Search Results (19,040)

Kidney cells are exposed to a wide range of physiological and pathological stresses, including hormonal changes, mechanical forces, hypoxia, hyperglycemia, and inflammation. These insults can trigger adaptive responses, but when they persist, they can lead to organelle stress. Organelles such as mitochondria, the endoplasmic reticulum, and primary cilia sustain cellular metabolism and tissue homeostasis. When organelle stress occurs, it disrupts cellular processes and organelle communication, leading to metabolic dysfunction, inflammation, fibrosis, and progression of kidney disease. Sex and hormonal factors play a significant role in the development of renal disorders. Many glomerular diseases show distinct differences between the sexes. Chronic Kidney Disease is more common in women, while men often experience a faster decline in kidney function, partly due to the influence of androgens. Additionally, the loss of female hormonal protection after menopause highlights the importance of sex as a factor in renal susceptibility. This narrative review synthesizes preclinical evidence on how sexual dimorphism and sex hormones affect organelle stress in mitochondria, the endoplasmic reticulum, and primary cilia, from 33 studies identified through a non-systematic literature search of the PubMed database, to provide an overview of how these mechanisms contribute to sex-specific differences in kidney disease pathophysiology. Full article

Taking climate funds (e.g., the Green Climate Fund) as the main financial mechanism for providing funding to developing countries, this paper examines a long-term climate funding relationship between two parties—the rich country and the poor country. Conflicts between the rich and poor countries arise when determining (1) the size of climate funding that the rich country contributes to the poor country and (2) the funding allocation between climate adaptation and mitigation projects in the poor country. In addition, the rich country cannot be forced to commit contractual contributions to the poor country, and in each period, there is a probability that the countries can renegotiate the contract. This paper derives two main dynamic comparative–static results: (1) climate funds converge to the first-best in the long run, both in the size of climate funding in adaptation and mitigation projects, if and only if climate damage becomes sufficiently severe; (2) fewer renegotiations between the rich and poor countries make climate funding contracts more efficient, remedying inequality between the poor and rich countries. These results highlight how increasing climate damages and reducing the frequency of renegotiation can push climate funds closer to a first-best allocation, suggesting design principles for climate funding mechanisms like the Green Climate Fund. Full article

In the ecosystem, birds are important indicators that can sensitively reflect changes in the ecological environment and its health. However, bird monitoring has challenges due to species diversity, variable behaviors, and distinct morphological characteristics. Therefore, we propose a parallel dual-branch hybrid CNN–Transformer architecture for feature extraction that simultaneously captures local and global image features to address the “local feature similarity” issue in dual tasks of bird species and behaviors. The dual-task framework comprises three main components: the Token Re-segmentation Module (TRM), the Multi-scale Adaptive Module (MAM), and the Feature Interleaving Structure (FIS). The designed MAM fuses hybrid attention to address the problem of different-scale birds. MAM models the interdependencies between spatial and channel dimensions of features from different scales. It enables the model to adaptively choose scale-specific feature representations, accommodating inputs of different scales. In addition, we designed an efficient feature-sharing mechanism, called FIS, between parallel CNN branches. FIS interleaving delivers and fuses CNN feature maps across parallel layers, combining them with the features of the corresponding Transformer layer to share local and global information at different depths and promote deep feature fusion across parallel networks. Finally, we designed the TRM to address the challenge of visually similar but distinct bird species and of similar poses with distinct behaviors. TRM adopts a two-step approach: first, it locates discriminative regions, and then performs fine segmentation on them. This module enables the network to allocate relatively more attention to key areas while merging non-essential information and reducing interference from irrelevant details. Experiments on the self-made dataset demonstrate that, compared with state-of-the-art classification networks, the proposed network achieves the best performance, achieving 79.70% accuracy in bird species recognition, 76.21% in behavior recognition, and the best performance in dual-task recognition. Full article

Reliable road pothole detection remains challenging in complex environments, where low contrast, shadows, water films, and strong background textures cause frequent false alarms, missed detections, and boundary instability. Thin rims and adjacent objects further complicate localization, and model robustness often deteriorates across regions and sensor domains. To address these issues, we propose OCDBMamba, a unified and efficient framework that integrates omnidirectional context modeling with consensus-driven boundary selection. Specifically, we introduce the following: (1) an Omnidirectional Channel-Selective Scanning (OCS) mechanism that aggregates long-range structural cues by performing multidirectional scans and channel similarity fusion with cross-directional consistency, capturing comprehensive spatial dependencies at near-linear complexity and (2) a Dual-Branch Consensus Thresholding (DBCT) module that enforces branch-level agreement with sparsity-regulated adaptive thresholds and boundary consistency constraints, effectively preserving true rims while suppressing reflections and redundant responses. Extensive experiments on normal, shadowed, wet, low-contrast, and texture-rich subsets yield 90.7% $mA{P}_{50}$, 67.8% $mA{P}_{50:95}$, a precision of 0.905, and a recall of 0.812 with 13.1 GFLOPs, outperforming YOLOv11n by 5.4% and 5.6%, respectively. The results demonstrate more stable localization and enhanced robustness under diverse conditions, validating the synergy of OCS and DBCT for practical road inspection and on-vehicle perception scenarios. Full article

