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Abstract Background Particulate matter (PM), including the major risk factor for lung cancer (LC), greatly impacts human health. Although numerous studies have highlighted spatiotemporal patterns and PM-LC associations, these studies have not been well-reviewed. Thus, we examined epidemiological studies linked with PM-LC and provided concise, up-to-date data. Methods We used certain keywords to review articles published in PubMed, Web of Science, Scopus, and Google Scholar until 30th June 2024 and identified 1474 research articles. We then filtered the research articles based on our criteria and ultimately dropped down to 30 for this review. Results Out of the thirty reviewed studies on the PM-LC relation, twenty-four focused on PM2.5, four on PM10, and two on both, indicating that approximately 80% of the respondents were inclined toward fine particles and their health impacts. The study revealed that 22 studies used visualization, 12 used exploration, and 15 used modeling methods. A strong positive relationship was reported between LC and PM2.5, ranging from 1.04 to 1.60 (95% CI) for a 10 µg/m3 increase in PM2.5 exposure. However, compared to PM2.5, PM10 was found to have a significantly less positive association. Conclusions Very few studies have used advanced spatiotemporal methods to examine the association between LC and PM. Advanced spatiotemporal analysis techniques should be employed to explore this association in specific geographical locations. Further research should utilize spatiotemporal epidemiological approaches to study the link between PM and lung cancer.

Abstract Background As we delve into the intricate world of venetoclax combination therapy in relapsed or refractory acute myeloid leukemia (AML), our exploration not only aims to contribute to the current body of knowledge but also strives to inform future research directions, clinical decision-making, and the ongoing evolution of therapeutic strategies in the relentless pursuit of improved outcomes for patients facing this formidable hematologic malignancy. Methods We systematically searched PubMed, Embase, and Cochrane databases from inception to November 2023 for English-language studies on venetoclax combination therapy in relapsed/refractory AML. We excluded duplicate published studies, incomplete studies, those with incomplete data, animal experiments, literature reviews, and systematic studies. Meta-analysis was performed using STATA 15.1. Results Out of 58 identified articles, seven were included in the meta-analysis. The pooled complete remission (CR) rate was 15.4%, and the composite complete remission (CRc) rate was 35.7%. The partial remission (PR) rate was 2.6%, while the non-remission (NR) rate was 24.4%. The minimal residual disease status in CRc patients (MRD-CRc) rate was 39.4%, and the morphologic leukemia-free state (MLFS) rate was 10.3%. Incidence of adverse events included diarrhea (10.0%), nausea (4.3%), vomiting (2.6%), hypokalemia (16.4%), hypomagnesemia (0.8%), decreased appetite (4.2%), fatigue (9.1%), febrile neutropenia (39.6%), and thrombocytopenia (28.4%). Subgroup analysis based on combined drugs revealed varying CR and CRc rates. the combination of venetoclax and azacitidine + demonstrates superior outcomes, displaying the highest rates of CR at 31.3% and CRc at 62.7%. In contrast, venetoclax and idasanutlin exhibits a moderate CR rate of 6.1% and a CRc rate of 26.5%, while venetoclax and mivebresib shows the lowest CR rate at 3.3% and a moderate CRc rate of 8.0%. Conclusion In conclusion, while venetoclax combination therapies, particularly with azacitidine + , show promise in achieving favorable treatment responses in relapsed/refractory AML patients, a comprehensive evaluation of safety profiles is essential. Nevertheless, it is essential to underscore the markedly increased incidence rates of febrile neutropenia and thrombocytopenia observed among adverse events.

SiOC-based ceramics are considered promising electromagnetic wave-absorbing materials because of their lightweight, high-temperature resistance, and heat insulation properties. Herein, SiOC@C ceramic nanospheres were prepared using a liquid-phase method combined with a polymer-derived ceramic (PDC) method, followed by heat treatment in N2 and Ar atmospheres at different temperatures. The morphology, microstructure, phase composition, and electromagnetic wave absorption performance of the SiOC@C ceramic nanospheres were investigated in detail. The SiOC@C ceramic nanospheres obtained in the Ar atmosphere showed a minimum reflection loss (RLmin) of −67.03 dB, whereas the SiOC@C ceramic nanospheres obtained in the N2 atmosphere exhibited an RLmin value of −63.76 dB. The outstanding electromagnetic wave absorption performance of the SiOC@C ceramic nanospheres was attributed to the synergistic effect between conductive loss, interfacial/defect polarization loss, multiple reflections, and scattering. Therefore, this research provides valuable insights into the design and fabrication of SiOC ceramic-based electromagnetic wave absorbers.

Abstract In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

Abstract Objective Type 2 diabetes mellitus (T2DM) has beenis known as an important risk factor for cognitive impairment. Meanwhile, the liver plays a central role in the development of T2DM and insulin resistance. The present study attempted to identify and validate marker genes for mild cognitive impairment (MCI) in patients with T2DM. Methods In this study, insulin resistance-related differentially expressed genes were identified from the liver tissues of individuals with T2DM and those with normal glucose tolerance using the Gene Expression Omnibus database and MCI-associated genes were identified using the GeneCards database. Next, enrichment analysis was performed with overlapping T2DM and MCI genes, followed by the identification of specific genes using the LASSO logistic regression and SVM-RFE algorithms. An important experiment involved the implementation of clinical and in vitro validation using real-time quantitative polymerase chain reaction (RT-qPCR). Finally, multiple linear regression, binary logistic regression, and receiver operating characteristic curve analyses were performed to investigate the relationship between the key gene and cognitive function in these patients. Result The present study identified 40 overlapping genes between MCI and T2DM, with subsequent enrichment analysis revealing their significant association with the roles of neuronal and glial projections. The marker gene complement receptor 1(CR1) was identified for both diseases using two regression algorithms. Based on RT-qPCR validation in 65 T2DM patients with MCI (MCI group) and 65 T2DM patients without MCI (NC group), a significant upregulation of CR1 mRNA in peripheral blood mononuclear cells was observed in the MCI group (P

ABSTRACTConvex and concave hulls originating from computational geometry are widely applied in practice. For instance, to determine the boundaries of a geographical area within a group of cities, convex hulls can represent the approximate boundaries of the areas. Concave hulls can accurately describe the shape of the area. The traditional methods for solving two-dimensional convex hull problems include the Jarvis March algorithm and the Graham scanning algorithm. The K-nearest neighbours and alpha-shape methods are designed for solving the concave hull problem. Other than traditional algorithms, we consider using deep neural networks to handle the convex and concave hull problems. General neural networks cannot deal with the problem whose output is a variable-length sequence. Our study provides a machine-learning approach for solving the convex hull and concave hull problems. We combine the Pointer network (Ptr-Net) with the Transformer model and propose the DeepHullNet. The trained DeepHullNet can provide a feasible solution in the majority of cases. The experimental results show that DeepHullNet outperforms the original Ptr-Net regarding accuracy. Compared with traditional methods, DeepHullNet can generate a good solution quickly, and the running time is shortened by nearly 100 times based on GPU.

