Search Results (2,793)

Fenjiu distillers’ grains are a major by-product of Fenjiu production and contain various bioactive components derived from cereal raw materials and microbial fermentation. Colorectal cancer (CRC) remains a global health challenge, and the development of safe and effective therapeutic agents is urgently needed. In this study, we successfully obtained DGAE-1 (distillers’ grains ethanol extract fraction 1), an ethanol-extracted bioactive fraction derived from Fenjiu distillers’ grains, with anti-colorectal cancer activity. LC-MS analysis tentatively annotated the chemical constituents of DGAE-1 fraction based on MS/MS fragmentation patterns and database similarity matching, with the major annotated compounds belonging to carboxylic acids and derivatives, organic nitrogen compounds, organic phosphoric acid derivatives, and other chemical classes. These annotated compounds may contribute to the biological activity of DGAE-1 fraction, although the specific active constituents remain to be further clarified. DGAE-1 fraction inhibited colorectal cancer cell proliferation and induced autophagy. Further studies indicated that the PI3K/AKT pathway is involved in DGAE-1 fraction-induced autophagy. The results of this study demonstrate that DGAE-1 fraction exerts anti-colorectal cancer activity in both cellular and animal models. These findings provide new insights into the development and utilization of Fenjiu distillers’ grains and highlight their potential as a source of bioactive compounds for health-related applications. Full article

Radio occultation offers high vertical resolution, global coverage, and low sensitivity to clouds and precipitation, making it a useful complement to existing atmospheric sounding techniques. The YunYao constellation, the first commercial meteorological remote sensing constellation of China, supplies a large number of GNSS-RO temperature profile products, yet the quality of its wet atmospheric profiles remains undocumented. Using operational radiosonde data from 43 stations over southern China (97–123°E, 17–31°N) during boreal winter and early spring (1 November 2025 to 31 March 2026) as reference, this study evaluates the accuracy, error characteristics, and applicability of the YunYao wet atmospheric temperature profile product, with COSMIC-2 as an independent benchmark. YunYao yields 4.77 times the matched sample volume of COSMIC-2 over the same domain and period, while its RMSE is only 0.20–0.42 °C higher. Within 50–925 hPa, the YunYao profiles agree closely with radiosondes, with RMSE generally within 1–2 °C, a weak, spatially uniform cold bias, and small diurnal differences. Below 925 hPa, near-surface error grows markedly, dominated by enhanced random error rather than systematic error, with inland cold and coastal warm bias. A January maximum of random error is possibly related to super-refraction during peak winter monsoon cold surges, and a March sign reversal of the bias possibly related to the seasonal increase in boundary layer moisture ahead of the pre-flood season. With weak cold bias correction, YunYao temperature profiles are highly usable within 50–925 hPa, while data below 925 hPa require strict quality control. Full article

The synthesis of efficient reversible logic circuits is critical for fault-tolerant quantum computing (FTQC). The primary motivation of this work is to overcome the inherent disadvantages of existing synthesis techniques: approximate heuristic methods often miss optimal solutions, while pure exact computational methods suffer from combinatorial explosion on deep circuits. While the strict NCT library (NOT, CNOT, Toffoli) is often preferred due to the high cost of distilling non-Clifford states required for arbitrary gates, standard physical implementations frequently utilize the broader NCV library (NOT, CNOT, V, V-dagger), requiring the decomposition of Toffoli gates into five elementary operations. To bridge this gap, this paper presents a unified, highly scalable methodology for the optimal design of reversible circuits across both libraries. First, a Mixed-Integer Non-Linear Programming (MINLP) formulation, linearized for the high-performance IBM ILOG CPLEX solver, is introduced to automate the exact generation of globally optimal strict NCT topologies. Second, a systematic four-phase optimization framework is proposed to reduce NCV costs. By replacing Toffoli gates with specific NCV decompositions, permuting control lines to match subsequent linear gates, and applying exact local searches via an extended MINLP solver on bounded sliding windows, significant gate cancellations are achieved. Applying this methodology to prominent primitives (MIG, SAYEM, URG, TSG, and MKG), we match global NCT optimality constraints and achieve highly optimized NCV Quantum Costs of 7, 14, and 12 for the MIG, TSG, and MKG gates, respectively, establishing best-known upper bounds that significantly outperform heuristic literature benchmarks. Full article

