PennScience Spring 2012 Artificial Intelligence

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VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

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About PennScience PennScience is a peer reviewed journal of undergraduate research published by the Science and Technology Wing at the University of Pennsylvania. PennScience is an undergraduate journal that is advised by a board of faculty members. PennScience presents relevant science features, interviews, and research articles from many disciplines including biologial sciences, chemistry, physics, mathematics, geological sciences, and computer sciences. PennScience is a SAC funded organization. For additional information about the journal including submission guidelines, visit www.pennscience.org or email us at pennscience@gmail.com.

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Staff

Table of Contents

EDITOR-IN-CHIEF

COVER DESIGN

Isabel Fan

Jennie Shapira

ASSOCIATE EDITOR-

WRITING

IN-CHIEF

Glen Brixey

Steven Chen

Sanders Chang Jason (Jiayi) Fan

EDITING MANAGERS

Vinayak Kumar

Emily Xue

Sarah Murray

Kevin Zhang

Natalie Neale Jaimin Shah

LAYOUT MANAGERS Lucy Shi

EDITING

Jenny Xiang

Ali Ahmad Paul Blazek

PUBLICITY MANAGER Glen Brixey Jenny Yan

Siyuan Cao Sally Chu

WRITING MANAGERS Kate Kerpen Sally Chu

Sarah Murray

Susan Sheng

Vihang Nakhate Kathryn Rooney

BUSINESS MANAGER Hulbert Soh Lisa Pang

Indu Subbaraj Adrianna Weiss

FACULTY ADVISORS

INTRODUCTION

Staff List ...................................................................................................... 3 Letter from the Editors ............................................................................. 4

FEATURES

The History of Artificial Intelligence ................................................... 6 - 8 By Sally Chu & Sarah Murray AI Laboratories at Penn ....................................................................... 9 -10 By Sanders Chang Robots Take Over Penn .................................................................... 10 - 11 By Jaimin Shah & Susan Sheng Biological vs. Algorithmic Approaches to AI ................................ 12 - 14 By Natalie Neale Glory, Glory, Robots United ............................................................. 14 - 15 By Vinayak Kumar

RESEARCH IQGAP Proteins as Oncogenic Signal Regulators in Human Squamous Cell Carcinoma .................................................................................. 16 - 21 Submitted by Emily Schapira Delayed recovery of core body temperature from repeated social defeat may be indicative of stress vulnerability ....................................... 22 - 26 Submitted by Arjunan Gnanendran

Dr. M. Krimo Bokreta Dr. Jorge Santiago-Aviles

Academic Excellence. Professional Success.

Dedicated to: t "DBEFNJD &YDFMMFODF t 2VBMJUZ 1BUJFOU $BSF t 1SPGFTTJPOBM -FBEFSTIJQ Degree Programs include: t %PDUPS PG $IJSPQSBDUJD t .BTUFS PG 4DJFODF JO "DVQVODUVSF t .BTUFS PG 4DJFODF JO "DVQVODUVSF BOE 0SJFOUBM .FEJDJOF t .BTUFS PG 4DJFODF JO "QQMJFE $MJOJDBM t /VUSJUJPO POMJOF EFMJWFSZ

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For more information call NYCC at 1-800-234-6922 or visit www.nycc.edu. Finger Lakes School of Acupuncture & Oriental Medicine of New York Chiropractic College School of Applied Clinical Nutrition 236 3PVUF Seneca Falls, NY 1314

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

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Letter from the Editors Dear Readers, We are proud to introduce you to the second issue of the 10th volume of PennScience. The theme of this issue, artificial intelligence, was inspired by recent developments and larger impact artificial intelligence is having in not only fiction, movies, and research, but in our daily lives. Sally Chu and Sarah Murray provide the early history of artificial intelligence to current developments. Sanders Chang, Jaimin Shah, Susan Sheng, and Vinayak Kumar write about artificial intelligence initiatives at the University of Pennsylvania. Natalie Neale compares the biological and algorithmic approaches to artificial intelligence. We are pleased to showcase the stellar research of two Penn undergraduates. Emily Schapira presents her research on IQGAP proteins as oncogenic signal regulators in human squamous cell carcinoma. Arjunan Gnanendran presents work on the delayed recovery of core body temperature from repeated social defeat may be indicative of stress vulnerability We would like to thank the groups and individuals that have made PennScience possible. We would like to thank our staff for their dedication and enthusiasm for the journal. We owe our funding to the Student Activities Council and the Science and Technology Wing, without which we could not publish a high-quality journal. We would also like to thank our faculty advisors for their constant support and insight. Finally, we would like to thank the Penn faculty who took the time to meet with us to discuss their research. We have enjoyed our time leading PennScience and are pleased to introduce Lucy Shi and Sally Chu as the new co-Editors in Chief of PennScience Journal of Undergraduate Research. As we leave, we would like to thank the groups and individuals that have made our work at PennScience possible--our staff for their dedication; the Student Activities Council and Science & Technology Wing for the resources, and our faculty advisors for their support. Thank you for reading PennScience and we hope you enjoy our latest issue! Sincerely, Isabel Fan, Editor-In-Chief Steven Chen, Associate Editor-In-Chief

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FEATURES

The History of

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THE GOLDEN YEARS (1956-1974)

The first era of Artificial Intelligence is aptly named,

After 1956, the field for AI expanded immensely. In

“The Beginning.� It begins in 1943 with the publishing

1956, John McCartney officially defined the term

of Warren McCulloch and Walter Pitts’ “A Logical

“Artificial Intelligence� at the Dartmouth Conference

Calculus of the Ideas Immanent in Nervous Activity�

and in doing so, created the field of AI. Many leading

and ends in about 1956. McCulloch and Pitts’ work

researchers of this time in many advanced research

laid the foundation for neural networks, and in 1950,

topics were invited to Dartmouth to discuss AI as

AM Turing expanded on their ideas by publishing

well as the consequences of the rapid growth rate of

“Computing Machinery and Intelligence,� which

electronic capacity and functionality. In the end, many

created the Turing Test. The Turing Test was a test

of these participants concluded that if technology

used to determine whether machines could think and

continued to evolve at the same exponential rate, it

expanded on Descartes’ Discourse on the Method,

would not be long until computers had the resources

where Descartes claimed a machine could be “so

to be as intelligent as human beings.5 Furthermore, at

constructed that it utters words, and even utters words

this conference, the first running AI program (Logic

that correspond to bodily actions causing a change

Theorist by Allen Newell, JC Shaw, and Herbert

in its organs. ...But it is not conceivable that such

Simon) was revealed. By now, the Turing Test had

a machine should produce different arrangements

become one of the criteria used to conclude whether

of words so as to give an appropriately meaningful

a machine could be considered AI or not.

answer to whatever is said in its presence.�3 However, the Turing Test failed to fully implement Descartes’

In 1966, researchers began to create programs that

philosophy, and some people suggested that the

seemed to pass the Turing test. Joseph Weizenbaum

ability to sustain a conversation with humans was

was the first researcher, creating the program

not necessarily a valid measurement. It is entirely

ELIZA.2 ELIZA worked by examining a user’s typed

possible that intelligent things may converse but

commands for keywords. ELIZA would respond to

cannot converse with humans because we do not

the keywords with a comment (many times generic)

share the same language. Alternatively, it is entirely

if she could find the keyword in her database.6 Six

possible that we are able to converse with something

years later, Kenneth Colby created PARRY, which

that only repeats random sentences.

was a program described as “ELIZA with attitude.�7

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technology had not been able to store for AIs’ use. This, along

and when a group of experienced psychiatrists were told to

with the inability to mimic true vision in AIs, set progress back.

analyze a combination of real patients and computers running

In the end, funding disappeared, leading to the stagnation of the

PARRY, they were only able to identify their patients as human or

development in the field of AI between 1974 and 1980.

PARRY only 48% of the time.

8

THE BOOM (1980-1987) While researchers were developing programs that passed the

In the 1980s, AI notably emerged in the corporate and commercial

Turing Test, others delved into making machinery that would

sector, allowing more funding towards research and more

aid humans. The General Problem Solver (GPS) was created in

projects. A large part of this move to the corporate sector was

1959 by Herbert Simon, JC Shaw, and Allen Newell.1 Unlike what

in the applications of expert systems like MYCIN. Additionally,

is currently thought of by the term GPS (a machine that guides

further into the 1980s, the US and British governments restored

people to different destinations), the original GPS was intended

some funding to academic AI research, primarily due to the threat

to work as a universal problem solver machine. It could work

of competition as Japan began inventing new technologies in AI.

through theorems proof, geometric problems, and even play chess.9 However, it could not solve any real-world problems.

Another popular technology was the machine vision system.

