Advances and Challenges in Machine Learning for RNA-Small Molecule Interaction Modeling: Review.

RNA plays a pivotal role in biological processes such as gene expression regulation and protein synthesis. Targeting RNA with small molecules offers a novel therapeutic strategy for various diseases by directly modulating these processes. However, the structural diversity and complexity of RNA pose significant challenges for experimentally characterizing RNA-small molecule interactions.

Evaluation of probiotic potential of β-galactosidase producing and their charaterization.

Unlabelled: Lactose intolerance is defined as the inability to digest lactose due to insufficient activity of the β-galactosidase enzyme, which catalyzes the hydrolysis of lactose into glucose and galactose. This study evaluated the potential probiotic properties of isolated S8, which exhibiting high β-galactosidase activity. The strain demonstrated higher survival rate under gastrointestinal stress, with 80% and 63% viability after 3 h in simulated gastric fluid and 8 h in intestinal fluid, respectively, while retaining 60.

Uncovering the protective mechanisms of Bacillus subtilis fmbj against LPS-induced hepatic immune stress and redox imbalance in broilers through transcriptomic profiling.

This study investigated the protective effects of Bacillus subtilis fmbj (BS) in alleviating hepatic immune stress and redox imbalance induced by lipopolysaccharide (LPS) in broilers. A total of 240 chickens were randomly assigned to three groups, each consisting of ten replicates with eight birds per replicate. Birds in the LPS and BSLPS groups received intraperitoneal injections of LPS (1 mg/kg body weight), whereas the CON group was administered an equivalent volume of 0.

Hybrid feature-based machine vision method for objective evaluation of textile pilling and fuzzing.

The degree of pilling and fuzzing in textile fabrics is a crucial indicator of textile product quality. Current evaluation methods predominantly rely on subjective judgments, leading to issues such as rating errors and inefficiency. To achieve objective assessment of pilling and fuzzing grades, this study proposes a Hybrid Feature-Based Machine Vision Method for Objective Evaluation of Textile Pilling and Fuzzing.

Leveraging data analytics to revolutionize cybersecurity with machine learning and deep learning.

The exponential growth of digital technologies has brought about a surge in the complexity and frequency of cyber-attacks, necessitating robust cyber security measures. This study introduces an innovative approach to cyber security data analysis by leveraging Convolutional Neural Network (CNN) technology. The primary objective is to explore the potential of CNNs in accurately and efficiently detecting and classifying cyber security threats.

Sugar-responsive packaging system combined with zein nanoparticle-enhanced coacervates for controlled release of natural preservatives and food preservation.

Food spoilage contributes significantly to global food waste, while concerns over the health risks of traditional chemical preservatives have accelerated the search for natural alternatives. Tea polyphenols (TP) are natural antioxidants with antibacterial effects. However, their poor stability leads to oxidation and degradation, reducing their functional activity.

Preparation of rare earth metal hybridized magnetic chitosan nanoparticles for efficient DNA capture.

The advancement of biomedicine and molecular diagnostics depends on effective nucleic acid separation methods. Nevertheless, creating high-performing and environmentally friendly nucleic acid adsorbent materials is still challenging. In order to achieve high-quality DNA capture, this work creatively created a rare earth metal hybridized magnetic chitosan nanoparticle (FeO₄@CS@La(OH)₃) by combining the biocompatibility of chitosan with the effective coordination ability of La.

Computer-aided design for enhancing the tolerance of ω-amine transaminase against methanol as a cosolvent.

A significant challenge hindering the application of enzymes in industrial biotechnology is their inadequate stability during processing. Employing organic solvents rather than aqueous systems for enzymatic catalysis presents several key benefits. However, excessive organic solvents as cosolvent that added to reaction system often inactivate enzymes.

An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease.

Despite the widespread use of vaccines against SARS-CoV-2, COVID-19 continues to pose global health challenges, requiring efficient drug screening and repurposing strategies. This study presents a novel hybrid framework that integrates deep learning (DL) with molecular docking to accelerate the identification of potential therapeutics. The framework comprises three crucial steps: (1) a previously developed DL model is employed to rapidly screen candidate compounds, selecting those with predicted interaction scores above a cut-off value of 0.