Urban flooding is shaped by urban morphology, but the inferred relationships can change with the spatial units used to represent flooding and urban form. Existing studies often report results for a selected spatial configuration, leaving unclear whether predictive performance and identified dominant predictors remain robust when analytical scale, grid placement, and spatially separated validation are varied. Using the 22 May 2020 Guangzhou storm as an event-specific case, this study evaluates the robustness of multi-scale morphology–flood associations to these spatial analytical choices. The analysis integrated 119 unique official waterlogging locations with 67 geocoded social-media locations. After removing four cross-source matches within 100 m, 182 unique observations were used to construct a kernel density response surface and examine 1–5 km analytical grids. Spatial autocorrelation, repeated nested geographic cross-validation of XGBoost, out-of-fold SHAP attribution, and accumulated local effects (ALEs) were used to quantify scale-dependent patterns. Global Moran’s I increased from 0.125 at 1 km to 0.524 at 4 km and decreased to 0.479 at 5 km (all permutation p < 0.001). Mean spatially validated R² ranged from 0.483 to 0.610, with the highest R² and lowest RMSE at 4 km, although residual spatial autocorrelation remained. Road density was the largest individual SHAP contributor at every scale (35.54–40.89%). ALE indicated broad positive associations for road density, building density, and impervious surface ratio and a negative association for elevation, without supporting universal sharp thresholds. These event-specific results show that analytical scale and grid placement should be reported explicitly when morphology-based evidence is used for flood screening and climate-resilient planning. Full article

Fuel cells (FCs) are critical for decarbonizing the transport industry, with Life Cycle Assessment (LCA) serving as the standard evaluation framework. However, existing literature exhibits severe methodological heterogeneities and divergent system boundaries that introduce deep epistemic uncertainties. This review conducts a systematic analysis to critically harmonize FC environmental performance across the road, aviation, and maritime sectors. Quantitative synthesis reveals global warming potential (GWP) as the dominant metric. For light-duty vehicles, GWP drops to around 30 gCO₂eq/km, matching battery-electric configurations exclusively under deeply decarbonized grids. Manufacturing FC stacks and advanced storage imposes a severe upfront carbon debt, particularly prominent in heavy-duty freight (60–130 tCO₂eq). In aviation, 80–90% in-flight GWP reductions trigger massive burden-shifting, transferring 60–70% of lifecycle damages to ground-based infrastructure. Maritime FCs shrink GWP to 0.06–0.60 kgCO₂eq/kWh, strictly contingent on upstream hydrogen production. Crucially, despite long-term GWP advantages, FC pathways face systematic penalties in acidification, eutrophication, and ecotoxicity, heavily driven by platinum-group catalysts and fluoropolymer membranes. By isolating software-driven biases and database discrepancies, this work delivers an actionable methodological roadmap, establishing a policy-aligned baseline for future FC transportation sustainability frameworks. Full article

Background: Lead (Pb) exposure remains a major global health issue, and Pb stable isotope ratio analysis is a powerful tool for identifying potential exposure sources. However, traditional whole blood collection is difficult to implement in remote or resource-limited settings. We developed and validated a novel method for measuring Pb isotopic composition in dried blood spots (DBS) using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) and applied it in a pilot study of DBS collected from children in Suriname, South America. Methods: Method accuracy was evaluated by comparing isotopic ratios (²⁰⁸Pb/²⁰⁶Pb and ²⁰⁷Pb/²⁰⁶Pb) measured in laboratory-spiked DBS (15 µL of blood per DBS) with those from matched whole blood samples (1 mL) across a range of Pb concentrations (2.1–28.9 µg/dL). The method was then applied to DBS collected from 18 children, ages 6 months to 5 years, in Suriname. Potential local exposure sources, including soil, shotgun pellets, and cooking utensils, were also analyzed for lead isotopic composition and compared with DBS isotope ratios to assess source concordance. Results: DBS isotopic ratios accurately reflected whole blood values when Pb concentrations exceeded ~315 pg/punch (~2.1 µg/dL), with greater accuracy observed above 5 µg/dL. In the Suriname DBSs, blank-corrected ²⁰⁸Pb/²⁰⁶Pb ratios were consistent across regions (mean 2.12 ± 0.018; n = 15) and showed no significant geographic stratification (p = 0.11). DBS signatures (isotope ratios) most closely aligned with shotgun pellets and soil samples, whereas those from cooking utensils did not match DBS signatures. A positive correlation was observed between estimated blood total Pb and soil Pb concentrations (ρ = 0.60, p = 0.20). Conclusions: Overall, DBS provided reliable Pb isotopic ratio measurements above ~2–5 µg/dL, spanning the current CDC reference value of 3.5 µg/dL at which children’s exposures warrant investigation. These findings support DBS as a minimally invasive, scalable tool for exposure source identification in environmental epidemiology, particularly in remote settings where traditional venipuncture is impractical. What this study adds: This study establishes the first validated method for high-precision lead (Pb) stable isotopic ratio analysis using dried blood spots (DBS). By implementing a sample blank correction, we demonstrate that DBS can accurately resolve isotopic signatures even at low blood lead concentrations. Applied in DBS collected in Suriname, the method successfully linked DBS signatures to local shotgun pellets and soils, illustrating a common exposure profile across diverse regions. This work expands the utility of DBS beyond simple screening, offering a minimally invasive, scalable tool for source attribution in global health research and environmental epidemiology in remote regions. Full article