ABSTRACTThe circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.

ABSTRACTHillside urbanization currently provides the most economical solution for urban expansion. However, limited attention was paid to the characteristics, processes, and patterns of gradient expansion of construction land (GEC) in mountainous regions and its impact on landscape fragmentation. This study suggests an approach to identify GEC in mountainous regions and quantify its spatial impact on the fragmentation of cultivated land and ecological land. The results are as follows: (1) GEC can be categorized as ‘unidirectional’, ‘bidirectional’, or ‘multidirectional’ expansion, influenced by the proportion of topography in different directions of the cities. The magnitude of expansion is correlated with the city's economic level. (2) GEC leads to an increasing trend of landscape fragmentation, with nearly a three-fold increase in arable land fragmentation and a two-fold increase in ecological land fragmentation within the focus areas over 20 years. (3) The impact of GEC on the fragmentation of cultivated and ecological land diminishes with distance, primarily influencing within 3 km. The results of this study provide a methodological reference for identifying key areas and characteristics of GEC, and serve as a theoretical basis for reducing landscape ecological risks in the process of mountainous urban development and promoting sustainable development.

ABSTRACTUrban spatial decision analysis is a critical component of spatial optimization and has profound implications in various fields, such as urban planning, logistics distribution, and emergency management. Existing studies on urban facility location problems are based on heuristic methods. However, few studies have used deep learning to solve this problem. In this study, we introduce a unified framework, SpoNet. It combines the characteristics of location problems with a deep learning model SpoNet can solve spatial optimization problems: p-Median, p-Center, and maximum covering location problem (MCLP). It involves modeling each problem as a Markov Decision Process and using deep reinforcement learning to train the model. To improve the training efficiency and performance, we integrated knowledge SpoNet. The results demonstrated that the proposed method has several advantages. First, it can provide a feasible solution without the need for complex calculations. Second, integrating the knowledge model improved the overall performance of the model. Finally, SpoNet is more accurate than heuristic methods and significantly faster than modern solvers, with a solution time improvement of more than 20 times. Our method has a promising application in urban spatial decision analysis, and further has a positive impact on sustainable cities and communities.

The interior of a high-voltage cable causes a state of extrusion due to thermomechanical effects; the resulting stress and deformation can easily lead to the destruction of the cable structure. Aiming at the problem, we establish an electromagnetic–thermal–force field model of a high voltage cable. By considering the influence of temperature on the material properties, a bidirectional couple simulation of electromagnetic and thermal fields is established. The thermal load excitation is introduced into the structural field, and the simulation calculation of thermal strain and thermal stress of the axial and radial layers is realized. The influence of ambient temperature, load current, and circulating current defect factors on the thermal–force characteristics of each layer of the cable is studied. The results show that the response of the insulating layer to the thermomechanical effect is the most significant, and its characteristic change law shows the nonlinear trend. In addition, the thermal strain of the cable has an approximate axial symmetry; the axial thermomechanical effect is significantly higher than the radial direction. The maximum axial and radial deformations of the cable when running at full load are 1.21 and 0.16 mm respectively. Among the three influencing factors, the impact of circulating current defects on the thermomechanical effect of cables is the most obvious. The thermomechanical effect of the cable approximately is positively related to the square of the circulating current defect factors, the load current, and the ambient temperature.

ObjectiveThis study aimed to investigate the role of galectin-3 (Gal-3; coded by LGALS3 gene), as a biomarker for MCI in T2DM patients and to develop and validate a predictive nomogram integrating galectin-3 with clinical risk factors for MCI prediction. Additionally, microRNA regulation of LGALS3 was explored.MethodsThe study employed a cross-sectional design. A total of 329 hospitalized T2DM patients were recruited and randomly allocated into a training cohort (n = 231) and a validation cohort (n = 98) using 7:3 ratio. Demographic data and neuropsychological assessments were recorded for all participants. Plasma levels of galectin-3 were measured using ELISA assay. We employed Spearman’s correlation and multivariable linear regression to analyze the relationship between galectin-3 levels and cognitive performance. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for MCI in T2DM patients. Based on these analyses, a predictive nomogram incorporating galectin-3 and clinical predictors was developed. The model’s performance was evaluated in terms of discrimination, calibration, and clinical utility. Regulatory miRNAs were identified using bioinformatics and their interactions with LGALS3 were confirmed through qRT-PCR and luciferase reporter assays.ResultsGalectin-3 was identified as an independent risk factor for MCI, with significant correlations to cognitive decline in T2DM patients. The developed nomogram, incorporating Gal-3, age, and education levels, demonstrated excellent predictive performance with an AUC of 0.813 in the training cohort and 0.775 in the validation cohort. The model outperformed the baseline galectin-3 model and showed a higher net benefit in clinical decision-making. Hsa-miR-128-3p was significantly downregulated in MCI patients, correlating with increased Gal-3 levels, while Luciferase assays confirmed miR-128-3p’s specific binding and influence on LGALS3.ConclusionOur findings emphasize the utility of Gal-3 as a viable biomarker for early detection of MCI in T2DM patients. The validated nomogram offers a practical tool for clinical decision-making, facilitating early interventions to potentially delay the progression of cognitive impairment. Additionally, further research on miRNA128’s regulation of Gal-3 levels is essential to substantiate our results.

Abstract The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2–4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.

