Search Results (7,747)

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) remains a major cause of both community-onset and hospital-acquired infections, yet longitudinal data from Latin American hospitals spanning the COVID-19 pandemic are scarce. We characterized temporal trends, seasonality, and the impact of the COVID-19 pandemic on MRSA prevalence and incidence density among clinical S. aureus isolates at a tertiary-care hospital in western Mexico over 9.5 years. Methods: We analyzed 6625 non-duplicate clinical S. aureus isolates (6609 with valid resistance data) from June 2016 to December 2025. Temporal trends were assessed using Mann–Kendall tests, Theil–Sen estimation, and binomial generalized linear models. Seasonality was evaluated through STL decomposition, generalized additive models, and Fourier analysis. An interrupted time series (ITS) model with GLS-AR(1) and Newey–West corrections compared three COVID-19 phases: pre-pandemic (2016–2020), high viral circulation (2020–2022), and post-peak stabilization (2022–2025). Exposure-adjusted incidence densities (per 1000 patient-days) were analyzed in parallel. Results: MRSA prevalence declined from 28.1% pre-pandemic to 14.0% post-peak (Mann–Kendall z = −9.03, p < 0.001; OR = 0.85 per year, 95% CI: 0.829–0.871). MRSA incidence density decreased by 50%, from 1.27 to 0.63 per 1000 patient-days, while aggregate S. aureus incidence density remained stable (z = −0.17, p = 0.868). The ITS joint Wald test confirmed a significant cumulative shift in MRSA trajectory post-pandemic (p = 0.019 counts; p = 0.012 incidence density), with a significant post-peak level drop (p = 0.008). S. aureus exhibited moderate seasonality peaking in May–July (GAM edf = 7.26, p < 0.001), whereas MRSA showed only marginal seasonal variation. Conclusions: MRSA declined markedly across the study period, with the steepest reduction following the Omicron peak. The decline persisted after adjustment for pandemic-related fluctuations in hospital volume, supporting periodic reassessment of empiric anti-MRSA prescribing policies in similar settings. Full article

Accumulating evidence over the past decades has established that people conceptualize elapsing time along a sagittal mental timeline (MTL). A recent study discovered that representations of autobiographical memories (AMs) also proceed along a sagittal back-to-front MTL, consistent with the direction of sensorimotor experiences such as walking or running. The present investigation attempted to clarify and extend that work by exploring if the back-to-front axis for the temporal organization of AMs is a universal phenomenon across linguistic communities. An experiment that recruited Mandarin speakers as participants was conducted. The experimental task asked participants to categorize personal events retrieved from their AMs as past- or future-related via distinct key arrangements that corresponded to a back-to-front and a front-to-back line respectively. Results show that cross-linguistic variations may exist in the directionalities of MTL underlying AM processes. Contrary to the back-to-front MTL observed among Italian speakers in the aforementioned research, Mandarin speakers conceived of AM progression as oriented from front to back. The findings of the present study provide preliminary evidence to validate the predictive power of spatiotemporal metaphors rather than sensorimotor experience in shaping a sagittal MTL for AM representations, especially when the two forces contradict each other in terms of spatial directions. Full article

The diurnal sea surface temperature variation (DSV) influences atmospheric convection and precipitation through air–sea interactions in the equatorial Pacific. Deep learning-based DSV forecasting has been less explored compared to traditional methods, presenting the potential for a substantial leap in forecast accuracy. In this study, a forecast model is developed for 24 h DSV in the equatorial Pacific using an improved coupled Transformer-CNN (CoTCN-DSV) by incorporating a new loss function including maximal and minimal values. The CoTCN-DSV forecasts diurnal variation in SST at the interval of 3 h based on 3 h SST from the WHOI dataset. The CoTCN-DSV captures DSV well with root mean square error (RMSE) of DSA below 0.03 °C/0.13 °C at 3 h/12 h lead times and maintains high forecast skill with the temporal correlation coefficient (R) of 0.78 at the lead times of 12 h in the equatorial Pacific. The CoTCN-DSV reduces RMSE for daily max/min SST by 10.9% and 12.8% due to replacing the new loss function, then significantly improving DSV forecast. There are systematic SST biases in the WHOI dataset and this leads to relatively large RMSEs when DSV forecasts trained using WHOI are evaluated against TAO observations. Replaced WHOI SST by TAO SST, the forecasted DSA RMSE by CoTCN-DSV is reduced by an average of 43%. This confirms that the CoTCN-DSV has good generalization ability and high-quality data are important to advance the forecast accuracy. These finding show that CoTCN-DSV has the potential to forecast extreme values for different scenarios. Full article

