Search Results (4,998)

Diet plays a central role in shaping the composition and metabolic activity of the oral microbiota, thereby influencing both oral and systemic health. Disturbances in this delicate host–microbe balance, triggered by dietary factors, smoking, poor oral hygiene, or antibiotic use, can lead to microbial dysbiosis and increase the risk of oral diseases such as periodontitis, as well as chronic systemic disorders including diabetes, cardiovascular disease, Alzheimer’s disease, and certain cancers. Among dietary contaminants, exposure to toxic heavy metals such as cadmium (Cd), lead (Pb), mercury (Hg), nickel (Ni), and arsenic (As) represents an underrecognized modifier of the oral microbial ecosystem. Even at low concentrations, these elements can disrupt microbial diversity, promote inflammation, and impair metabolic homeostasis. Saliva has recently emerged as a promising, non-invasive biofluid for monitoring nutritional status and early metabolic alterations induced by diet and environmental exposures. Salivary biomarkers, including metabolites, trace elements, and microbial signatures, offer potential for assessing the combined effects of diet, microbiota, and toxicant exposure. This review synthesizes current evidence on how diet influences the oral microbiota and modulates susceptibility to heavy metal toxicity. It also examines the potential of salivary biomarkers as integrative indicators of nutritional status and metabolic health, highlights methodological challenges limiting their validation, and outlines future research directions for developing saliva-based tools in personalized nutrition and precision health. Full article

As demand for rare earth elements (REEs) rises and environmental concerns about the extraction of primary resources grow, biological methods for removing these elements have gained significant attention as eco-friendly alternatives. This study assessed the ability of the green macroalga Ulva lactuca to remove europium (Eu) from aqueous solutions, evaluated the cellular partition of this element and investigated the toxicological effects of Eu exposure on its biochemical performance. U. lactuca was exposed to variable concentrations of Eu (ranging from 0.5 to 50 mg/L), and the amount of Eu in both the solution and algal biomass was analyzed after 72 h. The results showed that U. lactuca successfully removed 85 to 95% of Eu at low exposure concentrations (0.5–5.0 mg/L), with removal efficiencies of 75% and 47% at 10 and 50 mg/L, respectively. Europium accumulated in algal biomass in a concentration-dependent manner, reaching up to 22 mg/g dry weight (DW) at 50 mg/L. The distribution of Eu between extracellular and intracellular fractions of U. lactuca demonstrated that at higher concentrations (5.0–50 mg/L), 93–97% of Eu remained bound to the extracellular fraction, whereas intracellular uptake accounted for approximately 20% at the lowest concentration (0.5 mg/L). Biochemical analyses showed significant modulation of antioxidant defenses. Superoxide dismutase activity increased at 10 and 50 mg/L, while catalase and glutathione peroxidase activities were enhanced at lower concentrations (0.5–1.0 mg/L) and inhibited at higher exposures. Lipid peroxidation levels remained similar to controls at most concentrations, with no evidence of severe membrane damage except at the highest Eu level. Overall, the results demonstrate that U. lactuca is an efficient and resilient biological system for Eu removal, combining high sorption capacity with controlled biochemical responses. These findings highlight its potential application in environmentally sustainable remediation strategies for REE-contaminated waters, while also providing insights into Eu toxicity and cellular partitioning mechanisms in marine macroalgae. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

Background/Objectives: Lithium (Li), silicon (Si), and boron (B) are proposed nutritional trace elements with potential roles in metabolic, neurobiological, endocrine, inflammatory, and bone-related processes. This review provides a critical synthesis of data on Li–Si–B, emphasizing (i) physiological and mechanistic pathways, (ii) human clinical relevance, (iii) shared biological domains, and (iv) safety considerations. Methods: A narrative review was conducted across PubMed, Scopus, and Web of Science from inception to January 2025. Predefined search strings targeted dietary, environmental, and supplemental exposures of lithium, silicon, or boron in relation to metabolism, endocrine function, neurobiology, inflammation, bone health, and the gut microbiome. Inclusion criteria required peer-reviewed studies in English. Data extraction followed a structured template, and evidence was stratified into human, animal, cellular, and ecological tiers. Methodological limitations were critically appraised. Results: Li, Si, and B influence overlapping molecular pathways including oxidative stress modulation, mitochondrial stability, inflammatory signaling, endocrine regulation, and epithelial/gut barrier function. Human evidence remains limited: Li is supported primarily by small trials; Si by bone-related observational studies and biomarker-oriented interventions; and B by metabolic, inflammatory, and cognitive studies of modest sample size. Convergence across elements appears in redox control, barrier function, and neuroimmune interactions, but mechanistic synergism remains hypothetical. Conclusions: Although Li–Si–B display compelling mechanistic potential, current human data are insufficient to justify dietary recommendations or supplementation. Considerable research gaps—including exposure assessment, dose–response characterization, toxicity thresholds, and controlled human trials—must be addressed before translation into public health policy. Full article

