Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test = 0.30 ± 0.09; depressive symptoms: r Test = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.

Plasma per- and polyfluoroalkyl substance mixtures during pregnancy and duration of breastfeeding in the New Hampshire birth cohort study.

BACKGROUND: Prior studies suggest that prenatal per- and polyfluoroalkyl substances (PFAS) exposures are associated with shorter breastfeeding duration. Studies assessing PFAS mixtures and populations in North America are sparse. METHODS: We quantified PFAS concentrations in maternal plasma collected during pregnancy in the New Hampshire Birth Cohort Study (2010-2017). Participants completed standardized breastfeeding surveys at regular intervals until weaning (n = 813). We estimated associations between mixtures of 5 PFAS and risk of stopping exclusive breastfeeding before 6 months or any breastfeeding before 12 months using probit Bayesian kernel machine regression. For individual PFAS, we calculated the relative risk and hazard ratio (HR) of stopping breastfeeding using modified Poisson regression and accelerated failure time models respectively. RESULTS: PFAS mixtures were associated with stopping exclusive breastfeeding before 6 months, primarily driven by perfluorooctanoate (PFOA). We observed statistically significant trends in the association of perfluorohexane sulfonate (PFHxS), PFOA, and perfluorononanoate (PFNA) (p-trends≤0.02) with stopping exclusive breastfeeding. Participants in the highest PFOA quartile had a 28% higher risk of stopping exclusive breastfeeding before 6 months compared to those in the lowest quartile (95% Confidence Interval: 1.04, 1.56). Similar trends were observed for PFHxS and PFNA with exclusive breastfeeding (p-trends≤0.05). PFAS were not associated with stopping any breastfeeding before 12 months. CONCLUSIONS: In this cohort, we observed that participants with greater overall plasma PFAS concentrations had greater risk of stopping exclusive breastfeeding before 6 months and associations were driven largely by PFOA. These findings further support the growing literature indicating that PFAS may be associated with shorter duration of breastfeeding.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Developmental regulation of matrix metalloproteinases in response to multi-factorial, severe TBI injuries during immaturity.

INTRODUCTION: A striking pattern in young children after severe TBI is when the entire cortical ribbon displays tissue damage: hemispheric hypodensity (HH). HH is often a result of abusive head trauma (AHT). We previously reported a model of HH in a gyrencephalic species where a combination of injuries consisting of 1) cortical impact, 2) midline shift, 3) subdural hematoma/subarachnoid hemorrhage, 4) traumatic seizures, and 5) brief apnea and hypoventilation, resulted in extensive, hypoxic-ischemic type injury. Importantly, this mechanism closely resembles that seen in children, with relative sparing of the contralateral cortex, thus, ruling out a pure asphyxia mechanism. In this model, piglets of similar developmental stage to human toddlers (postnatal day 30, PND30) have extensive hypoxic-ischemic damage to the cortical ribbon with sparing of the contralateral hemisphere and deep gray matter areas. However, piglets of similar developmental stage to human infants (postnatal day 7, PND7) have less hypoxic-ischemic damage that is notably bilateral and patchy. We therefore sought to discover whether the extensive tissue damage observed in PND30 was due to a greater upregulation of matrix metalloproteinases (MMPs). MATERIALS AND METHODS: In PND 7 or PND 30 piglets receiving AHT injuries (cortical impact, midline shift, subdural hematoma/subarachnoid hemorrhage, traumatic seizures, and brief apnea and hypoventilation) or a sham injury, the pattern of albumin extravasation and MMP-9 upregulation throughout the brain was determined via immunohistochemistry, brain tissue adjacent to the cortical impact where the tissue damage spreads was collected for Western blots, and the gelatinase activity was determined over time in peripheral plasma. EEG was recorded and piglets survived up to 24 hours after injury administration. RESULTS: The pattern of albumin extravasation, indicating vasogenic edema, as well as increase in MMP-9, were both present at the same areas of hypoxic-ischemic tissue damage. Evidence from immunohistochemistry, western blot, and zymogens demonstrate that MMP- 2,- 3 or -9 are constitutively expressed during immaturity and are not different between developmental stages; however, active forms are upregulated in PND30 but not PND7 after in response to AHT model injuries. Furthermore, peripheral active MMP-9 was downregulated after model injuries in PND7. CONCLUSIONS: This differential response to AHT model injuries might confer protection to the PND7 brain. Additionally, we find that immature gyrencephalic species have a greater baseline and array of MMP's than previously demonstrated in rodent species. Treatment with an oral or intravenous broad-spectrum matrix metalloproteinase inhibitor might reduce the extensive spread of injury in PND30, but the exposure to metalloproteinase inhibitors must be acute as to not interfere with the homeostatic role of matrix metalloproteinases in normal postnatal brain development and plasticity as well as post-injury synaptogenesis and tissue repair.

