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SPECTRUM Journal of Student Research at Saint Francis University

Volume 7 (3) Winter 2017


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SPECTRUM: Journal of Student Research at Saint Francis University Volume 7 Issue 3 Table of Contents Effect of Seat Location on Classroom Participation Kindra R. Witthus; Kaylyn Andress; Holly A. Lawrence; Marnie L. Moist

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The Housing of Endotoxins in Palythoa sp. Zoanthid Corals Joshua N. Ankeny; Devonna S. Shoemaker

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Neuroscience: Recent Advances, Common Misconceptions, and Ethical Concerns Lauren A. Olek; Shlomit Flaisher-Grinberg

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Call for papers

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(Student authors’ names underlined.)

Faculty Editors: Balazs Hargittai Professor of Chemistry bhargittai@francis.edu

Grant Julin Associate Professor of Philosophy gjulin@francis.edu

Student Editorial Board: Allison Bivens ’12 Hayden Elliott Paul Johns ’07 Jonathan Miller ’08 Morgan Onink Miranda Reed William Shee Stephanie Wilson

Kayla Brennan Eric Horell ’13 Elise Lofgren ‘14 Steven Mosey ‘14 Shaelyn Parry Hannah Retherford Margaret Thompson Staci Wolfe

Managing Designer: Grace McKernan

Cover: Photo by Barbara Doll. Courtesy of the Environmental Action Society.


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Effect of Seat Location on Classroom Participation [Research conducted for PSYC 202 (Research Methods and Statistics II)] Kindra R. Witthus Occupational Therapy Department School of Health Sciences krw103@francis.edu

Kaylyn Andress Occupational Therapy Department School of Health Sciences kxa115@francis.edu

Holly A. Lawrence Psychology Department School of Arts & Letters hal106@francis.edu

Marnie L. Moist, Ph.D. Psychology Department School of Arts & Letters mmoist@francis.edu

The problem investigated was if seating arrangement in a classroom affected participation. For this study, there were 104 male and female college students participated, ranging from approximately 18-25 years old.Each of six college classrooms were broken up into three sections, front, middle, and back, before observing actual classes. The classes we observed were discussion-based, therefore participation was necessary. The first person to raise a hand to participate in class each time, was counted up on the observation sheet in the relevant section. We found that there was a significantly higher percentage of classroom participation from people in the front and middle section of class than back section. These results could be utilized in order to promote engagement and participation of students during class. Classroom participation has always been an issue for students throughout their education years. Some students are shy or scared to participate, while others are not engaged in class enough to participate. Lack of engagement is often a problem for students that sit in the back of the classroom. Many teachers become frustrated with the lack of participation from students and some associate points to motivate students. Also the more a student engages in class by participating, the more they will learn and understand what is being taught. The goal of this study was to prove that sitting in the front of the classroom facilitates more participation than when sitting in the back of the classroom. As said in her article regarding the impact of seating arrangement on student’s learning, Roy (2014) explains that, “if the seating arrangement can be organized properly then it will create a purposeful setting for learning and teaching that will affect and motivate the teacher and student. (p. 23). We can use this information to help inform students and

teachers, where the best place to sit in the classroom is for maximum participation. Some key terms we used were participation, seat location and observation. Participation is defined as the action of taking part in an activity Komori and Nagaoka (2011) define participation in a different way by saying, “synchronization between speaker and listener, which we call entrainment, reflects the relationship between the speaker and the listener.” (p.108). In our study, hand-raising was the start of this synchronization in a formal classroom setting. Our definition of seat location was where the seat was in relationship to the class room. More specifically, front, middle or back rows. Montello (1988) describes how, “Empirical evidence also suggests, however, that seating location influences class participation and several self-report variables pertaining to attitudes about the course” (p.152). The sources showed generally that sitting in the middle to front lead to an increase in class participation and a higher GPA.


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Our study was unique in the fact we focused solely on the first person who raised their hand and had a total tally over entire class session. Other factors that have been known to cause an increase in participation were the level of interest in the class and how long each class was. In a past study, student’s seating location preference was hoped to be related to performance and success in the class (Benedict & Hoag, 2004). This study took place over the course of a year in two college courses (Marco and Micro Economics). The researchers conducted a study where they compared the chosen location of seats to the student’s grades. They calculated the average grade among students who regularly sat in either the front or the back of the classroom individually. The first set of conditions in this study were the front of the classroom or the back of the classroom. The next set of conditions in this study was the final course grades A, B, C, D and F. The dependent variable in this study was the number of people in each row that fell into all pairs of conditions. Benedict and Hoag concluded that, “individuals who prefer to sit near the front of the room have a higher probability of receiving A’s, whereas those sitting in the back, regardless whether one chose to do so, increased probability of receiving a D or F by 23 percentage points” (p.219). This study can be very applicable to real life. From this knowledge students can be more conscious of what seat will allow them to have the greatest opportunity to excel in the classroom. Another study was done by Fritschner in 2000, who wanted to show that students and faculty interpret the meaning of student participation in different ways. During this study 32 students were trained to perform consistent observation. Each researcher observed 10-12 sessions of a course during all parts of the spring semester of 1996 and 1997. The first set of conditions in this study were whether the student participated or did not participate. Another set of conditions was whether the student was in biology, physics, anthropology, political science, psychology, sociology, women’s studies, history of literature. The third set of conditions was if the student was male or female.

4 The fourth set of conditions was if they are traditional (under 25) or nontraditional students (over 25). The final set of conditions in this study was whether the class was an introductory class, a 200 level class or an upper division class. The dependent variable in this study was the mean number of people who participated in each level of class from each condition (type of student). The results of this study found that “an average of 7 students participated verbally in class. An average of 4.4 students made two or more comments” (Fritschner, p.354). Fritschner was also able to conclude that participation increased as the class level got higher, but gender and traditional or nontraditional students didn’t matter. Howard and Baird conducted a study in 2000 to prove that “learning occurs most effectively in a situation where students are actively engaged with the material, other students and their instructor” (p.713). This study had seven students who were trained to perform observational techniques. Each student then picked an undergraduate course, class size ranging from 50 to 100 students, at a Midwestern U.S. university. The observers recorded types of participation in different level courses at the school. In this study there were 5 sets of conditions. First, was if the student was male or female. Second, was if the student was traditional (under 25 years old) or nontraditional (over 25 years old). The third set being what type of participation the student was involved in, studentinitiated, instructor -initiated or direct question. Next the classes tested were either 100, 200 or 300 level classes. The final set of this study was whether the instructor was a male or a female. The number of people who participated in each pair of conditions was counted up. The results of this study concluded that there was an average of “41 interactions per 75- minute session. The mean total interactions per student in a given session was 1.9” (p. 720). This study showed also that nontraditional students were more likely to participate than traditional students but the gender of the professor didn’t affect student participation. It also showed that female and male participation rates were fairly similar. This study also showed that student-


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initiated participation had the highest number of participation out of the three types. This study can be applicable for professors who are seeking to make expectations for levels of participation in their classes. Hiraoka (n.d.) conducted a study where the purpose was to analyze, “the effect of classroom seating preference on academic motivation, participation and GPA” (p.2). The first condition of this study was if the students sat in the front, front middle, back middle or back of the class room. Cumulative GPA, amount of participation per student and amount of academic motivation were measured. Participation and motivation were each measured on 5- point Likert scale, with 5 being very much. This study concluded that while sitting in the front correlated with a higher GPA, there was no correlation between higher participation and higher academic motivation when sitting in the front of the classroom. The implications of this study are quite inconclusive. Also, most students were aware of the seats that average the highest GPA. Although it can’t be concluded that sitting in the front of class causes a higher GPA, we can say there was a predictive value of being able to estimate students in front will have higher grades. Our study was related to past studies in many ways. Our study compared to Hiraoka’s used a different way to measure participation. Hiraoka’s study used the 5-pt. scale to measure participation while we used tally marks on our behavioral checklist for each student that participated. Our study offered clear measurable results by using tallies in each section whenever someone participated. Our definition of participation was also clear because it was when a student willingly raises a hand, regardless of who initiated discussion. Our study was simple, clear and measurable which offered more than past studies have. We used the tally mark as a measurement because it was easy for each observer to make a simple tally when they saw a student raise their hand. Our study also had similar procedures as other studies. Fritschner conducted a study where each researcher observed 10-12 sessions of a

