Protein A-like peptide generation based on generalized diffusion model.

With the rapid advancement of biotechnology, protein generation and design based on generative models have demonstrated extensive applications in drug development, vaccine research, and biocatalysis. This research proposes a protein generation method based on the generalized diffusion model, which breaks through the traditional diffusion model's reliance on Gaussian noise, enables more flexible protein sequence generation, and preliminarily verifies its advantages. Specifically, protein sequences were first encoded using one-hot encoding and input into the diffusion model to generate novel sequences.

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

Objective: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy.

Materials And Methods: A total of 93 patients with advanced NSCLC were included in this study.

Recent advances in TiO modification for improvement in photocatalytic purification of indoor VOCs.

TiO-based photocatalytic oxidation technology has been widely used for the purification of indoor VOCs. However, the fast recombination rates of photoexcited charge carriers and wide energy band gaps have limited the practical application of TiO-based photocatalysts. Therefore, developing highly efficient catalysts is crucial for efficiently separating charge carriers and hindering their recombination, fully utilizing visible light.

SpaOmicsVAE: A deep learning framework for integrative analysis of spatial multi-omics data.

Spatial multi-omics technologies have revolutionized our understanding of biological systems by enabling simultaneous measurement of multiple molecular features while preserving spatial information. However, the effective integration and analysis of these complex datasets remain challenging. Here we introduce SpaOmicsVAE, a novel computational framework that combines variational autoencoder architecture with a dual graph neural network to address these challenges.

DCE-UNet: a transformer-based fully automated segmentation network for multiple adolescent spinal disorders in x-ray images.

In recent years, spinal x-ray image segmentation has played a vital role in the computer-aided diagnosis of various adolescent spinal disorders. However, due to the complex morphology of lesions and the fact that most existing methods are tailored to single-disease scenarios, current segmentation networks struggle to balance local detail preservation and global structural understanding across different disease types. As a result, they often suffer from limited accuracy, insufficient robustness, and poor adaptability.

Carboxymethyl chitosan/PVA-based dynamic cross-linked dual network hydrogel loaded with berberine with self-healing, self-adaptive, sustainable release and antibacterial ability for dressing.

Developing novel dressings with superior antibacterial properties to resist bacterial infections during wound healing remains a significant challenge. Hydrogels have emerged as prominent materials due to their excellent biocompatibility and adjustable functionality, and are widely applied in the field of wound therapy. However, the majority of hydrogels exhibit suboptimal mechanical properties, which hinder their practical application.

How age-inclusive human resource practices relate to career sustainability: the role of work-family enrichment and protean career orientation.

Drawing on conservation of resource theory and work-home resources model, this study explores how age-inclusive human resource (HR) practices related to employees' career sustainability through their work-family enrichment. In line with work-home resource model, we introduce a boundary condition, protean career orientation, to explain when our proposed associated may unfold. We collected data in three waves from 244 employees to test our hypotheses.

Embedding Dual-Responsive Polymer@Enzyme in Zeolitic Imidazolate Frameworks with a Hollow Space for Improving Its Catalytic Performance.

Herein, a stimuli-responsive polymer-support and sacrificial template strategy was introduced to modulate enzyme conformation freedom and generate nanoconfinement in porous zeolitic imidazolate framework-8 (ZIF-8), addressing challenges in realizing improved catalytic performance, tolerance, and stability. A thermo- and pH-responsive poly(styrene-maleic anhydride-2-hydroxyethyl methacrylate) was synthesized via a reversible addition-fragmentation chain transfer polymerization protocol. Through a chemical bonding strategy, alanine aminotransferase was immobilized on the smart polymer.

Spacer Group Modified Naphthalimide with Acidichromism and Mechanochromism for Anti-Counterfeiting Application.

Stimuli-responsive fluorescence materials are crucial in developing intelligent materials and have wide applications in anti-counterfeiting and information encryption. However, organic fluorescence dyes with strong π-π intermolecular interactions and dense molecular packing lead to inertia ability of stimuli-responsive. Here, we design and synthesize spacer group modified naphthalimide compounds (P1 and P2) with acidichromism and mechanochromism properties.

Affinity prediction of inhibitor-kinase based on mixture of experts enhanced by multimodal feature semantic analysis.

