Matching Items (125)
Description
In response to a national call within STEM to increase diversity within the sciences, there has been a growth in science education research aimed at increasing participation of underrepresented groups in science, such as women and ethnic/racial minorities. However, an underexplored underrepresented group in science are religious students. Though 82% of the United States population is religiously affiliated, only 52% of scientists are religious (Pew, 2009). Even further, only 32% of biologists are religious, with 25% identifying as Christian (Pew, 2009; Ecklund, 2007). One reason as to why Christian individuals are underrepresented in biology is because faculty may express biases that affect students' ability to persist in the field of biology. In this study, we explored how revealing a Christian student's religious identity on science graduate application would impact faculty's perception of the student during the biology graduate application process. We found that faculty were significantly more likely to perceive the student who revealed their religious identity to be less competent, hirable, likeable, and faculty would be less likely to mentor the student. Our study informs upon possible reasons as to why there is an underrepresentation of Christians in science. This further suggests that bias against Christians must be addressed in order to avoid real-world, negative treatment of Christians in science.
ContributorsTruong, Jasmine Maylee (Author) / Brownell, Sara (Thesis director) / Gaughan, Monica (Committee member) / Barnes, Liz (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.
ContributorsBrooks, Meilia Catherine (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Ghirlanda, Giovanna (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in the context of a high-enrollment active-learning undergraduate biology course. Using surveys and semistructured interviews, we investigated whether students perceived that instructors know their names, the importance of instructors knowing their names, and how instructors learned their names. We found that, while only 20% of students perceived their names were known in previous high-enrollment biology classes, 78% of students perceived that an instructor of this course knew their names. However, instructors only knew 53% of names, indicating that instructors do not have to know student names in order for students to perceive that their names are known. Using grounded theory, we identified nine reasons why students feel that having their names known is important. When we asked students how they perceived instructors learned their names, the most common response was instructor use of name tents during in-class discussion. These findings suggest that students can benefit from perceiving that instructors know their names and name tents could be a relatively easy way for students to think that instructors know their names. Academic self-concept is one's perception of his or her ability in an academic domain compared to other students. As college biology classrooms transition from lecturing to active learning, students interact more with each other and are likely comparing themselves more to students in the class. Student characteristics, such as gender and race/ethnicity, can impact the level of academic self-concept, however this has been unexplored in the context of undergraduate biology. In this study, we explored whether student characteristics can affect academic self-concept in the context of a college physiology course. Using a survey, students self-reported how smart they perceived themselves in the context of physiology compared to the whole class and compared to the student they worked most closely with in class. Using logistic regression, we found that males and native English speakers had significantly higher academic self-concept compared to the whole class compared with females and non-native English speakers, respectively. We also found that males and non-transfer students had significantly higher academic self-concept compared to the student they worked most closely with in class compared with females and transfer students, respectively. Using grounded theory, we identified ten distinct factors that influenced how students determined whether they are more or less smart than their groupmate. Finally, we found that students were more likely to report participating less than their groupmate if they had a lower academic self-concept. These findings suggest that student characteristics can influence students' academic self-concept, which in turn may influence their participation in small group discussion.
ContributorsKrieg, Anna Florence (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Historically, studies of condition-dependent signals in animals have been male-centric, but recent work suggests that female ornaments can also communicate individual quality (e.g., disease state, fecundity). There has been a surge of interest in how urbanization alters signaling traits, but we know little about if and how cities affect signal expression in female animals. We measured carotenoid-based plumage coloration and coccidian (Isospora spp) parasite burden in desert and city populations of house finches to examine urban impacts on male and female health and attractiveness. In earlier work, we showed that male house finches are less colorful and more parasitized in the city, and we again detected that pattern in this study for males. However, though city females are also less colorful than their rural counterparts, we found that rural females were more parasitized. Also, regardless of sex and unlike rural birds, more colorful birds in the city were more heavily infected with coccidia. These results show that urban environments can disrupt signal honesty in female animals and highlight the need for more studies on how cities affect disease and condition-dependent traits in both male and female animals.
ContributorsSykes, Brooke Emma (Author) / McGraw, Kevin (Thesis director) / Sweazea, Karen (Committee member) / Hutton, Pierce (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The free-base tetra-tolyl-porphyrin and the corresponding cobalt and iron porphyrin complexes were synthesized and characterized to show that this class of compound can be promising, tunable catalysts for carbon dioxide reduction. During cyclic voltammetry experiments, the iron porphyrin showed an on-set of ‘catalytic current’ at an earlier potential than the cobalt porphyrin’s in organic solutions gassed with carbon dioxide. The cobalt porphyrin yielded larger catalytic currents, but at the same potential as the electrode. This difference, along with the significant changes in the porphyrin’s electronic, optical and redox properties, showed that its capabilities for carbon dioxide reduction can be controlled by metal ions, allotting it unique opportunities for applications in solar fuels catalysis and photochemical reactions.