5 course during the spring semester. Our study also observed students in multiple classroom settings in the spring semester. However, Fritschner observed the same classroom multiples times, while we visited 6 different classrooms once each. The method of sample we used during this study was time sampling and event sampling because we were randomly selecting the classes we were going to observe. While the studies may have procedural and theoretical differences, the results of all the past studies were fairly consistent. Each study found that seating arrangement had some effect on the student participation in the classroom. Our study went above and beyond these studies. Our study had higher external validity because we measured many discussion-based classes, which increased the diversity and variety of students in the study aside from the higher likelihood of student participation. By observing a variety of classes our study provided a realistic sample of classroom participation. We put tally marks on our own recording sheet in the relevant section of the classroom when the first person raised a hand, regardless of how often any one person did so. We left the class only after the class period was finished. We predicted some mean difference in the proportion of people who raised their hand first in the front third of the classroom, the middle third of the classroom and the back third of the classroom. Fernandes, Juang and Rinaldo (2011) supported our hypothesis by reinforcing that, “where one sits in the classroom, the front of the class or the back of the class, has the potential to affect student participation” (p.69). Past information lead us to form our hypothesis regarding participation and location of seating in the classroom. We predicted that we would find more participation from the front section of the classroom because students who sit there are often times more engaged when it is a discussion-based class. Methods Participants. There were 104 observed college students (61 females, 43 males) who participated only once in this study. The mean percentage of


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Caucasian students between the 6 classes was 88.33%. The rest of the percentage of students in each class was African American. All of our observations are estimates based on appearance. These participants were college students at a small, rural Catholic pre-professional mixed liberal arts college in west central Pennsylvania. Time sampling and event sampling were used to randomly select each class to observe, after constraining class times based on the observers own available schedule. Time sampling was used because we had a mixture of morning and afternoon classes along with Monday, Wednesday, and Friday classes because they are fifty minutes. Then from that we used event sampling to choose which classes to observe, after constraining courses to only those taught by required university wide professors known to encourage class discussion. Discussion-based classes such as freshman and senior seminars were chosen to observe to make sure participation would occur frequently. We were unable to observe all sections of Core 113 and Core 407 classes but we observed 3 sections of each Core class. Core 113 was a freshmen seminar course, also taken by some non-first years, while Core 407 was a senior seminar course, which some juniors took. All sections of Core 113 and Core 407 classes were required to have no more than eighteen students to encourage discussion. With the variety of ages and majors, upper and lower class levels, we were able to gather a variety of generalized data. The estimated age range of our participants was 18 to 25 years old. The sample was all college undergraduates. Based on observation, every student in each class room was physically able to participate. Materials. We used a full list of Saint Francis University professors teaching a core course to choose the classes we wanted to observe. We used the school email to contact professors we wanted in our study and to send them our debriefing sheets. Also we used a spring 2016 class schedule to choose what courses and times were available for our sample. Appendix 1 shows our self-created recording sheet and behavioral checklist. The

6 recording sheet was used to put tally marks in each section where the first person to raise their hand sat in the classroom throughout the entire class period. Each person could be counted more than once if they raised their hand first multiple different times. Therefore, regardless if a participant had previously been counted, each time they raised their hand first it was recorded in the appropriate section. For each of the three sections we recorded the number of rows, number of seats, and number of people physically able to raise their hand and participate. In the classrooms we observed, there were normal school desks or tables with multiple people at them in rows facing toward the front of the room. For each class we also recorded the amount of males and females in class, total number of students, and whether it was an upper or lower level class. At the end of our observation, on the recording sheet we included a question to ask the professor at the end of class. We asked the professor if this was a typical class participation rate and they gave a yes or no answer. Every professor, 100% stated that it was a typical participation day for their class. Since we only observed each class once we needed to make sure the participation during the time period was typical of that class. Design and Procedure. In our study seat location and participants fell into one of three conditions, front third of the classroom, middle third of the classroom, orback third of the classroom. We observed the total number of people who raised a hand in each condition over an entire 6 sections of college courses. To separate people into one of the three conditions first we made a map of where the three thirds of each classroom were and then where the person sat in the class automatically placed them into a condition. Our study was an unobtrusive observational study where we observed classroom participation in 6 college classes. This study was also a betweensubjects design and a quasi-experiment. For each class we observed, we emailed the professor asking permission and the date we were going to observe their class.


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Then we visited each class room we tested and mapped out the different thirds of the classroom. If the rows per section were uneven, we put the majority in the middle section. We then printed out the diagram for each class room to collect data on. Then before class we met with the professor and explained our study, why we did it, if they were willing to facilitate the deception and tell the class we were there simply to observe the professor and told them we would email them the debriefing sheet after we complied the results. Before each class we observed the professor would introduce us by name and state that we were there to observe the professor’s teaching style. Professors were thanked for their time and after class was observed were asked to rate if the participation was normal for that class. At least 2 of 3 observers were at each of the six 50-minute classes, (2 Monday, 2 Wednesday and 2 Friday) selected for the entire class period. For each class we attempted to be in the back row behind all students. If the back row was filled we brought in extra chairs so we could sit behind the students. We each had our own recording sheet and independently put tally marks on the recording sheet in the relevant section the first person that raised a hand was sitting in. The person needed to raise their hand at least three-fourths arm length up to be counted. We put a tally mark for each participation opportunity available, using the first person to raise his/her hand even if it was not the first time that person raised their hand. We also filled in the entire behavioral checklist. Scoring. The behavioral checklist we used can be found in Appendix 1. The observers wrote down demographics such as race, gender, age, and highest level of education. Throughout the six classes, between 13 and 20 seats were filled because the class sizes were small. The mean number of seats filled was 17.33. Then throughout the fifty minute class period each observer made a tally mark in the appropriate section of someone who raised their hand first to participate. The average number of hand raises per section was calculated by the total tallies per section divided by

7 the number of seats in each section. This was needed because there were more seats in particular sections and to account for that the average needed to be adjusted by dividing the total number of seats. The two observers who agreed most across classes showed a correlation 0.994 in the amount of tally marks in each section for each class, which is almost perfect agreement. Due to schedule constraints there were always at least two observes at every classroom and the two observers that agreed the most contributed the data for the correlation. Results The priori alpha level used in this study was 0.05. Therefore all effects significant at p is less than 0.05 were reported. All of our means were based on subject means. The type of analysis we used was a 1-way repeated measures ANOVA and it was significant, obs. F (1.11,5.53) = 6.62. p=.044. We then examined all pairwise comparisons on the each section (front, middle, and back) to measure the average hand raises per section. The comparison between front and middle was not significant at 0.176 but comparing front and back was significant at 0.049. Also comparing middle and back was significant at 0.025. Since both comparisons with the back section were significant because they were less than 0.05 this shows that more people raised their hand in the front and middle sections compared to the back. We also ran a correlation between the average number of hand raisers in the front section with the total number of seats filled in each class. Although are results were not significant our Pearson’s correlation observed r (4) = 0.263, p = 0.614 Effect size= 0.57.There is a weak negative correlation. Table 1 shows the mean number of hand raises per section as a function of seat location. This table shows the mean and standard deviation for the front, middle, and back section. As seen in Table 1 the mean number of hand raisers from each section decreases from front to middle to back.


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Seat Location Mean hand raises per section

Front

Middle

Back

M

1.8333

1.567

0.983

SE

0.256

0.156

0.226

Table 1. Mean number of hand raises per section as a Function of Seat Location. The table demonstrates that on average there was more participation in the front than any other section, the mean hand raises for the front was 1.83 which is greater than 1.567 and 0.983. Also there was more participation in the middle section (1.567) compared to the back section (0.983). Therefore showing that on average students who sat closer to the front of the room participated more.

