Search Results (411)

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

The interaction between epigenetic mechanisms and the gut microbiome is potentially crucial for the development and maintenance of intestinal health. Lysine acetylation, an important post-translational modification, plays a complex and critical role in the epigenetic regulation of the host by the gut microbiota. However, there are currently no reports on how gut microbiota dysbiosis affects host physiology in early life through global lysine acetylation. In this study, we constructed a mouse model of gut microbiota dysbiosis using antibiotic cocktail therapy (ABX). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the cecum, we analyzed the cecal lysine acetylome and proteome. As a result, we profiled the lysine acetylation landscape of the cecum and identified a total of 16,579 acetylation sites from 5218 proteins. Differentially acetylated proteins (DAPs) are involved in various metabolic pathways, including the citrate cycle (TCA cycle), butanoate metabolism, pyruvate metabolism, glycolysis/gluconeogenesis, and fatty acid biosynthesis. Moreover, both glycolysis and gluconeogenesis are significantly enriched in acetylation and protein modifications. This study aimed to provide valuable insights into the epigenetic molecular mechanisms associated with host protein acetylation as influenced by early-life gut microbiota disturbances. It reveals potential therapeutic targets for metabolic disorders linked to gut microbiota dysbiosis, thereby establishing a theoretical foundation for the clinical prevention and treatment of diseases arising from such dysbiosis. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

The investigation of phosphorus metabolism and regulatory mechanisms is conducive to maintaining stable production of crops within a low-phosphorus environment. In phosphorus signal transduction, a few phosphorus starvation response (PHR) transcription factors were identified to bind to the characteristic cis-element, namely the PHR1 binding sequence (P1BS). While the molecular function of the PHR transcription factor has been intensively elucidated, here, we explore a novel transcription factor, BnaA01.KAN3, that undergoes specific binding to the P1BS by yeast one-hybrid and electrophoretic mobility shift assays, and its expression is induced with low-phosphorus stress. BnaA01.KAN3 possessed transcriptional activation and was located in the nucleus. The spatiotemporal expression pattern of BnaA01.KAN3 exhibited tissue specificity in developmental seed, and its expression level was especially high 25–30 days after pollination. Regarding the phenotype analysis, the independent heterologous overexpression lines of BnaA01.KAN3 in Arabidopsis thaliana exhibited not only significantly longer taproots but also an increased number of lateral roots compared to that of the wild type undergoing low-phosphorus treatment, while no differences were seen under normal phosphorus conditions. Furthermore, these lines showed higher anthocyanin and inorganic phosphorus contents with normal and low-phosphorus treatment, suggesting that BnaA01.KAN3 could enhance phosphorus uptake or remobilization to cope with low-phosphorus stress. In summary, this study characterized the transcription factor BnaA01.KAN3 that modulates low-phosphate adaptation and seed development, providing insights for improving phosphorus use efficiency and yield traits in Brassica napus. Full article

Circular RNAs (circRNAs) are single-stranded noncoding RNAs with a covalently closed loop structure. They are known for their stability, abundance, and highly conserved nature. Their expression is often specific to tissues or developmental stages. They interact with microRNAs (miRNAs) and RNA-binding proteins (RBPs) and they undergo N⁶-methyladenosine (m⁶A) modifications, further affecting gene transcription and translation. Increasing evidence over the past decades has revealed that dysregulated circRNA expression is associated with various neurological disorders, particularly the glioma, one of the most malignant tumors with a poor prognosis. Due to the presence of the blood–brain barrier (BBB) and drug resistance, conventional therapeutic approaches have shown limited efficacy. Recently, increasing attention has been directed toward precisely targeted therapies, with circRNAs emerging as promising molecules for cancer treatment. Studies indicate that circRNAs play a key role in glioma proliferation and metastasis. Substantial evidence indicates that exosomes can package circRNAs and facilitate their transport across the BBB into brain tissue, highlighting the potential of circRNAs as therapeutic targets for glioma. This review summarizes circRNAs’ functional mechanisms, clinical application relevance, and current limitations. It offers future research directions in this evolving field, aiming to encourage further research on circRNAs’ therapeutic applications and contribute to the development of novel glioma-treatment strategies. Full article