Background/Objectives. Lactiplantibacillus plantarum CRL681 has previously demonstrated a strong antagonistic effect against Escherichia coli O157:H7 in food matrices; however, the molecular mechanisms underlying this activity remain poorly understood. Since initial interactions between beneficial bacteria and pathogens occur mainly at the cell surface and in the extracellular environment, the characterization of the bacterial secretome is essential for elucidating these mechanisms. In this study, the secretome of L. plantarum CRL681 was comprehensively characterized using an integrated in silico and in vitro approach. Methods. The exoproteome and surfaceome were analyzed by LC-MS/MS under pure culture conditions and during co-culture with E. coli O157:H7. Identified proteins were functionally annotated, classified according to subcellular localization and secretion pathways, and evaluated through protein–protein interaction network analysis. Results. A total of 275 proteins were proposed as components of the CRL681 secretome, including proteins involved in cell surface remodeling, metabolism and nutrient transport, stress response, adhesion, and genetic information processing. Co-culture with EHEC induced significant changes in the expression of proteins associated with energy metabolism, transport systems, and redox homeostasis, indicating a metabolic and physiological adaptation of L. plantarum CRL681 under competitive conditions. Notably, several peptidoglycan hydrolases, ribosomal proteins with reported antimicrobial activity, and moonlighting proteins related to adhesion were identified. Conclusions. Overall, these findings suggest that the antagonistic activity of L. plantarum CRL681 against E. coli O157:H7 would be mediated by synergistic mechanisms involving metabolic adaptation, stress resistance, surface adhesion, and the production of non-bacteriocin antimicrobial proteins, supporting its potential application as a bioprotective and functional probiotic strain. Full article

In response to the critical demand for improved characterization of atmospheric stability effects in wind turbine wake prediction, this study proposes and systematically validates a new analytical wake model that incorporates atmospheric stability effects. In recent years, research on wake models with atmospheric stability effects has primarily followed two approaches: incorporating stability through high-fidelity numerical simulations or modifying classical analytical wake models. While the former offers clear mechanical insights, it incurs high computational costs, whereas the latter improves efficiency yet often suffers from near-wake prediction biases under stable stratification, lacks a unified framework covering the entire wake region, and relies heavily on case-specific calibration of key parameters. To overcome these limitations, this study introduces a stability-dependent turbulence expansion term with a square of a cosine function and the stability sign parameter, enabling the model to dynamically respond to varying atmospheric conditions and overcome the reliance of traditional models on neutral atmospheric assumptions. It achieves physically consistent descriptions of turbulence suppression under stable conditions and convective enhancement under unstable conditions. A newly developed far-field decay function effectively coordinates near-wake and far-wake evolution, maintaining computational efficiency while significantly improving prediction accuracy under complex stability conditions. The Present model has been validated against field measurements from the Scaled Wind Farm Technology (SWiFT) facility and the Alsvik wind farm, demonstrating superior performance in predicting wake velocity distributions on both vertical and horizontal planes. It also exhibits strong adaptability under neutral, stable, and unstable atmospheric conditions. This proposed framework provides a reliable tool for wind turbine layout optimization and power output forecasting under realistic atmospheric stability conditions. Full article

