Prevalence of Mental Health Disorder Symptoms and Rates of Help-seeking Among University-Enrolled, Black Men

Background. Black men in college represent a subgroup of emerging adults who are at increased risk of developing mental health disorders (MHDs), such as anxiety and depression. Such risk has been attributed to disproportionate experiences with everyday racial discrimination and high levels of psychological distress. Despite being at higher risk, university-enrolled, Black men are not utilizing mental health or health resources at optimal rates. The current evidence base describing prevalence of MHDs and health services utilization among Black men in college is limited. The present study addresses this by examining mental health prevalence among university-enrolled, Black men and their rates of health services utilization. Methods. We analyzed data (N ~ 2500) from a student survey, Spit for Science, a longitudinal, ongoing, research study at a mid-Atlantic, public university. Participants are given surveys in their freshman year and follow-up surveys every spring thereafter. Measures included: mental health disorders (depression and anxiety, as measured by the Symptom Checklist 90) and campus health service utilization (counseling center, health services, wellness center, and recreational sports). We conducted descriptive analyses to determine MHD symptom prevalence and utilization rates; Mann Whitney U tests to compare prevalence rates to White men and Black women; and, Chi-squared tests to compare rates of utilization among groups. Results. During their Freshman year, greater than 60% of students from each ethnic group reported at least one anxiety symptom and greater than 80% reported at least one depressive symptom. By senior year, reporting rates decreased significantly for Black men (49.6%) but remained high for White men (69.1%) and Black women (63%); p \u3c0.000. For depression, results were similar; however, only significant differences between Black men (72.7%) and Black women (87.1%); p\u3c0.000. Black men (20.4%), though reporting high levels of symptoms, still utilized counseling services at lower rates compared to White men (37.76%); p = 0.024. Conclusion. Findings suggest that Black men underutilize available campus health resources despite reporting one or more symptoms associated with anxiety and depression. Further research and prevention efforts are needed to improve help-seeking among this vulnerable population.https://scholarscompass.vcu.edu/gradposters/1077/thumbnail.jp

A combinatorial approach to knot recognition

This is a report on our ongoing research on a combinatorial approach to knot recognition, using coloring of knots by certain algebraic objects called quandles. The aim of the paper is to summarize the mathematical theory of knot coloring in a compact, accessible manner, and to show how to use it for computational purposes. In particular, we address how to determine colorability of a knot, and propose to use SAT solving to search for colorings. The computational complexity of the problem, both in theory and in our implementation, is discussed. In the last part, we explain how coloring can be utilized in knot recognition

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page

The STAR MAPS-based PiXeL detector

The PiXeL detector (PXL) for the Heavy Flavor Tracker (HFT) of the STAR experiment at RHIC is the first application of the state-of-the-art thin Monolithic Active Pixel Sensors (MAPS) technology in a collider environment. Custom built pixel sensors, their readout electronics and the detector mechanical structure are described in detail. Selected detector design aspects and production steps are presented. The detector operations during the three years of data taking (2014-2016) and the overall performance exceeding the design specifications are discussed in the conclusive sections of this paper

Synthetic RNA modules for fine-tuning gene expression levels in yeast by modulating RNase III activity

The design of synthetic gene networks requires an extensive genetic toolbox to control the activities and levels of protein components to achieve desired cellular functions. Recently, a novel class of RNA-based control modules, which act through post-transcriptional processing of transcripts by directed RNase III (Rnt1p) cleavage, were shown to provide predictable control over gene expression and unique properties for manipulating biological networks. Here, we increase the regulatory range of the Rnt1p control elements, by modifying a critical region for enzyme binding to its hairpin substrates, the binding stability box (BSB). We used a high throughput, cell-based selection strategy to screen a BSB library for sequences that exhibit low fluorescence and thus high Rnt1p processing efficiencies. Sixteen unique BSBs were identified that cover a range of protein expression levels, due to the ability of the sequences to affect the hairpin cleavage rate and to form active cleavable complexes with Rnt1p. We further demonstrated that the activity of synthetic Rnt1p hairpins can be rationally programmed by combining the synthetic BSBs with a set of sequences located within a different region of the hairpin that directly modulate cleavage rates, providing a modular assembly strategy for this class of RNA-based control elements

The Identity Construction of Christian Women in Haiti

Identity construction is not an isolated reality; it is always the result of relationships with others and an inevitable process of socialization. The identity construction of women raises questions about their relationship with the Christian religion. This study on the identity construction of Christian women is situated within a constructivist and interactionist perspective of human and social relations. The objective of this article is, on the one hand, to assess the extent of Christian influence in the process of identity construction; on the other hand, the research seeks to understand the dynamics of subjectivation among these women. The results of this study, conducted with 12 women of Christian faiths, reveal an identity process that varies between internalization of religious values, adherence and discussion of religious values, and contestation of religious values

Squirrelpox virus: assessing prevalence, transmission and environmental degradation

Red squirrels (Sciurus vulgaris) declined in Great Britain and Ireland during the last century, due to habitat loss and the introduction of grey squirrels (Sciurus carolinensis), which competitively exclude the red squirrel and act as a reservoir for squirrelpox virus (SQPV). The disease is generally fatal to red squirrels and their ecological replacement by grey squirrels is up to 25 times faster where the virus is present. We aimed to determine: (1) the seropositivity and prevalence of SQPV DNA in the invasive and native species at a regional scale; (2) possible SQPV transmission routes; and, (3) virus degradation rates under differing environmental conditions. Grey (n = 208) and red (n = 40) squirrel blood and tissues were sampled. Enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qPCR) techniques established seropositivity and viral DNA presence, respectively. Overall 8% of squirrels sampled (both species combined) had evidence of SQPV DNA in their tissues and 22% were in possession of antibodies. SQPV prevalence in sampled red squirrels was 2.5%. Viral loads were typically low in grey squirrels by comparison to red squirrels. There was a trend for a greater number of positive samples in spring and summer than in winter. Possible transmission routes were identified through the presence of viral DNA in faeces (red squirrels only), urine and ectoparasites (both species). Virus degradation analyses suggested that, after 30 days of exposure to six combinations of environments, there were more intact virus particles in scabs kept in warm (25°C) and dry conditions than in cooler (5 and 15°C) or wet conditions. We conclude that SQPV is present at low prevalence in invasive grey squirrel populations with a lower prevalence in native red squirrels. Virus transmission could occur through urine especially during warm dry summer conditions but, more notably, via ectoparasites, which are shared by both species

Solving order constraints in logarithmic space.

We combine methods of order theory, finite model theory, and universal algebra to study, within the constraint satisfaction framework, the complexity of some well-known combinatorial problems connected with a finite poset. We identify some conditions on a poset which guarantee solvability of the problems in (deterministic, symmetric, or non-deterministic) logarithmic space. On the example of order constraints we study how a certain algebraic invariance property is related to solvability of a constraint satisfaction problem in non-deterministic logarithmic space

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems