Meghan Jin, Akash Kotian, Colin Ly, Kailey Matthews, Annabel Mendoza, Sakshi Parikh, Yash Parikh, Irene Quan, Esha Shah, Isabella Stagg, Megha Thomas, Anthea Zhang
Advisor: Dr. Graham Cousens
Assistant: Ali Greulich
The olfactory system is a neuronal sensory complex involved in the processing of odorants. While major olfactory structures such as the olfactory bulb (OB) and piriform cortex (PC) have been studied in depth, much less is known about the medial olfactory cortex (MOC) and by extension, the tenia tecta (TT). The TT is hypothesized to serve as a feedback mechanism response, but its other functions remain unclear. This study aims to explore the cells within the MOC and their role in odor responsiveness and selectivity. Electrophysiological recordings were performed on C57BL/6 mice, with a range of monomolecular odorants being presented for 2 second intervals to anesthetized mice. We found odor responsive and selective cells within the MOC and striatum. The firing rate of non-odor responsive cells was correlated to the respiration cycle; however, each cell studied increased its firing rate at different points during respiration. In this explorative study, possible explanations for the observed odor responsiveness and respiratory linkage are discussed, along with future directions in optogenetics to further explore the MOC.
Abbreviations: AOC, anterior olfactory cortex; AON, anterior olfactory nucleus; LOT, lateral olfactory tract; MOC, medial olfactory cortex; MOP, medial olfactory peduncle; OB, olfactory bulb; OT, olfactory tubercle; PC, piriform cortex; pE, pars externa; pP, pars principalis; TT, tenia tecta
Keywords: anterior olfactory nucleus, medial olfactory cortex, odor responsive, odor selective, olfactory peduncle, olfactory system, tenia tecta
Athira Arayath, Ethan Chee, Richa Desai, Julia Holz, Rachel Lee, Paras Nahar, Lakshay Patel, Rishi Patel, Melissa Pathil, Sakhi Shah, Elaine Wang, Jenny Xu
Advisor: Dr. Minjoon Kouh
Assistant: Jesse Murray
Machine learning algorithms can process and analyze large and complex datasets to make predictions. With easy-to-program languages like Python, easy access to big data, and cheap computers, machine learning is more popular than ever (1). Over the course of three weeks, machine learning was applied to three different datasets. The first project, NFL Game Prediction, used data from prior games to predict the result of later games, achieving 56.2% accuracy on the 2017 games. The second project predicted a person’s sex based on their name using 12,299 male and 17,970 female names as a dataset, yielding an accuracy of over 70%. The third project used speech data to determine the accent of the speaker, with an accuracy of 78%. These projects incorporated logistic regression and Support Vector Machine as binary classifiers. The algorithms that were tested against humans were able to perform as well as or better than humans in their respective tasks.
Kathleen Bailey, Emily Berkow, Michael Calixto, John Chen, Sudhish Devadiga, Alberto Gaytan, Alan Ji, Jason Li, Fatmata Nallo, Jeffrey Pan, Lauren Taylor, Matthew Zhao
Advisor: Daryl Van Ry
Assistant: Hunter Muratore
Electronic waste, or e-waste, consists of discarded electronic devices such as cell phones, televisions, cameras, and computers. As the world develops, a growing number of electronic devices are being manufactured and ultimately discarded. This increase in waste, combined with improper disposal methods around the world, has created a severe health issue as the toxic metals in these devices leach into the surrounding environments.
This study was performed to assess the magnitude of the leaching of these toxic elements. Components from several electronic devices were placed under a range of environmental conditions, which were simulated with various techniques. Acid rain and stomach acid were simulated using low pH solutions created with hydrochloric acid and either rainwater or distilled water, while natural waters were taken from local sources and incineration was simulated using a Meker-Fisher burner. The results showed that the levels of electronic waste leaching into solvents were dependent on the acidity of the solvent and the type of electronic waste. It was determined that incineration and exposure to acid rain or stomach acid had the greatest impact on mobilizing heavy metals from electronic waste, while neutral pH pond water had relatively little effect on heavy metals. At the same time, sand, along with a sand and loamy soil mixture were both very efficient retainers of electronic waste leachate.
Erin Chang, David Chen, Shrey Dalwadi, CJ Faulhaber, Serena Huang, Brian Katat, Joo Un Lee, Ethan Liu, Sydney Mullin, Jeremy Rasmussen, Beyer White, Claudia Zhang
Advisor: Dr. Mary-Ann Pearsall
Assistant: Sarah Costa
In the modern world, catalysts are essential in the synthesis of many compounds. Often, these are organometallic complexes, compounds with a central metal atom and bonded ligands. Before these catalysts can be implemented commercially, it is necessary for them to be synthesized and analyzed in a laboratory setting to fully understand their formation, structure, and properties.
This study focuses on the synthesis of an octahedral trans-chelating diphosphine molybdenum complex, an organometallic complex with a central molybdenum atom, two phosphines linked by a carbon chain on opposite sides of the molecule, and carbonyl groups on the remaining peripheral atoms. We varied the lengths of carbon chain that were used to determine the range of carbon chain length that allowed for the isomerization of the cis– substituted molecule to the trans– isomer.
The production of the molybdenum compound comprised of the initial formation of a cis– molybdenum-dipiperidine complex followed by the replacement of the piperidines with a specific diphosphine, then thermal isomerization of the cis- complex into the desired trans- configuration. The structures of the intermediates and products were analyzed by nuclear magnetic resonance (NMR) and infrared (IR) spectroscopy, which yielded valuable insight into the identity of the reaction products and the extent of isomerization.
The study was a successful proof of concept and determined that the minimum hydrocarbon chain length for trans- isomerization is 5 carbons.
Alyssa Amorim, Dakota Black, Pranati Borkhetaria, Mary Gambill, Megan Gray, Hannah Jin, Sean Pergola, Kelly Qiang, Michael Rosenberger, Aidan Smires, Kevin Tang and Nolan Vilim
Advisor: Dr. Lisa Jordan
Assistant: Molly Murphy, David Van Dongen
Objectives: To demonstrate the application of life cycle assessment (LCA) to firearms and ammunition in order to connect injury prevention and control research to scholarship in environmental and occupational health.
Methods: We collected data from the ATF on manufacturers, from the EPA and OSHA compliance databases, from the CDC detailed mortality tables, and from two state firearm law databases to explore the upstream and downstream impacts of firearms and ammunition. Results: We identified significant environmental and occupational health violations among firearms manufacturers, and occupational health violations among shooting range facilities, most commonly related to lead emissions and exposures. An exploration of the correlation between firearm deaths and state firearm laws revealed significant associations between all deaths due to firearms, and suicide deaths due to firearms, but insignificant results for firearm homicides. Last, we found evidence that state implementation of Stand Your Ground policies is associated with firearm homicide rates that significantly exceed the national average.
Conclusion: The LCA approach to firearms and ammunition opens up new avenues for public health interventions that extend beyond traditional injury prevention and control research to include negative health outcomes attributed to environmental and occupational health hazards.