Eclipsing binaries (EBs) are systems in which two stars orbit and pass in front of each other as observed from Earth. They are important astrophysical tools utilized in this project to directly measure the fundamental properties of stars, such as their masses and radii. We create a sample of 35 bright, detached EB targets from the Villanova Catalogue, based on high-quality photometry from the Kepler Satellite and spectroscopy from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Here, we present mass, radius, and orbital solutions for a subset of these EBs. For each system, we extract radial velocities (RVs), the speed at which each star in the system is moving away from the observer, from the APOGEE spectra. Then, we combine the RV with Kepler light curve (LC) information to simultaneously model the system light and line-of-sight speed as a function of time. Because our model has high dimensions with 18 free parameters, we first solve for the LC and RV solutions separately, and then simultaneously solve them. We use a nonlinear least-squares optimization method to determine the best fit solution and quantify the uncertainties in model parameters running Monte Carlo Markov Chain (MCMC) simulations. The model parameters that we find will further our understanding of fundamental stellar properties and binary star orbital parameters.

Our current project is one stage in a larger research goal: to build multiple yeast strains capable of detecting toxic water contaminants. We searched for the optimal contamination concentrations for yeast response to obtain robust, accurate RNA sequencing analysis results. This data will then be used to determine which contaminants elicit a strong enough genetic response for promoter attachment. The fluorescent yeast will emit a recognizable signal in this manner, and allow for reliable water assessment without the use of expensive equipment or testing services. Thus, we investigated the optimal times for RNA extraction, when the cells are expressing the most contaminant-induced genes but are still sustaining metabolic processes. To this end, we must consider factors such as contaminant concentration and time after exposure. At this stage, data collection involves the use of a plate reader, which periodically measures absorbance of the yeast solution. Absorbance, which is proportional to population density, is limited by the solution’s carrying capacity. We then generate population trend graphs and identify the greatest instantaneous growth time in each sample. Through repeated experimentation, we compile a spectrum of contaminant types, concentrations, and yeast carrying capacities. Analysis of these growth curves indicate the time of incubation at which RNA expression is ideal for sequencing. Future work will involve identifying additional contaminants of interest and additional modification of our algorithms to obtain more precise, accurate, and meaningful data.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder consisting of altered social and communication difficulties and restrictive and repetitive behaviors. In the United States, 1 in 68 children are diagnosed with ASD, however, the underlying causes of ASD are unknown. Neuroscientists commonly use magnetic resonance imaging (MRI) to noninvasively study the brains of children with ASD. However, many MRI studies suffer from a small sample size. To address this issue, 17 institutions came together to share anatomical and functional brain images and formed the Autism Brain Imaging Data Exchange (ABIDE). Although ABIDE tackles sample size issues, new pipelines introduced by the human connectome project (HCP) require reassessment of data quality. For our study, an ABIDE dataset went through a rigorous, yet teachable quality control assessment protocol created by the Developmental Cognition and Neuroimaging Lab (DCAN) at Oregon Health & Science University. The scans were graded as usable, probable, and unusable. Grades were determined by precise delineation of gray and white matter brain segmentations in anatomical scans, signal dropout using blood oxygen level detection (BOLD) in functional scans, and proper atlas registration of functional and anatomical data. Preliminarily, 116 subjects were analyzed and numerous issues were found. Anatomical scan segmentation showed signs of poor delineation, and functional brain images had notable loss of signal. Thus far, the analysis yielded a 35% acceptance rate. Early results of this analysis indicate that the ABIDE dataset would benefit from an improved quality assessment protocol along with updates to processing pipelines to increase data quality.

Water can have many contaminants that are harmful to humans and animals alike. Since water is so essential to life on this planet, it is important to know whether or not a water source is contaminated. However, testing water samples can be expensive. In order to make it easier to test water for contaminants, we are working to create a device to sense this contamination within a matter of hours by using yeast that produce a fluorescent protein in the presence of various water contaminants. In order to make the yeast easier to transport, we first need to dry the yeast before packaging it, similar to dry yeast that can be bought at the grocery store. To test a sample, the device utilizes a light to frequency sensor to convert the intensity of light from the glowing yeast into a signal that can be processed by an arduino board contained within the device. Our device can be used as a reusable, low cost diagnostics machine for testing water for contamination. This could provide access to advanced technology in parts of the world that don’t have access to expensive testing equipment, as well as  be used by an ecologist in the field.

