With coral reefs increasingly threatened by rapid environmental changes, understanding genetic diversity at microgeographic scale is critical for assessing their capacity to respond to local stress regimes. Theory for continuous populations predicts that brooding corals with restricted dispersal should exhibit fine-scale genetic structure and isolation-by-distance, yet such patterns remain poorly resolved in marginal and environmentally extreme reef ecosystems. Here, we investigated the genetic structure of the catch bowl coral, Isopora cf. palifera, across 11 sites within ~ 14 km in Kenting National Park (KNP), southern Taiwan, a reefscape characterized by strong small-scale environmental heterogeneity, including chronic thermal influence from a nuclear power plant and tidally driven upwelling. We genotyped 466 colonies (six microsatellite loci yielding 302 unique multilocus genotypes) and sequenced nuclear PaxC 46/47-intron from 322 colonies of I. cf. palifera. Microsatellite data revealed strong genetic structure (K = 2, K = 5): principal coordinate analyses identified four geographic groupings, and Bayesian clustering (STRUCTURE) supported two major clusters separating Nanwan (plus Tantzei Bay) from the remaining coastal sites, with one site (Shiaowan) showing admixture. The PaxC marker resolved ten haplotypes, with H1 widespread, H2 concentrated along Nanwan, and H3 dominant at thermally influenced sites near the nuclear power plant outfall. Overall, populations showed high site differentiation, significant isolation-by-distance, and high self-recruitment (68-92%), indicating limited effective dispersal. A temporal comparison (2000-2015) at Tantzei Bay indicated stable genetic structure through time despite repeated regional disturbances. Generalized estimating equation (GEE) models showed that site-level seawater temperature was positively associated with both host haplotype composition (GEE; coefficient = 0.0479, p < 0.001) and Symbiodiniaceae genera (GEE; coefficient = 0.0462, p < 0.001, symbiont data from a previous work in KNP), suggesting non-random host-symbiont-environment associations at microgeographic scale. Together, these results indicate that I. cf. palifera in KNP exhibits pronounced fine-scale genetic structure consistent with restricted dispersal and possible microgeographic adaptation of the holobiont to local thermal regimes. While such structuring may enhance local resilience by maintaining diverse, site-specific host-symbiont combinations, it also implies limited scope for rescue via gene flow if future warming pushes populations beyond their adapted tolerances. Our findings underscore the importance of accounting for microgeographic genetic structure and local adaptation when designing management and conservation strategies for reefscape such as those in KNP.
Persistent and mobile chemicals (PMs) threaten groundwater quality and drinking water safety, yet many remain undetected because analytical methods insufficiently address highly polar and ionic substances, while regulatory frameworks lack monitoring requirements for these compound classes. Here, we developed a supercritical fluid chromatography-high-resolution mass spectrometry-based smart-screen approach that integrates three key prioritisation strategies: (i) sampling site prioritisation, (ii) suspect-level prioritisation through tiered suspect lists, and (iii) candidate prioritisation using stepwise scoring. Additionally, the method achieved the sensitive identification and reliable quantification of PMs in groundwater, with a median limit of quantification of 6.8 ng/L, stable recoveries (75%), and low matrix effects (-12%) across diverse groundwater types. Prioritisation reduced 599 groundwater wells to 10 representative sites, yielding an 8.6-fold reduction in analytical workload while maintaining chemical diversity. The tiered suspect lists and stepwise scoring strategies improved confirmation efficiency and facilitated the detection of substances of high environmental relevance. Collectively, 34 PMs were detected across six substance groups including polar per- and polyfluoroalkyl substances, polyfluorinated inorganic species, transformation products, and amide or ether solvents at concentrations of 0.1-22,300 ng/L. Among these, 16 substances were newly detected in ambient groundwater and four were reported for the first time in any environmental compartment. Several substances (e.g. 2-phenylpropane-2-sulphonic acid) are not classified as persistent under EU regulation on registration, evaluation, authorisation and restriction of chemicals (REACH) yet occur ubiquitously in groundwater, suggesting an underestimation of PMs under aquifer conditions. These findings advance monitoring of PMs, supporting their regulation for groundwater and drinking water protection.
