Throughout the United States, children and adults are forced to engage in sexual acts and use illicit drugs against their will, in what many would call modern day slavery, commercial sexual exploitation, or sex trafficking. Victims are controlled through coercion, force, fraud, physical and sexual violence and they endure many emotional and physical consequences. Approximately 80% of victims access healthcare during their time of exploitation for health reasons such as acute injuries, sexually transmitted infections, and mental illnesses, such as depression, anxiety, trauma, and suicidal ideation. The emergency department (ED) is the frontline to these patients. To date, little is known about nurses' confidence or competence in identifying or caring for this vulnerable population in Seattle, WA. The aim of this study is to examine what current practices exist at emergency departments in three, large referral hospitals in Seattle to identify victims, and to assess facility and nurse readiness for improved identification. Nine ED nurses in Seattle were interviewed to gain insight into how they currently identify trafficking victims, their opinion of the acceptability of existing screening questions (compiled from nationwide piloted studies) in their ED setting, and whether they felt such a standardized screening tool may be effective in identifying victims and improving subsequent care. Through transcription of audio interviews, nurses’ responses were analyzed and coded for common themes. Improved identification of trafficking victims by nurses and other frontline health workers in emergency settings may lead to more efficient and effective linking with vital support services and resources to assist them in safely exiting the sex trafficking industry. Results from this study will be shared with the participating hospitals, as well as public health officials and stakeholder organizations in order to improve awareness of sex trafficking, victims, and provision of necessary care and resources to support this vulnerable population.

Traumatic fracture of the bone surrounding the eye (the orbit) can result in functional impairment of vision. Double vision, diplopia, is one such sequelae. The cause of diplopia in orbital fracture patients has most commonly been attributed to loss of orbital volume caused by an open fracture that allows the contents of the orbit to shift. However, in cases where the orbit is surgically repaired and orbital volume is restored, some continue to have diplopia. Our hypothesis in these cases is the anatomy of extraocular muscle pulleys is affected by the trauma. The aim of this study is to test this research question by analyzing computed tomography (CT) scans. Patients were selected from a previous study of orbital fracture patients in which all patients had CT scans available for review and consented to be part of a research study on orbital fractures. Locations of the extraocular muscle pulleys were measured relative to the center of the orbit in the lateral, anterior and superior directions. Pulley location was determined by a tangent plane drawn where the optic nerve meets the orbit and crosses the muscle. The measurements were input into Orbit, simulation software, to produce a prediction of visual changes in Lancaster plots. The Lancaster plots were compared to the data from the patient’s clinical visual testing to determine the accuracy of the prediction at each point in the visual field. Our results showed that the accuracy of our prediction varied, as the location of the pulleys is not the only factor that contributes to diplopia. Other factors that may contribute include weakened muscle, entrapment and damage that is not visible on CT. More research is needed to determine how the muscle pulley system is affected in orbital fracture, and to determine how clinical interventions including surgery may help improve diplopia.

Orbital fractures are one of the most common facial fractures and often result in functional deficits if not treated appropriately. No prior reports have assessed dynamic visual acuity (DVA) in these patients, which is critical in visually tracking objects during head movement. Our research seeks to determine the effect of orbital fractures on DVA and assess whether DVA will change over time in those who undergo surgical reconstruction of the orbit. We hypothesize that DVA is adversely affected in orbital fracture patients and improves after surgical reconstruction. We designed a prospective pilot study at a Level I trauma center. Adult patients who presented with an orbital fracture between November 2017-January 2019 without prior ocular history were eligible. All enrolled subjects underwent static visual acuity (SVA) and DVA testing twice: once within one month post-injury before surgery, and another at least ten days following surgery. SVA was assessed using a mobile Snellen eye chart. DVA was measured by having the subject rotate their head at a standard frequency while again reading a Snellen chart. DVA is defined as abnormal if acuity falls more than two lines on the Snellen chart compared to SVA. Eleven patients have been enrolled and tested. Four(36%) patients exhibited abnormal DVA prior to surgery, compared to two(18%) after surgery. For all patients, the mean change in visual acuity (DVA logMAR minus SVA logMAR per subject) before surgery was 0.17 (SD=0.09). Following surgery, the mean difference in visual acuity across all patients was 0.07 logMAR (SD=0.07). Our data shows that, surprisingly, the majority of orbital fractures patients tested did not have abnormal DVA. In patients that did demonstrate abnormal DVA, half improved after surgery. The mean difference in visual acuity of all patients also improved after surgery. A larger study is needed to determine the prevalence of abnormal DVA in this patient population.

