No significant differences in mARC2 transcript levels (Figure 1A) were found in HFD feed mice compared to LFD controls, consistent with published reports[15].

Additionally, mARC2 protein levels are not different in livers of HFD feed mice (Figure 1B). Previous studies demonstrated that mARC2 protein are elevated in the liver of C57BL/6W mice feed a HFD for 10 weeks (approximately 16 weeks old mice), but not changed after extended feeding (42 weeks on HFD). Thus it is possible that the mice (kindly provided by Dr. Lai) and feed HFD for ~ 20 weeks are beyond the initial phase of weight gain and have normalized mARC2 levels. Interestingly, mARC2 levels are much higher in the AKR strain than the C57BL/6J stain (Figure 1A and B); which is more susceptible to DIO [20].

95

Deleting mARC2 in mice causes decreased body weight in LFD feed mice and delayed weight gain in mice feed a HFD (figure 3). The unchallenged mARC2 KO mice maintain a lower body weight compared WT littermates feed LFD over the course of this investigation. These data are consistent with published phenotype data collected with young mice[17]. However, the HFD feed mARC2 KO do not maintain a lower body weight than the WT controls thought-out the 20-week experiment. Instead, the mARC2 KO mice gained weight at a slower rate than HFD feed WT control mice.

Significant differences (p-value < 0.01) in body weight among the WT and KO mice were observed between 1 and 10 weeks of HFD exposure, but not at the latter time points.

5. Conclusion

This investigation has shown that deleting mARC2 in mice protects against DIO by slowing weight gain over time. Future experiments will examine the role of different organs (adipose, liver, etc.) in weight gain and adiposity with inducible cell specific mARC2 KO mice.

Acknowledgments

We are grateful to Yen-Chun Lai for providing us with the samples used for Western Blot and qPCR. Funding from the Swanson School of Engineering to Jimmy Zhang and the Pittsburgh Liver Research Center (PLRC) pilot award to Courtney Sparacino-Watkins.

References

1. Hales, C.M., et al., Prevalence of obesity among adults and youth: United States, 2015-2016. 2017: US Department of Health and Human Services, Centers for Disease Control and â&#x20AC;Ś.

2. Bauer, U.E., et al., Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. The Lancet, 2014. 384(9937): p.

45-52.

3. Heron, M.P., Deaths: Leading causes for 2016. 2018.

4. Mitchell, N.S., et al., Obesity: overview of an epidemic.

Psychiatric Clinics, 2011. 34(4): p. 717-732.

5. Ott, G., A. Havemeyer, and B. Clement, The mammalian molybdenum enzymes of mARC. JBIC Journal of Biological Inorganic Chemistry, 2015. 20(2): p. 265-275.

6. Kotthaus, J., et al., Reduction of NĎ&#x2030;-hydroxy-L-arginine by the mitochondrial amidoxime reducing component (mARC).

Biochemical Journal, 2011. 433(2): p. 383-391.

7. Sparacino-Watkins, C.E., et al., Nitrite reductase and nitricoxide synthase activity of the mitochondrial molybdopterin enzymes mARC1 and mARC2. Journal of Biological Chemistry, 2014.

289(15): p. 10345-10358.

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8. Havemeyer, A., et al., Reduction of N-hydroxy-sulfonamides, including N-hydroxy-valdecoxib, by the molybdenum-containing enzyme mARC. Drug Metabolism and Disposition, 2010. 38(11): p.

1917-1921.

9. Krompholz, N., et al., The mitochondrial amidoxime reducing component (mARC) is involved in detoxification of N-hydroxylated base analogues. Chemical research in toxicology, 2012. 25(11): p. 2443-2450.

10. Plitzko, B., et al., The Pivotal Role of the Mitochondrial Amidoxime Reducing Component 2 in Protecting Human Cells against Apoptotic Effects of the Base Analog N6Hydroxylaminopurine. Journal of Biological Chemistry, 2015.

290(16): p. 10126-10135.

11. Schneider, J., et al., Detoxification of Trimethylamine N-Oxide by the Mitochondrial Amidoxime Reducing Component mARC. Chem Res Toxicol, 2018. 31(6): p. 447-453.

12. Malik, A.N., et al., Glucose regulation of CDK7, a putative thiol related gene, in experimental diabetic nephropathy.

Biochemical and Biophysical Research Communications, 2007.

357(1): p. 237-244.

