Browse Articles

Cognitive difference text classification in online knowledge collaboration based on SA-BiLSTM hybrid model

High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction

Influence of reduced nitrogen fertilizer combined with sheep manure on root-soil interaction in Korla fragrant pear orchards in xinjiang, China

Metformin and weight loss medication impact on survival outcomes in older women with obesity-related cancers

Abstract While the association between metformin and mortality in obesity-related cancers (ORC: breast (BrCa), colorectal (CRC), endometrial (ECa), and ovarian (OCa)) remains inconsistent, the impact of weight loss medication (WLM) on mortality in ORC is largely unexplored, particularly among older women. This study aims to investigate the association between pre-diagnostic use of metformin and WLM with all-cause and cancer-specific mortality in older women with ORC. A retrospective cohort of 63,907 women (≥ 65 years) with ORC was identified using SEER-Medicare 2007–2015 data. Pre-diagnostic prescriptions of metformin and WLM were ascertained, and inverse probability of treatment weighting using propensity score (IPTW-PS) was utilized to balance baseline patient characteristics. Cox proportional hazards and competing-risks models were conducted. Metformin was associated with all-cause- (HR:1.86; 95% CI: 1.81–1.92) and ORC-specific mortality (HR: 1.71; 95% CI: 1.63–1.77). Likewise, WLM was associated with all-cause- (HR:1.64; 95% CI: 1.59–1.71) and ORC-specific mortality (HR: 1.55; 95% CI: 1.48–1.62). Dual use of metformin and WLM was associated with all-cause- (HR:1.39; 95% CI: 1.34–1.45) and ORC-specific mortality (HR: 1.28; 95% CI: 1.21–1.53). Significant associations were observed in ORC subgroup analyses, except that WLM was inversely associated with ECa-specific morality. Pre-diagnostic metformin and WLM were associated with an increased mortality risk in older women with ORC, with effects more pronounced among metformin users. Further prospective studies are needed to substantiate these findings.

Ultrahigh energy storage in process-engineered NaNbO₃-based thin films with superior thermal and cyclic stability

Abstract Dielectric thin film capacitors are essential for miniaturized electronics and energy storage systems, offering ultrafast charge-discharge rates and high reliability. However, achieving high energy density, efficiency, and stability in lead-free systems remains challenging, particularly with scalable and cost-effective methods. Here, we demonstrate relaxor sodium niobate-based thin films with Bi and Mg substitution, synthesized via optimized chemical solution deposition. By tailoring crystallization temperature and heating rate, we achieved a recoverable energy density of 37 J cm−3 and an efficiency of 80% at 2.45 MV cm−1. The films exhibit exceptional thermal stability, with energy density variation below 10% up to 310 °C, and superior charge-discharge stability beyond 16 million cycles at high fields. Microstructural engineering, involving grain size reduction and enhanced granularity, was critical for achieving a high Weibull breakdown strength (2.29 MV cm−1) and reliability. These films outperform previously reported sodium niobate systems, surpassing lead-based alternatives in environmental sustainability and scalability. Our approach highlights the potential of sodium niobate-based thin films for high-performance dielectric capacitors in harsh environments, offering a scalable pathway for environmentally sustainable energy storage technologies.

Improving YOLOv11 for marine water quality monitoring and pollution source identification

Overcoming toxicity: How nonantagonistic microbes manage to thrive in boom-and-bust environments

Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and nonantagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains of Saccharomyces cerevisiae , we show that “boom-and-bust” dynamics—periods of rapid growth punctuated by episodic mortality events—caused by temporal environmental fluctuations can favor nonantagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom-and-bust environments where population dilutions occur either deterministically or stochastically over time. Our mathematical investigation reveals that optimal toxin regulation is much more beneficial to killers in stochastic, rather than deterministic, boom-and-bust environments. Overall, our findings show how both antagonistic and nonantagonistic microbes can thrive under varying environmental conditions.

Adaptive electronics for photovoltaic, photoluminescent and photometric methods in power harvesting for wireless wearable sensors

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

Abstract During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation and real-world data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalk’s capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalk’s capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways.

Emergence of cellular nematic order is a conserved feature of gastrulation in animal embryos

Physical education teaching design under the STEAM concept using the convolutional neural network

Analysis of monomer ratios in a dimethacrylate mixture using positron annihilation spectroscopy

Abstract Dimethacrylate based samples suitable for photopolymerization are usually used in mixtures with different ratios. In the case of the monomers D3MA and UDMA, the ideal monomer ratio of 1:1 was reported in the literature and designed as 2 M. Dimethacrylate-based photopolymers are highly cross-linked polymer networks, indispensable in applications requiring rapid polymerization processes, such as light-curable materials for dental applications. Different ratios of the starting monomers D3MA and UDMA in a mixture with the addition of a 0.1 mol% photoinitiator was used in near-UV light photopolymerization. The lifetime of the ortho-positronium was monitored by PALS and the local free volume were determined. NIR was used to follow the double bond transformation in the dimethacrylates region. Bulk density was determined using Archimedes’ law. The phase transitions and the glass transition temperatures (Tg´s) were determined using the dynamic mechanical thermal analysis (DMTA). Based on the free volume size, double bonds and density of the material, the most suitable ratio of monomers used in dentistry and other application was determined. Correlations between the local free volume, density, and chemical bonding of the studied samples with different ratios are discussed.

