gcPathogen: a comprehensive genomic resource of human pathogens for public health.

Here, we present the manually curated Global Catalogue of Pathogens (gcPathogen), an extensive genomic resource designed to facilitate rapid and accurate pathogen analysis, epidemiological exploration and monitoring of antibiotic resistance features and virulence factors. The catalogue seamlessly integrates and analyzes genomic data and associated metadata for human pathogens isolated from infected patients, animal hosts, food and the environment. The pathogen list is supported by evidence from medical or government pathogenic lists and publications.

Blind Quality Assessment of Images Containing Objects of Interest.

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results.

Assessing Effects of van der Waals Corrections on Elasticity of MgBiSb in DFT Calculations.

As a promising room-temperature thermoelectric material, the elastic properties of MgBiSb (0 ≤ x ≤ 2), in which the role of van der Waals interactions is still elusive, were herein investigated. We assessed the effects of two typical van der Waals corrections on the elasticity of MgBiSb nanocomposites using first-principles calculations within the frame of density functional theory. The two van der Waals correction methods, PBE-D3 and vdW-DFq, were examined and compared to PBE functionals without van der Waals correction.

Unveiling the metal mutation nexus: Exploring the genomic impacts of heavy metal exposure in lung adenocarcinoma and colorectal cancer.

Mutations that activate oncogenes and deactivate tumor suppressor genes are widely recognized as significant contributors to cancer development. We investigated relationships between heavy metal exposure and the frequencies and types of gene mutations in patients with lung adenocarcinoma (LUAD) and colorectal cancer (CRC). Plasma concentrations of arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), and lead (Pb) were measured using inductively coupled plasma mass spectrometry (ICPMS), and next-generation sequencing (NGS) of 1123 cancer-related genes was performed using the tumor tissues.

Selective Interfacial Excited-State Carrier Dynamics and Efficient Charge Separation in Borophene-Based Heterostructures.

Borophene-based van der Waals heterostructures have demonstrated enormous potential in the realm of optoelectronic and photovoltaic devices, which has sparked a wide range of interest. However, a thorough understanding of the microscopic excited-state electronic dynamics at interfaces is lacking, which is essential for determining the macroscopic optoelectronic and photovoltaic performance of borophene-based devices. In this study, photoexcited carrier dynamics of β , χ , and α΄ borophene/MoS heterostructures are systematically studied based on time-domain nonadiabatic molecular dynamics simulations.

HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction.

Identification of MKI67, TPR , and TCHH Mutations as Prognostic Biomarkers for Patients With Defective Mismatch Repair Colon Cancer Stage II/III.

Background: Stage II/III disease is the most predominant form of colorectal cancer, accounting for approximately 70% of cases. Furthermore, approximately 15% to 20% of patients with stage II/III disease have deficient mismatch repair or microsatellite instability-high colorectal cancer. However, there are no identified significant prognostic biomarkers for this disease.

Divergence and convergence of gut microbiomes of wild insect pollinators.

Pollination services provided by wild insect pollinators are critical to natural ecosystems and crops around the world. There is an increasing appreciation that the gut microbiota of these insects influences their health and consequently their services. However, pollinator gut microbiota studies have focused on well-described social bees, but rarely include other, more phylogenetically divergent insect pollinators.

Case Report: SMARCA4 (BRG1)-deficient undifferentiated carcinoma of gallbladder with genetic analysis.

-deficient undifferentiated carcinoma is a rare and highly aggressive malignancy. It has been reported to occur in a multiple range of organs. However, to the best of our knowledge, -deficient undifferentiated carcinoma of gallbladder has not yet been reported.

RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data.

Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems.

Deep learning-based classification model for GPR151 activator activity prediction.

Background: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming.

RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches.

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.

Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction.

In order to capture the spatial-spectral (x,y,λ) information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, , i.

The coherent motions of thermal active Brownian particles.

Active matter exhibits many intriguing non-equilibrium characteristics, for instance, without any attractive and aligned interactions, the active Brownian particle (ABP) system undergoing motility-induced phase separation forms a high-density phase with both structural ordering and dynamical coherence. Recently, the velocity correlation among the particles in this high-density phase was found in non-thermal overdamped ABP systems. However, it seemed to disappear if thermal noises were included, bringing some confusion about the generality of the consistency between structures and dynamics in ABPs.

Detecting liquid remnants of solid tumors treated with curative intent: Circulating tumor DNA as a biomarker of minimal residual disease (Review).

Circulating tumor DNA (ctDNA) has emerged as a promising biomarker of minimal residual disease (MRD) in solid tumors. There is increasing evidence to suggest that the detection of ctDNA following curative‑intent treatments has high potential in anticipating future relapse in various solid tumors. Multiple liquid biopsy technical approaches and commercial platforms, including tumor‑informed and tumor‑agnostic ctDNA assays, have been developed for ctDNA‑based MRD detection in solid tumors.

A corpus of CO electrocatalytic reduction process extracted from the scientific literature.

The electrocatalytic CO reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms.

Development and validation of a mutation-based model to predict immunotherapeutic efficacy in NSCLC.

Background: Immunotherapy has become increasingly important in the perioperative period of non-small-cell lung cancer (NSCLC). In this study, we intended to develop a mutation-based model to predict the therapeutic effificacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC.

Methods: Random Forest (RF) classifiers were generated to identify tumor gene mutated features associated with immunotherapy outcomes.

MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction.

Drug-target interaction (DTI) prediction can identify novel ligands for specific protein targets, and facilitate the rapid screening of effective new drug candidates to speed up the drug discovery process. However, the current methods are not sensitive enough to complex topological structures, and complicated relations between multiple node types are not fully captured yet. To address the above challenges, we construct a metapath-based heterogeneous bioinformatics network, and then propose a DTI prediction method with metapath-based hierarchical transformer and attention network for drug-target interaction prediction (MHTAN-DTI), applying metapath instance-level transformer, single-semantic attention and multi-semantic attention to generate low-dimensional vector representations of drugs and proteins.

Numerical prediction of portal hypertension by a hydrodynamic blood flow model combing with the fractal theory.

Portal hypertension (PH) can cause a series of complications, therefore, early prediction of PH is important. Traditional diagnostic methods are harmful to the human body, while other non-invasive methods are inaccurate and lack physical meaning. Combining various fractal theories and flow laws, we establish a complete portal system blood flow model from the Computed Tomography (CT) and angiography images.

Persistent response of furmonertinib plus anlotinib in a lung adenocarcinoma patient with an EGFR exon 20 insertion mutation: A case report.

Insertions in exon 20 represent the third most common type of mutation following in-frame deletions in exon 19 and the point mutation L858R in exon 21. They are generally associated with primary resistance to EGFR-TKIs. Although mobocertinib and amivantamab were approved for adult patients with non-small cell lung cancer (NSCLC) harboring exon 20 insertion mutations, the efficacy of these two agents was rather moderate.

Kylin 1.0: An ab-initio density matrix renormalization group quantum chemistry program.

The accurate evaluation of electron correlations is highly necessary for the proper descriptions of the electronic structures in strongly correlated molecules, ranging from bond-dissociating molecules, polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, in this paper, a new ab-initio quantum chemistry program Kylin 1.0 for electron correlation calculations at various quantum many-body levels, including configuration interaction (CI), perturbation theory (PT), and density matrix renormalization group (DMRG), is presented.