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Abstract BackgroundEmerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. ObjectiveThe purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. MethodsA risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variables by the weight of evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. ResultsA model with 48 predictive variables yielded significant predictive performance of subsequent musculoskeletal injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at baseline and acute timepoints. At a specified false-positive rate of 6.67%, the model achieves a true-positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well-calibrated composite risk score. ConclusionsThese results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student athletes most at risk for post-concussion musculoskeletal injury. 

Abstract BackgroundEmerging evidence indicates an elevated risk of post-concussion musculoskeletal (MSK) injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. ObjectiveThe purpose of this study was to model post-concussion MSK injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. MethodsA risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery specific criteria, and outcomes from a diverse array of concussions assessments. The machine learning approach involved transforming variables by the Weight of Evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. ResultsA model with 48 predictive variables yielded significant predictive performance of subsequent MSK injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at Baseline and Acute timepoints. At a specified false positive rate of 6.67%, the model achieves a true positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well calibrated composite risk score. ConclusionThese results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student-athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student-athletes most at risk for post-concussion MSK injury. Key PointsThere is a well-established elevated risk of post-concussion subsequent musculoskeletal injury; however, prior efforts have failed to identify risk factors.This study developed a composite risk score model with an AUC of 0.82 from common concussion clinical measures and participant demographics.By identifying athletes at elevated risk, clinicians may be able to reduce injury risk through targeted injury risk reduction programs. 

There is a national need to increase the number of minority students entering STEM fields with essential computing skills. To increase minority students’ interest and engagement in computing, a researcher-practitioner partnership between the University of Texas at El Paso and the El Paso Independent School District, developed and implemented a culturally and linguistically responsive curriculum and pedagogy to introduce computational thinking (CT) in two middle schools across different subject areas in a borderland region. The curriculum leveraged the Sol y Agua game – a bilingual, culturally-responsive game designed to engage students of this region in CT. This paper describes the process and initial findings of this project. The quantitative data from in-game analyses show that students utilized the language change feature to switch from English to Spanish more frequently than the other way – highlighting the need for educational platforms relatable to students through language, environment, and cultural context. Analyses of the qualitative data indicate that while teachers/team members understood CT and translanguaging concepts and taught lesson units that provided opportunities to practice both, CT and translanguaging were largely implicit in the curriculum. In collaborative analyses of these patterns, teachers described additional supports that would help them to make CT instruction and translanguaging strategies more explicit in the content and pedagogy, highlighting the need for systematic, targeted integration of these concepts. 

The transition from subduction to transform motion along horizontal terminations of trenches is associated with tearing of the subducting slab and strike-slip tectonics in the overriding plate. One prominent example is the northern Tonga subduction zone, where abundant strike-slip faulting in the NE Lau back-arc basin is associated with transform motion along the northern plate boundary and asymmetric slab rollback. Here, we address the fundamental question: how does this subduction-transform motion influence the structural and magmatic evolution of the back-arc region? To answer this, we undertake the first comprehensive study of the geology and geodynamics of this region through analyses of morphotectonics (remote-predictive geologic mapping) and fault kinematics interpreted from ship-based multibeam bathymetry and Centroid-Moment Tensor data. Our results highlight two notable features of the NE Lau Basin: 1) the occurrence of widely distributed off-axis volcanism, in contrast to typical ridge-centered back-arc volcanism, and 2) fault kinematics dominated by shallow-crustal strike slip-faulting (rather than normal faulting) extending over ∼120 km from the transform boundary. The orientations of these strike-slip faults are consistent with reactivation of earlier-formed normal faults in a sinistral megashear zone. Notably, two distinct sets of Riedel megashears are identified, indicating a recent counter-clockwise rotation of part of the stress field in the back-arc region closest to the arc. Importantly, the Riedel structures identified in this study directly control the development of complex volcanic-compositional provinces, which are characterized by variably-oriented spreading centers, off-axis volcanic ridges, extensive lava flows, and point-source rear-arc volcanoes. This study adds to our understanding of the geologic and structural evolution of modern backarc systems, including the association between subduction-transform motions and the siting and style of seafloor volcanism. 

The Sol y Agua researcher-practitioner partnership (RPP) project introduces computational thinking (CT) in the middle school of the Paso del Norte region using a linguistically and culturally responsive approach. At the core of this RPP is the Sol y Agua game, a bilingual, culturally- and environmentally-relevant educational game developed at the University of Texas at El Paso to introduce computing and STEM topics in middle school. The Sol y Agua RPP includes some critical areas for a successful RPP, including partnership building and the focus on a linguistically and culturally-responsive pedagogy and content development. We describe our approach to build a sustainable RPP, incorporating bilingual pedagogy, and integrating CT through a culturally- and environmentally-relevant game as part of our RPP experience.