Search Results (49,371)

Silk production is a classic example of a domestication trait, yet the cell-type-specific driver of its enhancement in the silkworm Bombyx mori remains unresolved. To address this, we integrated extensive bulk RNA-seq data with a single-nucleus RNA-seq atlas of silk glands (SGs) from domestic B. mori and wild B. mandarina for deconvolution analysis. This identified phenotype-associated cell subpopulations (Scissor+ and Scissor− cells) that enrich in B. mori and B. mandarina, respectively. Transcriptomic characterization revealed that B. mori SG cells exhibit a pervasive “pro-synthesis” transcriptional state, with concerted upregulation of silk protein genes and metabolic pathways. Conversely, B. mandarina cells maintained a “protective–adaptive” state, enriched for stress response and xenobiotic metabolism genes. Pseudotime analysis further delineated the cell state transitions, pinpointing key dynamic gene expression linked to high silk yield. Our findings demonstrate that domestication reshaped the silk gland cellular landscape, promoting a systemic shift toward a synthesis-optimized cell state. This study offers a new framework at the cellular level to elucidate the evolution of complex traits under selection. Full article

Clarifying the complex interaction between climate risks and the new energy industry chain is of key significance to advancing the energy transition and strengthening industrial chain robustness. This research pairwise-matches the climate physical risk and the climate transition risk with the entire range of the new energy industry chain segments, comprehensively examining the pairwise interactive relationships. By applying the MF-ADCCA series of methods, it was revealed that there are prevalent asymmetric cross-correlated multifractal characteristics between climate risks and the new energy industry. The long-term memory under the upward trend of the market is distinctly stronger than that under the downward trend. Given that this correlation can indirectly reflect market efficiency differences, this paper constructs the Hurst Volatility Sensitivity Index (HVI) and the Hurst Asymmetry Index (HAI) and further proposes the Unified Market Efficiency Index (UMEI). Its innovative advantage resides in the balanced integration of volatility efficiency and structural symmetry, in turn enabling a comprehensive assessment of the new energy market efficiency under climate risk perturbations. Static analysis reveals that the overall market efficiency of the new energy industry under the climate transition risk is generally higher than that under the climate physical risk, and the market efficiency of mature upstream and midstream new energy segments is significantly superior to that of the downstream. Dynamic evolution characteristics indicate that market efficiency has typical time-varying traits, the evolution of which is often driven by significant policies or extreme events. The climate transition risk tends to trigger aperiodic structural adjustments, while the climate physical risk mostly induces periodic efficiency fluctuations. This study furnishes solid evidence for the new energy market in coping with climate risks. Full article

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance ($R^2$ = 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article

Dental caries is an example of an oral infectious disease that affects many people worldwide, but it is not well studied in deterministic mathematical modeling. Therefore, we are interested in studying the dynamics of tooth cavity disease using a deterministic modeling approach. We propose a delay differential equation system (DDEs) to describe the phenomenon. The breakthrough of the constructed model is the formulation of the recovery rate as a saturation function constrained by healthcare capacity and the plausibility of caries reformation. In addition, we consider two controls, such as a health campaign and a post-treatment intervention. The mathematical analysis yields equilibrium solutions and their stability, which is determined by the basic reproduction number (\({\Re_0}\)). Furthermore, backward bifurcation occurs as the medical facility’s capacity decreases, driven by an increasing infectious population. The sensitivity analysis results indicate that both considered controls are the most influential parameters. The optimal control problem is formulated using the Pontryagin Maximum Principle to obtain an optimal solution in suppressing the number of caries formation cases. At the end, a numerical simulation shows that interventions reduce the risk of transmission and suppress the number of infectious individuals. The constructed model has excellent future potential, such as generating a function for relapse cases or other preventive actions into an optimal control problem. Full article