ContributorsSkibo, Edward Kim (Author) / Moore, Gary (Thesis director) / Woodbury, Neal (Committee member) / School of Molecular Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Course-Based Undergraduate Research Experiences, or CUREs have become an increasingly popular way to integrate research opportunities into the undergraduate biology curriculum. Unlike traditional cookbook labs which provide students with a set experimental design and known outcome, CUREs offer students the opportunity to participate in novel and interesting research that is of interest to the greater biology community. While CUREs have been championed as a way to provide more students with the opportunity to experience, it is unclear whether students benefit differently from participating in different CURE with different structural elements. In this study we focused in on one proposed element of a CURE, collaboration, to determine whether student's perception of this concept change over the course of a CURE and whether it differs among students enrolled in different CUREs. We analyzed pre and post open-ended surveys asking the question "Why might collaboration be important in science?" in two CUREs with different structures of collaboration. We also compared CURE student responses to the responses of senior honors thesis students who had been conducting authentic research. Five themes emerged in response to students' conceptions of collaboration. Comparing two CURE courses, we found that students' conceptions of collaboration were varied within each individual CURE, as well as what students were leaving with compared to the other CURE course. Looking at how student responses compared between 5 different themes, including "Different Perspectives", "Validate/Verify Results", "Compare Results", "Requires Different Expertise", and "Compare results", students appeared to be thinking about collaboration in distinct different ways by lack of continuity in the amount of students discussing each of these among the classes. In addition, we found that student responses in each of the CURE courses were not significantly different for any of the themes except "Different Expertise" compared to the graduating seniors. However, due to the small (n) that the graduating seniors group had, 22, compared to each of the CURE classes composing of 155 and 98 students, this comparison must be taken in a preliminary manner. Overall, students thought differently about collaboration between different CUREs. Still, a gap filling what it means to "collaborate", and whether the structures of CUREs are effective to portray collaboration are still necessary to fully elaborate on this paper's findings.
ContributorsWassef, Cyril Alexander (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
There are two electrophysiological states of sleep in birds (rapid-eye-movement sleep [REM] and slow-wave sleep [SWS]), which have different functions and costs. REM improves memory consolidation, while SWS is neuro-restorative but also exposes the animal to more risk during this deep-sleep phase. Birds who sleep in more exposed microsites are known to invest proportionally less in SWS (presumably to ensure proper vigilance), but otherwise little else is known about the ecological or behavioral predictors of how much time birds devote to REM v. SWS sleep. In this comparative analysis, we examine how proportional time spent in SWS v. REM is related to brain mass and duration of the incubation period in adults. Brain mass and incubation period were chosen as predictors of sleep state investment because brain mass is positively correlated with body size (and may show a relationship between physical development and sleep) and incubation period can be a link used to show similarities and differences between birds and mammals (using mammalian gestation period). We hypothesized that (1) species with larger brains (relative to body size and also while controlling for phylogeny) would have higher demands for information processing, and possibly proportionally outweigh neuro-repair, and thus devote more time to REM and that (2) species with longer incubation periods would have proportionally more REM due to the extended time required for overnight predator vigilance (and not falling into deep sleep) while on the nest. We found, using neurophysiological data from literature on 27 bird species, that adults from species with longer incubation periods spent proportionally more time in REM sleep, but that relative brain size was not significantly associated with relative time spent in REM or SWS. We therefore provide evidence that mammalian and avian REM in response to incubation/gestation period have convergently evolved. Our results suggest that overnight environmental conditions (e.g. sleep site exposure) might have a greater effect on sleep parameters than gross morphological attributes.
ContributorsRaiffe, Joshua Sapell (Author) / McGraw, Kevin (Thesis director) / Deviche, Pierre (Committee member) / Hutton, Pierce (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Humans have greatly altered the night-time photic environment via the production of artificial light at night (ALAN; e.g. street lights, car traffic, billboards, lit buildings). ALAN is problematic because it may significantly alter the seasonal/daily physiological rhythms or behaviors of animals. There has been considerable interest in the impacts of ALAN on health in humans and lab animals, but most such work has centered on adults and we know comparatively little about effects on young animals. We exposed 3-week-old king quail (Excalfactoria chinensis) to a constant overnight blue-light regime for 6 weeks and assessed weekly bactericidal activity of plasma against Escherichia coli - a commonly employed metric of innate immunity in animals. We found that chronic ALAN exposure significantly increased immune function, and that this elevation in immune performance manifested at different developmental time points in males and females. These results counter the pervasive notion that overnight light exposure is universally physiologically harmful to diurnal organisms and indicate that ALAN can provide sex-specific, short-term immunological boosts to developing animals.
ContributorsSaini, Chandan (Author) / McGraw, Kevin (Thesis director) / Hutton, Pierce (Committee member) / Sweazea, Karen (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022