Rare diseases affect small, dispersed populations and are often studied through multisite designs where equity-relevant demographic data are essential for inclusive recruitment and accurate interpretation. This study examined how sociodemographic variables are collected and reported in rare disease research and evaluated their alignment with the PROGRESS-Plus framework, which outlines Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, social capital, and additional "Plus" factors such as age and disability status. A systematic review of peer-reviewed articles was conducted alongside an environmental scan of demographic instruments from governmental, health-system, academic, and rare disease organizations. Screening and extraction coded variables as reported, indirectly derivable, or not reported and compared them with established standards. Of 647 records identified, 37 met inclusion criteria. Reporting was dominated by age and sex, while most other equity-relevant variables including gender identity, sexual orientation, race/ethnicity, distinctions-based Indigenous identity, socioeconomic position, language, migration, disability/function, religion, occupation, and social capital, were inconsistently captured. Environmental scan instruments were more comprehensive, revealing a capture-to-reporting gap. Demographic reporting in rare disease research is heterogeneous and insufficient for equity-focused analyses. A concise, standards-aligned sociodemographic dataset is needed to improve transparency, comparability, and detection of inequities across rare disease populations.
Catalysis was an effective method for uranium recovery and environmental remediation. However, the weak flexoelectric response, despite being universal in dielectric materials, greatly limited its appeal for research and catalytic applications. Here, we proposed a strategy to enhance the flexoelectric response by bridging inorganic chains with metal-organic chains within the structure. The resulting hybrid material, Co[C4H4N2]V2O6, demonstrated excellent uranyl removal performance, surpassing that of state-of-the-art piezocatalysts. Co[C4H4N2]V2O6 showed flexocatalytic uranyl activity across a broad pH range and under high-salinity conditions. Under a dynamic experimental setup, Co[C4H4N2]V2O6 showed strong potential for practical flexocatalytic applications. Co[C4H4N2]V2O6 could efficiently separate uranyl in contaminated potable water, reducing the uranium concentration (~2.0 ppm) to below the drinking water standard (30 ppb). It could also lower the uranium concentration (~5.6 ppm) in mining wastewater to below the discharge limit (300 ppb). Its intrinsic anisotropic mechanical properties and cantilever-like morphology endowed high deformability, which, together with a large dielectric constant, enhanced its flexoelectric polarization. The revealed flexocatalytic mechanism confirmed that uranyl was converted into insoluble (UO2)O2·2H2O by active species generated through dynamic polarization. This work provided a promising avenue for the design of advanced flexocatalysts and offered an effective strategy for uranium recovery and environmental remediation.
Human biomonitoring (HBM) is crucial for evaluating exposure to diet-related contaminants, whose effects may pose substantial health risks. Saliva is recognized as a promising non-invasive biological matrix due to its ease of collection and potential to reflect external and systemic exposure. However, suitability for monitoring dietary hazardous compounds remains uncertain. To assess the potential of saliva as a biomonitoring matrix for diet-related contaminants, identify compounds with robust diet-related associations, and highlight knowledge gaps. A systematic literature review was conducted to screen over 500 diet-related contaminants analyzed in saliva. Detailed information was extracted only for contaminants quantitatively measured in saliva, including concentration ranges, sample sizes, and analytical methods. Evidence of correlations with systemic concentrations, exposure pathways, and individual or lifestyle factors was compiled into a FAIR database to provide an integrated evaluation of saliva's biomonitoring potential. Only a limited subset of contaminant groups, including nitrite/nitrate, heavy metals, bisphenols, polycyclic aromatic hydrocarbons (PAHs), biogenic amines, pesticides, advanced glycation end products (AGEs), perchlorate, microplastics (MPs), parabens and phthalates, have been quantitatively measured in saliva. Compounds such as nitrate, arsenic, AGEs, pesticides and perchlorate demonstrate moderate to strong correlations between salivary and systemic levels, supporting saliva's potential to estimate exposure. Conversely, substances like PAHs, MPs, phthalates and parabens generally show weak or no correlation, reflecting recent or localised exposures rather than cumulative burden. Salivary composition is influenced by intrinsic and extrinsic factors, including diet, oral microbiota, physiology, and sampling conditions, resulting in high interindividual variability. Despite challenges, low salivary concentrations and lack of standardized collection protocols, saliva offers advantages for biomonitoring vulnerable populations, such as children and pregnant women. Harmonized collection procedures, validated sensitive methods, predictive models accounting for variability and exposure context, could establish saliva as a reliable complementary or alternative matrix for assessing human exposure to dietary and environmental contaminants. This systematic review synthesizes findings from 104 studies, covering over 500 diet-related contaminants measured in saliva, and compiles them into a FAIR database, providing the most comprehensive resource to date for saliva-based biomonitoring. Compounds such as nitrate, arsenic, advanced glycation end-products (AGEs), pesticides, and perchlorate show meaningful correlations with systemic levels, supporting saliva's potential as a non-invasive matrix for assessing human exposure. To fully realize saliva's potential, standardized collection protocols, validated analytical methods, and predictive models that account for interindividual variability and exposure context are urgently needed, enabling more accurate and ethical monitoring of vulnerable populations.