13. Uhlen, M., et al., Proteomics. Tissue-based map of the human proteome. Science, 2015. 347(6220): p. 1260419.

14. Neve, E.P., et al., Expression and Function of mARC: Roles in Lipogenesis and Metabolic Activation of Ximelagatran. PloS one, 2015. 10(9): p. e0138487.

15. Jakobs, H.H., et al., The N-Reductive System Composed of Mitochondrial Amidoxime Reducing Component (mARC), Cytochrome b5 (CYB5B) and Cytochrome b5 Reductase (CYB5R) Is Regulated by Fasting and High Fat Diet in Mice. PLoS ONE, 2014.

9(8): p. e105371.

16. Neve, E.P.A., et al., Amidoxime Reductase System Containing Cytochrome b5 Type B (CYB5B) and MOSC2 Is of Importance for Lipid Synthesis in Adipocyte Mitochondria. Journal of Biological Chemistry, 2012. 287(9): p. 6307-6317.

17. Koscielny, G., et al., The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic acids research, 2013.

42(D1): p. D802-D809.

18. Khoo, N.K.H., et al., Nitrite augments glucose uptake in adipocytes through the Protein Kinase A-dependent stimulation of mitochondrial fusion. Free radical biology & medicine, 2014. 70: p.

45-53.

19. Meng, Q., et al., Development of a Mouse Model of Metabolic Syndrome, Pulmonary Hypertension, and Heart Failure with Preserved Ejection Fraction. American Journal of Respiratory Cell and Molecular Biology, 2017. 56(4): p. 497-505.

20. Alexander, J., et al., Distinct phenotypes of obesity-prone AKR/J, DBA2J and C57BL/6J mice compared to control strains.

International journal of obesity, 2006. 30(1): p. 50.

Ingenium 2019

Student and Mentor Bios Deep learning for hyperspectral image classification on embedded platforms....................................................................................7 Siddharth Balakrishnan is a student with a dual-degree in Bioenginnering and Electrical Engineering. He is also an undergraduate researcher at the NSF Center for Space, High-performance, and Resilient Computing (SHREC) at Pitt. After graduation, he hopes to work in the field of Artificial Intelligence.

Dr. Alan George is Department Chair, R&H Mickle Endowed Chair, and Professor of Electrical and Computer Engineering at the University of Pittsburgh. He is also Founder and Director of the NSF Center for Space, High-performance, and Resilient Computing (SHREC), a national research center and consortium founded in 2017 and headquartered at Pitt.

Smarter riversheds: Real-time water sensors.................................................12 Kathleen Beaudoin is from Greenville, SC, but attends the University of Pittsburgh.

Her interest in water resources motivates her to pursue a career as an environmental engineer.

David Sanchez, PhD is an Assistant Professor in the Civil & Environmental Engineering department and the Assistant Director for the Mascaro Center for Sustainable Innovation. Current projects include creating predictive water quality/ quantity models for the US, creating water quality profiles for vertical agriculture/ recirculating aquaculture systems, and improving biofilm-anode interactions for bio-electrochemical systems.

Modal analysis of human brain dynamics after head impact..........................17 Ryan Black was born in Lake Forest, IL, and grew up early on in Houston, TX, and later on in Pittsburgh, PA. His goal is to pursue a career in research and development that uses computational modeling.

Dr. Hessam Babaee is an Assistant Professor in the Mechanical Engineering and Materials Science department at Pitt. His research interests are uncertainty quantification and reduced order modeling in bio/mechanical engineering application.

Evaluating occlusion success of Esophocclude prototypes in comparison to diameter and radial force....................................................21 Gordon Bryson grew up in Newtown, PA, but he has loved getting to know Pittsburgh in the past three years. He is looking forward to using his experience in medical product design to help make new healthcare technology safe and affordable.

Dr. Youngjae Chun obtained his PhD in Mechanical Engineering at UCLA working on the development of biomedical devices to treat vascular diseases. He is currently an Associate Professor in the Department of Industrial Engineering, where his research interests include designing metallic medical devices and investigating biocompatibility.

Investigating the influence of carbon nanomaterials on the mechanical properties of concrete............................................................25 Nathanial Buettner is passionate about structural engineering and concrete material science. After graduation, he plans to pursue his PhD in Civil and Environmental Engineering to research concrete pavements.

