The Paper Chase: A Team Science Training Exercise.

Over the past few decades of psychological research, there has been an important increase in both the application of multidisciplinary or collaborative science and in training and research that emphasizes social justice and cultural humility. In the current paper, we report on the use of the "Paper Chase" as a team science training and research experience that also facilitates cultural humility in research and when working in teams. The Paper Chase is a synchronous writing exercise originally conceptualized by a cohort of health service psychology interns to reduce lag time between manuscript writing and submission (Schaumberg et al., 2015). The Paper Chase involves a group of trainees coming together for a predetermined amount of time (e.g., 9 or more hours) with the aim of writing and submitting a full manuscript for publication. In the current paper, we extend a previous report on the Paper Chase by formally linking the training experience to the four phases of team science: development, conceptualization, implementation, and translation. We also discuss ways in which the Paper Chase as a training experience can promote cultural humility. Finally, we provide updated recommendations for successfully completing a Paper Chase project. Overall, the authors of this manuscript who were predoctoral psychology interns across two recent cohorts at one academic medical center reported positive experiences from the Paper Chase. In addition, the current study suggests the Paper Chase can be used as one activity that facilitates critical training in team science.

Biobehavioral Predictors of Pain Intensity, Pain Interference, and Chronic Pain Episodes: A Prospective Cohort Study of African-American Adults.

Racial disparities in pain experiences are well-established, with African-American (AA) adults reporting higher rates of daily pain, increased pain severity, and greater pain-related interference compared to non-Hispanic Whites. However, the biobehavioral factors that predict the transition to chronic pain among AA adults are not well understood. This prospective cohort study provided a unique opportunity to evaluate predictors of chronic pain onset among 130 AA adults (81 women), ages 18 to 44, who did not report chronic pain at their baseline assessment and subsequently completed follow-up assessments at 6- and 12-months. Outcome measures included pain intensity, pain-related interference, and chronic pain status. Comprehensive assessments of sociodemographic and biobehavioral factors were used to evaluate demographics, socioeconomic status, stress exposure, psychosocial factors, prolonged hypothalamic-pituitary-adrenal secretion, and quantitative sensory testing responses. At baseline, 30 adults (23.1%) reported a history of prior chronic pain. Over the 12-month follow-up period, 13 adults (10.0%) developed a new chronic pain episode, and 18 adults (13.8%) developed a recurrent chronic pain episode. Whereas socioeconomic status measures (ie, annual income, education) predicted changes in pain intensity over the follow-up period, quantitative sensory testing measures (ie, pain threshold, temporal summation of pain) predicted changes in pain interference. A history of chronic pain and higher depressive symptoms at baseline independently predicted the onset of a new chronic pain episode. The present findings highlight distinct subsets of biobehavioral factors that are differentially associated with trajectories of pain intensity, pain-related interference, and onset of chronic pain episodes in AA adults. PERSPECTIVE: This prospective study sought to advance understanding of biobehavioral factors that predicted pain outcomes over a 12-month follow-up period among AA adults without chronic pain at their initial assessment. Findings revealed distinct subsets of factors that were differentially associated with pain intensity, pain-related interference, and onset of chronic pain episodes.

Diversity in emotion regulation strategy use: Resilience against posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) is associated with impaired emotion regulation (ER). ER diversity, the variety, prevalence, and relative abundance of ER strategy use, may provide resilience against PTSD. This study examined the prospective relation between ER diversity and PTSD, while accounting for negative and positive life events, in interpersonal violence (IPV) survivors. IPV-exposed women with PTSD onset (PTSD; n = 22), without PTSD onset (IPV; n = 37), and non-traumatized control participants (NTC; n = 41) rated their ER strategy use and experience of negative and positive life events. The ER diversity index differentiated the participant groups. Importantly, group differences in ER diversity depended on the experience of life events. When experiencing fewer positive life events and more negative life events, the IPV and NTC groups, but not the PTSD group, demonstrated higher ER diversity. Thus, greater ER diversity during periods with more negative life events and fewer positive life events may play a protective role against PTSD onset for IPV survivors.

Physical activity, sitting time, and thermal quantitative sensory testing responses in African Americans.

Introduction: Prior research suggests that African Americans (AAs) have more frequent, intense, and debilitating pain and functional disability compared with non-Hispanic Whites (NHWs). Potential contributing factors to this disparity are physical activity and sedentary behavior, given that AAs are less physically active, and physical activity is associated with antinociception (whereas sedentary behavior is linked to pronociception). However, impact of these factors on pain processing has largely been unexplored in AAs, especially before chronic pain onset. Objective: This study examined relationships between physical activity, sedentary behavior (sitting time), and laboratory measures of pain and pain modulation in adult AAs. These included heat pain threshold and tolerance, temporal summation of pain (TSP, a marker of central sensitization), and conditioned pain modulation (CPM, a marker of descending pain inhibition). Methods: Multiple regressions were conducted to examine the effects of physical activity and sitting time on heat threshold and tolerance. Multilevel models were conducted to assess the relationship between physical activity, sitting time, and temporal summation of pain. Additional multilevel models were conducted to assess the relationship between physical activity, sitting time, and conditioned pain modulation. Results: Higher level of physical activity, but not sitting time, was associated with reduced TSP slopes. Neither physical activity nor sitting time was associated with CPM slopes. No significant relationships between physical activity or sitting time and heat pain threshold or tolerance were detected. Conclusions: These findings suggest that physical activity is associated with reduced TSP, an effect which may be driven by reduced spinal hyperexcitability in more active individuals. Thus, structural and individual interventions designed to increase physical activity in healthy, young AAs may be able to promote antinociceptive processes (ie, reduced TSP/reduced pain facilitation) potentially protective against chronic pain.

Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.

The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.

Adversity type and timing predict temporal summation of pain in African-American adults.

African Americans are disproportionately exposed to adversity across the lifespan, which includes both stressful and traumatic events. Adversity, in turn, is associated with alterations in pain responsiveness. Racial differences in pain responsiveness among healthy adults are well established. However, the extent to which adversity type and timing are associated with alterations in pain responsiveness among healthy African-American adults is not well understood. The present study included 160 healthy African-American adults (98 women), ages 18 to 45. Outcome measures included pain tolerance and temporal summation of pain to evoked thermal pain. Composite scores were created for early-life adversity (childhood trauma, family adversity) and recent adversity (perceived stress, chronic stress burden). A measure of lifetime racial discrimination was also included. Higher levels of recent adversity were associated with higher temporal summation of pain, controlling for gender, age, and education. Neither early-life adversity nor lifetime racial discrimination were associated with temporal summation of pain. The present findings suggest that heightened temporal summation of pain among healthy African-American adults is associated with exposure to recent adversity events. Improved understanding of how recent adversity contributes to heightened temporal summation of pain in African Americans could help to mitigate racial disparities in pain experiences by identifying at-risk individuals who could benefit from early interventions.

Dynamics of diurnal cortisol and alpha-amylase secretion and their associations with PTSD onset in recent interpersonal trauma survivors.

BACKGROUND: Dysfunction in major stress response systems during the acute aftermath of trauma may contribute to risk for developing posttraumatic stress disorder (PTSD). The current study investigated how PTSD diagnosis and symptom severity, depressive symptoms, and childhood trauma uniquely relate to diurnal neuroendocrine secretion (cortisol and alpha-amylase rhythms) in women who recently experienced interpersonal trauma compared to non-traumatized controls (NTCs). METHOD: Using a longitudinal design, we examined diurnal cortisol and alpha-amylase rhythms in 98 young women (n = 57 exposed to recent interpersonal trauma, n = 41 NTCs). Participants provided saliva samples and completed symptom measures at baseline and 1-, 3-, and 6-month follow-up. RESULTS: Multilevel models (MLMs) revealed lower waking cortisol predicted the development of PTSD in trauma survivors and distinguished at-risk women from NTCs. Women with greater childhood trauma exposure exhibited flatter diurnal cortisol slopes. Among trauma-exposed individuals, lower waking cortisol levels were associated with higher concurrent PTSD symptom severity. Regarding alpha-amylase, MLMs revealed women with greater childhood trauma exposure exhibited higher waking alpha-amylase and slower diurnal alpha-amylase increase. CONCLUSIONS: Results suggest lower waking cortisol in the acute aftermath of trauma may be implicated in PTSD onset and maintenance. Findings also suggest childhood trauma may predict a different pattern of dysfunction in stress response systems following subsequent trauma exposure than the stress system dynamics associated with PTSD risk; childhood trauma appears to be associated with flattened diurnal cortisol and alpha-amylase slopes, as well as higher waking alpha-amylase.

Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach.

Introduction: Benzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions could assist in prevention efforts. Methods: The present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (N = 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (N = 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features. Results: For predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878). Discussion: Results suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.

Racial, Gender, and Neighborhood-Level Disparities in Pediatric Trauma Care.

BACKGROUND: Disparities in trauma outcomes and care are well established for adults, but the extent to which similar disparities are observed in pediatric trauma patients requires further investigation. The objective of this study was to evaluate the unique contributions of social determinants (race, gender, insurance status, community distress, rurality/urbanicity) on trauma outcomes after controlling for specific injury-related risk factors. STUDY DESIGN: All pediatric (age < 18) trauma patients admitted to a single level 1 trauma center with a statewide, largely rural, catchment area from January 2010 to December 2020 were retrospectively reviewed (n = 14,398). Primary outcomes were receipt of opioids in the emergency department, post-discharge rehabilitation referrals, and mortality. Multivariate logistic regressions evaluated demographic, socioeconomic, and injury characteristics. Multilevel logistic regressions evaluated area-level indicators, which were derived from abstracted home addresses. RESULTS: Analyses adjusting for demographic and injury characteristics revealed that Black children (n = 6255) had significantly lower odds (OR = 0.87) of being prescribed opioid medications in the emergency department compared to White children (n = 5883). Children living in more distressed and rural communities had greater odds of receiving opioid medications. Girls had significantly lower odds (OR = 0.61) of being referred for rehabilitation services than boys. Post hoc analyses revealed that Black girls had the lowest odds of receiving rehabilitation referrals compared to Black boys and White children. CONCLUSION: Results highlight the need to examine both main and interactive effects of social determinants on trauma care and outcomes. Findings reinforce and expand into the pediatric population the growing notion that traumatic injury care is not immune to disparities.

Cognitive-Affective-Behavioral Pathways Linking Adversity and Discrimination to Daily Pain in African-American Adults.

The tendency to ruminate, magnify, and experience helplessness in the face of pain - known as pain catastrophizing - is a strong predictor of pain outcomes and is associated with adversity. The ability to maintain functioning despite adversity - referred to as resilience - also influences pain outcomes. Understanding the extent to which pain catastrophizing and resilience influence relations between adversity and daily pain in healthy African-American adults could improve pain risk assessment and mitigate racial disparities in the transition from acute to chronic pain. This study included 160 African-American adults (98 women). Outcome measures included daily pain intensity (sensory, affective) and pain impact on daily function (pain interference). Adversity measures included childhood trauma exposure, family adversity, chronic burden from recent stressors, and ongoing perceived stress. A measure of lifetime racial discrimination was also included. Composite scores were created to capture early-life adversity (childhood trauma, family adversity) versus recent-life adversity (perceived stress, chronic burden). Increased pain catastrophizing was correlated with increased adversity (early and recent), racial discrimination, pain intensity, and pain interference. Decreased pain resilience was correlated with increased recent-life adversity (not early-life adversity or racial discrimination) and correlated with increased pain intensity (not pain-related interference). Bootstrapped multiple mediation models revealed that relationships between all adversity/discrimination and pain outcomes were mediated by pain catastrophizing. Pain resilience, however, was not a significant mediator in these models. These findings highlight opportunities for early interventions to reduce cognitive-affective-behavioral risk factors for persisting daily pain among African-American adults with greater adversity exposure by targeting pain catastrophizing.