Polyacrylonitrile (PAN) electrospun nanofibrous membrane for removing indoor gaseous phthalates: A health-driven framework for macro-channel adsorption module design and its implications.

This study developed a polyacrylonitrile (PAN) electrospun nanofibrous membrane (PEM)-based macro-channel adsorption module for removing indoor gaseous phthalates, integrating material design, mass transfer modeling and health-benefits-oriented optimization. The fabricated PEM demonstrated exceptional adsorption capacity, exhibiting partitioning coefficients (K) 4-30 times higher than conventional textile materials. Surface chemical analysis confirmed that the adsorption of phthalates on PEM is predominantly governed by physical adsorption mechanisms.

EvidenceMap: Learning evidence analysis to unleash the power of small language models for biomedical question answering.

When addressing professional questions in the biomedical domain, humans typically acquire multiple pieces of information as evidence and engage in multifaceted analysis to provide high-quality answers. Current LLM-based question answering methods lack a detailed definition and learning process for evidence analysis, leading to the risk of error propagation and hallucinations while using evidence. Although increasing the parameter size of LLMs can alleviate these issues, it also presents challenges in training and deployment with limited resources.

A Review on the Degradation of Antibiotic Resistance Genes During Composting of Livestock Manure.

As emerging pollutants, antibiotic resistance genes (ARGs) have been recognized as originating from diverse sources. Among these, the use of livestock feed and veterinary drugs was identified as the primary source of ARGs in livestock manure. ARGs were found to be widely distributed in global environments, particularly in agriculture-related soils, water bodies, and the atmosphere, posing potential threats to ecological environments and human health.

Origin centric and part based pose decomposition for 3D human pose estimation.

Transformer-based approaches have recently made significant advancements in 3D human pose estimation from 2D inputs. Existing methods typically either consider the entire 2D skeleton for global features extraction or break it into independent parts for local features learning. However, capturing the spatial dependencies of the entire 2D skeleton does not effectively facilitate learning local spatial features, while partitioning the skeleton into independent segments disrupts the relevance of individual joints to the whole.

Biochar suppresses conjugative transfer of antibiotic resistance genes in manure-amended soils.

The environmental dissemination of antibiotic resistance genes (ARGs), particularly in manure-amended soils, poses a growing threat to public health due to the potential transfer of ARGs to humans and animals. Effective strategies are urgently needed to mitigate ARG spread in agricultural settings. Biochar, an eco-friendly soil amendment, shows promise for pollution control, yet its role in suppressing ARG horizontal gene transfer remains unclear.

Synergistic adsorption mechanisms of self-N-doped biochar for levofloxacin elimination: integrated experimental and DFT studies.

The efficient removal of fluoroquinolone antibiotics from water is critical for water pollution control. In this study, a novel self-nitrogen doped porous biochar (KBC) was synthesized using waste peanut meal as a precursor, combined with high-temperature pyrolysis (900 °C) and an activating agent (CHKO) at an optimal mass ratio of 1:2. The KBC exhibited excellent adsorption capacity for levofloxacin (LEV) with the maximum capacity of 717.

Structural and surface hydrophobic remodeling of whey protein-linoleic acid-EGCG complex suppresses co-oxidation, attenuates allergenicity, and enhances bioactive functionalities under oxidative stress.

This study developed a whey protein concentrate (WPC)-linoleic acid (LA)-(-)-epigallocatechin-3-gallate (EGCG) ternary complex, evaluating EGCG concentration effects (0-0.14 %, w/v) under Fenton oxidation condition. The complex suppressed WPC-LA co-oxidation in a dose-dependent manner and was maximally inhibited by 0.

Temporal Adaptivity Enabled High-Efficiency In-Sensor Reservoir Computing Based on MoS Phototransistors.

Reservoir computing (RC) excels in temporal signal processing, driving advances in efficient reservoir hardware. However, dynamic target recognition faces challenges due to mismatches between event time scales and temporal properties of the optoelectronic RC system. In this work, a bridge is built between the event chronological information and the temporal dynamic of optoelectronic physical nodes in RC.

Improving CNN predictive accuracy in COVID-19 health analytics.