Eventually, the GPS paradigm evolved into the Soar architecture

The machine vision system consists of a computer with cameras

for AI, which was created by John Laird, Allen Newell, and Paul

and lighting to capture images of objects. The computer then

Rosenbloom at Carnegie Mellon in 1983.2

compares these new images with a predefined image or quality standard to detect defects. This sort of quality control was ideal

In the early 1970s, a multitude of new programs and advancements

for manufacturing companies, who began using these systems in

occurred, mainly due to DARPA, a government funding source.

the 1980s. However, these machines were limited by the quality of

One program that emerged was SHRDLU, a project at MIT that

computing; they were difficult to install, limited in performance,

demonstrated that computer programs could solve spatial

complex in programming, challenging to maintain, and not cost-

problems and logic problems when confined to a small subject

effective.11

matter. SHRDLU allowed a robot arm to be manipulated using instructions typed in English. Additionally, the first expert

However, toward the end of the 1980s, the corporate sector

systems began emerging. Expert systems are computers that

stumbled. These machine vision systems eventually became too

are programmed to make different levels of decisions based on

expensive to maintain, due to their lack of cost-effectiveness.

a great deal of information and rules set in the programs. One

Additionally, the emergence of the personal computer (PC) made

of the first expert systems, developed in 1974, was MYCIN,

expert systems pale in comparison. Lisp machines and other

which diagnosed bacterial infections of the blood and suggested

expert systems were far more expensive, slower, and less efficient

possible treatments.10 However, soon after this, AI development

overall. There were no longer any good reasons for companies and

slowed down.

individuals to buy these AI technologies. This drop in purchases correspondingly cut funding to research, known as the Second AI

THE FIRST WINTER (1974-1980)

Winter, where little research was able to occur.

Beginning in the mid-1970s, AI was criticized and subjected to financial setbacks. Researchers involved in the field did not fully

Despite the funding troubles at the end of the 1980s, the boom

comprehend the complexity of the problems they faced, and

in this decade represented a turning point in AI. For the first

their optimism had raised hopes too high. They had not realized

time, AI technology had real life uses. It was applicable to

how limited they were technologically, which caused their AI

business, industry, and the average customer. Before, as recently

programs to be limited. While the AI could handle trivial versions

as the 1970s, AI could not apply its problem-solving abilities

of problems they were expected to solve, they were unable to

to real problems. Now, AI machines were identifying defects

apply such knowledge to real-world problems. Also, they did not

in manufacturing products. This major turn from obscure to

have enough processing power to accomplish anything useful:

applicable in the lives of the public (and the consumers) was a

Ross Quillian’s work could only fit in twenty words into memory.

critical turning point in AI development, and has continued to

Furthermore, AIs needed to be considered as newborn children.

spur AI growth.

They had not experienced the world before, so programmers and researchers needed to give them the information about the

THE PRESENT (1990-NOW)

world in order to survive. If we consider all the things we have

In the 1990s, AI expanded again. The strength of AI compounded

learned since we were born, we realize that there is quite a bit

due to the increasing computational power of computers and

of information stored in our brain – information that current

cooperative work between AI researchers and other fields.

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

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FEATURES

PARRY attempted to model a paranoid schizophrenic’s behavior,


FEATURES

This new strength and proliferation of research led to great

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Programmed by Gerry Tesauro, TD-Gammon played backgammon,

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showing the power of reinforcement learning, up to the level of playing against world-class players. Deep Blue also showed the

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power of this sort of learning against humans, famously beating

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the world chess champion Garry Kasparov on May 11, 1997. The

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1990s also saw the first official Robo-Cup soccer match featuring

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table-top matches with 40 teams of interacting robots and over

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5000 spectators.12

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However, AI research is not confined to game-playing robots.

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Other equally brilliant projects emerged throughout the 1990s and

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2000s in all fields of AI, many including practical applications. In

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the 1990s, AI research became essential in the widespread use of the world-wide-web, mainly through extraction programs like

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web crawlers. In the 2000s, MIT’s Cynthia Breazeal developed

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KISMET, a robot with a face that can express emotions.

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In

addition, with a project that began in 2004 and launched February

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2011, IBM developed the question answering system Watson,

which defeated two Jeopardy! Champions: Brad Rutter and Ken

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Jennings.

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Navigational AI technology also became a big hallmark of the

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2000s. The Nomad robot was launched to explore remote regions

of Antarctica, looking for meteorite samples. In 2005, a Stanford robot won DARPA Grand Challenge by driving autonomously

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for driving 131 miles in a desert the robot had no familiarity

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with. Later, in 2007, CMU won the DARPA Urban Challenge by

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autonomously navigating 55 miles in an urban environment (while adhering to traffic hazards and laws!).12 Also in the 2000s, AI research moved into the commercial sector. For example, the Kinect for the Xbox 360 uses algorithms from AI research to create a 3D body-motion interface. Additionally, many programs utilizing AI are popular on personal computers, allowing users to “plays against the computer,� in games such as chess. As we move forward into the future, AI will surely develop by leaps and bounds.

Currently, technology can overcome the

limitations it had in the past, allowing researchers to build AI with enough memory. It is possible that in the future, more successful AI projects will populate the world. In the meantime, we can entertain thoughts of what AI can do in fiction and other means.

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FEATURES

$, /DERUDWRULHV DW 3HQQ BY SANDERS CHANG The General Robotics, Automation, Sensing and Perception (GRASP) laboratory is the hub of most studies in robotics at Penn.1 Founded in 1979, the lab has facilitated student and faculty cooperation in robotics research projects. GRASP offers a variety of undergraduate and graduate courses, including control systems design, computer vision, and machine learning, as well as a MSc in Robotics program. Examples of projects from the laboratory are listed below: The Scalable sWarms of Autonomous Robots and Mobile Sensors (SWARMS) project is cutting-edge research aiming to understand swarming behaviors in nature and apply them to the ways autonomous vehicles function and communicate with one another.2 The main challenge is to understand how these mobile robots are able to function collectively as a group to work RISE PROJECT: RiSE V2 Robot. Kod*Lab: University of Pennsylvania. http://kodlab.seas.upenn.edu/RiSE/Home (accessed Nov 1, 2011).

towards a goal without minimal human instruction. A practical application for this research is the deployment of autonomous vehicles that can function in dangerous environments during military operations. The Situation Understanding Bot Through Language and Evironment (SUBTLE) project seeks to improve the way autonomous robots can interpret and respond to linguistic instructions.3 An important application of this research is in the task of Urban Search and Rescue, in which robots can reliably communicate with victims and provide valuable information to rescuers during emergencies.

Accomplishing this research

demands the collaboration of researchers in computer science, cognitive science, computational linguistics, and robotics engineering. The Kod*Lab focuses on designing machines that move X-RHEX: X-RHex Robot. Kod*Lab: University of Pennsylvania. http:// kodlab.seas.upenn.edu/XRHex/XRHex (accessed Nov 1, 2011)

intelligently in ways inspired by biomechanics.4 This research will enable autonomous robots to adapt their movements to the characteristics of their environment. One example is the RiSE project, which aims to develop a robot that can climb on vertical surfaces. Another project is the X-RHex robot, which is engineered to sense dynamics in the terrain and to tailor its movements accordingly. This robot has managed to climb up elaborate platforms like stairwells.

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

9


FEATURES

The Image Segmentation and Object Recognition project focuses on developing methods that can help machines perceive their environments similar to the way humans do. Some specific perceptive tasks taught to these machines include recognizing patterns in the environment and detecting familiar objects. The strategy behind this research lies in the use of image segmentation, in which perceived images are partitioned into meaningful pixels or groups of pixels that can be manipulated and analyzed computationally.

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BY JAIMIN SHAH & SUSAN SHENG

What do self-driving cars, self-aware and danger-detecting Outside GRASP, there are other departments engaged in robotics

military land rovers, and movie recommendation systems have

research. As a partnership between the School of Engineering &

in common? Besides seeming to hail from futuristic movies, all

Applied Science and the Wharton School, the Penn Research in

are in fact examples of Penn’s involvement in the growing push

Machine Learning (PRiML) seeks to utilize statistical theory and

towards artificially intelligent technology. Over the last decade,

computational methods to develop machine intelligence that is

Penn engineers have competed in a variety of innovative AI

both structured and dynamic.5 Machine learning refers to the

competitions and produced award-winning

study of how machines acquire knowledge through instruction or

and prototypes. As more and more industries are integrating AI

experience. One specific focus of research at PRiML is “Structured

into their products, competitions and challenges for increasingly

Prediction,� which investigates computational methods by which

advanced designs continue to appear across the globe and, Penn

autonomous machines can use to solve problems more efficiently.

continues to set new in the field of artificial intelligence.

