The increasing number of polluted watersheds and water bodies with total maximum daily loads (TMDLs) has resulted in increased research to find methods that effectively and universally identify fecal pollution sources. A fundamental requirement to identify such methods is understanding the microbial and chemical processes that influence fate and transport of fecal indicators from various sources to receiving streams. Using the Watauga River watershed in northeast Tennessee as a model to better understand these processes, multivariate statistical analyses were conducted on data collected from four creeks that have or are expected to have pathogen TMDLs. The application of canonical correlation and discriminant analyses revealed spatial and temporal variability in the microbial and chemical parameters influencing water quality, suggesting that these creeks differ in terms of the nature and extent of fecal pollution. The identification of creeks within a watershed that have similar sources of fecal pollution using this data analysis approach could change prioritization of best management practices selection and placement. Furthermore, this suggests that TMDL development may require multiyear and multisite data using a targeted sampling approach instead of a 30-d geometric mean in large, complex watersheds. This technique may facilitate the choice between watershed TMDLs and single segment or stream TMDLs.

The authors conclude that somatization disorder is a frequent and serious comorbid disorder among patients with dissociative disorders.

A high proportion of psychiatric inpatients have significant dissociative pathology, and these symptoms are underrecognized by clinicians. The proper diagnosis of these patients has important implications for their clinical course.

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+‐somatic bacteriophage detections. Models were validated using a bootstrapping cross‐validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation. Core Ideas Maxent models were developed to infer source and transport of two fecal indicators. Canonical correlation analysis was applied to select parameters for Maxent models. Canonical selection and sensitivity analysis improved E. coli models. Elevated concentrations of E. coli are associated with hardness and heterotrophic activity. Bacteriophage detection is inhibited by sediment coliforms and enzyme activity.

Child maltreatment and household dysfunction have long been linked to delinquency, adult criminality, and sexual offending. However, the association between adverse childhood experiences (ACEs), factors related to out-of-home placement, and the onset of maladaptive behaviors has not thoroughly been explored in adolescents who have engaged in sexually abusive behavior. In the present study, we examined archival records of 120 male youths who have received treatment for sexually abusive behavior. As expected, the male adolescents in this sample have experienced higher rates of ACEs than samples of adult males in the community, adult males who committed sexual offenses, and juvenile justice-involved males as reported in the literature. Discrete-time survival analyses yielded increased risks of onset of aggression and sexually abusive behavior during early childhood and mid-to-late childhood, with significant associations between higher ACE scores and a greater number of out-of-home placements. Implications and future directions are discussed.