Sensorless speed estimation replaces fragile shaft encoders in cost-sensitive Permanent Magnet Direct Current (PMDC) motor drives, but classical model-based observers degrade under brush friction, commutation ripple, and thermal drift, while purely data-driven estimators sacrifice physical interpretability. This paper presents a Hybrid Physics-Data-Driven Observer (HPDDO) that couples an identified lumped-parameter electrical model with a compact multilayer-perceptron residual compensator, which is executed in real time on a low-cost ESP8266 microcontroller. Global parameters are identified from a short labeled recording, after which the network corrects only the nonlinear residual that the physics model cannot explain. Under a strictly time-series-aware evaluation (chronological 80/20 split), the proposed estimator achieves an average root mean square error (RMSE) of 4.11 RPM across dynamic PWM sweeps, abrupt load transitions, and a long-duration thermal-drift test, outperforming an extended Kalman filter (9.49 RPM), a sliding mode observer (10.89 RPM), and a pure neural-network estimator (7.14 RPM) implemented on the identical dataset. An ablation study shows that accuracy is insensitive to network size, with a 0.9 kB variant matching the deployed model, and a residual-clamping safeguard bounds the estimation error under unseen operating conditions. The framework provides an accurate, interpretable, and computationally lightweight solution for industrial PMDC drives without dedicated speed sensors. Full article

Chronic kidney disease (CKD) may reduce physiologic reserve among patients with esophageal cancer, but evidence beyond postoperative cohorts is limited. Using the TriNetX Global Collaborative Network, we studied adults with esophageal cancer diagnosed during 2010–2023. CKD stages 3–5 were defined by diagnostic codes plus estimated glomerular filtration rate < 60 mL/min/1.73 m² within 6 months before or on the index date; dialysis-dependent patients were excluded. Non-CKD patients served as comparators. Prespecified outcomes from day 1 to up to 3 years included all-cause mortality, subsequent recorded metastatic diagnosis, pneumonia, sepsis, blood transfusion, and major adverse cardiovascular events. Propensity score matching produced 832 pairs. All-cause mortality was not significantly higher in the overall cohort with CKD stages 3–5 than in the non-CKD cohort (HR, 1.13; 95% CI, 0.98–1.31; p = 0.099), whereas CKD stages 4–5 were associated with higher mortality in the stage-specific analysis (HR, 1.46; 95% CI, 1.03–2.06; p = 0.031). CKD stages 3–5 were also associated with higher risks of blood transfusion (HR, 1.45; 95% CI, 1.04–2.01; p = 0.025) and MACEs (HR, 1.22; 95% CI, 1.02–1.47; p = 0.027), and with a lower risk of subsequent recorded metastatic diagnosis (HR, 0.80; 95% CI, 0.65–0.99; p = 0.036). These findings suggest that the associations of CKD with post-diagnostic outcomes varied by outcome type, with higher mortality observed in the separate CKD stages 4–5 analysis. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Federated learning (FL) enables cellular traffic prediction without centralizing raw base-station data, but statistical heterogeneity makes a single global model unsuitable for many clients. This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation. Clients are grouped using training-only daily traffic profiles, after which an LSTM backbone is trained by FedAvg within each cluster. Each client then freezes the cluster backbone and optimizes a residual bottleneck adapter locally. The adapter contains 4241 trainable parameters, 6.19% of the 68,483-parameter three-feature backbone and prediction head, and personalization transmits no model updates. In a shared-seed-42 comparison across 11 methods and four public datasets, FedCAP ranks first or second in 12 of 16 dataset–metric combinations. Across five shared seeds, its mean MAE is 6.66%, 7.47%, 2.64%, and 3.86% below FedAvg on the Milan, Trentino, Bihar, and Taiwan datasets, respectively. Holm-adjusted paired t-tests identify 6 significant dataset–metric differences, whereas exact Wilcoxon tests are not significant because each comparison contains only five nonzero seed-matched pairs; the statistical evidence is therefore interpreted conservatively. Full article