Discussion Our results supported our hypothesis that there was a mean difference in the average number of people who raised their hand first for those sitting in the front section, middle section, and back section of the classroom. Although based on the pairwise comparisons we were unable to prove that people in the front participate more than people in the middle, we were able to show that students participated more in the front and middle section than the back. Our results showed that theoretically people are more likely to participate the closer they are to the front of the classroom and people in the back of the class participate less. Besides our hypothesis, the correlation we ran is an additional alternative explanation of our results. There was a weak negative correlation between the average number of people who raised their hand in the front sections and total number of seats filled in each class. These results show that when the class size is smaller people in the front section are more likely to participate. This was relevant to our study because we observed mostly small classes between 13-20 students. If we would have observed larger class sizes between 50 and 100 students our correlation would may have been significant and there likely would have been much less overall class participation. The main flaw in our study was that our sample size was too small. N=6 classes which is not enough to have a large power size related to our

correlation. This flaw is important to note because our effect size was high (0.57), which fits the criteria for a large effect size (0.35). With our effect size being so high it shows that our results have real world applicability despite our extremely small sample size. Since the pairwise comparisons showed that people in the back participated less than the front and middle, if we had a larger sample size it can be inferred that results would then show people in the front participated more than people in the middle. Another flaw in our study was professors seem to overlook students in the front row. After talking with numerous professors they said they often overlook students in the front row because they are uncomfortably close to them and making eye contact is awkward. Although our study was focused on students that raise their hand, if students in the front continuously raise their hand and then never get called on they might be less likely to participate. One way to fix this flaw is to move the front row of each classroom to comfortable distance away from the professor. One other flaw in our study was that the middle section of each classroom was two rows because there was an unequal number of rows, while the front and back section were each only one row. This discrepancy in number of rows caused the middle section of each class to have more people in it that the other two sections. We fixed this flaw, however, by dividing the average number of hand raises per section by the total number of people in each section. Our results were consistent with what was found in previous studies Earlier studies focused on seat location in relation to GPA, such as Fritschner (2000), Benedict and Hoag (2004), Hiaroka (n.d.). These studies found that students sitting in the front of the classroom on average received high grades than those in the back. Our study is similar in the fact we found students sitting in the front part of the class tended to participate more than those in the back, so our study was focused on participation not GPA. Fernandes, Huang, and Rinaldo concluded that where someone sat in the classroom affected participation. Our study was consistent with Fernandes, Huang, and Rinaldo’s conclusions


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that classroom seating affected participation but more specifically, our study showed people participated more in the front of the class room. Our study brought unique results to previous studies because our procedure was more clear and understandable, therefore our results were clearer in stating that people participate more in the front than in the back. There are multiple practical implications of our results relevant to students and teachers throughout the world. If teachers know that students are more likely to participate if seated in the front of the class room they can have students who may be struggling to pay attention or be engaged in class move to the front. Teachers could have assigned seating for students and place the ones that need to participate more in the front. Another way teachers could utilize our results is by making sure the classroom seats get filled from front to back. Therefore, there shouldn’t be any open seats in the front or middle section of the classroom. There may be open seats but they will be in the back depending on how may seats and students there are. Students may also benefit from our results. If a student knows they are often quiet in class or disengaged, they could sit in the front section to facilite their own participation. Also, during the semester, if a student feels themselves falling behind or not participating they could request to be moved to the front section to help stay focused and engaged. The results of our correlation between average number of hand raises per section and total seats filled per section is also a benefit for the real world. With only 6 classes we found a hint at a negative weak correlation but if we had a larger sample size and class sizes we would most likely find a strong negative correlation. If teachers and administrators knew that students in the front were less likely to participate if the class population is large, they could have an argument for keeping the class sizes relatively small. With an average or small class size people in the front would be more willing to raise their hand to participate. Some different areas researchers could experiment would be to expand their style of class. Our study was limited to discussion based classes

9 so it would be interesting to expand to all types of classes, including lecture based classes. For example, the classes we observed were meant to facilitate discussion, but if researchers also observed lecture based classes such as math, they could get a better understanding of participation across populations. Another suggestion for future research in this area is to focus more on gender and ethnicity. We wrote down the total number of males and females in each class but did not specify which rows they were in or what gender specifically participated. Researchers could see which gender or ethnicity participated more based on where they were sitting in the classroom.

Appendix Amount of males in class: Amount of females in class: Lower Level Class (100/200) or Upper Level Class (300/400):

Estimated Race/Ethnicity: African American/Black:____ Asian-American/Pacific Isl.:____ Caucasian/White:____ Hispanic/Latino:____ Multiracial:____ Native American/ Alaskan Native:____ Unknown:____

Total number of seats filled: Estimated Age of all students: 18-25 all College Undergraduates Front Section # of rows:____ # of seats:____ # of people able to raise hand and or participate:____

Middle Section # of rows:____ # of seats:____ # of people able to raise hand and or participate:____

Back Section # of rows:____ # of seats:____ # of people able to raise hand and or participate:____

Was this a typical day of participation?

Yes____ No____


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Works Cited Benedict, M., & Hoag, J. (2004). Seating Location in Large Lectures: Are Seating Preferences or Location Related to Course Performance? The Journal of Economic Education, 35(3), 215-231. Retrieved October 27, 2015, from PROQUEST database. Fernandes, A., Huang, J., & Rinaldo, V. (2011). Does Where A Student Sits Really Matter? - The Impact of Seating Locations on Student Classroom Learning. International Journal of Applied Educational Studies, 10(1), 66-77. Retrieved October 27, 2015, from PROQUEST database. Fritschner, L. (2000). Inside the Undergraduate College Classroom: Faculty and Students Differ on the Meaning of Student Participation. The Journal of Higher Education, 71(3), 342-365. Retrieved October 27, 2015, from PROQUEST database. Hiraoka, P. (n.d) Effect of Classroom Seating Preference on Academic Motivation, Participation, and Performance. Psychology 9b. 74(2), 1-7. Retrieved October 28, 2015, from PROQUEST database. Howard, J., & Baird, R. (2000). The Consolidation of Responsibility and Students' Definitions of Situation in the Mixed-Age College Classroom. The Journal of Higher Education, 71(6), 700-721. Retrieved October 28, 2015, from PROQUEST database. Komori, M., & Nagaoka, C. (2011). The Relationship between Classroom Seating Locations and InstructorStudent Entrainment: A Video Analysis Study. 2011 International Conference on Biometrics and Kansei Engineering, 10(1), 106-111. Retrieved October 28, 2015, from PROQUEST database.

10 Montello, D. (1988). Classroom seating location and its effect on course achievement, participation, and attitudes. Journal of Environmental Psychology, 8, 149-157. Retrieved October 27, 2015, from http://www.Geog.ucsb.edu. Roy, J. E. (2014). The Impact of Seating Arrangement on Students' Learning in Secondary Schools. International Journal of Information, Business and Management, 6(2), 128. Retrieved October 28, 2015, from PROQUEST database.

Kindra Witthus (’17) is an Occupational Therapy major with a double major in Psychology. She will have played 4 years of softball for the Red Flash after this spring season. Kindra has received an NFCA All-American Scholar Athlete award every semester at Saint Francis. She is a member of the Psi Chi International Honor Society in Psychology and the Student Occupational Therapy Association. Kindra will graduate with her Master of Occupational Therapy degree in 2018. Kaylyn Andress (’17) is an Occupational Therapy major. Holly Lawrence (’17) is a Psychology major with a minor in marketing. She is president of the Psi Chi National Honor Society and Captain of the Saint Francis University Women's Lacrosse team.


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The Housing of Endotoxins in Palythoa sp. Zoanthid Corals Joshua N. Ankeny Biology Department School of Sciences jna100@francis.edu

Devonna S. Shoemaker Biology Department School of Sciences smorra@francis.edu

Zoanthid corals are one of the most common corals in the aquaria trade. They also contain one of the most deadly toxins found in marine environments; palytoxin. Palytoxin is an organic toxin that when ingested/injected can cause disruptions in the peripheral nervous system and in some cases, death. In this study, the common Zoanthid, Palythoa grandis (Cinnamon Zoanthids) were used to make three treatments, each of which contained a different part of the coral; the whole healthy coral, the tissue without the dinoflagellates, and the dinoflagellates alone. The three treatments were tested against a control to determine where palytoxin is housed in the coral. It was predicted that the toxin was produced by the dinoflagellates alone, due to the nature of some dinoflagellates such as those that cause red tides and ciguatera food poisoning. To test this, the treatments were injected into Pimephales promelas (Fathead Minnows) and compared against a control of only holding water. The survivability of each fish was recorded over a 108 hour window. Final data analysis concluded that there is in fact a significant difference in the treatments types. Using the survival package in R version 3.2.3, a chi-square (X2) test was ran on the data and resulted in a P-Value of 0.0534 (α = 0.10). Interestingly enough, the survivorship graphs suggest that the tissue alone treatment effects the health of the fishes the most. Introduction Many of the lesser known toxins on Earth are present in marine ecosystems. Philip Helfrich discovered one of these toxins in the late 1950’s while exploring an ancient Hawaiian legend. The coral he discovered was Palythoa toxica, and its unearthing opened the eyes of aquarists worldwide [2]. Dr. Helfrich’s coral is a member of the order Zoantharia and family Sphenopidae. Corals in this phylogeny are commonly referred to as Zoanthids. They produce a deadly toxin, known as palytoxin, which is present in three forms: palytoxin (C129H224N3O54), 42-hydroxy-palytoxin (C129H225N3O55), and deoxy-palytoxin (C129H224N3O53) [1]. All three analogs are toxic and are capable of being found simultaneously in the same organism. All three analogs affect the peripheral nervous system by disrupting the Na+/K+ pumps. Contact with the toxin increases the internal Na+ and leads to a subsequent flood of Ca2+. Actin filament system distortion is brought about by the altering of the neuron signaling cascade [4].

