Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Demographics and Characteristics of US-Based Minimally Invasive Gynecologic Surgery Fellowship Program Directors: A 2023 Descriptive Analysis.

STUDY OBJECTIVE: Fellowship program directors (FPDs) play an important role in the development of fellows and learners, but little is known about their demographics. This cross-sectional study aims to examine the characteristics of minimally invasive gynecologic surgery (MIGS) FPDs. DESIGN: A retrospective cross-sectional study. SETTING: Data obtained from publicly available information on official websites of the program directors studied. SUBJECTS: MIGS fellowship program directors. INTERVENTIONS: All US-based MIGS programs affiliated with the AAGL in 2023 were included. Information about FPD gender, medical school attended and graduation year, residency program attended and graduation year, any additional graduate degrees earned, fellowship programs completed, and the year of their appointment as FPD was collected through publicly available sources. Scholarly activity was measured by peer-reviewed articles and the Hirsch index. MEASUREMENTS AND MAIN RESULTS: Of the 54 FPDs, 28 (51.85%) were female and 26 (48.15%) were male. Male FPDs were significantly older (54.6 ± 8.7 years) than female FPDs (46.2 ± 5.0 years), p <.05. Average age at appointment was 43.1 ± 6.7 years, with female FPDs being appointed at significantly younger ages (39.4 ± 5.1 years) compared to male FPDs (44.5 ± 6.8 years), p <.05. Male FPDs had statistically significant higher Hirsch indices (14 ± 11.4) compared to female FPDs (8 ± 5.8), p <.05. Of the FPDs who completed a fellowship, 27 (50%) did so in MIGS, eight (14.81%) in Gynecologic-Oncology, 6 (11.11%) in Urogynecology, and 4 (7.41%) in Reproductive Endocrinology/Infertility. CONCLUSIONS: MIGS fellowships have a uniquely equal representation of male and female FPDs, as surgical subspecialties historically tend to be male dominant. Notably, there is diversity in the type of fellowship pursued by MIGS FPDs, with nearly half of FPDs completing a fellowship outside of MIGS. The reasons for differences in scholarly contributions, indicated by Hirsch index, of male versus female FPDs is unclear.

We returned home empty-handed: COVID-19, care, and contested citizenship of Naga migrant workers in northeast India.

COVID-19 highlighted interconnections between matters of identity and citizenship, vulnerability, and inclusion in/exclusion from systems of care in times of crisis. Migrant workers from Nagaland state, northeast India, were disproportionately impacted by the pandemic's socioeconomic consequences. The public health emergency brought into question who is 'Indian' and the citizenship rights attached to that identity, heightening migrants' exclusion from central structures. Communitarian responses in Nagaland enhanced resilience in the face of often inadequate government responses; however, COVID-19 also exposed structural inequalities within and between Naga communities. This study shows that identity-based citizenship regimes and multi-nation federalism interact to increase minorities' exclusion during crises, and that crises can strengthen both divisions and solidarity at the local level in multi-nation federal systems. Inclusion in and exclusion from systems of care are shaped by and can reshape notions of identity and citizenship, underlining the need for inclusive sociopolitical systems to mitigate crises in multi-nation federal states.

Episignatures Stratifying Helsmoortel-Van Der Aa Syndrome Show Modest Correlation with Phenotype.

Helsmoortel-Van der Aa syndrome (HVDAS) is a neurodevelopmental condition associated with intellectual disability/developmental delay, autism spectrum disorder, and multiple medical comorbidities. HVDAS is caused by mutations in activity-dependent neuroprotective protein (ADNP). A recent study identified genome-wide DNA methylation changes in 22 individuals with HVDAS, adding to the group of neurodevelopmental disorders with an epigenetic signature. This methylation signature segregated those with HVDAS into two groups based on the location of the mutations. Here, we conducted an independent study on 24 individuals with HVDAS and replicated the existence of the two mutation-dependent episignatures. To probe whether the two distinct episignatures correlate with clinical outcomes, we used deep behavioral and neurobiological data from two prospective cohorts of individuals with a genetic diagnosis of HVDAS. We found limited phenotypic differences between the two HVDAS-affected groups and no evidence that individuals with more widespread methylation changes are more severely affected. Moreover, in spite of the methylation changes, we observed no profound alterations in the blood transcriptome of individuals with HVDAS. Our data warrant caution in harnessing methylation signatures in HVDAS as a tool for clinical stratification, at least with regard to behavioral phenotypes.

