Introduction Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health. Methods The sample population consisted of more than 179,000 high school students living in Utah and participating in the Communities That Care (CTC) Youth Survey from 2011-2017. The dataset includes responses to 300+ questions from the CTC and 8000+ demographic factors from the American Census Survey for a total of 1.2 billion values. Machine learning techniques were employed to extract the survey questions that were best able to predict answers indicative of STB, using recent work in interpretable machine learning. Results Analysis showed strong predictive power, with the ability to predict individuals with STB with 91% accuracy. After extracting the top ten questions that most affected model predictions, questions fell into four main categories: familial life, drug consumption, demographics, and peer acceptance at school. Conclusions Modern machine learning approaches provide new methods for understanding the interaction between root causes and outcomes, such as STB. The model developed in this study showed significant improvement in predictive accuracy compared to previous research. Results indicate that certain risk and protective factors, such as adolescents being threatened or harassed through digital media or bullied at school, and exposure or involvement in serious arguments and yelling at home are the leading predictors of STB and can help narrow and reaffirm priority prevention programming and areas of focused policymaking.
This study aimed to discover how individuals with autism spectrum disorders (ASD) fare in psychotherapy within a university counseling setting, compared to their neurotypical peers. Clients with ASD showed no difference in level of distress at intake compared to their neurotypical peers, and improved about the same amount from pre- to post-treatment. However, students with ASD stayed in treatment for significantly more sessions than neurotypical clients, and took significantly longer to achieve maximum improvement on Outcome Questionnaire-45 reports.
Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes rash, fever and severe polyarthritis that can last for years in humans. Murine models display inflammation and macrophage infiltration only in the adjacent tissues at the site of inoculation, showing no signs of systemic polyarthritis. Monocyte-derived macrophages are one cell type suspected to contribute to a systemic CHIKV infection. The purpose of this study was to analyze differences in CHIKV infection in two different cell lines, human U937 and murine RAW264.7 monocyte derived macrophages. PMA-differentiated U937 and RAW264.7 macrophages were infected with CHIKV, and infectious virus production was measured by plaque assay and by reverse transcriptase quantitative PCR at various time points. Secreted cytokines in the supernatants were measured using cytometric bead arrays. Cytokine mRNA levels were also measured to supplement expression data. Here we show that CHIKV replicates more efficiently in human macrophages compared to murine macrophages. In addition, infected human macrophages produced around 10-fold higher levels of infectious virus when compared to murine macrophages. Cytokine induction by CHIKV infection differed between human and murine macrophages; IL-1, IL-6, IFN-γ, and TNF were significantly upregulated in human macrophages. This evidence suggests that CHIKV replicates more efficiently and induces a much greater pro-inflammatory cytokine profile in human macrophages, when compared to murine macrophages. This may shed light on the critical role that macrophages play in the CHIKV inflammatory response.
Highway safety improvement projects are identified by using either (i) a site-specific or (ii) a systemic approach. In the site-specific approach, locations for improvements are ranked according to different performance measures such as critical crash rate, expected crash rate or equivalent property damage only. Alternatively, in the systemic approach, roadway characteristics such as number of lanes, shoulder width, etc. are flagged as a ‘risk’ (or ‘preventative’) feature that increases (decreases) the risk of negative outcomes. Using the Highway Safety Information System database, we seek to merge the two approaches by, first, identifying roadway factors associated with an increased occurrence of car crashes (features we call ‘risk factors’) and, subsequently, identifying roadway segments with a higher crash risk. Specifically, we model the locations of crashes as a realization from a spatial point process. We then parameterize the associated intensity surface of this spatial point process as the sum of a regression on roadway characteristics and spatially correlated error terms. Thus, through the regression piece, we identify hazardous roadway features and through the spatially correlated error terms, we identify locations of high risk.
Io is the most volcanically active and tidally influenced body in the solar system. Its paterae and mountains are among its most distinguishing features. Paterae, similar to calderas, are volcanotectonic collapse features, often with active lava flows on their floors. Io's mountains are some of the highest in the solar system and contain many linear features that reveal global and regional stresses. This study investigates the relationship of linear features associated with paterae and mountains to stress fields associated with proposed mechanisms of formation: tidal forces, crustal loading, and local tectonics.
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