Search Results (2,112)

Background: Neutrophil extracellular traps (NETs) have been known to be involved in the gestational diabetes mellitus (GDM), but the underlying role remains poorly understood. Methods: We conducted an integrated analysis of bulk RNA-seq data, single-cell transcriptomic sequencing (scRNA-seq) data, clinical laboratory findings, and cell models to identify hub genes linked to NET formation (NETosis) and to elucidate how NETs contribute to placental injury in GDM. Results: We found that circulating NET levels were increased in pregnant women with GDM both under fasting conditions and following an oral glucose tolerance test (OGTT). Primary neutrophils isolated from healthy pregnant women produced more NETs upon high-glucose stimulation in vitro. The mRNA expression of PADI4, a key regulator of NETosis, was upregulated and positively correlated with MMP8 mRNA in patients with GDM. Inhibition of MMP8 suppressed intracellular reactive oxygen species (ROS) generation and attenuated NETosis in neutrophils. scRNA-seq analysis of placental tissues from patients with GDM identified a neutrophil subset with higher PADI4 expression. In vitro stimulation with NETs induced functional impairment of HTR8/SVneo cells. Conclusions: We uncovered that MMP8, a novel NETosis-promoting molecule, is a promising target for GDM intervention. Full article

Low temperature during germination of late-seeded rapeseed disrupts multiple physiological and biochemical processes and thus limits growth and yield. Accordingly, methods to improve cold tolerance in late-sown rapeseed are needed. In this study, Zhongshuang 11 seeds were primed for 10 h with different concentrations of erucic acid (EA) or glucosinolates (GSLs). After drying, seeds were germinated at low temperature (15 °C/10 °C, 16 h/8 h light/dark) for 14 days. Compared with the control (distilled water priming), the optimal treatments—500 mg/L EA and 300 mg/L GSLs—increased germination rates by 2.9% and 15.6%, respectively, and raised total seedling biomass by 14–24%. Physiological assays on day 14 showed that EA priming increased peroxidase (POD) activity by 28.3%, while GSL priming enhanced superoxide dismutase (SOD) and POD activities by 12.6% and 36.2%, respectively. EA seed priming increased auxin (IAA), brassinolide (BR), cytokinin (CTK), and gibberellin (GA) contents in underground tissues by 37.2%, 18.7%, 53.9%, and 46.7%, respectively, while GSL priming raised IAA, BR, and GA levels in aerial tissues by 74.0%, 59.0%, and 26.6%. Moreover, EA seed priming significantly increased the activities of long-chain acyl-CoA synthetase (LACS) and carnitine acyltransferase (CPT) in rapeseed seedlings, whereas GSL priming elevated glutathione S-transferase (GST) and thioredoxin reductase (TrxR) activities. Field experiments confirmed that EA and GSL priming enhanced seedling biomass accumulation, producing 31.4% and 23.8% increases in total dry weight, respectively, and increased silique number per plant by 15.6% and 17.3%, ultimately raising grain yield by 12.9% and 20.0%. These results indicate that EA or GSL seed priming can improve cold tolerance and yield of late-seeded rapeseed, although further multi-environment and mechanistic studies are required. Full article

As urban agglomerations reshape China’s urban–rural relations, existing evaluations still focus mainly on development levels within administrative units, with limited attention to spatial interactions between urban and rural territorial systems. Taking the Chengdu–Chongqing Economic Circle (CCEC) as a case, this study develops a spatial interaction-based framework for measuring urban–rural integration. The framework integrates population, land and economic factors to characterize urban–rural development status, orientation, coordination and interaction intensity. The Geographical Detector is further employed to identify the driving mechanisms. Compared with conventional within-unit assessments, this framework captures the direction, intensity, and cross-regional dependence of urban–rural linkages, thereby providing a more relational understanding of urban–rural integration. The results show that the CCEC experienced a transition from factor differentiation toward functional restructuring, characterized by continued urban concentration of population and land factors and a relative balancing of economic factors between urban and rural areas. Urban–rural integration exhibited clear directional and spatial dependence: urban-to-rural interactions were mainly concentrated around the Chengdu–Chongqing dual cores and major development corridors, whereas rural-to-urban interactions formed a more dispersed support network. The driving mechanisms were not dominated by individual factors but emerged from the interaction of economic foundations, spatial conditions, policy environments and regional linkages, with different factors playing varying roles across development stages. This study provides new insights into the evolution mechanisms of urban–rural integration in emerging urban agglomerations characterized by strong core–periphery disparities, highlighting the importance of spatial interactions, policy guidance and regional development conditions. Full article