Portland, Oregon takes the lead in the highest strip clubs per capita, however, strip clubs in Portland have gone unregulated, allowing for poor management and oversight of health and safety issues. When violence and exploitation occurs, exotic dancers have scarce resources to turn to due to the continued stigma of being a sex worker. Past research has explored limited issues related to exotic dancers, including drug use, sexually transmitted infections, and mental health problems, but few have questioned what safety measures are needed to protect women in this industry. This study aims to explore the exploitation and abuse that exotic dancers experience in order to improve the laws and regulations of strip clubs. Through this qualitative study, I will use semi-structured interviews in order to better understand experiences of interpersonal violence against exotic dancers including various forms of violence from clients, employees and law enforcement in Portland. Data will be analyzed using thematic analysis in order to establish working themes. The anticipated results of this study is that the interviewed participants will express various experiences of interpersonal violence and that there may be common variables to violence experienced in strip clubs such as race, age, structural safety features in clubs, and the availability of outside resources to report violence. It is also expected that violence perpetrated by clients and club managers will be among the highest reports by the participants. Lastly, it is hypothesized that women who have not experienced abuse and exploitation (or limited amounts) while at work will still fear experiencing it, which is still a cause for concern. This study provides a platform for exotic dancers’ voices to be heard, while also attempting to improve better working conditions of adult entertainment establishments.

The Jasmonic Acid (JA) sensitive system is an important stress and pathogen resilience pathway in plants. Understanding the kinetics of the components of this system allows for a more rational approach to improving crop resilience than traditional breeding.  MYC transcription factors upregulate the expression of particular defense or stress response genes, and these are inhibited by JASMONATE-ZIM-DOMAIN (JAZ) proteins, which are in turn degraded in the presence of JA-Ile, the bioactive form of JA. This leads to a system whereby MYC transcription factors are only active when JA-Ile is present. The variable defenses plants are capable of mounting may be regulated by differential interactions between some of the 12 JAZ proteins and 3 relevant MYC proteins in Arabidopsis thaliana. We have used a fluorescent yeast model to characterize the differences in those interactions in order to predict changes in plant responses when particular JAZ proteins are up or down regulated. Although we have been unable to replicate the canonical direct repression of MYC transcription factors by JAZ proteins, repression was observed when the JAZ protein was fused with an Mxi1 repression domain. This allows us to determine the relative effect of each JAZ protein on MYC transcription factors. A rough model involving related Hill functions is built using this information which can be used to predict the effect on the downstream response based on the abundance of the different JAZ proteins. The model can then be verified in planta using a dCas9 transcription factor with an appropriate gRNA, and a MYC-responsive GUS gene in a transient transformation.

G Protein-Coupled Receptors (GPCRs) are seven-transmembrane proteins present throughout the body that can be activated upon binding of drugs, hormones, or neurotransmitters. Acting like an inbox for messages, these multi-protein complexes play a significant role in the body by regulating cell expression and signaling, making them attractive targets for drug development. One class of GPCRs, known as the adrenergic receptors (ARs) are critical in modulating the function of numerous targets, including cardiac muscle, vascular smooth muscle, and bronchial smooth muscle. There are nine AR subtypes: three α1s, three α2s, and three βs. In my research, I focus on deciphering the interactions of proteins with the α1D-AR due to a recent publication of the Hague Lab, which discovered that α1D-AR forms a multi-PDZ protein complex. To determine the participants of this complex and identify the presence of associated proteins, I first purified the proteins that potentially bind to α1D-AR. In my purification process, I first conducted Polymerase Chain Reactions (PCRs), transformed my bacteria, grew my bacteria, and finally purified my proteins of interest. With this information, my lab and I intend to identify the complexes using mass spectrometry and Octet. Determining how α1D-ARs are organized can create a deeper understanding of how drugs work in the body and assist in new drug development.

Telehealth is being widely adopted in medicine, but is still novel in the field of dentistry, especially in therapy. Dentists rely on in-clinic visits and application of therapies rather than prescribing drug treatments outside the clinic. In this paper, we have developed and introduced an app that dentists can use to establish and maintain trust for fluoride treatments to be conducted correctly at home. Pediatric patients and their caregivers can access the client app on their phone to guide them remotely and record progress. A unique aspect of this app is rapid and accurate display of the child’s own emerging teeth and the sites for fluoride therapy. In our study, we surveyed 16 dentists that were equally divided into two groups, half being exposed to a demonstration of the tool before taking the survey, and the other half taking the survey without any exposure. Although the randomly selected group which saw the app received their dental degrees on average 8 years earlier, four times the number of these dentists would allow off-label at-home use of the highest level of fluoride varnish for the youngest ages of patients. The tools present in this pilot study appear to influence pediatric dentists' management plans and attitudes towards home treatments, demonstrating the potential of expanding teledentistry to incorporate at-home care and treatment in addition to self-monitoring and tracking.