The grassland ecosystems of Xilingol, China, characteristically part of the vast Eurasian steppe, are currently facing two challenges: natural variations and anthropogenic stress, which are leading to significant degradation. This article harnesses a sequence of high-resolution (30 m) land cover and greenness trend maps derived from multiyear Landsat imagery to describe these ecologically critical shifts over a landscape spanning more than 200 000 km2. By leveraging random forest models complemented with phenological patterns, we streamlined the generation of land cover maps, securing overall accuracies upwards of 94% across eight categorical classifications, as substantiated by rigorous validation. Between 1985 and 2000, there were significant changes in the landscape, such as an increase in farmland of about 4.0 × 103 km2, mostly at the expense of natural grasslands and wetlands. Throughout the study period, an ongoing trend is the noticeable shrinkage of water bodies with the biggest reduction of wetlands reported between 1995 and 2015. Open-pit mining regions began to increase with the start of the 21st century, and from 1985 to the present, urbanization drove the growth of impervious surfaces. These maps offer powerful visual representations of major land use changes, capturing the expansion of surface mining, the retreat of wetland areas, and the growth of urban areas. Therefore, our findings compose an essential part in the documentation and comprehension of the details of wetland reduction, cropland intensification, surface water decline, and rapid urban growth, providing crucial information to conservationists and policymakers working toward sustainable ecosystem management.

The detection of station area topology change is a critical part of guaranteeing the accuracy of a data-driven station area topology identification algorithm, for which a detection method for station area topology change based on the multi-time scale is proposed. The detection method comprehensively constructs the topology change detection indicator mechanism with multi-source data such as voltage and current and improves the robustness of the algorithm to the error of single voltage and current data. Firstly, the transient moment comparison indicator, the adjacent moment comparison indicator, and the adjacent sliding window comparison indicator with short to long-term scales are constructed. Subsequently, a three-level station area topology change tracking and detection strategy based on multi-time scale indicators is proposed for the identification of three types of topology changes: user migration into the area, user migration out of the area, and user phase adjustment. Finally, a simulation model is built using actual user data to verify the effectiveness of the proposed algorithm, explore the performance of the proposed algorithm in different scenarios, and compare it with existing recognition algorithms. The case study shows that the proposed algorithm is effective in recognizing topology change categories and change locations compared with existing algorithms, and is robust to data measurement errors.

Intersections are crucial and high-risk areas in urban road networks due to dense traffic and complex scenarios. Deploying Roadside Units (RSUs) can enhance safety and efficiency by providing real-time traffic information. However, the impact of traffic accident risks on RSU deployment is largely ignored. This study introduces an innovative RSU deployment strategy that prioritizes the risk of traffic accidents at intersections. The approach begins with analyzing environmental conditions, traffic patterns, and historical accident data at target intersections to identify key risk dimensions: road, accident, and environmental. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are used to weigh the indicators to evaluate their importance in accident risk assessment. Then, construct an objective function based on the accident risk value of the intersection. To overcome the redundancy problem in risk assessment, this study proposes an improved 0-1 knapsack algorithm that considers the redundancy of intersection accident risk to determine the optimal deployment location of RSUs. Simulations with SUMO, TraCI, Veins, and OMNeT++ demonstrate the algorithm’s superiority over traditional methods in all metrics. The results show that the vehicle coverage of this strategy is on average 2.63% and 2.86% higher than that of the IIA-ORD and UDA algorithms, respectively. It also leads by about 5.04% in traffic accident coverage and 5.72% in accident risk coverage. This intersection-focused RSU deployment method ensures timely information dissemination after incidents, providing valuable insights and practical guidelines for improving urban intersection safety and efficiency.

Against the backdrop of the partially idle resources of conventional railways and the continuous growth of suburban leisure tourism demand, it is required to develop railway tourism by utilizing the disused resources of conventional railways in China. Therefore, restoring, analyzing, and evaluating the visual landscapes seen when traveling on conventional trains are extremely important. The innovation of this study is proposing a dynamic visual landscape interface for disused railways, which establishes a connection between the “seeing” and “perceiving” for passengers and describes the visual landscape they observe. By analyzing the dynamic visual factors of passengers and combining DEM data, the temporal-spatial relationship of the visual landscapes along the railway is described as a dynamic visual landscape interface model. Based on this, the study proposes criteria and indicators for analyzing and evaluating the dynamic visual landscapes of railways, using typical landscapes along the Jingmen railway as an example for evaluation and validation. The research results provide analytical and decision-making tools for repurposing disused railways as tourist railways.

Semantic folding theory (SFT) is an emerging cognitive science theory that aims to explain how the human brain processes and organizes semantic information. The distribution of text into semantic grids is key to SFT. We propose a sentence-level semantic division baseline with 100 grids (SSDB-100), the only dataset we are currently aware of that performs a relevant validation of the sentence-level SFT algorithm, to evaluate the validity of text distribution in semantic grids and divide it using classical division algorithms on SSDB-100. In this article, we describe the construction of SSDB-100. First, a semantic division questionnaire with broad coverage was generated by limiting the uncertainty range of the topics and corpus. Subsequently, through an expert survey, 11 human experts provided feedback. Finally, we analyzed and processed the feedback; the average consistency index for the used feedback was 0.856 after eliminating the invalid feedback. SSDB-100 has 100 semantic grids with clear distinctions between the grids, allowing the dataset to be extended using semantic methods.

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture.

FeCoNi-based high-entropy alloys (HEAAs) attracted much attention because of their good thermodynamic stability, mechanical properties and soft magnetic properties. In this paper, Fe22Co22Ni22B34-xMnx (x = 6–22 at%) HEAAs were designed and the effects of Mn on the crystallization behaviors, magnetic performance, and mechanical property were investigated. The crystallization behaviors of the FeCoNiBMn HEAAs are: 1) 12–16 at% Mn: am → am’ + M23B6 → M23B6 + fcc; 2) 18 at% Mn: am → am’ + bcc + M23B6 → bcc + M23B6 → fcc + M23B6; and 3) 20–22 at% Mn: am → am’ + bcc → bcc + M23B6 → fcc + M23B6. Two parameters ΩA and VEC are proposed to predict the primary precipitates of FeCoNi-based HEAAs. All as-spun ribbons exhibit excellent soft magnetic properties with a very low coercivity (Hc) of less than 1.5 A/m and a maximum saturation magnetization (Bs) of 0.82 T. The 14 at% Mn alloy exhibits very low Hc of less than 4 A/m even after complete crystallization. Magnetic properties and Vickers hardness of crystalline ribbons are closely related to crystallization behaviors.

