Search Results (2,915)

Regional reassessments of agricultural carbon emission efficiency are essential for improving the sustainability of food production systems under climate constraints. This study evaluates agricultural carbon emission efficiency (ACEE) across China’s major grain-producing zone (GPZ), major grain-consuming zone (GSZ), and grain production–consumption balanced zone (GBZ) during 2003–2022, excluding Hong Kong, Macao, Taiwan, and Tibet due to data limitations. A super-efficient EBM–GML model incorporating both desirable and undesirable outputs is employed to measure ACEE at the provincial level, with comparisons conducted within each functional zone and nationally unified efficiency values used as a benchmark. Spatial dependence is examined using Moran’s I, and a spatial Durbin model is applied to identify driving factors and spatial spillover effects. The results indicate that the average efficiency levels differ systematically across functional grain zones, following the order GBZ > GPZ > GSZ, while several provinces experience notable changes in their relative rankings. Carbon emissions increase in the earlier period and decline in later years, whereas efficiency exhibits an opposite temporal pattern, reflecting a gradual transition of grain production systems from extensive input-driven growth toward more sustainability-oriented practices. Substantial regional disparities in ACEE are also observed. Rational industrial organization and efficient allocation of production resources contribute to positive spillover effects on neighboring regions, whereas natural disasters and inefficient resource distribution tend to weaken such effects. These findings suggest that functional grain zones provide an effective framework for capturing intra-regional heterogeneity and should be adopted as the basic unit for efficiency assessment and the formulation of differentiated governance strategies. Full article

The river ecosystems of the Qinghai–Tibet Plateau, recognized as a vital component of the “Asian Water Tower,” possess unique hydrological conditions and extreme environments that have shaped key indicator groups, most notably zooplankton. The community dynamics and structural characteristics of these zooplankton exhibit regular spatio-temporal distribution patterns across elevational gradients and seasonal successions. However, the intrinsic mechanisms underlying community succession and their correlations with environmental factors remain poorly understood, and the primary environmental drivers influencing community structure require further elucidation. Based on systematic zooplankton surveys and environmental data collection conducted across the Lhasa River basin from 2019 to 2021, this study established a comprehensive species inventory comprising 113 taxa across four major groups, alongside a multi-dimensional environmental dataset. We analyzed the spatio-temporal heterogeneities of zooplankton community structures—including abundance, biomass, and diversity indices—across different seasons and river reaches. The results revealed the composition and seasonal turnover of dominant taxa, with rotifers accounting for 39.82% of the total taxonomic richness. Mean zooplankton abundance and biomass across the basin were 1.18 ind./L and 343.60 × 10⁻⁵ mg/L, respectively, with peak values observed during autumn and within the Chabalang Wetland. The zooplankton community structure in the upstream, midstream, and downstream reaches, as well as associated wetlands, was significantly correlated with specific environmental factors (p < 0.05), including ammoniacal nitrogen (NH₄⁺-N), magnesium (Mg²⁺), total hardness (TH), potassium (K⁺), iron (Fe²⁺), sodium (Na⁺), sulfite (SO₃²⁻), nitrate ion (NO₃⁻), chloride ion (Cl⁻), total phosphorus (TP), and sulfide (S²⁻). Cl⁻, TH, Mg²⁺, SO₃²⁻, and elevation (Ele) were the key environmental drivers significantly influencing zooplankton abundance across seasons (p < 0.05). Furthermore, zooplankton abundance decreased significantly with increasing elevation during the winter. This research deepens our understanding of community assembly mechanisms in plateau river ecosystems and provides a scientific foundation for aquatic biodiversity conservation and ecological management in the Lhasa River basin. Full article

Drought stress severely limits the growth and productivity of Morus alba, yet the molecular mechanisms underlying its adaptation remain poorly understood. Trehalose, an important osmoprotectant and signaling molecule, plays a key role in plant responses to abiotic stress, and its biosynthesis is primarily regulated by trehalose-6-phosphate synthase (TPS). However, the characteristics and potential functions of TPS genes in M. alba have not been systematically investigated. In this study, we identified 11 TPS genes (MaTPSs) in the M. alba genome and performed comprehensive analyses, including phylogenetic relationships, gene structures, conserved motifs, cis-regulatory elements, and expression profiles. Phylogenetic analysis classified MaTPSs into TPS I and TPS II subfamilies, with closer evolutionary relationships to Populus trichocarpa than to Arabidopsis thaliana. Promoter analysis revealed the presence of multiple stress- and hormone-responsive elements, suggesting their potential involvement in abiotic stress regulation. Physiological measurements showed that drought stress significantly increased trehalose accumulation, with a 1.6-fold increase in leaves and a 2.2-fold increase in roots. Expression profiling further demonstrated that six MaTPS genes were upregulated under drought stress, among which MaTPS4, MaTPS9, MaTPS10, and MaTPS11 exhibited significant induction (approximately 5-, 5-, 8-, and 10-fold, respectively). Correlation analysis further indicated that trehalose accumulation was positively associated with all upregulated MaTPS genes (p < 0.05). Taken together, these results suggest that MaTPS genes may be involved in drought-responsive regulation of trehalose metabolism in M. alba. This study provides a valuable foundation for future functional validation and the genetic improvement of drought tolerance in mulberry. Full article