Cancer is the 2nd leading cause of death in the United States. Of these cases, 6-7% are pancreatic cancer (PC), yet this disease is ranked 1st in mortality because what causes PC and how it manifests is still relatively unknown. Early detection is difficult and in a majority of cases, patients are diagnosed after the cancer has progressed to final stages. For the last 7 years, the Human Photonics Laboratory (HPL) has been dedicated to early detection and diagnosis of PC through the next generation 3D pathology (and all aspects of its infrastructure: 3D microscopy, 3D reconstructions/supercomputing, millifluidic device development/whole tissue processing, clinical evalution/validation). For PC, cell and tissue specimens which have been imaged in 3D and reconstructed in an advanced 3D visualization software (Amira) require an appropriate gray scale and RGB color scheme to determine accurate cancer diagnoses. Ideal and non-ideal samples may then be passed onto collaborating pathologists for clinical evaluation and validation. This research project seeks to satisfy three primary aims: (1) properly match the color metrics/scheme of the raw optical 3D microscope datasets to 3D computer reconstructions and analyze (qualitatively/quantitatively) the gray scale/RGB color histogram between normal/cancerous (human) pancreatic samples, (2) compare/contrast the pathological metric known as the nuclear-to-cytoplasmic (N/C) ratio between a small sample of normal/cancerous specimens, (3) choose 5-10 ideally processed/matched specimens and submit to pathologists for clinical evalution. The outcome of this project is expected to accelerate the diagnosis of PC.

The goal of this project is to explore how Brain-Computer Interface (BCI) devices are depicted in the media, especially news media. BCI technology is not new, but it is still very much in its infancy, with few feasible embodiments usable for practical, non-medical applications. Additionally, a majority of BCIs that are (only somewhat) feasible require dangerous invasive surgical procedures. Yet in the last year, the technology has received a higher-than-average level of press coverage. Media coverage of upcoming medical technology is not a trivial issue. Substantial research has shown that positive portrayals of novel medical technology in the media can indirectly affect patient consent to undergo treatment. Consequently, we argue that it is essential that the media reports and discusses the ethical impacts of BCIs. We aim to discover whether media coverage depicts the technology realistically, discussing its shortcomings, risks associated with its use, and ethical issues related to neural implantation. We use a research software called FACTIVA to survey and analyze the depiction of BCI technology inn English-speaking media such as news publications, radio transcripts, press releases, etc. This content analysis allows us to understand mass media values and narratives in three general ways: 1) it generates evidence to demonstrate whether there is any positively-biased and over-enthusiastic depiction of BCI in mass media; 2) it sheds light on whether there is an absence of discussion of risks and ethics associated with BCI technology; and 3) it exposes unrealistic discourse, such as wide-reaching claims of the panacean nature of BCIs (i.e. transhumanist arguments, the race against artificial intelligence, etc.). In brief, this study allows us to explore whether media misrepresentations of BCI could influence the narrative about the technology in ways that may increase the risk of harms for prospective patients and their families.

90% of drugs fail in clinical trials. As a result, a successful FDA-approved drug costs an average of $2.6 billion to develop and takes a decade to reach the market. Pharmaceutical companies are unable to comprehensively evaluate drug toxicity before clinical trials because no methods exist to screen the thousands of protein-protein interactions (PPI) that a drug could unintentionally disrupt in the human body. The ability to disqualify drug candidates with off-target effects before clinical trials would greatly increase the speed at which useful drugs are introduced to the market and reduce the expense of developing new therapeutics for those who need them. Here, we demonstrate a novel synthetic biology-based method, SynAg, that enables thorough drug characterization by linking PPI binding strength to the cellular agglutination of engineered S. cerevisiae cells. SynAg is a library-on-library approach that conserves the accuracy of pairwise PPI toxicity screening in a high-throughput, one-pot format. Specifically, we construct a SynAg library to characterize a 20-by-20 protein interaction network involved in cellular apoptosis to demonstrate the accurate profiling of a complex and highly interconnected family of proteins. Finally, we use the SynAg library to characterize the on- and off-target effects of small-molecule cancer therapeutics, which are shown to be consistent with previous studies. 

