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Abstract As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.

Abstract Background Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. Purpose To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. Methods Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. Results The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson’s and Spearman’s correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p

An optimization method of fracturing fluid volume strength was introduced taking well X-1 in Biyang Sag of Nanxiang Basin as an example. The characteristic curves of capillary pressure and relative permeability were obtained from history matching between forced imbibition experimental data and core-scale reservoir simulation results and taken into a large scale reservoir model to mimic the forced imbibition behavior during the well shut-in period after fracturing. The optimization of the stimulated reservoir volume (SRV) fracturing fluid volume strength should meet the requirements of estimated ultimate recovery (EUR), increased oil recovery by forced imbibition and enhancement of formation pressure and the fluid volume strength of fracturing fluid should be controlled around a critical value to avoid either insufficiency of imbibition displacement caused by insufficient fluid amount or increase of costs and potential formation damage caused by excessive fluid amount. Reservoir simulation results showed that SRV fracturing fluid volume strength positively correlated with single-well EUR and an optimal fluid volume strength existed, above which the single-well EUR increase rate kept decreasing. An optimized increase of SRV fracturing fluid volume and shut-in time would effectively increase the formation pressure and enhance well production. Field test results of well X-1 proved the practicality of established optimization method of SRV fracturing fluid volume strength on significant enhancement of shale oil well production.

Abstract Facial features are important sources of information about perceived trustworthiness. Masks and protective clothing diminish the visibility of facial cues by either partially concealing the mouth and nose or covering the entire face. During the pandemic, the use of personal protective equipment affected and redefined who trusts whom in society. This study used the classical investment game of interpersonal trust with Chinese participants to explore the impact of occlusion on interpersonal trust. Faces with moderate initial trustworthiness were occluded by a mask or protective clothing in Experiment 1 and were digitally occluded by a square in Experiment 2, and faces with three levels of initial trustworthiness were occluded by a mask in Experiment 3. Results showed that both undergraduates (Experiment 1a) and non-student adults (Experiment 1b) perceived the faces with protective clothing as more trustworthy than faces wearing standard masks and faces not wearing masks. Faces with the top halves showing were perceived as trustworthy as full faces, while faces with the bottom halves showing were perceived as less trustworthy. The effect of masks is weak and complex. Masks reduced participants’ trust in faces with high initial trustworthiness, had no effect on faces with low and moderate initial trustworthiness, and only slightly increased the trust of undergraduates in faces with moderate initial trustworthiness. Our findings indicate that the lack of information caused by occlusion and the social significance associated with occlusion collectively affect people’s trust behavior in Chinese society. We believe the findings of this study will be useful in elucidating the effects of personal protective equipment usage on perceptions of trustworthiness.

Abstract Background Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations.

Abstract Digital modeling is an essential means for preserving and passing down historical culture within cultural heritage. Point cloud registration technology, by aligning point cloud data captured from multiple perspectives, enhances the accuracy of reconstructing the complex structures of artifacts and buildings and provides a reliable digital foundation for their protection, exhibition, and research. Due to the challenges posed by complex morphology, noise, and missing data when processing cultural heritage data, this paper proposes a point cloud registration method based on the Diffusion Transformer (PointDT). Compared to traditional methods, the Diffusion Transformer can better capture both the global features and local structures of point cloud data, more accurately capturing the geometric and semantic information of the target point cloud, thereby achieving precise digital reconstruction. In this study, we trained our method using indoor datasets such as 3DMatch and large-scale outdoor datasets like KITTI, and validated it on various cultural heritage datasets, including those of the Terracotta Warriors and heritage buildings. The results demonstrate that this method not only significantly improves accuracy but also shows advantages in computational efficiency.

Abstract Background China experienced an overwhelming COVID-19 pandemic from middle December 2022 to middle January 2023 after lifting the zero-COVID-19 policy on December 7, 2022. However, the infection rate was less studied. We aimed to investigate the SARS-CoV-2 infection rate in children shortly after discontinuation of the zero-COVID-19 policy. Methods From February 20 to April 10, 2023, we included 393 children aged 8 months to less than 3 years who did not receive COVID-19 vaccination and 114 children aged 3 to 6 years who received inactivated COVID-19 vaccines based on the convenience sampling in this cross-sectional study. IgG and IgM antibodies against nucleocapsid (N) and subunit 1 of spike (S1) of SARS-CoV-2 (anti-N/S1) were measured with commercial kits (Shenzhen YHLO Biotech, China). Results Of the 393 unvaccinated children (1.5 ± 0.6 years; 52.2% boys), 369 (93.9%) were anti-N/S1 IgG positive. Of the 114 vaccinated children (5.3 ± 0.9 years; 48.2% boys), 112 (98.2%) were anti-N/S1 IgG positive. None of the unvaccinated or vaccinated children was anti-N/S1 IgM positive. The median IgG antibody titers in vaccinated children (344.91 AU/mL) were significantly higher than that in unvaccinated children (42.80 AU/mL) (P

Abstract Human multidrug resistance protein 5 (hMRP5) effluxes anticancer and antivirus drugs, driving multidrug resistance. To uncover the mechanism of hMRP5, we determine six distinct cryo-EM structures, revealing an autoinhibitory N-terminal peptide that must dissociate to permit subsequent substrate recruitment. Guided by these molecular insights, we design an inhibitory peptide that could block substrate entry into the transport pathway. We also identify a regulatory motif, comprising a positively charged cluster and hydrophobic patches, within the first nucleotide-binding domain that modulates hMRP5 localization by engaging with membranes. By integrating our structural, biochemical, computational, and cell biological findings, we propose a model for hMRP5 conformational cycling and localization. Overall, this work provides mechanistic understanding of hMRP5 function, while informing future selective hMRP5 inhibitor development. More broadly, this study advances our understanding of the structural dynamics and inhibition of ABC transporters.

Abstract Background Horsfieldia hainanensis Merr., an indicator species of China’s humid tropical rainforests, is endangered due to difficulties with population regeneration. In this study, the biological characteristics and germination adaptability of the seeds were studied for the first time, in order to provide a basis for analyzing the causes of endangerment and strategies for the artificial cultivation of H. hainanensis. The effects of biological characteristics (population, arils, seed coat, seed weight, seed moisture content) and environmental factors (temperature, light, drought, substrate, burial depth) on seed germination and seedling growth of H. hainanensis were studied. Results and discussion The fruits were found to be capsules containing seeds wrapped in a pericarp and fleshy aril, which provide protection and assist in seed dispersal, but also pose risks to the seeds, as the peel and fleshy aril can become moldy under high temperature and humidity conditions. There were significant differences in fruit morphology and germination characteristics among different populations, and the seed quality of populations in Niandian village, Daxin County, Chongzuo City, Guangxi Zhuang Autonomous Region was better. The arils significantly inhibited seed germination, the germination of large seeds was better, and seedling growth from medium seeds was superior. H. hainanensis seeds were sensitive to dehydration, and intolerant to drought and low temperature, which is typical of recalcitrant seeds. The seeds are suitable for germination on a moist substrate surface with good water retention and breathability at 30–35℃.

