EVALUATION OF A DIVISION I MID-MAJOR UNIVERSITY’S STUDENT-ATHLETE MENTAL HEALTH PROGRAM

The following executive summary provides high level findings of a student-athlete mental health program (SAMHP) at a National Collegiate Athletic Association Division I mid-major university. Various elements of the SAMHP were evaluated to create a well-rounded understanding of the program to determine sustainability, goals, and stakeholder expectations. Findings from this study provided insight on stakeholder needs, program successes, and implications for program improvements

EVALUATION OF A DIVISION I MID-MAJOR UNIVERSITY’S STUDENT-ATHLETE MENTAL HEALTH PROGRAM

The following executive summary provides high level findings of a student-athlete mental health program (SAMHP) at a National Collegiate Athletic Association Division I mid-major university. Various elements of the SAMHP were evaluated to create a well-rounded understanding of the program to determine sustainability, goals, and stakeholder expectations. Findings from this study provided insight on stakeholder needs, program successes, and implications for program improvements

Relationships Among Depression, Anxiety, and Insomnia Symptoms in Perinatal Women Seeking Mental Health Treatment

Background: Depression and anxiety symptoms are commonly experienced by women during the perinatal period. Changes in sleep and sleep quality are typical throughout pregnancy and early postpartum. However, little is known about relationships between insomnia symptoms and psychiatric symptoms in perinatal women. The objective of the present study is to characterize the burden of insomnia symptoms in perinatal women seeking outpatient psychiatric treatment and to examine relationships between insomnia and symptoms of depression and anxiety. Methods: Data from 257 pregnant or postpartum women who sought outpatient psychiatric treatment at a university hospital-affiliated clinic were extracted from an existing clinical management database. Data included validated self-report measures assessing insomnia (Insomnia Severity Index [ISI]), mood (Edinburgh Postnatal Depression Scale [EPDS]), and generalized anxiety (Penn State Worry Questionnaire [PSWQ]). Results: Fifty-two percent of women reported symptoms of insomnia, 75% reported symptoms of depression, and 61% reported symptoms of generalized anxiety. After controlling for PSWQ, the partial correlation between EPDS and ISI was 0.15 and 0.37 for pregnant and postpartum women, respectively. After controlling for EPDS, the partial correlation between PSWQ and ISI was 0.20 and 0.12 for pregnant and postpartum women, respectively. Women with clinically significant ISI scores had significantly higher odds for reporting symptoms consistent with depression (odds ratio [OR] 7.7) and generalized anxiety (OR 2.55) compared to women with lower ISI scores. Conclusions: Insomnia symptoms affected a significant proportion of the perinatal women in this sample. These symptoms are linked to symptoms of depression and anxiety in treatment-seeking pregnant and postpartum women. Perinatal women seen in psychiatric treatment settings should be routinely screened for sleep problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90453/1/jwh-2E2010-2E2371.pd

Machine Learning Analyses of Highly-Multiplexed Immunofluorescence Identifies Distinct Tumor and Stromal Cell Populations in Primary Pancreatic Tumors

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians. OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression. METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME. RESULTS: A random forest algorithm accurately predicted tumor and stromal-rich areas with 87% accuracy using 31 markers and 77% accuracy using only five markers. Top tumor-predictive markers guided downstream analyses to identify immune populations effectively invading into the tumor, including dendritic cells, CD4+ T cells, and multiple immunoregulatory subtypes. CONCLUSIONS: Immunoprofiling of PDAC to identify differential distribution of immune cells in the TME is critical for understanding disease progression, response and/or resistance to treatment, and the development of new treatment strategies

RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data

Human endogenous retroviruses (HERVs) integrated into the human genome as a result of ancient exogenous infections and currently comprise ∼8% of our genome. The members of the most recently acquired HERV family, HERV-Ks, still retain the potential to produce viral molecules and have been linked to a wide range of diseases including cancer and neurodegeneration. Although a range of tools for HERV detection in NGS data exist, most of them lack wet lab validation and they do not cover all steps of the analysis. Here, we describe RetroSnake, an end-to-end, modular, computationally efficient, and customizable pipeline for the discovery of HERVs in short-read NGS data. RetroSnake is based on an extensively wet-lab validated protocol, it covers all steps of the analysis from raw data to the generation of annotated results presented as an interactive html file, and it is easy to use by life scientists without substantial computational training. Availability and implementation: The Pipeline and an extensive documentation are available on GitHub

Nautical Research Platform for Water-Bound Experiments

Conducting research in lakes and rivers requires large crews and heavy-duty equipment, making even simple tests more costly and time consuming. Newer research methods are evolving constantly as new technology enables more precise and accessible experiments to be conducted. The need for simple execution of water-bound experiments exists and must be addressed to aid our understanding of these environments. We at the Microgravity Undergraduate Research Team have taken our previous research in autonomous Unmanned Surface Vehicles (USVs) and applied our efforts to relieving this problem. Our current research aims to provide a universal platform for research and experiments to be conducted in lakes and rivers, where we can then expand our efforts to more broad applications. The design allows for remote-control navigation by one user and easy portability. To address precision in experimentation, we have integrated autonomous GPS waypoint navigation which removes user error in sensitive measurements. The most important factor in its design is modularity; the ability to accommodate a wide range of equipment for research. Our platform succeeds in making water-bound experiments more accessible and more precise for a multitude of potential applications

Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD

Lions and Prions and Deer Demise

Background: Contagious prion diseases – scrapie of sheep and chronic wasting disease of several species in the deer family – give rise to epidemics that seem capable of compromising host population viability. Despite this prospect, the ecological consequences of prion disease epidemics in natural populations have received little consideration. Methodology/Principal Findings: Using a cohort study design, we found that prion infection dramatically lowered survival of free-ranging adult (.2-year-old) mule deer (Odocoileus hemionus): estimated average life expectancy was 5.2 additional years for uninfected deer but only 1.6 additional years for infected deer. Prion infection also increased nearly fourfold the rate of mountain lions (Puma concolor) preying on deer, suggesting that epidemics may alter predator–prey dynamics by facilitating hunting success. Despite selective predation, about one fourth of the adult deer we sampled were infected. High prevalence and low survival of infected deer provided a plausible explanation for the marked decline in this deer population since the 1980s. Conclusion: Remarkably high infection rates sustained in the face of intense predation show that even seemingly complete ecosystems may offer little resistance to the spread and persistence of contagious prion diseases. Moreover, the depression of infected populations may lead to local imbalances in food webs and nutrient cycling in ecosystems in which deer ar

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr

Early brain development in infants at high risk for autism spectrum disorder

Brain enlargement has been observed in children with Autism Spectrum Disorder (ASD), but the timing of this phenomenon and its relationship to the appearance of behavioral symptoms is unknown. Retrospective head circumference and longitudinal brain volume studies of 2 year olds followed up at age 4 years, have provided evidence that increased brain volume may emerge early in development.1, 2 Studies of infants at high familial risk for autism can provide insight into the early development of autism and have found that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life3,4. These observations suggest that prospective brain imaging studies of infants at high familial risk for ASD might identify early post-natal changes in brain volume occurring before the emergence of an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that cortical surface area hyper-expansion between 6-12 months of age precedes brain volume overgrowth observed between 12-24 months in the 15 high-risk infants diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep learning algorithm primarily using surface area information from brain MRI at 6 and 12 months of age predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81%, sensitivity of 88%). These findings demonstrate that early brain changes unfold during the period in which autistic behaviors are first emerging