209 research outputs found

    TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning

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    Pathological alterations in the human vascular system underlie many chronic diseases, such as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the vascular system, such as computed tomographic angiograms (CTAs) is a time-consuming and tedious process. To address this issue, we propose a deep learning model to segment the vascular system in CTA images of patients undergoing surgery for peripheral arterial disease (PAD). Our study focused on accurately segmenting the vascular system (1) from the descending thoracic aorta to the iliac bifurcation and (2) from the descending thoracic aorta to the knees in CTA images using deep learning techniques. Our approach achieved average Dice accuracies of 93.5% and 80.64% in test dataset for (1) and (2), respectively, highlighting its high accuracy and potential clinical utility. These findings demonstrate the use of deep learning techniques as a valuable tool for medical professionals to analyze the health of the vascular system efficiently and accurately. Please visit the GitHub page for this paper at https://github.com/pip-alireza/TransOnet.Comment: Accepted for the 2023 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, US

    An analysis of fast photochemistry over high northern latitudes during spring and summer using in-situ observations from ARCTAS and TOPSE

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    Observations of chemical constituents and meteorological quantities obtained during the two Arctic phases of the airborne campaign ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) are analyzed using an observationally constrained steady state box model. Measurements of OH and HO2 from the Penn State ATHOS instrument are compared to model predictions. Forty percent of OH measurements below 2 km are at the limit of detection during the spring phase (ARCTAS-A). While the median observed-to-calculated ratio is near one, both the scatter of observations and the model uncertainty for OH are at the magnitude of ambient values. During the summer phase (ARCTAS-B), model predictions of OH are biased low relative to observations and demonstrate a high sensitivity to the level of uncertainty in NO observations. Predictions of HO2 using observed CH2O and H2O2 as model constraints are up to a factor of two larger than observed. A temperature-dependent terminal loss rate of HO2 to aerosol recently proposed in the literature is shown to be insufficient to reconcile these differences. A comparison of ARCTAS-A to the high latitude springtime portion of the 2000 TOPSE campaign (Tropospheric Ozone Production about the Spring Equinox) shows similar meteorological and chemical environments with the exception of peroxides; observations of H2O2 during ARCTAS-A were 2.5 to 3 times larger than those during TOPSE. The cause of this difference in peroxides remains unresolved and has important implications for the Arctic HOx budget. Unconstrained model predictions for both phases indicate photochemistry alone is unable to simultaneously sustain observed levels of CH2O and H2O2; however when the model is constrained with observed CH2O, H2O2 predictions from a range of rainout parameterizations bracket its observations. A mechanism suitable to explain observed concentrations of CH2O is uncertain. Free tropospheric observations of acetaldehyde (CH3CHO) are 2–3 times larger than its predictions, though constraint of the model to those observations is sufficient to account for less than half of the deficit in predicted CH2O. The box model calculates gross O3 formation during spring to maximize from 1–4 km at 0.8 ppbv d−1, in agreement with estimates from TOPSE, and a gross production of 2–4 ppbv d−1 in the boundary layer and upper troposphere during summer. Use of the lower observed levels of HO2 in place of model predictions decreases the gross production by 25–50%. Net O3 production is near zero throughout the ARCTAS-A troposphere, and is 1–2 ppbv in the boundary layer and upper altitudes during ARCTAS-B

    Depression and Anxiety Change from Adolescence to Adulthood in Individuals with and without Language Impairment

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    This prospective longitudinal study aims to determine patterns and predictors of change in depression and anxiety from adolescence to adulthood in individuals with language impairment (LI). Individuals with LI originally recruited at age 7 years and a comparison group of age-matched peers (AMPs) were followed from adolescence (16 years) to adulthood (24 years). We determine patterns of change in depression and anxiety using the Child Manifest Anxiety Scale-Revised (CMAS-R) and Short Moods and Feelings Questionnaire (SMFQ). In addition to examining associations with gender, verbal and nonverbal skills, we use a time-varying variable to investigate relationships between depression and anxiety symptoms and transitions in educational/employment circumstances. The results show that anxiety was higher in participants with LI than age matched peers and remained so from adolescence to adulthood. Individuals with LI had higher levels of depression symptoms than did AMPs at 16 years. Levels in those with LI decreased post-compulsory schooling but rose again by 24 years of age. Those who left compulsory school provision (regardless of school type) for more choice-driven college but who were not in full-time employment or study by 24 years of age were more likely to show this depression pathway. Verbal and nonverbal skills were not predictive of this pattern of depression over time. The typical female vulnerability for depression and anxiety was observed for AMPs but not for individuals with LI. These findings have implications for service provision, career/employment advice and support for individuals with a history of LI during different transitions from adolescence to adulthood

    The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

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    OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

    Synaptic scaffold evolution generated components of vertebrate cognitive complexity

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    The origins and evolution of higher cognitive functions, including complex forms of learning, attention and executive functions, are unknown. A potential mechanism driving the evolution of vertebrate cognition early in the vertebrate lineage (550 million years ago) was genome duplication and subsequent diversification of postsynaptic genes. Here we report, to our knowledge, the first genetic analysis of a vertebrate gene family in cognitive functions measured using computerized touchscreens. Comparison of mice carrying mutations in each of the four Dlg paralogs showed that simple associative learning required Dlg4, whereas Dlg2 and Dlg3 diversified to have opposing functions in complex cognitive processes. Exploiting the translational utility of touchscreens in humans and mice, testing Dlg2 mutations in both species showed that Dlg2\u27s role in complex learning, cognitive flexibility and attention has been highly conserved over 100 million years. Dlg-family mutations underlie psychiatric disorders, suggesting that genome evolution expanded the complexity of vertebrate cognition at the cost of susceptibility to mental illness