ABSTRACT Clostridioides difficile infection (CDI) with high morbidity and high mortality is an urgent threat to public health, and C. difficile pathogenesis studies are eagerly required for CDI therapy. The major surface layer protein, SlpA, was supposed to play a key role in C. difficile pathogenesis; however, a lack of isogenic slpA mutants has greatly hampered analysis of SlpA functions. In this study, the whole slpA gene was successfully deleted for the first time via CRISPR-Cas9 system. Deletion of slpA in C. difficile resulted in smaller, smother-edged colonies, shorter bacterial cell size, and aggregation in suspension. For life cycle, the mutant demonstrated lower growth (changes of optical density at 600 nm, OD600) but higher cell density (colony-forming unit, CFU), decreased toxins production, and inhibited sporulation. Moreover, the mutant was more impaired in motility, more sensitive to vancomycin and Triton X-100-induced autolysis, releasing more lactate dehydrogenase. In addition, SlpA deficiency led to robust biofilm formation but weak adhesion to human host cells.IMPORTANCEClostridioides difficile infection (CDI) has been the most common hospital-acquired infection, with a high rate of antibiotic resistance and recurrence incidences, become a debilitating public health threat. It is urgently needed to study C. difficile pathogenesis for developing efficient strategies as CDI therapy. SlpA was indicated to play a key role in C. difficile pathogenesis. However, analysis of SlpA functions was hampered due to lack of isogenic slpA mutants. Surprisingly, the first slpA deletion C. difficile strain was generated in this study via CRISPR-Cas9, further negating the previous thought about slpA being essential. Results in this study will provide direct proof for roles of SlpA in C. difficile pathogenesis, which will facilitate future investigations for new targets as vaccines, new therapeutic agents, and intervention strategies in combating CDI.

Summary: The Covering Location Problem (CLP) is widely used for the efficient facility distribution. However, existing algorithms for this problem suffer from long computation times or suboptimal solutions. To address this, we propose two methods based on graph convolutional networks (GCN) to solve two types of covering location problems: the location set covering problem and the maximum covering location problem. The first method, GCN-Greedy, is a supervised algorithm that synergized with the Greedy algorithm as decoder. It designs a specialized loss function to train the model, tailored to the characteristics of the two covering location problems. The second method, reinforcement learning based on GCN with auto-regressive decoder (GCN-AR-RL), represents a reinforcement learning framework that integrates a GCN encoder with an auto-regressive decoder. The experimental results of these models demonstrate the remarkable accuracy and performance advantages. Additionally, we apply these two models to the realistic dataset and achieve good performance.

The multiple traveling salesman problems (MTSP), which arise from real world problems, are essential in urban logistics. Variations such as MinMax-MTSP and Bounded-MTSP aim to distribute workload evenly among salesmen and impose constraints on visited cities, respectively. Branch-and-bound (B&B) provides an exact algorithm solution for these problems. The Learn to Branch (L2B) approach guides branch node selection through deep learning. In this study, we utilize mathematical modeling of Bipartite Graph Neural Network (BiGNN) and an attention mechanism to support B&B in exploring solution spaces through imitation learning. The problems are framed to formulate mixed integer linear programming, which is different from conventional algorithms. It is proposed that a bipartite graph network approach makes a feature representation by setting a structure of constraints and variables. Experimental results showed that our model can generate more accurate solutions than three benchmark models. The BiGNN model can effectively learn the strong branch strategy, which reduces solution time by replacing complex calculations with fast approximations. Additionally, the small-scale instances model can be applied to larger-scale ones.

‘Allen Eureka’ is a bud variety of Eureka lemon with excellent fruiting traits. However, it suffers from severe winter defoliation that leads to a large loss of organic nutrients and seriously affects the tree’s growth and development as well as the yield of the following year, and the mechanism of its response to defoliation is still unclear. In order to investigate the molecular regulatory mechanisms of different leaf abscission periods in lemon, two lemon cultivars (‘Allen Eureka’ and ‘Yunning No. 1’) with different defoliation traits were used as materials. The petiole abscission zone (AZ) was collected at three different defoliation stages, namely, the pre-defoliation stage (CQ), the mid-defoliation stage (CZ), and the post-defoliation stage (CH). Transcriptome sequencing was performed to analyze the gene expression differences between these two cultivars. A total of 898, 4,856, and 3,126 differentially expressed genes (DEGs) were obtained in CQ, CZ, and CH, respectively, and the number of DEGs in CZ was the largest. GO analysis revealed that the DEGs between the two cultivars were mainly enriched in processes related to oxidoreductase, hydrolase, DNA binding transcription factor, and transcription regulator activity in the defoliation stages. KEGG analysis showed that the DEGs were concentrated in CZ and involved plant hormone signal transduction, phenylpropanoid biosynthesis, glutathione metabolism, and alpha-linolenic acid metabolism. The expression trends of some DEGs suggested their roles in regulating defoliation in lemon. Eight gene families were obtained by combining DEG clustering analysis and weighted gene co-expression network analysis (WGCNA), including β-glucosidase, AUX/IAA, SAUR, GH3, POD, and WRKY, suggesting that these genes may be involved in the regulation of lemon leaf abscission. The above conclusions enrich the research related to lemon leaf abscission and provide reliable data for the screening of lemon defoliation candidate genes and analysis of defoliation pathways.

Maximizing billboard coverage with limited resources and different objective goals plays a vital role in social activities. The Maximal Coverage Billboard Location Problem (MCBLP) is complex, especially for multi-objective functions. A multi-objective spatial optimization model was developed using mixed-integer linear programming based on MCBLP to formulate the spatial optimization problem of determining billboard locations. Combining the distinctive features of location problems, we have developed a new approach called ReCovNet that utilizes Deep Reinforcement Learning (DRL) to solve the MCBLP. We applied the ReCovNet to address a real-world billboard location problem in New York City. To assess its performance, we implemented various algorithms such as Gurobi solver, Genetic Algorithm (GA) and a deep learning baseline called Attention Model (AM). The Gurobi reports the optimal solutions, while GA and AM serve as benchmark algorithms. Our proposed approach achieves a good balance between efficiency and accuracy and effectively solves MCBLP. The ReCovNet introduced in our study has potential to improve advertising effectiveness, and our proposed approach offers novel insights for addressing the MCBLP.