Understanding the relationships between diversity and functional traits in plant communities is essential for elucidating ecosystem functions, forecasting community succession, and informing ecological restoration efforts in arid regions. Although the current research on plant functional traits and diversity has improved our ability to predict ecological functions, there are still many problems, such as how environmental changes affect the relationship between species diversity and plant functional traits, and how these interactions affect plant community functions. We examined the relationships among leaf and fine root functional traits, species diversity, and functional diversity at the community level, along with their environmental interpretations, in a plant community within the desert–oasis transition zone of the Hexi Corridor, where habitats are undergoing significant small-scale changes. During dune succession, plant community composition and diversity exhibited significant variation. Plants are adapted to environmental changes through synergistic combinations of above-ground and below-ground traits. Specifically, plants in fixed dunes adopted a “slow investment” strategy, while those in semi-fixed and mobile dunes employed a “fast investment” approach to resource acquisition. A strong coupling was observed between plant community functional traits and species diversity. Soil phosphorus content and compactness emerged as primary factors influencing differences in plant community functional traits and composition. These soil factors indirectly regulated fine root functional traits and diversity by affecting species diversity, thereby driving community succession. Our study elucidates the “soil—diversity—community functional trait” linkage mechanisms in the successional process of desert plants. This research provides scientific support for the restoring and reconstruction of degraded ecosystems in arid zones. Full article

Novel peptide-zinc chelates (DSPs-Zn) with a zinc content of 186.94 mg/g were synthesized from deer tendon peptides at pH 6, 60 °C, 60 min, and peptide-zinc mass ratio of 1:3. Ultraviolet-visible absorption spectroscopy (UV) and Fourier transform infrared spectroscopy (FTIR) demonstrated that the chelation sites of the deer tendon polypeptides (DSPs) with zinc ions were located at the carboxyl oxygen and amino nitrogen atoms of the peptides. Amino acid analysis showed that aspartic acid, glutamic acid, lysine, and arginine play important roles in the chelation process. In vitro simulated gastrointestinal digestion studies showed that DSPs-zinc exhibited higher stability than zinc sulfate and zinc gluconate in the pH range 2–8 and in a simulated gastrointestinal digestion environment. The above experimental results suggest that DSPs-Zn has the potential to be used as a novel zinc nutritional supplement. Full article

Due to the difficulty of creating directional fractures efficiently and accurately with existing non-explosive rock-breaking methods, a directional fracturing technique utilizing a coal-based solid waste expansive agent, termed the instantaneous expansion with a single fracture (IESF), has been developed. IESF can generate high-pressure gases within 0.05–0.5 s and utilize gas pressure to achieve directional rock fragmentation. The rock-breaking mechanisms under double-borehole conditions of conventional blasting (CB), shaped charge blasting (SCB), and IESF were studied by theoretical analysis, numerical simulation, and in situ test. The gas pressure distribution within directional fractures of IESF was determined, and the crack propagation criterion between double-borehole was established. Numerical simulation results indicated that the stress distribution in CB was random. SCB exhibited tensile stress of −10.89 MPa in the inter-borehole region and −8.33 MPa on the outer-borehole region, while IESF generated −14.47 MPa and −12.62 MPa in the corresponding regions, demonstrating that stresses generated between adjacent boreholes can be superimposed in the inter-hole region. In CB, strain was concentrated along main fractures. SCB exhibited strains of 7 mm and 8 mm in the shaped charge direction, while non-shaped charge directions showed a strain of 1.5 mm. For IESF, strain in the shaped charge direction measured 6 mm, compared to 1 mm in non-shaped charge directions, resulting in superior directional fracture control. In situ test results from Donglin Coal Mine demonstrated that IESF can form superior directional rock-breaking efficacy compared to both CB and SCB, with the average crack rates of 95.5% by IESF higher than 85.0% by SCB. This technique provides a non-explosive method that realizes precise control of the direction of cracks while avoiding the high-risk and high-disturbance problems of explosives blasting. Full article