In the department of Penn Computational Linguistics, professor,

2007 DARPA GRAND CHALLENGE

Fernando Pereira, has done research on machine learning,

In 2007, the Defense Advanced Research Projects Agency

particularly in the context of language and sequential data like

(DARPA) held the Urban Grand Challenge, a prize competition

biological DNA and proteins. Some examples of his projects

for driverless, self-guiding vehicles. Teams across the country

that employ machine learning include CRAIG, which can predict

competed to build the most innovative vehicles that could

and locate gene sequences that encode proteins, and BioTagger,

complete a 60 mile California urban area course in under 6 hours.

which can search for and tag genes and their mutant variants in

Vehicles had to obey California state driving laws, operate entirely

biomedical journals.

autonomous using sensors and public signals such as GPS, and hit

6

robots, software,

certain checkpoints. The vehicles also had to operate in parking

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areas, perform complicated maneuvers such as U-turns, and be able to operate in rain and fog with their GPS blocked.

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Penn engineers teamed up with top robotics students at Lehigh University and researchers at defense contractor Lockheed

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Martin in order to make “Little Ben,� a heavily modified 2006

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Toyota Prius, in order to compete in the urban challenge. The

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car was modified to have autonomous controls, sensor detection

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of surroundings, and GPS for self-navigation. After preliminary

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rounds and proposals, eleven teams, including MIT, Carnegie

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Mellon, Cornell, and Penn, were shortlisted to compete in the final

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challenge on November 3, 2007.

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Penn Engineering professor and General Robotics, Automation, Sensing and Perception (GRASP) lab member Daniel Lee coordinated this cooperative effort, named the Benjamin Franklin racing team. Lee claimed that advancing to the finals was a “great testament to dedication, quality, and passion of Penn and Lehigh students.� The final challenge consisted of a typical urban setting and competition vehicles had to perform routine maneuvers such as merging, lane changing, and obeying traffic signals.

10 PENNSCIENCE JOURNAL |

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Penn


movie recommendation system which uses past viewing history to

brainchild of Carnegie Mellon and General Motor, took home

predict whether a user will enjoy a particular movie.5 On October

the gold.

DARPA commended the competitors, claiming that

2, 2006, the Netflix Prize Challenge began, in which Netflix

the technology had the potential to reduce emissions, improve

challenged participants to develop a recommendation algorithm

efficiency, and reduce traffic jams. The use of AI in vehicles will

that would be at least 10% better than the existing algorithm used

hopefully one day eliminate the hazards and pitfalls imposed by

by Cinematch. Contestants were provided with anonymous rating

human driving.

data from Netflix’s customers, and the accuracy of the submitted algorithms was determined by “how closely the [algorithm’s]

2010 MAGIC TEAM

predicted ratings of movies match subsequent actual ratings.�5

Penn has demonstrated its prowess in commercial vehicles, but

The grand prize: $1 million. Additionally, each year a $50 000

what about military grade self-aware robots? In 2010, members

Progress Prize was up for grabs for the team which could make the

of the GRASP lab, led again by professor Daniel Lee, found

most improvement over the previous year’s accuracy. The contest

themselves on the shortlist to compete in the Multi Autonomous

was set to run for 5 years, or until a team succeeded in meeting

Ground Robot International Challenge (MAGIC) lead by a joint

the 10% mark. To be considered for the final prize, algorithms had

sponsorship between the US and Australian Departments of

to meet a minimum threshold on a qualifying “Quiz� data set. The

Defense.

final winner would be determined based on the accuracy of a test

The goal of competition was to attract innovative

proposals from worldwide research organizations to develop

set.5

next-generation fully autonomous ground vehicle systems that can be deployed effectively in military operations and civilian

David Weiss, a graduate student in Penn’s Computer and

emergency situations.

Shortlisted competitors had to field

Information Science department, joined forces with two college

cooperatives of unmanned vehicle prototypes with the ability

friends, David Lin and Lester Mackey, to enter the contest as “Team

to autonomously and dynamically coordinate, plan and carry

Dinosaur Planet�.1 By the end of the first year of the competition,

out tasks against changing priorities. Among the six finalists

they had merged with a Hungarian team, forming “When Gravity

were researchers from Penn, the University of Michigan, Tokyo,

and Dinosaurs Unite� and finished second overall for 2006-2007.1

Turkey, Maryland, and Australia. In July 2009, BellKor’s Pragmatic Chaos announced that it had The challenge consisted of a crash course with eight dangers that

passed the 10% mark. This meant that the remaining teams had

had to be neutralized, to encourage the development of robots

30 days to try and beat BellKor’s submission before the contest

that could replace the need for human intervention in clearing

would be officially closed.3 Weiss’s team then formed an open

danger zones. GRASP members Jon Butzke, Alex Kushleyev,

collaborative team, which wound up joining up with another large

Cody Phillips and Mike Phillips built seven self-guiding robots,

open collaborative group to form the global consortium “The

and designed the software and visual system to allow the robots

Ensemble.� With less than 24 hours left in the competition, The

to navigate a course by themselves. At the conclusion of the

Ensemble managed to meet BellKor’s Quiz test score by just one-

challenge, the top three teams received US$750,000, US$250,000

hundredth of a percent better!1 On September 21, 2009, Netflix

and US$100,000 respectively in research grants; Penn took home

announced that BellKor’s Pragmatic Chaos won the $1 million

the silver with their team of robots.

prize. In the end, The Ensemble and Bellkor scored the same on the test set of data, so it came down to time of submission;

AI technology has the potential to reduce the need for and the

Bellkor had submitted their system a mere 20 minutes before The

number of human soldiers while also creating a more “humane�

Ensemble.2

type of warfare. The Director of US Tank Automotive Research, Development and Engineering Center, Dr Grace Bochenek said the inaugural MAGIC 2010 competition brought together top researchers from around the world, competing for a common

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goal. Although the technology is still far from the battlefields,

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in emergency situations.

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NETFLIX PRIZE CHALLENGE

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Netflix is an Internet subscription service that allows members to

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instantly stream unlimited movies and TV episodes for a monthly rate.4 Netflix utilizes a system called CinematchSM, which is a

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VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

11

FEATURES

took home fourth prize in the competition, while “Boss�, the


FEATURES

Biological vs. Algorithmic

Approach to AI BY NATALIE NEALE

$V RXU GHYHORSPHQW RI DUWLĂ€FLDO LQWHOOLJHQFH DGYDQFHV ZH PXVW GHWHUPLQH ZKDW LV WKH PRVW HIIHFWLYH ZD\ WR PRGHO WKLV LQWHOOLJHQFH %HFDXVH ´LQWHOOLJHQFHÂľ LV VXFK DQ DEVWUDFW FRQFHSW LW LV RIWHQ GLIĂ€FXOW WR GHWHUPLQH KRZ WR DFKLHYH LW :KLOH FXUUHQW WHFKQRORJ\ RIWHQ UHOLHV RQ DOJRULWKPV LQ LQWHUSUHWLQJ LQIRUPDWLRQ DQG SURGXFLQJ UHVXOWV VRPH UHVHDUFKHUV DUH EHJLQQLQJ WR XVH WKH ELRORJ\ RI WKH KXPDQ EUDLQ DV D PRGHO IRU DUWLĂ€FLDO LQWHOOLJHQFH %\ VWUXFWXULQJ WHFKQRORJ\ EDVHG RQ QHXURQV DQG V\QDSVHV ZH FRXOG SRWHQWLDOO\ FUHDWH DUWLĂ€FLDO FUHDWXUHV ZKRVH LQWHOOLJHQFH PDWFKHV WKDW RI KXPDQV 7KLV WHFKQRORJ\ SRVHV WKH SRVVLELOLW\ RI UDGLFDOO\ DOWHULQJ WKH ZD\ ZH FUHDWH DUWLĂ€FLDO LQWHOOLJHQFH EXW WKHUH DUH PDQ\ FKDOOHQJHV LQ SXUVXLQJ WKLV SDWK %RWK DSSURDFKHV KDYH WKHLU SURV DQG FRQV DQG ZLOO KDYH XQLTXH HIIHFWV RQ VRFLHW\ LQ WKH IXWXUH +RZHYHU WKH DOJRULWKPLF DSSURDFK LV XOWLPDWHO\ OLPLWHG DQG LV OLNHO\ WR EH UHSODFHG E\ WKH QHZHU ELRORJLFDOO\ EDVHG PHWKRG First, let us consider the algorithmic approach. The majority of our technology today is programmed by algorithms, which are sets of rules for solving a problem given in a finite number of steps. Despite the fact that this type of artificial intelligence is not modeled after the human brain, many of these technologies are fairly impressive and can pass the Turing test (Artificial Intelligence). If the machine can produce the same information that a human would, regardless of understanding, it passes the test. This approach to artificial intelligence has produced impressive results, with machines that can perform “human tasksâ€? such as carry out conversations, compute integrals, and play chess. However, since the machine is programmed by algorithms, it does not actually understand the information that it produces. For example, the Instant Messenger robot “Smarter Childâ€? is programmed to reply to dialogue in the same way a human does, but does not really understand the meaning behind the information. Similarly, philosopher John Searle uses the hypothetical example of learning how to write Chinese characters without understanding their meaning as an example. He argues that if one does not understand the information they are conveying, then this does not constitute a “mind.â€? Most current computers rely on “Neumann architecture,â€? in which processing and storage of information are kept separate (Knapp). This type of technology relies on constant updates and reprogramming by humans, so it is somewhat limited in its intelligence and its ability to grow on its own. Its capabilities depend on available resources and input on the part of humans. In fact, it is a point of contention among philosophers as

