The effect of changing the military's sexual assault laws on law enforcement investigative findings in the U.S. Army.

OBJECTIVE: In 2007, Congress changed the military's sexual assault laws as part of an effort to improve sexual assault case processing. This study looked at the U.S. Army law enforcement investigative finding for every sexual assault reported to the Army from 2004 through June 2012, along with every nonsexual assault. Our objective was to measure whether the legal intervention affected the investigative findings made by Army law enforcement officers in sexual assault cases (penetrative, nonpenetrative, and combined) as compared to assault cases (aggravated, simple, and combined). HYPOTHESES: We hypothesized that we would not find evidence that the legal intervention affected the rate of sexual assault cases labeled as "founded" by Army law enforcement, such that for the best-fitting time-series models, any difference in the residuals of the means before and after the intervention would not be statistically significant. METHOD: We received data from the U.S. Army on all sexual assaults and nonsexual assaults from 2004 through June 2012. The data comprised 47,058 observations. We used time-series analysis with autoregressive integrated moving average modeling. The variable tracked over time was the ratio of the proportion of founded sexual assault cases to the proportion of founded nonsexual assault cases. We then conducted t tests of the means of the residuals before and after the legal intervention. RESULTS: The difference in the means of the residuals before and after the intervention was not statistically significant for combined sexual assaults versus combined assaults, penetrative sexual assaults versus aggravated assaults, or nonpenetrative sexual assaults versus simple assaults. CONCLUSIONS: This reform to sexual assault laws does not appear to have affected sexual assault case processing by U.S. Army law enforcement. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.

Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.

coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes.

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Lessons Learned From Miami-Dade County's COVID-19 Epidemic: Making Surveillance Data Accessible for Policy Makers.

INTRODUCTION: COVID-19 represents an unprecedented challenge to policy makers as well as those entrusted with capturing, monitoring, and analyzing COVID-19 data. Effective public policy is data-informed policy. This requires a liaison between public health scientists and public officials. OBJECTIVE: This article details the experience, challenges, and lessons learned advising public officials in a large metropolitan area from March to October 2020. METHODS: To effectively do this, an R Markdown report was created to iteratively monitor the number of COVID-19 tests performed, positive tests obtained, COVID-19 hospitalization census, intensive care unit census, the number of patients with COVID-19 on ventilators, and the number of deaths due to COVID-19. RESULTS: These reports were presented and discussed at meetings with policy makers to further comprehension. DISCUSSION: To facilitate the fullest understanding by both the general public and policy makers alike, we advocate for greater centralization of public health surveillance data, objective operational definitions of metrics, and greater interagency communication to best guide and inform policy makers. Through consistent data reporting methods, parsimonious and consistent analytic methods, a clear line of communication with policy makers, transparency, and the ability to navigate unforeseen externalities such as "data dumps" and reporting delays, scientists can use information to best support policy makers in times of crises.

PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

Empirically contrasting urine drug screening-based opioid use disorder treatment outcome definitions.

BACKGROUND AND AIMS: A lack of consensus on the optimal outcome measures to assess opioid use disorder (OUD) treatment efficacy and their precise definition and computation has hampered the pooling of research data for evidence synthesis and meta-analyses. This study aimed to empirically contrast multiple clinical trial definitions of treatment success by applying them to the same dataset. METHODS: Data analysis used a suite of functions, developed as a software package for the R language, to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results. Outcome definitions were derived from clinical trials that are among the most influential in the OUD treatment field. Outcome functions were applied to a harmonized dataset from three large-scale National Drug Abuse Treatment Clinical Trials Network (CTN) studies, which tested various medication for OUD (MOUD) options (n = 2492). Hierarchical clustering was employed to empirically contrast outcome definitions. RESULTS: The optimal number of clusters identified was three. Cluster 1, comprising eight definitions focused on detecting opioid-positive UDS, did not include missing UDS in outcome calculations, potentially resulting in inflated rates of treatment success. Cluster 2, with the highest variability, included 10 definitions characterized by strict criteria for treatment success, relying heavily on UDS results from either a brief period or a single study visit. The 43 definitions in Cluster 3 represented a diverse range of outcomes, conceptualized as measuring abstinence, use reduction and relapse. These definitions potentially offer more balanced measures of treatment success or failure, as they avoid the extreme methodologies characteristic of Clusters 1 and 2. CONCLUSIONS: Clinical trials using urine drug screening (UDS) for objective substance use assessment in outcome definitions should consider (1) incorporating missing UDS data in outcome computation and (2) avoiding over-reliance on UDS data confined to a short time frame or the occurrence of a single positive urine test following a period of abstinence.

Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment.

Importance: No existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder. Objective: To develop an individual-level prediction tool for risk of return to use in opioid use disorder. Design, Setting, and Participants: This decision analytical model used predictive modeling with individual-level data harmonized in June 1, 2019, to October 1, 2022, from 3 multicenter, pragmatic, randomized clinical trials of at least 12 weeks' duration within the National Institute on Drug Abuse Clinical Trials Network (CTN) performed between 2006 and 2016. The clinical trials covered a variety of treatment settings, including federally licensed treatment sites, physician practices, and inpatient treatment facilities. All 3 trials enrolled adult participants older than 18 years, with broad pragmatic inclusion and few exclusion criteria except for major medical and unstable psychiatric comorbidities. Intervention: All participants received 1 of 3 medications for opioid use disorder: methadone, buprenorphine, or extended-release naltrexone. Main Outcomes and Measures: Predictive models were developed for return to use, which was defined as 4 consecutive weeks of urine drug screen (UDS) results either missing or positive for nonprescribed opioids by week 12 of treatment. Results: The overall sample included 2199 trial participants (mean [SD] age, 35.3 [10.7] years; 728 women [33.1%] and 1471 men [66.9%]). The final model based on 4 predictors at treatment entry (heroin use days, morphine- and cocaine-positive UDS results, and heroin injection in the past 30 days) yielded an area under the receiver operating characteristic curve (AUROC) of 0.67 (95% CI, 0.62-0.71). Adding UDS in the first 3 treatment weeks improved model performance (AUROC, 0.82; 95% CI, 0.78-0.85). A simplified score (CTN-0094 OUD Return-to-Use Risk Score) provided good clinical risk stratification wherein patients with weekly opioid-negative UDS results in the 3 weeks after treatment initiation had a 13% risk of return to use compared with 85% for those with 3 weeks of opioid-positive or missing UDS results (AUROC, 0.80; 95% CI, 0.76-0.84). Conclusions and Relevance: The prediction model described in this study may be a universal risk measure for return to opioid use by treatment week 3. Interventions to prevent return to regular use should focus on this critical early treatment period.

No evidence of accelerated epigenetic aging among black heroin users: A case vs control analysis.

This study sought to assess the association between illicit opioid use and accelerated epigenetic aging (A.K.A. DNAm Age) among people of African ancestry who use heroin. DNA was obtained from participants with opioid use disorder (OUD) who confirmed heroin as their primary drug of choice. Clinical inventories of drug use included: the Addiction Severity Index (ASI) Drug-Composite Score (range: 0-1), and Drug Abuse Screening Test (DAST-10; range: 0-10). A control group of participants of African ancestry who did not use heroin was recruited and matched to heroin users on sex, age, socioeconomic level, and smoking status. Methylation data were assessed in an epigenetic clock to determined and compare Epigenetic Age to Chronological Age (i.e., age acceleration or deceleration). Data were obtained from 32 controls [mean age 36.3 (±7.5) years] and 64 heroin users [mean age 48.1 (±6.6) years]. The experimental group used heroin for an average of 18.1 (±10.6) years, reported use of 6.4 (±6.1) bags of heroin/day, with a mean DAST-10 score of 7.0 (±2.6) and ASI Score of 0.33 (±0.19). Mean age acceleration for heroin users [+0.56 (± 9.5) years] was significantly (p< 0.05) lower than controls [+5.19 (± 9.1) years]. This study did not find evidence that heroin use causes epigenetic age acceleration.

Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values.

INTRODUCTION: The efficacy of treatments for substance use disorders (SUD) is tested in clinical trials in which participants typically provide urine samples to detect whether the person has used certain substances via urine drug screenings (UDS). UDS data form the foundation of treatment outcome assessment in the vast majority of SUD clinical trials. However, existing methods to calculate treatment outcomes are not standardized, impeding comparability between studies and prohibiting reproducibility of results. METHODS: We extended the concept of a binary UDS variable to multiple categories: "+" [positive for substance(s) of interest], "-" [negative for substance(s)], "o" [patient failed to provide sample], "*" [inconclusive or mixed results], and "_" [no specimens required per study design]. This construct can be used to create a standardized and sufficient representation of UDS datastreams and sufficiently collapses longitudinal records into a single, compact "word", which preserves all information contained in the original data. RESULTS: We developed the R software package CTNote (available on CRAN) as a tool to enable computers to parse these "words". The software package contains five groups of routines: detect a substance use pattern, account for a specific trial protocol, handle missing UDS data, measure the longest period of consecutive behavior, and count substance use events. Executing permutations of these routines result in algorithms which can define SUD clinical trial endpoints. As examples, we provide three algorithms to define primary endpoints from seminal SUD clinical trials. DISCUSSION: Representing substance use patterns as a "word" allows researchers and clinicians an "at a glance" assessment of participants' responses to treatment over time. Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. We discuss some caveats when applying this data summarization technique in practice and areas of future study.

Risk of Experiencing an Overdose Event for Patients Undergoing Treatment With Medication for Opioid Use Disorder.

OBJECTIVE: Overdose risk during a course of treatment with medication for opioid use disorder (MOUD) has not been clearly delineated. The authors sought to address this gap by leveraging a new data set from three large pragmatic clinical trials of MOUD. METHODS: Adverse event logs, including overdose events, from the three trials (N=2,199) were harmonized, and the overall risk of having an overdose event in the 24 weeks after randomization was compared for each study arm (one methadone, one naltrexone, and three buprenorphine groups), using survival analysis with time-dependent Cox proportional hazard models. RESULTS: By week 24, 39 participants had ≥1 overdose event. The observed frequency of having an overdose event was 15 (5.30%) among 283 patients assigned to naltrexone, eight (1.51%) among 529 patients assigned to methadone, and 16 (1.15%) among 1,387 patients assigned to buprenorphine. Notably, 27.9% of patients assigned to extended-release naltrexone never initiated the medication, and their overdose rate was 8.9% (7/79), compared with 3.9% (8/204) among those who initiated naltrexone. Controlling for sociodemographic and time-varying medication adherence variables and baseline substance use, a proportional hazard model did not show a significant effect of naltrexone assignment. Significantly higher probabilities of experiencing an overdose event were observed among patients with baseline benzodiazepine use (hazard ratio=3.36, 95% CI=1.76, 6.42) and those who either were never inducted on their assigned study medication (hazard ratio=6.64, 95% CI=2.12, 19.54) or stopped their medication after initial induction (hazard ratio=4.04, 95% CI=1.54, 10.65). CONCLUSIONS: Among patients with opioid use disorder seeking medication treatment, the risk of overdose events over the next 24 weeks is elevated among those who fail to initiate or discontinue medication and those who report benzodiazepine use at baseline.

Evaluating the effect of acute diesel exhaust particle exposure on P-glycoprotein efflux transporter in the blood-brain barrier co-cultured with microglia.