The Qinghai–Tibet Plateau and surrounding mountain regions form one of the world’s most distinctive freshwater environmental gradients. Schizothoracinae are among the most representative endemic fish lineages in these systems and provide a useful model for studying how drainage history, genome evolution, adaptation, and conservation interact. In this review, we synthesize schizothoracine research within an environment–evolution–conservation framework. We examine how drainage history and connectivity shape divergence and gene exchange, how polyploidy and genome remodeling provide the genomic background for adaptive inference, and how phenotypic and population-genomic evidence can be translated into conservation units and management priorities. Across current studies, cold-associated metabolic remodeling and UV-related DNA damage response and repair emerge as the most recurrent molecular themes, whereas hypoxia-related signals are more context-dependent. We further show that morphology, otolith chemistry, age–growth traits, and population structure can strengthen MU/ESU interpretation when integrated with genomic evidence. Future progress will depend on broader chromosome-level genome coverage, more systematic comparison of structural genomic variation, standardized stressor-linked designs, and denser sampling in geomorphic transition zones and putative hybrid regions. Full article

To explore whether the causal chain of “Industry–University–Government Integrated Innovation Ecosystem → Novel Energy Storage Technology Innovation → Dual-Control Energy Targets” can be achieved, this study analyzes panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2010 to 2022. By employing a complex mediation effect model combining dynamic Qualitative Comparative Analysis (QCA) and the dynamic panel system Generalized Method of Moments (GMM) model, this study identifies five configuration pathways for driving innovation in novel energy storage technologies within an integrated innovation ecosystem. These include two industry digitalization–university innovation resource-dominant pathways: a government-light and digitally driven “university–industry” resource-sharing and knowledge-conversion synergy, and an industry leadership pathway embedded with university collaborative innovation under a digitalization framework. Two policy-driven hybrid and industry–leadership synergistic pathways are also extracted: a growth pathway for policy-supported hybrid organizations under insufficient industry digitalization and a policy-driven innovation substitution pathway compensating for the absence of university niche roles. Additionally, a multidimensional collaborative development pathway is identified, reflecting comprehensive collaboration. In the dynamic panel system GMM model, all five pathways collectively suppress total energy consumption and energy intensity, while also indirectly driving the achievement of dual-control energy targets through innovation in novel energy storage technologies. Pathways driven by government-light and digitally facilitated collaboration, industry leadership, and comprehensive collaboration show significant direct negative effects on energy consumption and intensity. However, the policy-driven innovation substitution pathway exhibits limited contribution due to the absence of university innovation components, thereby failing to significantly advance regional dual-control energy goals. Full article

Winter rapeseed (Brassica rapa L.) is an important crop for vegetable oil production in China. However, its productivity is frequently threatened by severe cold waves during winter. To investigate the role of the microtubule cytoskeleton in cold adaptation of winter rapeseed, a microtubule stabilizer paclitaxel (Tax) and a microtubule depolymerizer colchicine (Col) were sprayed on winter rapeseed and transgenic proBrAFP1 Arabidopsis, respectively. The mRNA levels of cold-induced genes, along with cell membrane stability, antioxidant enzyme activities, and hormone levels were assessed under cold stresses of 4 °C and −4 °C. The results showed that low temperature significantly activated the proBrAFP1 promoter activity and increased the mRNA levels of core cold signaling pathway genes, such as C-REPEAT BINDING FACTORS (CBFs), Cyclic Nucleotide-Gated Channel (CNGC), OPEN STOMATA 1 (OST1) and Inducer of CBF EXPRESSION 1 (ICE1). Notably, under low-temperature stress, exogenous application of the microtubule stabilizer Tax markedly suppressed proBrAFP1-driven reporter activity in transgenic Arabidopsis, with consistent inhibition observed across both stem and leaf tissues; meanwhile, the Tax application alleviated reactive oxygen species (ROS) accumulation and mitigated membrane damage. In contrast, under the same low-temperature stress, the Col treatment exacerbated oxidative stress, enhanced lipid peroxidation, and elevated membrane damage. Collectively, these findings establish that microtubule regulators play indispensable roles in the cold stress response of winter rapeseed. It provides new insights into the mechanism by which plant microtubule cytoskeleton regulators mediate the cold response. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