12 PENNSCIENCE JOURNAL |

SPRING 2012 | VOLUME 10, ISSUE 2


Electronics) aims to let computers reason for themselves rather

they do not actually learn or have an understanding of the tasks

than rely on constant updates by humans (IBM SyNAPSE). This

they perform. For example, philosopher John Searle argues that

cognitive computing combines neuroscience, nanotechnology,

the machines are not truly intelligent because they do not have

and supercomputing. The Defense Advanced Research Projects

consciousness. This rejects the theory of “Strong AI,” which

Agency has awarded approximately $21 million to fund the

claims that the appropriately programmed computer could have

SyNAPSE project, and four universities (Cornell, University of

a mind in the same sense that humans have minds. The focus

Wisconsin, University of Califonria at Merced, and Columbia) are

of this approach is clearly output over method, but the lack of

currently working on it (IBM SyNAPSE). Researchers envision

flexibility and growth ultimately restricts the output.

artificial intelligences based on this concept than can predict weather patterns and natural disasters.

Another example of

To overcome this limit and expand away from what many consider

this type of technology is the ZenRobotics Recycler, which is

to be “Weak AI,” scientists are currently taking a different approach

modeled after the human brain’s cerebellum to coordinate fine

to artificial intelligence.

Based on the human conception of

motor movement (“Super-Efficient Recycling Robot”). It uses

intelligence, there is arguably no better place to start than the

sensor fusion to combine data from multiple sources. This is an

human brain. The biological approach attempts to replicate the

example of an application of the biological approach to cleaning

neurobiology of the human brain into technology. While these

the environment.

technologies will not necessarily be biologically based, they will

potentially monitor produce in a grocery store to determine

function similarly to the way neurons and synapses do in the

ripeness. Clearly, these machines could have positive effects on a

human mind. They contain “neural networks” that can make

variety of fields from public safety to green technology. In addition,

intelligent decisions based on past experiences. The “neurons”

technologies using current programming techniques take up

compute information, the “synapses” are the foundation for

much more space and energy than these new technologies would.

learning and memory, and the “axons” connect the tissue of the

The minimal amount of resources necessary for the “cognitive

computer (Knapp). Like the human brain, this computing power

computers” would have positive effects on the environment and

will be distributed rather than centralized.

These cognitive

is a large reason why many believe we should pursue this type of

computers would be radically different than any current artificial

artificial intelligence. Machines that can program themselves and

intelligence. This technology will be more adaptive than that of

do not rely on input require far fewer resources to maintain. As

the algorithmic approach and will function on a level more akin

population increases and the effects of climate change increase,

to human intelligence.

it would be wise to pursue technology that has a relatively small

Scientists will attempt to incorporate

qualities of the brain such as plasticity, critical thinking, and

Another type of cognitive computer could

carbon footprint.

emotion into artificial intelligence. Since these computers will be able to analyze patterns, they will be able to perform cost-benefit

These technologies would undeniably perform tasks that

analysis and come to informed decisions.

were previously thought to be impossible and have positive environmental repercussions. However, not everyone is on board

This type of technology is already starting to take off. For example,

with this modern approach. There is some fear that this approach

IBM has created a computer chip that is designed to interpret its

to artificial intelligence could create a “post singularity” world

environment in the same way a human brain would. A prototype

and have drastic implications for the human race. There are also

of this chip has 262,114 programmable synapses, while the other

many difficulties in modeling parts of the human brain that we

has 65,536 learning synapses (Knapp). The neurons of this chip

have little understanding about, such as consciousness. Until

would be acting simultaneously rather than in a linear fashion,

significant advances in neuroscience occur, it will be impossible

so much more intricate connections can be established. Axons

to replicate these aspects of the human mind. Of course, we know

control power going through the synapses, and in response the

much more now about the human brain than we did even a decade

neurons will “spike” and send signals (Knapp). These neurons

ago, and our knowledge will grow in the future. On another note,

use a very small amount of energy since they do not rely on input

the creation of machines that are able to feel emotions will also

of external instructions. By replicating the complex system of

have ethical implications. Ultimately, we could create a new race

synapses in the human brain, engineers are essentially creating

of “post-humans” that would possibly be more intelligent than

nonbiological minds.

These “cognitive computers” would

the biological human species. While this seems far-fetched at

learn through their own experiences and would not have to be

the moment, it should certainly be taken into consideration when

constantly reprogrammed. These computers, unlike those that

pursuing the development of artificial intelligences.

exist today, will be considered “right-brained” in that they can identify patterns in the environment and respond accordingly. The

Overall, the current trend in artificial intelligence seems to be

SyNAPSE project (Systems of Neuromorphic Adaptive Scalable

towards modeling the intricate neurobiology of the human brain.

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

13

FEATURES

to whether these technologies actually have intelligence, since


FEATURES

The algorithm approach is starting to appear limited and energy

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intelligence as we know it. The possibility that we could soon

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create intelligence that matches, or even surpasses, our own, is both exciting and frightening. We will need to carefully consider

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how this change will affect society and make sure it is mostly

for the better before diving in headfirst.

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It is human nature to

want to progress and produce greater and greater results, but we must make sure that these technologies created will have specific purposes that will provide important benefits.

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that the two approaches could be combined, and society could determine which approach is more appropriate to use in particular situations. In some cases, we could design artificial intelligence that can use both approaches, depending on the situation.

Glory, Glory, Robots United BY VINAYAK KUMAR The Robo-craze that was earlier attributed solely to Japanese

‘Nao’ humanoids (Figure 1). This toddler-sized robot is extremely

anime seems to have now taken the rest of the world by storm.

versatile with a range of motions and functions that mirror human

Recent movies like “Transformers� and “Real Steel� provide

capabilities. This is a true brains-over-brawn competition, as

evidence to the growing role of robots in popular imagination.

teams must focus on software development only, with a particular

However, this imagination is slowly becoming reality, especially in

emphasis on decision-making (after all, soccer is still a team

the engineering department at Penn, where even undergraduates

sport).

can get involved. One group in particular seems to be actively involved in advancing the nature of robotics on the world stage: the UPennalizers, the robotics team that actively participates in the RoboCup each year. The RoboCup is an international robotics competition where teams build and program robots in hopes of developing smarter and more versatile robots. One competition in particular captured public attention recently: autonomous robotic soccer.1 The game originally started with Sony AIBO robotic dogs, and has now advanced to Aldebaran’s Nao humanoids robots.2 This competition is not just for kicks, but has the overarching goal of defeating the human soccer world champions by 2050.3 The RoboCup is divided into several leagues to accommodate different categories of humanoid and non-humanoid robots.1 The UPennalizers compete in two of these leagues: The Standard Platform League (since 2003) and the Humanoid Kid-Sized League (since 2010). Each league has its own restrictions on the robots’ capabilities. In the Standard Platform League, all teams are required to use identical robots, which have been standardized to Aldebaran’s

14 PENNSCIENCE JOURNAL |

SPRING 2012 | VOLUME 10, ISSUE 2

Figure retrieved from http://www.sony. net/

Figure retrieved from http://www.cenrob.org/ FIGURE 1: (Left) At 22.8 inches tall, the 11 lb Nao is the new standard robot used in the Standard Platform League, having replaced; (Right) the Sony AIBO in the RoboCup.


FEATURES

In the Kid-Sized Humanoid league, the shape and specifications of the robots are more relaxed. In this competition, teams are encouraged to design new robots to suit the purposes of the game, and there is a wider variety of robotic appearances in this competition. The UPennalizers have been working closely with Virginia Tech in this competition, entering themselves in the

FIGURE 3: A map of the virtual processes used to provide autonomous action to the robots.6

competition as the joint-team DARwIn. Their robot is the DARwIn OP (Figure 2). FIGURE 2: The DARwIn OP robot is the joint product of the University of Pennsylvania and Virginia Tech, and competes in the Kid-Sized Humanoid league. Figure retrieved from http://www.trossenrobotics.com

It seems that all the long hours in the lab have paid off for the UPennalizers, who have recently attracted international attention. In the most recent competition, the 2011 Robocup in Istanbul, the joint-team DARwIn won 1st place in Kid-Sized Humanoid division, while the UPennalizers themselves were quarter-finalists in the Standard Platform League. These successes showcase Penn’s significant robotic presence in the international arena. These robots may not transform into a truck or a car, but they are certainly evolving in the right direction toward the final goal set by RoboCup. But objectives aside, it is still fun just to sit back and watch the gameplay.