As the primary living environment for disabled older adults, families play a crucial role in disease prevention and maintaining their health. However, research has found that both disabled older adults and their family members experience numerous physiological, psychological, and social adaptation problems when adjusting to the changes brought by disability, severely impacting the overall health status of the family. Therefore, guided by the ERG (Existence-Relatedness-Growth) theory, this study aims to understand the family health needs of families with disabled older adults in the community, providing a basis for improving the health level of these families and developing targeted intervention programs. From December 2024 to February 2025, this study employed purposive and snowball sampling to select 12 pairs of disabled older adults and their primary caregivers from communities under the jurisdiction of Zhengzhou City, Henan Province for semi-structured interviews. Thematic analysis was applied to organize and analyze the interview data. Deductive analysis indicated that the famliy health needs of families with disabled older adults in the community can be summarized into the following three themes: existence needs (daily living needs, economic support needs, environmental modification needs), relatedness needs (family communication needs, social resource connection needs, social participation needs), and growth needs (autonomy and dignity maintenance needs, family development needs, demand for technology-enabled solutions). The results show that the family health needs of families with disabled older adults in the community are unique and diverse. Community health workers and social workers can develop and implement effective strategies based on the different levels of family needs to promote the health level of families with disabled older adults and improve the overall quality of life of these families.
The increasing frequency of freshwater cyanobacterial blooms has emerged as a critical ecological and environmental concern, yet long-term time series data documenting such blooms remain scarce. This study presents a 13-year dataset (2010-2022) from two adjacent subtropical reservoirs (Shidou and Bantou) in Xiamen, Fujian Province, Southeast China. It provides a monthly and quarterly overview of 20 physicochemical parameters (348 samples), microscope-based phytoplankton (348 samples), and DNA sequence-based data for bacteria (342 samples) and microeukaryotes (348 samples). The dataset highlights recurrent cyanobacterial blooms dominated by Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii). This long-term dataset serves as a valuable resource for investigating, predicting, and controlling cyanobacterial blooms, and will support efforts in biodiversity forecasting, ecological restoration, and targeted management of freshwater ecosystems.
Air pollution is a major environmental and public health challenge in Greater Cairo due to rapid urbanization, intense traffic activity, and recurrent regional dust intrusions. This study presents a data driven analytical framework for identifying and interpreting recurring air pollution regimes in the city. The framework combines unsupervised K-means clustering with supervised Decision Tree (DT) and Random Forest (RF) models using atmospheric reanalysis data derived from the Copernicus Atmosphere Monitoring Service (CAMS) for the period 2023-2024. The optimized K-means model identified four distinct pollution regimes. Low pollution conditions accounted for approximately 75.1% of the analyzed period, whereas higher pollution regimes were mainly associated with traffic related emissions and episodic dust events. To assess the separability of the identified regimes, DT and RF classifiers were trained to predict cluster membership. The optimized Decision Tree achieved an accuracy of 93.10%, while the Random Forest model showed better classification performance, reaching a maximum accuracy of 97.49%, with a practical optimum of 97.43% obtained using 300 trees. Feature importance analysis showed that NO₂ was the dominant variable for distinguishing traffic related pollution regimes, whereas PM₁₀ played a key role in identifying dust related events. Overall, the findings indicate that integrating clustering with tree based classification provides an interpretable and effective approach for characterizing urban air pollution patterns. The resulting framework may support regime based air quality interpretation and targeted management strategies in Greater Cairo.