    Comparative Genomic Analysis of six Glossina Genomes, Vectors of African Trypanosomes

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    Background: Tsetse flies (Glossina sp.) are the vectors of human and animal trypanosomiasis throughout subSaharan Africa. Tsetse flies are distinguished from other Diptera by unique adaptations, including lactation and the birthing of live young (obligate viviparity), a vertebrate blood-specific diet by both sexes, and obligate bacterial symbiosis. This work describes the comparative analysis of six Glossina genomes representing three sub-genera: Morsitans (G. morsitans morsitans, G. pallidipes, G. austeni), Palpalis (G. palpalis, G. fuscipes), and Fusca (G. brevipalpis) which represent different habitats, host preferences, and vectorial capacity. Results: Genomic analyses validate established evolutionary relationships and sub-genera. Syntenic analysis of Glossina relative to Drosophila melanogaster shows reduced structural conservation across the sex-linked X chromosome. Sex-linked scaffolds show increased rates of female-specific gene expression and lower evolutionary rates relative to autosome associated genes. Tsetse-specific genes are enriched in protease, odorant-binding, and helicase activities. Lactation-associated genes are conserved across all Glossina species while male seminal proteins are rapidly evolving. Olfactory and gustatory genes are reduced across the genus relative to other insects. Visionassociated Rhodopsin genes show conservation of motion detection/tracking functions and variance in the Rhodopsin detecting colors in the blue wavelength ranges. Conclusions: Expanded genomic discoveries reveal the genetics underlying Glossina biology and provide a rich body of knowledge for basic science and disease control. They also provide insight into the evolutionary biology underlying novel adaptations and are relevant to applied aspects of vector control such as trap design and discovery of novel pest and disease control strategies

    Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Kenya: a cluster-randomised controlled trial.

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    BACKGROUND: Poor nutrition and exposure to faecal contamination are associated with diarrhoea and growth faltering, both of which have long-term consequences for child health. We aimed to assess whether water, sanitation, handwashing, and nutrition interventions reduced diarrhoea or growth faltering. METHODS: The WASH Benefits cluster-randomised trial enrolled pregnant women from villages in rural Kenya and evaluated outcomes at 1 year and 2 years of follow-up. Geographically-adjacent clusters were block-randomised to active control (household visits to measure mid-upper-arm circumference), passive control (data collection only), or compound-level interventions including household visits to promote target behaviours: drinking chlorinated water (water); safe sanitation consisting of disposing faeces in an improved latrine (sanitation); handwashing with soap (handwashing); combined water, sanitation, and handwashing; counselling on appropriate maternal, infant, and young child feeding plus small-quantity lipid-based nutrient supplements from 6-24 months (nutrition); and combined water, sanitation, handwashing, and nutrition. Primary outcomes were caregiver-reported diarrhoea in the past 7 days and length-for-age Z score at year 2 in index children born to the enrolled pregnant women. Masking was not possible for data collection, but analyses were masked. Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01704105. FINDINGS: Between Nov 27, 2012, and May 21, 2014, 8246 women in 702 clusters were enrolled and randomly assigned an intervention or control group. 1919 women were assigned to the active control group; 938 to passive control; 904 to water; 892 to sanitation; 917 to handwashing; 912 to combined water, sanitation, and handwashing; 843 to nutrition; and 921 to combined water, sanitation, handwashing, and nutrition. Data on diarrhoea at year 1 or year 2 were available for 6494 children and data on length-for-age Z score in year 2 were available for 6583 children (86% of living children were measured at year 2). Adherence indicators for sanitation, handwashing, and nutrition were more than 70% at year 1, handwashing fell to less than 25% at year 2, and for water was less than 45% at year 1 and less than 25% at year 2; combined groups were comparable to single groups. None of the interventions reduced diarrhoea prevalence compared with the active control. Compared with active control (length-for-age Z score -1·54) children in nutrition and combined water, sanitation, handwashing, and nutrition were taller by year 2 (mean difference 0·13 [95% CI 0·01-0·25] in the nutrition group; 0·16 [0·05-0·27] in the combined water, sanitation, handwashing, and nutrition group). The individual water, sanitation, and handwashing groups, and combined water, sanitation, and handwashing group had no effect on linear growth. INTERPRETATION: Behaviour change messaging combined with technologically simple interventions such as water treatment, household sanitation upgrades from unimproved to improved latrines, and handwashing stations did not reduce childhood diarrhoea or improve growth, even when adherence was at least as high as has been achieved by other programmes. Counselling and supplementation in the nutrition group and combined water, sanitation, handwashing, and nutrition interventions led to small growth benefits, but there was no advantage to integrating water, sanitation, and handwashing with nutrition. The interventions might have been more efficacious with higher adherence or in an environment with lower baseline sanitation coverage, especially in this context of high diarrhoea prevalence. FUNDING: Bill & Melinda Gates Foundation, United States Agency for International Development
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