Background: High-fat diet-induced metabolic disorders are associated with trimethylamine (TMA)/trimethylamine N-oxide (TMAO), whose production is linked to gut microbial choline metabolism. However, changes in specific gut microbiota under a high-fat diet and the relationship between these changes and choline in TMA/TMAO production remain unclear. Methods: A total of 48 7-week-old male C57BL/6J mice were subjected to one-week acclimatization feeding, and then randomly divided into four groups (12 mice per group) to establish a 2 × 2 factorial design animal experiment: the control group (CON, basal diet), the choline-supplemented control group (CON + C, basal diet supplemented with 1% choline), the high-fat diet group (HF, high-fat diet), and the high-fat plus choline group (HF + C, high-fat diet supplemented with 1% choline). The experiment lasted for 9 weeks, during which dynamic monitoring of TMAO levels in mice was performed in the first 4 weeks. At the ninth week, the mice were sacrificed and samples were collected for subsequent assays, including the concentrations of TMA and TMAO in serum, colonic contents and feces; the pathological morphology of liver tissue, adipocyte staining characteristics and serum biochemical parameters; and the expression levels of key genes and proteins in liver, small intestine and colon tissues. Meanwhile, metagenomic analysis was conducted on colonic contents, combined with machine learning to predict the correlation between gut microbiota and TMA. In addition, gene cloning, multiple sequence alignment, molecular simulation and in vitro culture experiments were carried out to verify the TMA-producing function of the target strain. Results: This study elucidated that high-fat diet and high choline exert a significant interaction in TMA/TMAO production through a 2 × 2 animal experiment; meanwhile, the significantly increased TMA/TMAO levels co-induced by the two factors further exacerbate metabolic disorders. Notably, through combined metagenomics and machine learning, we identified Serratia marcescens as the primary TMA-producing microorganism under high-fat/choline diet induction. In vitro cultures simulating the intestinal environment revealed that the TMA conversion ability of Serratia marcescens is time-dependent, reaching 60 ± 2.49% after 24 h of anaerobic culture with choline chloride. Multiple sequence alignment and molecular simulation further demonstrated that the CutC enzyme of Serratia marcescens has a conserved amino acid sequence and high affinity for choline. Conclusions: We uncovered a two-factor synergistic effect of a high-fat/choline diet on TMA/TMAO, and for the first time identified the genus Serratia as a TMA-producing bacterium. These findings provide a new potential target for intervening in metabolic disorders mediated by high-fat diet-induced TMAO elevation. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article

Recurrent massive strandings of pelagic Sargassum have severely impacted Caribbean and Gulf of Mexico coastlines over the past decade, generating major environmental, sanitary, and socioeconomic consequences. Accurate monitoring of Sargassum dynamics in nearshore waters remains challenging, as most existing satellite products rely on moderate-resolution sensors that inadequately resolve coastal processes. Here, we present a high-spatial- and -temporal-resolution Sargassum detection dataset derived from the VENµS (Vegetation and Environment New Micro-Satellite) mission, providing daily observations at 4 m resolution for five coastal zones in Guadeloupe, Martinique, and the Yucatán Peninsula over the 2022–2024 period. VENµS imagery consists of 12 multispectral bands, and the analysis specifically uses the red, the red-edge/near-infrared and the short-wave infrared bands. Detection is based on the Alternative Floating Algae Index (AFAI), combined with land and cloud masking, background estimation, and adaptive thresholding. We demonstrate the capability of this dataset to resolve fine-scale Sargassum raft dynamics, characterize the seasonal influx of Sargassum along the coastline, and assess exposure across different coastal typologies. By offering the highest combined spatial and temporal resolution currently available for these regions, this dataset provides a novel resource for coastal impact assessment, nearshore drift analysis, and validation of Sargassum transport and stranding models. Full article

The integration of Foamed Concrete (FC) into 3D Concrete Printing (3DCP) processes facilitates the design of energy-efficient building envelopes. However, strategies for optimizing material porosity and printing topology to balance winter and summer performance remain underexplored. This study presents a 2D numerical thermal analysis of an innovative 3D-printed building envelope block characterized by sinusoidal internal partitions. Through a parametric variation in porosity (ranging from 10% to 50%) and internal geometry (amplitude and period of the partitions), 45 distinct configurations were simulated. Performance was evaluated by calculating the steady-state thermal transmittance (U) and the periodic thermal transmittance (Y_ie) under dynamic climatic conditions. The results demonstrate that porosity is the governing parameter; increasing porosity from 10% to 50% reduces U by 31% and, contrary to traditional assumptions for massive structures, also improves Y_ie by 12.3%. These outcomes are physically driven by the drastic reduction in thermal conductivity, which overcompensates for the loss of thermal mass, leading to a net reduction in overall thermal diffusivity. While internal topology plays a secondary role, its optimization allows for fine-tuning dynamic damping without compromising insulation. The study confirms that 3D printing with foamed concrete enables the overcoming of the traditional trade-off between insulation and thermal inertia. High-porosity configurations (50%) with optimized internal topology emerge as the most effective solution, simultaneously guaranteeing beneficial steady-state and dynamic thermal performance for sustainable buildings. Full article