Accurate wildlife detection in Unmanned Aerial Vehicle (UAV)-captured imagery is crucial for biodiversity conservation, yet it remains challenging due to the visual similarity of species, environmental disturbances, and the small size of target animals. To address these challenges, this paper introduces YOLO-WL, a wildlife detection algorithm specifically designed for UAV-based monitoring. First, a Multi-Scale Dilated Depthwise Separable Convolution (MSDDSC) module, integrated with the C2f-MSDDSC structure, expands the receptive field and enriches semantic representation, enabling reliable discrimination of species with similar appearances. Next, a Multi-Scale Large Kernel Spatial Attention (MLKSA) mechanism adaptively highlights salient animal regions across different spatial scales while suppressing interference from vegetation, terrain, and lighting variations. Finally, a Shallow-Spatial Alignment Path Aggregation Network (SSA-PAN), combined with a Spatial Guidance Fusion (SGF) module, ensures precise alignment and effective fusion of multi-scale shallow features, thereby improving detection accuracy for small and low-resolution targets. Experimental results on the WAID dataset demonstrate that YOLO-WL outperforms existing state-of-the-art (SOTA) methods, achieving 94.2% mAP@0.5 and 58.0% mAP@0.5:0.95. Furthermore, evaluations on the Aerial Sheep and AI-TOD datasets confirm YOLO-WL’s robustness and generalization ability across diverse ecological environments. These findings highlight YOLO-WL as an effective tool for enhancing UAV-based wildlife monitoring and supporting ecological conservation practices. Full article

Nanotechnology is becoming a key tool in plant biotechnology, enabling nanoparticles (NPs) to deliver biomolecules with high precision and to enhance plant and tissue resilience under stress. However, the literature remains fragmented across genetic delivery, in vitro regeneration, stress mitigation, and germplasm cryopreservation, and it still lacks standardized, comparable protocols and robust long-term safety assessments—particularly for NP use in cryogenic workflows. This review critically integrates recent advances in NP-enabled (i) genetic engineering and transformation, (ii) tissue culture and regeneration, (iii) nanofertilization and abiotic stress mitigation, and (iv) cryopreservation of plant germplasm. Across these areas, the most consistent findings indicate that NPs can facilitate targeted transport of DNA, RNA, proteins, and regulatory complexes; modulate oxidative and osmotic stress responses; and improve regeneration performance in recalcitrant species. In cryopreservation, selected nanomaterials act as multifunctional cryoprotective adjuvants by suppressing oxidative injury, stabilizing cellular membranes, and improving post-thaw viability and regrowth of sensitive tissues. At the same time, NP outcomes are highly context-dependent, with efficacy governed by dose, size, and surface chemistry; formulation; plant genotype; and interactions with culture media or vitrification solutions. Evidence of potential phytotoxicity, persistence, and biosafety risks highlights the need for harmonized reporting, mechanistic studies on NP–cell interfaces, and evaluation of environmental fate. Expected outcomes of this review include a consolidated framework linking NP properties to biological endpoints, identification of design principles for application-specific NP selection, and a set of research priorities to accelerate the safe and reproducible translation of nanotechnology into sustainable plant biotechnology and long-term germplasm preservation. Full article

Sustained abiotic stress severely impairs fruit crop growth and development. As plants’ primary environmental sensing organ, fruit tree roots experience disrupted morphogenesis and physiological functions, reducing yield, lowering fruit quality, and threatening orchard ecosystem stability. Abiotic stress is diverse: water deficit from drought, extreme temperature fluctuations, and salinization-induced ion imbalance, heavy metal accumulation, or nutrient disorders. Its complexity requires synergistic and crosstalk regulation of multiple root-specific signaling modules and pathways in root stress perception and transduction. When responding to stress, roots activate hormone, reactive oxygen species (ROS), and calcium ion (Ca²⁺) signaling. These pathways mediate early stress recognition and regulate downstream gene expression and physiological metabolic reprogramming via transcription factors (TFs) and other regulators, determining stress tolerance and adaptability. Using typical abiotic stresses as models, this review outlines the composition, activation mechanisms, specificity, and synergistic effects of root-specific signaling modules/pathways, along with modern biotechnologies for decoding these modules and current research limitations, aiming to reveal the root signal network’s integration mode. Full article