Evaluating machine learning models to classify occupants' perceptions of their indoor environment and sleep quality from indoor air quality.

A variety of factors can affect a person's perception of their environment and health, but one factor that is often overlooked in indoor settings is the air quality. To address this gap, we develop and evaluate four Machine Learning (ML) models on two disparate datasets using Indoor Air Quality (IAQ) parameters as primary features and components of self-reported IAQ satisfaction and sleep quality as target variables. In each case, we compare models to each other as well as to a simple model that always predicts the majority outcome. In the first analysis, we use open-source data collected from 93 California residences to predict occupant's satisfaction with their indoor environment. Results indicate building ventilation rate, Relative Humidity (RH), and formaldehyde are most influential when predicting IAQ perception and do so with an accuracy greater than the simplified model. The second analysis uses IAQ data gathered from a field study we conducted with 20 participants over 11 weeks to train similar models. We obtain accuracy and F1 scores similar to the simplified model where PM2.5 and TVOCs represent the most important predictors. Our results underscore the ability of IAQ to affect a person's perception of their built environment and health and highlight the utility of ML models to explore the strength of these relationships.Implications: The results from this study show that two outcome variables - occupant's indoor air quality (IAQ) satisfaction and perceived sleep quality - are related to the measured IAQ parameters but not heavily influenced by typical values measured in apartments and homes. This study highlights the ability of machine learning models as exploratory analysis tools to determine underlying relationships within and across datasets in addition to understanding the importance of certain features on the outcome variable. We compare four different models and find that the random forest classifier has the best performance in both analysis on IAQ satisfaction and perceived sleep quality. It is a suitable model for predicting IAQ-related subjective metrics and also provides value insight into the feature importance of the IAQ parameters. The accuracy of any of these machine learning models in predicting occupants' comfort or sleep quality is limited by the dataset size, how data is collected, and range of data. This study identifies the factors that are important to IAQ perception: ventilation rate, relative humidity, and concentrations of formaldehyde, NO2, and particulate matter. It indicates that sensors that can measure these variables are necessary for future, related studies that model occupants' IAQ satisfaction. However, this study does not find strong relationships between any of the IAQ parameters measured in this study and perceived sleep quality despite the logical pathway between these many pollutants and respiratory issues. A prediction model of IAQ perception or sleep quality can be integrated into home management systems to automatically adjust building operations such as ventilation rates in smart buildings. Once buildings are equipped with a network of low-cost sensors that measure concentrations of pollutants and operating conditions of the ventilation system, the prediction model can be used to predict the occupants' comfort and facilitate the control of the ventilation system.

Hurricane María drives increased indoor proliferation of filamentous fungi in San Juan, Puerto Rico: a two-year culture-based approach.

Extensive flooding caused by Hurricane María in Puerto Rico (PR) created favorable conditions for indoor growth of filamentous fungi. These conditions represent a public health concern as contamination by environmental fungi is associated with a higher prevalence of inflammatory respiratory conditions. This work compares culturable fungal spore communities present in homes that sustained water damage after Hurricane María to those present in dry, non-flooded homes. We collected air samples from 50 houses in a neighborhood in San Juan, PR, 12 and 22 months after Hurricane María. Self-reported data was used to classify the homes as flooded, water-damage or dry non-flooded. Fungi abundances, composition and diversity were analyzed by culturing on two media. Our results showed no significant differences in indoor fungal concentrations (CFU/m3) one year after the Hurricane in both culture media studied (MEA and G25N). During the second sampling period fungal levels were 2.7 times higher in previously flooded homes (Median = 758) when compared to dry homes (Median = 283), (p-value < 0.005). Fungal profiles showed enrichment of Aspergillus species inside flooded homes compared to outdoor samples during the first sampling period (FDR-adjusted p-value = 0.05). In contrast, 22 months after the storm, indoor fungal composition consisted primarily of non-sporulated fungi, most likely basidiospores, which are characteristic of the outdoor air in PR. Together, this data highlights that homes that suffered water damage not only have higher indoor proliferation of filamentous fungi, but their indoor fungal populations change over time following the Hurricane. Ultimately, after nearly two years, indoor and outdoor fungal communities converged in this sample of naturally ventilated homes.