Accurate identification of inhibitor-kinase binding affinity is crucial for drug discovery. However, many deep learning models often overlook high-order feature information from biological networks and face challenges related to the cold-start problem when extracting biomolecular features. The EGFR family among human kinases serves as a primary drug target in cancer and other diseases.

Enhanced Metal Surface Processing Through the No-Stray-Corrosion Controllable Electrolyte DistributionElectrochemical Machining Method Utilizing a Water-Absorbent Porous Ball.

The Electrochemical Machining (ECM) method is one of the most widely used processing methods in metal surface processing, due to its unique advantages. However, the electrolyte in ECM causes stray corrosion on the workpiece. To overcome these shortcomings, we have developed a no-stray-corrosion ECM method called the controllable electrolyte distribution ECM (CED-ECM) method.

A Modified Two-Temperature Calibration Method and Facility for Emissivity Measurement.

Measuring the emissivity of an infrared radiant sample with high accuracy is important. Previous studies reported on the multi- or two-temperature calibration methods, which used a reference blackbody (or blackbodies) to eliminate the background radiation, and assumed that the background radiation was independent of temperature. However, in practical measurements, this assumption does not hold.

Predictive iterative learning control based on averaging technology for networked systems with fading channels and data loss.

This paper proposes a unique general averaging (GA)-based predictive iterative learning control (PILC) method for networked systems with both data loss and fading channels. The objective is to eliminate the adverse effects of data loss and fading channels via prediction error. First, an averaging technique using all historical information is introduced into the prediction model and controller, and the control input is updated only when the data is successfully transmitted.

Comparative effectiveness of colonic stenting alone and with neoadjuvant chemotherapy for patients with left-sided obstructive colon cancer: a meta-analysis.

Background: The effective management of acute left-sided obstructive colon cancer (LSOCC) remains challenging. This meta-analysis compared the short-term and long-term outcomes between two prevalent treatment approaches: selective surgery following colonic stenting (CS) and colonic stenting combined with neoadjuvant chemotherapy (CS-NAC).

Methods: We conducted a comprehensive literature search of databases, including PubMed and Web of Science, covering the period from January 2000-May 2025.

Translation of Fundus Image to Fundus Fluorescein Angiography Boosted by Structure Self-Supervised Representation Cycle Learning.

Introduction: Fundus fluorescein angiography captures detailed images of fundus vasculature, enabling precise disease assessment. Translating fundus images to fundus fluorescein angiography images can assist patients unable to use contrast agents due to physical constraints, facilitating disease analysis. Previous studies on this translation task were limited by the use of only 17 image pairs for training, potentially restricting model performance.

GraphCellNet: A deep learning method for integrated single-cell and spatial transcriptomic analysis with applications in development and disease.

Spatial transcriptomics (ST) integrates gene expression with spatial location, enabling precise mapping of cellular distributions and interactions within tissues, and is a key tool for understanding tissue structure and function. Single-cell RNA sequencing (scRNA-seq) data enhances spatial transcriptomics by providing accurate cell type deconvolution, yet existing methods still face accuracy challenges. We propose GraphCellNet, a model combining cell type deconvolution and spatial domain identification, featuring the Kolmogorov-Arnold Network layer (KAN) to enhance nonlinear feature representation and contextual integration.

A comprehensive dataset of therapeutic peptides on multi-function property and structure information.

This paper presents a comprehensive dataset comprising 58,583 experimentally validated therapeutic peptides with annotated structure information. These peptides are grouped into 47 categories based on their function or therapeutic property like antimicrobial or glucose-regulatory, of which 21,130 are multi-function peptides and 54,722 possess structural annotation information. We believe this dataset can be useful for the relevant research of therapeutic peptides, especially for computational tool developments in therapeutic peptide discovery and further exploration of the 'sequence-structure-function' relationship for therapeutic peptides.

The impact of school climate on college students' socio-emotional competence: the mediating role of psychological resilience and emotion regulation.

Given that previous studies have mostly focused on single pathways and neglected the complex relationships between school climate, psychological resilience, emotional regulation, and college students' social-emotional competencies, this study constructed a structural equation model. The aim was to explore the relationship between school climate and college students' socio-emotional competence, as well as the possible mediating roles of psychological resilience and emotional regulation between school climate and social-emotional competencies. To this end, 800 students from two universities in China were selected for the study, and 761 valid questionnaires were collected, of which 152 (20%) were male and 609 (80%) were female, and the majority of the participants, 657 (86.