Abies georgei var. smithii (Viguie & Gaussen) is a dominant conifer along the southeastern margin of the Qinghai–Tibet Plateau, where heart rot often develops covertly, complicating forest health monitoring and disease management. Fomitopsis subpinicola B.K. Cui, M.L. Han & Shun Liu is an important causal agent of heart rot affecting A. georgei var. smithii in this region, yet rapid, field-deployable molecular diagnostics of this pathogen remain limited. Here, we developed and evaluated two TEF1α-based isothermal platforms for specific detection of F. subpinicola: RAA and LAMP. To reduce potential cross-reactivity, TEF1α sequences from representative taxa within the F. pinicola species complex and closely related non-complex species were aligned for primer/probe design. Candidate RAA primers were screened by gel electrophoresis to select an optimal pair, and two LAMP primer sets were compared by specificity testing to identify the best-performing set. Both assays specifically detected F. subpinicola with no cross-amplification in the tested non-target fungi. Limits of detection were 9.97 copies/μL for fluorescent RAA (25 min), 9.97 × 10² copies/μL for RAA-LFD (15 min), and 9.97 × 10³ copies/μL for LAMP (35 min). In 30 increment core samples from A. georgei var. smithii, all methods consistently detected samples with obvious decay, while fluorescent RAA additionally yielded positives in some apparently asymptomatic samples, indicating promise for early or low-abundance screening. Together, these assays constitute a tiered and application-oriented detection system, enabling flexible selection of diagnostic approaches according to sensitivity requirements, operational conditions, and field surveillance needs for heart rot of A. georgei var. smithii. Full article

Deep learning has significantly improved landslide identification from remote sensing imagery, but accurately detecting multi-scale landslides under complex backgrounds remains challenging. This study proposes a lightweight YOLOv8-based model, namely YOLO-BEG, incorporating three improvements: a bidirectional feature pyramid network (BiFPN) for enhanced multi-scale feature fusion, an embedded Gaussian attention system (EGS) to improve discrimination under complex backgrounds, and a generalized intersection over union (GIoU) loss to optimize boundary localization. The model was evaluated on two datasets: a vegetation-covered Southwest landslide database and the Sichuan–Tibet Highway database. On the Southwest database, YOLO-BEG improved Precision, Recall, and F1-score by 16%, 13%, and 15% compared with YOLOv8, while using only one tenth of the parameters of Mask R-CNN. In the Sichuan–Tibet Highway database, which has more diverse background conditions, YOLO-BEG outperformed Mask R-CNN and Faster R-CNN by 32% and 13% in F1-score, respectively. These results demonstrate that YOLO-BEG is able to operate with fewer parameters and yield high-precision identification of landslides with different scales under complex backgrounds, making it a rapid and accurate tool for landslide identification. Full article

Wuweiganlu (WGL) is a traditional formulation widely applied in the treatment of rheumatoid arthritis (RA), yet the identity of its bioactive constituents remains inadequately defined. In this study, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and untargeted serum metabolomics were employed to characterize the active components of WGL. Fraxin was identified as a principal compound from WGL. To investigate its therapeutic mechanism in RA, a series of in silico and experimental approaches were conducted. Network pharmacology analysis and RNA sequencing identified heat shock protein family member 8 (HSPA8) as a potential molecular target of Fraxin, which was further validated by molecular docking studies. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that Fraxin exerts its effects primarily by modulating cell apoptosis through the PI3K signaling pathway. In vitro experiments demonstrated that Fraxin significantly reduced inflammatory responses and downregulated HSPA8 expression in lipopolysaccharide (LPS)-stimulated fibroblast-like synoviocytes (FLs) and macrophages. In vivo, Fraxin administration markedly reduced paw swelling, alleviated bone deformities, and improved bone volume fraction (BV/TV) in male IL1RA-deficient mice exhibiting spontaneous arthritis. Histological analysis confirmed that Fraxin attenuated joint inflammation by modulating the inflammatory microenvironment. Additionally, Fraxin inhibited synovial hyperplasia by regulating mitochondrial membrane potential collapse in FLs. Functional assays revealed that this regulation occurred via the inhibition of HSPA8/PI3K/AKT signaling axis, thereby suppressing aberrant FLS proliferation and contributing to the attenuation of RA progression. Full article

