OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages.

Foodborne Disease Risk Prediction Using Multigraph Structural Long Short-term Memory Networks: Algorithm Design and Validation Study.

Background: Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management.

Objective: We aimed to design a spatial-temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases.

BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.

Motivation: Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities.

Longitudinal influence of COVID-19-related stress on sexual compulsivity symptoms in Chinese undergraduates.

Background: The coping theory shows that stressful life events are associated with individuals' psychology/behaviors; meanwhile, the coronavirus disease of 2019 (COVID-19) pandemic is known to have impacted individuals' physical and mental health. Prior studies revealed that undergraduates have many sexual behavior and emotion disorders, which may be impacted during an isolation period, such as the one brought by COVID-19. However, few studies have explored the longitudinal associations between COVID-19-related stress and sexual compulsivity symptoms (SCS), and the mediating effect of emotions (i.

Optimizing two-electron repulsion integral calculations with McMurchie-Davidson method on graphic processing unit.

In this article, several optimization methods of two-electron repulsion integral calculations on a graphic processing unit (GPU) are presented. These methods are based on the investigations of the method presented by McMurchie and Davidson (MD). A new Boys function evaluation method for the GPU calculation is introduced.

Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer.

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC.

Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China.

Echinococcosis, caused by genus Echinococcus, is the most pathogenic zoonotic parasitic disease in the world. In Tibet of the People's Republic of China, echinococcosis refers principally to two types of severe zoonosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), which place a serious burden on public health and economy in the local community. However, research on the spatial epidemiology of echinococcosis remains inadequate in Tibet, China.

DIVIS: Integrated and Customizable Pipeline for Cancer Genome Sequencing Analysis and Interpretation.

Next-generation sequencing (NGS) has drastically enhanced human cancer research, but diverse sequencing strategies, complicated open-source software, and the identification of massive numbers of mutations have limited the clinical application of NGS. Here, we first presented GPyFlow, a lightweight tool that flexibly customizes, executes, and shares workflows. We then introduced DIVIS, a customizable pipeline based on GPyFlow that integrates read preprocessing, alignment, variant detection, and annotation of whole-genome sequencing, whole-exome sequencing, and gene-panel sequencing.

DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks.

Identification of a novel heterozygous germline RAD52 missense mutation in a patient with gallbladder carcinoma: A case report.

Rationale: Gallbladder carcinoma is a malignant biliary tract tumor which is characterized by poor prognosis. Recent advances in genomic medicine have identified a few novel germline mutations that contribute to the increased risk of gallbladder carcinoma. RAD52 is a crucial human deoxyribonucleic acid (DNA) repair gene involved in maintaining genomic stability and preventing tumor occurrence.

Genetic alteration of Chinese patients with rectal mucosal melanoma.

Background: Rectal mucosal melanoma (RMM) is a rare and highly aggressive disease with a poor prognosis. Due to the rarity of RMM, there are few studies focusing on its genetic mechanism. This retrospective study aimed to analyze the genetic spectrum and prognosis of RMM in China and lay a foundation for targeted therapy.

High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or erroneous reporting in FDMRS, which significantly increase the cost of related epidemic investigations. To solve this problem, we designed a model to identify suspected outbreaks from the data generated by the FDMRS of CFSA.

Comprehensive review and evaluation of computational methods for identifying FLT3-internal tandem duplication in acute myeloid leukaemia.

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis.

Comprehensive analysis of immune-related prognostic genes in the tumour microenvironment of hepatocellular carcinoma.

Background: The mortality rate of hepatocellular carcinoma (HCC) remains high worldwide despite surgery and chemotherapy. Immunotherapy is a promising treatment for the rapidly expanding HCC spectrum. Therefore, it is necessary to further explore the immune-related characteristics of the tumour microenvironment (TME), which plays a vital role in tumour initiation and progression.

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics.

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz.

Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study.

Background: Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life.

Objective: We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested.

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

Motivation: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations.

gcType: a high-quality type strain genome database for microbial phylogenetic and functional research.

Taxonomic and functional research of microorganisms has increasingly relied upon genome-based data and methods. As the depository of the Global Catalogue of Microorganisms (GCM) 10K prokaryotic type strain sequencing project, Global Catalogue of Type Strain (gcType) has published 1049 type strain genomes sequenced by the GCM 10K project which are preserved in global culture collections with a valid published status. Additionally, the information provided through gcType includes >12 000 publicly available type strain genome sequences from GenBank incorporated using quality control criteria and standard data annotation pipelines to form a high-quality reference database.

Portably parallel construction of a configuration-interaction wave function from a matrix-product state using the Charm++ framework.

The construction of configuration-interaction (CI) expansions from a matrix product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations in a large Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS employing the parallel object-oriented Charm++ programming framework, upon which automatic load-balancing and object migrating facilities can be employed. This procedure was employed in the MPS-to-CI utility (Moritz et al.

RabbitQC: high-speed scalable quality control for sequencing data.

Motivation: Modern sequencing technologies continue to revolutionize many areas of biology and medicine. Since the generated datasets are error-prone, downstream applications usually require quality control methods to pre-process FASTQ files. However, existing tools for this task are currently not able to fully exploit the capabilities of computing platforms leading to slow runtimes.

Variational Multistate Density Functional Theory for a Balanced Treatment of Static and Dynamic Correlations.

Here, an approach to variational multistate density functional theory (vMSDFT) is explored. In this approach, the Kohn-Sham orbitals as well as configuration coefficients were simultaneously optimized, thus yielding a full variational minimum. Furthermore, this work also proposes two important improvements on the MSDFT framework.

Red Blood Cells as Potential Repositories of MicroRNAs in the Circulatory System.

The amount of erythrocyte-derived microRNAs (miRNAs) represents the majority of miRNAs expressed in whole blood. miR-451, miR-144, and miR-486, which are abundant in red blood cells (RBCs), are involved in the process of erythropoiesis and disease occurrence. Moreover, erythrocyte-derived miRNAs have been reported to be potential biomarkers of specific diseases.

A density functional theory study of high-performance pre-lithiated MS (M = Mo, W, V) Monolayers as the Anode Material of Lithium Ion Batteries.

Recent experimental study shows that the pre-lithiated MoS monolayer exhibits an enhanced electrochemical performance, coulombic efficiency of which is 26% higher than the pristine MoS based anode. The underlying mechanism of such significant enhancement, however, has not yet been addressed. By means of density functional theory (DFT) calculations, we systematically investigated the adsorption and diffusion behavior of lithium (Li) atoms on the MS (M = Mo, W, V) monolayers.