Mineral-dependent release, migration and enrichment of toxic elements during black shale weathering: An integrated study from profile scale to mineral scale.

Chemical weathering of lithologies with high geochemical backgrounds such as black shale has been proposed to be a critical source for toxic elements in soil and water systems. However, mechanisms controlling the release, migration and enrichment of toxic elements during black shale weathering are poorly understood. This study utilized a suite of micro analytical techniques such as TESCAN integrated mineral analyzer (TIMA), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), X-ray photoelectron spectroscopy (XPS) and electron micro-probe analysis (EMPA) to elucidate the intimate relationship between mineralogical transformations and elemental behaviors from profile scale to mineral scale.

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necessity to discover reliable biomarkers for predicting treatment outcomes.

Systemic inflammatory regulators and age-related macular degeneration: a bidirectional Mendelian randomization study.

Introduction: We investigated the relationship between systematic regulators of inflammation and the risk of age-related macular degeneration (AMD), both wet and dry forms, by using bidirectional, two-sample Mendelian randomization (MR).

Methods: We performed bidirectional two-sample Mendelian randomization analysis using genome-wide study (GWAS) data for 91 plasma proteins from 14,824 individuals of European descent across 11 study groups. Next, we utilized data from the FinnGen consortium to study AMD using the inverse- variance-weighted approach for Mendelian randomization.

STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution.

Motivation: Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios.

The PB1 protein of H9N2 influenza A virus inhibits antiviral innate immunity by targeting MAVS for TRIM25-mediated autophagic degradation.

The proteins encoded by Influenza A virus (IAV) evade the innate immune system through diverse strategies to facilitate their replication. However, the regulatory mechanisms remain not fully understood. In this study, we identified that the H9N2 PB1 protein suppressed the activities of the IFN-β, ISRE, and NF-κB promoters.

Dual recombinase polymerase amplification system combined with lateral flow immunoassay for simultaneous detection of Staphylococcus aureus and Vibrio parahaemolyticus.

Development of a highly sensitive visualization platform for multiplex genetic detection could significantly improve efficiency and reliability of on-site detection of foodborne pathogens. In this study, coupling recombinase polymerase amplification (RPA) with lateral flow immunoassay (LFIA) readout system was proposed for Staphylococcus aureus and Vibrio parahaemolyticus detection. Taking the advantage of the isothermal amplification of RPA, dual primers modified with different labeling groups were designed to realize target signal amplification.

iMFP-LG: Identification of Novel Multi-Functional Peptides by Using Protein Language Models and Graph-Based Deep Learning.

Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous research focused on mono-functional peptides, but a growing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small fraction of millions of known peptides have been explored.

Evolutionary dynamics and functional characterization of proximal duplicated sorbitol-6-phosphate dehydrogenase genes in Rosaceae.

Sorbitol is a critical photosynthate and storage substance in the Rosaceae family. Sorbitol 6-phosphate dehydrogenase (S6PDH) functions as the pivotal rate-limiting enzyme in sorbitol synthesis. The origin and functional diversification of S6PDH in Rosaceae remain unclear, largely due to the complicated interplay of gene duplications.

SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning.

Spatial transcriptomics technologies enable the generation of gene expression profiles while preserving spatial context, providing the potential for in-depth understanding of spatial-specific tissue heterogeneity. Leveraging gene and spatial data effectively is fundamental to accurately identifying spatial domains in spatial transcriptomics analysis. However, many existing methods have not yet fully exploited the local neighborhood details within spatial information.

Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.

The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model.

A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

Motivation: Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.

Background: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.

Efficacy and comfort following the implantation of extended depth of focus, multifocal, and monofocal intraocular lenses in cataract patients.

Background: Extended depth of focus (EDOF) and multifocal (Multi) intraocular lenses (IOL) can provide a fixed distance of near vision, which may result in some discomfort for patients who prefer different near distances. The aim of this study was to compare the vision, comfortable near distance (CND) and visual comfort in patients who underwent implantation of EDOF, Multi, and monofocal (Mono) IOLs.

Methods: A total of 100 eyes were implanted with Tecnis ZXR00, ZMB00 or ZCB00 IOLs.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia.

Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI).

Scalable, Controlled Bimodal Pore-Structured Polymer Coating for Efficient Passive Daytime Radiative Cooling.

Outdoor thermal irritation poses a serious threat to public health, with the frequent occurrence of increasingly intense heat waves. With the global goal of carbon peaking and carbon neutrality, there is an urgent need for a strategy that is efficient and can provide localized outdoor cooling without an intensive energy input. This paper demonstrated a rapidly formable polyurethane-based coating with controlled bimodal spherical micropores.

Pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries under topical anesthesia.

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery. Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery. However, patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery.

Drug repositioning based on residual attention network and free multiscale adversarial training.

Background: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets.

Ultrafast Dual-Shock Chemistry Synthesis of Ordered/Disordered Hybrid Carbon Anodes: High-Rate Performance of Li-Ion Batteries.

Graphite exhibits crystal anisotropy, which impedes the mass transfer of ion intercalation and extraction processes in Li-ion batteries. Herein, a dual-shock chemical strategy has been developed to synthesize the carbon anode. This approach comprised two key phases: (1) a thermal shock utilizing ultrahigh temperature (3228 K) can thermodynamically facilitate graphitization; (2) a mechanical shock (21.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents.

Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.

Investigating the Impact of Pineapple-Whey Protein Fermentation Products on Cefixime-Induced Intestinal Flora Dysbiosis in Mice Using 16S Sequencing and Untargeted Metabolomics Techniques.

In our previous study, a new fermented food (PWF) created by utilizing pineapple by-products and whey proteins as a matrix via co-fermentation with lactic acid bacteria and yeast was developed, and, in the current study, we examined the impact of a pineapple-whey protein fermentation product on a cefixime-induced dysbiosis model in mice using 16S sequencing and untargeted metabolomics techniques. The results indicated that the pineapple-whey protein fermentation product played a positive role in restoring the intestinal flora. In this study, cefixime reduced the overall abundance of intestinal flora and decreased the relative abundance of probiotics in the gut, while also inhibiting amino acid metabolism.

Neural network enabled fringe projection through scattering media.

The projection of fringes plays an essential role in many applications, such as fringe projection profilometry and structured illumination microscopy. However, these capabilities are significantly constrained in environments affected by optical scattering. Although recent developments in wavefront shaping have effectively generated high-fidelity focal points and relatively simple structured images amidst scattering, the ability to project fringes that cover half of the projection area has not yet been achieved.