Background: Sheep (Ovis aries) exhibit significant diversity in adipose tissue deposition, which influences meat quality, environmental adaptation, and economic value. Tail fat, in particular, varies widely among breeds, yet the transcriptomic basis of this variation remains incompletely understood. This study aims to systematically compare the transcriptional profiles of five adipose depots across five sheep breeds to identify molecular mechanisms underlying fat deposition and tail phenotype divergence. Methods: We analyzed 250 publicly available RNA-seq samples from five adipose tissues (caul, subcutaneous, perirenal, intermuscular, and tail fat) of five sheep breeds (Altay, Tibetan, Merino, Wadi, Small-tailed Han). Data were processed using FastQC, STAR, and featureCounts. Differential expression analysis was performed with DESeq2, followed by GO and KEGG enrichment analyses. Breeds were grouped into three tail phenotypes: fat-tailed, short fat-tailed, and thin-tailed. Cross-tissue and phenotype-specific pathway analyses were conducted to identify key regulatory genes. Results: Transcriptional divergence was most pronounced in subcutaneous and intermuscular fat, while tail fat exhibited both conserved and phenotype-specific pathways. Fat-tailed breeds showed enrichment in mitochondrial oxidative phosphorylation and lipid biosynthesis genes (TAFAZZIN, GPAM, COQ family). Short fat-tailed breeds were characterized by extracellular matrix remodeling genes (MMP9, MMP12, MMP19). Thin-tailed sheep lacked these pro-lipogenic and structural remodeling pathways. A dual-axis model of tail fat development is proposed to explain phenotypic diversity. Conclusions: This study reveals that distinct molecular mechanisms underpin tail fat phenotypes in sheep: fat-tailed breeds prioritize metabolic efficiency, short fat-tailed breeds rely on ECM remodeling, and thin-tailed breeds lack these enhancements. The identified candidate genes may serve as potential targets for molecular breeding strategies aimed at optimizing fat deposition and adaptive traits in sheep. Full article

Cnidium monnieri (L.) Cusson is a species of Umbelliferae plants, and it is one of China’s traditional medicinal herbs, widely distributed in China owing to its strong adaptability in fields. In this article, the research progress on the taxonomy, distribution, cultivation techniques, active components, analysis methods, antibacterial and insecticidal properties, and ecological applications of C. monnieri was reviewed. The main active components in C. monnieri are coumarins (mainly osthole) and volatile compounds, exhibiting multiple pharmacological effects, e.g., anti-inflammatory, antibacterial, antioxidant, anti-tumor, and immune-regulating effects. Some modern analytical techniques (e.g., HPLC, GC-MS, and UPLC-QTOF-MS) have enabled more precise detection and quality control of these chemical components in C. monnieri. The specific active constituents in C. monnieri (e.g., coumarins and volatile components) exhibit significant inhibitory effects against various pathogenic fungi and insect pests. Simultaneously, the resources provided during its flowering stage (e.g., pollen and nectar) and the specific volatiles released can repel herbivorous insect pests while attracting natural enemies, such as ladybugs, lacewings, and hoverflies, thereby enhancing ecological control of insect pests in farmland through a “push–pull” strategy. Additionally, C. monnieri has the ability to accumulate heavy metals, e.g., Zn and Cu, indicating its potential value for ecological restoration in agroecosystems. Overall, C. monnieri has medicinal, ecological, and economic value. Future research should focus on regulating active-component synthesis, improving our understanding of ecological mechanisms, and developing standardized cultivation systems to enhance the applications of C. monnieri in modernized traditional Chinese medicine and green agriculture production. Full article

High-Resolution Range Profile (HRRP) represents the electromagnetic backscattering distribution of targets and plays a pivotal role in remote-sensing-based Automatic Target Recognition (RATR). However, in non-cooperative sensing scenarios, acquiring sufficient measured data is severely constrained by operational costs and physical limitations, leading to data scarcity that hampers model robustness. To overcome this, we propose SpecM-DDPM, a spectral-aware Denoising Diffusion Probabilistic Models (DDPM) tailored for generating high-fidelity HRRPs that preserve physical scattering properties. Unlike generic generative models, SpecM-DDPM incorporates radar signal physics into the diffusion process. Specifically, a parallel multi-scale block is designed to adaptively capture both local scattering centers and global target resonance structures. To ensure spectral fidelity, a spectral gating mechanism serves as a physics-constrained filter to calibrate the energy distribution in the frequency domain. Furthermore, a Frequency-Aware Curriculum Learning (FACL) strategy is introduced to guide the progressive reconstruction from low-frequency structural components to high-frequency scattering details. Experiments on measured aircraft data demonstrate that SpecM-DDPM generates samples with high physical consistency, significantly enhancing the generalization performance of radar recognition systems in data-limited environments. Full article

Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physics-informed neural network-graph neural network (PINN-GNN) approaches suffer from redundant physics residual calculations (over 70% of flat regions contain little information) and poor model generalization (requiring retraining for new box types), making them inefficient for deployment on edge devices. This paper proposes the PhysGraphIR framework, which employs an Adaptive Residual Sampling (ARS) mechanism to dynamically identify hotspot region nodes through a physics-aware gating network, calculating physics residuals only at critical nodes to reduce computational overhead by over 80%. In this study, a `hotspot region’ is explicitly defined as a localized area exhibiting significant temperature elevation relative to the background—typically concentrated around electrical connection terminals or wire entrances—which is critical for identifying potential thermal faults under sparse data conditions. Additionally, it utilizes a Physics Knowledge Distillation Graph Neural Network (Physics-KD GNN) to decouple physics learning from geometric learning, transferring universal heat conduction knowledge to specific meter box geometries through a teacher–student architecture. Experimental results demonstrate that on both synthetic and real-world meter box datasets, PhysGraphIR achieves a hotspot region mean absolute error (MAE) of 11.8 °C under 60% infrared data missing conditions, representing a 22% improvement over traditional PINN-GNN. The training speed is accelerated by 3.1 times, requiring only five infrared samples to adapt to new box types. The experiments prove that this method significantly enhances prediction accuracy and computational efficiency under sparse infrared data while maintaining physical consistency, providing a feasible solution for edge intelligence in power systems. Full article