Zoanthid species that produce palytoxin have been known to cause ciguatera food poisoning [2]. This food poisoning is contracted by humans who consume fish that contain the palytoxin in their flesh. It is uncertain how the fishes contract the toxin, but it has been proposed that it occurs due to either zooxanthellae bleaching (the expelling of symbiotic zooxanthellae due to stress) events, or direct feeding on the coral. Since palytoxin has been discovered in both Zoanthid corals and unrelated dinoflagellates, both means seem possible [6]. This research was designed to determine where the palytoxin is housed in Palythoa grandis (Cinnamon Zoanthids). Cinnamon Zoanthids were chosen for this experiment due to their abundance in the home aquaria trade. Though previous studies have not determined which palytoxin analog is present in the Cinnamon Zoanthids, it is known that they contain some form of it. However, it is unknown where they produce and store the toxin, be it in their symbiotic dinoflagellates or their tissue. Though common survivability tests on aquatic organisms involve placing the specimen in a set


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concentration of the toxin, our procedure was to inject multiple Pimephales promelas (Fathead minnow) with the toxin. This method insured that the toxin was immediately present in the bloodstream and the surrounding tissue. Using this process, we exposed the specimen to the symbiotic zooxanthellae alone, the bleached coral tissue (containing no zooxanthellae), and the whole coral (both the tissue and the zooxanthellae). With these three injection levels, three hypothesis could be tested: P. grandis contain palytoxin in their zooxanthellae symbionts alone, P. grandis contain palytoxin in their tissues alone, and P. grandis do not contain palytoxin. Fathead minnows were selected for this research due to their many benefits. They are commonly used for aquatic based research as they are simple to house, relatively cheap, and abundant. Due to these attributes, they are approved by the US EPA as toxicity testing specimen for organic compounds [5]. They are specifically important for this research because they are freshwater species. Since palytoxin is exclusive to marine environments, this detail ensures that the minnows have not previously contacted palytoxin and therefore have minimal tolerance towards the toxin. Materials and methods Minnow Systems. Four 40 liter aquaria were fitted with undergravel filters, small pebble substrate and Tetra Whisper (Blacksburg, VA) 10 air pumps. A temperature of 18-20ºC was maintained in these systems throughout the research. These aquariums were then inoculated with bacteria from a previously established system to begin the cycling process. After a week of cycling and daily HACH tests for ammonia, nitrite and nitrate, 50 fathead minnows ranging from 2.5cm to 5.0cm were evenly distributed between the aquariums. The fish were fed daily with equal amounts of 0.5mm sinking pellet fish food. During the following two weeks the fish were watched closely for any health issues. Dead or diseased fishes were removed from the aquariums. Research was not initiated until the fishes reached a period of three days without a death.

12 Coral System. A single 21 liter aquarium was established with an Aqueon Quietflow (Franklin, WI) 10 overhead filter, and 1.4 kilograms of live rock. A single colony of Palythoa grandis containing five polyps was purchased from Zoanthids.com (Lindon, UT). The polyps were acclimated and allowed to grow over a two month period. During this time the corals were fed with Two Little Fishies (Miami Gardens, FL) MarineSnowⓇ Plankton Diet. The aquarium was equipped with a coral growth light and set to a ten hour light cycle. Extraction of Dinoflagellates. A single polyp of Palythoa grandis was removed from the colony and placed in a 50mL beaker containing deionized water. The water was then slowly heated by means of a hot plate until bleaching of the coral occurred. Different levels of bleaching occurred throughout the heating process at varying temperatures. Due to this, the coral was intently watched and the heating process was stopped when the polyp reached a bleached state. The coral tissue was removed from the beaker and placed in a 50mL homogenizer vial. The remaining solution was placed in a 50mL centrifuge vial, and pelleted in a SurvallⓇ RC-5B Refrigerated Superspeed Centrifuge at a speed of 15,000 rev/min. Homogenization of Tissue. Both the bleached tissue and another healthy polyp were placed in individual 50mL homogenizer vials. Using a Talboys Instrument Corporation 115V tissue homogenizer, the polyps were homogenized. During this time, the vial was kept in an ice bath to insure the generated heat did not denature the proteins. Using a squirt bottle filled with deionized water, the homogenized tissue was transferred to another 50mL centrifuge vial. Using the same centrifuge system and speed, the vials were pelleted. Injection. There were four aquariums of five fish each in the experiment, and each aquarium received a different treatment. Aquarium 1 (Control) fishes were injected with 0.5mL of water from the aquarium that they were housed in. Aquarium 2 (Total Coral) fishes were injected with the homogenized healthy, unbleached coral tissue resuspended in aquarium water from their housing


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system. Aquarium 3 (Tissue Alone) fishes were injected with homogenized bleached coral tissue resuspended in aquarium water that they were housed in. Aquarium 4 (Dinoflagellate Alone) fishes were injected with the resuspended dinoflagellate solution. The use of holding aquarium water to resuspend the pellet for injection ensured the internal salinity level of the specimen remained consistent. Injections were administered in the caudal sinus as this was the easiest and most reliable way to ensure the sample entered the bloodstream. Data Collection. The systems were inspected every 12 hours for the next 108 hours. At every inspection, the number of fish dead as well as the number of fish living were recorded. Fish that showed any sign of life (ie, operculation, appendage movement) were considered still alive and allowed to remain in the aquarium till the next observation period. All fishes declared dead were recorded, removed from the system and disposed of in a biohazard waste bin. Data Analysis. Due to this being a preliminary study with limited sample size, an alpha value higher than 0.05 is acceptable [7]. Therefore, the alpha value for this study was set to 0.10. Survival curves were compared using the survival package in R version 3.2.3 (R Foundation for Statistical Computing). The Kaplan-Meier formula was used to fit the survival curves for each treatment using the survfit() function, combining both replicate aquariums in the analysis. The curves were then compared using a non-parametric log-rank test using the survdiff() function. R was also used to run a X2 test of the data. Power analysis tests were conducted using Minitab software. Results Twenty eight fathead minnows were separated into four identical aquaria. Each group was then injected with one of four treatments; aquarium water, homogenized healthy coral, bleached coral tissue, or dinoflagellates. The survivability of the fishes were observed and deaths were recorded every 12 hours throughout the experiment for a total of 108 hours.

13 Microscopic investigations during the formation of the treatments revealed unexpected errors in the purifying process of the samples. Figure 1A shows the coral tissue prior to bleaching. The small cylindrical objects are the symbiotic dinoflagellates. Figure 1B is the same tissue sample after the bleaching process; while dinoflagellates were substantially reduced, not all were successfully removed from the tissue. Figure 1C shows the supernatant that was collected after the bleaching process and depicts an extracted dinoflagellate (bold arrow). Nevertheless, the sample was also contaminated with coral tissue cells (hollow arrows).

Figure 1. Multiple anecdotal discoveries. A) The healthy coral tissue containing abundant zooxanthellae. B) The same tissue sample after bleaching C) The supernatant gathered after the coral was bleached, containing expelled dinoflagellates (bold arrow) and coral tissue cells (hollow arrows). D) A specimen from the study, depicting the swelling and bruising of the caudal peduncle. This was only present in fishes injected with one of the three treatments and not in the control fishes.

Anecdotally, swelling and bruising were noticed around the caudal sinuses of the fishes injected with the three coral substances (Figure 1D). The same swelling and bruising was not noticed in the control fishes whom were injected with solely aquarium water. Figure 1D depicts these findings. A X2 value of 7.7 was obtained for three degrees of freedom, resulting in a P-Value of 0.0534 (Îą = 0.10). A power analysis test was conducted on Minitab, concluding that this study had a power of 0.60. A concordant power analysis test showed that achieving an acceptable power of 0.90, would require a sample size of 16 replications per treatment.


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The X2 test revealed that somewhere there is difference between our treatment levels. This signifies that survivability of the fishes is dependent on the treatment type that they received. The survivability package in R version 3.2.3 output a series of graphs comparing the control to each treatment (Figure 2). In these graphs, the solid line is the actual data, and the dotted line is the 95% confidence intervals for these values.

Figure 2. Survivability graphs containing actual data (bold lines) and the calculate 95% confidence intervals (dotted lines). A) Control vs Total Coral. B) Control vs Tissue Alone. C) Control vs Dinoflagellate Alone.

Discussion Figure 2A and 2C compare the total coral treatment to the control, and the dinoflagellates alone to the control, respectively. In these figures, the confidence intervals between the injections are overlapping for the most part. This implies that there is not much difference in their means and in turn palytoxin could be absent in these samples. Figure 2B, on the other hand, has 95% confidence intervals that do not overlap as much. With this information

14 we can assume that the difference found in the X2 test lies in the tissue only treatment. Further statistical analysis is needed to confirm these claims. With future studies, how treatment type or types affect the health of fathead minnows can be better explained. Many types of corals are known to contain symbiotic dinoflagellates, referred to as zooxanthellae (Figure 1A). Bleaching is a term used to describe the act of a coral expelling its zooxanthellae due to high stress, such as increased water temperature or sickness. Although the use of heat to induce bleaching is a common technique for extracting dinoflagellates in a lab setting, it does not extract all the dinoflagellates. After bleaching the coral, we found evidence that there were still dinoflagellates inside the coral tissue (Figure 1B), albeit in smaller numbers. Though the number of dinoflagellates in the coral tissue was drastically reduced during the bleaching process, the remnant of dinoflagellates could be enough to confound the results.. Additionally, tissue cells from the coral were found in the supernatant (Figure 1C). The presence of coral tissue cells in the dinoflagellate sample could have the same negative effects. Future studies could look into better methods of extracting the dinoflagellates from the coral tissue, as well of removing the coral tissue cells from the dinoflagellate supernatant. While removing the deceased fishes from the system, we recognized a pattern in the treatment injected fishes. Each fish that was injected with any of the three samples had swelling and bruising in the caudal peduncle region (Figure 1D). Hemolysis (the rupturing of red blood cells) is a possible contributor to this symptom. It is still unknown as to whether or not palytoxin has a hemolytic nature. In further observations, we did not see any paralysis in the treatment injected fishes that would be implied by palytoxin’s effect on the actin filament system. However, this does not mean that paralysis did not occur, as it could have been mistaken as death or occurred and led to immediate death. Another limitation to this study lies in the sample size. Due to the small size of this study (7 replications per treatment) a power of only .60 was


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15

achieved. To achieve a power of .90, at least 16 replications per treatment are necessary. Time and funding constraints did not allow subsequent or larger data sets. However, further research could improve the power and overall findings of the study. This research would benefit greatly from a second trial. More significant results could be achieved in future studies. Greater sample size would increase the power of the findings. Another trial could focus on creating more pure treatments than what were seen in this study. Future research could include the use of yet another control in the form of a known nontoxic coral. This step would give an insight into the swelling and bleeding of the caudal peduncles that was witnessed in this study.

fathead minnows were separated into four identical aquaria. Each group was then injected with one of four treatments; aquarium water, homogenized healthy coral, bleached coral tissue, or dinoflagellates. The survivability of the fishes were observed and deaths were recorded every 12 hours throughout the experiment for a total of 108 hours. The use of the R survivability package to run a X2 test revealed a P-Value of 0.0534 (α=0.10). With this data we can conclude that there is in fact a difference between treatment types. The output graphs (Figure 2) showed that this difference probably lied in the tissue alone treatment. This outcome contradicted our hypothesis that the dinoflagellate housed/produced palytoxin. Works Cited

Fish Deaths Hours

Control

Total Coral

Tissue Alone

Dinoflagellate Alone

12

2

2

1

3

24

0

0

1

0

36

0

1

2

0

48

0

0

0

1

60

0

0

2

0

72

0

0

1

1

84

0

2

0

1

96

0

0

0

0

108

0

0

0

0

Total Deaths

2

5

7

6

Total Alive

5

2

0

1

Table 1. Fish deaths per treatment by 12 hour intervals. Total Dead and Total Alive refer to the systems after the 108 hour period.

Conclusion The goal of this experiment was to localize where palytoxin is housed and/or produced in the P. grandis Zoanthid coral. To test this question, 28

1. Ciminiello, P., Dell’Aversano, C., Dello Iacovo, E., Fattorusso, E., Forino, M., Grauso, L., Tartaglione, L., Florio, C., Lorenzon, P., and De Bortoli, M. et al. (2009). Stereostructure and Biological Activity of 42-Hydroxypalytoxin: A New Palytoxin Analogue from Hawaiian Palythoa Subspecies. Chem. Res. Toxicol. 22, 1851-1859. 2. Deeds, J., Handy, S., White, K., and Reimer, J. (2011). Palytoxin Found in Palythoa sp. Zoanthids (Anthozoa, Hexacorallia) Sold in the Home Aquarium Trade. PLoS ONE 6, e18235. 3. Deeds, J. and Schwartz, M. (2010). Human risk associated with palytoxin exposure. Toxicon 56, 150-162. 4. Louzao, M., Ares, I., and Cagide, E. (2008). Marine toxins and the cytoskeleton: a new view of palytoxin toxicity. FEBS Journal 275, 6067-6074. 5. Mattson, Vincent R. et al. “Acute Toxicity of Selected Organic Compounds to Fathead Minnows”. Ecological Research Series. EPA Form 2220-1 (9-73). 6. Oku, N., Sata, N., Matsunaga, S., Uchida, H., and Fusetani, N. (2004). Identification of palytoxin as a principle which causes morphological changes in rat 3Y1 cells in the zoanthid Palythoa aff. margaritae. Toxicon 43, 21-25. 7. Hampton R., Havel J. Introductory Biological Statistics. 3rd ed. Long Grove, IL: Waveland Press; 2014:47.

Josh Ankeny ('12, B.S. Biology) is currently applying to graduate positions with a strong interest in freshwater ecology. He spends most of his free time hunting, fly fishing, and enjoying the great outdoors in preparation for long hours in the classroom.


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16

Neuroscience: Recent Advances, Common Misconceptions, and Ethical Concerns Lauren A. Olek Psychology Department School of Arts & Letters lao101@francis.edu

Shlomit Flaisher-Grinberg Psychology Department School of Arts & Letters sfgrinberg@francis.edu

The field of neuroscience is an extremely broad and complex field spanning study areas that include molecules, cells, behavior and cognition of humans and animals. The purpose of this study was to explore recent advances in the research methodologies that are used in the field of neuroscience. The research methodologies that were explored include whole brain imaging, molecular and cellular techniques, and genetic techniques. Through the review of multiple studies, it was found that numerous advances have occurred in the field of neuroscience during the last ten years. In addition, it was found that misunderstandings of different research methodologies can lead to the development of misconceptions in the opinions of the general public. Lastly, some ethical concerns were also addressed which could have a strong impact on future advances in the field of neuroscience. Introduction If we could look into the brain, could we find an explanation for the occurrence of typical adolescent risk-taking behaviors? Could we understand the nature of empathy? Can stress change the cellular structure of the brain? How much insight can genetics provide into the study of different neurological, developmental, and/or psychiatric conditions? The field of neuroscience can help answer all of these questions. Neuroscience is a highly interdisciplinary field which aims to study the nervous systems structure and function under normal and abnormal circumstances. The field of neuroscience has many different branches that are used to inspire different perspectives from which to conduct research. The five main branches of neuroscience aim to answer different questions about the nervous system’s structure and function [1]. Behavioral Neuroscience aims to examine the biological processes and/or mechanisms which underlie the occurrence of different human and animal behaviors. Clinical Neuroscience is the application of the science behind the brain and nervous system in the medical setting. Practitioners in this branch of neuroscience are more commonly known as neurologists and

psychiatrists. Neurologists study problems of the nervous system such as brain tumors and epilepsy [2]. While psychiatrist study problems of the mind such as affective disorders like Bipolar Disorder [1]. Cognitive Neuroscience aims to study the neural basis of higher cognitive functions, such as decision making and problem solving [2]. Developmental neuroscience is dedicated to the study of the nervous system across the lifetime and what the mechanisms are that cause or direct these changes [1]. Lastly, Molecular and Cellular Neuroscience uses the principles of molecular and cellular biology to answer questions relating to the impact of genes, proteins, and other molecules on the cellular components of the nervous system [2]. It is important to note that the branches can, and do, work together to answer specific questions about the nervous system and its function. There are many different ways to conduct research in the field of neuroscience. The first purpose of this study was to explore what methodologies that are used in the field of neuroscience to conduct research. This particular literature review focuses on the methodologies of Whole Brain Imaging, Cellular and Molecular Techniques, and Genetic Engineering Techniques.


