Criminologists largely rely on national de-identified data sources to study homicide in the United States. The National Death Index (NDI), a comprehensive and well-established database compiled by the National Center for Health Statistics, is an untapped source of homicide data that offers identifiable linkages to other data sources while retaining national coverage. This study's five aims follow. First, we review the data sources in articles published in Homicide Studies over the past decade. Second, we describe the NDI, including its origins, procedures, and uses. Third, we outline the procedures for linking a police gang intelligence database to the NDI. Fourth, we introduce the St. Louis Gang Member-Linked Mortality Files database, which is composed of 3,120 police-identified male gang members in the St. Louis area linked to NDI records. Finally, we report on preliminary cause-of-death findings. We conclude by outlining the benefits and drawbacks of the NDI as a source of homicide data for criminologists.The pilot-scale production of the peroxygenase from Agrocybe aegerita (rAaeUPO) is demonstrated. In a fed-batch fermentation of the recombinant Pichia pastoris, the enzyme was secreted into the culture medium to a final concentration of 0.29 g L-1 corresponding to 735 g of the peroxygenase in 2500 L of the fermentation broth after 6 days. Due to nonoptimized downstream processing, only 170 g of the enzyme has been isolated. The preparative usefulness of the so-obtained enzyme preparation has been demonstrated at a semipreparative scale (100 mL) as an example of the stereoselective hydroxylation of ethyl benzene. Using an adjusted H2O2 feed rate, linear product formation was observed for 7 days, producing more than 5 g L-1 (R)-1-phenyl ethanol. The biocatalyst performed more than 340.000 catalytic turnovers (942 g of the product per gram of rAaeUPO).Since the late 1990s, mortality rates for middle-aged (45-55), White non-Hispanic (WNH) Americans began to rise while rates declined for all other demographic and age groups. Coinciding with the rise in mortality, rates of death due to suicide, drug- and alcohol-related overdoses, and alcohol-related liver diseases increased as well for this demographic. Research suggests these causes of death (i.e., suicide, poisoning, alcohol-related liver disease) are driving the overall mortality rate for middle-aged WNHs and have been described as "deaths of despair" in the literature. In the current paper, we describe the social and clinical features of "deaths of despair," explore theoretical models of psychopathology (e.g., depression, posttraumatic stress disorder) that may inform our understanding of mechanisms of risk for negative mental health outcomes, and propose an initial conceptual model of "deaths of despair" to identify intervention targets. We then review an applied case example demonstrating how this model could be used for clinical application. We conclude our paper by describing how current cognitive-behavioral interventions may address these mechanisms of "despair."Experiencing a sexual assault can have long-lasting negative consequences including development of posttraumatic stress disorder (PTSD) and alcohol misuse. Intervention provided in the initial weeks following assault can reduce the development of these chronic problems. This study describes the iterative treatment development process for refining a brief intervention targeting PTSD and alcohol misuse for women with recent sexual assault experiences. Experts, treatment providers, and patients provided feedback on the intervention materials and guided the refinement process. Based on principles of cognitive change, the final intervention consists of one in-person session and four coaching calls targeting beliefs about the assault and about drinking behavior. Initial feasibility and acceptability data are presented for patients enrolled in an open trial (N = 6). The intervention was rated as helpful, not distressing, and interesting by patients and all patients completed the entire treatment protocol. A large decrease in PTSD symptoms pre- to post-intervention was observed. A small effect on decreasing alcohol consequences also emerged, although drinks consumed per week showed a slight increase, not a decrease, over the course of the intervention. Applications of this intervention and next steps for testing efficacy are presented.The Dynamic Haptic Robotic Trainer (DHRT) was developed to minimize the up to 39% of adverse effects experienced by patients during Central Venous Catheterization (CVC) by standardizing CVC training, and provide automated assessments of performance. Specifically, this system was developed to replace manikin trainers that only simulate one patient anatomy and require a trained preceptor to evaluate the trainees' performance. While the DHRT system provides automated feedback, the utility of this system with real-world scenarios and expertise has yet to be thoroughly investigated. Thus, the current study was developed to determine the validity of the current objective assessment metrics incorporated in the DHRT system through expert interviews. The main findings from this study are that experts do agree on perceptions of patient case difficulty, and that characterizations of patient case difficulty is based on anatomical characteristics, multiple needle insertions, and prior catheterization.Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through space-time, but its computational burden prevents its wider use.  https://www.selleckchem.com/products/ly2157299.html  Crucial MDS model calculations scale quadratically in the number of observations. We partially mitigate this limitation through massive parallelization using multi-core central processing units, instruction-level vectorization and graphics processing units (GPUs). Fitting the MDS model using Hamiltonian Monte Carlo, GPUs can deliver more than 100-fold speedups over serial calculations and thus extend Bayesian MDS to a big data setting. To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe. We examine 5392 viral sequences and their associated 14 million pairwise distances arising from the number of commercial airline seats per year between viral sampling locations.