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.

Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms.

Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.

Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year.

INTRODUCTION: Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population. METHODS: To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15-17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features. RESULTS: The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience. DISCUSSION: This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience.

Magnetic resonance imaging-guided stereotactic laser ablation therapy for the treatment of pediatric epilepsy: a retrospective multiinstitutional study.

OBJECTIVE: The authors of this study evaluated the safety and efficacy of stereotactic laser ablation (SLA) for the treatment of drug-resistant epilepsy (DRE) in children. METHODS: Seventeen North American centers were enrolled in the study. Data for pediatric patients with DRE who had been treated with SLA between 2008 and 2018 were retrospectively reviewed. RESULTS: A total of 225 patients, mean age 12.8 ± 5.8 years, were identified. Target-of-interest (TOI) locations included extratemporal (44.4%), temporal neocortical (8.4%), mesiotemporal (23.1%), hypothalamic (14.2%), and callosal (9.8%). Visualase and NeuroBlate SLA systems were used in 199 and 26 cases, respectively. Procedure goals included ablation (149 cases), disconnection (63), or both (13). The mean follow-up was 27 ± 20.4 months. Improvement in targeted seizure type (TST) was seen in 179 (84.0%) patients. Engel classification was reported for 167 (74.2%) patients; excluding the palliative cases, 74 (49.7%), 35 (23.5%), 10 (6.7%), and 30 (20.1%) patients had Engel class I, II, III, and IV outcomes, respectively. For patients with a follow-up ≥ 12 months, 25 (51.0%), 18 (36.7%), 3 (6.1%), and 3 (6.1%) had Engel class I, II, III, and IV outcomes, respectively. Patients with a history of pre-SLA surgery related to the TOI, a pathology of malformation of cortical development, and 2+ trajectories per TOI were more likely to experience no improvement in seizure frequency and/or to have an unfavorable outcome. A greater number of smaller thermal lesions was associated with greater improvement in TST. Thirty (13.3%) patients experienced 51 short-term complications including malpositioned catheter (3 cases), intracranial hemorrhage (2), transient neurological deficit (19), permanent neurological deficit (3), symptomatic perilesional edema (6), hydrocephalus (1), CSF leakage (1), wound infection (2), unplanned ICU stay (5), and unplanned 30-day readmission (9). The relative incidence of complications was higher in the hypothalamic target location. Target volume, number of laser trajectories, number or size of thermal lesions, or use of perioperative steroids did not have a significant effect on short-term complications. CONCLUSIONS: SLA appears to be an effective and well-tolerated treatment option for children with DRE. Large-volume prospective studies are needed to better understand the indications for treatment and demonstrate the long-term efficacy of SLA in this population.

Evaluating the Impact of Mask Mandates and Political Party Affiliation on Mental Health Internet Search Behavior in the United States During the COVID-19 Pandemic: Generalized Additive Mixed Model Framework.

BACKGROUND: The impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. OBJECTIVE: The goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. METHODS: This study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates-with 16 of these states being Republican-to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. RESULTS: The results of generalized additive mixed models revealed that search volume for "restless" significantly increased following a mask mandate across all states, whereas the search volume for "irritable" and "anxiety" increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. CONCLUSIONS: These findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health-related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States.

Sidelobe Suppression Techniques for Near-Field Multistatic SAR.

Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.

OBJECTIVE: Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention. METHODS: Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP). RESULTS: Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions. LIMITATIONS: A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female. CONCLUSIONS: This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.