Accurate prediction of embankment settlement and evaluation of its serviceability in permafrost regions are significantly challenged by scarce monitoring data and dynamic, non-stationary settlement processes. To address this, an integrated framework combining change-point detection with a novel dynamic prediction model is proposed. Analysis of long-term monitoring data from the Qinghai–Tibet Railway using the Pettitt test revealed a key change point around 2015, indicating a transition towards stabilization. Subsequently, an SAA-GRU-LSTM hybrid model, employing a dynamic compensation prediction strategy, was developed. The model successfully utilized only early-stage data to forecast future settlement trends, demonstrating robust performance in adapting to the identified abrupt change. Furthermore, by applying established engineering serviceability criteria to both historical and predicted data, the framework enables a dynamic and prospective serviceability assessment. This methodology provides a practical tool for the maintenance and risk management of infrastructure in permafrost environments under conditions of data scarcity and process uncertainty. Full article

Grain-scale heterogeneity strongly influences hydraulic fracture initiation and trajectory in crystalline rocks, yet its contributions to non-planar growth and the interaction of multiple nearby cracks remain insufficiently quantified. To address this gap, we perform numerical experiments on a model containing two parallel pre-existing cracks using a hydro-mechanical phase-field framework, systematically quantifying how mineral distribution and axial compression govern non-planar hydraulic fracture growth and inter-fracture competition. The results demonstrate that mineral distribution is the primary driver of fracture complexity. Even within the same Voronoi tessellation, redistributing minerals alone yields markedly different trajectories, deflections, branching patterns, and final morphologies. Furthermore, non-planar growth follows a stepwise, energy-threshold-driven mechanism. When cracks penetrate strong grains or undergo large-angle deflections, propagation is impeded, and injection pressure builds up. Once a critical energy threshold is reached, accumulated energy is rapidly released along the path of minimum incremental energy, manifested as abrupt pressure drops and rapid crack advance. Additionally, the two nearby fractures exhibit strong mechanical competition. Despite negligible hydraulic interference in low-permeability granite, early growth of one fracture redistributes stresses and suppresses the driving force of the other, resulting in asymmetric development. Finally, axial compression primarily governs the overall propagation orientation and influences local failure modes but has a limited effect on peak pressure relative to mineral distribution. Full article

The Qinghai–Tibet Plateau is undergoing rapid warming and humidification, with altered precipitation regimes increasingly affecting soil nitrogen cycling and N₂O emissions. Denitrification—a key nitrogen transformation pathway—is particularly sensitive to these hydrological changes. Here, we investigated the response of nirK-type denitrifying microbial communities to altered precipitation in an alpine wetland on the northern shore of Qinghai Lake. Using a long-term precipitation manipulation platform with five gradients (ambient, ±25%, and ±50%), we integrated high-throughput sequencing with bioinformatics to systematically assess community shifts. Short-term precipitation treatments did not significantly alter alpha diversity, but markedly restructured community composition. Extreme wetting (+50%) increased within-group heterogeneity. At the phylum level, Proteobacteria remained dominant across all treatments, whereas extreme drought (−50%) suppressed Planctomycetes. At the genus level, Ochrobactrum was enriched under reduced precipitation, while Rhodopseudomonas increased under increased precipitation. Functional predictions indicated that reduced precipitation enhanced nitrogen fixation potential, whereas increased precipitation favored nitrate respiration. Soil pH and carbon fractions were the key environmental filters driving community variation. Ecological process analysis revealed that community assembly was entirely governed by deterministic processes, specifically variable selection. Together, these findings elucidate how precipitation shifts reconfigure the structure and functional potential of denitrifying microbial communities in alpine wetlands, primarily via changes in soil pH and moisture under variable selection. This work provides critical insights into microbial regulation of the nitrogen cycle on the Tibetan Plateau under ongoing climate change. Full article

Groundwater is a strategic resource for maintaining ecological balance and supporting human development in arid inland basins. However, under the dual pressures of climate change and human activities, it faces threats in both quantity and quality. This study selects the Chahan Usu River watershed in the eastern Qaidam Basin, a typical arid inland basin on the Tibetan Plateau, to assess the current quality of groundwater resources and reveal the formation mechanisms and material sources of its hydrochemistry. The results show that the groundwater in the watershed is generally weakly alkaline, with some areas exhibiting high salinity. The dominant cations and anions are Na⁺ and Cl⁻, respectively. The hydrochemical type is mainly Cl-Na, with a minority being mixed Cl-Mg·Ca. Overall, the groundwater in the watershed is suitable for domestic use. However, in the middle and lower reaches of the Chahan Usu River, nitrate and ammonia nitrogen contamination reduce its suitability. Meanwhile, although long-term use of this groundwater would not lead to soil degradation, its widespread high salinity and high sodium content make it unsuitable for irrigation. Water–rock interactions with evaporites and silicate rocks are the main mechanisms controlling groundwater chemistry in the watershed. Among them, halite minerals contribute most of the Na⁺ and Cl⁻, while sulfate minerals provide Ca²⁺ and SO₄²⁻. In addition, cation exchange is widespread. This study provides a reference for ensuring the security and sustainable development of groundwater resources on the plateau. Full article