We compiled and verified a comprehensive inventory dataset of communication tower infrastructure across the range of the greater sage-grouse (Centrocercus urophasianus) and Gunnison sage-grouse (Centrocercus minimus), two species of conservation concern that are viewed as ecosystem health indicators for the entire sagebrush biome within the United States. Our dataset includes all known towers with emphasis on validating construction year and month for towers built between 1990-2023. The annual spatial time series format of the data allows users to visualize, assess, and download tower locations and duration (including dates of construction and dismantlement) across the sagebrush biome of the western U.S. Tower data were acquired from publicly available infrastructure databases and records were filtered to include communication tower structures within the area of interest. Data records were validated and checked for accuracy with high resolution aerial and satellite imagery, and a subset were verified during field visits. The final filtered dataset comprises 4,322 tower sites, of which 3,528 tower site locations were verified via satellite imagery or field visits, and 794 were unverified tower sites (tower presence could not be confirmed via satellite imagery). Each tower record includes geographic coordinates, structure height, estimated date of construction, number of towers at each site, and, if applicable, date of dismantlement. The data product closes spatiotemporal gaps and resolves discrepancies present in other public versions of similar data and can be used in ecological research, infrastructure planning/siting/permitting, decision support tools for biological or landscape management, environmental assessments, or general use pertaining to the historic and current locations of communication infrastructure across sagebrush ecosystems.
Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis framework that effectively combines statistical independence modeling and geometric contour evolution to achieve high-precision motion detection and segmentation. In the first phase, an enhanced Fast Independent Component Analysis (Fast-ICA) algorithm is employed to perform statistical decomposition of video sequences, exploiting temporal independence to distinguish moving foregrounds from static backgrounds. This process generates an initial motion mask with strong robustness to illumination variation and noise artifacts. In the second phase, a hybrid level set segmentation model integrating the global Chan-Vese formulation and a locally adaptive Yezzi-based energy function refines object boundaries through an adaptive energy minimization process. A stabilization term and a self-regulating convergence criterion are further incorporated to ensure contour smoothness, numerical stability, and resilience to topological changes. Comprehensive experiments conducted on the CDNet-2014 benchmark dataset demonstrate that the proposed method achieves an average recall of 0.9613, precision of 0.9089, and F-measure of 0.9310, outperforming several state-of-the-art supervised, semi-supervised and unsupervised background subtraction algorithms. The proposed Fast-ICA-Level Set fusion framework thus provides a robust, adaptive, and computationally efficient solution for real-world intelligent surveillance and autonomous visual monitoring applications.
This study focuses on evaluating the thermoregulatory performance of a graphene-based electric heating cape and determining its optimal temperature for use in cool indoor environments during winter. Through controlled experiments with 30 participants in a climate chamber maintained at 15.5 °C, five heating conditions (no heating, 35 °C, 40 °C, 45 °C, and 50 °C) were systematically tested. Skin temperature and heart rate were continuously monitored, and subjective thermal sensation and comfort votes were collected using 7-point scales. Results demonstrated that the electric heating cape significantly improved thermal comfort, with 40 °C identified as the optimal temperature setpoint: overall thermal sensation shifted from slightly cool to slightly warm, and overall thermal comfort state attained 'slightly comfortable' level on the adaptive comfort scale. Repeated-measures ANOVA revealed that heating temperature had a highly significant effect on both overall thermal sensation and overall thermal comfort. Post-hoc tests identified 40 °C as the optimal temperature setpoint under these environmental conditions (15.5 °C). At this temperature, the overall thermal sensation vote improved significantly from - 1.23 (no heating) to -0.13 (p < 0.01), approaching a neutral sensation, while overall thermal comfort increased significantly from - 1.08 to 0.35, reaching a "slightly comfortable" level. The most significant improvements were observed in the abdomen and lumbar regions. While skin temperature showed a positive correlation with thermal perception, heart rate remained stable (± 5 bpm), indicating a low physiological burden. Marked individual differences in temperature preference underscore the importance of personalized thermal regulation. This study provides empirical evidence to guide the application and control of localized heating devices in cool indoor office settings during winter.