Reciprocating compressor air volume control systems have been extensively investigated, with a primary objective of reducing energy consumption and associated carbon footprints. As a multi-actuator system, failures in this energy-efficient configuration can trigger severe operational disruptions with cascading consequences. To address this, we initially constructed numerical models of the multi-actuator energy-efficient system to decode the variational patterns of compressor dynamic pressure pulsations and connecting-rod small-end bush tribological behaviors under partial actuator fault conditions, thereby establishing foundational data for fault degradation stratification. Building upon this, we propose a Prognostics and Health Management (PHM) algorithm using fault degradation analysis, thereby materializing self-recovery functionality in response to various fault conditions. Experimental validation demonstrates that the self-recovery algorithm successfully contained deterioration propagation through proactive intervention. The system achieved autonomous healing within 8 s (mild faults) and 13 s (moderate faults), constraining discharge fluctuations and vibration amplitude within allowable thresholds. This study establishes a solution framework for preserving multi-actuator energy-efficient systems’ health, accuracy, and economy. Full article

Postprandial plasma amino acid (AA) kinetics serve as essential indicators of digestive efficiency and systemic metabolic status in pigs. Traditional kinetic analysis relies on Non-Linear Least Squares (NLS) regression using compartmental models, yet these methods typically demand repeated blood sampling and precise initialization to ensure convergence. In this study, we developed a Physics-Informed Neural Network (PINN) framework by integrating mechanistic Ordinary Differential Equations (ODEs) directly into the deep learning loss function. The framework was evaluated using a benchmark dataset. Specifically, we performed a retrospective analysis by downsampling the original high-frequency data to simulate dense and sparse sampling strategies. The results demonstrate that while both models exhibit high fidelity under dense sampling, PINN maintains superior robustness and predictive accuracy under data-constrained conditions. Under the sparse sampling scenario, PINN reduced the Root Mean Square Error (RMSE) compared to NLS in key metabolic profiles, such as Methionine in the FAA group (p < 0.01) and Lysine in the HYD group (p < 0.05). Unlike NLS, which is sensitive to initial guesses, PINN successfully utilized physical laws as a regularization term to robustly solve the inverse problem, demonstrating superior parameter identification stability and predictive consistency under data-constrained conditions compared to NLS. We concluded that the PINN framework provides a reliable and consistent alternative for modeling the AA dynamics. In the future, it may be possible to reconstruct highly accurate physiological trajectories under optimized sparse sampling conditions. Full article

Accumulation of stem non-structural carbohydrates (NSC) at heading is crucial for mitigating grain-setting defects in large-panicle rice. While traditional panicle nitrogen fertilizer application at the emergence of the fourth leaf from the flag leaf stage (TL4) may weaken stem sink strength, delaying application to the emergence of the third leaf from the flag leaf stage (TL3) significantly enhances NSC accumulation. This study aimed to elucidate the molecular mechanisms through which TL3 remodels stem sink strength to promote NSC storage. Using two large-panicle rice varieties (Huiliangyou 280 and Yangliangyou 228), we compared stem NSC dynamics under TL4 and TL3 treatments and integrated sugar-related metabolite profiling with transcriptome analysis during the critical NSC accumulation phase. The results showed that TL3 treatment significantly increased stem NSC content and NSC per spikelet at heading, leading to a higher percentage of filled grains. The period from 5 days before heading (DBH) to heading showed the highest NSC accumulation rate. At the molecular level, TL3 treatment specifically up-regulated eight key genes in the sucrose–starch metabolism pathway, increasing the activities of sucrose phosphate synthase, sucrose synthase, and ADP–glucose pyrophosphorylase, and thereby promoting the accumulation of sucrose, trehalose, and D-fructose. In summary, delaying panicle nitrogen application to TL3 enhances stem NSC storage by remodeling sink strength via coordinated regulation of the sucrose–starch metabolic network. Full article