AIM: To investigate the short-term efficacy and safety of inebilizumab for neuromyelitis optica spectrum disorders (NMOSD). METHODS: A total of 33 patients with NMOSD treated with inebilizumab (Group INB, n=15) or rituximab (Group RTX, n=18) in addition to high-dose glucocorticoids were included. Both groups underwent hormone shock therapy during the acute phase. Subsequently, Group INB received inebilizumab injections during the remission phase, while Group RTX received rituximab injections. A comparison of aquaporins 4 (AQP4) titer values, peripheral blood B lymphocyte counts, and visual function recovery was conducted before and 8wk after treatment. Additionally, adverse reactions and patient tolerability were analyzed after using inebilizumab treatment regimes. RESULTS: Following inebilizumab treatment, there was a significantly improvement in the visual acuity of NMOSD patients (P0.05). Furthermore, inebilizumab exhibited higher safety levels than that of rituximab injections. CONCLUSION: The combination of inebilizumab and high-dose glucocorticoids proves to be effective. In comparison to rituximab injections, inebilizumab displays better tolerance and safety. Moreover, it demonstrates a partial effect in preventing optic nerve atrophy. Thus, it stands as an effective method to reduce the disability rates and improve the daily living ability of patients with NMOSD.

Abstract Federated learning is a distributed machine learning paradigm where the goal is to collaboratively train a high quality global model while private training data remains local over distributed clients. However, heterogenous data distribution over clients is severely challenging for federated learning system, which severely damage the quality of model. In order to address this challenge, we propose global prototype distillation (FedGPD) for heterogenous federated learning to improve performance of global model. The intuition is to use global class prototypes as knowledge to instruct local training on client side. Eventually, local objectives will be consistent with the global optima so that FedGPD learns an improved global model. Experiments show that FedGPD outperforms previous state-of-art methods by 0.22% ~1.28% in terms of average accuracy on representative benchmark datasets.

Abstract Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor’s (TF) binding strength, affect enhancer’s chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.

Abstract Background Stroke is a devastating disease affecting populations worldwide and is the primary cause of long-term disability. The inflammatory storm plays a crucial role in the progression of stroke. In the acute phase of ischemic stroke, there is a transient increase in anti-inflammatory M2 microglia followed by a rapid decline. Due to the abundant phospholipid in brain tissue, lipid peroxidation is a notable characteristic of ischemia/reperfusion (I/R), constituting a structural foundation for ferroptosis in M2 microglia. Slowing down the decrease in M2 microglia numbers and controlling the inflammatory microenvironment holds significant potential for enhancing stroke recovery. Results We found that the ferroptosis inhibitor can modulate inflammatory response in MCAO mice, characterizing that the level of M2 microglia-related cytokines was increased. We then confirmed that different subtypes of microglia exhibit distinct sensitivities to I/R-induced ferroptosis. Adipose-derived stem cells derived exosome (ADSC-Exo) effectively decreased the susceptibility of M2 microglia to ferroptosis via Fxr2/Atf3/Slc7a11, suppressing the inflammatory microenvironment and promoting neuronal survival. Furthermore, through plasmid engineering, a more efficient M2 microglia-targeted exosome, termed M2pep-ADSC-Exo, was developed. In vivo and in vitro experiments demonstrated that M2pep-ADSC-Exo exhibits significant targeting specificity for M2 microglia, further inhibiting M2 microglia ferroptosis and improving neurological function in ischemic stroke mice. Conclusion Collectively, we illustrated a novel potential therapeutic mechanism that Fxr2 in ADSC-Exo could alleviate the M2 microglia ferroptosis via regulating Atf3/Slc7all expression, hence inhibiting the inflammatory microenvironment, improving neurofunction recovery in cerebral I/R injury. We obtained a novel exosome, M2pep-ADSC-Exo, through engineered modification, which exhibits improved targeting capabilities toward M2 microglia. This provides a new avenue for the treatment of stroke. Graphical Abstract

Abstract Background We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. Methods Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. Results The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. Conclusions The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

Ag-based solid solutions have gained significant attention in the electronic packaging field due to their remarkable plasticity. In this work, the generalized planar fault energy (GPFE), critical strength and stresses, and electronic properties after adding solutes with different types and size misfits were studied for Ag-based solid solutions. Furthermore, we explored the underlying mechanism behind the impact of solutes on the plasticity of Ag and confirmed it through transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analyses. Importantly, we introduced the novel concepts of size and chemical misfit factors, electronic factors, and twinning factor into the plasticity enhancement mechanism of Ag-based solution systems. The predictions based on these factors were in agreement with experimental observations using TEM and HRTEM. Remarkably, the (Ag)–Bi system exhibited the lowest critical resolved shear stress for slip and twinning, as well as the most superior plasticity, positioning it as a crucial candidate for future development. This study presents a scientific framework for designing and developing Ag-based solid solutions with excellent plasticity, encompassing size/chemical factors, electronic factors, and twinning factor.

The management of chronic wounds in diabetes remains challenging due to the complexity of impaired wound healing, delayed healing, susceptibility to infection, and elevated risk of reopening, highlighting the need for effective chronic wound management with innovative approaches such as multifunctional hydrogels. Here, we have produced HA-DA@rhCol hydrogels consisting of dopamine-modified hyaluronic acid and recombinant human collagen type-III (rhCol) by oxidative coupling of the catechol group using the H2O2/HRP catalytic system. The post-reactive hydrogel has a good porous structure, swelling rate, reasonable degradation, rheological and mechanical properties, and the catechol group and dopamine impart to the hydrogel tissue adhesiveness, antioxidant capacity, and excellent photothermal effects leading to superior in vitro antimicrobial activity. In addition, the ability of rhCol to confer hydrogels to promote angiogenesis and wound repair has also been investigated. Cytotoxicity and hemolysis tests demonstrated the good biocompatibility of the hydrogel. Wound closure, collagen deposition and immunohistochemical examination confirmed the ability of the hydrogel to promote diabetic wound healing. In summary, the adhesive hemostatic antioxidative hydrogel with rhCol to promote wound healing in diabetic rat is an excellent chronic wound dressing.