Abstract ‘Shuijingmiyou’ pummelo (SJ), one of the most popular fruits in Yunnan province of China, is of relatively low fruit shape (FS) quality. In this study, we compared the FS promoting effects of cross pollinations using pollens from seven pummelo varieties, and found that ‘Guanximiyou’ pummelo (GX) cross-pollination showed the best FS promoting effects on SJ fruits by shortening its fruit neck. To explore the underlying mechanism, physiochemical and transcriptomic differences between self- and cross-pollinated SJ ovaries (SJO and GXO) were investigated. Higher salicylic acid, gibberellin and indole acetic acid contents and superoxide dismutase, peroxidase and catalase activities, and lower polyphenol oxidase activity were determined in GXO compared with SJO. Enrichment analysis of the identified 578 differentially expressed genes (123 up-regulated and 455 down-regulated) in GXO showed that genes involved in solute transport, RNA biosynthesis, phytohormone action and cell wall organization were significantly enriched. The results obtained in this study will be helpful in understanding the influences of cross-pollination on pummelo ovary and fruit development, and can provide the basis for clarifying the underlying mechanism of cross-pollination improved fruit quality.

The digital economy now drives China’s new economic growth. However, with various industries’ digital transformation (DT), the consequent environmental pollution problems must be addressed. To achieve a better balance between economic growth and environmental protection, digitalization and carbon dioxide (CO2) emissions reduction targets should be achieved. In promoting the DT of manufacturing, it is essential to consider the industry’s CO2 emissions and the CO2 emission spillover effects (CO2SE). This paper first constructs a model of CO2SE generated and suffered in the DT of industries using input–output techniques, i.e., the perpetrator and victim effects, and measures these effects. Then, the cross-sectional inter-industry differences and vertical time-series characteristics are analyzed based on the measurement results. Finally, the convergence test of CO2 emission spillover effects in the digital transformation (CO2SE-DT) of China’s manufacturing industry is conducted, and the test results are analyzed. The research results show that (1) digital infrastructure-dependent and technology-intensive manufacturing have the largest CO2SE-DT, followed by capital-intensive and labor-intensive manufacturing in that order. (2) The whole victim effect in the DT of China’s manufacturing industry shows an “N” trend. In addition, according to the time-series changes of the perpetrator effect in the DT of China’s manufacturing industry between 2000 and 2014, China’s manufacturing industry can be classified into three types: stable, fluctuating downward, and fluctuating upward, among which the fluctuating upward type is the key industry of concern for CO2 emissions. (3) There is no gradual decrease in the industry differences in CO2SE-DT of China’s manufacturing industry, but manufacturing industries with low CO2SE-DT levels have faster growth rates compared with high-level manufacturing industries and eventually achieve convergence of CO2SE-DT rate of change. However, there is a significant difference in the results of the industry difference analysis for different types of manufacturing industries.

Abstract It is well established that nighttime light brightness value, which is measured from satellites, correlates with economic prosperity across the globe. Researchers have diverged over whether economic factors cluster in coastal areas or move to interior areas. By using nighttime light data and applying the random forest algorithm to measure the proportion of global “near regions” GDP, it was seen that global GDP decreased from 67.25% in 2000 to 63.02% in 2018. This research reveals that under the continuous promotion of economic globalization, there is still a spatial imbalance of economic development between global “near regions” and “far regions”; however, economic factors are gradually shifting to interior areas and forming a “coastal remoteness” evolution pattern. Within the intercontinental range, there are obvious differences in the evolution patterns and spatial structure of economic development between the sub-regions. The reduction of overseas transportation costs and diseconomies of scale are the primary reasons for the evolution of “coastal remoteness” in global economic development. Our findings can facilitate future policymaking and the management of global coastal and interior areas, as well as establish new horizons for relevant research topics within the context of land and marine-coordinated development.

With the development of ecological civilization construction, urban planning and development in China have entered a phase in which optimizing and constructing ecological spaces is required. As a national livable city, Xuchang has experienced rapid economic development in recent years, leading to significant urban expansion that has impacted the layout of ecological space networks in the central urban area and its surroundings. Therefore, identifying and optimizing the spatial layout of ecological corridors in Xuchang City are crucial for ecological development and park city construction. This study utilizes multisource geospatial data to identify and extract ecological corridors in the central urban area of Xuchang City. Ecological resistance and gravity models are employed to identify and verify that the primary ecological corridor pattern in Xuchang City is situated in Weidu District, which is a central urban area. Finally, 11 main ecological corridors in the central urban area are delineated. In response to the identification of ecological corridors, this study integrates spatial analysis methods and text analysis methods to evaluate the characteristics of urban ecological corridors. The results indicate that Xudu Park extends outward, serving as the hub of the ecological network, and that West Lake Park and Luming Lake Park form the core of the urban park system. Finally, based on the spatial relationships, ecological benefits, and citizen experience of each ecological corridor and the green parks it traverses, strategies for optimizing the layout of urban ecological corridors are proposed.

Clostridioides difficile is a widely distributed anaerobic pathogen. C. difficile infection is a serious problem in healthcare. Its biofilms have been found to exhibit biocorrosivity, albeit very little, but sufficient for it to correlate with biofilm growth/health. This work demonstrated the use of a disposable electrochemical biofilm test kit using two solid-state electrodes (a 304 stainless steel working electrode, and a graphite counter electrode, which also served as the reference electrode) in a 10 mL serum vial. It was found that the C. difficile 630∆erm Adp-4 mutant had a minimum inhibitory concentration (MIC) for vancomycin twice that of the 630∆erm wild type strain in biofilm prevention (2 ppm vs. 1 ppm by mass) on 304 stainless steel. Glutaraldehyde, a commonly used hospital disinfectant, was found ineffective at 2% (w/w) for the prevention of C. difficile 630∆erm wild type biofilm formation, while tetrakis(hydroxymethyl)phosphonium sulfate (THPS) disinfectant was very effective at 100 ppm for both biofilm prevention and biofilm killing. These antimicrobial efficacy data were consistent with sessile cell count and biofilm imaging results. Furthermore, the test kit provided additional transient biocide treatment information. It showed that vancomycin killed C. difficile 630∆erm wild type biofilms in 2 d, while THPS only required minutes.