Oligodendrocytes (OLs) are the myelinating cells of the central nervous system (CNS). The PLP1 gene, predominantly expressed in OLs, encodes proteolipid protein (PLP), a major structural component of CNS myelin that also regulates oligodendrocyte precursor cell (OPC) proliferation, differentiation, and maturation. Pelizaeus–Merzbacher disease (PMD) is a rare X-linked leukodystrophy caused by PLP1 mutations and characterized by defective myelination. Clinical manifestations range from severe connatal PMD to classic PMD and the milder spastic paraplegia type 2 (SPG2), reflecting substantial phenotypic heterogeneity. Beyond disrupting myelin structure, PLP1 mutations impair oligodendrocyte development and function. Increasing evidence indicates that PMD is fundamentally a proteostasis disorder, in which misfolded PLP accumulates within the endoplasmic reticulum (ER), overwhelms ER quality control mechanisms, and triggers chronic unfolded protein response (UPR) activation. Persistent ER stress and maladaptive UPR signaling ultimately promote oligodendrocyte dysfunction and degeneration. Using PMD as a representative model, this review summarizes the relationships between PLP1 mutations and disease phenotypes and discusses the cellular mechanisms by which ER stress and UPR signaling contribute to oligodendrocyte pathology. Full article

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management. Full article

Background/Objectives: Maternal cell contamination (MCC) is a pervasive challenge in prenatal single-gene testing, as it can cause both false-positive and false-negative results. Despite its clinical significance, quantitative tolerance thresholds for MCC in whole exome sequencing (WES) and Sanger sequencing remain limited. Methods: We established a gradient contamination model (5–95%) using genomic DNA from 20 mother–child pairs and assessed the detection fidelity for single-nucleotide variants (SNVs) and insertions/deletions (InDels) in two clinically relevant scenarios using both WES and Sanger sequencing—Scenario 1 (Fetus Heterozygous/Mother Wild-type) and Scenario 2 (Fetus Wild-type/Mother Heterozygous). Results: In Scenario 1, analysis of the 20 loci evaluated by both WES and Sanger sequencing revealed false-negative rates of 0% when MCC ≤ 30%, which then increased sharply across the 30–70% MCC range and reached 100% at 95% MCC. In Scenario 2, analysis of 12,627 WES loci showed a false-positive rate of ≤1.0% when MCC ≤ 10%, which then increased from 1.0% to 76.6% within the 10–30% MCC range and climbed to 89.8% at 50% MCC. A similar pattern was observed for the 20 loci analyzed by both WES and Sanger sequencing in this scenario. These findings potentially support a three-tier stratification: low-risk (MCC ≤ 30% in Scenario 1 and ≤10% in Scenario 2), moderate-risk (30% < MCC < 70% in Scenario 1, 10% < MCC < 30% in Scenario 2), and high-risk (MCC ≥ 70% in Scenario 1 and ≥30% in Scenario 2). Conclusions: Our findings reveal a clear directional asymmetry of MCC tolerance between the two scenarios, indicating that MCC tolerance depends not only on contamination level but also on the genotype of the contaminating DNA. We further propose a clinical reference framework based on a three-tier risk stratification for prenatal single-gene testing, which can guide result interpretation and laboratory decision-making, including decisions about reliable reporting, orthogonal validation, or re-sampling. Full article