A number of factors must be taken into account when coding a robot, even for the simplest of motions, such as walking. As humans, we learned to walk and move naturally, as our innate neural circuitry developed in response to various unconscious

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stimuli. For robots, these motions and correction algorithms must

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sense the position of its center of mass and maintain it above one

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leg at all times to prevent itself from falling. When losing balance,

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the robot’s legs will compensate for this uncontrolled motion

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by dropping lower to recover its balance, much as human do.4

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This sensing pattern allows the robots to walk even on a slightly

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inclined surface, as its programming will correct for its plane of

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motion and ensure that the robot does not tip over.

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However, walking is not the only action required for playing soccer (Figure 3). The robot must also understand its objective: scoring goals. The cameras located in its eyes observe the shape and color of an object in its field of vision and assigns actions. For example, the two goal posts are colored either blue or yellow, and the ball is an orange tennis ball. Upon recognition, the robots dribble the ball to the correct goal and use a preprogrammed kicking motion to score. A match consists of two 10-minute halves with a 5 minute half-time in between.5 While these robots are designed for 90 minutes of continuous walking, Spencer Lee, a sophomore UPennalizer, states that their robots have a significantly reduced battery life, though it is sufficient to participate in the match itself. 4

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

15


RESEARCH

IQGAP Proteins as Oncogenic Signal Regulators in Human Squamous Cell Carcinoma Emily Schapira, Eschap@sas.upen.edu Dr. Todd Ridky, M.D.,Ph.D. (650) 248-6784 Ridky@mail.med.upenn.edu February 1, 2012 ABSTRACT IQGAP1 and IQGAP3 function as scaffolds in the mitogen activated protein kinase pathway, an extracellular signal-regulated kinase cascade that plays a critical role in the development and progression of cancer. I investigated the role of IQGAP1 and IQGAP3 in supporting human squamous cell carcinoma (SCC). A population of SCC cells was generated from primary human keratinocytes in a genetically defined manner through induced expression of constitutive human Ras (RasG12V) and cyclin- dependent kinase 4 (Cdk4R24C). RNAi vectors were designed for knockdown of IQGAP1 or IQGAP3. Despite sharing a set of highly homologous, evolutionarily conserved domains, either IQGAP1 or IQGAP3 knockdown was sufficient to attenuate in vivo tumor growth in both the genetically designed keratinocyte SCC population and the A431 human epithelial cell line. This result suggests that IQGAP1 and IQGAP3 are not functionally redundant. Importantly, IQGAP1 or IQGAP3 knockdown alone seem to have no effect on primary keratinocytes or their ability to make functioning three-dimensional epidermis. Ongoing studies are being conducted to confirm the tumor-specific requirement for IQGAP signal modulating activity to initiate and maintain the malignant phenotype. In such a case of oncogene addiction, IQGAP1 and/or IQGAP3 would represent novel targets for rationally designed therapeutics. 1. INTRODUCTION

2. MATERIALS & METHODS

The IQGAP family of proteins function as scaffolds in the map

Isolation and culture of primary epithelial cells

kinase pathway, modulating cellular processes important in

Primary human epithelial keratinocytes were isolated and

cancer progression, including cell growth and division, cell

cultured from discarded surgical foreskin. Foreskin was washed

motility and metastasis, and cell-to-cell communication (Johnson,

in phosphate buffered saline solution (PBS). Any residual

M., M. Sharma, et al. 2009; White, C. D., M. D. Brown, et al. 2009).

subcutaneous fat was removed from the foreskin. Foreskin was

IQGAP1 and IQGAP3 are highly homologous proteins and share

incubated in 1X dispase overnight at 4 degrees Celsius and then

a set of evolutionary conserved functional domains (Nojima, H.,

washed in PBS. The epidermis was separated from the underlying

M. Adachi, et al. 2008). Analysis of primary tumors has revealed

dermis using sterile forceps. The epidermis was incubated in 5 ml of

consistent upregulation of IQGAP1 in human neoplasia (Johnson,

warm trypsin for approximately ten minutes. To quench trypsin, 10

M., M. Sharma, et al. 2009), while the expression of the more

ml of Dulbecco’s modified Eagle’s medium (DMEM), supplemented

recently discovered IQGAP3 is less well characterized.

with 10% Fetal Bovine Serum (FBS) was added to solution. Cells and media mixture were spun at 1500g for 5 minutes. Cells were

I investigated the role of IQGAP1 and IQGAP3 in supporting

resuspended in keratinocyte serum-free media supplemented

human squamous cell carcinoma (SCC). Primary keratinocytes

with epidermal growth factor and bovine pituitary extract. Cells

were transformed to SCC using lentiviral delivery of constitutively

were plated on standard tissue culture polystyrene plates. Media

active human Ras (Ras

was changed every two days. Cells were split after achieving

G12V

) and active cyclin-dependent kinase 4

(Cdk4R24C), impeding retinoblastoma-mediated cell-cycle arrest. These two alterations mimic the genetic profile of clinically

approximately 90% confluence. Explant human skin cultures

2D skin culture

observed human SCC and are therefore able to produce clinically relevant results (Ridky, T., Chow, J. Chow, et al. 2010). GIPZ shRNA vectors specific for IQGAP1 or IQGAP3 diminished expression in both the genetically engineered keratinocytes and the A431 human epithelial cell line. Cell populations were injected into the subcutaneous space of non SCID or non SCID gamma mice with the hypothesis that cells harboring the IQGAP1 or IQGAP3 knockdown would show attenuated tumor growth in vivo. Reprinted with permission of Dr. Todd Ridky

Design of RNAi vectors Primary keratinocytes were cultured from human tissue samples.

16 PENNSCIENCE JOURNAL |

SPRING 2012 | VOLUME 10, ISSUE 2


cell lysate was performed and proteins were transferred to PVDF

IQGAP1 and IQGAP3 RNAi GIPZ shRNA transfer vectors. “DNA

membrane. PVDF membrane was blocked for 30 minutes in

oligonucleotides encoding two portions of the open reading frames

Odyssey Blocking Buffer (Licor) at room temperature. The PVDF

were cloned into GIPZ shRNA transfer vectors.

was incubated overnight at 4 degrees Celsius in solution containing appropriately diluted primary antibodies and 0.1% Tween in

Lentiviral and Retroviral production

Odyssey Blocking Buffer. The following primary antibodies and

32 ml of DMEM was added to a 15-cm polystyrene plate of

dilutions were used: Anti-IQGAP1 (Millipore) at 1:1000, and Anti-

293-T based Phoenix packaging cells, which was cultured to

IQGAP3 (Abcam, Cambridge, MA) at 1:1000, H-Ras (Santa Cruz

approximately 40% confluency. A solution of 1800 µl of DMEM

Biotechnology Inc.) at 1:500. PVDF was washed three times in

Free and 135 µl of Fugene HD Transfection Agent was prepared.

PBS-Tween. PVDF was incubated at room temperature for 1 hour

A solution containing 22.5 µg of GIPZ shRNA transfer vectors, 16.9

in dark in solution of appropriately diluted secondary antibody and

µg of CMV R8.91, and 5.6 µg of PVC MDG viral packaging DNA was

0.1% Tween in Odyssey Blocking Buffer.

prepared. These two solutions were mixed and allowed to incubate for fifteen minutes at room temperature. The solution was added

Tumor Formation Assays

drop by drop to the appropriate plate of 293-T cells. The 293-T

Approximately 1 million transduced epithelial cells engineered

cells were returned to 37 degrees Celsius. After 24 hours, sodium

to express oncogenic Ras and CDK4 (plus IQGAP knockdowns)

butyrate was added to the 293-T media in appropriate volume to

were suspended in a volume of 150 µl containing 50% Matrigel (BD

achieve a concentration of 10 mM. After 12 hours, the media on

Biosciences, San Jose, CA). Suspension was injected with a 27 gage

the transfected 293-T plates was changed, and the plates were

needle into the subcutaneous space of two immunodeficient NOD

moved to 32 degrees Celsius. After 24 hours, the 293-T media was

SCID Gamma mice (Jackson, Bar Harbor, MA).

filtered through a 0.45 µm filter (Millipore, Billireca, MA). Lenti-X Viral concentrator (Clontech) was added to filtrate in a 1:3 ratio.