The transition period in dairy cows involves profound metabolic adaptations that challenge energy balance and liver function. This study evaluated the effects of rumen-protected methionine (RPM) and choline (RPC) on hepatic and skeletal muscle metabolism. Twenty-one multiparous Holstein cows from a 2 × 2 factorial design (CON, RPM, RPC) underwent liver and semitendinosus biopsies at −10, +7, and +20 d relative to parturition. Untargeted LC-MS metabolomics detected 2288 and 1454 molecular features in liver and muscle. Data were analyzed using mixed-model ANOVA (FDR ≤ 0.05), complemented by multivariate approaches including sparse PLS-DA and PERMANOVA to assess global metabolic variation. Metabolite annotation was performed using HMDB (±0.005 Da). Dietary supplementation significantly affected 105 hepatic metabolites, whereas time influenced 552 metabolites, generally reflecting increases or decreases in concentration from the prepartum to early postpartum periods. Network analysis identified nine hepatic co-expression modules associated with RPM and RPC. Hub metabolites included glucose-6-phosphate, mannose-6-phosphate, and sphingomyelins, indicating modulation of carbohydrate and lipid metabolism. In muscle, treatment effects were modest, with PERMANOVA and PLS-DA confirming limited discrimination among groups and a predominant temporal effect. Overall, RPM and, to a lesser extent, RPC modulated key hepatic metabolic pathways, supporting energy and redox homeostasis during early lactation. These findings highlight the potential of methyl-donor supplementation to enhance metabolic resilience at the tissue level in transition cows. Full article

During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. Full article

Instance segmentation of 3D point clouds is a fundamental task for scene understanding in applications such as autonomous driving, robotics, and augmented reality. The inherent irregularity and sparsity of point clouds, compounded by scale variations and instance adhesion, pose significant challenges to accurate segmentation. Existing grouping-based methods are often limited by the loss of geometric details in single-path backbones and by error propagation near complex boundaries. To address these issues, a Multi-grained Dual-aware Grouping algorithm (MDGroup) is proposed, which explicitly integrates multi-grained feature representation with dual awareness of class and boundary. The algorithm features a Dual-Resolution 3D U-Net (DRNet) that preserves local geometric details while aggregating global semantics through adaptive alignment. A four-branch prediction scheme enhances semantic and offset estimation with boundary and directional cues, enabling fine-grained boundary modeling. Furthermore, a Hierarchical Adaptive Multi-grained Feature fusion framework (HAMF) achieves efficient cross-scale alignment by combining Class-Aware Dynamic Voxelization and Class-Aware Pyramid Scaling. Finally, a Boundary-Aware Weighted Aggregation mechanism (BAWA) refines instance grouping by dynamically weighting semantic confidence, geometric distance, boundary probability, and directional consistency. To extend the model to dynamic scenes, a Temporal Adaptive Gating (TAG) module is introduced to leverage historical frame correlations. Extensive experiments on the ScanNet v2, S3DIS, STPLS3D, SemanticKITTI, LiDAR-Net, and OCID benchmarks demonstrate that MDGroup achieves state-of-the-art performance among grouping-based methods, particularly on small objects, complex boundaries, and dynamic environments. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by persistent synovial inflammation and vascular abnormalities. Emerging evidence suggests that dysbiosis of the microbiome contributes to the pathogenesis of this disease, while seasonal and meteorological variations represent significant factors influencing microbial community dynamics. However, the specific pathological mechanisms mediated by microbial populations within knee joint effusions of RA patients remain poorly elucidated. The present study employs 16S rRNA high-throughput sequencing technology to characterize seasonal variation patterns affecting microbial communities in knee joint effusions of RA patients and to investigate the relationship between microbial community structures and climatic lag effects. Methods: Microbial communities in knee joint effusion samples obtained from RA patients were analyzed using 16S rRNA high-throughput sequencing methodologies. A Distributed Lag Non-linear Model (DLNM) was applied to quantify the delayed effects of climatic variables on microbial community composition. The correlation patterns between meteorological parameters and community structure were elucidated through the integration of ridge regression and redundancy analysis (RDA). Preliminary identification of potential biomarkers was conducted using random forest algorithms. Results: According to research findings, the microbial composition of knee joint effusions in RA patients shows seasonal fluctuation patterns that are compatible with those seen in RA patients, even though there is no discernible seasonal change in β-diversity. Compared with samples obtained during other seasons, spring specimens exhibited significantly elevated relative abundances of both beneficial microorganisms and opportunistic pathogenic taxa. Random forest modeling identified Escherichia-Shigella and Curtobacterium as preliminary candidate biomarkers; however, external validation is required to establish their specificity as disease indicators. Further analysis revealed that although short-term meteorological fluctuations exert minimal influence on overall microbial diversity, specific alterations in mean wind speed (MWS) and relative humidity (RH) drive compositional changes in the microbial community, manifested as rapid responses from dominant bacterial taxa and compensatory buffering effects from rare taxa. Conclusions: This study suggests that the synovial cavity microbiota in RA patients may exhibit seasonal variation patterns that are statistically associated with environmental parameters, particularly humidity and temperature. Due to the inherent limitations of the cross-sectional study design, the preliminary candidate biomarkers identified herein require validation through external cohorts. Additional investigations incorporating healthy controls and osteoarthritis (OA) cohorts are necessary to confirm specificity and to elucidate the therapeutic potential of these microbial targets for RA microbiome interventions. Currently, insufficient evidence exists to establish causal relationships among microbial populations, joint pathology, and climatic factors. Longitudinal cohort studies are imperative to validate the temporal dynamics and clinical significance of these associations. Full article

