Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significa...
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Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency a...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, developm...
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Background Calls for healthcare systems to reduce disparities in cancer care access and outcomes draw on comparisons of existing measures across race and ethnicity subgroups. This approach may hide inequities driven b...
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Background Calls for healthcare systems to reduce disparities in cancer care access and outcomes draw on comparisons of existing measures across race and ethnicity subgroups. This approach may hide inequities driven by systematic bias in the timing of care delivery. The goals of this study were to: (1) identify differences in the timing of care delivery between racial groups, and (2) determine whether these differences could be identified from quality measures. Methods Retrospective decedent follow-back study of hospitals treating Medicare fee-for-service beneficiaries with advanced cancer aged 65–99 who died April–December 2016. Among hospitals serving at least 11 decedents of color (including Black or African-American, Asian/Pacific Islander, Hispanic, American Indian/Alaska Native, and Other) and 11 White decedents, we calculated hospital-level differences between White decedents and decedents of color for 1) any use of palliative care and hospice (Measures) and 2) daily counts of palliative care and hospice use for each day in the 6 months before death (Signatures). Findings The cohort included 30,319 decedents across 217 hospitals, of whom 7,852 (25.9%) were people of color (POC). The median of the hospital-specific aggregate measure difference was −5.35% (IQR = 12.83) for palliative care, indicating more POC received any palliative care, and 3.66% (IQR = 12.45) for hospice care, indicating more White people (WP) received any hospice care. We identified 5 high-level cluster-group descriptions of inequality from signatures. Inequality information from signatures matched those from measures in only 46.5% and 39.2% of hospitals for palliative and hospice care, respectively. Interpretation Signatures incorporating timing of care delivery using longitudinal data revealed patterns of racial-ethnic inequalities in end-of-life cancer care otherwise missed by traditional aggregate quality measures. Funding This work was supported by the American Cancer Society Award (RS
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We ...
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Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad
We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5σ-lev...
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Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent ca...
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Blue Horizontal Branch (BHB) stars, excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the (g−r)0 vs (i−z)0 color space from another class of stars, blue straggler stars ...
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Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehen...
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Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.
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