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

To elucidate the gas adsorption characteristics of medium-rank coal, this study collected samples from fresh mining faces in the Qinshui Basin. A series of experiments were conducted, including low-temperature carbon dioxide adsorption, low-temperature liquid nitrogen adsorption, mercury intrusion, and methane isothermal adsorption experiments, which clarify the pore structure characteristics of medium-rank coals, reveal the gas adsorption behavior in medium-rank coal, and identify the control mechanism. The results demonstrate that the modified Dubinin–Radushkevich (D-R) isothermal adsorption model accurately describes the gas adsorption in medium-rank coal, with fitting errors remaining below 1%. Comprehensive pore structure analysis reveals that the coal pore volume consists primarily of absorption pores (<2 nm), transitional pores (10–100 nm), and seepage pores (>100 nm), while the specific surface area is predominantly contributed by absorption pores (<2 nm). At low pressures, gas molecules form monolayer adsorption on absorption pore (<2 nm) and adsorption pore (2–10 nm) surfaces. With increasing pressure, multilayer adsorption dominates. As pore filling approaches the maximum capacity, the adsorption rate decreases progressively until reaching an equilibrium, at which point the adsorption capacity attains its saturation limit. The adsorption data of the gas in medium-rank coal can be explained by the improved D-R isothermal adsorption model. The priority of gas filling in pores is different, and the absorption pore is normally better than the adsorption pore. The results provide a new idea and understanding for the further study of the coalbed gas adsorption mechanism. Full article

The formation of unstable solid electrolyte interphases (SEIs) on the surface of lithium metal anodes poses a significant barrier to the commercialization of lithium metal batteries (LMBs). Rational modulation of solvation structures within the electrolytes emerged as one of the most effective strategies to enhance interfacial stability in LMBs; however, this approach often compromises the structural stability of the bulk electrolyte. Herein, we present an innovative method that improves interface stability without adversely affecting the bulk electrolyte’s structural stability. By employing ZSM molecular sieves as efficient ion channels on the lithium metal anode surface—termed ZSM electrolytes—a more aggregated solvation structure is induced at the lithium metal interface, resulting in an anion-rich interphase. This anion-enriched environment promotes the formation of an SEI derived from anions, thereby enhancing the stability of the lithium metal interface. Consequently, Li||Cu cells utilizing the ZSM electrolyte achieve an average coulombic efficiency (CE) of 98.76% over 700 h. Moreover, LiFePO₄||Li batteries exhibit stable cycling performance exceeding 900 cycles at a current density of 1 C. This design strategy offers robust support for effective interfacial regulation in lithium metal batteries. Full article

Herein, a facile strategy was proposed to enhance the gas sensing performance of SnO₂ for H₂ by regulating its crystalline phase composition. Sn-based metal–organic framework (Sn-MOF) precursors with different morphologies were synthesized by introducing the surfactant cetyltrimethylammonium bromide (CTAB). Upon calcination, these precursors yielded either mixed-phase (orthorhombic and tetragonal, SnO₂-C) or single-phase (pure tetragonal, SnO₂-NC) SnO₂ nanoparticles. Structural characterization and gas sensing tests revealed that SnO₂-C exhibited a high response of 7.73 to 100 ppm H₂ at 280 °C, more than twice that of SnO₂-NC (3.75). Moreover, SnO₂-C demonstrated a faster response/recovery time (10/56 s), high selectivity, a ppb-level detection limit (~79 ppb), and excellent long-term stability. Notably, although the addition of CTAB reduced the specific surface area of SnO₂, the resulting lower surface area minimized oxygen exposure during calcination, facilitating the formation of a mixed-phase heterostructure. In addition, the calcination atmosphere of SnO₂-C (flowing air or Ar) was adjusted to further investigate the role of the crystal phase in gas sensing performance. The results clearly demonstrated that mixed-phase SnO₂ exhibited superior sensing performance, achieving a higher sensitivity and a faster response to H₂. These findings underscored the critical role of crystal phase engineering in the design of high-performance gas sensing materials. Full article