Accurate assessment of the cooling effect from urban green space (UGS) is largely hindered by insufficient field samples or consideration of the internal and surrounding three-dimensional (3D) structure. This study developed a transferable modeling-optimization framework that integrated a multi-temporal sampling strategy, multimodal 3D environmental reconstruction, and Bayesian-based optimization. First, the potential influencing factors of the cooling effect were quantified from three aspects of inner 2D/3D structure, surrounding building ventilation, and background meteorology through fusing field measurements, multi-spectral UAV images, and Sentinel-2 images. Then, a generalized additive mixed-effects model was used to explore cooling-related patterns of UGS, and a Bayesian network was further applied to identify potential optimized configurations. The results suggest the following: (1) The adopted multi-temporal sampling strategy enhances the stability of detected cooling signals and minimizes spatial interference among neighboring UGS patches and water bodies. (2) Temporal changes in the cooling effect are mainly driven by average air temperature and maximum wind speed, while the spatial variation by the UGS inner characteristics of area and shape index and surrounding ventilation. (3) The “win–win” situation of cooling intensity and range occurred in UGSs with larger areas, higher shape regularity, and medium ventilation. This approach is useful for model-based planning of climate-responsive green infrastructure and city-scale ventilation systems in heat-vulnerable environments. Full article

Gestational diabetes mellitus (GDM), one of the most common metabolic complications of gestation, affects approximately 10–15% of all pregnancies and represents a significant challenge for obstetricians and diabetologists aiming to reduce adverse feto-maternal outcomes. Medical nutrition therapy remains the cornerstone of GDM management, alongside lifestyle modification and pharmacological treatment in the presence of unmet glycemic targets. However, current dietary recommendations primarily emphasize nutrient composition and caloric intake, often without fully considering the temporal aspects of food intake. Chrono-nutrition is an emerging field that investigates the interaction between meal timing, circadian rhythms, and metabolic regulation. Increasing evidence indicates that glucose metabolism and insulin sensitivity exhibit marked diurnal variations, which may be further amplified in women with GDM, resulting in time-dependent differences in postprandial glycemic responses. This narrative review summarizes the current evidence on the role of chrono-nutrition in GDM by integrating mechanistic insights with findings from observational and interventional human studies. Although the available literature is limited by heterogeneity and a paucity of well-designed randomized controlled trials, the convergence of biological plausibility and emerging clinical data suggests that chrono-nutrition may represent a potential low-risk refinement of standard medical nutrition therapy. Incorporating temporal aspects of eating into dietary counseling may help frame glycemic management within a more physiologically aligned and personalized nutritional approach for pregnancies complicated by GDM. Full article

We report a dual-readout self-resetting CMOS image sensor that achieves a signal-to-noise ratio (SNR) exceeding 70 dB and resolves sub-percent optical contrast variations by effectivly suppressing reset artifacts. The proposed sensor employs a Dual-Readout architecture with two independent scanners operating with a temporal offset; while one readout system is in the self-reset “dead time”, the other remains active, thereby physically ensuring continuous data acquisition. To minimize pixel area while achieving high reconstruction accuracy, a minimum frame-to-frame difference algorithm is utilized for signal restoration without requiring in-pixel counters. A prototype chip fabricated in a 0.35-$\mathsf{\mu }$m process demonstrated SNR characteristics near the shot-noise limit, with a peak SNR exceeding 70 dB. Vascular phantom experiments using a carbon black suspension successfully visualized ±0.25% contrast fluctuations—dynamic signals previously undetectable by conventional sensors. This device provides a powerful platform for high-precision bio-imaging applications, including brain surface blood flow monitoring, where both wide dynamic range and high SNR are essential. Full article