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17

The second purpose of this study was to review possible misconceptions and ethical concerns that can result from the misinterpretation of research that has be conducted in the field of neuroscience. Whole Brain Imaging The innovation that has become widely known today as whole brain imaging aims to provide detailed and mostly accurate pictures of the brain’s anatomical structure and physiological function [3]. Through the use of this non-invasive technique scientists can observe not only the normal brain but also the developing brain and the abnormal brain. The two most commonly used whole brain imaging research methodologies are Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) [3]. MRI is a type of technique which helps to determine the different anatomical structures of the brain “through the interaction between radio waves and a strong magnetic field [22].” There are numerous different research studies which utilize MRI technology in order to help find an answer to their research question. For instance, in a study published in 2014 [4], the researchers examined a series of structural MRI images that were taken between late childhood and early adulthood it was found that areas involved with reward and emotion processing (the amygdala and nucleus accumbens respectively) develop sooner than the brain areas involved in cognitive control (such as the prefrontal cortex), an effect could explain typical adolescence behaviors of impulsivity and risk-taking. The findings are significant because they provide a link between the appearance of the continued development of several brain structures and the behaviors that are considered typical in adolescents. Therefore, it could be concluded that when an adolescent appears to be impulsive and taking too many risks this could be a sign that their brain is still developing the structures that are influential in helping to think through these actions. While this is an assumption that could be made from the results it is also important to note that this does not excuse an adolescent from doing something grievously wrong.

Figure 1. Structural www.brainfacts.org

MRI scan

image.

Retrieved

from

In another study, published in 2016 [5], the researchers used MRI imaging to measure the differences in gray matter (brain cells in individuals diagnosed as being high-functioning with autism spectrum disorder and individuals who are described as being of typical development). The results of this study found that there was an increased volume of gray matter in individuals who were diagnosed as high functioning on the autism spectrum. This was suspected to result from an increase in early brain development in individuals with Autism Spectrum Disorder which is then directly followed by a sudden stop, or decrease, in brain development by the time early adolescents occurs. This differential development to brain wiring may explain different cognitive and behavioral symptoms that are commonly associated with Autism Spectrum Disorder. While Autism Spectrum Disorder is a complex disorder, these findings are significant in its addition to the growing body of knowledge about the disorder and its contribution to the research into its development and treatment. While the MRI methodology is used to view the anatomical structure of the brain and spinal cord, fMRI is used to view the physiological function or neural activity that occurs in the brain and spinal cord [3]. FMRI works by detecting the increase of oxygen in the specific brain areas which then indirectly shows neural activity [3]. In a study published in 2013 it was researched whether or not the hippocampus, a brain structure important to emotion and memory, can be linked to the experience of social emotions, such as compassion and empathy to another person’s circumstances [6].


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This study used fMRI scans while the participants were asked to identify the feeling of strong emotion in response to a narrative being read to them. It was hypothesized that the hippocampus was used more and for a longer period of time when trying to understand someone else’s mental circumstances, such as the experience of sadness, compared to trying to understand someone’s physical circumstances, such as when they get physically injured. The results of this study confirmed the hypothesis. To explain these findings, the researchers suggested that in order to be able to empathize with a person’s mental circumstances one would need to recall an event that would personally result in the same type of circumstance. For example, if a friend were experiencing intense sadness- one would need to recall a time when they also felt incredibly sad in order to understand how to empathize with their friend. This particular study was important because it elucidated the brain structures involved in normal experience of social emotion and emotional processing, which can help identify why dementia patients, who have some hippocampal structure loss, also experience deficits in the social emotion (e.g. compassion) regarding another person’s mental or physical situation [6].

18 commonly used methodologies are fluorescent microscopy, electron microscopy, and immunohistochemistry. It is also important to know that these molecular and cellular techniques are often used in tandem with each other in order to provide comprehensive answers to the research question. The methodology of Fluorescent microscopy uses fluorescent molecule that attach to a specific antibody which then attaches to a specific target within the brain (such as a specific protein) in order to visualize small structures that are present [7]. The major appeal of Fluorescent microscopy is that a researcher can use more than one fluorescent molecule at a time in a single brain sample in order to determine the spatial proximity and interaction between two different molecules[3]. Another major benefit of this particular techniques is that it can be used in living samples as well as non-living samples. While there are many benefits to the use of this molecular and cellular technique, one major disadvantage is that once the fluorescent molecules are illuminated, the illumination will fade after a certain amount of time being exposed to light [3].

Figure 3. Image of Fluorescent Microscopy images in a variety of body systems/areas. Retrieved from physiologyonline.physiology.org

Figure 2. Example of a final fMRI scan after analysis of data. Retrieved from en.wikipedia.org

Molecular and Cellular Techniques Another way of conducting brain research is by studying the molecular and cellular components which make up the brain [3]. A few of the most

An example which highlights the use of fluorescent microscopy methods in order to improve neurosurgery was published in 2016 [8]. The aim of the study was to come up with a novel approach that could useful in classifying brain tumors through the automatic intraoperative analysis of the microscopic structure. Currently, during a brain tumor removal surgery, a sample of the tumor tissue if often sent to


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the pathology lab in order to tell if the tumor is cancerous, and if it is, what type of cancer is present. The problem is that these procedures are timeconsuming and the fact that the samples can occasionally be altered structurally due to the preparation procedures. This new method, used fluorescent microscopy and an automated detection system to classify the type of tumor tissue present. The classification happened through the comparison of the sample tissue from the current patient with a database of stored images of meningioma and glioblastomas. It was found that the particular method that was tested was useful in detecting the difference between these two types of brain tumor. It is the belief of the researchers that after the further testing of this novel method of classification it could become extremely useful in terms of speed and accuracy in the detection of two different types of brain tumor. This study was included because it shows how useful cellular research methodologies in the field of neuroscience can be used in many different areas in order to try benefit the general public by creating new technologies and procedures. The method of Electron microscopy replaces the use of fluorescent molecules with electrons to help create an image of the smaller structures and components of the cell [3]. The greatest benefit of electron microscopy is that it helps scientists and researchers to be able to visualize the smallest cellular structures in the brain. A major disadvantage to this research tool is that it cannot be used on living specimens because it requires the use of different substances and chemicals to make the structures visible. There are two major types of electron microscopy which are used in the field of neuroscience are transmission electron microscopy and scanning electron microscopy [3]. A study published in 2015 [9] focused on the effects of stress on the structure of the blood-brain barrier in male adult rats. Using immunohistochemistry, electron microscopy, and fluorescent microscopy molecular markers of endothelial cells and astroglia, it was found that exposure to stress can cause structural changes to the blood-brain barrier along with the hippocampus and the frontal lobe. These structural changes occur

19 almost immediately but only become permanent after exposure to stress for three days. These results suggest that the effects of stress on the brain can be harmful in a way that does not allow the maintenance of normal blood-brain barrier structure. It was reported in previous research that some neurological and psychiatric conditions can be linked to blood-brain barrier structural dysfunction. This study helped to provide more information on the possible causes of neurological and psychiatric conditions and could thus help to provide better treatments for those who suffer from them. One last method used to study the molecular and cellular components of the brain is immunohistochemistry. Immunohistochemistry detects antigens in tissues through the use of immunological and chemical reactions [10]. There are two different types of immunohistochemistry: direct and indirect. Direct immunohistochemistry takes advantage of the fact that the immune system creates certain antibodies agents, particular proteins (antigens) which can recognized and bound to. This attachment between antibody and antigen to tissues in the brain can then be identified and quantified through use of several different chemical treatments. Indirect immunohistochemistry uses a primary antibody and a labeled secondary antibody to visualize and quantify, in the same manner as direct immunohistochemistry, the binding of an antibody to particular antigens to different cellular components of the brain. A major benefit of immunohistochemistry is that one can use it to visual the presence of different proteins which can then be indirectly linked backed to the genes which create them [3]. A study that was reported in 2016 [11] used immunohistochemistry to visualize the effect of repeated, excessive, alcohol exposure on the brain. By looking at microglia (a type of support cell in the brain reported to be a part of the neuroimmune system) it was found that it rats which were exposed to two episodes of “binge-like” alcohol consumption, the first “drinking” episode “primed” microglia to become overactive during the second episode. This effect occurred in the hippocampus (an area important for emotion and memory) and the


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entohinal cortex (an area important for memory and navigation). In fact, using a second method called enzyme-linked immunosorbent assay it was found that the activated microglia displayed a proinflammatory phenotype- which can lead to brain degeneration. This study is significant because it provides more information about the effects of excessive alcohol consumption on the cellular components of the brain and could lead to the development of better treatment methods to offset the neurodegenerative effects of binge episodes.

