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.
Tracking of trajectories of mutually interacted collectively moving agents such as fish, birds, insects, and even humans is an active field in computer vision. However, the trajectories produced by multi-object tracki...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
Tracking of trajectories of mutually interacted collectively moving agents such as fish, birds, insects, and even humans is an active field in computer vision. However, the trajectories produced by multi-object tracking methods might consist of unconstructed segments of trajectories due to the natural phenomena such as occlusion, change of illumination, etc., which require robust tracking methods. Some tracking methods employ computationally expensive approximation schemes to connect these segments. In this work, we utilize mutual interactions and dependencies between the agents to reconstruct the missing segments of the trajectories. Due to these interactions, the coordinate matrix representing the particles' trajectories of collective motion is often low-rank. Thus, we utilize a low-rank matrix completion technique to reconstruct incomplete trajectories. We apply this approach for two representative self-propelled particle swarms, simulated by the classic Vicsek model, that imitate two real-life collective motion scenarios and use low-rank approximations to analyze their incomplete trajectories.
We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photometric and astrometric data. We outline...
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DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 20...
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Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution...
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is ...
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Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati
In this perspective, we argue that despite the democratization of powerful tools for datascience and machine learning over the last decade, developing the code for a trustworthy and effective datascience system (DSS...
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The relationship between magnetic field strengthBand gas densitynin the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of theB–nrelation for two comprehensive observational...
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The relationship between magnetic field strengthBand gas densitynin the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of theB–nrelation for two comprehensive observational data sets: a Zeeman data set and 700 observations using the Davis–Chandrasekhar–Fermi (DCF) method. Using a hierarchical Bayesian analysis we present a general, multiscale broken power-law relation,|$B=B_0(n/n_0)^{\alpha }$|, with|$\alpha =\alpha _1$|for|$n< n_0$|and|$\alpha _2$|for|$n>n_0$|, and with|$B_0$|the field strength at|$n_0$|. For the Zeeman data, we find:|$\alpha _1={0.15^{+0.06}_{-0.09}}$|for diffuse gas and|$\alpha _2 = {0.53^{+0.09}_{-0.07}}$|for dense gas with|$n_0 = 0.40^{+1.30}_{-0.30}\times 10^4$|cm|$^{-3}$|. For the DCF data, we find:|$\alpha _1={0.26^{+0.01}_{-0.01}}$|and|$\alpha _2={0.77_{-0.15}^{+0.14}}$|, with|$n_0=14.00^{+10.00}_{-7.00}\times 10^4$|cm|$^{-3}$|, where the uncertainties give 68 per cent credible intervals. We perform a similar analysis on nineteen numerical magnetohydrodynamic simulations covering a wide range of physical conditions from protostellar discs to dwarf and Milky Way-like galaxies, computed with thearepo, flash, pencil, andramsescodes. The resulting exponents depend on several physical factors such as dynamo effects and their time-scales, turbulence, and initial seed field strength. We find that the dwarf and Milky Way-like galaxy simulations produce results closest to the observations.
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