Traditional track-driven deep-sea nodule mining solutions significantly disrupt seabed ecosystems, making them unsuitable for commercial application. In contrast, ROV-like alternatives, such as the hovering mining vehicle, or HMV, offer substantial improvement in this regard and are deemed to be a viable way forward. This paper proposes an adaptive neural network fuzzy sliding mode controller architecture for the underwater trajectory tracking of HMV. The algorithm, named the Adaptive Radial Basis Function Neural Network Fuzzy Sliding Mode Controller (ARFSMC), replaces modeled vehicle dynamics with a radial basis function neural network (RBFNN). To enhance disturbance rejection, an adaptive mechanism is applied to the RBFNN output weighting matrix. Additionally, a fuzzy inference system (FIS) is implemented as the switching term, replacing the traditional signum function, to reduce high-frequency oscillations in the control signal. The stability of the algorithm under unknown external disturbance was confirmed via Lyapunov stability analysis. To validate the ARFSMC’s performance, three numerical simulation cases were conducted, each designed to reflect an expected operation scenario of the HMV, through which the tracking performance of the ARFSMC under time-varying system inertia is validated and benchmarked against conventional sliding mode control (CSMC) and double-loop sliding mode control (DSMC). The simulation results confirm that comparing the above two controllers, the root mean square error (RMSE) of the ARFSMC is reduced by 15.0% and 11.4%, respectively. And when comparing the CSMC, the chattering is reduced by 97.8%. Both indicate their high robustness and superior performance in tracking control. The controller development and numerical validation in this work are aimed at the trajectory tracking challenge of the HMV in deep-sea mining operation. The dynamical modeling of the vehicle is based on parameters of the HaiMa ROV. External disturbance from currents were considered as sinusoidal functions modified with random noise. Full article

α-Lipoic acid (ALA) is a naturally occurring compound with diverse biological functions, widely distributed in animal and plant tissues. It has attracted considerable attention due to its versatile therapeutic potential. However, despite these promising prospects, the clinical application of ALA remains limited by its low bioavailability and chemical instability and an incomplete understanding of its multifaceted mechanisms across various diseases. This review provides a comprehensive overview of the biochemical properties of ALA, including its direct free-radical-scavenging activity, regeneration of endogenous antioxidants, chelation of metal ions, and modulation of inflammatory responses. We also highlight the current evidence regarding ALA’s therapeutic roles and efficacy in major diseases, such as neurodegenerative disorders, lung diseases, cardiovascular diseases, and diabetes. Furthermore, recent advancements and innovative strategies in ALA-based derivatives and drug-delivery systems are summarized, emphasizing their potential to address complex diseases and the necessity for further translational studies. This review aims to provide a theoretical foundation for the rational design of ALA-based therapies, thereby supporting future clinical applications and the optimization of therapeutic strategies. Full article

Pemphigus-associated interstitial lung disease (P-ILD) is a severe complication observed in pemphigus patients that is characterized by pulmonary interstitial inflammation and fibrosis. This study investigated the role of anti-desmoglein (Dsg) 1/3 antibodies in P-ILD pathogenesis and evaluated the therapeutic potential of molecular hydrogen (H₂). Using a BALB/cJGpt mouse model, we demonstrated that anti-Dsg 1 antibodies, but not anti-Dsg 3 antibodies, induced interstitial inflammation and fibrosis. Immunofluorescence staining confirmed IgG deposition in the alveolar epithelium, suggesting immune complex formation and epithelial damage. Gene expression analysis revealed elevated pro-inflammatory cytokines (IL-1β, IL-13) and upregulated pro-fibrotic markers (α-SMA, S100A4, TGF-β, and collagen genes) in P-ILD progression. Elevated oxidative stress and impaired ROS metabolism further implied the role of oxidative damage in disease pathogenesis. To assess H₂’s therapeutic potential, hydrogen-rich water was administered to P-ILD mice. H₂ treatment significantly reduced oxidative stress, attenuated interstitial inflammation, and prevented pulmonary fibrosis. These protective effects were attributed to H₂’s antioxidant properties, which restored the pro-oxidant–antioxidant balance. Our findings underscore the critical role of anti-Dsg 1 antibodies and oxidative stress in P-ILD and highlight H₂ as a promising therapeutic agent for mitigating anti-Dsg 1 antibody-induced lung injury. Full article