Figure 4. Example of the visualization in immunohistochemistry. Retrieved from journal.frontiersin.org

Genetic Techniques There are several different types of genetic methodologies which includes genetic engineering, genetic targeting, and gene therapy [3]. This section focuses on developments in the section of genetic targeting, which will lay the foundations for what one day could become a form of gene therapy. Gene targeting is the incorporation of an engineered DNA sequence into an animal’s genetic code. There are two main types of gene targeting: Knock out and Knock in. Knockout gene targeting is used to remove a particular genetic sequence. On the other

20 hand, Knock-in gene targeting is when a particular genetic sequence is inserted where another functioning sequence once was. In simpler terms, these types of genetic techniques are used in order to investigate the effects of genetic alterations on the brain and behavior. In a 2014 study titled “Mouse Model of Chromosome 15q13.3 Microdeletion Syndrome Demonstrates Features Related to Autism Spectrum Disorder” [12] focused on the effects of a chromosomal abnormality in humans, which has been linked to the occurrence of epilepsy, intellectual disability, schizophrenia, and autism spectrum disorder, in mice. This study engineered a genetic knockout mouse with a mouse equivalent of the Chromosomes 15q13.3 microdeletion that is found in humans. The goal of this study was to explore a possible cause for several different neurological, psychiatric, and developmental disorders. The results showed that knock out mice had enlarged brains and lateral ventricles, decreased social interaction, and increased repetitive behaviors. These characteristics of decreased social interaction and increased repetitive behaviors are commonly seen in patients who are diagnosed with an Autism Spectrum disorder [11]. This study was significant because it provided insight into the possible genetic causation of several disorders and highlighted candidate genes for the development of treatment interventions. In the research study “Mice that lack the Cterminal region of Reelin exhibit behavioral abnormalities related to neuropsychiatric disorders”, published in 2016 [13] the goal was to generate a knock-in mouse which might elucidate the effects of altered Reelin overproduction. Reelin is extremely important in normal neuronal function and thus, very important in synaptic plasticity. It was hypothesized that if it were to be overexpressed in the brain, than it could play a role in the occurrence of different neurodevelopmental and psychiatric disorders. This experiment found that the overexpression of Reelin in the brain of knock-in mice led to increased activity, anxiety-like behaviors, and a significant decrease in social behavior. It was further concluded that increased


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research into the roles of Reelin and its dysfunction may provide more information into characteristics of several different psychiatric conditions. This study is important because it explores a possible causative factor which can lead to different neurodevelopmental and psychiatric disorders. Misconceptions and Ethical Concerns While there are many different improvements and advancements in the field of neuroscience as shown through the explanation of several different research methodologies, several issues can result from different research studies, such as the development of misunderstandings or misinterpretation of the field. These misunderstandings are so common, that in the field of neuroscience they have become known as “neuromyths.” In the study titled “Neuromyths in education: Prevalence and predictors of misconceptions among teachers” the authors define a neuromyth as “a misconceptions generated by a misunderstanding, a misreading, or a misquoting, of fact scientifically established [by brain research] to make a case for the use of brain research in education and other contexts [14].” There are numerous different neuromyths which are either widely heard of and/or believed in by the general public today which are talked about in a study published in 2008 [15]. The first most common neuromyth is the 10% myth, which suggests that humans only used 10% of their brains. However, this statement is entirely false because even though not all of the cells of the brain are active for a given task, the brain is still constantly active in some way. This constant activity is due to the fact that the brain needs to be ready for whatever may happen next. The possible origin of this myth lies in the way that the results of fMRI experiments are displayed publicly. Specifically, since the areas of the brain that are highlighted with different colors are often about the size of at least 10% of the brain, then it could be assumed by the general public that this is the only area of the brain that is active during whatever process the fMRI image is trying to show. In order to try to eradicate the belief in this neuromyth, it is important to know that data that is

21 often displayed in these fMRI images as are the results of several different participants summed up into one or more pictures where the resulting colors show overlapping points of statistical significance [16]. A second neuromyth, is the belief that an individual is a left- or right-brained thinker [15]. In fact, through personal experience, there are numerous social media outlets which report this belief as a fact. The belief that an individuals is a left- or right- brained thinker is false, because the brain is largely interconnected and each hemisphere has to communicate with the other in order to complete some tasks associated with higher cognitive function. This neuromyth could be due to the misunderstanding of research which was completed using epileptic patients who underwent split-brain surgery which destroys the main communication pathway between the two hemispheres. In these studies, it was found that when the patient was asked to complete a certain task only the task that was being processed in the left hemisphere was reported to have occurred. Although these possible neuromyth origination points are still a bit elusive, it is still highly important to understand the truth behind these neuromyths. It was found that not only the public opinion of but the field of education is largely impacted by the belief in neuromyths [14]. In fact, it has been suggested that the belief in, and application, of tools inspired by neuromyths in the classroom can be harmful to the current and future education of young students. In a study published in 2012 [14], the authors focused on the prevalence of belief in different neuromyths in educators from the United Kingdom and the Netherlands who taught in the primary or secondary schooling levels. The survey results found that 49% of the neuromyths included in the survey were believed to be true by the teachers. In another study, published in 2012 [23], it was suggested that teachers are highly interested in improving their teaching techniques in a way that will maximize their students learning. This is an important fact to point out because most teachers would not dream of using false information in order


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to improve their teaching styles if they knew it to not be accurate. This study suggested that teachers are interested in using the information out of neuroscience research to try improve their students learning potential. Finally, it was suggested that in order to minimize the danger of the adoption of neuromyths in the classroom is to educate all teachers with the truth about the brain in the hopes that the truth would allow them to determine the difference between faulty teaching methods and methods which are based on facts [17]. One final study which highlights the prevalence of neuromyths in education [18] was published in 2016. This article studied the use of learning styles categorization in the college classroom. The learning styles tool suggests that an individual has a preference for a learning style, and if taught information through this style, will have an increased chance of learning and understanding the material which is being presented. This myth is not true since the learning and understanding of a particular concept is not limited to one preferred sensory domain alone. In fact, it is through the repetition of presented information using numerous different senses that the material is learned and understood. This study used two databases that were thought to be easily accessible to those that participate in the education of young minds at higher education institutions. It was found that even though it is known that the learning styles method is false, it is still widely publicized which could suggest that it is still prevalently used. While the possible motivation behind teachers wanting to learn more about neuroscience to help improve the education of their students is honorable; it is still important to understand the dangers of applying false information to teaching technique. Ethical concern must also be addressed in relationship to research methodologies that are used in the field of neuroscience. One ethical concern is associated with the use of whole brain imaging [19]. This concern revolves around the question of what to do in the case incidental findings, such as a brain abnormality in a participant who is participating in a research study. According to the authors, there is no designated protocol for such an occurrence. When

22 considering the present concerns for this present study, no literature was present to suggest guidelines in this type of scenario; however, it is probably best to inform the participant of this abnormality. A second ethical concern is with the use of animals in neuroscientific research. First, and foremost, it is important to understand there are numerous different institutional review boards and laws which govern and approve the use of animals for an experiment [16]. These review boards and laws are in place in order to ensure the proper health and treatment of laboratory animals, including the minimization of pain and discomfort. The use of animals in research follows a strict code of conduct to ensure no unnecessary procedures are carried out, and no unnecessary animals are used for the benefit of science. However, even though all of these measures are put in place, the ethical concerns associated with the use of animals are still important to be aware of and reflect on. A final ethical concern that is associated with the use of research methodologies in the field of neuroscience is related to gene therapy. Gene therapy is the use of gene manipulation in order to attempt to treat and/or prevent diseases [20]. Gene manipulation is achieved through the addition of a “healthy” gene sequence to replace an “unhealthy” gene sequence, completely removing a gene, and/or introducing a new gene sequence entirely. The ethical concerns associated with gene therapy are that if for some reason when gene therapy is applied it were to affect the cells which are responsible for the reproduction of future generations the costs could out way the benefits since there is no guarantee that the treatment would work for future generations [21]. Gene Therapy is controversial because the long-term effects of this type of therapy are not fully known. Ethical concerns are diverse and are important to be aware of and acknowledge due to their significance in understanding how future research methodologies should improve for the good of humanity as a whole. Conclusions and Future Directions The purpose of this study was to explore and gain a better understand of different research methodologies that are used in the field