Evaluation study on gas-phase passivation inhibiting the spontaneous combustion tendency of ferrous sulfide compounds.

The iron sulfide compound FeS, present in petrochemical plants, is prone to spontaneous combustion when exposed to air, posing a significant threat to the safety of production processes in the petrochemical industry. Accurately assessing the spontaneous combustion tendency of FeS is crucial for preventing fire and explosion hazards in petrochemical storage tanks. In this study, FeS was passivated with 3%, 6%, and 9% concentrations of passivator for 2 to 8 h, and the effects of gas-phase passivation on FeS were investigated.

A Tough Anti-Freezing Thermophoto-, and Hydrochromic Hydrogel.

As an intelligent responsive material, photochromic hydrogel possesses broad application prospects in fields such as information storage and anti-counterfeiting, owing to its unique light-responsive color-changing properties. However, existing photochromic hydrogels typically exhibit inherent drawbacks, including poor mechanical properties, susceptibility to freezing at low temperatures, and singular response performance, which significantly restrict their practical applications. To address these challenges, this study innovatively employs a drying-swelling method to prepare a novel multifunctional photochromic hydrogel (V-NxM50) based on polyvinyl alcohol (PVA), N-hydroxymethylacrylamide (NHMA), and ammonium molybdate (Mo7).

Long-term removal characterization of trichloroethylene from groundwater using sustained-release iron-carbon composite: Investigations through batch and column experiments.

Nano-zero-valent iron (nZVI) is commonly used as a reactive material for trichloro-ethylene (TCE) remediation in groundwater systems, and the long-term removal capacity serves as a critical indicator for evaluating the material efficacy. In this study, a novel sustained-release iron-carbon composite (BC@nZVI-βCD) was synthesized by the integration of biochar (BC) matrix and β-cyclodextrin (βCD) encapsulation, and the materials before and after modification were analyzed by various characterization methods. Subsequently, the kinetics of TCE removal by different materials were investigated, and the sustained-release properties of BC@nZVI-βCD were explored by batch and column experiments.

Mechanistic Insights into HO-Enhanced Iron Sulfide Formation: A DFT Study for Sulfur Fire Prevention and Environmental Hazard Mitigation.

The presence of water (HO) has been found to increase the susceptibility of sulfur to ignition during production and transportation, primarily due to the spontaneous combustion of iron-sulfide compounds generated by friction. In this study, the interaction mechanisms of iron-sulfur reactions under wet and dry conditions were investigated using density functional theory (DFT). Reactant cluster models were constructed, and the structures of all stationary points along the reaction potential energy surface were fully optimized for the 13-fold spin state.

Efficient and Green Strategy of Ultrasound-Assisted Liquid-Phase Exfoliation of MoS Nanosheets.

Two-dimensional molybdenum disulfide (2D MoS) holds significant promise for the development of various photoelectric devices due to its unique atomic thin layer and band gap characteristics. Numerous techniques have made strides in exfoliating 2D MoS nanosheets from their bulk counterparts. However, achieving environmentally benign and efficient preparation of high-quality MoS nanosheets remains challenging.

The Influence of Several Polymer Oil Displacement Agents on the Stability of Produced Water and the Treatment Performance of Typical Nonionic Water Clarifier.

In this paper, first, the effects of four polymers, including partially hydrolyzed polyacrylamide (HPAM), hydrophobically modified polyacrylamide (HMPAM), surface-active polymer (SAP), and heavy oil activator (HOA), on the stability of produced water were studied, and then, the effects of the four polymers on the performance of a nonionic polyether water clarifier AE411 were compared. The results of turbiscan stability index and heart-shaped structure microfluidic chip experiments showed that the four polymers can enhance the stability of oil droplets, ranked as SAP > HMPAM > HOA > HPAM. They can increase the absolute value of oil droplet ζ-potential, the elastic modulus of the oil-water interface, and the repulsion force between oil droplets.

Integrating hydrothermal pretreatment and co-digestion of activated sludge and kitchen waste for enhanced biogas production and microbial mechanism explanation.

Waste activated sludge (AS) and kitchen waste (KW) recently emerged as major environmental problems. Aiming at sustainable development and carbon emission reduction, AS and KW could be transformed to resourceful substance through anaerobic digestion (AD), and energy (i.e.