Acetate supplementation rescues social deficits and alters transcriptional regulation in prefrontal cortex of Shank3 deficient mice.

BACKGROUND: The pathophysiology of autism spectrum disorder (ASD) involves genetic and environmental factors. Mounting evidence demonstrates a role for the gut microbiome in ASD, with signaling via short-chain fatty acids (SCFA) as one mechanism. Here, we utilize mice carrying deletion to exons 4-22 of Shank3 (Shank3KO) to model gene by microbiome interactions in ASD. We identify SCFA acetate as a mediator of gut-brain interactions and show acetate supplementation reverses social deficits concomitant with alterations to medial prefrontal cortex (mPFC) transcriptional regulation independent of microbiome status. METHODS: Shank3KO and wild-type (Wt) littermates were divided into control, Antibiotic (Abx), Acetate and Abx + Acetate groups upon weaning. After six weeks, animals underwent behavioral testing. Molecular analysis including 16S and metagenomic sequencing, metabolomic and transcriptional profiling were conducted. Additionally, targeted serum metabolomic data from Phelan McDermid Syndrome (PMS) patients (who are heterozygous for the Shank3 gene) were leveraged to assess levels of SCFA's relative to ASD clinical measures. RESULTS: Shank3KO mice were found to display social deficits, dysregulated gut microbiome and decreased cecal levels of acetate - effects exacerbated by Abx treatment. RNA-sequencing of mPFC showed unique gene expression signature induced by microbiome depletion in the Shank3KO mice. Oral treatment with acetate reverses social deficits and results in marked changes in gene expression enriched for synaptic signaling, pathways among others, even in Abx treated mice. Clinical data showed sex specific correlations between levels of acetate and hyperactivity scores. CONCLUSION: These results suggest a key role for the gut microbiome and the neuroactive metabolite acetate in regulating ASD-like behaviors.

Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder.

Genome-wide association studies (GWAS) have identified several risk loci for post-traumatic stress disorder (PTSD); however, how they confer PTSD risk remains unclear. We aimed to identify genes that confer PTSD risk through their effects on brain protein abundance to provide new insights into PTSD pathogenesis. To that end, we integrated human brain proteomes with PTSD GWAS results to perform a proteome-wide association study (PWAS) of PTSD, followed by Mendelian randomization, using a discovery and confirmatory study design. Brain proteomes (N = 525) were profiled from the dorsolateral prefrontal cortex using mass spectrometry. The Million Veteran Program (MVP) PTSD GWAS (n = 186,689) was used for the discovery PWAS, and the Psychiatric Genomics Consortium PTSD GWAS (n = 174,659) was used for the confirmatory PWAS. To understand whether genes identified at the protein-level were also evident at the transcript-level, we performed a transcriptome-wide association study (TWAS) using human brain transcriptomes (N = 888) and the MVP PTSD GWAS results. We identified 11 genes that contribute to PTSD pathogenesis via their respective cis-regulated brain protein abundance. Seven of 11 genes (64%) replicated in the confirmatory PWAS and 4 of 11 also had their cis-regulated brain mRNA levels associated with PTSD. High confidence level was assigned to 9 of 11 genes after considering evidence from the confirmatory PWAS and TWAS. Most of the identified genes are expressed in other PTSD-relevant brain regions and several are preferentially expressed in excitatory neurons, astrocytes, and oligodendrocyte precursor cells. These genes are novel, promising targets for mechanistic and therapeutic studies to find new treatments for PTSD.

Wearable and Non-Invasive Sensors for Rock Climbing Applications: Science-Based Training and Performance Optimization.