Steven Sachs received a PhD in Civil Engineering from the University of Pittsburgh in 2016. He has previously been employed by the Municipal Authority of Westmoreland County and PennDOT. He is presently employed as an Assistant Professor in the Department of Civil Engineering. His primary research interests include finite element modeling of pavement structures, rigid pavement design and analysis, and mechanistic-empirical design procedures.

Dr. Leanne Gilbertson joined the University of Pittsburgh in 2015 as an Assistant Professor of Environmental Engineering and has degrees in Chemistry (BA from Hamilton College) and Environmental Engineering (MS, PhD from Yale University).

Her research aims to inform sustainable design of emerging materials and technologies proposed for use in environmental and public health applications.

Vision field testing with virtual reality.............................................................29 Ava Chong is pursuing a major in Computer Engineering with a minor in Industrial Engineering and a certificate in Innovation, Product Design, and Entrepreneurship.

Her passion is for the intersection of innovative computing and social good. She hopes to continue exploring social impact technology through research and industry experience!

Murat Akcakaya received his PhD in Electrical Engineering from Washington University in St. Louis in 2010. He is currently an Assistant Professor in the Electrical and Computer Engineering department. His research interests include statistical signal processing and machine learning with applications to noninvasive EEG-based brain-computer interface (BCI) systems, array signal processing, and physiological signal analysis for health informatics.

Myocardin-related transcription factor’s role in cell migration.......................33 Aidan Dadey was born and raised in Pittsburgh, PA and currently studies Bioengineering. He has worked with Dr. Roy since January 2018 and conducts research with triple negative breast cancer.

Dr. Partha Roy is an Associate Professor of Bioengineering and Pathology at the University of Pittsburgh. He received his PhD in Biomedical Engineering from the University of Texas Southwestern Medical Center. After completing his post-doctoral fellowships in Cell Biology at Harvard Medical School and the University of North Carolina, Chapel Hill, he began his independent academic career at the University of Pittsburgh. Dr. Roy’s laboratory studies cell migration, cancer metastasis, and angiogenesis.

Design of a wearable upper limb exoskeleton................................................37 Zach Egolf has been interested in robotics since his freshman year as a project member in the Pitt Robotics and Automation Society. Due to his passion for robotics and rapid prototyping, Zach hopes to attend graduate school to obtain his PhD in Mechanical Engineering.

Nitin Sharma received his PhD degree in Mechanical Engineering from the University of Florida, Gainesville, in 2010. Since 2012, he has been an Assistant Professor with the Department of Mechanical Engineering and Materials Science at Pitt. His research interests include the modeling, optimization, and control of functional electrical stimulation-elicited walking.

In vivo dopamine sensors for basic neuroscience and biomedical research: A review...........................................................................................................42 Noah Chaim Freedman was born and raised in the Squirrel Hill neighborhood of Pittsburgh, PA. His deep interest in understanding mechanisms of neural computation and his desire to help others with neurological disorders have driven him to pursue a career as a medical research scientist.

Dr. Tracy Cui is the William Kepler Whiteford Professor of Bioengineering at the University of Pittsburgh. She earned her PhD in Macromolecular Science and Engineering at the University of Michigan. Her research interests lie in neural engineering with special focuses on neural electrode-tissue interface, neural tissue engineering, central nervous system drug delivery, and biosensors.

Diffusion tensor image analysis of stroke damaged brains treated with combined neural stem cell and physical therapy............................................46 Lauren Grice is a junior bioengineering student motivated by understanding the merger of healthcare and technology. After graduation, she plans to attend graduate school to study the integration of electrical systems into the brain for the treatment of neurological disease.

Dr. Michel Modo holds a PhD in Neuroscience from King’s College London. He joined the Department of Radiology at the University of Pittsburgh in 2011 and his laboratory’s main focus is to develop novel imaging tools to visualize brain repair using stem cells and biomaterials.

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Student and Mentor Bios Continued from page 97 Delamination of soft thin films from dynamic wrinkling substrates................51 Joseph Hamm was raised in Kintnersville, Pennsylvania. Prior conducted research was focused on applications of thin film wrinkling. He possesses a strong concern for sustainability and wishes to align future pursuits with interests.

Sachin Velankar is in the Chemical Engineering department and is broadly interested in soft materials. His current research is on rheology, interfacial phenomena, thin film mechanics, and arterial mechanics.