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neuroscience in order better understand the structure and function of the brain. It was through this exploration of different research methodologies that different improvements and discoveries in the field of neuroscience were located. This literature review looked at multiple studies that used the most common research methodologies. It was found that many advances in the field of neuroscience are due to the development and use of the new technologies based upon these common research methodologies. Findings also suggest that there are several misconceptions and misunderstandings which exist in the general public and the field of neuroscience that can be linked to the misunderstanding of several different research methodologies. Lastly, several different ethical concerns were reviewed which could have implications for further advancement of different research methodologies in the field of neuroscience. For future research about the use of different research methodologies in the field of neuroscience, it would be recommended to look further into additional cellular, molecular, and genetic techniques to be able to fully understand the true impact these methodologies. Works Cited 1. Nordqvist C. 2016. About Neuroscience. Georgetown University; [accessed 2016 Jul 10]. https://neuro.georgetown.edu/about-neuroscience 2. What are the different areas of neuroscience? 2013 Sep 27: NIH; [accessed 2016 July 10]. https://www.nichd.nih.gov/health/topics/neuro/conditioninf o/Pages/areas.aspx 3. Carter M, Shieh J. 2010. Guide to Research Techniques in Neuroscience. New York(NY): Elsevier, Inc. 4. Mills K, Goddings A, Clasen L, Giedd J, Blakemore S. 2014. The developmental mismatch in structural brain maturation during adolescence. Developmental Neuroscience [accessed 2016 Jul 10]; 36:147–160. http://www.ncbi.nlm.nih.gov/pubmed/24993606 5. Eilam-Stock T, Wu T, Spagna A, Egan L, Fan J. 2016. Neuroanatomical Alterations in High-Functioning Adults with Autism Spectrum Disorder. Frontiers in Neuorscience [accessed 2016 Jul 10]; 10:237. http://doi.org/10.3389/fnins.2016.00237 6. Immordino-Yang MH, Singh V. 2013. Hippocampal Contributions to the Processing of Social Emotions. Human Brain Mapping [accessed 2016 Jul 10]; 34:945–955. http://doi.org/10.1002/hbm.21485 7. Rice G. 2013 Nov 19. Fluorescent Microscopy. Montana State University; [accessed 2016 Jul 10].

23 http://serc.carleton.edu/microbelife/research_methods/micr oscopy/fluromic.html 8. Kamen A, Sun S, Wan S, Kluckner S, Chen T, Gigler AM, Charalampaki P. 2016. Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.BioMed Research International [accessed 2016 Jul 10]; http://doi.org/10.1155/2016/6183218 9. Santha P, Veszelka S, Hoyk Z, Meszaros M, Walter FR, Toth AE, Deli MA. 2015. Restraint Stress-Induced Morphological Changes at the Blood-Brain Barrier in Adult Rats.Frontiers in Molecular Neuroscience [accessed 2016 Jul 10]; http://doi.org/10.3389/fnmol.2015.00088 10. Immunohistochemistry. The Human Protein Atlas; [accessed 2016 Jul 10]. http://www.proteinatlas.org/learn/method/immunohistoche mistry 11. Marshall SA, Geil CR, Nixon K. 2016. Prior Binge Ethanol Exposure Potentiates the Microglial Response in a Model of Alcohol-Induced Neurodegeneration.Brain Sciences [accessed 2016 Jul 10]; http://doi.org/10.3390/brainsci6020016 12. Kogan JH, Gross AK, Featherstone RE, Shin R, Chen Q, Heusner CL, Adachi M, Lin A, Walton NM, Miyoshi S, et al. 2016. Mouse Model of Chromosome 15q13.3 Microdeletion Syndrome demonstrates Features Related to Autism Spectrum Disorder.Journal of Neuroscience [accessed 2016 Jul 10]; http://www.ncbi.nlm.nih.gov/pubmed/26658876 13. Sakai K, Shoji H, Kohno T, Miyakawa T, Hattori M. 2016. Mice that lack the C-terminal region of Reelin exhibit behavioral abnormalities related to neuropsychiatric disorders.Scientific Reports [accessed 2016 Jul 10]; http://doi.org/10.1038/srep28636 14. Dekker S, Lee NC, Howard-Jones P, Jolles J. 2012. Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers.Frontiers in Psychology [accessed 2016 Jul 10]; 3:429. http://doi.org/10.3389/fpsyg.2012.00429 15. Geake J. 2008. Neuromythologies in education.Educational Research [accessed 2016 Jul 10]; :123–133. http://dx.doi.org.francis.idm.oclc.org/10.1080/0013188080 2082518 16. Harrington M. 2006. The design of experiments in neuroscience. Belmont, CA: Thomson/Wadsworth. 17. Pasquinelli E. 2012. Neuromyths: Why do they exist and persist? Mind, Brain, and Education [accessed 2016 Jul 10]; :89–96. http://francis.idm.oclc.org/login?url=http://search.proquest. com.francis.idm.oclc.org/docview/1023528775?accountid= 4216 18. Newton PM. 2015. The Learning Styles Myth is Thriving in Higher Education.Frontiers in Psychology [accessed 2016 Jul 10]; 6. http://doi.org/10.3389/fpsyg.2015.01908


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19. Neuroimaging: Visualizing Brain Structure and Function. Colombia University; [accessed 2016 Jul 10]. http://ccnmtl.columbia.edu/projects/neuroethics/module1/fo undationtext/ 20. What is gene therapy? . 2015 Jul 19. Genetics Home Reference; [accessed 2016 Jul 10]. https://ghr.nlm.nih.gov/primer/therapy/genetherapy 21. Fact Sheet 23| GENE THERAPY . 2015 Sep 30. Genetics Home Reference; [accessed 2016 Jul 10]. http://www.genetics.edu.au/Publications-andResources/Genetics-Fact-Sheets/FactSheetGeneTherapy 22. Carlson, N. 2013. Physiology of Behavior. Upper Saddle River (NJ): Pearson Education, Inc. 23. Hook, C. J., & Farah, M.J. 2013. Neuroscience for Educators: What Are They Seeking, and What Are They Finding?. Neuroethics, 6 (2), 331-341. http://dx.doi.org/10.007/s12152-012-9159-3

24 Lauren Olek ('17) is a Psychology major with a dual minor in History and Neuroscience. She is actively engaged in community service in addition to her academic studies. Lauren has assisted as a work study with research in the laboratory of Dr. Shlomit Flaisher-Grinberg for the past two years. After completing her undergraduate studies at Saint Francis University, Lauren hopes to move on to graduate school in order to obtain a doctoral degree in Neuroscience. Lauren would like to thank Dr. Shlomit Flaisher-Grinberg for her continued support and guidance over the course of the research study which led to the creation of this paper and her parents and sister for their continued support which have helped her on her journey to reach her dreams.

Call for papers for SPECTRUM Submission Guidelines The purpose of SPECTRUM is not merely to disseminate new results, but also to inform and enlighten. Our readership is a general and multidisciplinary audience who may not be an expert in your field of study. Consequently, please explain all pertinent concepts essential to understanding your article as well as any concepts that might not be common knowledge. Please submit your file in Microsoft Word format as an attachment to the following email address: spectrum@francis.edu. The text should be single spaced, using 12-point Times New Roman font. Please use italics, rather than underlining, for emphasis. Organization of Manuscripts SPECTRUM is an interdisciplinary journal accepting submissions from the natural sciences, the humanities, as well as the professional schools (health sciences and business), therefore, the structure and style of each manuscript will differ from discipline to discipline. Regardless, all submissions must provide a cover sheet, a thorough introduction of the problem your research addresses, the conclusion(s), result(s) or findings of your research, as well as some form of bibliographic citation. Below are the general guidelines for these requirements, some of which may not apply to your area of research. Introduction – Include general background of the relevant field and the larger problem your research addresses as well as its relevance within the field. In addition, explain what prompted your investigation, a summary of previous findings related to your research problem and what contributions your project brings (or was expected to bring) to the issue. Methods and Materials (If applicable) – Summarize important methods and materials used in your research. Results/Conclusions – Give detailed report of the results and or conclusions reached through your research. Discussion – Results should be evaluated in the context of general research problem, the implications of which should be explained with conclusions, predictions or suggestions (if applicable) for further study. Tables (if applicable) – Create tables in Microsoft Word format and insert into general text accompanied by a table legend. Each table needs a number based on its appearance in the paper, where it is referenced. Figures (if applicable) – Please submit figures at the end of the article, one image per page; we will fit these in as we organize the manuscript. Each figure needs a number (the figures shall be numbered consecutively in the order of their appearance in the paper) and a title. SPECTRUM will be printed black and white, but there will be an on-line version where figures submitted in color will appear in color. References – You may use any referencing style you choose so long as it is a standard format or your discipline (IEE, APA, ACS, PubMed) and that you use it consistently and to the appropriate bibliographical standards.

Spectrum volume 7(3)  
Spectrum volume 7(3)