Club cell secretory protein (CC16) is considered a biological marker indicating lung epithelial and lung permeability. The joint effect of polycyclic aromatic hydrocarbons (PAHs) exposure on CC16 levels and the association between CC16 levels and long-term lung function changes lacks epidemiological evidence. To investigate the effect of PAHs exposure on plasma CC16 levels and the association between CC16 levels and long-term lung function changes, this study enrolled 307 coke oven workers in 2014, measured their baseline concentrations of urinary PAHs metabolites and plasma CC16, with follow-up after nine years. Bayesian kernel machine regression (BKMR) was employed to analyze the effect of mixed PAHs metabolites. The dose-effect association between baseline CC16 levels and lung function during 2014–2023 was explored using restricted cubic spline (RCS) models, and stratified analysis investigated the effect modification of PAHs exposure and smoking status on this association. The median age of the participants was 40 years, with 93.81 % male. The results showed that plasma CC16 levels decreased by 2.02 ng/mL (95 % CI: −3.77, −0.27) among all participants and FVC (% predicted) decreased by 2.87 % (95 % CI: −5.59, −0.14) in the low CC16 group with each unit increase in log-transformed 2-OHNAP. The BKMR model revealed a negative association between PAHs metabolites and both plasma CC16 levels and FVC (% predicted). Plasma CC16 decreased by 1.05 units when all PAHs metabolites at P65 compared to those at P50. After 9 years of follow-up, baseline CC16 levels were significantly associated with follow-up FVC (% predicted), FEV1 (% predicted), and small airway dysfunction risk. Furthermore, high PAHs exposure and smoking enhanced the association between CC16 and lung function. In conclusion, PAHs exposure decreases CC16 levels, and coking workers with low baseline CC16 levels may experience more severe future lung function decline.

The escalating prevalence of non-alcoholic fatty liver disease (NAFLD), driven by inadequate dietary habits and metabolic imbalances, underscores the urgent need for effective dietary interventions. This study investigated the potential of 80 % peanut diacylglycerol (DAG) as a functional lipid component in preventing NAFLD and associated intestinal microenvironment based on a high-cholesterol diet (HCD)-induced zebrafish model. We first found that the zebrafish fatty liver model shared multiple similar pathways like lipid-induced atherosclerosis, insulin resistance with NAFLD patient in terms of lipid metabolism. Employing larvae and grown zebrafish, we aimed to elucidate the preventive and alleviating effect of a DAG diet on NAFLD respectively. Results showed that DAG post-intervention significantly decreased hepatic lipid accumulation and diminished intestinal inflammatory in fatty liver zebrafish model. Notably, transcriptomic sequencing illuminated upregulation in genes related to lipid metabolism (e.g., fasn, mogat2, cd36), suggesting that peanut DAG accelerated lipid transportation and fostered a metabolic environment less conducive to NAFLD symptoms. Besides, reduced liver lipid droplets and hepatic inflammatory suggested DAG feeding could also prevent the formation of NAFLD. The study provided compelling evidence supporting the incorporation of DAG into the diet as a strategy for the prevention and management of fatty liver disease, proposing a novel approach to functional food development aimed at combating metabolic diseases.

The efficient utilization of biomass energy and the optimal operation of integrated renewable energy sources in virtual power plants have become critical issues that need addressing to achieve carbon neutrality targets in rural areas. This paper introduces a model for rural biomass energy generation based on the characteristics of rural biogas availability, fermentation efficiency, and utilization status. A model for a rural virtual power plant is proposed, taking into account the flexible response capacity of renewable energy sources consumption and load demand response. Additionally, the correlated uncertainties of photovoltaic and wind turbine outputs are addressed by leveraging the flexible biogas power generation in the rural virtual power plant. The scenario generation method is proposed based on the Copula function to describe the correlated uncertainties. Aiming to minimize the system operating cost while ensuring the safe operation of the system, a two-stage distributionally robust optimization operation model for the rural virtual power plant is proposed. The dual vertices fixing based algorithm is developed to solve the two-stage distributionally robust optimal operation problem. Simulation results demonstrate that the proposed model and method effectively reduce over-robustness by addressing correlated uncertainties and achieve the safe, economical, and green operation of the rural energy system under uncertain conditions.

BackgroundExisting studies investigating the impact of serum calcium (Ca), phosphate (P), 25 hydroxyvitamin D (25[OH]D), and parathyroid hormone (PTH) levels on kidney function have produced inconsistent results. Further research is needed to establish the direct causal relationship between these factors and kidney function.MethodsThe study used genome-wide association study datasets for exposure and outcome, mainly derived from the UK Biobank and CKDGen Consortium, with sample sizes ranging from 3,310 to 480,699 individuals of European ancestry. Heritability and genetic correlations among these phenotypes were assessed using linkage disequilibrium score regression (LDSC) and phenotypes with a heritability z-score

Abstract Hybrid excitons, characterized by their strong oscillation strength and long lifetimes, hold great potential as information carriers in semiconductors. They offer promising applications in exciton‐based devices and circuits. MoSe2/WS2 heterostructures represent an ideal platform for studying hybrid excitons, but how to regulate the exciton lifetime has not yet been explored. In this study, layer hybridization is modulated by applying electric fields parallel or antiparallel to the dipole moment, enabling us to regulate the exciton lifetime from 1.36 to 4.60 ns. Furthermore, the time‐resolved photoluminescence decay traces are measured at different excitation power. A hybrid exciton annihilation rate of 8.9 × 10−4 cm2 s−1 is obtained by fitting. This work reveals the effects of electric fields and excitation power on the lifetime of hybrid excitons in MoSe2/WS2 1.5° moiré heterostructures, which play important roles in high photoluminescence quantum yield optoelectronic devices based on transition‐metal dichalcogenides heterostructures.