Rock climbing has evolved from a method for alpine mountaineering into a popular recreational activity and competitive sport. Advances in safety equipment and the rapid growth of indoor climbing facilities has enabled climbers to focus on the physical and technical movements needed to elevate performance. Through improved training methods, climbers can now achieve ascents of extreme difficulty. A critical aspect to further improve performance is the ability to continuously measure body movement and physiologic responses while ascending the climbing wall. However, traditional measurement devices (e.g., dynamometer) limit data collection during climbing. Advances in wearable and non-invasive sensor technologies have enabled new applications for climbing. This paper presents an overview and critical analysis of the scientific literature on sensors used during climbing. We focus on the several highlighted sensors with the ability to provide continuous measurements during climbing. These selected sensors consist of five main types (body movement, respiration, heart activity, eye gazing, skeletal muscle characterization) that demonstrate their capabilities and potential climbing applications. This review will facilitate the selection of these types of sensors in support of climbing training and strategies.

Dissociation Among Women with Chronic Pelvic Pain: Relation to Surgical Treatment, Pelvic Pain Severity, and Health-Related Quality of Life.

Chronic pelvic pain (CPP) is associated with a history of trauma and symptoms of somatoform dissociation. We aimed to describe how somatoform dissociation impacts CPP symptoms, surgical treatment, and health-related quality of life (HRQOL). Patients (N = 133) diagnosed with CPP presenting for an appointment at a women's health clinic between November, 2019 - July, 2021 were recruited to participate in a cross-sectional study and complete a survey assessing symptoms of somatoform dissociation, post-traumatic stress disorder (PTSD), pelvic pain severity, history of CPP-related surgeries, and mental and physical HRQOL. We also conducted a post-hoc analysis assessing correlations of individual symptom items on the Somatoform Dissociation Questionnaire (SDQ-20) with HRQOL outcomes. We did not find a relationship between somatoform dissociation and pelvic pain severity or surgical history. Physical HRQOL outcomes were related to somatoform dissociation, PTSD symptoms, and pelvic pain severity, while mental HRQOL outcomes were connected to somatoform dissociation and PTSD symptoms. Our study reveals preliminary evidence suggesting that among CPP patients, HRQOL outcomes are affected by unique profiles of positive and negative symptoms of somatoform dissociation, including sensory disturbances, localized genital pain, and generalized numbness and bodily analgesia. Addressing specific symptoms of somatoform dissociation may enhance HRQOL among trauma-exposed women with CPP. Replication studies are needed to validate our findings. Integrating trauma-informed approaches, including standardized evaluations of trauma exposure and symptoms of somatoform dissociation into routine care for women with CPP is encouraged.

Integrating Wearable Sensors and Video to Determine Microlocation-Specific Physiologic and Motion Biometrics-Method Development for Competitive Climbing.

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance.

Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds.

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.

Gain-Scanning for Protein Microarray Assays.

Protein microarrays consist of known proteins spotted onto solid substrates and are used to perform highly multivariate assessments of protein-binding interactions. Human protein arrays are routinely applied to pathogen detection, immune response biomarker profiling, and antibody specificity profiling. Here, we describe and demonstrate a new data processing procedure, gain-scan, in which data were acquired under multiple photomultiplier tube (PMT) settings, followed by data fitting with a power function model to estimate the incident light signals of the array spots. Data acquisition under multiple PMT settings solves the difficulty of determining the single optimal PMT gain setting and allows us to maximize the detection of low-intensity signals while avoiding the saturation of high-intensity ones at the same time. The gain-scan data acquisition and fitting also significantly lower the variances over the detectable range of signals and improve the linear data normalization. The performance of the proposed procedure was verified by analyzing the profiling data of both the human polyclonal serum samples and the monoclonal antibody samples with both technical replicates and biological replicates. We showed that the multigain power function was an appropriate model for describing data acquired under multiple PMT settings. The gain-scan fitting alone or in combination with the linear normalization could effectively reduce the technical variability of the array data and lead to better sample separability and more sensitive differential analysis.

Deletion of the KH1 Domain of Fmr1 Leads to Transcriptional Alterations and Attentional Deficits in Rats.