Biofilm formation represents a key survival strategy employed by Vibrio cholerae to adapt to the complex intestinal environment of the host. While most previous studies on V. cholerae biofilms have focused on genetic regulation and monospecies cultures, its ability to form dual-species biofilms with other intestinal pathogens is still poorly understood. In this study, using samples from both cholera patients and healthy individuals, Fusobacterium nucleatum was identified as a bacterium capable of co-aggregating with V. cholerae. Untargeted metabolomic analysis revealed that F. nucleatum-derived metabolites, specifically 6-hypoxanthine, enhance biofilm formation in V. cholerae. Further validation confirmed that these F. nucleatum-derived metabolites upregulate the biofilm-associated regulatory gene vpsT. In an adult mouse model, co-infection with F. nucleatum and V. cholerae significantly enhanced the intestinal adaptability of V. cholerae compared to infection with V. cholerae alone. Together, these findings elucidate the mechanism enabling the co-infection of F. nucleatum and V. cholerae in the host intestine, thereby shedding new light on how other pathogenic bacteria can assist in V. cholerae infection. Full article

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments. Full article

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), are becoming more prevalent and still lack effective disease-modifying therapies (DMTs). However, translational efficiency remains critically low. For example, a ClinicalTrials.gov analysis of AD programs (2002–2012) estimated ~99.6% attrition, while PD programs (1999–2019) achieved an overall success rate of ~14.9%. In vitro platforms are assessed, ranging from immortalized neuronal lines and primary cultures to human-induced pluripotent stem cell (iPSC)-derived neurons/glia, neuron–glia co-cultures (including neuroinflammation paradigms), 3D spheroids, organoids, and blood–brain barrier (BBB)-on-chip systems. Complementary in vivo toxin, pharmacological, and genetic models are discussed for systems-level validation and central nervous system (CNS) exposure realism. The therapeutic synthesis focuses on AD, covering symptomatic drugs, anti-amyloid immunotherapies, tau-directed approaches, and repurposed drug classes that target metabolism, neuroinflammation, and network dysfunction. This review links experimental models to translational decision-making, focusing primarily on AD and providing a brief comparative context from other NDDs. It also covers emerging targeted protein degradation (PROTACs). Key priorities include neuroimmune/neurovascular human models, biomarker-anchored adaptive trials, mechanism-guided combination DMTs, and CNS PK/PD-driven development for brain-directed degraders. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

With the explosive growth in the quantity of electrical equipment in distribution networks, traditional manual inspection struggles to achieve comprehensive coverage due to limited manpower and low efficiency. This has led to frequent equipment failures including partial discharge, insulation aging, and poor contact. These issues seriously compromise the safe and stable operation of distribution networks. Real-time monitoring and defect identification of their operation status are critical to ensuring the safety and stability of power systems. Currently, commonly used methods for defect identification in distribution network electrical equipment mainly rely on single-image or voiceprint data features. These methods lack consideration of the complementarity and interleaved nature between image and voiceprint features, resulting in reduced identification accuracy and reliability. To address the limitations of existing methods, this paper proposes distribution network electrical equipment defect identification based on multi-modal image voiceprint data fusion and channel interleaving. First, image and voiceprint feature models are constructed using two-dimensional principal component analysis (2DPCA) and the Mel scale, respectively. Multi-modal feature fusion is achieved using an improved transformer model that integrates intra-domain self-attention units and an inter-domain cross-attention mechanism. Second, an image and voiceprint multi-channel interleaving model is applied. It combines channel adaptability and confidence to dynamically adjust weights and generates defect identification results using a weighting approach based on output probability information content. Finally, simulation results show that, under the dataset size of 3300 samples, the proposed algorithm achieves a 8.96–33.27% improvement in defect recognition accuracy compared with baseline algorithms, and maintains an accuracy of over 86.5% even under 20% random noise interference by using improved transformer and multi-channel interleaving mechanism, verifying its advantages in accuracy and noise robustness. Full article