Adventitial delivery of therapeutic cells for localization in porcine aortas..............................................................................................55 Trevor Kickliter was raised in Allentown, PA. His desire to save lives and his passion for engineering motivate him to pursue a PhD in either mechanical or biomedical engineering.

David A. Vorp, PhD, is the Associate Dean for Research at the Swanson School of Engineering. He is also John A. Swanson Professor of Bioengineering, with secondary appointments in the Department of Cardiothoracic Surgery, the Department of Surgery, the Department of Chemical and Petroleum Engineering, and the Clinical and Translational Science Institute at the University of Pittsburgh.

The influence of nitrogen doping on electrocatalytic activity of FeN4 embedded graphene...............................................................................59 Based on her passion for the environment and electronic materials, Lydia Kuebler hopes to further pursue research in photovoltaic cells and applications for sustainable energy through a career in academia.

Dr. Guofeng Wang is an Associate Professor in the Department of Mechanical Engineering and Materials Science. He has been developing and applying first-principles density functional theory based multi-scale approach and novel computational algorithms to solve problems related to a broad range of materials, which include catalysts, nanostructured metals, and high-temperature alloys, as well as advanced manufacturing of these materials.

Combined neural stem cells and physical therapy improve somatosensory cortex activity after stroke...............................................................................65 Nikhita Perry is from Greenville, PA, and is studying bioengineering and chemistry.

She aspires to one day be a neurologist and to continue studying neurodegenerative diseases.

Dr. Michel Modo holds a PhD in Neuroscience from King’s College London. He joined the Department of Radiology at the University of Pittsburgh in 2011 and his laboratory’s main focus is to develop novel imaging tools to visualize brain repair using stem cells and biomaterials.

Angiogenic response to abdominal and vaginal polypropylene mesh implants in a rabbit model.....................................................................69 McKenzie Sicke is from Rochester, NY, and is interested in pursuing medical school to specialize in Emergency Medicine. Her family of many teachers inspires her love for working as an Engineering Peer Advisor and Teaching Assistant on campus.

Dr. Bryan Brown is an Assistant Professor in the Department of Bioengineering.

He is also the Director of Educational Outreach at the McGowan Institute for Regenerative Medicine, an Adjunct Assistant Professor of Clinical Sciences at the Cornell University College of Veterinary Medicine, and Chief Technology Officer of Renerva, LLC, a Pittsburgh-based start-up company.

The effect of pivot-bearing surface roughness on thrombus formation: An in-vitro study..............................................................................................72 Katherine Stevenson is from Lumberton, NJ. After she moved to Pittsburgh to pursue a degree in bioengineering, she got involved in artificial lung research due to her interest in medical devices, biocompatibility, and fluid dynamics. Post-graduation, she hopes to continue working on medical devices in the biotech industry.

Dr. William Federspiel is the William Kepler Whiteford Professor in the Department of Bioengineering and is the Director of the Medical Devices Laboratory at the McGowan Institute. Additionally, Dr. Federspiel is a Founder of ALung Technologies, a Pittsburgh based medical start-up company, for which he serves as the head of the scientific advisory board.

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Undergraduate Research at the Swanson School of Engineering

Infant feed thickening characterization at UPMC Children’s Hospital of Pittsburgh...........................................................76 Kelsey Toplak was born and raised in the North Hills of Pittsburgh, PA. After graduation, Kelsey plans to user her bioengineering degree and passion for healthcare to pursue a career in the medical device industry.

Dr. Mark Gartner is a Professor of Practice in the Department of Bioengineering and a co-founder of Ension, Inc. Dr. Gartner’s laboratory focuses on research and development of medical products for at-risk pediatric and adult patient populations as well as design of devices for a range of conservation engineering applications in partnership with the Pittsburgh Zoo and PPG Aquarium.

Analyzing right ventricular response to Sacubitril/Valsartan in pulmonary hypertension..................................................................................80 Claire Tushak is originally from Erie, Pennsylvania. She is pursuing a degree in bioengineering to make an impact in the medical device industry.

Dr. Marc Simon is an Associate Professor of Medicine, Director of Heart Failure Research & Translational Pulmonary Hypertension Research, and Medical Director of the Montefiore Clinical & Translational Research Center of the Clinical Translational Science Institute. Dr. Simon’s current research interests include the study of right ventricular structural and functional adaptation to pressure overload in pulmonary hypertension and heart failure.

Thermal resistance and stiffness of iSIM-90 support blades for CASPR camera system....................................................................................84 Michael Ullman is from Graysville, Ohio, and is currently pursuing a Bachelor’s degree in Mechanical Engineering with a minor in Physics. He plans to continue his education by pursuing a graduate degree in Aerospace Engineering or a related field Dr. Alan George is Department Chair, R&H Mickle Endowed Chair, and Professor of Electrical and Computer Engineering at the University of Pittsburgh. He is also Founder and Director of the NSF Center for Space, High-performance, and Resilient Computing (SHREC), a national research center and consortium founded in 2017 and headquartered at Pitt.

Modeling and energy calculations of perovskite methylammonium lead iodide grain boundaries...........................................................................89 Philip Williamson’s main research interests are using modelling, simulation, and data science to research and develop materials for renewable and alternative energy technologies. He is the Treasurer and Business Manager for Pitt’s chapter of Material Advantage and a member of the Engineering Honor Society Tau Beta Pi. Wissam A. Saidi is an Associate Professor in the Department of Mechanical Engineering and Materials Science. The Saidi group uses multiscale simulation tools to understand, predict, and design novel materials for applications in energy conversion and storage, surfaces and interfaces, spectroscopy, and nanoparticles.

Mitochondrial amidoxime reducing component 2 (mARC2) knockout mice are partly protected from diet-induced obesity......................93 Jimmy Zhang is a student research assistant at the Vascular Medicine Institute.

He plans on pursuing a career as a process engineer in the pharmaceutical or consumer healthcare industry.

Dr. Courtney Sparacino-Watkins earned her BS in Chemistry and Biochemistry at Slippery Rock University, and her PhD at Duquesne University. She is currently a junior faculty member in the University of Pittsburgh, School of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, and in the Vascular Medicine Institute. Her research interests center on the role of molybdenum enzymes in human physiology and pathophysiology.

Ingenium 2019

Index Category: Experimental research

Category: Computational research

Kathleen Beaudoin Smarter riversheds: Real-time water sensors.................................................12

Siddharth Balakrishnan Deep learning for hyperspectral image classification on embedded platforms....................................................................................7

Nathanial Buettner Investigating the influence of carbon nanomaterials on the mechanical properties of concrete............................................................25 Aidan Dadey Myocardin-related transcription factor’s role in cell migration.......................33 Lauren Grice Diffusion tensor image analysis of stroke damaged brains treated with combined neural stem cell and physical therapy....................................46 Joseph Hamm Delamination of soft thin films from dynamic wrinkling substrates................51 Trevor M. Kickliter Adventitial delivery of therapeutic cells for localization in porcine aortas..............................................................................................55 Nikhita Perry Combined neural stem cells and physical therapy improve somatosensory cortex activity after stroke.....................................................65

Ryan T. Black Modal analysis of human brain dynamics after head impact..........................17 Lydia Kuebler* The influence of nitrogen doping on electrocatalytic activity of FeN4 embedded graphene...............................................................................59 Philip A. Williamson Modeling and energy calculations of perovskite methylammonium lead iodide grain boundaries...........................................................................89

Category: Review Noah Freedman In vivo dopamine sensors for basic neuroscience and biomedical research: A review..................................................................42

McKenzie Sicke Angiogenic response to abdominal and vaginal polypropylene mesh implants in a rabbit model.....................................................................69 Kelsey Toplak Infant feed thickening characterization at UPMC Children’s Hospital of Pittsburgh...........................................................76 Claire Tushak Analyzing right ventricular response to Sacubitril/Valsartan in pulmonary hypertension..................................................................................80 Jimmy Zhang Mitochondrial amidoxime reducing component 2 (mARC2) knockout mice are partly protected from diet-induced obesity......................93

Category: Device design Gordon Bryson Evaluating occlusion success of Esophocclude prototypes in comparison to diameter and radial force....................................................21 Ava Chong Vision field testing with virtual reality.............................................................29 Zach Egolf Design of a wearable upper limb exoskeleton................................................37 Katherine Stevenson The effect of pivot-bearing surface roughness on thrombus formation: An in-vitro study.............................................................72 Michael Ullman Thermal resistance and stiffness of iSIM-90 support blades for CASPR camera system....................................................................................84

* Editors’ choice

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Ingenium: Undergraduate Research at the Swanson School of Engineering, 2019

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