Panax notoginseng is a traditional Chinese medicine rich in many pharmacological components. The root of the ‘Miaoxiang Sanqi No. 2’ is yellow or greenish yellow, while a novel cultivar-‘Wenyuan Ziqi No. 1’ shows purple root and is thought to have high medicinal value. Little information is available about the anthocyanin biosynthesis in P. notoginseng root. In this study, we compared the ‘Miaoxiang Sanqi No. 2’ and ‘Wenyuan Ziqi No. 1’ in morphological, transcriptional and metabolic levels. The results showed that purple rich in the periderm, rhizome and phloem around cambium of the ‘Wenyuan Ziqi No. 1’ root and cyanidin 3-O-galactoside was the main anthocyanin causing purple. Moreover, ‘Wenyuan Ziqi No. 1’ highly accumulated in 155 metabolites, including flavones, phenylpropanoids and lipids. Transcriptome data showed that phenylpropanoid biosynthesis pathway genes are highly expressed in ‘Wenyuan Ziqi No. 1’. Conjoint analysis showed that anthocyanin biosynthesis pathway substances were highly accumulated in ‘Wenyuan Ziqi No. 1’, and the expression level of structural genes involved in anthocyanin biosynthesis pathway was higher in ‘Wenyuan Ziqi No. 1’. Meanwhile, eight R2R3-MYB genes that might be involved in anthocyanin biosynthesis were identified. The comprehensive analysis of two cultivars provides new insights into the understanding of root coloration in P. notoginseng.

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatio-temporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%–10%.

The aurora arc is a separate auroral structure from the aurora oval, whose location and morphology are related to various solar-terrestrial circumstances. However, because of the low occurring frequency of aurora arc and the lack of the automatic identification technique, it can only be manually distinguished from a huge number of observed images, which is very inefficient. In order to improve the identification efficiency, we propose an identification algorithm based on YOLOX network and Convolutional Block Attention Module attention mechanism. Using the aurora images observed by Special Sensor Ultraviolet Spectrographic Imager carried by the Defense Meteorological Satellite Program F16-F19 satellites from 2013 to 2019, the automatic detection models for global and local areas were trained separately. The identification outputs will be integrated by calculating the intersection. According to the test results, the event identification precision is 86% and the position identification precision is 79%, both of which are greater than the results before integration. Therefore, the proposed method is not only able to identify whether the image contains the aurora arcs, but also accurately locate them, making it a highly effective tool for the advancement of future study.

PurposeAimed to assess the impact of wearable device-based aerobic exercise on the physical and mental well-being of women with breast cancer (BC) undergoing chemotherapy.MethodsForty adult women with BC who underwent anthracycline-based chemotherapy were randomly allocated to the exercise group (n = 21) or the control group (n = 19). Both groups received standard health education and oncology care. In addition, the exercise group wore wearable devices to engage in moderate to high-intensity (50–90% HRmax) aerobic exercise during chemotherapy, while the control group did not carry out exercise intervention. Health-related physical fitness level, physical activity energy expenditure (PAEE), anxiety and depression scores, sleep quality, cancer-related fatigue, and overall quality of life (QoL), were assessed both before (prior to the first chemotherapy session) and after (prior to the fifth chemotherapy session) the exercise intervention. Exercise-related adverse events, exercise compliance, number and severity of gastrointestinal reactions and myelosuppression occurred were recorded during the exercise intervention.ResultsAfter the intervention, compared to the control group, the exercise group (1) had significantly higher relative VO2peak (p = 0.003) and handgrip strength (p

Background and aims: Alcohol-related liver disease (ALD) is a worldwide burden. Cuproptosis has been shown to play a key role in the development of several diseases. However, the role and mechanisms of cuproptosis in ALD remain unclear. Methods: The RNA-sequencing data of ALD liver samples were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatical analyses were performed using the R data package. We then identified key genes through multiple machine learning methods. Immunoinfiltration analyses were used to identify different immune cells in ALD patients and controls. The expression levels of key genes were further verified. Results: We identified three key cuproptosis-related genes (CRGs) (DPYD, SLC31A1, and DBT) through an in-depth analysis of two GEO datasets, including 28 ALD samples and eight control samples. The area under the curve (AUC) value of these three genes combined in determining ALD was 1.0. In the external datasets, the three key genes had AUC values as high as 1.0 and 0.917, respectively. Nomogram, decision curve, and calibration curve analyses also confirmed these genes’ ability to predict the diagnosis. These three key genes were found to be involved in multiple pathways associated with ALD progression. We confirmed the mRNA expression of these three key genes in mouse ALD liver samples. Regarding immune cell infiltration, the numbers of B cells, CD8 (+) T cells, NK cells, T-helper cells, and Th1 cells were significantly lower in ALD patient samples than in control liver samples. Single sample gene set enrichment analysis (ssGSEA) was then used to estimate the immune microenvironment of different CRG clusters and CRG-related gene clusters. In addition, we calculated CRG scores through principal component analysis (PCA) and selected Sankey plots to represent the correlation between CRG clusters, gene clusters, and CRG scores. Finally, the three key genes were confirmed in mouse ALD liver samples and liver cells treated with ethanol. Conclusions: We first established a prognostic model for ALD based on 3 CRGs and robust prediction efficacy was confirmed. Our investigation contributes to a comprehensive understanding of the role of cuproptosis in ALD, presenting promising avenues for the exploration of therapeutic strategies.

ObjectiveNumerous studies emphasize the pivotal role of inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD) development. Some link specific systemic immune biomarkers (e.g., systemic immuno-inflammatory index [SII], neutrophil-to-albumin ratio [NPAR] and neutrophil-to-lymphocyte ratio [NLR]) to hepatic steatosis risk. However, the relevance of other markers like systemic immune-inflammation index [SIRI], platelet-to-lymphocyte ratio [PLR] and lymphocyte/monocyte ratio [LMR] in MASLD remains unclear. Limited literature covers all six markers together. This study aims to investigate the association between SII, SIRI, LMR, NLR, PLR, and NPAR and MASLD, assessing their predictive value.MethodsIn this cross-sectional analysis of adults from NHANES (2007–2018), we investigated the relationship between six systemic immune biomarkers, stratified by quartiles: quartile1 (Q1), quartile2 (Q2), quartile3 (Q3) and quartile4 (Q4), and the outcome of MASLD assessed by Fatty Liver Index (FLI) and United States Fatty Liver Index (USFLI). Logistic regression and restricted cubic splines (RCS) were employed to assess the association between systemic immune biomarkers and MASLD risks. Propensity score matching controlled for potential confounders, and receiver operating characteristic (ROC) curve analysis evaluated the biomarkers’ predictive performances for MASLD. Subgroup and interaction analysis were conducted to explore the effects of systemic immune biomarkers on MASLD risks. Multicollinearity was quantified using the variance inflation factor.ResultsIn total, 14,413 participants were included and 6,518 had MASLD. Compared with non-MASLD, participants with MASLD had higher SII, SIRI, NLR, PLR, and NPAR (p 0.05). The prevalence of MASLD increased with the Q4 of SII [OR = 1.47, 95%CI (1.24, 1.74)], SIRI [OR = 1.30, 95%CI (1.09, 1.54)], NLR [OR = 1.25, 95%CI (1.04, 1.49)], PLR [OR = 1.29, 95%CI (1.09, 1.53)] and NPAR [OR = 1.29, 95%CI (1.09, 1.54)] compared to the Q1 after adjusting for the bias caused by potential confounders. However, the propensity score matching analysis only supported an association between the highest SII, SIRI, NLR NPAR and the risk of MASLD. The results of the subgroup analysis showed considerable robustness in the relationship.ConclusionHigher SII, SIRI, NLR and NPAR were positively associated with a heightened risk of MASLD. NPAR showed the superior predictive value, followed by SII, SIRI and NLR. This needs to be validated in additional longitudinal studies and clinical trials.