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article

Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts in dynamic scenes due to the temporal gap between sequential exposures. In this article, we categorize the literature on two important topics on HDR imaging: multi-exposure fusion (MEF) and ghost removal. Conventional filter-based and data-driven methods are studied in pixel space and feature space. For popular deep learning-based approaches, we provide a granular taxonomy based on their alignment and fusion domains: pixel-space methods, which typically employ explicit motion compensation such as optical flow or spatial transformers, and feature-space methods, which leverage implicit alignment through deformable convolutions, attention mechanisms, or latent representation merging. Representative works are compared across different supervision settings, and key design principles are summarized. In addition, this survey summarizes commonly used datasets and evaluation metrics, discussing their applicability under diverse output forms. Finally, major bottlenecks and promising directions for future research are outlined. Full article

The probability estimation of agrometeorological damage risk is the core technical support for consolidating the agricultural disaster prevention and reduction system and ensuring the sustainable development of national agriculture. It has been widely applied in agricultural production and agricultural insurance. Based on the crop planting area data, as well as the damaged crop area data (damage-affected, damage-stricken, and dead harvest) of 31 provinces and municipalities from 1980 to 2018, this study creatively builds a comprehensive damage strength index. After that, this study obtains accurate risk probability estimation results of five meteorological damage types by using the parameters of the nonparametric normal information diffusion model. The results show that the risk probability of comprehensive meteorological damage is the largest, followed by drought, flood, wind and hail, and freezing. Flood in Hubei, drought in NeiMenggol, windstorm and hailstorm in Qinghai, freezing damage in Hainan, and comprehensive meteorological damage in NeiMenggol have the highest risk probability. The regions where various meteorological damage occur show different distribution characteristics, which is closely related to the latitude and longitude and topography of China. These findings indicate that it is necessary to understand the overall patterns of agrometeorological damage risks and consider their internal heterogeneity, in order to take targeted prevention and control measures to avoid systemic risks in agricultural production and safeguard sustainable and high-quality agricultural development. Full article

Under the dual pressures of increasing fragmentation of farmland and profound changes in the agricultural labor force, the problem of abandoned farmland in rural China has become increasingly prominent, posing a serious threat to food security. Previous studies have mainly focused on the impact of socioeconomic factors on farmland abandonment behavior; however, there remains a lack of systematic investigations into the underlying mechanisms through which farmland fragmentation influences farmers’ land-use decisions. Therefore, this study applies behavioral theory to construct a “fragmentation–mechanization–abandonment” analytical framework to analyze the potential mechanisms of land-use decision-making. Using micro-level household survey data from multiple provinces across China, we conduct empirical tests through Probit models and Karlson Holm and Breen (KHB) mediation effect analysis. Our findings include: (1) Farmland fragmentation significantly increases the probability of farmland abandonment, which remains robust across multiple tests. (2) Farmland fragmentation indirectly influences abandonment behavior through the mediating variable of agricultural mechanization level, but this mechanism plays only a partial mediating role. (3) Heterogeneity tests indicate that the positive effect of farmland fragmentation is significantly stronger among households with low access to finance than among those with high access to finance. This influence is also stronger among households with high farmland accessibility. This study indicates that abandonment governance requires moving beyond a singular focus on land consolidation and shifting toward a synergistic approach that combines differentiated regional strategies with targeted support for smallholders. From a micro-mechanism perspective, this study provides empirical evidence for targeted implementation of land consolidation and livelihood support for smallholders, offering important policy implications for achieving agricultural modernization. Full article