The human brain is comprised of many billions of neurons, which then connect with each other many trillions of times. Modeling how these neurons function provides insight into language, thought, and behavior. However, neurons are not all identical, so an approach that performs well on some neurons will perform poorly on others. I focused on a learning system to detect and model neurons that use pattern-recognition in order to decide when to fire, which would not be expressed well under existing approaches. We researched a strategy for describing these process in a non-Markov state, which means that we do not have enough information to correctly model the neuron, by approximating to Markov models using a subset of the required information. This means that we produce a set of probabilities for the next state of the neuron given the previous state. We approximate the Markov model using Monte-Carlo Tree Search (MCTS) optimizing for smallest confidence interval to select sequences to measure. In order to calculate a confidence interval on a given sequence we apply A*, pronounced A star, which is a targeted pathfinding algorithm, in order to produce execution paths that in turn find confidence intervals. We use two strategies for pruning sequences; a MCTS to find the measured sequence that differs least from chance for long term learning, and exponential decay for short term learning. We assume that after we process information it cannot be recovered, so when we measure new sequences we only measure from the time of consideration onward. If our process successfully locates important patterns the neuron is looking for, it produces an accurate approximation for the neuron that can be rapidly evaluated.

A pH measurement of oral biofilms is helpful for monitoring the impact of acidogenic bacteria in the caries process. Demineralization of dental enamel is closely related to the time dependent pH of human plaque. Therefore, providing a means to easily measure the local pH of biofilms is a useful clinical diagnostic in the arsenal of caries prevention tools. Optical measurement methods of plaque metabolism can use intrinsic fluorescence or extrinsic fluorescence from added dyes. Autofluorescence spectral features of human oral biofilms at green (500 nm) and red (634 nm) fluorescence wavelengths using 405 nm excitation did not demonstrate a spectral or intensity shift between neutral and acidic conditions. Chlorin e6, an ingredient in chlorophyllin food supplement, exhibited a spectral and intensity shift of fluorescence emission in buffered solutions, but this quantitative pH-dependence was not transferable to a human plaque environment. Finally, a ratiometric quantitative pH measure was achieved by exciting (405 nm laser) a mixture of two dyes, fluorescein and rhodamine B. This two-dye mixture produced two strong fluorescent bands centered at 515 nm (fluorescein) and 580 nm (rhodamine B), where the 515 nm band was pH sensitive and the 580 nm band served as a pH insensitive reference. This dual-dye fluorescence ratio exhibited a linear response over pH 7 to 5 in human oral biofilms during a sugar challenge. We have explored methods to use non-contact, optical measures of local acidity levels in difficult to access dental locations such as occlusal fissures using various pH sensitive fluorescent dye systems.

On top of being iconic symbols of climate change, glaciers integrate year-to-year precipitation and temperature variability, which can mask their response to a climatic trend. For this reason, it is vital to identify the variability in their records to better detect regional climate change. With time-varying sea-level pressure (SLP) and sea-surface temperature (SST) fields, we examine the influence of natural climate variability on 14 glaciers in the North Atlantic region by using a statistical method called dynamical adjustment. The SLP and SST fields each explain 50-60% of variance in the winter mass-balances and 30-40% in the summer mass-balances of the maritime glaciers. The continental glaciers are less closely linked to oceanic variability, with SST explaining roughly 30% of variance in winter and summer mass-balances. The glaciers located further south fluctuate with the North Atlantic Oscillation (NAO) in the winter, while glaciers near the Arctic show influence from the Atlantic Multidecadal Oscillation (AMO) in the winter. To investigate how this variability affects glacier trends, adjusted mass-balances are compared to observational mass-balances with no signs of variability forcing trends. The observed and adjusted summer mass-balance trends of the southern glaciers are significantly negative, while the observed and adjusted winter mass-balances of the northern glaciers are significantly negative. This indicates that winter trends are more likely to emerge in glaciers near the Arctic, while summer trends are more likely to emerge in glaciers further south which is consistent with surface temperature time of emergence for global warming.