Racial and demographic disparities in emergency department utilization for mental health concerns before and during the COVID-19 pandemic.

This study investigated whether emergency department (ED) visits for mental health concerns increased during the COVID-19 pandemic, taking a health disparities lens. ED encounters from the only academic medical center in Mississippi were extracted from March-December 2019 and 2020, totaling 2,842 pediatric (ages 4-17) and 17,887 adult (ages 18-89) patients. Visits were coded based on primary ED diagnosis. For adults, there were fewer depression/anxiety ED visits during the pandemic, not moderated by any demographic factor, but no differences for serious mental illness or alcohol/substance use. For youth, there were significantly fewer ED visits for behavior problems during the pandemic among children in the lower socioeconomic status (SES) category; there were no differences for depression/anxiety. Regardless of year, adults in the lower SES category were more likely to visit the ED for mental health, Black adults were less likely to visit the ED for depression/anxiety or alcohol/substance use, and Black children were less likely to visit the ED for behavioral concerns. Results suggest that access to outpatient and telehealth services remains critical for mental health care during the pandemic and underline the importance of race- and SES-related factors in use of the ED for mental health concerns beyond the pandemic.

Social determinants of trauma care: Associations of race, insurance status, and place on opioid prescriptions, postdischarge referrals, and mortality.

BACKGROUND: Racial disparities in trauma care have been reported for a range of outcomes, but the extent to which these remain after accounting for socioeconomic and environmental factors remains unclear. The objective of this study was to evaluate the unique contributions of race, health insurance, community distress, and rurality/urbanicity on trauma outcomes after carefully controlling for specific injury-related risk factors. METHODS: All adult (age, ≥18 years) trauma patients admitted to a single Level I trauma center with a statewide, largely rural, catchment area from January 2010 to December 2020 were retrospectively reviewed. Primary outcomes were mortality, rehabilitation referral, and receipt of opioids in the emergency department. Demographic, socioeconomic, and injury characteristics as well as indicators of community distress and rurality based on home address were abstracted from a trauma registry database. RESULTS: Analyses revealed that Black patients (n = 13,073) were younger, more likely to be male, more likely to suffer penetrating injuries, and more likely to suffer assault-based injuries compared with White patients (n = 10,946; all p < 0.001). In adjusted analysis, insured patients had a 28% lower risk of mortality (odds ratio, 0.72; p = 0.005) and were 92% more likely to be referred for postdischarge rehabilitation than uninsured patients (odds ratio, 1.92; p = 0.005). Neither race- nor place-based factors were associated with mortality. However, post hoc analyses revealed a significant race by age interaction, with Black patients exhibiting more pronounced increases in mortality risk with increasing age. CONCLUSION: The present findings help disentangle the social determinants of trauma disparities by adjusting for place and person characteristics. Uninsured patients were more likely to die and those who survived were less likely to receive referrals for rehabilitation services. The expected racial disparity in mortality risk favoring White patients emerged in middle age and was more pronounced for older patients. LEVEL OF EVIDENCE: Prognostic and epidemiological, Level III.

Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study.

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally 1) registered a greater amount of midday movement than movement in the morning (8-10 a.m.) and in the evening (7-9 p.m.), as opposed to the other way around; 2) exhibited significantly less intradaily variability in their daily movement; 3) visited less places and stayed at home more everyday, and; 4) had a significant lower correlation between their mobility patterns and negative mood.

Human-Infrastructure Interactions during the COVID-19 Pandemic: Understanding Water and Electricity Demand Profiles at the Building Level.