Establishment of 3-D Organotypic Tissue

The solution was mixed by inversion and allowed to incubate on

Dermis Preparation

wet ice for 30 minutes. The solution was centrifuged at 4 degrees

Human dermis was washed in PBS containing. Dermis was

Celsius for 45 minutes at 1500xg. Supernatant was discarded, and

incubated at 37 degrees Celsius for more than a week, with PBS

the viral pellet was resuspended in 1 ml of PBS. Virus was stored at

changed every three days. Sterile forceps were used to peel the

-80 degrees Celsius.

epidermis off of the dermis. The epidermis was discarded.

Lentiviral and retroviral transduction of primary epithelial

Seeding Keratinocytes onto Dermis

cells

The dermis was elevated to an annular dermis support (ADS)

Retrovirus driving expression of Ras

, or Non-Silencing

tissue culture insert device with the basement membrane oriented

Control (Open Biosystems) was used to transduce keratinocytes.

, Cdk

up. The ADS insert was placed into a 6 cm tissue culture plate. 5 ml

GIPZ lentiviral vectors diminishing expression of protein of

of keratinocyte growth media (KGM) was added to the 6 cm culture

interest were also transduced. Primary keratinocytes were cultured

plate. KGM is a 3:1 mixture of DMEM:Ham’s F12 supplemented

to approximately 30% confluency in standard tissue culture

with FBS (10%), adenine (1.8 x 10-4 M), hydrocortisone (0.4 µg ml-1),

polysterene flasks. Keratinocyte growth medium was removed

insulin (5 µg ml-1), cholera toxin (1-10 M), EGF (10 ng ml-1),

and immediately replaced with an equal volume of lentivirus and

transferrin (5 µg ml-1), and triiodo-L-thyronine (1.36 ng ml-1). A total

retrovirus with growth media and 5 µg per milliliter polybrene.

of 5 x 105 keratinocytes were suspended in 100 µl of KGM and then

Flasks were centrifuged at 500g for 1 hour at 32 degrees Celsius.

added dropwise to the basement membrane side of the elevated

Flasks were returned to 37 degrees Celsius.

ADS. The ADS was cultured at 37 degrees Celsius with daily KGM

G12V

4R24C

media changes. Protein Collection Cell media was removed and 400 microliters of RIPA buffer was added to each plate. Cells were scraped off of polystyrene plate

Dermal support organotypic tissue culture insert Organotypic skin culture

using cell lifter. Cell solution was transferred to a 1.5 ml tube and incubated on wet ice for five minutes. The cell solution was microcentrifuged at top speed for 1 minute. The supernatant was removed and stored at -80 degrees Celsius. Immunoblotting SAS-polyacrylamide gel electrophoresis (SDS-PAGE) on total Reprinted with permission of Dr. Todd Ridky VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

17

RESEARCH

Expression of IQGAP1 and IQGAP3 was diminished using


RESEARCH

Immunoflourescence Microscopy Tissue samples from the organotypic tissue set-ups and tumor FIGURE 1: Original pGIPZ lentiviral vector

formation assays were embedded in OCT compound (Sakura, Torrance, CA). Samples were cut to 8 Âľm thick on cryostat and allowed to air dry on Superfrost Plus Microscope Slides (Fisher Scientific). The ImmEdge pen (Vector Laboratories, Burlingame, California) was used to establish a hydrophobic barrier around samples. Samples were fixed in 50/50 methanol:acetone solution at 20 degrees Celsius for three minutes. Solution was removed, and then sample was blocked in a 1:20 dilution of Horse Serum

Reprinted from http://www.openbiosystems.com/collateral/rnai/pi/ GIPZ_TechnicalManual.pdf

(Invitrogen) in PBS for 30 minutes before being washed 4 times in PBS. Samples were incubated in primary antibodies for 30 minutes. The following primary antibodies and were used: AntiIQGAP1 (Millipore), Anti-IQGAP3 (103372, Abcam, Cambridge, MA), Anti-Collagen Type VII (234192, Calbiochem), Anti-Keratin5 (PRB-160P, Covance, Emeryville, California). After washing 4 times

FIGURE 2

in PBS, samples were incubated with secondary antibodies for 30 minutes. Samples were washed 2 times in PBS. Samples were fixed in Prolong Gold Antifade with DAPI (Life Technologies, Carlsbad, California). Slides were stored at 4 degrees Celsius. 3. RESULTS Redesigned the GIPZ vector to elimininate GFP gene and replace CMV promoter The GFP gene was initially included in the original GIPZ vector (Figure 1) as a visual marker of shRNAmir expression levels. Due to concerns about the cellular toxicity, the GFP gene was excised. A restriction digest at the XbaI and Not1 sites excised GFP, but also removed the CMV promoter, which was religated into the vector (Figure 2). In an effort to avoid in vivo silencing of shRNA hairpin expression, the viral CMV promoter (573 bp) was replaced with the mammlian JeT promoter (195 bp) in the GIPZ vector from which GFP was already excised. The CMV promoter was excised at the Xba and NotI restriction sites. The JeT promoter was excised from the

FIGURE 3

IQGAP1

IQGAP3

CDK4 CDK4

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H-Ras


RESEARCH

FIGURE 4 Non-Silencing

IQGAP1 knockdown

IQGAP3 knockdown

PUC57 vector at the XbaI and NotI sites, gel purified, and

FIGURE 5

ligated into the GIPZ vector. Genetically defined human model of squamous cell carcinoma generated from primary human keratinocytes Figure 3: Western Blot of Engineered Keratinocytes Cultured primary human keratinocytes were transformed into genetically defined human squamous cell carcinoma with constitutive human Ras sand cyclin dependent kinase-4. Cells were divided into three groups, with the control group harboring GIPZ non-silencing shRNA, and two additional groups harboring shRNA mediated IQGAP1

FIGURE 6

and IQGAP3 knockdowns, respectively. IQGAP knockdowns of genetically engineered human SCC show decreased growth in vivo Figure 4: Immunoflourescent Microscopy of Sectioned Tumors The nuclei are stained blue with DAPI, IQGAP1 is stained yellow, and IQGAP3 is stained red. Figure 5: Tumor Formation Assay Cells were injected subcutaneously into NSG mice, and tumors were extracted 48 days later. The cells harboring shRNA mediated IQGAP1 or IQGAP3 knockdown showed a marked decrease in mass in comparison to the nonIQGAP1 knockdown B

silencing control cells. IQGAP1 knockdown A

Non-silencing control B

Non-silencing control A

FIGURE 7

A431 human epithelial cell line tumors Figure 6: Tumor Formation Assay Cultured A431 cells received the IQGAP1 knockdown or non-silencing control. Cells were injected subcutaneously into SCID mice, and tumors were extracted 25 days later. Figure 7: Western Blot of Tumors Whole cell lysate from extracted tumors was run on a gel and immunoblotted for IQGAP1. Each lane contained 30 micrograms of protein. VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

19


RESEARCH

Human Tissue is Viable with IQGAP1 and IQGAP3

4. DISCUSSION

Knockdowns

The genetically defined human model of SCC generated from

Figure

8:

Immunofluorescence

Images

of

Viable

Three

primary human keratinocytes suggests the importance of IQGAP1

Dimensional Organotypic Tissue

and IQGAP3 in tumor formation. The Western Blot (Figure 3)

Primary human keratinocytes are viable even with IQGAP1 and

shows that IQGAP1 and IQGAP3 were successfully knocked

IQGAP3 knockdown. The keratinocytes were seeded onto human

down without unintentional knockdown of the homologue. CDK4

dermis in their native environment above the basement membrane

was constitutively expressed and H-Ras was overexpressed.

zone. Fibroblasts were seeded in the stroma, below the basement

The ubiquitous expression of IQGAP1 and IQGAP3 in the IF

membrane zone. The basement membrane zone is stained green,

of non-silencing control tumor serves as a negative control for

the nuclei are stained blue, and keratin is stained red.