Cell-type specific sensory and motor activity in the cuneiform nucleus and pedunculopontine nucleus in mice

Abstract The activity of neurotransmitter-based cell types in the cuneiform and pedunculopontine nuclei during locomotion, non-locomotor behaviors, and following sensory stimulation is not fully understood. Using fiber photometry in mice, we found cell-type specific responses to sensory stimuli. Glutamatergic and GABAergic cells responded to sound, visual looming, and air puffs, except for pedunculopontine GABAergic cells, which did not respond to visual looming. Cholinergic cells responded to air puffs. Air puffs triggered high-speed locomotion, whereas visual looming and sound stimuli evoked low-speed locomotion. During air puff–evoked locomotion, cuneiform glutamatergic neuron activity was higher than in trials without locomotion. In contrast, during locomotion evoked by visual looming or sound, activity in pedunculopontine glutamatergic neurons was higher than when no locomotion occurred. In the open-field arena, mice exhibited spontaneous low-speed locomotion during which activity increased in pedunculopontine glutamatergic cells. Activity also increased in a cell type-specific manner during grooming or rearing. Our study shows cell type-specific activity in the cuneiform or pedunculopontine nuclei during locomotion, non-locomotor behaviors, and following sensory stimulation. Sensory responsiveness likely has relevance in Parkinson’s disease, where sensory circuits are increasingly targeted to improve walking.

Effects of oral hygiene and oral exercise on oral hypofunction in residents of long-term care facilities

A hybrid AI-Blockchain security framework for smart grids

Seismic stratigraphy and evolution of mesozoic deposits in the central Arabian basin

Molecular insights into pangenome localization and constructs design for Hemophilus influenza vaccine

Abstract Haemophilus influenza, a major contributor to respiratory infections such as pneumonia, meningitis, sinusitis, chronic bronchitis, and acute otitis, poses a significant public health challenge, driven by rising antibiotic resistance particularly among the non-typeable H. influenza (NTHi) strains given their ability to evade immune surveillance. To address this, we employed a comprehensive immunoinformatics pipeline integrated with extensive pan-genome analysis of 59 strains of H. influenzae to design a novel multiepitope vaccine (MEV) candidate targeting most virulent and clinically significant proteins. Key surface exposed and virulence associated proteins, including Protein E, PilA, Protein D, P4, TolC, YadA, and HifC were prioritized based on their roles in bacterial adhesion, immune evasion, biofilm formation, and nutrient acquisition. Advanced in silico epitope prediction and verification strategies were utilized to map highly immunogenic regions across these proteins, followed by codon optimization to enhance expression efficiency in human systems. To further stabilize the vaccine construct, we performed disulfide engineering to enhance structural integrity and resilience. Comprehensive validation through in silico immune simulations, molecular dynamics (MD) simulations and binding free energy calculations confirmed the structural stability, immunogenic potential, and strong receptor affinity of the MEV candidate. Phylogenetic and virulence factor analysis further corroborated the broad coverage of the pathogenic relevance of the selected proteins. Together, our integrative approach presents a robust pipeline for rational vaccine design, offering a promising avenue toward combating multidrug resistant and immune evasive H. influenza strains.

Soil heterotrophic and autotrophic respiration respond differently to seasonal variations in temperature and water content under monsoon continental climate

Characterization of glutamine synthetase involved in the fecundity of Rhopalosiphum padi

Abstract Glutamine synthetase (GS) is a pivotal enzyme crucial for the synthesis of glutamine (Gln), an important precursor in amino acid biosynthesis, essential for the growth, development, and reproduction of insects through its involvement in nitrogen metabolism. Despite its recognized significance in insect biology, the specific functions of GS in aphids have not been fully elucidated. Here, we cloned and characterized two GS genes, RpGS1 and RpGS2, from Rhopalosiphum padi and analyzed their expression profiles and explored the contribution of RpGS to aphid fecundity. The two isoforms, which are predicted to localize in the mitochondria and cytoplasm respectively, were successfully cloned and heterologously expressed in Escherichia coli. Despite exhibiting 92% amino acid similarity, the isoforms displayed distinct enzymatic kinetic properties and demonstrated variations in mRNA expression levels across developmental stages and tissues. Notably, RpGS1 was highly expressed in the head, whereas RpGS2 was highly expressed in the intestine. Both RpGS genes were significantly expressed in alate adult aphids. Treatment with the specific inhibitor L-methionine S-sulfoximine (MSX) not only suppressed enzyme activity but also downregulated gene expression. Furthermore, inhibition of RpGS led to a marked decrease in the abundance of the obligate symbiont Buchnera and reduced the fecundity of R. padi. The transcript levels of RpVg and RpGT were also downregulated. These findings underscore the significant role of RpGS in regulating fecundity, suggesting its potential as a target for insecticide development in pest management strategies.

Neurons whisper, tissues respond: neurons as orchestrators of stress responses