Computer science and neurobiology have an undeniable entanglement with one another in the 21st century with the advent of advanced artificial intelligence and attempts to understand the black-box model of a brain. However, most neurobiology research has revolved around understanding existing behaviors in large animals, with commonly researched organisms including zebrafish and mice. Introducing novel behaviors to animals in a controlled environment is undeniably attractive in modern neurobiology. With the use of mass data-collection in the online Aquarium lab environment, this study shows the potential to model and produce novel behaviors in Hydra vulgaris (freshwater anemones with simple nervous systems). We aim to create a high-throughput, data-oriented, model for gene-based behavior analysis and editing in neurobiology research. By creating a single controlled environment to perform all research jobs on mass-produced H. vulgaris strains with clockwork husbandry, extraneous factors affecting the organisms are significantly reduced. By taking data on the success of every modification of these Hydra, we can model the effects of specific factors on these animals and progress toward creating a model organism for neurobiology research akin to knockout mice in medicinal genetics. With these methods we have been able to produce a significantly more efficient process of gene editing in Hydra and with several different kinds of DNA integrants. Over the long term, the wide availability of the online Aquarium lab can yield the discovery of unknown trends in genetic knockouts and the discovery of novel behaviors through analysis of all data collected in this controlled environment. Here we highlight successes thus far and future developments necessary to make this system a viable model for gene-based neural engineering over this century.

In humans, CYP2J2 is involved in both drug metabolism and converting arachidonic acid to epoxyeicosatrienoic acids (EETs), which then act as signaling molecules. EETs are involved in angiogenesis, vasodilation, opening of ion channels, and general protection against cardiotoxicity. Currently, the Totah lab is testing the hypothesis that CYP2J2 has a critical role in the heart as one of the main contributors to EETs production. This research project seeks to determine whether CYP2J2 regulation is similar among different tissues, specifically between the heart and the liver, under oxidative stress conditions. A critical function in the heart would mean that under times of stress, CYP2J2 expression will increase. In contrast, CYP2J2 expression in the liver is expected to decrease similar to other CYP proteins under oxidative stress. To test this, reagents that increase reactive oxygen species (ROS) levels have been incubated with hepatic cells (HepG2) and the levels of CYP2J2 mRNA were measured and compared to untreated control cells. In the heart, CYP2J2 has a more important function in creating protective EETs and the benefits from increasing production of EETs have been shown in our lab to protect against the harmful effects of ROS. A few of the conditions being tested are: varying concentrations of hydrogen peroxide, cobalt chloride, and hypoxic conditions. MTT, a tetrazolium based dye, assays were used to quantify cell viability and rt-PCR was used to determine gene upregulation or downregulation of CYP2J2. Afterwards, the results were compared to similar treatments and conditions in cardiomyocytes. If transcription in HepG2 cells is downregulated but increased in cardiomyocytes, it supports the hypothesis that CYP2J2 has a critical role in the heart compared to the liver, specifically under times of ROS stress.

Lipopolysaccharide (LPS) is a bacterial membrane surface antigen that potently stimulates the immune system. In response to LPS, immune cells release cytokines and chemokines that propagate the immune response. LPS and cytokines can increase in the circulation during infections, which may cause disruption of the blood-brain barrier (BBB). BBB disruption results in leakage of circulating factors into the brain, which can damage neurons. Because there are known sex differences in the immune response, we aimed to determine in mice whether sex and strain affect LPS-induced BBB disruption, and assess cytokine expression patterns in blood and brain that may contribute to sex or strain differences. To test this, we injected different strains of male and female mice, C57 and CD-1, with three different LPS doses or with saline. BBB disruption and the levels of 23 cytokines were evaluated 28 hours later. We found that only the highest dose of LPS induced significant BBB disruption in all sexes and strains. The severity of BBB disruption to the highest LPS dose significantly differed between sexes and strains, and ranked CD-1 male < CD-1 female < C57 male < C57 female. Two-way ANOVA revealed significant sex and strain effects on BBB disruption in the high-dose LPS group, with 14.83 % of the variance attributable to sex, and 24.46% attributable to strain. Cytokines showed distinct profiles in brain and blood among sex and strain following LPS treatment. In blood, interleukin-10, keratinocyte chemoattractant, and interleukin-12(p40) trends in sex and strain paralleled those found for BBB disruption, and IL-12(p70) showed an opposite trend. Our results indicate that the severity of BBB disruption to LPS is partially dependent on sex and strain, with mouse strain having a greater effect. Future directions are warranted to determine whether cytokines identified in this study are causal in the BBB response to LPS.