IQGAP1 and IQGAP3 expression in the knockdown tumors. In the tumor harboring the IQGAP1 knockdown, some cells expressed

FIGURE 8

normal levels of IQGAP1. This resurgence in IQGAP1 expression, despite a process of drug selection with resistance conferred by the vector harboring the hairpin, may explain the increased size of the IQGAP1 knockdown tumor as compared with the IQGAP3 knockdown tumor (Figure 5). This discrepancy in tumor mass may reflect the varying degree of the protein knockdown, not the relative importance of IQGAP1 and IQGAP3 as a Ras scaffold. The A431 cells infected with IQGAP1 knockdown also showed diminished tumor growth (Figure 6). When the tumors were

Non-silencing control

harvested, IQGAP1 protein was beginning to be reexpressed in the knockdown tissue, indicating negative selection against the knockdown cells. These results were consistent in both trial A and trial B. In both the A431 human epithelial cell line and the keratinocytes harboring RasG12V and Cdk4R24C, either IQGAP1 or IQGAP3 knockdown is sufficient to attenuate tumor growth in vivo as compared with the non-silencing control. This result suggests that IQGAP1 is unable to compensate for diminished levels of IQGAP3 and vice versa. If the domains of IQGAP1 and IQGAP3 were functionally redundant, then IQGAP1 would be able to compensate for diminished levels of IQGAP3, resulting in a phenotype that

IQGAP1 knockdown

largely resembles the control. The second possibility is that IQGAP1 and IQGAP3 are functionally redundant, but protein expression levels are not high enough to achieve normal levels of function. As a scaffold protein, IQGAPs help to organize the pathway, regulating localization and selectivity of the Ras- RafMek-ERK protein kinase cascade. Since both IQGAP1 and IQGAP3 act as Ras scaffolds, it is likely that IQGAP1 and IQGAP3 knockdowns abrogate Ras-dependent signaling. Experiments to simultaneously knock down both IQGAP1 and IQGAP3 are in progress. IQGAP1 or IQGAP3 knockdown alone seem to have no effect on primary keratinocytes or their ability to make functioning three-

IQGAP3 knockdown

dimensional epidermis. Figure 8 shows that cell morphology and viability of the IQGAP1 and IQGAP3 knockdowns closely resembles that of the non-silencing control. This has significant therapeutic applications, as drugs targeting IQGAP1 or IQGAP3

20 PENNSCIENCE JOURNAL |

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organotypic culture. Figure 1 shows an intact BMZ, while Figure 2

unharmed.

shows a fragmented and disrupted BMZ associated with invasive cells.

The tumor formation assay has several shortcomings as a model

Figure 1

Figure 2

system. In order to inject the cells, they must be removed from a two-dimensional culture using trypsin, disrupting important cell-to-cell contacts. Furthermore, the cells are injected into the subcutaneous space of a mouse, a nonnative environment that does not require cells to recapitulate the process of invasion, which is an essential gained function for cancers of epithelial origin. To address these shortcomings, the next set of experiments

Reprinted with permission of Dr. Todd Ridky

will use a xenografting procedure. Genetically engineered human keratinocytes will be seeded on human dermis, which allows cells

5. ACKNOWLEDGEMENTS: First and foremost, I would like

to interact with the extracellular matrix and stroma. This dermis

to thank to Dr. Ridky for his extraordinary guidance throughout

seeded with cells will be grafted onto the back of NOG mice. Skin

my time as a member of the Ridky Lab, and particularly for his

is the only human organ in which such accurate replication of the

support with this independent study. I would also like to thank the

native environment is possible.

other members of the Ridky Lab: Andrew McNeal, Liz Kennedy,

Organotypic skin culture

In vivo xenograft

Kevin Liu, and Vihang Nikhate.

I appreciate the guidance of

my Independent Study Advisor, Dr. Greg Guild, as well as the generous funding support of the Goldfederer Undergraduate Research Grant. 6. AUTHOR CONTRIBUTIONS: Dr. Todd Ridky (T.R.) designed the research, T.R., Andrew Mcneal (A.M.), Kevin Liu (K.L.), and

Reprinted with permission of Dr. Todd Ridky

Vihang Nikhate, and Emily Schapira (E.S.) acquired the data, T.R., A.M., K.L., and E.S. analyzed and interpreted the data, and E.S.

Rescuing the phenotype is also an essential part of this study to

drafted the manuscript. T.R., A.M., Liz Kennedy, and Seung Ja Oh

confirm the specificity of the IQGAP shRNA knockdowns and

commented on the manuscript.

ultimately validate the significance of the IQGAP proteins as oncogenic signal regulators. Expression clones resistant to the shRNA mediated knockdown will be used to recover normal levels of IQGAP protein. To design a clone with such resistance, I

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used the degeneracy of the amino acid code to find possible silent

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mutations in the target region of the IQGAP1 gene (2727 – 2747

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bp of coding region), eliminating sequence homology between the

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IQGAP1 mRNA and RNAi machinery while maintaining normal

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IQGAP1 protein structure and function. If the shRNA hairpin

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does lacks off- target effects that are functionally significant,

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then cells harboring RasG12V, Cdk4R24C, and IQGAP1 GIPZ shRNA

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should phenotypically resemble cells harboring RasG12V, Cdk4R24C,

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and GIPZ non-silencing control. I will complete the same process

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for IQGAP3.

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Overexpression vectors are needed to determine whether

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IQGAP1 and/or IQGAP3 are bonafide oncogenes. Overexpression

of an oncogene would be sufficient to trigger a cancerous transformation of primary keratinocytes.

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Overexpression

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of IQGAP1 and IQGAP3 will be studied in two contexts: the

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xenografts and the 3D organotypic culture. If IQGAP1 or IQGAP3

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can function as oncogene, then invasion through the basement membrane zone (BMZ) of the dermis will be occur in the 3D

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

21

RESEARCH

could selectively disrupt cancer cells while leaving normal tissue


RESEARCH

Delayed recovery of core body temperature from repeated social defeat may be indicative of stress vulnerability Arjunan Gnanendran Sponsor: Seema Bhatnagar, Ph.D. Department: Anesthesiology and Critical Care Perelman School of Medicine, University of Pennsylvania

Co-sponsor: Phillip Rea, Ph.D. Department: Biology, University of Pennsylvania

ABSTRACT Exposure to stress has been shown to lead to certain psychopathological disorders such as depression and anxiety, including posttraumatic stress disorder. However, it is unclear why only certain individuals develop stress-related diseases whereas other individuals remain resilient. The answer may be linked to the type of strategy used to cope with stress. Passive coping has been linked with the development of stress-induced disorders. Our goal is to determine the biomarkers of resilience using a rodent model of the stress of social defeat. In this experiment, we used fully implantable telemetry to measure body temperate prior to, during, and after repeated social stress events, using a rodent resident/intruder paradigm of social defeat. For seven consecutive days, experimental intruder rats underwent a 30-minute social defeat. We found that the behavioral coping differences to the stressor between LL and SL rats are associated with different core body temperature responses. Specifically, following social defeat, SL animals expressed a prolonged elevated body temperature hours after the stress event. This is in line with numerous studies that have shown that repeated social defeat induces chronic hyperthermia in rats. However, our data suggests that this chronic hyperthermia is not present in all individuals following repeated stress events, but that it is prevalent mostly in animals that exhibit passive coping behavior (SL). Since this chronic hyperthermia has also been associated with depressive-type behavior in rats, a body temperature response characterized by a prolonged recovery to baseline following stress may be a biomarker of stress vulnerability. 1. INTRODUCTION

Exposure to social stressors has been shown to induce

One of the most studied paradigms of social stress is the resident/

hyperthermia in rats (5,6). The elevation in core body temperature

intruder test developed by Miczek (1). In this paradigm, the intruder

is seen during the social stress event itself, rising rapidly at its

(experimental) rats are placed into the home cage territory of an

onset and gradually returning to baseline within several hours.

unfamiliar resident previously screened for high aggression. A

In experiments of repeated social stress, the elevations in

typical encounter results in the intruder showing “defeat,” signaled

temperature may be seen long after the acute stress events occur

by the intruder assuming a supine position for approximately

(5). A study by Tsuji showed that the effects of repeated social

3 seconds. In this homogenous population of intruders, two

stress induces chronic hyperthermia in rats, along with depressive-

subpopulations emerge, each exhibiting different coping behavior

type behavior. Following a four week social stress experiment,

(2). These two subpopulations have been classified by their latency

showed and increase in baseline body temperature of about 0.2-

time to defeat. The first group, termed long latency animals (LL),

0.3°C. Furthermore, 8 days following the last social stressor,

resists defeat for more than 300 seconds and exhibit more proactive

hyperthermia and depression-like behavior were still observed in a

coping behavior. Short latency (SL) animals usually resist defeat

forced-swim test (5). A study by Koolhaas showed that the effects

for less than 300 seconds and typically show passive behavior

on body temperature depended on the rats’ counter-aggressiveness

toward the resident.

during the defeat (7). They saw that intruders that counterattacked the resident more often were least affected by the stress, in that

Since most of the stress humans encounter is of a social nature, this

their daily temperature amplitude did not shrink as much as

animal model of defeat can provide valuable information regarding

more passive intruders (7). In this experiment, we determined

the neural mechanisms that govern responses to stress in the human

the effects of a repeated social defeat stress on body temperature

brain (3). Repetitive social stress in rat and mice models has been

and assessed whether there were differences in the homeostatic

shown to remodel the dendrites of the hippocampus and alter the

response between the two subpopulations LL and SL. By focusing

neuroendocrine response to stress by the hypothalamic-pituitary-

on these two subpopulations, we seek to further understand the

adrenocortical (HPA) axis (3,4). These changes in the brain and

physiological changes that occur in animals that exhibit proactive

neuroendocrinology can have lasting effects on the individual, and

vs. passive coping and further explore the link to stress resilience

studying their mechanisms may explain why certain individuals are

vs. vulnerability.

vulnerable to developing psychopathological disorders.