Modern cells discriminate among the amino acids chosen to be included in protein synthesis: some, like leucine, serine, glycine, and alanine appear in most proteins across most cells, whereas other amino acids, like (g)-aminobutyric acid and aminoisobutyric acid do not appear. This raises the question of how selectivity among amino acids arose – does the selectivity rely on modern protein machinery or could it have arisen in the first protocells of the Early Earth? For example, could certain amino acids have, by some mechanism, associated more strongly with protocell membranes, increasing their chances of being integrated into the first peptide chains? To test this hypothesis, our group assembles rudimentary protocells from molecules that would have been present on the early-Earth: decanoic acid (a fatty acid), sodium mono-phosphate, salt, and water. Vesicles of the decanoic acid spontaneously form. We then add different amino acids to the solutions and measure their turbidity to determine whether each amino acid causes the number of lamellae in the vesicles to increase or decrease. Increased lamellarity correlates with a sturdier vesicle. If certain amino acids increase lamellarity of protocells, that could serve as a method of selection for certain amino acids rather than others. Our results are that particular amino acids (most notably serine, glycine, and alanine) do in fact increase the lamellarity of fatty acid vesicles significantly, whereas other, less common, amino acids do not. We are currently exploring the plausibility of a mechanism for this occurrence involving ease of rotation around the alpha carbon of the amino acids, and we are investigating other ways in which interactions between amino acids and fatty acid membranes might be manifested, for example by a shift in the solution’s critical vesicle concentration. Our results will fit into the overarching goal of understanding peptide formation and protocell stability in order to gain insight into the origins of life on Earth.

Alzheimer’s disease (AD) is a neurodegenerative illness affecting millions of individuals in the United States and represents the leading cause of dementia worldwide. AD is pathologically defined by abnormal accumulation of interneuronal plaques composed of amyloid beta protein and intraneuronal tangles composed of tau protein. The focus of our current research involves understanding how tau protein homeostasis is restored using the model organism C. elegans. One process by which protein homeostasis is modulated is through the endoplasmic reticulum unfolded protein response (UPRER), which is composed of three signaling branches. Initially, the UPRER acts to restore normal protein folding, but if restoration is impossible, it triggers apoptosis. Our lab became interested in this signaling pathway after we identified the UPRER master transcription factor XBP-1s as a modifier of tau pathology (tauopathy). Specifically, XBP-1s overexpression protects against tauopathy in our C. elegans model. Previous RNA sequencing (RNAseq) analysis identified several downstream target genes upregulated with XBP-1s overexpression in our pathological tau C. elegans model. To follow up, I am working on understanding the molecular basis of XBP-1s-mediated tauopathy suppression by generating C. elegans models containing target genes of interest. Assessment of the effect on tauopathy phenotypes includes behavioral analysis by performing a liquid thrashing assay and tau protein analysis via the Western blot technique. Using the microinjection technique, I have generated a transgenic C. elegans model overexpressing hsp-4, an upregulated XBP-1s target gene identified by RNAseq that potentially modulates tauopathy. We are currently determining whether overexpression of hsp-4 can ameliorate tauopathy similar to XBP-1s overexpression. Gaining further information about the role of these XBP-1s target genes in tauopathy using the model organism C. elegans is valuable for future translational studies in mammals, along with being integral to research for neuroprotective therapies for neurodegenerative diseases such as AD.

Computer vision models are used to help analyze biomedical images for diagnosis and treatment through looking for differences between images by a comparison to a template image. For instance, optical coherence tomography (OCT) is used to diagnose and treat retinal issues. When looking at the brain, injury, cellular uptake and characteristic features vary across regions, therefore images are often segmented into established brain regions to determine how the brain is impacted in a particular study. Current models fail to work in segmenting brain regions because each brain has variation in local microstructure, making it difficult to compare one brain to another. Furthermore, when brains are sliced, the exact location within the brain can be difficult to pinpoint, particularly in regard to depth, because the regions vary slice to slice. Therefore, my research addresses the increasing need for a method of analysis to align and compare images from brain regions across slices from a single brain, and from brain to brain. Using scikit-image analysis tools, I extracted information from cell images and videos of nanoparticles obtained in brain slices and determined trends within various regions. My program extracted cell density, shape, and death, then analyzed the uptake of nanoparticles to determine where a small segment of an image is most likely located within the brain. Iterating over the entire image generated a rough map of the regions within the brain which is refined using mapping descriptions detailed in literature. This research resulted in a systematic program that uses image analysis tools to extract features of defined brain regions. This program allows for quick, accurate and consistent analysis of regional differences of cellular features, nanoparticle distribution, toxicity, and other important measures.

Black bears are the most abundant bear in North America and Washington, but present knowledge is dominated by telemetry studies on home range size or vegetation class preference. Camera trapping data indicating black bear presence were used to build an occupancy model for the two study sites in Washington, one in Okanogan county and one in the northeast. The camera traps are intended for a predator-prey study focused on wolves, cougars, and ungulates, but detected a large number of black bears, suggesting the importance of black bears in these ecosystems. Black bear distribution may be driven by land-use patterns including variables such as elevation, vegetation and habitat type, approximate percent canopy cover, slope, aspect, and land management type. The constructed single season occupancy models of black bear presence can be used to better understand habitat selection by black bears in different regions of the state. This study seeks to fill in gaps of knowledge about black bears in Washington and provides a framework for future occupancy studies on black bear habitat use.

Microbial communities are essential to numerous biological systems from aquatic and land ecosystems to the human gut. Our understanding of interactions among bacterial strains can help us understand how these communities form and function. Such interactions may depend on the species themselves but can also be sensitive to the abiotic environment. Harsh environments, where mortality is increased, can reverse the outcome of a competition between species. Simple models predict that increasing mortality favors faster growers, and past experiments repeatedly confirmed this prediction. In these pairwise competition experiments, slow growers excluded fast growers in low-mortality environments, but increasing mortality caused fast growers to dominate. A necessary condition for observing the reversal is that slow growers are good competitors in low-mortality, high-density environments. While this tradeoff between growth rate and competitive ability was common in well-characterized lab strains of bacteria, it was unclear if it might exist elsewhere. In this project, we tested for the existence of a growth/competition tradeoff in a natural community of bacteria collected from MIT’s Killian Court. Contrary to past results and some evidence from the literature, we found a weak positive correlation between competitive ability and growth rate, which does not support the idea of a tradeoff.

The #MeToo movement has sparked a national conversation about what constitutes sexual consent, and a series of debates have erupted over the actions of famous men like Brett Kavanaugh, Louis C.K., and Aziz Ansari. Popular hits like Robin Thicke’s song “Blurred Lines” have also come under fire for promoting sexual violence by celebrating confusion around sexual consent. At this moment, when norms and expectations around sexual interactions seem to be changing quickly, it is especially important that we have comprehensive and systematic data on how people define sexual consent and interpret ambiguous sexual experiences. Existing studies have shown characteristics of an individual matter when it comes how they conceptualize appropriateness. However, we still do not know to what extent these characteristics matter. This study addresses this gap by answering the question: How do people of varying groups define appropriate sexual behaviors and boundaries? A Q-Method study of over 200 people will reveal how individuals of different characteristics sort scenarios of various sexual interactions into categories ranging from “most appropriate” to “most inappropriate.” Q method is similar to a rank-order techinque and allows for representation or reconstruction of perspectives concerning any given topic of interest. In addition to providing insight about how different people make sense of sexual behaviors, this study shows how Q-sorts can provide a more detailed look at sexual norms than the survey and vignette studies currently used in the field.

The influenza virus is known for its rapid evolution, or the ability to fix many mutations over a short period of time. Some of these mutations lead to amino acid substitutions in regions of the virus targeted by the immune system. Such changes are often selected for because they confer a fitness advantage by allowing the virus to "escape" immune response. This pattern of repeated immune escape is a detriment to public health because it necessitates an annual update to the influenza vaccine. Therefore, identifying sites on the influenza virus which are targeted by the immune system could help predict which influenza strain will circulate in the future, inform vaccine design, and help understand basic evolutionary questions. Using molecular phylogenetic techniques, we can identify sites potentially targeted by the immune system by looking for "positive selection", a phenomenon which manifests as a higher than expected rate of evolution. To identify sites evolving faster than expected, we defined a null expectation of the evolutionary rate of influenza in the absence of immune pressure. This null model is defined using empirical measurements from a high-throughput functional assay known as deep mutational scanning. This null model differs from traditional phylogenetic models in that it describes the constraints on influenza on a site-specific basis and, as a result, has been shown to be a more accurate and powerful null model. I have implemented an empirical Bayes approach to identify sites which deviate from the null model by an unexpectedly high evolutionary rate, suggesting positive selection. Preliminary results show that my method outperforms other methods for identifying sites under positive selection. Next, I will apply these methods to the influenza virus surface protein, hemagglutinin, which is a major target of the immune system.

Self-efficacy has been shown to be associated with fewer problem behaviors in adolescence. However, the research investigating this association has been primarily based on self-report measurements of delinquency. The purpose of this study is to see if this association is maintained with a more objective measure of delinquency by examining juvenile offenders’ self-efficacy, and its predictive relationship with probation violations. Using self-report survey data of juvenile offenders’ self-efficacy along with probation violation data from Pierce County Juvenile Court, we conducted a statistical analysis of self-efficacy measurements and official probation violations. The self-reported self-efficacy measures address feelings in desire of academic and career success, prosocial behavior, and a reduction in the desire to express aggressive behaviors. If self-efficacy is shown to be a good predictor of fewer probation violations, further research can investigate determinants of high versus low self-efficacy, or how to improve self-efficacy, in order to promote probation adherence of juvenile offenders.