Deep learning models have made great accomplishments in space weather forecasting. The critical frequency of the ionospheric F2 layer (foF2) is a key ionospheric parameter, which can be understood and predicted by some advanced new deep learning technologies. In this paper, we utilized an Informer architecture model to predict foF2 for several hours up to 48 h and analyzed its variations during periods of quiet, moderate, and intense geomagnetic conditions. The Informer method forecasts the temporal variations of foF2 by processing and training the past and present foF2 data from the Haikou station, China, during 2006–2014. It is evident that the Informer–foF2 model achieves better prediction performance than the widely used long short-term memory model. The Informer–foF2 model captures the correlation features within the foF2 time series and better predicts the variations ranging for hours up to days during different geomagnetic activities.

Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 104 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.

Background The majority of extant methodologies for text classification prioritize the extraction of feature representations from texts with high degrees of distinction, a process that may result in computational inefficiencies. To address this limitation, the current study proposes a novel approach by directly leveraging label information to construct text representations. This integration aims to optimize the use of label data alongside textual content. Methods The methodology initiated with separate pre-processing of texts and labels, followed by encoding through a projection layer. This research then utilized a conventional self-attention model enhanced by instance normalization (IN) and Gaussian Error Linear Unit (GELU) functions to assess emotional valences in review texts. An advanced self-attention mechanism was further developed to enable the efficient integration of text and label information. In the final stage, an adaptive label encoder was employed to extract relevant label information from the combined text-label data efficiently. Results Empirical evaluations demonstrate that the proposed model achieves a significant improvement in classification performance, outperforming existing methodologies. This enhancement is quantitatively evidenced by its superior micro-F1 score, indicating the efficacy of integrating label information into text classification processes. This suggests that the model not only addresses computational inefficiencies but also enhances the accuracy of text classification.

Attitude measurement is a basic technique for monitoring vehicle motion states and safety. The spin motion of a vehicle couples the attitude angles with each other, which has an impact on the navigation and control of the vehicle. Global navigation satellite system (GNSS) signals-based roll angle measurement methods are important for vehicle attitude measurement. Most of existing studies use continuous signal power, but the case of loop lock loss leading to discontinuous power reception has not been considered. A robust estimation method for the roll angle based on the Tukey weight function is proposed to improve the measurement accuracy in cases of discontinuous reception. The characteristics of the GNSS signals, the geometric relationship between the signal power and roll angle of the vehicle are discussed. By installing a GNSS receiver with a single patched antenna on a rotating platform with a controllable rolling speed, the proposed method was verified by experiments. The robust estimation errors of different weight functions are analyzed. According to the characteristics of the gross measurement errors, a robust estimation method of multisatellite power observations is proposed to obtain a high-precision and stable estimation of the vehicle roll angle. The results show that the proposed algorithm can improve the accuracy of roll angle estimation even with gross measurement errors. As a result of the experiments, the estimation errors of the algorithm are 6.57° at a confidence level of 68 % and 15.49°at the confidence level of 95 %. In contrast, they are 11.38° and 37.31° for the traditional LS method. Moreover, the estimation accuracy of the algorithm is not significantly correlated with the vehicle rotational speed. Therefore, the vehicle roll angle can be estimated with high accuracy under a variety of rotational speeds.

Introduction Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features. Methods A retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model. Results A 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC. Conclusions This study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.

Technological advances are crucial for the optimization of gastric cancer surgery, and the success of any gastric cancer surgery is based on the correct and precise anatomical determination of the primary tumour and tissue structures. Real-time imaging-guided surgery is showing increasing potential and utility, mainly because it helps to aid intraoperative decision-making. However, intraoperative imaging faces many challenges in the field of gastric cancer. This article summarizes and discusses the following clinical applications of real-time optical imaging and fluorescence-guided surgery for gastric cancer: (1) the potential of quantitative fluorescence imaging in assessing tissue perfusion, (2) vascular navigation and determination of tumour margins, (3) the advantages and limitations of lymph node drainage assessment, and (4) identification of peritoneal metastases. In addition, preclinical study of tumour-targeted fluorescence imaging are discussed.

BackgroundColorectal cancer (CRC) is an aggressive primary intestinal malignancy with the third-highest incidence and second-highest mortality among all cancer types worldwide. Transcription factors (TFs) regulate cell development and differentiation owing to their ability to recognize specific DNA sequences upstream of genes. Numerous studies have demonstrated a strong correlation between TFs, the etiology of tumors, and therapeutic approaches. Here, we aimed to explore prognosis-related TFs and comprehend their carcinogenic mechanisms, thereby offering novel insights into the diagnosis and management of CRC.Materials and MethodsDifferentially expressed TFs between CRC and normal tissues were identified leveraging The Cancer Genome Atlas database, Weighted correlation network analysis and Cox regression analysis were performed to identify prognosis-related TFs. The cellular functions of hub TF zinc finger E-box binding homeobox 1 (ZEB1) were determined using by 5-ethynyl-2′-deoxyuridine and cell invasion assays in CRC cells. RNA-sequencing, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses were used to identify the cellular processes in which ZEB1 participates. Immunoaffinity purification, silver staining mass spectrometry, and a chromatin immunoprecipitation assay were conducted to search for proteins that might interact with ZEB1 and the target genes they jointly regulate.ResultsThirteen central TFs related to prognosis were identified through bioinformatics analysis techniques. Among these TFs, ZEB1 emerged as the TF most closely associated with CRC, as determined through a combination of regulatory network diagrams, survival curves, and phenotype analyses. ZEB1 promotes CRC cell growth by recruiting the NuRD(MTA1) complex, and the ZEB1/NuRD(MTA1) complex transcriptionally represses glycolysis-associated tumor suppressor genes.ConclusionOur study not only identified a hub biomarker related to CRC prognosis but also revealed the specific molecular mechanisms through which ZEB1 affects cancer progression. These insights provide crucial evidence for the diagnosis of CRC and potential treatment opportunities.

BackgroundCancers arise from genetic and epigenetic abnormalities that affect oncogenes and tumor suppressor genes, compounded by gene mutations. The N6-methyladenosine (m6A) RNA modification, regulated by methylation regulators, has been implicated in tumor proliferation, differentiation, tumorigenesis, invasion, and metastasis. However, the role of m6A modification patterns in the tumor microenvironment of gastric cancer (GC) remains poorly understood.Materials and methodsIn this study, we analyzed m6A modification patterns in 267 GC samples utilizing 31 m6A regulators. Using consensus clustering, we identified two unique subgroups of GC. Patients with GC were segregated into high- and low-infiltration cohorts to evaluate the infiltration proportions of the five prognostically significant immune cell types. Leveraging the differential genes in GC, we identified a “green” module via Weighted Gene Co-expression Network Analysis. A risk prediction model was established using the LASSO regression method.ResultsThe “green” module was connected to both the m6A RNA methylation cluster and immune infiltration patterns. Based on “Module Membership” and “Gene Significance”, 37 hub genes were identified, and a risk prediction model incorporating nine hub genes was established. Furthermore, methylated RNA immunoprecipitation and RNA Immunoprecipitation assays revealed that YTHDF1 elevated the expression of DNMT3B, which synergistically promoted the initiation and development of GC. We elucidated the molecular mechanism underlying the regulation of DNMT3B by YTHDF1 and explored the crosstalk between m6A and 5mC modification.Conclusionm6A RNA methylation regulators are instrumental in malignant progression and the dynamics of tumor microenvironment infiltration of GC. Assessing m6A modification patterns and tumor microenvironment infiltration characteristics in patients with GC holds promise as a valuable prognostic biomarker.

Achieving superior ductility, microstructure, and strength simultaneously in tailor-welded blanks remains challenging. To address this, pre-aging blanks before friction stir welding was proposed. Systematic studies were conducted on the microstructure, ductility, and post-aging strength of pre-aged 2219 aluminium alloy after friction stir welding. Uniaxial tensile tests evaluated the ductility of tailor-welded blanks at room and cryogenic temperatures, along with their mechanical properties after artificial aging. Microstructural characterization and Vickers hardness measurements elucidated the strengthening mechanisms between the welded region and base material. No abnormal grain growth was observed in the welded region post pre-aging welding; instead, a finer grain structure prevailed. Ductility of the tailor-welded blanks initially increased and then decreased with increasing rotational speed. Excellent elongations, 27.8% at room temperature and 32.4% at cryogenic temperatures, were obtained at a rotational speed of 1300 rpm, reaching 91.5% of the base material. This attributed to the finer grain boundary strengthening in the welded regions. After artificial aging, a joint efficiency of 93.5% was achieved, owing to effective compensation of the reduction in precipitation strengthening caused by the welded heat input through finer grain strengthening. This approach represents a novel method for preparing ultrawide blanks for forming integral large-sized aluminum alloy components.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Soil Cd pollution is a significant environmental issue faced by contemporary society. Kentucky bluegrass is considered a potential phytoremediation species, as some varieties have excellent cadmium (Cd) tolerance. However, the mechanisms of Cd accumulation and transportation in Kentucky bluegrass are still not fully understood. The Cd-tolerant Kentucky bluegrass cultivar ‘Midnight’ (M) exhibits lower Cd translocation efficiency and a higher leaf Cd concentration compared to the Cd-sensitive cultivar ‘Rugby II’ (R). We hypothesized that Cd translocation from roots to shoots in cultivar M is hindered by the endodermal barriers and cell wall polysaccharides; hence, we conducted Cd distribution, cytological observation, cell wall component, and transcriptomic analyses under Cd stress conditions using the M and R cultivars. Cd stress resulted in the thickening of the endodermis and increased synthesis of cell wall polysaccharides in both the M and R cultivars. Endodermis development restricted the radical transport of Cd from the root cortex to the stele, while the accumulation of cell wall polysaccharides promoted the binding of Cd to the cell wall. These changes further inhibited the long-distance translocation of Cd from the roots to the aerial parts. Furthermore, the M cultivar exhibited limited long-distance Cd translocation efficiency compared to the R cultivar, which was attributed to the enhanced development of endodermal barriers and increased Cd binding by cell wall polysaccharides. This study provides valuable insights for screening high Cd transport efficiency in Kentucky bluegrass based on anatomical structure and genetic modification.

Radiation-induced brain injury (RIBI) is a significant challenge in radiotherapy for head and neck tumors, impacting patients' quality of life. In exploring potential treatments, this study focuses on memantine hydrochloride and hydrogen-rich water, hypothesized to mitigate RIBI through inhibiting the NLRP3/NLRC4/Caspase-1 pathway. In a controlled study involving 40 Sprague-Dawley rats, divided into five groups including a control and various treatment groups, we assessed the effects of these treatments on RIBI. Post-irradiation, all irradiated groups displayed symptoms like weight loss and salivation, with notable variations among different treatment approaches. Particularly, hydrogen-rich water showed a promising reduction in these symptoms. Histopathological analysis indicated substantial hippocampal damage in the radiation-only group, while the groups receiving memantine and/or hydrogen-rich water exhibited significant mitigation of such damage. Molecular studies, revealed a decrease in oxidative stress markers and an attenuated inflammatory response in the treatment groups. Immunohistochemistry further confirmed these molecular changes, suggesting the effectiveness of these agents. Echoing recent scientific inquiries into the protective roles of specific compounds against radiation-induced damages, our study adds to the growing body of evidence on the potential of memantine and hydrogen-rich water as novel therapeutic strategies for RIBI.

Land-use changes dominated by high-intensity human activities have seriously threatened regional ecosystem health (EH) and will continue to increase in the future, especially in mountainous cities. Therefore, there is a need to systematically assess the contribution and spatial effects of future land use conversion on EH from a multi-scenario simulation perspective, which is essential for ecological conservation and management. For this purpose, taking Chongqing as an example, this study assessed the static distribution and dynamic evolution of EH over historical periods (2000–2020) and future multi-scenarios in 2030 using the vigor-organization-resilience-ecosystem services balance (VORSB) framework and patch-level land use simulation (PLUS) model, then analyzed the influence of land use conversion on EH and identified sensitive areas under multiple scenarios based on the contribution index and ecological sensitivity index. The results showed that Chongqing ecosystems were generally at a moderate healthy level during 2000–2020, with an increasing trend in EH level, with high-EH located in the eastern mountainous areas, while low-EH clustered in the western urban core and its peripheral areas. From 2020 to 2030, the EH level was improved by about 0.21% under the ecological protection scenario due to the transformation from farmland to forestland, which has the largest positive contribution of 0.111. In contrast, the EH level under the natural development and economic development scenarios declined by 1.95% and 3.07%, especially in the metropolitan developed circle, which was significantly related to the occupation of farmland resources by urban land, with negative contribution of −0.103 and −0.189, respectively. In the future, these regions should strictly control the urban expansion scale and strengthen high-quality farmland protection to achieve the balance of ecological protection and economic development. The findings will provide scientific guidance for the regional ecosystem conservation, land use planning, and urban sustainable development of mountainous cities.

Dual-comb photoacoustic spectroscopy (DC-PAS) advances spectral measurements by offering high-sensitivity and compact size in a wavelength-independent manner. Here, we present a novel cantilever-enhanced DC-PAS scheme, employing a high-sensitivity fiber-optic acoustic sensor based on an optical cantilever and a non-resonant photoacoustic cell (PAC) featuring a flat-response characteristic. The dual comb is down-converted to the audio frequency range, and the resulting multiheterodyne sound waves from the photoacoustic effect, are mapped into the response frequency region of the optical cantilever microphone. This cantilever-enhanced DC-PAS method provides advantages such as high sensitivity, compact design, and immunity to electromagnetic interference. Through 10 seconds averaging time, the proposed approach experimentally achieved a minimum detection limit of 860 ppb for acetylene. This technology presents outstanding opportunities for highly sensitive detection of trace gases in a wavelength-independent manner, all within a compact volume.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events. HIGHLIGHTS Machine learning models, particularly the LSTM model, outperformed traditional methods in estimating RDII in sewer systems.; Feature engineering techniques allowed for the extraction of physically significant features in sewer flow modelling.; Machine learning models successfully simulated the temporal variation in RDII processes, resulting in improved accuracy for predicting peak flows and RDII volumes during storm events.;

Abstract Background CDC6 is an oncogenic protein whose expression level fluctuates during the cell cycle. Although several E3 ubiquitin ligases responsible for the ubiquitin-mediated proteolysis of CDC6 have been identified, the deubiquitination pathway for CDC6 has not been investigated. Methods The proteome-wide deubiquitinase (DUB) screening was used to identify the potential regulator of CDC6. Immunofluorescence, protein half-life and deubiquitination assays were performed to determine the protein stability of CDC6. Gain- and loss-of-function experiments were implemented to analyse the impacts of OUTD6A-CDC6 axis on tumour growth and chemosensitivity in vitro. N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-induced conditional Otud6a knockout (CKO) mouse model and tumour xenograft model were performed to analyse the role of OTUD6A-CDC6 axis in vivo. Tissue specimens were used to determine the association between OTUD6A and CDC6. Results OTUD6A interacts with, depolyubiquitinates and stabilizes CDC6 by removing K6-, K33-, and K48-linked polyubiquitination. Moreover, OTUD6A promotes cell proliferation and decreases sensitivity to chemotherapy by upregulating CDC6. CKO mice are less prone to BCa tumorigenesis induced by BBN, and knockdown of OTUD6A inhibits tumour progression in vivo. Furthermore, OTUD6A protein level has a positive correlation with CDC6 protein level, and high protein levels of OTUD6A and CDC6 are associated with poor prognosis in patients with bladder cancer. Conclusions We reveal an important yet missing piece of novel DUB governing CDC6 stability. In addition, our findings propose a model for the OTUD6A-CDC6 axis that provides novel insights into cell cycle and chemosensitivity regulation, which may become a potential biomarker and promising drug target for cancer treatment.

Highlights Si/C Composite and Nanostructure Engineering: Advanced Si/C composites and multidimensional nanostructures address key challenges in silicon anodes, like volume expansion and unstable SEI, enhancing LIBs performance. Artificial SEI, Prelithiation, and Binders: Focus on stable artificial SEI layers, efficient prelithiation, and cutting-edge binders to improve Coulombic efficiency and reduce capacity loss, enhancing Si anode durability and efficiency. Real-World Application and Scalability: Analysis of these strategies highlights scalability and commercial viability, transitioning Si-anode technologies to practical, high-performance LIBs applications.

Abstract Xanthomonas oryzae pv. oryzae (Xoo) injects major transcription activator-like effectors (TALEs) into plant cells to activate susceptibility (S) genes for promoting bacterial leaf blight in rice. Numerous resistance (R) genes have been used to construct differential cultivars of rice to identify races of Xoo, but the S genes were rarely considered. Different edited lines of rice cv. Kitaake were constructed using CRISPR/Cas9 gene-editing, including single, double and triple edits in the effector-binding elements (EBEs) located in the promoters of rice S genes OsSWEET11a, OsSWEET13 and OsSWEET14. The near-isogenic lines (NILs) were used as tracers to detect major TALEs (PthXo1, PthXo2, PthXo3 and their variants) in 50 Xoo strains. The pathotypes produced on the tracers determined six major TALE types in the 50 Xoo strains. The presence of the major TALEs in Xoo strains was consistent with the expression of S genes in the tracers, and it was also by known genome sequences. The EBE editing had little effect on agronomic traits, which was conducive to balancing yield and resistance. The rice-tracers generated here provide a valuable tool to track major TALEs of Xoo in Asia which then shows what rice cultivars are needed to combat Xoo in the field.

Abstract Background Asthma is a prevalent respiratory inflammatory disease. Abnormal apoptosis of bronchial epithelial cells is one of the major factors in the progression of asthma. Peripheral benzodiazepine receptors are highly expressed in bronchial epithelial cells, which act as a component of the mitochondrial permeability transition pore to regulate its opening and closing and apoptosis of bronchial epithelial cells. We aimed to investigate the mechanisms by which peripheral benzodiazepine receptor and its ligands, agonist 4’-Chlorodiazepam (Ro5-4864) and antagonist 1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinolinecarboxamide (PK 11,195), modulate the mitochondrial function and cell apoptosis in the treatment of asthma. Methods In vitro study, Ro5-4864 and PK 11,195 were utilized to pretreat cells prior to the inflammatory injury induced by Lipopolysaccharide. The reactive oxygen species, the apoptosis of cell, the mitochondrial membrane potentials, the ultrastructures of the mitochondria and the expression levels of peripheral benzodiazepine receptors and apoptosis-related proteins and genes were detected. In vivo study, mice were administrated intraperitoneally with Ro5-4864 and PK 11,195 before sensitized and challenged by ovalbumin. Serum IgE and bronchoalveolar lavage fluid cytokines were detected, and lung tissues were underwent the histopathological examination. Results The ligands of peripheral benzodiazepine receptor counteracted the effects of the increase of reactive oxygen species, the elevated extent of apoptosis, the decrease of mitochondrial membrane potentials and the disruption of mitochondrial ultrastructures induced by Lipopolysaccharide. The ligands also promoted the expression of anti-apoptosis-related proteins and genes and inhibited the expression of pro-apoptosis-related proteins and genes. Besides, the ligands reduced the levels of serum IgE and bronchoalveolar lavage fluid cytokines in asthmatic mice and attenuated the histopathological damage of lungs. Conclusion Peripheral benzodiazepine receptor serves as a potential therapeutic target for the treatment of asthma, with its ligands exerting mitochondrial protective and anti-apoptotic effects on bronchial epithelial cells.