Asparagine synthetase (AS) is a key enzyme in plant nitrogen metabolic network. Beyond its canonical role as a major nitrogen transport and storage molecule, asparagine also serves critical functions in plant immunity and tolerance to environmental stresses. This review systematically summarizes the characteristics of the core AS-mediated asparagine biosynthesis pathway and two other minor pathways in plants. It details the distribution of the AS gene family, protein structure, and evolutionary classification. The mechanisms governing AS expression are analyzed, revealing tissue-specific patterns and precise regulation by nitrogen availability, abiotic stresses, and exogenous hormones, mediated through an interactive network of cis-acting elements and transcription factors. Furthermore, the biological functions of AS are multifaceted: it influences plant biomass and nitrogen use efficiency by regulating nitrogen uptake, transport, and recycling during growth and development; it contributes to abiotic stress tolerance by synthesizing asparagine to maintain cellular osmotic balance and scavenge reactive oxygen species; and it indirectly enhances antibacterial and antiviral capacity by activating the SA signaling pathway and modulating programmed cell death. Current knowledge gaps remain regarding the crosstalk between AS-mediated signaling pathways, the upstream transcriptional regulatory network, and the balance between nitrogen utilization and disease resistance in crop breeding. Future research aimed at addressing these questions will provide a theoretical foundation and molecular targets for improving crop nitrogen use efficiency and breeding resistant cultivars. Full article

Background/Objectives: Sensory deprivation, defined as a reduction or absence of external sensory input across one or more modalities, has long been investigated in extreme and experimental settings. More recently, its relevance has expanded to clinical contexts and environmental conditions. The present narrative review aims to synthesize current evidence on the neurobiological mechanisms, psychological effects, and clinical implications of sensory deprivation, with particular attention to its dual role as both a risk factor and, under controlled conditions, a potential therapeutic tool. Methods: A narrative literature search was conducted using PubMed, Scopus, and PsycINFO, covering studies published up to August 2025. Search terms included sensory deprivation, neuroplasticity, neurotransmitters, HPA axis, neuro-inflammation, circadian rhythms, psychopathology, extreme environments, and spaceflight. Preclinical and clinical studies examining biological, cognitive, and psychological consequences of reduced sensory stimulation were included. Data were synthesized thematically without quantitative meta-analysis. Results: Evidence indicates that sensory deprivation induces widespread neurobiological adaptations involving neurotransmitter systems (particularly dopaminergic pathways), dysregulation of the hypothalamic–pituitary–adrenal axis, neuroimmune activation, circadian rhythm disruption, and structural and functional brain changes, notably affecting the hippocampus. These alterations are associated with increased vulnerability to depression, anxiety, hallucinations, dissociative symptoms, and cognitive impairment. Duration, voluntariness, and individual differences (e.g., baseline vulnerability/resilience, trait anxiety, and prior psychiatric history) critically modulate outcomes. However, short-term and voluntary sensory restriction, such as Floatation-REST, may promote relaxation and emotional regulation under specific conditions. Conclusions: Sensory deprivation exerts complex, context-dependent effects on brain function and mental health. Duration, individual vulnerability, and voluntariness critically modulate outcomes. Understanding these mechanisms is increasingly relevant for clinical practice and for developing preventive strategies in extreme environments, including future long-duration space missions. Full article

Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting for both environmental loads and the reaction force generated during water cannon operation. Unlike conventional DP architectures in which DP control and thrust allocation are treated as separate modules, the proposed framework integrates both functions within a unified MPC formulation, enabling real-time optimization under actuator constraints. Environmental loads are modeled by incorporating nonlinear second-order wave drift effects, while nonlinear rudder–propeller interaction forces are derived through computational fluid dynamics (CFD) analysis and embedded in a control-oriented dynamic model. This modeling approach allows operational constraints, including rudder angle limits and propeller thrust saturation, to be explicitly considered in the control formulation. Simulation results demonstrate that the proposed MPC-based DP system achieves improved station-keeping accuracy, enhanced stability, and increased robustness against combined environmental disturbances and water cannon reaction forces, compared to a conventional PID controller. Full article