When engineers design and manage a building's water and electricity utilities, they must make assumptions about resource use. These assumptions are often challenged when unexpected changes in demand occur, such as the spatial and temporal changes observed during the coronavirus (COVID-19) pandemic. Social distancing policies (SDPs) enacted led many universities to close their campuses and implement remote learning, impacting utility consumption patterns. Yet, little is known about how consumption changed at the building level. Here, we aim to understand how water and electricity consumption changed during the pandemic by identifying characteristic weekly demand profiles and understanding how these changes were related to regulatory and social systems. We performed k-means clustering on utility demand data measured before and as the pandemic evolved from five buildings of different types at the University of Texas at Austin. As expected, after SDPs were enacted both water and electricity use shifted, with most buildings seeing a sharp initial decline that remained low until the university partially reopened. In contrast to electricity use, we found that water use was tightly coupled with SDPs. Our study provides actionable information for managers to mitigate negative impacts (e.g., water stagnation) and capitalize on opportunities to minimize resource use.

Development of a reproducible method for monitoring SARS-CoV-2 in wastewater.

Monitoring the genetic signal of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through RNA titers in wastewater has emerged as a promising strategy for tracking community-scale prevalence of coronavirus disease 2019 (COVID-19). Although many studies of SARS-CoV-2 in wastewater have been conducted around the world, a uniform procedure for concentrating the virus in wastewater is lacking. The goal of this study was to comprehensively evaluate how different methods for concentrating the suspended solids in wastewater affect the associated SARS-CoV-2 RNA signal and the time required for processing samples for wastewater-based epidemiology efforts. We additionally consider the effects of sampling location in the wastewater treatment train (i.e., following preliminary or primary treatment), pasteurization, and RNA extraction method. Comparison of the liquid phase to suspended solids obtained via centrifugation or vacuum filtration suggests that the RNA signal of SARS-CoV-2 preferentially occurs in the solids. Therefore, we assert that the recovery of SARS-CoV-2 from wastewater should focus on suspended solids. Our data indicate that the measured SARS-CoV-2 signal is higher among samples taken from the primary clarifier effluent, as opposed to those taken after preliminary treatment. Additionally, we provide evidence that sample pasteurization at 60 °C for 90 min reduces the SARS-CoV-2 signal by approximately 50-55%. Finally, the results indicate that a magnetic bead approach to RNA extraction leads to a higher SARS-CoV-2 signal than does a silica membrane approach.

Neural mechanisms and predictors of SSRI and CBT treatment of anxiety: A randomized trial focused on emotion and cognitive processing.

Anxiety disorders (ADs) are common and difficult to treat. While research suggests ADs are characterized by an imbalance between bottom-up and top-down attention processes and that effective treatments work by correcting this dysfunction, there is insufficient data to explain how and for whom treatments work. The late positive potential (LPP), an event-related potential reflecting elaborative processing of motivationally salient stimuli, is sensitive to both bottom-up and top-down processes. The present study examines the LPP in healthy controls (HC) and patients with ADs under low and high working memory (WM) load to assess its utility as a predictor and index of symptom reduction in patients who underwent cognitive behavioral therapy (CBT) or selective serotonin reuptake inhibitor (SSRI) treatment. The LPP when viewing negative and neutral distractor images and WM performance were assessed in 96 participants (40 HC, 32 CBT, 24 SSRI) during a letter recall task at Week 0 and in a subset of the study sample (23 CBT, 16 SSRI) at Week 12. Patients were randomly assigned to twelve weeks of CBT or SSRI treatment. Participants completed self-reported symptom measures at each time point. Greater Week 0 LPP to negative images under low WM load predicted greater symptom reduction in the SSRI, but not the CBT, group. Regression analyses examining the LPP to negative images as an index of symptom reduction revealed a smaller decrease in the LPP to negative images under low WM load was associated with less anxiety reduction across treatment modalities. Findings suggest the LPP during low WM load may serve as a cost-effective predictor and index of treatment outcome in ADs. Clinical Trials Registration: ClinicalTrials.gov (Identifier: NCT01903447).

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts.

BACKGROUND: As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. RESULTS: To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants' mood, sleep, behavior, and living environment. CONCLUSIONS: We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.