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intruder assuming a supine position for approximately 3 sec. After

Animals

defeat, a wire mesh enclosure was placed in the cage to prevent

Intruders were male Sprague-Dawley rats (Charles River) weighing

physical contact between the resident and intruder but allowing

225-250g on arrival were used as the experimental animals.

visual, auditory, and olfactory contact for the remainder of the 30-

Residents were retired male Long Evans breeders (Charles River).

min defeat session. Latency to assume a submissive posture was

Rats were individually housed with a 12-h light, 12-h dark cycle

recorded and averaged over the seven daily defeat exposures. If

(lights on at 0700 h) in a climate-controlled room with ad libitum

an intruder resisted defeat for 15 min, rats were separated with

food and water. Studies were approved by the Children’s Hospital

the wire partition for the remainder of the session. Controls were

of Philadelphia Institutional Animal Care and Use Committee and

placed behind a wire partition in a novel cage for 30 min daily. Rats

conformed to the National Institutes of Health Guide for the Use

were returned to their home cage after each session.

of Laboratory Animals. Animals were given 5 days of acclimation prior to any procedures. All defeats took place between 1030h and

Experimental Design

1100h.

Residents were randomly assigned to either a social defeat or control group. Defeat animals were exposed to a 30-min social

Telemetry

defeat, while control animals underwent novel cage exposure, for

Data was recorded from hardware and software purchased from

7 consecutive days. Following the 7-day defeat period, on day 8,

Data Sciences International (St. Paul, MN). DSI PhysioTel® F40-

residents were placed in a resident cage where the resident was

EET transmitters were implanted into the peritoneal cavity of each

not present in an “anticipation” test. After 30 minutes had passed,

rat anesthetized with Isoflurane. The animals were given 4 days

rats were returned to their home cages. Prior to sac, the defeat

to recover from the surgery before any experimental procedures

and control groups underwent a novel stressor, a 30- minute

began. Animals were housed individually and were placed atop a

restraint, followed by a 30-minute recovery. All defeats and novel

wireless receiver. Temperature and locomotor activity (parameter

cage placements took place between 1030h and 1100h. (See figure

values) were continuously recorded every 30 seconds throughout

below)

the experiment. These recordings were then averaged into 10-minute bins and graphed and analyzed using Prism software.

Statistical Analysis

We have focused on the 9:30 (1 hour prior to defeat) to 5:30 time

For analysis of the body temperature response to the repeated

period.

social defeat and anticipation test on days 1-8, two way [Stress Group × Day] repeated measures ANOVAS were conducted on

Social Defeat Stress

each time point (averaged 10-minuted bin) from 9:30 AM to 5:30

During each episode of social stress, a rat was placed into the home

PM on days -1,1,5,7, and 8. All data was analyzed using PRISM 5.0c.

cage territory of an unfamiliar Long-Evans resident previously

P) 0.05 was considered statistically significant for all analyses.

screened for high aggression. A typical agonistic encounter resulted in intruder subordination or defeat, signaled by the

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

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RESEARCH

2. MATERIALS & METHODS


RESEARCH

3. RESULTS: Temperature Response to Repeated Social Defeat

FIGURE 1: Body temperature is shown in rats exposed to acute social defeat or novel cage placement (CTRL n=5) for 30 minutes for seven consecutive days. Based on average latency times to defeat over the course of the 7-day defeat stress period, two groups, designated long latency (LL; n=11) and short latency (SL; n=4) were defined. Temperatures are shown during a one-hour baseline period (9:30 – 10:30 AM), 30-minute social defeat or novel cage period (10:30 – 11:00 AM), a 30-minute recovery period (11:00 – 11:30 AM), and a prolonged recovery period (11:30 AM – 5:30 PM) on days 1,5, and 7 of social defeat. *Both SL & LL rats significantly higher than CTRL rats at the time points indicated (P ) 0.05) +Only LL rats significantly higher than CTRL rats at the time points indicated (P ) 0.05) ++Only SL rats significantly higher than CTRL rats at the time points indicated (P ) 0.05) **SL rats significantly higher than CTRL & LL rats at the time points indicated (P ) 0.05) *** SL rats significantly higher than LL (but not CTRL) rats at the time points indicated (P ) 0.05)

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RESEARCH FIGURE 2: Body temperature (Mean & SEM) is shown for LL, SL and control rats exposed to either social defeat or novel cage placement. Data is from days: -2, -1, 1, 4, 5, 7, and 8 (Anticipation Test). Starting on day four, we begin to see an increase in the SL’s body temperature following defeat, with a slower recovery to a baseline temperature. During the anticipation test, where stress group rats were placed in a empty resident cage, we see an temperature response very similar to an actual defeat.

VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

25


RESEARCH

4. DISCUSSION

and SL animals have very similar temperature responses but as

The present study supports others that show repeated social defeat

the experiment progresses, SL animals peak temperature during

increases rats’ body temperature long after the stress events have

defeat remains elevated while LL temperature decreases. This

concluded. However, our analysis shows that all individuals of

along with the aforementioned prolonged recovery to a baseline

the population do not show the same temperature response both

temperature may be evidence of poor habituation to the repeated

during and after the stress event. As the experiment progressed,

social defeat. Although more analysis must be conducted to find

the baseline temperature of the LL animals tracked the control

definitive evidence, these findings are exciting, since they may

group closely, while the SL animals’ temperature remained elevated

provide a noninvasive, readily measured biomarker of stress

hours after defeat. The chronic hyperthermia induced by the social

vulnerability, easily translatable to humans. Further analysis in this

defeat is much more pronounced in the SL group, with many time

experiment will look at the changes to sleep architecture caused by

points significantly higher (P ) 0.05) than control in the recovery

the repeated social defeat. Changes to circadian rhythms will also

period, than the LL group. For example, on day 5 of defeat, SL rats

be analyzed, since they are an important part of HPA regulation.

were significantly above control at 20 10-minute time periods while

Stress is regulated by a complex interconnected response, and so

the LL group was only significantly higher in 1 10-minute period.

the differences shown in the temperature response must be seen

Furthermore, at three of those time points, the SL group was higher

as just one endpoint in the broader physiological changes found

than both the LL and CTRL groups. Splitting the population in the

in stress vulnerable individuals. Hopefully, this data can help

two subpopulations based on latency time to defeat, as detailed

elucidate the changes caused by chronic stress in the underlying

by Wood et al. (2010), has illuminated this trend, which would

neural circuitry.

have been hidden in an average of the entire stress group. SL rats showed an elevated temperature during defeat as well as a delayed

5. ACKNOWLEDGEMENTS: I thank Dr. Seema Bhatnagar and

temperature recovery to baseline following defeat. This trend

Elizabeth Ver Hoeve for taking the time to teach me and help steer

developed over the course of the experiment, becoming more

my analysis during the course of this experiment.

pronounced after days 4 and 5 of defeat. This is interesting because it is thought that the two subpopulations begin to “split� in latency

6. AUTHOR CONTRIBUTIONS: Project conception and planning

time, with the LL animals taking longer to defeat each successive

provided by S. Bhatnagar. Experiments were conducted by E. Ver

day, after day 4. Thus, the temperature differences we observe

Hoeve and A.G. Set up of hardware, software and data collection

may be linked to other changes occurring to the neuroendocrine

management by A.G. Data analyses preformed by A.G.

response, particularity in the HPA axis. 5HIHUHQFHV

HPA hypo-responsiveness, due to excessive negative feedback caused by stress, may negatively affect the normal homeostatic

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response, leading to depressive disorders (6). In normal

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it is thought that poor habituation to stress may be one cause of

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poor habituation, for example high levels of ACTH, we may see

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evidence supporting this in the temperature response.

Habituation refers to the decline in magnitude of the stress

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social defeat experiment, and decrease over the course of the

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experiment. Looking at our temperature data, we see that SL

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26 PENNSCIENCE JOURNAL |

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RESEARCH VOLUME 10, ISSUE 2 | SPRING 2012 | PENNSCIENCE JOURNAL

27


RESEARCH www.pennscience.org PennScience is sponsored by the Science & Technology Wing at the University of Pennsylvania SAC Funded

28 PENNSCIENCE JOURNAL |

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