The family care of left-behind children often combines in-person grandparental caregiving with parental remote parenting; yet, how these two forms of care jointly relate to children’s mental health remains underexplored. Drawing on family systems theory, this study used data from 6317 rural left-behind children cared for by grandparents in the 2023 Hubei Province Survey of Vulnerable Children. Latent class analysis (LCA) and regression models were applied to identify grandparenting styles, parental remote parenting, and their interaction. Grandparenting styles were identified as warmly controlling, indifferent punitive, and mildly interventional. Compared with the mildly interventional style, the warmly controlling style was associated with higher resilience and lower psychological distress, whereas the indifferent punitive style showed the opposite pattern. Parental remote parenting was associated with higher resilience and lower psychological distress, but its strength varied across grandparenting contexts. In the indifferent punitive style, its positive association with resilience was weaker; in the warmly controlling style, its negative association with psychological distress was weaker. These findings suggest that parental remote parenting serves as a conditional family resource embedded in grandparental care environments, contributing to the mental health of left-behind children. The study shows the asymmetric role of intergenerational coparenting in psychological resilience and distress. Full article

The Duanfengshan deposit, located in the Mufushan area of the middle Jiangnan Orogen, is a large-scale pegmatite-type Nb-Ta deposit. Previous studies have explored the genetic linkage between regional magmatic evolution and Nb-Ta mineralization; however, the specific roles played by magmatic and hydrothermal processes in rare-metal mineralization remain poorly constrained. Focusing on the newly discovered No. 124 dyke with Li-Nb-Ta mineralization, this study systematically divides it into four internal zones, including the graphic texture zone (GT), coarse-grained albite zone (CGA), fine-grained albite zone (FGA), and lepidolite–quartz core (LQ). Monazite U-Pb age of 134.6 ± 1.1 Ma precisely constrains the emplacement age of the dyke to the Early Cretaceous. Coltan in the CGA and FGA exhibit primary homogeneous or oscillatory zoning and are identified as columbite–(Mn), whereas those in the LQ display patchy texture or Ta-enriched overgrowth rims. Mica compositions exhibit continuous evolutionary trends from GT to LQ, with progressive decreases in K/Rb and K/Cs ratios and synchronous increases in F, Li, Cs, Rb, and Ta contents. Such compositional variations drive the transformation of mica species from muscovite, via Li–phengite and zinnwaldite (Li–muscovite), to metasomatic lepidolite. Integrated magmatic–hydrothermal evolution controls overall Li-Nb-Ta mineralization of Dyke No. 124. Early Nb-Ta-Li pre-enrichment within GT and CGA zones is dominated by magmatic fractional crystallization. Mica-based Li-Cs-Rb Rayleigh fractionation evidence demonstrates that the FGA may represent a transitional stage governed by volatile-rich, fluid-unsaturated melts. Massive albite crystallization elevates melt ASI values, lowers coltan solubility, and thus induces extensive Nb-Ta mineral precipitation. Subsequent metasomatism by Ta-Li-F-rich hydrothermal fluids further causes prominent secondary Li and Ta enrichment. Compared with the global typical LCT pegmatites, Dyke No. 124 possesses highly evolved magmatic differentiation. Zone-specific mica geochemical signatures are well coupled with Nb-Ta and Li mineralization, highlighting the favorable integrated Li-Nb-Ta mineralization potential of the dyke. This study improves regional Li-Nb-Ta mineralization theories and supplies reliable geological constraints for further rare-metal prospecting in the Duanfengshan area. Full article

The ecological risks brought by sulfadiazine (SDZ) residues in the environment have put forward requirements for efficient antibiotic treatment technologies. In this study, biogenic manganese oxides (BMOs) were synthesized using the bacterium Stenotrophomonas maltophilia DT1, and a BMO/syringaldehyde (SYR) system was constructed for SDZ degradation to investigate its performance, mechanism, and potential application. Results showed that the DT1-synthesized BMO contained Mn (II/III/IV) and defect-related oxygen species, which enabled the BMO to participate in SYR activation and SDZ transformation. A total of 99.67% of 10 mg/L SDZ was removed within 3 h under optimized conditions. Humic acid and most environmental ions had no significant interference with the system, except for slight inhibition by Fe³⁺ and Mn²⁺. Three degradation pathways of SDZ were elucidated through the identification of five transformation products and density functional theory calculations. ECOSAR toxicity prediction and growth inhibition of Escherichia coli revealed that the degradation products exhibited significantly reduced toxicity. Furthermore, the BMO exhibited 4.41–17.47 times higher SYR-mediated SDZ transformation efficiency than chemically synthesized manganese oxide (CMO) and showed good performance in reuse tests and real water matrices. This study provides an efficient and eco-friendly green technology for the remediation of SDZ pollution in aquatic environments and provides potential support for the removal of refractory pollutants mediated by BMO. Full article

