DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.

RNA N4-acetylcytidine (ac4C) is a crucial chemical modification involved in various biological processes, influencing RNA properties and functions. Accurate prediction of RNA ac4C sites is essential for understanding the roles of RNA molecules in gene expression and cellular regulation. While existing methods have made progress in ac4C site prediction, they still struggle with limited accuracy and generalization.

Exploring the landing speed of digital society PPP projects: A continuous-time event history analysis of 300 projects.

The Public-Private Partnership (PPP) model has become a viable alternative or supplement to traditional approaches in the development of a digital society. However, PPP projects in this domain often face significant landing (the launch of project implementation, typically marked by contract signing and the commencement of operational activities) challenges. Understanding the factors that influence the speed of project landing is thus of considerable practical importance.

Accurate and Scalable Quantum Hydrodynamic Simulations of Plasmonic Nanostructures Within OFDFT.

Quantum hydrodynamic theory (QHT) provides a computationally efficient alternative to time-dependent density functional theory for simulating plasmonic nanostructures, but its predictive power depends critically on the choice of ground-state electron density and energy functional. To construct ground-state densities, we adopt orbital-free density functional theory and numerically evaluate the effect of different exchange-correlation functionals and kinetic energy functionals. A suitable energy functional to reproduce both the DFT-calculated work function and charge density is identified.

Inequality in opportunities for utilizing community-based care services among the elderly in China - an analysis based on the "circumstance-effort" perspective.

Based on the "circumstance-effort" framework of inequality of opportunity, this study utilizes microdata from the 2020 China Longitudinal Aging Social Survey (CLASS), employing the ex-ante parametric estimation method and the Shapley decomposition approach to empirically measure the level of opportunity inequality in the utilization of community-based care services among the elderly, as well as the contribution and transmission pathways of various influencing factors. Key findings include: ① The levels of opportunity inequality in overall community-based care services, and their subcategories-medical care, daily-life assistance, and emotional support-range between 0.168 and 0.

Decoupling upper and lower face transformers for binary interactive video generation.

Current audio-driven binary interaction methods have limitations in capturing the uncertain relationship between a speaker's audio and an interlocutor's facial movements. To address this issue, we propose a video generation pipeline based on a cross-modal Transformer. First, a Transformer decoder partitions facial features into upper and lower regions, capturing lower features that are closely linked to the audio and upper features that remain independent of visual cues.

Why do Chinese migrant workers return? Exploring economic push-pull factors and emotional ties.

Over the past few decades, China's economic growth and urbanization have driven a significant migration of rural laborers to cities. Recently, however, an increasing number of migrant workers have chosen to return to their hometowns for employment opportunities. Understanding the factors influencing this return migration is crucial but challenging due to the complexity and diversity of these factors and their intricate interrelationships.

Spatio-temporal dynamics and drivers of cropland ecosystem services value during the past two decades in Yangtze river economic belt, China.

Introduction: Cropland ecosystem is one of the fundamental natural resources for human survival and development, serving not only as the core carrier of food production but also as an important provider of ecological services. Clarifying the spatio-temporal variation of the cropland ecosystem service value (Crop-ESV) and understanding its main drivers are critical for maintaining and regulating cropland ecosystem functions.

Methods: Thus, this study systematically assessed the Crop-ESV in the Yangtze River Economic Belt (YREB) in China and mapped it at 1 km spatial resolution from 2001 to 2020.

Robust SOH estimation for Li-ion battery packs of real-world electric buses with charging segments.

Accurate estimation of battery packs state of health (SOH) is essential for the timely maintenance and efficient reuse of batteries in pure electric buses, which paly an import role in modern public transportation. However, real-world SOH estimation faces significant challenges due to inconsistencies among cells and variations in charge-discharge depths. Based on extensive operational data from electric buses, a novel SOH labeling calibration method is proposed, forming the foundation of a robust SOH estimation framework.

Systematic review and randomized controlled trial meta-analysis of the effects of physical activity interventions and their components on repetitive stereotyped behaviors in patients with autism spectrum disorder.

Objective: To systematically evaluate the effect of exercise intervention and its components on repetitive stereotyped behaviors in patients with autism spectrum disorder (ASD).

Methods: A computer-based search was conducted in PubMed, Web of Science, The Cochrane Library, and EMbase databases for randomized controlled trials (RCTs) related to exercise interventions for repetitive stereotyped behaviors in patients with ASD. The search covered all available data from the inception of each database until January 2025.

Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.

lncRNAs are densely related to many human diseases. Identifying new lncRNA-disease associations (LDAs) conduces to better deciphering mechanisms of diseases, finding new biomarkers, and further promoting their diagnosis and treatment. In this manuscript, we devise an LDA prediction framework called LDA-GARB.

Xylitol promotes the antioxidant and biocontrol efficiency of the antagonistic yeast, .

The biocontrol efficiency of the antagonist yeast is significantly reduced under oxidative stress in adverse environments. However, effective strategies to improve under such abiotic stress remain limited. As an effective protectant of yeasts, xylitol has significant potential to improve the performance of under abiotic stress.

Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.

Alternative polyadenylation (APA) is a widespread post-transcriptional mechanism that diversifies gene expression by generating messenger RNA isoforms with varying 3' untranslated regions. Accurate identification and quantification of transcriptome-wide polyadenylation site (PAS) usage are essential for understanding APA-mediated gene regulation and its biological implications. In this review, we first review the landscape of computational tools developed to identify APA events from RNA sequencing (RNA-seq) data.

The effect of error aversion climate on impoverished leadership.

Introduction: Impoverished leadership, as a form of unethical leadership behavior, can have a wide range of negative impacts. It not only affects team morale, work efficiency, cohesion, and trust but also directly influences organizational performance, reputation, and the leader's own career development. However, previous research has rarely explored the antecedents of impoverished leadership.

SVM-LncRNAPro: An SVM-Based Method for Predicting Long Noncoding RNA Promoters.

Long non-coding RNAs (lncRNAs) are closely associated with the regulation of gene expression, whose promoters play a crucial role in comprehensively understanding lncRNA regulatory mechanisms, functions and their roles in diseases. Due to limitations of the current techniques, accurately identifying lncRNA promoters remains a challenge. To address this challenge, we propose a support vector machine (SVM)-based method for predicting lncRNA promoters, called SVM-LncRNAPro.

ACP-DPE: A Dual-Channel Deep Learning Model for Anticancer Peptide Prediction.

Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectively treating cancer. Identifying ACPs is challenging due to the limitation of experimental conditions.

Responses of root hairs to soil compaction: A review.

In recent years, many studies have investigated the effects of soil compaction on plant root growth. However, root hairs, which are important parts of plants that anchor the soil and absorb nutrients and water, under compacted conditions have received limited attention. We reviewed the responses of root hair structure (behaviors), the rhizosheath, water and nutrient uptake by root hairs, plant hormones and crop species associated with root hairs to soil compaction and proposed potential solutions to mitigate the impacts of soil compaction on root hairs.

EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.

Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fraction have been functionally characterized. While the experimental validation of lncRNAs (EVlncRNAs) using low-throughput methods is increasing, the expensive costs limit the validation to a small subset of HTlncRNAs.

An enhanced walrus optimization algorithm for flexible job shop scheduling with parallel batch processing operation.

The flexible job shop scheduling problem with parallel batch processing operation (FJSP_PBPO) in this study is motivated by real-world scenarios observed in electronic product testing workshops. This research aims to tackle the deficiency of effective methods, particularly global scheduling metaheuristics, for FJSP_PBPO. We establish an optimization model utilizing mixed-integer programming to minimize makespan and introduce an enhanced walrus optimization algorithm (WaOA) for efficiently solving the FJSP_PBPO.

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism.

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement.

Zeta Potential Change and curing effects on Shield Muck solidified by Metal Ion Complex Catalysis.

Using potentiometric testing, we investigated the zeta potential of shield muck curing materials' particle surfaces, varying the concentration of metal ion complex. We analyzed the microscopic characteristics of shield muck curing products by using the electron microscopy, revealing the impact of metal ion complex on curing. Results showed that the metal ion complex significantly reduces the surface zeta potential of shield muck and conventional curing materials, with cement showing the most substantial effect, followed by shield muck, calcium carbonate, and calcium sulfate.

Fault detection for Li-ion batteries of electric vehicles with segmented regression method.

Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection.

The spatial differentiation characteristics and influencing mechanisms of intangible cultural heritage in China.

Intangible cultural heritage represents a cultural evolution shaped by human responses to adapting and transforming environments. Unveiling the characteristics and patterns of its spatial distribution can offer a more scientific foundation for the protection of intangible cultural heritage and the development of heritage tourism. By using ArcGIS software and Geo-detector, the regional differentiation characteristics and influencing mechanisms of Intangible Cultural Heritage, specifically the Hometown of Chinese Folk Culture and Art (HCFCA), have been investigated.

Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.

Background: Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly.