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Abstract Background Insulin resistance is linked to atherosclerotic cardiovascular diseases and stroke, whereas less is known about adipose tissue specific insulin resistance and outcomes after ischemic stroke. This study aimed to estimate the association between adipose tissue specific insulin resistance and prognosis of nondiabetic patients with ischemic stroke. Methods Patients with ischemic stroke without a history of diabetes mellitus in the Third China National Stroke Registry were included. Adipose tissue specific insulin resistance index (Adipo-IR) was calculated by fasting serum insulin and free fatty acids and categorized into 5 groups according to the quintiles. Outcomes included stroke recurrence (ischemic or hemorrhagic), combined vascular events, all-cause death, and poor outcome (modified Rankin Scale, 3–6) at 12 months after stroke onset. We assessed the association between Adipo-IR and risk of prognosis by multivariable Cox/logistic regression models adjusted for potential covariates. Results Among 2,222 patients, 69.0% were men with a mean age of 62.5 years. At 12 months, 185 (8.3%) patients had recurrent stroke, 193 (8.7%) had combined vascular events, 58 (2.6%) died, and 250 (11.5%) had a poor outcome. Compared with patients with the lowest quintile, patients with the second, third, fourth, fifth quintiles of the Adipo-IR were associated with an increased risk of stroke recurrence (hazard ratio [HR], 1.77; 95% CI, 1.04–3.03; P = 0.04; HR, 2.19; 95% CI, 1.30–3.68; P = 0.003; HR, 1.84; 95% CI, 1.06–3.21; P = 0.03; HR, 2.11; 95% CI, 1.20–3.71; P = 0.01, respectively) and marginally associated with an increased risk of combined vascular events ( HR, 1.60; 95%CI, 0.97–2.64; P = 0.07; HR, 1.91; 95% CI, 1.17–3.13; P = 0.01; HR, 1.62; 95% CI, 0.96–2.75; P = 0.07; HR, 1.80; 95% CI, 1.05–3.09; P = 0.03, respectively) at 12 months after adjustment for potential covariates. Adipo-IR was not associated with mortality and poor outcome at 12 months. Conclusions These findings suggest that adipose tissue specific insulin resistance is independently associated with recurrent stroke and combined vascular events after acute ischemic stroke in nondiabetic patients.

Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) is a phenomenon that characterizes individuals with somatic mutations that are related to hematologic malignancy but without hematologic abnormalities. Presence of CHIP is associated with the atherosclerotic cardiovascular disease through the activation of the interleukin 6 (IL-6) pathway; however, its role on unfavorable functional outcomes in different etiologies of ischemic stroke remains unclear. We aimed to investigate the association between CHIP-related gene mutations and unfavorable functional outcomes of ischemic stroke with different etiologies. Methods We prospectively studied a cohort of 3396 stroke patients with identified etiologies, and identified CHIP and the presence of the IL6R variant (IL6R p.Asp358Ala) by whole-genome sequencing. The IL6R p.Asp358Ala coding mutation was used as a genetic inhibition for IL-6 signaling. The primary outcome was unfavorable functional outcome [(Modified Rankin Scale), mRS 2–6] at 3 months. Results Of the 3396 patients, 110 (3.2%) were CHIP carriers and the median age was 62 years (IQR, 54.0–69.0). The CHIP increased the risk of unfavorable functional outcome among patients with hyper-inflammation status of high-sensitivity C-reactive protein (hsCRP) > median levels in patients with large-artery atherosclerosis (LAA) (OR 2.45, 95% CI 1.00–5.98, p = 0.049, p interaction = 0.01). Presence of IL6R variant (IL6R p.Asp358Ala) could attenuate the risk of unfavorable functional outcome only in patients with CHIP (OR 0.30, 95%CI 0.12–0.76, p = 0.01, p interaction = 0.02), and especially in LAA patients with CHIP (OR 0.1, 95%CI 0.02–0.42, p = 0.002; p interaction = 0.001). Conclusion CHIP is associated with unfavorable functional outcomes in patients with LAA stroke and hyper-inflammation. Genetic IL-6 signaling inhibition might attenuate the risk of unfavorable functional outcomes in CHIP carriers, especially in LAA stroke patients.

Highlights A simple two-step multi-element co-doping strategy is proposed to fabricate core-shell structured LiCoO2 based on the different diffusivities of dopant ions. The high diffusivity Al3+/Mg2+ ions occupy the core of single-crystal grain while the low diffusivity Ti4+ ions enrich the shell layer. In-situ XRD demonstrates the mitigated structural distortion under a high cut-off voltage of 4.6 V, resulting in a significantly improved cycling stability.

Abstract Schistosomiasis is a neglected tropical disease that affects humans and animals in tropical and subtropical regions worldwide. Schistosome eggs are responsible for the pathogenesis and transmission of schistosomiasis, thus reducing egg production is vital for prevention and control of schistosomiasis. However, the mechanisms underlying schistosome reproduction remain unclear. Annexin proteins (ANXs) are involved in the physiological and pathological functions of schistosomes, but the specific regulatory mechanisms and roles of ANX A13 in the development of Schistosoma japonicum and host–parasite interactions remain poorly understood. Therefore, in this study, the expression profiles of SjANX A13 at different life cycle stages of S. japonicum were assessed using quantitative PCR. In addition, the expression profiles of the homolog in S. mansoni were analyzed in reference to public datasets. The results of RNA interference showed that knockdown of SjANX A13 significantly affected the development and egg production of female worms in vivo. The results of an immune protection assay showed that recombinant SjANX A13 increased production of immunoglobulin G-specific antibodies. Finally, co-culture of S. japonicum exosomes with LX-2 cells using a transwell system demonstrated that SjANX A13 is involved in host–parasite interactions via exosomes. Collectively, these results will help to clarify the roles of SjANX A13 in the development of S. japonicum and host–parasite interactions as a potential vaccine candidate.

Abstract Objectives Monetizing health has sparked controversy and has implications for pricing strategies of emerging health technologies. Medical insurance payers typically set up thresholds for quality-adjusted life years (QALY) gains based on health productivity and budget affordability, but they rarely consider patient willingness-to-pay (WTP). Our study aims to compare Chinese payer threshold and patient WTP toward QALY gain of advanced non-small cell lung cancer (NSCLC) and to inform a potential inclusion of patient WTP under more complex decision-making scenarios. Methods A regression model was constructed with cost as the independent variable and QALY as the dependent variable, where the regression coefficients reflect mean opportunity cost, and by transforming these coefficients, the payer threshold can be obtained. Patient WTP was elicited through a contingent valuation method survey. The robustness of the findings was examined through sensitivity analyses of model parameters and patient heterogeneity. Results The payer mean threshold in the base-case was estimated at 150,962 yuan (1.86 times per capita GDP, 95% CI 144,041–159,204). The two scenarios analysis generated by different utility inputs yielded thresholds of 112,324 yuan (1.39 times per capita GDP) and 111,824 yuan (1.38 times per capita GDP), respectively. The survey included 85 patients, with a mean WTP of 148,443 yuan (1.83 times per capita GDP, 95% CI 120,994–175,893) and median value was 106,667 yuan (1.32 times the GDP per capita). Due to the substantial degree of dispersion, the median was more representative. The payer threshold was found to have a high probability (98.5%) of falling within the range of 1–2 times per capita GDP, while the robustness of patient WTP was relatively weak. Conclusions In China, a country with a copayment system, payer threshold was higher than patient WTP, indicating that medical insurance holds significant decision-making authority, thus temporarily negating the need to consider patient WTP.

Abstract Background Teenage pregnancy is a global public health issue, and it poses a serious threat to the health and socioeconomic status of mothers and their newborn children. Although Papua New Guinea has recorded one of the highest teenage pregnancy rates among Asia-Pacific countries, few studies have conducted research on the related inequality in the country. Therefore, this study aimed to assess socioeconomic inequality in teenage pregnancy and its contributing factors in Papua New Guinea. Methods Data for this cross-sectional study were obtained from the 2016–2018 Papua New Guinea Demographic and Health Survey. The analytical sample consisted of 2,864 girls aged 15–19 years. We employed Erreygers normalized concentration index (ECI) and concentration curves to measure and depict socioeconomic inequality in teenage pregnancy. Decomposition analysis was likewise performed to identify the contributions of determinants to the observed inequality. Results Weighted ECI for teenage pregnancy was − 0.0582 (P

Abstract The development of heterogenous catalysts based on the synthesis of 2D carbon-supported metal nanocatalysts with high metal loading and dispersion is important. However, such practices remain challenging to develop. Here, we report a self-polymerization confinement strategy to fabricate a series of ultrafine metal embedded N-doped carbon nanosheets (M@N-C) with loadings of up to 30 wt%. Systematic investigation confirms that abundant catechol groups for anchoring metal ions and entangled polymer networks with the stable coordinate environment are essential for realizing high-loading M@N-C catalysts. As a demonstration, Fe@N-C exhibits the dual high-efficiency performance in Fenton reaction with both impressive catalytic activity (0.818 min−1) and H2O2 utilization efficiency (84.1%) using sulfamethoxazole as the probe, which has not yet been achieved simultaneously. Theoretical calculations reveal that the abundant Fe nanocrystals increase the electron density of the N-doped carbon frameworks, thereby facilitating the continuous generation of long-lasting surface-bound •OH through lowering the energy barrier for H2O2 activation. This facile and universal strategy paves the way for the fabrication of diverse high-loading heterogeneous catalysts for broad applications.

Abstract Quantum key distribution (QKD) holds the potential to establish secure keys over long distances. The distance of point-to-point QKD secure key distribution is primarily impeded by the transmission loss inherent to the channel. In the quest to realize a large-scale quantum network, increasing the QKD distance under current technology is of great research interest. Here we adopt the 3-intensity sending-or-not-sending twin-field QKD (TF-QKD) protocol with the actively-odd-parity-pairing method. The experiment demonstrates the feasibility of secure QKD over a 1002 km fibre channel considering the finite size effect. The secure key rate is 3.11 × 10 − 12 $3.11\times 10^{-12}$ per pulse at this distance. Furthermore, by optimizing parameters for shorter fiber distances, we conducted performance tests on key distribution for fiber lengths ranging from 202 km to 505 km. Notably, the secure key rate for the 202 km, the normal distance between major cities, reached 111.74 kbps.

Abstract The biosynthesis of neurotoxin aetokthonotoxin (AETX) that features a unique structure of pentabrominated biindole nitrile involves a first-of-its-kind nitrile synthase termed AetD, an enzyme that shares very low sequence identity to known structures and catalyzes an unprecedented mechanism. In this study, we resolve the crystal structure of AetD in complex with the substrate 5,7-di-Br-L-Trp. AetD adopts the heme oxygenase like fold and forms a hydrophobic cavity within a helical bundle to accommodate the indole moiety. A diiron cluster comprising two irons that serves as a catalytic center binds to the carboxyl O and the amino N of the substrate. Notably, we demonstrate that the AetD-catalyzed reaction is independent of the bromination of the substrate and also solved crystal structures of AetD in complex with 5-Br-L-Trp and L-Trp. Altogether, the present study reveals the substrate-binding pattern and validates the diiron cluster-comprising active center of AetD, which should provide important basis to support the mechanistic investigations into this class of nitrile synthase.

Highlights Highly thermally conductive composite films with elimination of graphene nanosheet wrinkles prepared by a constrained drying strategy of in-plane stretching. Reveals the mechanism of graphene nanosheet wrinkle elimination to improve the thermal conductivity of composite films and demonstrates its use as a heat sink film for cooling flexible LED chips and smartphones.

Abstract Objective Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF. Method The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers. Results The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified. Conclusion The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.

Abstract Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high-resolution forecast (HRES), which is widely considered as the world’s best physics-based weather forecasting system. Specifically, ML models have outperformed HRES in 10-day forecasts with a spatial resolution of 0.25∘. However, the challenge remains in mitigating the accumulation of forecast errors for longer effective forecasts, such as achieving comparable performance to the ECMWF ensemble in 15-day forecasts. Despite various efforts to reduce accumulation errors, such as implementing autoregressive multi-time step loss, relying on a single model has been found to be insufficient for achieving optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts at a temporal resolution of 6 hours and a spatial resolution of 0.25∘. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation demonstrates that FuXi has forecast performance comparable to ECMWF ensemble mean (EM) in 15-day forecasts. FuXi surpasses the skillful forecast lead time achieved by ECMWF HRES by extending the lead time for Z500 from 9.25 to 10.5 days and for T2M from 10 to 14.5 days. Moreover, the FuXi ensemble is created by perturbing initial conditions and model parameters, enabling it to provide forecast uncertainty and demonstrating promising results when compared to the ECMWF ensemble.

Dislocation core dominates dislocation mobility and mechanical properties of crystalline solids. To date, a complete landscape for describing dislocation with narrow core in metals like aluminum (Al) remains elusive, and thus deformation mechanisms of Al alloys are still unclear. This work investigates the dislocation core structure and deformation behaviors of Al alloyed with solutes X (X = Mg, Si, Cu, Zn, and Fe) within the framework of semi-discrete variational theory combined with first-principles calculations. Depending on the dislocation core characteristics, the deformation modes of Al alloys are governed by the compositional-dependent misfit potentials. Except for Fe, all other solutes in Al decrease the intrinsic SF energy, with considerable effects being Mg and Si. The deformation tends to occur via cross-slip of dislocations in Al alloyed with Mg, whereas Si additions can benefit to deform via emission of partial dislocations. Our investigation offers theoretical guidance for choosing solutes favorable to mechanical performances of Al alloys.

The non-equilibrium characteristics during the shock relaxation process hold a foundational position in various fields. In contrast to the propagation of a single shock wave, the collision process of two shock waves exhibits distinct non-equilibrium features. Employing non-equilibrium molecular dynamics, we simulated the collision of ultra-strong shock waves in a classical gas system, investigating the relationship between equilibrium relaxation time and shock intensity. Tracking the spatial migration of microscopic particles in the shock collision region during the relaxation process, we observed a significant contribution of particle migration to the average energy changes during relaxation. The discussion on particle migration provides a valuable new perspective for understanding the microscopic mechanisms of the relaxation process.

The discovery of Markov boundaries is highly effective at identifying features that are causally related to the target variable, providing strong interpretability and robustness. While there are numerous methods for discovering Markov boundaries in real-world applications, no single method is universally applicable to all datasets. Therefore, in order to balance precision and recall, we propose an ensemble framework of divide-and-conquer Markov boundary discovery algorithms based on U-I selection strategy. We put three divide-and-conquer Markov boundary methods into the framework to obtain an ensemble algorithm, focusing on judging controversial parent–child variables to further balance precision and recall. By combining multiple algorithms, the ensemble algorithm can leverage their respective strengths and more thoroughly analyze the cause-and-effect relationships of target variables through various perspectives. Furthermore, it can enhance the robustness of the algorithm and reduce dependence on a single algorithm. In the experiment, we select four advanced Markov boundary discovery algorithms as comparison algorithms and compare them on nine benchmark Bayesian networks and three real-world datasets. The results show that EDMB ranks first in the overall ranking, which illustrates the superiority of the integrated algorithm and the effectiveness of the adopted U-I selection strategy. The main contribution of this paper lies in proposing an ensemble framework for divide-and-conquer Markov boundary discovery algorithms, balancing precision and recall through the U-I selection strategy, and judging controversial parent–child variables to enhance algorithm performance and robustness. The advantage of the U-I selection strategy and its difference from existing methods is the ability to independently obtain the maximum precision and recall of multiple algorithms within the ensemble framework. By assessing controversial parent–child variables, it further balances precision and recall, leading to results that are closer to the true Markov boundary.

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation.

Background: Sterile α and Toll/IL-1 receptor motif-containing 1 (SARM1) is a central regulator of programmed axon death and a crucial nicotinamide adenine dinucleotide (NAD+) hydrolase (NADase) in mammalian tissues, hydrolyzing NAD+ and playing an important role in cellular NAD+ recycling. Abnormal SARM1 expression is linked to axon degeneration, which causes disability and disease progression in many neurodegenerative disorders of the peripheral and central nervous systems. Methods: In this study, we use PC6 assay of hydrolase activity, DRG axon regeneration and CIPN model to screen for potent SARM1 Inhibitors. Results: Two novel SARM1 inhibitors (compound 174 and 331P1) are charcterized for its high potency for SARM1 NADase. In a chemotherapy-induced peripheral neuropathy (CIPN) myopathy model, compound 331P1 treatment prevented the decline in neurofilament light chain (NfL) levels caused by axonal injury in a dose-dependent manner, associated with elevated intraepidermal nerve fiber (IENF) intensity in mouse foot paw tissue, suggesting its functionality in reversing axon degeneration. Conclusions: The newly designed SARM1 inhibitor 331P1 is a promising candidate due to its excellent in vivo efficacy, favorable CYP inhibition properties, and attractive safety profiles. The 331P1 compound possesses the potential to be developed as a novel neuroprotective therapy that can prevent or halt the neurodegenerative process in CIPN.

This study leverages the inherent curiosity of pigs and their competitive nature among conspecifics to explore behavioral transition paths and critical nodes that govern aggression during initial encounters between unfamiliar individuals. Two consecutive experiments were designed to investigate these dynamics under controlled conditions. In Experiment 1, unfamiliar pigs engaged in one-on-one fights with quick retreats, displaying a simple behavioral sequence of looking followed by attacking. In Experiment 2, the addition of new pigs to resident groups resulted in a more complex and structured behavioral sequence. Resident pigs exhibited a ‘four-step’ exploratory behavior pattern: looking, sniffing, touching, and attacking. Further analysis revealed distinct exploratory pathways. In Experiment 1, only short behavioral paths were observed, while Experiment 2 revealed both long and short paths. Specifically, Experiment 2 uncovered seven types of behavioral transition paths, four of which were long and three short, highlighting different combinations of the basic behaviors. The transition paths involving aggression were more varied in Experiment 2 compared to Experiment 1. Overall, the 15 most frequent and obvious behavioral transition paths were identified across both experiments. Eight types of paths were categorized based on the transitions between the basic behaviors. These findings enhance our understanding of the behavioral dynamics in unfamiliar pig encounters, emphasizing the complexity of social interactions and the conditions under which aggression occurs.

Factsage is a robust thermodynamic calculation software that enables simulation and computation of complex multi-component and multi-phase system reactions. It has a variety of application fields such as metallurgy, energy, and environmental domains. This article elucidates the key functionalities of Factsage’s diverse modules, including Equilib, Viscosity, EpH, Reaction, and Phase Diagram modules. Furthermore, it delineates the present usage and research progress of the software in the realms of air pollution, water pollution, and solid waste treatment. By predicting the thermodynamic properties of pollutants, their chemical reactions, and complex phase changes, Factsage provides a critical scientific foundation for environmental decision-making and optimization of waste treatment processes. It showed its greater contributions to environmental protection and sustainable development.

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and are thus computationally expensive, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders compared with the original SIFT method. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. Nine large images (over 2600 × 1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation.

To address the requirement of functioning as a transmit/receive isolation device in terahertz transceiver systems, in this paper, we present two high−isolation multi−branch waveguide directional couplers operating at a center frequency of 510 GHz. One is a high−performance five−branch directional coupler, and the other is a new type of three−branch waveguide coupler with lower processing difficulty. Both couplers were fabricated using low−cost CNC milling technologies. The performance of these couplers was verified through measurement results, demonstrating high isolation at the center frequency.

Exploring farmers’ willingness for cultivated land quality protection (WCQP) is crucial for preserving land quality. The existing sociopsychological research often examines farmers’ WCQP from a single perspective—either self-identity or social identity—overlooking the structural relationship between the two. This oversight hinders the development of synergistic policies for cultivated land quality protection. Based on a micro-survey of 439 farm households in Shaanxi Province, China, this study constructs a theoretical analysis framework for farmers’ WCQP that integrates the structural relationships of self-identity and social identity. Self-identity is further subdivided into cognitive identity, emotional identity, and behavioral identity. Using structural equation modeling (SEM), the study analyzes the impact of cognitive identity, emotional identity, behavioral identity, and social identity on farmers’ WCQP. Additionally, the moderating effects of social identity are explored. The results indicate that (1) based on the baseline regression results, farmers’ cognitive identity, emotional identity, behavioral identity, and social identity all promote WCQP among farmers; (2) the analysis of moderating effects further indicates that farmers’ social identity enhances the positive impact of cognitive identity on their WCQP. However, the moderating effect of social identity is conditional in shaping the impact of emotional identity on farmers’ WCQP. These findings remain valid after addressing endogeneity and conducting robustness tests. When farmers’ emotional identity is high, social identity strengthens its promotive effect on their WCQP, but when farmers’ emotional identity is low, social identity actually hinders this effect. Our research not only simultaneously considers both the self-identity and social identity of farmers but also delves into their structural relationship. This provides theoretical support and practical guidance for developing more targeted land quality conservation policies from a social–psychological perspective.

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions.

Many protein–protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand–protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand–protein complexes in a benchmark dataset for protein–protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein–protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein–protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug–target interactions in complex environments, such as in the case of protein–protein interactions.

Neutrophil extracellular traps (NETs) are three-dimensional reticular structures that release chromatin and cellular contents extracellularly upon neutrophil activation. As a novel effector mechanism of neutrophils, NETs possess the capacity to amplify localized inflammation and have been demonstrated to contribute to the exacerbation of various inflammatory diseases, including cardiovascular diseases and tumors. It is suggested that lysophosphatidylcholine (LPC), as the primary active component of oxidized low-density lipoprotein, represents a significant risk factor for various inflammatory diseases, such as cardiovascular diseases and neurodegenerative diseases. However, the specific mechanism of NETs formation induced by LPC remains unclear. Quercetin has garnered considerable attention due to its anti-inflammatory properties, serving as a prevalent flavonoid in daily diet. However, little is currently known about the underlying mechanisms by which quercetin inhibits NETs formation and alleviates associated diseases. In our study, we utilized LPC-treated primary rat neutrophils to establish an in vitro model of NETs formation, which was subsequently subjected to treatment with a combination of quercetin or relevant inhibitors/activators. Compared to the control group, the markers of NETs and the expression of P2X7R/P38MAPK/NOX2 pathway-associated proteins were significantly increased in cells treated with LPC alone. Quercetin intervention decreased the LPC-induced upregulation of the P2X7R/P38MAPK/NOX2 pathway and effectively reduced the expression of NETs markers. The results obtained using a P2X7R antagonist/activator and P38MAPK inhibitor/activator support these findings. In summary, quercetin reversed the upregulation of the LPC-induced P2X7R/P38MAPK/NOX2 pathway, further mitigating NETs formation. Our study investigated the potential mechanism of LPC-induced NETs formation, elucidated the inhibitory effect of quercetin on NETs formation, and offered new insights into the anti-inflammatory properties of quercetin.

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions.

In the construction industry, the professional evaluation of construction schemes represents a crucial link in ensuring the safety, quality and economic efficiency of the construction process. However, due to the large number and diversity of construction schemes, traditional expert review methods are limited in terms of timeliness and comprehensiveness. This leads to an increasingly urgent requirement for intelligent check of construction schemes. This paper proposes an intelligent compliance checking method for construction schemes that integrates knowledge graphs and large language model (LLM). Firstly, a method for constructing a multi-dimensional, multi-granular knowledge graph for construction standards is introduced, which serves as the foundation for domain-specific knowledge support to the LLM. Subsequently, a parsing module based on text classification and entity extraction models is proposed to automatically parse construction schemes and construct pathways for querying the knowledge graph of construction standards. Finally, an LLM is leveraged to achieve an intelligent compliance check. The experimental results demonstrate that the proposed method can effectively integrate domain knowledge to guide the LLM in checking construction schemes, with an accuracy rate of up to 72%. Concurrently, the well-designed prompt template and the comprehensiveness of the knowledge graph facilitate the stimulation of the LLM’s reasoning ability. This work contributes to exploring the application of LLMs and knowledge graphs in the vertical domain of text compliance checking. Future work will focus on optimizing the integration of LLMs and domain knowledge to further improve the accuracy and practicality of the intelligent checking system.

In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations.

Enteral nutrition (EN) and foods for special medical purposes (FSMPs) can be used to meet the specific nutritional needs of patients. There are multiple types of EN products and nutritionally complete FSMPs on the market. The peptides in these products are important nutritional components, while their presence in different products remains unclear. To provide better clinical guidelines, we analyzed and compared the molecular weight (MW) distribution, types, and quantities of peptides and bioactive peptides of two EN products (liquid products) and two FSMPs with nutritionally complete formulas (powder products). Our results showed that each product had a unique peptide profile. The two liquid products and one powder product (Samples 1–3) had a higher content of peptides. Sample 1 contained 75.60% peptides with an MW less than 375 Da and contained 95.21% peptides with an MW less than 1000 Da, being rich in short peptides. Sample 2 and 3 had high levels of peptides with MW values between 180 Da and 2000 Da. Additionally, Sample 4 contained high levels of proteins, containing 69.18% peptides with MW values larger than 10,000 Da. Further, Sample 1 had more bioactive dipeptides and Sample 2 had more long bioactive peptides. Our results suggest that peptides in different EN and FSMP products are very different and should be evaluated in more detail. This will provide valuable information for clinical medical professionals, help them to guide patients with different physiological conditions better, and ultimately benefit patients.

In recent years, TiO2-based catalysts have received extensive attention from researchers for their excellent oxidative desulfurization (ODS) performances. In this paper, a series of mesoporous TiO2/SiO2 catalysts with different TiO2 loadings are prepared, using an incipient wetness impregnation method with agricultural waste rice husk as both the silicon source and mesoporous template and tetrabutyl titanate as the titanium source. The effect of different TiO2 loadings on the ODS performance of the samples is investigated, and the appropriate TiO2 loading is 2.5%. Compared with pure TiO2, the 2.5%TiO2/SiO2 sample exhibits high catalytic activity for oxidative desulfurization. This is, on the one hand, due to the high specific surface area and mesopore volume of the 2.5%TiO2/SiO2 sample. On the other hand, it is due to the uniform dispersion of TiO2 grains with an average diameter of 6.1 nm on the surface of the mesoporous SiO2 carrier, which greatly increases the active sites of the 2.5%TiO2/SiO2 sample, thus improving the catalytic activity of the sample. The recycling performances of the 2.5%TiO2/SiO2 sample are further investigated. The results show that, after fifteen cycles, the 2.5%TiO2/SiO2 sample still maintains high conversions of dibenzothiophene (99.8%) and 4,6-dimethyldibenzothiophene (99.7%) without deactivation. In addition, the 2.5%TiO2/SiO2 sample treated with TBHP aqueous solution is characterized by the technique of UV-Vis, and the Ti-peroxo (Ti-OOtBu) species, the active intermediate for the ODS of bulky organic sulfides, is successfully captured. Finally, a possible reaction mechanism for the ODS process over the 2.5%TiO2/SiO2 sample is proposed.

Bacillus Calmette–Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle-invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG’s efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and an enhanced stimulation of anti-tumor immune responses. Key strategies involve using materials such as liposomes, polymers, and magnetic particles to encapsulate BCG or functional BCG cell wall components. Additionally, co-delivering BCG with chemotherapeutics enhances drug targeting and tumor-killing effects while reducing drug toxicity, with some studies even achieving synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG’s limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG’s mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.

The development of water-saving management relies on understanding the physiological response of crops to soil drought. The coordinated regulation of hydraulics and stomatal conductance in plant water relations has steadily received attention. However, research focusing on grain crops, such as winter wheat, remains limited. In this study, three soil water supply treatments, including high (H), moderate (M), and low (L) soil water contents, were conducted with potted winter wheat. Leaf water potential (Ψleaf), leaf hydraulic conductance (Kleaf), and stomatal conductance (gs), as well as leaf biochemical parameters and stomatal traits were measured. Results showed that, compared to H, predawn leaf water potential (ΨPD) significantly reduced by 48.10% and 47.91%, midday leaf water potential (ΨMD) reduced by 40.71% and 43.20%, Kleaf reduced by 64.80% and 65.61%, and gs reduced by 21.20% and 43.41%, respectively, under M and L conditions. Although gs showed a significant difference between M and L, Ψleaf and Kleaf did not show significant differences between these treatments. The maximum carboxylation rate (Vcmax) and maximum electron transfer rate (Jmax) under L significantly decreased by 23.11% and 28.10%, stomatal density (SD) and stomatal pore area index (SPI) under L on the abaxial side increased by 59.80% and 52.30%, respectively, compared to H. The leaf water potential at 50% hydraulic conduction loss (P50) under L was not significantly reduced. The gs was positively correlated with ΨMD and Kleaf, but it was negatively correlated with abscisic acid (ABA) and SD. A threshold relationship between gs and Kleaf was observed, with rapid and linear reduction in gs occurring only when Kleaf fell below 8.70 mmol m−2 s−1 MPa−1. Our findings demonstrate that wheat leaves adapt stomatal regulation strategies from anisohydric to isohydric in response to reduced soil water content. These results enrich the theory of trade-offs between the carbon assimilation and hydraulic safety in crops and also provide a theoretical basis for water management practices based on stomatal regulation strategies under varying soil water conditions.

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China’s South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVCSVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R2 = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation.

The shape of nanochannels plays a crucial role in the ion selectivity and overall performance of reverse electrodialysis (RED) systems. However, current research on two-dimensional nanochannel shapes is largely limited to a few fixed asymmetric forms. This study explores the impact of randomly shaped nanochannels using dimensionless methods, controlling their randomness by varying their length and shape amplitude. The research systematically compares how alterations in the nanochannel length and shape amplitude influence various system performance parameters. Our findings indicate that increasing the nanochannel length can significantly enhance the system performance. While drastic changes in the nanochannel shape amplitude positively affect the system performance, the most significant improvements arise from the interplay between the nanochannel length and shape amplitude. This compounding effect creates a local optimum, resulting in peak system performance. Within the range of dimensionless lengths from 0 to 30, the system reaches its optimal performance at a dimensionless length of approximately 25. Additionally, we explored two other influencing factors: the nanochannel surface charge density and the concentration gradient of the solution across the nanochannel. Optimal performance is observed when the nanochannel has a high surface charge density and a low concentration gradient, particularly with random shapes. This study advances the theoretical understanding of RED systems in two-dimensional nanochannels, guiding research towards practical operational conditions.

To evaluate the effects of varying proportions of yak meat in feed on the growth of rats and provide a theoretical basis for selecting the optimal feed proportion suitable for rats. This study was designed as a one-variable experiment. Fifty male rats were divided into five groups. The ratios of yak meat to basal feed of rats in four dietary treatment groups were 2:8, 4:6, 6:4, and 8:2, respectively, while those in the control group were only provided a basal diet. In the feeding experiment, the body weights of the rats were recorded on Day 0 and subsequently in the first, second, third, and fourth weeks, along with quantities of feed intake. The body and tail lengths, as well as the waist circumference of the rats, were measured, and blood samples were collected in the fourth week for routine blood and biochemistry investigations. The rats in the 4:6 feed group had the best body condition. They had normal body and tail lengths, smaller waist circumferences, good posture, and were in better overall health than rats in the other groups. The results indicate that the 4:6 diet was optimal for enhancing rats’ growth performance compared to the other diets.

Lysosomes are degradation centers of cells and intracellular hubs of signal transduction, nutrient sensing, and autophagy regulation. Dysfunction of lysosomes contributes to a variety of diseases, such as lysosomal storage diseases (LSDs) and neurodegeneration, but the mechanisms are not well understood. Altering lysosomal activity and examining its impact on the occurrence and development of disease is an important strategy for studying lysosome-related diseases. However, methods to dynamically regulate lysosomal function in living cells or animals are still lacking. Here, we constructed lysosome-localized optogenetic actuators, named lyso-NpHR3.0, lyso-ArchT, and lyso-ChR2, to achieve optogenetic manipulation of lysosomes. These new actuators enable light-dependent control of lysosomal membrane potential, pH, hydrolase activity, degradation, and Ca2+ dynamics in living cells. Notably, lyso-ChR2 activation induces autophagy through the mTOR pathway, promotes Aβ clearance in an autophagy-dependent manner in cellular models, and alleviates Aβ-induced paralysis in the Caenorhabditis elegans model of Alzheimer's disease. Our lysosomal optogenetic actuators supplement the optogenetic toolbox and provide a method to dynamically regulate lysosomal physiology and function in living cells and animals.

Imperfect external capital market and internal soft budget constraints are important reasons for the issue of enterprise financing constraints. This paper studies the impact of financing constraints and influencing factors of financing constraints in municipal infrastructure investment and construction for local governments by using the panel data from 2003 to 2018 in China. The results show that, first, there are great financing constraints in municipal infrastructure investment and construction for local government in China. Second, the increase of self-financing capacity and external financing capacity can reduce the level and uncertainty of financing constraints, thus improving the efficiency of municipal infrastructure investment and construction. Third, the expansion of the city scale may reduce the uncertainty of financing constraints, but improve the level of financing constraints thus reducing the efficiency of municipal infrastructure investment and construction.

Abstract Background Moyamoya disease (MMD) stands as a prominent cause of stroke among children and adolescents in East Asian populations. Although a growing body of evidence suggests that dysregulated inflammation and autoimmune responses might contribute to the development of MMD, a comprehensive and detailed understanding of the alterations in circulating immune cells associated with MMD remains elusive. Methods In this study, we employed a combination of single‐cell RNA sequencing (scRNA‐seq), mass cytometry and RNA‐sequencing techniques to compare immune cell profiles in peripheral blood samples obtained from patients with MMD and age‐matched healthy controls. Results Our investigation unveiled immune dysfunction in MMD patients, primarily characterized by perturbations in T‐cell (TC) subpopulations, including a reduction in effector TCs and an increase in regulatory TCs (Tregs). Additionally, we observed diminished natural killer cells and dendritic cells alongside heightened B cells and monocytes in MMD patients. Notably, within the MMD group, there was an augmented proportion of fragile Tregs, whereas the stable Treg fraction decreased. MMD was also linked to heightened immune activation, as evidenced by elevated expression levels of HLA‐DR and p‐STAT3. Conclusions Our findings offer a comprehensive view of the circulating immune cell landscape in MMD patients. Immune dysregulation in patients with MMD was characterized by alterations in T‐cell populations, including a decrease in effector T‐cells and an increase in regulatory T‐cells (Tregs), suggest a potential role for disrupted circulating immunity in the aetiology of MMD.

BackgroundHyperuricemia (HUA) is a prevalent metabolic disorder whose development is associated with intestinal microbiota. Therefore, probiotics have emerged as a potential and safe approach for lowering uric acid (UA) levels. However, the underlying mechanisms of many effective probiotic strains remain unknown.Methods and resultsC57BL/6 mice were randomly divided into two groups: control and model groups. The model group received 12 weeks of potassium oxonate. Through 16s sequencing we found that HUA resulted in a significant decrease in the total diversity of all intestinal segments. When each intestinal segment was analyzed individually, the reduction in diversity was only significant in the cecum and colon sections. RDA analysis showed that lactobacilli in the rat colon exhibited a strong correlation with model group, suggesting that Lactobacillus may play an important role in HUA. Consequently, the preventive effects of Lactobacillus johnsonii YH1136 against HUA were investigated. C57BL/6 mice were randomly divided into three groups: control, model and YH1136 groups. The results showed that administering Lactobacillus johnsonii YH1136 effectively reduced serum UA levels in vivo by inhibiting hepatic xanthine oxidase (XOD) activity and promoting renal ABCG2 transporter expression. Moreover, supplementation with Lactobacillus johnsonii YH1136 significantly ameliorated pathological damage in the kidney and liver, thereby reducing UA accumulation.ConclusionHyperuricemia is accompanied by an altered composition of multiple gut bacteria, of which Lactobacillus is a key genus. Lactobacillus johnsonii YH1136 may ameliorate renal involvement in HUA via the gut-kidney axis.

Stability analysis and benefit estimation have substantial implications for lane-change decision-making to reduce delay and variation. Connected platoons drive with minor headway to increase capacity, whereas dividing or reforming platoons significantly impacts traveling efficiency. Therefore, this article focuses on the instability of the platoon caused by an en-route lane-change. Construction of platoon forming, combination rules, and car-following models for various vehicle types are presented to describe driving behaviours. Then, a velocity adjustment and a model for lane-change preparation and recovery are proposed. In addition, a group of stability recognition indexes and related stability evaluation factors are presented. Experiments involving numerical comparisons of the proposed factors are conducted to demonstrate the propagation properties of the instability and reveal the fluctuation degree. The variation duration, velocity variation range, and total delay are the primary indicators for evaluating lane-change feasibility. The models and findings can be applied effectively in practice to determine the optimal time and location for en-route lane-change and to assist with traffic management and lane selection at the entrance.

The phenomenon of fire or even explosion caused by thermal runaway of lithium-ion power batteries poses a serious threat to the safety of electric vehicles. An in-depth study of the core-material thermal runaway reaction mechanism and reaction chain is a prerequisite for proposing a mechanism to prevent battery thermal runaway and enhance battery safety. In this study, based on a 24 Ah commercial Li(Ni0.6Co0.2Mn0.2)O2/graphite soft pack battery, the heat production characteristics of different state of charge (SOC) cathode and anode materials, the separator, the electrolyte, and their combinations of the battery were investigated using differential scanning calorimetry. The results show that the reaction between the negative electrode and the electrolyte is the main mode of heat accumulation in the early stage of thermal runaway, and when the heat accumulation causes the temperature to reach a certain critical value, the violent reaction between the positive electrode and the electrolyte is triggered. The extent and timing of the heat production behaviour of the battery host material is closely related to the SOC, and with limited electrolyte content, there is a competitive relationship between the positive and negative electrodes and the electrolyte reaction, leading to different SOC batteries exhibiting different heat production characteristics. In addition, the above findings are correlated with the battery failure mechanisms through heating experiments of the battery monomer. The study of the electro-thermal properties of the main materials in this paper provides a strategy for achieving early warning and suppression of thermal runaway in batteries.

IntroductionThis study examines the impact of environmental accidents on Generation Z’s purchase behavior towards green apparel, integrating the Theory of Planned Behavior and the Norm Activation Theory to conceptualize how different dimensions of environmental accidents influence consumer behavior.MethodsEmploying focus groups and questionnaires, this research captures data on Generation Z’s perceptions and behaviors towards green apparel in the context of environmental accidents. The collected data were analyzed using SPSS software, with structural equation modeling employed to test the research hypotheses.ResultsThe findings indicate that the scale (H12 = 0.545), the degree of suddenness (H18 = 0.357), nature of the effect (H14 = 0.295), and duration (H17 = 0.289) of environmental accidents significantly influence Generation Z’s awareness of consequences, behavioral attitudes, subjective norms, and perceived behavioral control regarding the purchase of green clothing. Notably, the scale of environmental accidents has a significant impact on perceived behavioral control, which in turn significantly affects Generation Z’s intention to purchase green apparel (H3 = 0.5).DiscussionThis study elucidates the impact of environmental accidents on Generation Z’s green apparel purchase intentions. The findings highlight Generation Z’s environmental awareness and social responsibility, influencing their purchasing decisions. This research offers practical insights for brands to enhance green marketing strategies, focusing on product quality, transparency, and consumer education to align with Generation Z’s values and expectations. Future research should explore additional factors affecting purchase behavior and strategies to bridge the intention-behavior gap.

IntroductionMany studies have shown that the functional adaptation of immigrants to high-altitude is closely related to oxygen transport, inflammatory response and autonomic nervous system. However, it remains unclear how human attention changes in response to hypoxia-induced neurophysiological activity during high-altitude exposure.MethodsIn the present study, we analyzed the relationship between hypoxic-induced neurophysiological responses and attention networks in 116 immigrants (3,680 m) using an attention network test to simultaneously record electroencephalogram and electrocardiogram in combination with specific routine blood markers.ResultsOur analysis revealed that red blood cells exert an indirect influence on the three attention networks, mediated through inflammatory processes and heart rate variability.DiscussionThe present study provides experimental evidence for the role of a neuroimmune pathway in determining human attention performance at high- altitude. Our findings have implications for understanding the complex interactions between physiological and neurocognitive processes in immigrants adapting to hypoxic environments.

Butyrylcholinesterase (BChE) is widely expressed in multiple tissues and has a vital role in several key human disorders, such as Alzheimer’s disease and tumorigenesis. However, the role of BChE in human disorders has not been investigated. Thus, to quantitatively detect and visualize dynamical variations in BChE activity is essential for exploring the biological roles of BChE in the progression of a number of human disorders. Herein, based on the substrate characteristics of BChE, we customized and synthesized three near-infrared (NIR) fluorescent probe substrates with cyanine-skeleton, and finally selected a NIR fluorescence probe substrate named CYBA. The CYBA demonstrated a significant increase in fluorescence when interacting with BChE, but mainly avoided AChE. Upon the addition of BChE, CYBA could be specifically hydrolyzed to TBO, resulting in a significant NIR fluorescence signal enhancement at 710 nm. Systematic evaluation revealed that CYBA exhibited exceptional chemical stability in complex biosamples and possessed remarkable selectivity and sensitivity towards BChE. Moreover, CYBA was successfully applied for real-time imaging of endogenous BChE activity in two types of nerve-related living cells. Additionally, CYBA demonstrated exceptional stability in the detection of complex biological samples in plasma recovery studies (97.51%–104.01%). Furthermore, CYBA was used to construct a high-throughput screening (HTS) method for BChE inhibitors using human plasma as the enzyme source. We evaluated inhibitory effects of a series of natural products and four flavonoids were identified as potent inhibitors of BChE. Collectively, CYBA can serve as a practical tool to track the changes of BChE activity in complicated biological environments due to its excellent capabilities.

Summary: Cerebrospinal fluid (CSF) samples are commonly collected via lumbar puncture (LP) in both clinical and research settings for measurement of biomarkers of Alzheimer’s disease (AD). To determine the effects of LP on CSF AD biomarkers, we collected CSF samples at seven different time points after an LP in rhesus monkeys. We find that amyloid-beta (Aβ) and Tau levels increased significantly on day 1, peaked on day 3, and returned to baseline on day 10 after LP. The NFL levels increased significantly on day 5, peaked on day 10, and returned to baseline after day 30. The increased AD biomarker levels were mainly due to CSF outflow and deep intrathecal invasion during LP. Therefore, if LPs are repeated within a short period of time, prior LP can affect Aβ and Tau levels within 10 days and NFL levels within 30 days, which may lead to clinical misdiagnosis or incorrect scientific conclusions.

Summary: This study significantly enhances the accuracy of exercise state identification in wearable devices through improved denoising techniques for sEMG and ECG signals. By adopting an optimized Variational Mode Decomposition (VMD) method, combined with the Improved Sparrow Search Algorithm and Second Generation Wavelet Transform (ISSA-VMD-SWT), and introducing chaos mapping to strengthen the algorithm’s initial population, this approach effectively reduces noise while preserving key fatigue-related features. In tests conducted on data from 32 participants, the method achieved accuracy rates of 93.25%, 95.16%, and 93.05% for identifying “Easy,” “Transition,” and “Tired” exercise states, respectively, showing significant advantages over traditional denoising techniques. These results indicate that the denoising technology developed in this study represents a significant technological advancement for the application of ECG and sEMG fatigue identification technologies in wearable health monitoring devices.

Muskmelon cracking during ripening is a physiological disorder that causes severe economic losses. Cracking is related with fruit growth, cuticular membrane (CM), biomechanics and cell wall. Nevertheless, the mechanism of cracking induced by irrigation strategy remains unclear. Therefore, we conducted an experiment in muskmelon grown under three irrigation levels: full irrigation (T1, 100% ET), mild deficit irrigation (T2, 75% ET of T1), and severe deficit irrigation (T3, 50% ET of T1). The results showed that fruit mass of T2 and T3 at maturity were decreased by 11.06% and 19.67%, while fruit cracking rate decreased by 53.13% and 84.38% compared to T1, respectively. Irrigation modified fruit growth rate during fruit expanding (Stage I) and maturation stage (Stage II), with a higher growing rate observed under high irrigation levels. Furthermore, fruits under T2 and T3 had a significantly less deformable (lower strain rate) cuticle than that under T1, resulting in higher CM per unit fruit surface area. The mechanical properties showed different dynamic mode during fruit growth. The pericarp break force and pericarp toughness increased at Stage I and then decreased at Stage II, while pericarp brittleness displayed an increasing trend before harvest. We concluded that deficit irrigation alleviated the fruit cracking by adjusting fruit growth rate, and increasing the pericarp break force and toughness at Stage II. However, both of T1 and T3 resulted in accelerated cell wall decomposition during fruit ripening. Therefore, mild deficit irrigation is an appropriate practice strategy that can greatly mitigate fruit cracking while slightly reducing fruit growth.