Neurodevelopmental disorder, speech impairment, and dysmorphic facies (NEDSID) is an autosomal dominant condition primarily driven by SETD1A haploinsufficiency. While most documented cases are sporadic, genomic mechanisms underlying intrafamilial phenotypic heterogeneity remain poorly understood. This study presents a comprehensive clinical, neurophysiological, and molecular characterization of a two-generation Mexican family segregating a novel heterozygous missense SETD1A variant. Whole-exome sequencing (WES) identified a c.1604G>A (p.Gly535Glu) substitution localized within the critical Functional Location on SETD1A (FLOS) domain, which was validated via automated Sanger sequencing across all family members and 100 ethnically matched controls. The proband exhibited a moderate NEDSID phenotype, including global developmental delay, macrocephaly, borderline IQ (79) with pronounced information-processing deficits, and abnormal EEG sharp waves. Conversely, first-degree relatives carrying the identical variant presented with mild, non-intellectually disabling phenotypes characterized primarily by isolated psychiatric disorders (bipolar disorder, depression) and minimal dysmorphism. Structural 3D modeling and multi-algorithmic in silico profiling confirmed high evolutionary conservation and a deleterious impact (CADD: 22.2, SIFT: 0.00, GERP: 2.924), classifying the variant as a Variant of Uncertain Significance (VUS)/Likely Pathogenic according to ACMG/AMP criteria (PM2, PP1, PP3, PP4). Furthermore, epigenetic dysregulation and chromatin remodeling defects increasingly link these histone-modifier variants to broader psychiatric landscapes. Emerging transcriptomic profiling confirms that dosage-sensitive COMPASS complex disruptions alter downstream neurodevelopmental gene cascades. Our findings present observational evidence of intrafamilial clinical variability in a single pedigree with a SETD1A missense alteration, supporting the hypothesis that non-catalytic domain substitutions may contribute to diverse neurodevelopmental outcomes. Full article

Perovskite tandem solar cells (TSCs) have emerged as a transformative photovoltaic technology, offering a viable pathway to surpass the Shockley-Queisser limit of single-junction devices by enabling broader solar spectrum utilization and reduced thermalization losses. This review provides a comprehensive overview of recent progress in perovskite-based TSCs, covering four major device architectures: perovskite/silicon, perovskite/CIGS, all-perovskite, and perovskite/organic TSCs. We systematically discuss the fundamental working principles, including bandgap engineering, charge generation and separation, and current-voltage matching, followed by an in-depth analysis of strategies for perovskite layer regulation, interface engineering, and transport-layer optimization. Key advancements, such as compositional engineering, defect passivation, crystallization control, and optical management, have synergistically pushed power conversion efficiencies (PCEs) beyond 34% for perovskite/silicon TSCs and over 28% for all-perovskite and perovskite/organic configurations. Despite these achievements, critical challenges remain, including material instability, halide phase segregation, lead toxicity, scalable fabrication, and cost-effective integration. This review also outlines future perspectives, emphasizing the development of lead-free perovskites, novel charge-transport materials, advanced encapsulation techniques, and large-area manufacturing processes. With continued interdisciplinary efforts, perovskite TSCs hold great promise for driving the global transition toward sustainable and low-carbon energy systems. Full article