The COVID-19 pandemic has underscored the critical necessity for robust and accurate predictive frameworks to bolster global healthcare infrastructures. This study presents a comprehensive examination of convolutional neural networks (CNNs) applied to the prediction of COVID-19-related health outcomes, with an emphasis on core challenges, methodological constraints, and potential remediation strategies. Our investigation targets two principal aims: the identification of COVID-19 infections through chest radiographic imaging, specifically X-rays, and the prognostication of disease severity by integrating clinical parameters and electronic health records.

Unraveling the characteristic structures of Cuscuta chinensis Lam. polysaccharides and its inhibitory effects on non-alcoholic fatty liver disease.

Cuscuta chinensis Lam. is a well-known edible and medicinal plant in China, generally used in traditional medicine with meridian tropism into liver and kidney. In this work, two fractions (CCL-A and CCL-B) were isolated and purified from the polysaccharides of Cuscuta chinensis Lam.

MetaRes-DMT-AS: A Meta-Learning Approach for Few-Shot Fault Diagnosis in Elevator Systems.

Recent advancements in deep learning have spurred significant research interest in fault diagnosis for elevator systems. However, conventional approaches typically require substantial labeled datasets that are often impractical to obtain in real-world industrial environments. This limitation poses a fundamental challenge for developing robust diagnostic models capable of performing reliably under data-scarce conditions.

Spin-Polarized DFT+ Study of Surface-Functionalized CrC MXenes: Tunable Electronic and Magnetic Behavior for Spintronics.

Surface functionalization is key for tuning the electronic and magnetic properties essential in spintronics, yet its impact on chromium-based MXenes (CrCT) is not fully understood. Using spin-polarized DFT+, this study investigates how O, F, and OH groups modify the magnetic state, electronic structure, and Curie temperature. Functionalization dramatically changes magnetism: O termination gives ferromagnetism, while F and OH yield ferrimagnetism.

Biological Function of Medium-Chain Fatty Acids and Their Application in Aquatic Animals: A Review.

Medium-chain fatty acid triglycerides (MCTs) possess antibacterial, antiviral, nutritional, and other biological activities and have demonstrated significant application potential in humans and terrestrial animals. In recent years, with the development of the green aquaculture industry, MCTs have been gradually applied to aquaculture animals, which can enhance growth performance, improve flesh quality, regulate lipid metabolism, boost immune activity, and modulate the intestinal flora, thereby improving the production efficiency of aquaculture. This paper elaborates in detail on the biological activities of MCTs and their applications in aquatic animals, providing a theoretical and practical basis for the application of MCTs in aquaculture.

Optimal task-priority projection for behavioral control of networked nonholonomic Mobile robots.

This paper addresses the task-priority projection problem within the Null-Space-based Behavioral Control (NSBC) framework. We propose a Reliable Intelligent Task Supervisor (RITS) for dynamic task-priority projection in networked Nonholonomic Mobile Robots (NMRs). Firstly, the NSBC task paradigm is extended to accommodate the NMRs by integrating nonholonomic constraints into both task functions and the projection operator.

Electrospinning fabrication of ethanol lignin/polyacrylonitrile composite carbon fibers for supercapacitor applications.

Supercapacitors, known for their high safety, long cycle life, rapid charging and discharging rates, and high power density, are widely used in portable electronic devices, aerospace applications, and new energy electric vehicles. However, the high cost and environmental impact of polyacrylonitrile (PAN)-based carbon fibers, the most commonly used electrode materials, limit their further development. In this study, low molecular weight ethanol lignin was mixed with PAN for the first time, and lignin-based carbon fibers with excellent electrochemical properties were prepared using electrospinning technology.

Synthesis, human carbonic anhydrase I and II inhibition, and in silico studies of 2-ethoxy-6-formylphenyl [1,1'-biphenyl]-4-sulfonate derived thiosemicarbazones.

This study describes the synthesis and biological evaluation of 2-ethoxy-6-formylphenyl [1,1'-biphenyl]-4-sulfonate-based thiosemicarbazones 5(a-s) as potential inhibitors of human carbonic anhydrase isoforms hCA I and hCA II. Among the synthesized compounds, 5 k exhibited potent inhibition, with IC values of 89.25 nM and 54.