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by mutations in the FMR1 gene. It is a leading monogenic cause of autism spectrum disorder and inherited intellectual disability and is often comorbid with attention deficits. Most FXS cases are due to an expansion of CGG repeats leading to suppressed expression of fragile X mental retardation protein (FMRP), an RNA-binding protein involved in mRNA metabolism. We found that the previously published Fmr1 knockout rat model of FXS expresses an Fmr1 transcript with an in-frame deletion of exon 8, which encodes for the K-homology (KH) RNA-binding domain, KH1. Notably, 3 pathogenic missense mutations associated with FXS lie in the KH domains. We observed that the deletion of exon 8 in rats leads to attention deficits and to alterations in transcriptional profiles within the medial prefrontal cortex (mPFC), which map to 2 weighted gene coexpression network modules. These modules are conserved in human frontal cortex and enriched for known FMRP targets. Hub genes in these modules represent potential therapeutic targets for FXS. Taken together, these findings indicate that attentional testing might be a reliable cross-species tool for investigating FXS and identify dysregulated conserved gene networks in a relevant brain region.

MRI Is Better Than CT Scan for Detection of Structural Pathologies After Traumatic Posterior Hip Dislocations in Children and Adolescents.

BACKGROUND: Traumatic posterior hip dislocations in children and adolescents requires emergent closed reduction. Postreduction imaging is necessary to assess the concentricity of reduction and structural injuries to the hip. There is no a consensus for which imaging is a modality of choice in such condition. The purposes of this study are to describe magnetic resonance imaging (MRI) findings of traumatic posterior hip dislocations and to compare the effectiveness of MRI with computerized tomography (CT) in detecting structural abnormalities of the hip that impact patient management. METHODS: This study is a retrospective review of imaging in traumatically dislocated hips in patients who were treated at our institution. All CT and MRI imaging were reviewed and specific osseous and soft tissue injuries documented by consensus among 2 musculoskeletal pediatric radiologists who interpreted the MRI and CT scans of each patient in a blinded manner. RESULTS: In total, 27 patients (23 males, 4 females) with mean age of 12.5 years (range, 2 to 19 y) with postreduction MRI were evaluated. MRI findings revealed femoral head injuries in 17 (62.9%), posterior labral entrapments in 6 (22.2%), posterior labral tears in 17 (62.9%), posterior wall fractures in 15 (55.5%), fracture of the posterior unossified part of acetabulum in 4 (14.8%), and ligamentum teres injuries in 8 (29.6%). Of 16 patients who had postreduction CT scans, 6 (37.5%) had femoral head fractures, 9 (56.3%) had posterior wall fractures, and 8 (50%) had intra-articular osseous entrapments. All bony fractures and intra-articular entrapment could be seen on MRI imaging. In 16 patients with both CT and MRI, posterior acetabular injury was detected in 10/16 (62.5%) on MRI and 9/16 (56.3%) on CT. Three patients with entrapment of labrums identified on MRI could not be seen on CT scan. One patient with persistently unstable hip after reduction had an entrapped unossified portion of acetabular fracture which was seen on MRI but not on CT. CONCLUSIONS: MRI is superior to CT scan for detection of structural injuries in children and adolescents with traumatic hip dislocation. The unique structural injuries included entrapment of posterior labrum and posterior unossified acetabular fractures could be seen only at MRI. These findings will impact surgical decision making of these injuries. LEVEL OF EVIDENCE: Level IV.

Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer.

BACKGROUND: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.

Immunological biomarkers as indicators for outcome after discontinuation of nucleos(t)ide analogue therapy in patients with HBeAg-negative chronic hepatitis B.

The optimal duration of treatment with nucleos(t)ide analogues (NAs) for patients with HBeAg-negative chronic hepatitis B (CHB) is unknown. The aim of this study was to identify an immune signature associated with off-treatment remission to NA therapy. We performed microarray analysis of peripheral blood mononuclear cell (PBMCs) from six patients with chronic hepatitis B who stopped NA therapy (three with off-treatment remission, three with relapse) and five patients with chronic HBV infection (previously termed 'inactive carriers') served as controls. Results were validated using qRT-PCR on a second group of 21 individuals (17 patients who stopped treatment and four controls). PBMCs from 38 patients on long-term NA treatment were analysed for potential to stop treatment. Microarray analysis indicated that patients with off-treatment remission segregated as a distinct out-group. Twenty-one genes were selected for subsequent validation. Ten of these were expressed at significantly lower levels in the patients with off-treatment remission compared to the patients with relapse and predicted remission with AUC of 0.78-0.92. IFNγ, IL-8, FASLG and CCL4 were the most significant by logistic regression. Twelve (31.6%) of 38 patients on long-term NA therapy had expression levels of all these four genes below cut-off values and hence were candidates for stopping treatment. Our data suggest that patients with HBeAg-negative CHB who remain in off-treatment remission 3 years after NA cessation have a distinct immune signature and that PBMC RNA levels of IFNγ, IL-8, FASLG and CCL4 may serve as potential biomarkers for stopping NA therapy.

Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: An exploratory machine-learning study.

OBJECTIVE: Features of posttraumatic stress disorder (PTSD) typically include sleep disturbances, impaired declarative memory, and hyperarousal. This study evaluated whether these combined features may accurately delineate pathophysiological changes associated with PTSD. METHOD: We recruited a cohort of PTSD-diagnosed individuals (N = 20), trauma survivors without PTSD (TE; N = 20), and healthy controls (HC; N = 20). Analyses of between-group differences and support vector machine (SVM)-learning were applied to participant features. RESULTS: Analyses of between-group differences replicated previous findings, indicating that PTSD-diagnosed individuals self-reported poorer sleep quality, objectively demonstrated less sleep depth, and evidenced declarative memory deficits in comparison to HC. Integrative SVM-learning distinguished HC from trauma participants with 80% accuracy using a combination of five features, including subjective and objective sleep, neutral declarative memory, and metabolite variables. PTSD and TE participants could be distinguished with 70% accuracy using a combination of subjective and objective sleep variables but not by metabolite or declarative memory variables. CONCLUSION: From among a broad range of sleep, cognitive, and biochemical variables, sleep characteristics were the primary features that could differentiate those with PTSD from those without. Our exploratory SVM-learning analysis establishes a framework for future sleep- and memory-based PTSD investigations that could drive improvements in diagnostic accuracy and treatment.

Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes.

Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We applied transcriptome-wide screens to 149 umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n = 20), depression (n = 31) and PTSD with comorbid depression (n = 13), compared to carefully matched trauma exposed controls (n = 23) and healthy mothers (n = 62). Analyses by maternal diagnoses revealed a clear pattern of gene expression signatures distinguishing neonates born to mothers with a history of psychopathology from those without. Co-expression network analysis identified distinct gene expression perturbations across maternal diagnoses, including two depression-related modules implicated in axon-guidance and mRNA stability, as well as two PTSD-related modules implicated in TNF signaling and cellular response to stress. Notably, these disease-related modules were enriched with brain-expressed genes and genetic risk loci for autism spectrum disorder and schizophrenia, which may imply a causal role for impaired developmental outcomes. These molecular alterations preceded changes in clinical measures at twenty-four months, including reductions in cognitive and socio-emotional outcomes in affected infants. Collectively, these findings indicate that prenatal exposure to maternal psychological distress induces neuronal, immunological and behavioral abnormalities in affected offspring and support the search for early biomarkers of exposures to adverse in utero environments and the classification of children at risk for impaired development.

Epistasis as the primary factor in molecular evolution.

The main forces directing long-term molecular evolution remain obscure. A sizable fraction of amino-acid substitutions seem to be fixed by positive selection, but it is unclear to what degree long-term protein evolution is constrained by epistasis, that is, instances when substitutions that are accepted in one genotype are deleterious in another. Here we obtain a quantitative estimate of the prevalence of epistasis in long-term protein evolution by relating data on amino-acid usage in 14 organelle proteins and 2 nuclear-encoded proteins to their rates of short-term evolution. We studied multiple alignments of at least 1,000 orthologues for each of these 16 proteins from species from a diverse phylogenetic background and found that an average site contained approximately eight different amino acids. Thus, without epistasis an average site should accept two-fifths of all possible amino acids, and the average rate of amino-acid substitutions should therefore be about three-fifths lower than the rate of neutral evolution. However, we found that the measured rate of amino-acid substitution in recent evolution is 20 times lower than the rate of neutral evolution and an order of magnitude lower than that expected in the absence of epistasis. These data indicate that epistasis is pervasive throughout protein evolution: about 90 per cent of all amino-acid substitutions have a neutral or beneficial impact only in the genetic backgrounds in which they occur, and must therefore be deleterious in a different background of other species. Our findings show that most amino-acid substitutions have different fitness effects in different species and that epistasis provides the primary conceptual framework to describe the tempo and mode of long-term protein evolution.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.