When LiDAR detects underwater targets, the water severely attenuates the laser beams, making it impossible to extract valid echo information during 3D reconstruction of urban water bodies. This study proposes a Multi-Scale Spectral Adaptive Loss Generative Adversarial Network Based on Morphology-Spatiotemporal Decoupled Attention (MSAGAN) that effectively enhances far-field underwater echo signals for LiDAR. Its core components consist of three parts: Morphology-Aware Dynamic Receptive Field Attention (MADRA), Spatial-Temporal Decoupled Frequency-Enhanced Global Feature Fusion Block (STDFBlock), and Adaptive Dynamic Adjustment Loss Function Based on Frequency-Domain Decomposition and Gradient Response (FGADLoss). The model precisely identifies the narrow and curved local structures of the echo signals during the feature extraction process, improving the precise detection of subtle structural changes in the echo signals and enabling the extraction of valid echo signal features from a background of numerous invalid echo signals. The model reduces image fragmentation and center-of-mass drift during echo signal augmentation, improving the accuracy of water body environments’ 3D reconstruction. Through this model, the average point cloud density per square meter for lakes and ponds increased by 2.12 and 3.54, respectively, enabling effective reconstruction of urban water bodies information and offering a high-quality data basis for underwater object recognition and bathymetric surveying. Furthermore, this method effectively addresses the challenge of simultaneously obtaining degraded and ideal streak images that match the echo signals of underwater detection targets, and it also offers advantages in terms of training data requirements, making it particularly well-suited for real-world underwater detection scenarios where paired ideal-degraded data is scarce. Full article

As the global sustainability agenda and carbon neutrality goals continue to advance, climate policy has become an important governance instrument for promoting firms’ green and low-carbon transformation. Adjustments in policy timing, regulatory intensity, and enforcement arrangements may create uncertainty for firms’ long-term technological investment and their capacity to sustain this transformation. Accordingly, this study focuses on corporate innovation resilience and examines firms’ ability to maintain, adjust, and recover innovation activities amid climate policy fluctuations. Using Shanghai and Shenzhen A-share-listed firms from 2010 to 2023, this study matches firm-level data with prefecture-level climate policy uncertainty indicators based on firms’ registered locations and empirically examines the impact of climate policy uncertainty on corporate innovation resilience. The results show that climate policy uncertainty is positively associated with corporate innovation resilience. This finding remains robust after changing fixed-effect specifications, adjusting the clustering level of standard errors, excluding special-year observations, and conducting instrumental variable, entropy balancing, and placebo tests. Mechanism tests provide evidence consistent with the channels of corporate sustainable transformation, external attention pressure, and managerial sustainable governance capability. Heterogeneity analysis further shows that this positive association is more pronounced in regions with stronger environmental regulation and among firms receiving higher government environmental subsidies. At the industry level, the effect is mainly observed among heavy-polluting firms and non-high-tech firms. These results indicate that the association is stronger where sustainable transition pressure or policy support is greater. This study extends the firm-level consequences of climate policy uncertainty from innovation quantity and green innovation to the adaptive capacity of corporate innovation systems and provides evidence on how firms sustain innovation while long-term low-carbon transition goals are implemented under fluctuating climate policies. Full article

To address the scarcity of high-precision control points on planetary surfaces and the accumulated drift of conventional relative-localization methods in deep-space exploration missions, this paper proposes a visual absolute-localization method based on salient-landmark contour matching and centroid-consistency constraints. Absolute localization is defined as estimating the rover position in the landing-site North-East-Down (NED) coordinate system or a map-projection coordinate system, rather than in image-pixel coordinates. Stable natural objects, including dunes and impact craters, are treated as generalized feature points. Local terrain is reconstructed from binocular navigation imagery; LiDAR is additionally used in the ground physical-equivalent experiment for multi-source terrain fusion. Multi-class cross-scale contour matching provides homologous landmark associations, after which centroid consistency aligns the local terrain with the global DOM/DEM reference frame. For ten Tianwen-1/Zhurong camera stations, the mean planar error was 0.458 m and the RMSE was 0.491 m. For five ground-test conditions, the mean planar error was 0.494 m and the RMSE was 0.526 m; all tested errors were below 1 m. Because the in-orbit reference DOM has a ground sampling distance of 1 m/pixel, the in-orbit sub-meter values indicate agreement with the adopted reference products and should not be interpreted as absolute accuracy independent of reference-map uncertainty. The results support the feasibility of natural-landmark-based map localization for future Chang’e and Tianwen missions. Full article