Abstract Due to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for the accuracy and computation speed of the power network security risk evaluation model, the study develops a fusion power network security risk evaluation algorithm by fusing the flash search algorithm with the support vector machine. This algorithm is then used as the foundation for building an improved power network security risk evaluation model based on the fusion algorithm.The study's improved algorithm's accuracy is 96.2%, which is higher than the accuracy of the other comparative algorithms; its error rate is 3.8%, which is lower than the error rate of the other comparative algorithms; and its loss function curve convergence is quicker than that of the other algorithms.The risk evaluation model's accuracy is 97.8%, which is higher than the accuracy of other comparative models; the error rate is 1.9%, which is lower than the error rate of other comparative models; the computing time of the improved power network security risk evaluation model is 4.4 s, which is lower than the computing time of other comparative models; and its expert score is high. These findings are supported by empirical analysis of the improved power network security risk evaluation model proposed in the study. According to the study's findings, the fusion algorithm and the upgraded power network security risk evaluation model outperform other approaches in terms of accuracy and processing speed. This allows the study's maintenance staff to better meet the needs of the community by assisting them in identifying potential security hazards early on and taking the necessary preventative and remedial action to ensure the power system's continued safe operation.

Abstract Background Potato (Solanum tuberosum L.) production is seriously threatened by the oomycete Phytophthora infestans (P. infestans). However, it remains unclear how the two potato cultivars, Q9 (moderately resistant) and Atl (susceptible), initiate distinct defense responses after inoculation with P. infestans. To uncover the underlying process, we investigated the dynamic gene expression profiles and the functions of the differentially expressed genes (DEGs). Results The results showed that the numbers of up-regulated DEGs were 1345, 1063 and 1694 in Q9 and 272, 603 and 2554 in Atl at 1 day post inoculation (dpi), 3 dpi and 5 dpi, respectively. Meanwhile, the down-regulated DEGs were 1557, 1417 and 389 in Q9 and 114, 364 and 2528 in Atl simultaneously. KEGG enrichment analysis revealed that plant-pathogen interaction, phenylpropanoid biosynthesis and MAPK signaling pathway were significantly enriched in Q9 at the later stage. Furthermore, DEGs associated with plant-pathogen interaction and phenylpropanoid biosynthesis showed higher expression levels in Q9 compared to Atl, especially at the later stage. The expressions of eight DEGs were validated by quantitative real-time PCR (qRT-PCR), which further verified the accuracy of the transcriptomics analysis. In addition, DEGs that were exclusively up-regulated and down-regulated in Q9 were analyzed, which may contribute to the resistance of Q9. Transient expression analysis revealed that four DEGs, including StHP1, StMYB2, StHSP3 and StNAC5, exhibited increased tolerance to P. infestans, indicating a positive role in enhancing the resistance of Q9. Conclusion Therefore, the DEGs associated with plant-pathogen interaction, phenylpropanoid biosynthesis and MAPK signaling pathway were involved in regulating late blight, and the expression level of the related genes significantly increased in Q9. In addition, StHP1, StHSP3 and the transcription factors, including StMYB2 and StNAC5, played positive roles in improving the resistance of potato Q9 to P. infestans. These findings provide useful information for further understanding the molecular mechanism of potato resistance to late blight. Furthermore, our study identified novel resistance genes that can be used in resistance breeding and functional research. Graphical abstract

Abstract Background GAox is a key enzyme involved in GA biosynthesis pathway and plays an important role in regulating various processes in plant life cycle. However, it has not been systematic, studies have been conducted in potato, which is the world's fourth largest food crop. Methods In this work, we systematically identified GAox gene family (StGAox) in potato by analyzing the potato genome sequence using a set of bioinformatics approaches, and analyze their physical, chemical properties, distribution on chromosomes, gene structure, conserved motifs, gene duplication events and expression patterns were analyzed. Results The results showed that a total of 33 GAox proteins were identified and unevenly distributed on 10 chromosomes. Based on their protein structure and phylogenetic characteristics, these 33 StGAoxes were divided into 5 distinct subclasses. Collinearity analysis revealed that there were 5 pairs of duplicated genes in the StGAox gene family, and all of which evolved under purifying selection. Analysis of RNA-seq data of double haploid (DM) potatoes under different tissues, abiotic stresses and hormone treatments showed that PG0002068, PG0024249 and PG0027963 were higher expressed in leaves, PG009427, PG0026762, PG0009021 and PG0021095 were higher expressed in tubers, PG2003479, PG0024249, PG0005698, and PG0009021were higher expressed in shoots than those of other tissues. In addition, the expression of PG0002068, PG2003479, PG0032156, PG0024249, and PG0021292 were up-regulated under mannitol and drought stress. Conclusions Comparative genome-wide analysis of StGAox genes and their expression analyses revealed that members of this family may be involved in tissue-specific developmental and abiotic stress responses. Graphical Abstract

Abstract Crop wild relatives offer natural variations of disease resistance for crop improvement. Here, we report the isolation of broad-spectrum powdery mildew resistance gene Pm36, originated from wild emmer wheat, that encodes a tandem kinase with a transmembrane domain (WTK7-TM) through the combination of map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, and transformation. Mutagenesis assay reveals that the two kinase domains and the transmembrane domain of WTK7-TM are critical for the powdery mildew resistance function. Consistently, in vitro phosphorylation assay shows that two kinase domains are indispensable for the kinase activity of WTK7-TM. Haplotype analysis uncovers that Pm36 is an orphan gene only present in a few wild emmer wheat, indicating its single ancient origin and potential contribution to the current wheat gene pool. Overall, our findings not only provide a powdery mildew resistance gene with great potential in wheat breeding but also sheds light into the mechanism underlying broad-spectrum resistance.