Accurately characterizing the anisotropic optoelectronic properties of crystals requires determining the band gaps at specific high-symmetry points in the Brillouin zone. Relying solely on the minimum band gap is insufficient. However, although Graph Neural Networks (GNNs) offer rapid property predictions, conventional models remain trapped in a local real-space paradigm, lacking the global symmetry information necessary to differentiate these high-symmetry energy states. To address this problem, we propose the Symmetry-Information-Enhanced Crystal Graph Neural Network (Symm-CGNN). It explicitly incorporates local atomic environments with global symmetry information, including space groups, crystal systems, material density and lattice constants. The evaluation is performed on a comprehensive dataset including 3D (Materials Project) and 2D (2DMatpedia). The results demonstrate that Symm-CGNN achieves an 18% reduction in Mean Absolute Error (MAE) for high-symmetry band gap prediction compared to the baseline Crystal Graph Convolutional Neural Networks (CGCNN). This approach bridges the representational gap between local atomic coordination and macroscopic symmetry. Consequently, it provides a robust and efficient machine-learning paradigm for the high-throughput screening of materials with anisotropic optoelectronic properties. Full article

Under the “dual carbon” goals (carbon peak and carbon neutrality), the new energy vehicle (NEV) industry has become a strategic focus of great-power competition, and the resilience of its international supply chain is critical to industrial security and development initiatives. As a traditional automobile manufacturing hub in China, Hubei Province faces increasingly prominent global risks in its supply chain during the transition to NEVs; scientifically evaluating and enhancing its international supply chain resilience is therefore of great practical significance. Drawing on supply chain resilience theory, this paper constructs an evaluation index system comprising 18 specific indicators across four dimensions: robustness, redundancy, agility, and innovativeness. To overcome the limitations of a single weighting method, a combined subjective and objective weighting approach that integrates the Analytic Hierarchy Process (AHP) and the entropy weight method was employed to determine indicator weights. Subsequently, the TOPSIS model was applied to measure the supply chain resilience level of Hubei Province from 2018 to 2025, with horizontal comparisons conducted against Shanghai and Guangdong. Finally, an obstacle degree model was introduced to quantitatively diagnose the key factors constraining resilience improvement. The results indicate that the international supply chain resilience of Hubei’s NEV industry has shown a continuous upward trend. By 2025, it ranks in the first tier alongside Guangdong (with closeness coefficients of 0.8180 and 0.8181, respectively), approaching the level of Shanghai. Weaknesses are concentrated primarily in the agility dimension, while upstream resource dependence remains a salient issue within the robustness dimension. “External dependence on key raw materials,” “average recovery time from logistics disruptions,” and “level of supply chain information sharing” are still the top three obstacle factors. Fuzzy-set qualitative comparative analysis (fsQCA) further reveals that low resource autonomy and slow logistics recovery are core conditions leading to low resilience, and the coupling of multiple obstacle factors amplifies the risk transmission effect. Based on this, this study proposes optimization recommendations focusing on foundation strengthening and chain consolidation, digital chain connectivity, and innovation–chain integration, in order to enhance the resilience of the international supply chain for new energy vehicles in Hubei. This research provides a methodological reference for evaluating the supply chain resilience of regionally distinctive industries and offers a quantitative basis for Hubei Province and related enterprises to formulate targeted improvement strategies. Full article