The development of stable, environmentally benign, and high-performance perovskite solar cells (PSCs) has increasingly focused on innovative inorganic absorber materials. In this study, we conduct a detailed evaluation of the optoelectronic and mechanical properties of Ca3AsBr3, a promising non-toxic halide perovskite, using density functional theory (DFT) alongside SCAPS-1D simulations. The DFT results indicate that Ca3AsBr3 possesses a direct bandgap of 1.66 eV, along with good mechanical stability and strong optical absorption, making it well-suited for photovoltaic applications. To further investigate device performance, four electron transport layers (ETLs)-WS2, SnS2, CdS, and TiO2 were incorporated into HTL-free FTO/ETL/Ca3AsBr3/Au architecture, allowing analysis of energy band alignment, defect tolerance, and overall efficiency. Among these configurations, the WS₂-based device demonstrated superior performance, achieving a power conversion efficiency (PCE) of 20.50%, with an open-circuit voltage (Voc) of 1.165 V, a short-circuit current density (Jsc) of 20.55 mA/cm², and a fill factor (FF) of 85.64%. Further simulation results highlight that an optimal absorber thickness of 1200 nm, along with reduced bulk and interface defect densities (≤ 10¹⁵ cm⁻³ and ≤ 10¹³ cm⁻²), plays a crucial role in minimizing non-radiative recombination losses and improving charge carrier collection. Overall, this work identifies Ca3AsBr3 as a viable eco-friendly absorber material and emphasizes the importance of ETL optimization in achieving efficient, stable, and scalable PSC devices.
The transition to sustainable agriculture requires technologies that simultaneously enhance crop yields and reduce environmental impacts. Solar-driven nitrate valorization, when coupled with CO2 capture from industrial flue gas, presents a promising dual strategy for producing high-value fertilizers while mitigating carbon emissions. However, its practical implementation is hindered by two interrelated challenges: (i) the intermittent nature of solar irradiation and (ii) the competitive hydrogen evolution reaction (HER), which severely compromises Faradaic efficiency (FE) of desired nitrogenous products. Here, we address these challenges by designing a heterogeneous CuPd electrocatalyst featuring an amorphous/crystalline heterojunction. This catalyst suppresses HER across a broad potential window (-0.4 to -1.4 V), maintaining >80% FE(ammonia) for >100 h. The catalytic robustness enables stable solar-powered electrolysis even under low irradiation (0.4 sun), achieving >70% FE(ammonia) and 6% solar-to-fuel conversion efficiency, while catholyte simultaneously captures CO2 at a rate of 6-20 mg h-1. Techno-economic analysis demonstrates cost competitiveness against biological counterparts. When applied to plant cultivation, this artificial photosynthesis system boosts solar-to-biomass conversion efficiency by 3.5-fold compared to natural photosynthesis. By unifying solar energy harvesting, waste nitrate reduction, and carbon sequestration, our work provides a scalable blueprint for a closed-loop agrochemical ecosystem and advanced catalyst design for intermittent renewable-powered electrosynthesis.