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha⁻¹ (95%CI: 1.8–1.9 SU·ha⁻¹), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management. Full article

To elucidate how pre-crack inclination affects the dynamic mechanical response, failure modes, and energy evolution of rocks, uniaxial impact compression tests were conducted on Φ50 mm Baima Iron Mine magnetite specimens with varying pre-crack angles using a split Hopkinson pressure bar (SHPB) system. The experiments were integrated with PFC2D discrete element simulations to investigate crack propagation and stress field characteristics. The results demonstrate that all specimens maintained dynamic stress equilibrium under impact loading. Crack inclination significantly influenced the dynamic stress–strain response: specimens with 0°~30°cracks exhibited gradual post-peak stress decay, indicating ductile behavior, while specimens with larger inclinations (≥45°) displayed pronounced brittle failure. Dynamic compressive strength followed a “U-shaped” trend with crack angle, reaching a minimum at 45°, whereas 0°and 90°specimens exhibited similar strength. Failure modes transitioned from axial splitting to wing-crack dominance, while anti-wing and shear cracks decreased significantly with increasing crack angle. Energy analysis indicated that reflected energy decreased and transmitted energy increased with increasing crack angle. Numerical simulations reproduced the experimental macroscopic failure patterns accurately, revealing the underlying mechanisms of crack-tip coalescence and stress concentration shifts as a function of crack inclination. These findings offer insights into the dynamic failure mechanisms of jointed rocks and provide guidance for engineering safety assessments. Full article

G-quadruplexes (G4s) are emerging as potential antiviral targets. SARS-CoV-2 genomic RNA contains 42 G-rich regions harboring putative G-quadruplex-forming sequences (PQSs). Here, we performed a systematic genomic and structural analysis of SARS-CoV-2 PQSs. It was proposed that non-G-tetrads or different triads may stabilize most G4s in this RNA. Many G4s may include the most stable U·A-U triad. Several G-quadruplexes may be significantly stabilized by 3′ U-tetrad. Large-scale mutational analysis of RNA structural elements containing PQSs showed that most PQSs are highly conserved, while persistent G4-destroying mutations were observed only for one PQS and were transient for two others. Based on G4 position and structural context, we propose that: (i) G4 370 in nsp1 may contribute to cap-independent translation initiation; (ii) certain putative G4s in different genes may assist in co-translational folding of viral proteins; (iii) G4 13385, located upstream of the frameshift stimulation element, may promote formation of a pseudoknot competent for −1 frameshifting. For putative G4s at positions 3467, 13385 and 28903, we analyzed binding to 13 compounds by molecular docking and selected four candidates for molecular dynamics simulations. The ligand EKM emerged as a promising antiviral candidate due to its specific binding to G4 3467. Full article

Reliable state-of-charge (SoC) estimation is crucial for safe and efficient battery management. However, it is challenging in practice. Terminal-voltage sensitivity becomes weak in open-circuit-voltage (OCV) plateau regions. Model uncertainty also persists at practical sampling periods. To tackle this issue, this paper proposes a discrete-time, model-based SoC estimation framework. This framework combines a dual-polarization equivalent-circuit model with a tuning-light sliding-mode observer. It is specifically designed for digitally sampled battery management systems. The modeling stage includes: (i) a discrete-time DP representation suitable for embedded use, (ii) a shape-preserving PCHIP reconstruction of the OCV–SoC curve and its derivative, and (iii) an effective-slope regularization mechanism that maintains non-vanishing output sensitivity even in flat OCV regions. On top of this structure, a boundary-layer SMO is developed with output-error shaping, model-driven gain scaling, and simple bias-compensation terms based on integral correction and leaky Coulomb counting. A discrete-time Lyapunov analysis is conducted directly on the surface dynamics. This analysis shows finite-time reaching to the boundary layer and a practical limit on the steady-state error that depends on the sampling period, disturbance level, and boundary-layer width. Numerical tests on a DP model identified from experimental data indicate that the proposed method achieves SoC accuracy similar to a switching-gain adaptive SMO. The results confirm the benefits of a model-centric design. The discrete-time formulation and convergence proof, which do not depend on high sampling rates, provide robustness advantages over traditional sliding-mode methods. The proposed method also performs better than a tuned EKF in plateau regions, requiring much less tuning effort. Full article