A growing public health concern, chronic Diesel Exhaust Particle (DEP) exposure is a heavy risk factor for the development of neurodegenerative diseases like Alzheimer's (AD). Considered the brain's first line of defense, the Blood-Brain Barrier (BBB) and perivascular microglia work in tandem to protect the brain from circulating neurotoxic molecules like DEP. Importantly, there is a strong association between AD and BBB dysfunction, particularly in the Aß transporter and multidrug resistant pump, P-glycoprotein (P-gp). However, the response of this efflux transporter is not well understood in the context of environmental exposures, such as to DEP. Moreover, microglia are seldom included in in vitro BBB models, despite their significance in neurovascular health and disease. Therefore, the goal of this study was to evaluate the effect of acute (24 hr.) DEP exposure (2000 µg/ml) on P-gp expression and function, paracellular permeability, and inflammation profiles of the human in vitro BBB model (hCMEC/D3) with and without microglia (hMC3). Our results suggested that DEP exposure can decrease both the expression and function of P-gp in the BBB, and corroborated that DEP exposure impairs BBB integrity (i.e. increased permeability), a response that was significantly worsened by the influence of microglia in co-culture. Interestingly, DEP exposure seemed to produce atypical inflammation profiles and an unexpected general downregulation in inflammatory markers in both the monoculture and co-culture, which differentially expressed IL-1ß and GM-CSF. Interestingly, the microglia in co-culture did not appear to influence the response of the BBB, save in the permeability assay, where it worsened the BBB's response. Overall, our study is important because it is the first (to our knowledge) to investigate the effect of acute DEP exposure on P-gp in the in vitro human BBB, while also investigating the influence of microglia on the BBB's responses to this environmental chemical.

Evaluating the endothelial-microglial interaction and comprehensive inflammatory marker profiles under acute exposure to ultrafine diesel exhaust particles in vitro.

Exposure to combustion-derived particulate matter (PM) such as diesel exhaust particles (DEP) is a public health concern because people in urban areas are continuously exposed, and once inhaled, fine and ultrafine DEP may reach the brain. The blood-brain barrier (BBB) endothelial cells (EC) and the perivascular microglia protect the brain from circulating pathogens and neurotoxic molecules like DEP. While the BBB-microglial interaction is critical for maintaining homeostasis, no study has previously evaluated the endothelial-microglial interaction nor comprehensively characterized these cells' inflammatory marker profiles under ultrafine DEP exposures in vitro. Therefore, the goal of this study was to investigate the in vitro rat EC-microglial co-culture under acute (24 h.) exposure to ultrafine DEP (0.002-20 µg/mL), by evaluating key mechanisms associated with PM toxicity: lactate dehydrogenase (LDH) leakage, reactive oxygen species (ROS) generation, cell metabolic activity (CMA) changes, and production of 27 inflammatory markers. These parameters were also evaluated in rat microglial and endothelial monocultures to determine whether the EC-microglial co-culture responded differently than the cerebrovasculature and microglia alone. While results indicated that ultrafine DEP exposure caused concentration-dependent increases in LDH leakage and ROS production in all groups, as expected, exposure also caused mixed responses in CMA and atypical cytokine/chemokine profiles in all groups, which was not expected. The inflammation assay results further suggested that the microglia were not classically activated under this exposure scenario, despite previous in vitro studies showing microglial activation (priming) at similar concentrations of ultrafine DEP. Additionally, compared to the cerebrovasculature alone, the EC-microglia interaction in the co-culture did not appear to cause changes in any parameter save in pro-inflammatory marker production, where the interaction appeared to cause an overall downregulation in cytokine/chemokine levels after ultrafine DEP exposure. Finally, to our knowledge, this is the first study to evaluate the influence of microglia on the BBB's ultrafine DEP-induced cytotoxic and inflammatory responses, which are heavily implicated in the pathogenesis of PM-related cerebrovascular dysfunction and neurodegeneration.

PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples.

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer's disease.

Interpreting pathways to discover cancer driver genes with Moonlight.

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.