The presence of synthetic dyes such as Methylene Blue (MB) and Methyl Orange (MO) in water sources presents a significant environmental issue, attributed to their stability and toxic properties. This study presents the fabrication and evaluation of a multifunctional ZIF-8/ZnO/Activated Carbon/Nanocellulose composite for its photocatalytic efficiency under optimized conditions. The characterization results confirmed the development of a hierarchical porous structure characterized by an increased surface area, enhanced light absorption, and improved charge separation. Photocatalytic degradation tests demonstrated that the composite attained maximum efficiencies of 91.8% for MB and 87.4% for MO under optimal conditions of pH 7, a catalyst dose of 0.3 g/L, a temperature of 30 °C, and 60 min of UV irradiation. The adsorption behavior conformed to the Langmuir model, exhibiting high monolayer capacities of qmax (53.0 mg/g for MB and 50.1 mg/g for MO), which indicates a strong affinity between the composite and the dye molecules. Kinetic analyses indicated a pseudo-second-order mechanism, and thermodynamic findings validated the spontaneous and exothermic characteristics of the process. The composite exhibited strong stability across five cycles, demonstrating negligible efficiency loss. The interaction between ZIF-8, ZnO, activated carbon, and nanocellulose markedly improved adsorption, charge transfer, and radical generation, rendering this composite an efficient material for sustainable wastewater treatment .
The growing worldwide population has increased the use of electric arc furnaces (EAF), resulting in a surge of EAF slag and a huge environmental concern. EAF slag's complex physical qualities have a considerable impact on concrete's mechanical performance, mainly its compressive strength (CS). This study introduces a novel framework for forecasting the CS of EAF slag concrete that uses advanced machine learning models such as Extreme Gradient Boosting (XGB), AdaBoost (ADB), Random Forest (RF), Hybrid XGB-RF, and Hybrid XGB-ADB. A full dataset of 730 samples was meticulously created, containing essential input parameters such as binders, aggregates, and other necessary variables, with CS as the desired outcome. Based on the findings, the XGB model showed highest accuracy, with an test R2 of 0.951, MAPE of 1.128, and RMSE of 1.393 MPa, indicating its potential for dependable performance forecasting. In addition, the hybrid XGB-RF model also demonstrated strong predictive accuracy, achieving an R2 value of 0.947 during the testing phase. Furthermore, explainability tools such as SHapley Additive ExPlanations (SHAP) and partial dependence curves (PDCs) identified the curing period and content of cement as the most influential factors to predict the CS of EAF slag concrete. The methodologies and outcomes of this study will help to reduce reliance on resource-intensive experimental methods, pave the way for efficient, precise, and ecologically conscientious concrete design.
Protecting and improving surface water quality is contingent upon understanding the trends and spatial patterns in physical, biological, and chemical conditions and their underlying drivers. This requires observational data, spanning a diverse range of water quality constituents, coupled with contextual environmental data. Here we present the first global-scale integration of stream water quality into large-sample hydrology (named Caravan-Qual), combining ~96 million observations of 100 constituents with streamflow measurements, meteorological forcing and catchment attributes covering the period 1980-2025. We envisage that the dataset can facilitate a diverse range of empirical analyses (e.g. spatio-temporal analysis across diverse regions, quantification of pollutant loadings and exports, concentration-discharge analysis), in addition to supporting development and evaluation of process-based and data-driven models for water quality prediction and management.
Repetitive noxious stimulation can increase perceived pain intensity, a phenomenon known as Temporal Summation of Pain (TSP), thought to reflect central sensitization via neuronal "wind-up" in the spinal cord. As neuronal wind-up occurs only at stimulation frequencies above 0.2 Hz, we have tested whether TSP also appears at two different frequencies using our recently developed TSP protocol in healthy volunteers. In a randomized crossover design, 30 healthy male participants (27±4 years) underwent two experimental sessions involving 90 repetitive heat stimuli applied to the forearm at individually determined pain tolerance temperatures. Stimuli were delivered using a thermode at either 0.4 or 0.15 Hz. Pain intensity was rated using a computerized visual analog scale (0-100). TSP was assessed via linear mixed-effects model (LMM), with pain intensity as the dependent variable. All participants finished the study. LMM revealed a significant main effect of stimulation frequency (F 1, 540=14.20, p<0.001), indicating TSP. Pain intensity was higher at 0.4 Hz compared with 0.15 Hz (β=14.77, 95 % confidence intervals (CI) 6.87-22.68, p<0.001). The presence of TSP at 0.4 Hz but not at 0.15 Hz aligns with previous findings on neuronal wind-up, supporting its reliance to central sensitization. These findings enhance our understanding of the physiological basis of TSP and offer a robust platform for future investigations into pain modulation and therapeutic intervention strategies.