Ultraviolet (UV) radiation is a significant environmental stressor that exerts profound impacts on insect physiology, behaviour and survival. Although some insects can use UV light for spatial orientation and navigation, it can induce DNA damage, oxidative stress, and impair critical biological functions, ultimately reducing ecological fitness. Sclerodermus alternatusi Yang (Hymenoptera: Bethylidae) is a dominant ectoparasitoid of the early instar larvae of Monochamus alternatus and plays a key role in the biological control of this pest in forestry systems; however, it faces intense UV exposure in the field environment. Despite its ecological importance, the molecular mechanisms underlying its responses to UV-induced stress remain poorly understood. In this study, newly emerged adult wasps (within 24 h post-eclosion) were exposed to UVA (365 nm) and UVC (253.7 nm) radiation for 9 h under controlled laboratory conditions. Total RNA was extracted from treated and control individuals for transcriptomic analysis using RNA-Seq. A total of 505 differentially expressed genes (DEGs) were identified; gene ontology enrichment analysis revealed that UVA exposure significantly upregulated genes involved in cellular respiration and oxidative phosphorylation, suggesting an enhanced metabolic response. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that UV stress modulates energy metabolism through the activation of oxidative phosphorylation and thermogenesis-related pathways, highlighting the reallocation of energy resources in response to UV-induced stress. To validate the RNA-Seq data, four representative DEGs were selected for quantitative real-time PCR (RT-qPCR) analysis. The qPCR results were consistent with the transcriptomic trends, confirming the reliability of the sequencing data. Collectively, this study provides a comprehensive overview of the molecular response mechanisms of S. alternatusi to UV stress, offering novel insights into its environmental adaptability and laying a theoretical foundation for its application in biological pest control under field conditions. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

The article presents the current state of knowledge on genetic modifiers of ovarian cancer risk in women carrying pathogenic variants (PVs) in the BRCA1 and BRCA2 genes, which are major contributors to hereditary susceptibility to this malignancy. Although PV carriers have high disease penetrance (BRCA1: ~40% and BRCA2: 11–27%), substantial variability in individual risk is observed, suggesting the influence of additional genetic variants. Background: Ovarian cancer is characterized by late detection and high mortality, and a significant portion of risk among BRCA1/2 carriers is shaped by reproductive and environmental factors as well as genetic modifiers. The article emphasizes that carriers of the same BRCA PV can exhibit markedly different risk levels depending on additional variants that modulate key biological processes, such as DNA repair, cell cycle regulation, and apoptosis. Methods: A systematic literature search covering the years 1996–2025 was conducted in the PubMed database. Initially, 734 publications were identified; after removing duplicates, thematically irrelevant articles, non-full-text papers, and studies not meeting the inclusion criteria, 47 articles were included in the review. These studies covered candidate gene analyses, GWAS, and data from the CIMBA consortium, which enables the examination of large cohorts of PV carriers. Results: The review identified numerous variants associated with increased or decreased ovarian cancer risk in BRCA1 carriers, including the following: OGG1, DR4, MDM2, CYP2A7, CASP8, ITGB3, HRAS1, TRIM61, and MTHFR. The reviewed studies also identified both protective and risk-increasing variants among BRCA2 PV carriers: UNG, TDG, and PARP2, and haplotypes in ATM, BRIP1, BARD1, MRE11, RAD51, and 9p22.2. The analysis identified 11 variants affecting both BRCA1 and BRCA2 carriers, most of which increase risk, including the following: IRS1, RSPO1, SYNPO2, BABAM1, MRPL34, PLEKHM1, and TIPARP. Protective variants include BNC2 and LINC00824. The only SNP reaching genome-wide significance (p < 5 × 10⁻⁸) was in BNC2. Conclusions: The article summarizes the growing number of genetic modifiers of ovarian cancer risk among BRCA1/2 carriers and highlights their potential to improve individualized risk assessment, enhance patient stratification, support personalized prevention and surveillance strategies, deepen the understanding of disease biology, and identify potential therapeutic targets. Full article

Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM’s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount. Full article