Titanium (Ti) is generally considered as an ideal bipolar plate (BPP) material because of its excellent corrosion resistance, good machinability and lightweight nature. However, the easy-passivation property, which leads to increased interfacial contact resistance (ICR) and subsequently decreased cell performance, limits its large-scale commercial application in proton exchange membrane fuel cells (PEMFCs). In this paper, we proposed a NiTi alloy prepared by suction casting as a promising bipolar plate for PEMFCs. This NiTi alloy exhibits significantly decreased ICR values (16.8 mΩ cm2 at 1.4 MPa) compared with pure Ti (88.6 mΩ cm2 at 1.4 MPa), along with enhanced corrosion resistance compared with pure nickel (Ni). The superior corrosion resistance of NiTi alloy is accredited to the nobler open circuit potential and corrosion potential, coupled with low corrosion current densities and passive current densities. The improved ICR can be interpreted by the existence of high-proportioned metallic Ni in the passive film, which contributes to the reduced capacitance characteristic of the passive film (compared with Ti) and enhances charge conduction. This work provides a feasible option to ameliorate BPP material that may have desirable corrosion resistance and ICR.

Background: Aortic dissection (AD) is a fatal cardiovascular disease for which the key involved genes are largely unknown. Here, we aimed to identify promising AD biomarkers from high-throughput RNA expressing data. Methods: In the GSE98770 dataset, differentially expressed mRNAs (DE-mRNAs) and microRNAs (DE-microRNAs) were identified through differentially expressed gene analysis and gene set enrichment analysis. The regulatory network between DE-mRNAs and DE-microRNAs was established, and hub genes were identified with Cytoscape. Relationships between hub genes and AD were confirmed in the Comparative Toxicogenomics Database (CTD). Potential key transcription factors were discovered with Cytoscape. Hub gene verification was performed by qPCR and immunofluorescence analyses of human specimens. Results: DE-mRNAs and DE-microRNAs were identified. Four mRNAs and microRNA-1321 (miR-1321) were found to have the most connections with other genes. CBL was connected to the most genes and interacted with miR-1321, which was also connected to the most genes among the DE-microRNAs. In addition, CBL was associated with AD in the CTD. Among the top five transcription factors potentially regulating CBL transcription, only HOXB13 was a DE-mRNA. The findings were further successfully verified in human specimens. Conclusion: CBL, which may be transcriptionally regulated by HOXB13 and post-transcriptionally regulated by miR-1321, was identified as the most promising potential biomarker for AD.

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively.

The carbon neutralization process continues to be a significant concern in China, particularly in the Yangtze River Economic Belt. This study defines carbon neutrality and presents a regional capacity measurement index system that includes 12 indicators across four dimensions: production reduction, lifestyle reduction, ecological removal, and technological removal. To assess the relative and absolute levels of carbon neutrality capacity in the Yangtze River Economic Belt, the study uses the time-weighted rough set theory and the uncertain measurement model. Additionally, the study also uses Kernel density estimation, Dagum Gini coefficient decomposition, and convergence analysis to examine the development differences in the relative capacity of carbon neutrality in the region. The findings indicate that from 2013 to 2021, the relative capacity of carbon neutrality in the Yangtze River Economic Belt exhibited an upward trend with fluctuations, and there were significant disparities in development between the upper, middle, and lower reaches of the region. However, these differences showed signs of convergence and a general convergence trend. Moreover, the production reduction and lifestyle reduction aspects in the Yangtze River Economic Belt region are currently at a moderate level, while there is still considerable room for improvement in terms of ecological reduction and technological reduction. Furthermore, there are obvious regional differences in the absolute carbon neutral capacity of the Yangtze River Economic Belt region: the upstream region of the Yangtze River Economic Belt is primarily driven by carbon emissions reduction, the middle reaches are driven by production reduction and the downstream region is driven by carbon removal. Consequently, it is crucial to consider the regional development characteristics of the Yangtze River Economic Belt and implement targeted strategies tailored to local conditions.

The semantic segmentation task is an essential issue in various fields, including land cover classification and cultural heritage investigation. The CNN and Transformer have been widely utilized in semantic segmentation tasks due to notable advancements in deep learning technologies. However, these methodologies may not fully account for remote sensing images' distinctive attributes, including the large intra-class variation and the small inter-class variation. Driven by it, we propose a category attention guided network (CAGNet). Initially, a local feature extraction module is devised to cater to striped objects and features at different scales. Then, we propose a novel concept of category attention for remote sensing images as a feature representation of category differences between pixels. Meanwhile, we designed the Transformer-based and CNN-based category attention guided modules to integrate the proposed category attention into the global scoring functions and local category feature weights, respectively. The network is designed to give more attention to the category features by updating these weights during the training process. Finally, a feature fusion module is developed to integrate global, local, and category multi-scale features and contextual information. A series of extensive experiments along with ablation studies on the UAVid, Vaihingen, and Potsdam datasets indicate that our network outperforms existing methods, including those based on CNN and Transformer.

Neurons of the basal forebrain nucleus basalis and posterior substantia innominata (NBM/SIp) comprise the major source of cholinergic input to the basolateral amygdala (BLA). Using a genetically encoded acetylcholine (ACh) sensor in mice, we demonstrate that BLA-projecting cholinergic neurons can ‘learn’ the association between a naive tone and a foot shock (training) and release ACh in the BLA in response to the conditioned tone 24 hr later (recall). In the NBM/SIp cholinergic neurons express the immediate early gene, Fos following both training and memory recall. Cholinergic neurons that express Fos following memory recall display increased intrinsic excitability. Chemogenetic silencing of these learning-activated cholinergic neurons prevents expression of the defensive behavior to the tone. In contrast, we show that NBM/SIp cholinergic neurons are not activated by an innately threatening stimulus (predator odor). Instead, VP/SIa cholinergic neurons are activated and contribute to defensive behaviors in response to predator odor, an innately threatening stimulus. Taken together, we find that distinct populations of cholinergic neurons are recruited to signal distinct aversive stimuli, demonstrating functionally refined organization of specific types of memory within the cholinergic basal forebrain of mice.