This study introduces two recently developed bio-inspired metaheuristic algorithms, Artificial Protozoa Optimizer (APO, 2024) and Dung Beetle Optimizer (DBO, 2023), into long short-term memory (LSTM) networks for monthly pan evaporation prediction under limited climatic data. Representing the first application of these algorithms to hydrological modeling, these models integrate APO and DBO into the LSTM framework to optimize hyperparameters and enhance accuracy and generalization. Their performance is benchmarked against the standard LSTM and two established hybrids, LSTM-GWO and LSTM-HHO. A case study in southeast China using 40 years of data from two stations shows that both LSTM-APO and LSTM-DBO consistently outperform the alternatives across three data-splitting scenarios (M1, M2, M3). For the best test case (M3, Station 1), LSTM-APO reduced RMSE and MAE by 46.5% and 47.2%, respectively, compared to the best LSTM, while in Station 2 (M2) it achieved reductions of 43.9% and 40.7%, with gains of about 9% in R² and NSE. LSTM-DBO also yielded notable improvements, reducing errors by 20-30% and demonstrating robust predictive stability. Visual analyses confirm that LSTM-APO provides predictions closely aligned with observations, with LSTM-DBO performing comparably well. These findings highlight the role of metaheuristic optimization in boosting LSTM performance for nonlinear evaporation processes with sparse inputs. Overall, APO- and DBO-based hybrids show strong promise for reliable pan evaporation forecasting. Future research should assess their real-time applicability and transferability across diverse climates.
Controlling peroxymonosulfate (PMS) activation at the atomic scale is crucial for steering reactive oxygen species (ROS) pathways, yet design principles that selectively bias PMS chemistry toward interfacial radical states remain elusive. Herein, we report an asymmetric Fe-Te dual-atom pair (FeTe DAs/NC), in which a p-block metalloid electronically modulates an Fe center through pronounced p-d hybridization. This atomic asymmetry reconstructs the local electronic structure, strengthens PMS binding, and directs PMS activation toward the generation and retention of surface-bound hydroxyl radicals. Mechanistic studies reveal surface-bound hydroxyl radicals (•OH) as the dominant ROS, while singlet oxygen (1O2) plays a secondary role. As a result, FeTe DAs/NC achieves complete degradation of carbamazepine within 60 min, markedly outperforming Fe or Te single-atom analogs, together with excellent reactivity and cycling stability across different water matrices and pollutant systems. This work establishes atomic-scale asymmetry and metal-metalloid p-d coupling as an effective strategy for steering PMS activation chemistry toward long-lived interfacial radical states.
Climate-driven heat stress disrupts metabolic homeostasis in livestock, yet the molecular mechanisms underlying adaptive responses remain poorly understood. Here, we integrated newly generated plasma metabolomic data from 111 heat-stressed cows with previously published whole-genome sequencing datasets from the same animals, identifying 30 metabolic markers and 27 copy number variations (CNVs) associated with 25 candidate genes involved in the regulation of these metabolites. Notably, a CNV hotspot encompassing CIITA emerged as a key pleiotropic locus strongly associated with acylcarnitine levels, body weight, and rectal temperature. Heat exposure suppressed CIITA expression in skeletal muscle, correlating with impaired myogenic development. We demonstrate that CIITA overexpression in vitro induces coordinated remodeling of cell cycle-related gene expression and partially alleviates heat-induced inhibition of myoblast proliferation. Moreover, CIITA overexpression markedly suppresses long-chain fatty acid β-oxidation and mitochondrial electron transport activity, accompanied by reduced adenosine triphosphate production, suggesting that CIITA may limit metabolic heat generation by constraining mitochondrial metabolic flux. Overall, these findings position CIITA as a central integrative regulator linking immune function, energy metabolism, and cell proliferation during bovine adaptation to heat stress, and highlight a potential genetic target for improving thermotolerance in livestock.