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article

With the development of polar regions and the deepening utilization of cold region resources, a large number of infrastructure projects are continuously being carried out. The freezing temperature of unsaturated soil is a critical factor governing the freezing depth and stability of foundations in cold regions or seasons. Concurrently, the supercooling state of soil significantly influences the assessment of its phase composition and physico-mechanical properties. This study employed physical experiments, theoretical formulas, and numerical simulations to reveal the influencing factors and underlying mechanisms of supercooling characteristics in unsaturated soils under controlled low-rate continuous cooling conditions. The results demonstrate that a reduced temperature gradient between the sample surface and the ambient environment correlates with a lower supercooling limit temperature and an extended supercooling duration. An excessively high cooling rate suppresses the supercooling phenomenon in the sample core due to boundary effects. In contrast, neither the temperature difference nor the external cooling rate exhibit a negligible influence on the freezing temperature. Analysis of the temperature–time curves reveals that the freezing process of silty clay is more stable, exhibiting fewer stepwise temperature declines during the phase change plateau, whereas mudstone shows heightened sensitivity to variations in the thermal gradient. Compared to conventional thermocouple measurements, the proposed methodology achieves an optimal balance between temporal efficiency and measurement accuracy. It not only enhances experimental controllability and data reliability, but also provides more scientific theoretical support and technical pathways for predicting freezing depth, designing foundation thermal systems, and preventing frozen ground disasters in cold region engineering. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

To accurately understand and investigate carbon fluxes in cropland ecosystems, this study adopted a machine learning ensemble model for estimation. Focusing on the Jinzhou station of the ChinaFLUX, we integrated eddy covariance carbon flux observations with multi-source satellite remote sensing data to construct a machine learning-based cropland carbon flux estimation model. For environmental driver selection, a strategy combining correlation analysis with ecological mechanism understanding was employed to screen LST, NDVI, and NDMI as model input variables, effectively avoiding multicollinearity issues. Using footprint-weighted integrated data from 2005 to 2014 for model training and validation, a Stacking ensemble model was constructed with the RF model serving as the meta-learner to stack the predictions of RF, CART, and GBM. The ensemble model further reduced the prediction error (RMSE = 39.82), maintaining an R² > 0.9 in most years and effectively improving predictive performance during anomalous years where single models underperformed. Based on these findings, the model was applied to analyze the spatiotemporal evolution of NEE in Jinzhou croplands from 2005 to 2014. The analysis revealed that while the region functioned overall as a carbon sink, it exhibited significant spatiotemporal heterogeneity. Spatially, the distribution followed a pattern of “strong intensity in the northeast and center, and weak intensity in the northwest and southwest.” Temporally, the sink intensity underwent significant interannual oscillations characterized by a “strengthening–weakening–re-strengthening–declining” trajectory. The high-precision prediction method proposed in this study is of great significance for revealing spatiotemporal variations in carbon sources/sinks, guiding green agricultural development, and supporting relevant policy formulation. Full article

Artificial intelligence (AI) is reshaping healthcare by supporting faster and more informed clinical decisions. However, the complexity of human health makes accurate predictive modeling challenging. In this study, we introduce a methodological framework for constructing intelligent digital twins of disease progression by combining patients’ sequential medical images with temporally aligned electronic health records (EHRs). EHRs in this context include structured clinical parameters such as laboratory test results, demographic characteristics, and medication information. The existing literature provides limited approaches that jointly forecast future medical images and clinical status using long-term historical data. Our framework integrates aligned temporal image sequences with these EHR features and employs either ConvLSTM or ViViT-based spatio-temporal encoders, optionally coupled with a generative module for future image synthesis. While awaiting access to patient datasets, we conducted an initial evaluation using a single-cell time-lapse microscopy dataset whose temporal dynamics resemble patient data. Both systems generate time-ordered image sequences that evolve under changing conditions, and the shifting nutrient environment in microfluidic channels parallels the temporal variations observed in patients’ EHR records. This preliminary study demonstrates the broader applicability of our model to datasets containing long-term sequential images and associated parameters, supporting its potential for future patient-specific digital twin development. Full article