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Drivers’ fault emergency response behavior can easily lead to crashes, resulting in significant economic and property losses. Exploring the causes of improper emergency response behavior is crucial for regulating driver behavior and preventing crash. Therefore, based on crashes data between commercial motor vehicles (CMVs) and noncommercial motor vehicles (NCMVs) that occurred in China from 2014 to 2018, this study established a binary logistic regression model. It systematically analyzed the key factors influencing drivers’ fault emergency response behaviors in terms of individuals, vehicles, road conditions, environment, and corporate management. Additionally, it compared the differences in the influencing factors of fault emergency response behaviors between drivers of CMV and NCMV. The results indicate that the model fits well. The presence of faulty emergency response behavior in drivers is significantly correlated with five factors: age, gender, fatigue driving, speeding, and weather conditions. Moreover, these factors have different impacts on CMV drivers and NCMV drivers. Fatigue driving and speeding have a more significant impact on CMV drivers, while other factors are more pronounced for NCMV drivers. This study can provide valuable insights for the development of measures aimed at reducing the severity of CMV-NCMV crashes.

'Allen Eureka' is a bud variety of Eureka lemon with excellent fruiting traits, but severe winter defoliation affects the following year's yield, and the response mechanism of lemon defoliation is currently unknown. Two lemon cultivars ('Allen Eureka' and 'Yunning No. 1') with different defoliation traits were used as materials to investigate the molecular regulatory mechanisms of different leaf abscission periods in lemons. The petiole abscission zone was collected at three different defoliation stages, namely, the predefoliation stage (k15), the middefoliation stage (k30), and the postdefoliation stage (k45). Transcriptome sequencing was performed to analyze the gene expression differences between these two cultivars. A total of 1141, 2695, and 1433 differentially expressed genes (DEGs) were obtained in k15, k30, and k45, respectively, and the number of DEGs in k30 was the largest. GO analysis revealed that the DEGs between the two cultivars were mainly enriched in processes related to hydrolase activity, chitinase activity, oxidoreductase activity, and transcription regulator activity in the defoliation stages. KEGG analysis showed that the DEGs were concentrated in k30, which involved plant hormone signal transduction, phenylpropanoid biosynthesis, and biosynthesis of amino acids. The expression trends of some DEGs suggested their roles in regulating defoliation in Lemon. Seven genes were obtained by WGCNA, including sorbitol dehydrogenase (CL9G068822012_alt, CL9G068820012_alt, CL9G068818012_alt), abscisic acid 8'-hydroxylase (CL8G064053012_alt, CL8G064054012_alt), and asparagine synthetase (CL8G065162012_alt, CL8G065151012_alt), suggesting that these genes may be involved in the regulation of lemon leaf abscission.

Abstract The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769–0.836) for OS nomogram and 0.807 (95% CI 0.769–0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789–0.847) for OS nomogram, while 0.804 (95% CI 0.773–0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions.

The error measurement of fiscal accounting data can effectively slow down the change of financial assets. Based on deep neural network theory, we constructed an error measurement model for fiscal and tax accounting data, and we analyzed the relevant theories of fiscal and tax performance evaluation. By establishing a batch evaluation index of finance and tax accounting, the model can monitor the changing trend of the error of urban finance and tax benchmark data scientifically and accurately, as well as solve the problem of high cost and delay in predicting the error of finance and tax benchmark data. In the simulation process, based on the panel data of credit unions, the entropy method and a deep neural network were used to measure the fiscal and tax performance of regional credit unions. In the example application, the model, combined with MATLAB programming, calculated the contribution rate of regional higher fiscal and tax accounting input to economic growth. The data show that the contribution rates of some fiscal and tax accounting input, commodity and service expenditure, other capital expenditure and capital construction expenditure to regional economic growth are 0.0060, 0.0924, 0.1696 and -0.0822, respectively. The results show that the proposed method can effectively map the relationships between variables.

Abstract Background H9N2 virus is mainly transmitted through the respiratory mucosal pathway, so mucosal immunity is considered to play a good role in controlling avian influenza infection. It is commonly accepted that no adequate mucosal immunity is achieved by inactivated vaccines, which was widely used to prevent and control avian influenza virus infection. Thus, an improved vaccine to induce both mucosal immunity and systemic immunity is urgently required to control H9N2 avian influenza outbreaks in poultry farms. Methods In this study, we constructed a novel Lactococcus lactis (L. lactis) strain expressing a recombinant fusion protein consisting of the HA1 proteins derived from an endemic H9N2 virus strain and chicken IgY Fc fragment. We evaluated the immunogenicity and protective efficacy of this recombinant L. lactis HA1-Fc strain. Results Our data demonstrated that chickens immunized with L. lactis HA1-Fc strain showed significantly increased levels of serum antibodies, mucosal secretory IgA, T cell-mediated immune responses, and lymphocyte proliferation. Furthermore, following challenge with H9N2 avian influenza virus, chickens immunized with L. lactis HA1-Fc strain showed reduced the weight loss, relieved clinical symptoms, and decreased the viral titers and the pathological damage in the lung. Moreover, oropharyngeal and cloacal shedding of the H9N2 influenza virus was detected in chicken immunized with L. lactis HA1-Fc after infection, the results showed the titer was low and reduced quickly to reach undetectable levels at 7 days after infection. Conclusion Our data showed that the recombinant L. lactis HA1-Fc strain could induce protective mucosal and systemic immunity, and this study provides a theoretical basis for improving immune responses to prevent and control H9N2 virus infection.

Abstract Background Venous invasion (VI) is an adverse prognostic indicator in esophageal squamous cell carcinoma. However, grading criteria for venous invasion in thoracic esophageal squamous cell carcinoma (ESCC) have not been established. Methods We enrolled 598 thoracic ESCC patients from 2005 to 2017. We detected the presence of venous invasion using the hematoxylin and eosin (H&E)-staining method and evaluated the VI grade on the basis of the number and maximal size of the involved veins. The degree of VI was classified as either 0, V1, V2, or V3, according to the combination of V-number and V-size. Results The 1-year, 3-year and 5-year disease-free survival rates were 79.7%, 64.7% and 61.2%, respectively. Multivariate analysis demonstrated that lymphatic invasion (HR: 1.457, 95% CI: 1.058–2.006, p = 0.021), T category (HR: 1.457, 95% CI: 1.058–2.006, p = 0.022), N category (HR: 1.535, 95% CI: 1.276–2.846, p

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal.

The proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate research on deployment stations selection, we propose a novel maximal covering location problem (MCLP) based on distance tolerance. The model aims to maximize the coverage of user demand while minimizing the sum of distances from users to deployment stations. A deep reinforcement learning (DRL) was devised to address this optimization model. An experiment was conducted focusing on areas with high concentrations of shared E-scooter trips in Chicago. The solutions of location selection were obtained by DRL, the Gurobi solver, and the genetic algorithm (GA). The experimental results demonstrated the effectiveness of the proposed model in optimizing the layout of shared E-scooter deployment stations. This study provides valuable insights into facility location selection for urban shared transportation tools, and showcases the efficiency of DRL in addressing facility location problems (FLPs).

The p-Center Problem (PCP) is a classical discrete facility location problem (FLP) with broad real-world application scenarios, such as public facilities and urban emergency services facilities. Most of the current methods for solving PCP are exact and heuristic. However, as the problem conditions are added or the problem scale is increased, the complexity of exact and heuristic methods for solving PCP increases significantly, which is reflected in the exponentially increased solving time and declined accuracy, respectively. Therefore, this research experiments with an attentional model with multiple decoders for solving the p-Center problem (AMMD-PC), considering different real-world contexts. AMMD-PC learns the construction heuristics with the deep reinforcement learning method, adopting an encoder-multiple decoder structure. The multi-head self-attention mechanism in the encoder learns the feature representation of the candidate facility nodes in different sub-spaces. Multiple decoders train the construction strategy of solutions separately to increase the diversity of solutions so as to find high-quality solutions. Experiments on PCPs of different scales and conditions show that AMMD-PC is superior to traditional heuristic methods in solving time and quality. In addition, the advantages of the AMMD-PC become more pronounced as the complexity of the problem increases to the point where the exact solver cannot compute the optimal solution in an acceptable time.

BackgroundAedes albopictus is the dominant mosquito species in residential areas in Shanghai. There are many types of small containers with accumulated water in residential areas, providing a large number of breeding environments for Aedes alpopicuts and leading to an increasing transmission risk of mosquito-borne diseases.ObjectiveTo use random forest to predict breeding of Aedes mosquitoes in small aquatic container habitat in two concentrated reconstruction communities of rural areas in Shanghai, and to understand associated influence of environmental factors on the breeding of Aedes mosquitoes in the process of urbanization.MethodsSmall-scale habitat surveys of Aedes mosquitoes were carried out in two suburb concentrated reconstruction communities (Community A and B) in Shanghai, and the environment where the habitat was located was recorded and analyzed in both communities. The habitat where eggs, larvae, or pupae were found was recorded as positive. Spatial weight matrix was applied on a household basis, and global Moran's I index was used to carry out spatial autocorrelation analysis on the small-scale habitat and positive habitat in the environment of the two communities. When Moran's I is greater than 0, it means that the data present a positive spatial correlation; when Moran's I is less than 0, it means that the data are spatially negatively correlated; when Moran's I is 0, the spatial distribution is random. Combining the results of P and Z values, we explored the spatial distribution characteristics of small-scale habitat and positive habitat in the community environment. Random forest algorithm in machine learning was used to classify and sort environmental-related factors, and predict the breeding of Aedes mosquitoes in small aquatic habitat; receiver operating characteristic (ROC) curve was used to carry out model fitting evaluation.ResultsThe environmental factors including building location (χ2=23.35, P

Abstract Background The diagnosis of adenoid cystic carcinoma (ACC) by cytopathology can be challenging. This study was aimed at testing the effectiveness of this technique and at assessing possible differences in the coincidence rate of fine-needle aspiration cytology(FNAC) and brush exfoliation. Methods The pathology database of Southwest Medical University( Luzhou, China) was searched for patients who had undergone surgery or biopsy for ACC between January 2017 and January 2022 and had preoperative cytopathologic results. Their cytologic and histologic data were then analyzed retrospectively and the coincidence rates of cytopathology in the diagnosis of ACC were calculated. Results Compared with histopathology, the total coincidence rate of the cytologic diagnosis of ACC was 76.8%, that of FNAC was 78.9%, and that of brush exfoliation was 55.6%. Conclusion In the diagnosis of ACC, cytopathology is an effective tool; this is especially true of FNAC, which plays an important role in the diagnosis of ACC. The authors further suggest that it is advisable for diagnosticians to master the cytopathological features of ACC to reduce the possibility of preoperative misdiagnoses.

Abstract Background The role of systemic inflammation in promoting cardiovascular diseases has attracted attention, but its correlation with various arrhythmias remains to be clarified. We aimed to comprehensively assess the association between various indicators of systemic inflammation and atrial fibrillation/flutter (AF), ventricular arrhythmia (VA), and bradyarrhythmia in the UK Biobank cohort. Methods After excluding ineligible participants, a total of 478,524 eligible individuals (46.75% male, aged 40–69 years) were enrolled in the study to assess the association between systemic inflammatory indicators and each type of arrhythmia. Results After covariates were fully adjusted, CRP levels were found to have an essentially linear positive correlation with the risk of various arrhythmias; neutrophil count, monocyte count, and NLR showed a non-linear positive correlation; and lymphocyte count, SII, PLR, and LMR showed a U-shaped association. VA showed the strongest association with systemic inflammation indicators, and it was followed sequentially by AF and bradyarrhythmia. Conclusions Multiple systemic inflammatory indicators showed strong associations with the onset of AF, VA, and bradyarrhythmia, of which the latter two have been rarely studied. Active systemic inflammation management might have favorable effects in reducing the arrhythmia burden and further randomized controlled studies are needed.