Predicting symptom response and engagement in a digital intervention among individuals with schizophrenia and related psychoses.

Introduction: Despite existing work examining the effectiveness of smartphone digital interventions for schizophrenia at the group level, response to digital treatments is highly variable and requires more research to determine which persons are most likely to benefit from a digital intervention. Materials and methods: The current work utilized data from an open trial of patients with psychosis (N = 38), primarily schizophrenia spectrum disorders, who were treated with a psychosocial intervention using a smartphone app over a one-month period. Using an ensemble of machine learning models, pre-intervention data, app use data, and semi-structured interview data were utilized to predict response to change in symptom scores, engagement patterns, and qualitative impressions of the app. Results: Machine learning models were capable of moderately (r = 0.32-0.39, R2 = 0.10-0.16, MAE norm = 0.13-0.29) predicting interaction and experience with the app, as well as changes in psychosis-related psychopathology. Conclusion: The results suggest that individual smartphone digital intervention engagement is heterogeneous, and symptom-specific baseline data may be predictive of increased engagement and positive qualitative impressions of digital intervention in patients with psychosis. Taken together, interrogating individual response to and engagement with digital-based intervention with machine learning provides increased insight to otherwise ignored nuances of treatment response.

An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia.

INTRODUCTION: Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information). METHODS: The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes. RESULTS: We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability. LIMITATIONS: However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information. CONCLUSIONS: Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.

Robust, long-term video EEG monitoring in a porcine model of post-traumatic epilepsy.

To date, post-traumatic epilepsy (PTE) research in large animal models has been limited. Recent advances in neocortical microscopy have made possible new insights into neocortical PTE. However, it is very difficult to engender convincing neocortical PTE in rodents. Thus, large animal models that develop neocortical PTE may provide useful insights that also can be more comparable to human patients. Because gyrencephalic species have prolonged latent periods, long-term video EEG recording is required. Here, we report a fully subcutaneous EEG implant with synchronized video in freely ambulatory swine for up to 13 months during epileptogenesis following bilateral cortical impact injuries or sham surgery The advantages of this system include the availability of a commercially available system that is simple to install, a low failure rate after surgery for EEG implantation, radiotelemetry that enables continuous monitoring of freely ambulating animals, excellent synchronization to video to EEG, and a robust signal to noise ratio. The disadvantages of this system in this species and age are the accretion of skull bone which entirely embedded a subset of skull screws and EEG electrodes, and the inability to rearrange the EEG electrode array. These disadvantages may be overcome by splicing a subdural electrode strip to the electrode leads so that skull growth is less likely to interfere with long-term signal capture and by placing two implants for a more extensive montage. This commercially available system in this bilateral cortical impact swine model may be useful to a wide range of investigators studying epileptogenesis in PTE.SignificancePost-traumatic epilepsy (PTE) is a cause of significant morbidity after traumatic brain injury (TBI) and is often drug-resistant. Robust, informative animal models would greatly facilitate PTE research. Ideally, this biofidelic model of PTE would utilize a species that approximates human brain anatomy, brain size, glial populations, and inflammatory pathways. An ideal model would also incorporate feasible methods for long-term video EEG recording required to quantify seizure activity. Here, we describe the first model of PTE in swine and describe a method for robust long-term video EEG monitoring for up to 13 months post-TBI. The relatively easy "out-of-the-box" radiotelemetry system and surgical techniques described here will be adaptable by a wide array of investigators studying the pathogenesis and treatment of PTE.

Advection-dominated transport past isolated disordered sinks: stepping beyond homogenization.

We investigate the transport of a solute past isolated sinks in a bounded domain when advection is dominant over diffusion, evaluating the effectiveness of homogenization approximations when sinks are distributed uniformly randomly in space. Corrections to such approximations can be non-local, non-smooth and non-Gaussian, depending on the physical parameters (a Péclet number Pe, assumed large, and a Damköhler number Da) and the compactness of the sinks. In one spatial dimension, solute distributions develop a staircase structure for large Pe , with corrections being better described with credible intervals than with traditional moments. In two and three dimensions, solute distributions are near-singular at each sink (and regularized by sink size), but their moments can be smooth as a result of ensemble averaging over variable sink locations. We approximate corrections to a homogenization approximation using a moment-expansion method, replacing the Green's function by its free-space form, and test predictions against simulation. We show how, in two or three dimensions, the leading-order impact of disorder can be captured in a homogenization approximation for the ensemble mean concentration through a modification to Da that grows with diminishing sink size.

LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction.

Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel generalizable deep learning framework, LM-GVP, composed of a protein Language Model (LM) and Graph Neural Network (GNN) to leverage information from both 1D amino acid sequences and 3D structures of proteins. Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and protein functions from Gene Ontology (GO). We also illustrated insights into how a GNN prediction head can inform the fine-tuning of protein LMs to better leverage structural information. We envision that our deep learning framework will be generalizable to many protein property prediction problems to greatly accelerate protein engineering and drug development.

Association of Selective Serotonin Reuptake Inhibitor Use With Abnormal Physical Movement Patterns as Detected Using a Piezoelectric Accelerometer and Deep Learning in a Nationally Representative Sample of Noninstitutionalized Persons in the US.

Importance: Selective serotonin reuptake inhibitors (SSRIs) are a common first-line treatment for some psychiatric disorders, including depression and anxiety; although they are generally well tolerated, SSRIs have known adverse effects, including movement problems, sleep disruption, and gastrointestinal problems (eg, nausea and upset stomach). No large-scale studies using naturalistic, longitudinal, objective data have validated physical activity findings, and actigraphy data are well suited to address this task. Objectives: To evaluate whether differences in physical movement exist among individuals treated with SSRIs compared with control participants and to identify the unique features of the movement of patients treated with SSRIs. Design, Setting, and Participants: This cross-sectional study examines longitudinally collected wearable movement data within a cross-sectional sample of 7162 participants from the 2005-2006 National Health and Nutrition Examination Survey (NHANES), a nationally representative population-based sample of noninstitutionalized persons in the US having both medication information and passive movement data. Statistical analysis was performed from April 1, 2021, to February 1, 2022. Exposures: The use of SSRIs (sertraline hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, paroxetine hydrochloride, and citalopram hydrobromide), as reported by participants interviewed by NNHANES personnel, was the primary exposure, measured as a binary variable (taking an SSRI vs not taking an SSRI). Main Outcomes and Measures: The primary outcome was the intensity of body movement as recorded by a piezoelectric accelerometer worn on the right hip for more than 1 week. Results: Of the 7162 participants included in the study, the mean (SD) age was 33.7 (22.6) years, 266 (3.7%) were taking an SSRI, 3706 (51.7%) were female, 1934 (27.0%) were Black, 1823 (25.5%) were Mexican American, 210 (2.9%) were other Hispanic, 336 (4.7%) were other or multiracial, and 2859 (39.9%) were White (per the NHANES data collection protocol). A cross-validated, deep learning classifier was constructed that achieved fair performance predicting SSRI use (area under the curve, 0.67 [95% CI, 0.64-0.71] for the validation set and 0.66 [95% CI, 0.64-0.68] for the test set). To account for possible confounding by indication, we constructed a parallel model incorporating depression severity, finding only marginal performance improvement. When averaged across individuals and across 7 days, the results show less overall movement in the SSRI group (mean, 120.1 vertical acceleration counts/min [95% CI, 115.7-124.6 vertical acceleration counts/min]) compared with the non-SSRI control group (mean, 168.8 vertical acceleration counts/min [95% CI, 162.8-174.9 vertical acceleration counts/min]). Conclusions and Relevance: This cross-sectional study found a moderate association between passive movement and SSRI use, as well as SSRI detection capacity of passive movement using time series deep learning models. The results support the use of passive sensors for exploration and characterization of psychotropic medication adverse effects.

Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom.

INTRO: As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive. METHODS: To build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of N = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework. RESULTS: The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (r > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones. CONCLUSION: A lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect.

BACKGROUND: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. OBJECTIVE: Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. METHODS: Using COVID-19-related news headlines from a database of online news stories in conjunction with mental health-related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. RESULTS: Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA ß=-.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA ß=.221; P<.001) contributing generally to an increase in online mental health search term frequency. CONCLUSIONS: This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health-related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.