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Microplastics have emerged as ubiquitous contaminants, attracting increasing global attention. Recent evidence confirms the presence of microplastics in human blood, suggesting their potential to interact with cells and induce adverse physiological reactions in various organs as blood circulates. To quantify the distribution of microplastics and assess their potential effects on human health, the effective separation of microplastics from blood is crucial. However, current methods for separating microplastics from blood are limited in effectiveness and simplicity. This study proposes a microfluidic device that utilizes traveling surface acoustic waves to separate microplastics from blood. While traveling surface acoustic waves have been employed to separate various particles, a systematic study on the separation of microplastics from blood samples has not been previously reported. Specifically, the theoretical values of the acoustic radiation factor for various types of microplastics and blood cells were investigated. The significant differences in resonant frequencies indicated the feasibility of separating microplastics of different sizes and types from blood cells. Experimental validation was performed using a polydimethylsiloxane microfluidic device on a piezoelectric lithium niobate substrate. The device successfully separated 5- and 10-micrometer polystyrene microplastics from blood samples. The effects of power and flow rate on separation efficiency were also systematically investigated. This study provides a novel approach for the effective separation of microplastics from blood, contributing to the assessment of their distribution and potential health impacts.

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.

The dam break flood is one of the potential causes of catastrophic events in cascade hydropower hub groups. Investigating the movement patterns of dam break flooding among reservoir groups under different dam break speeds is crucial for flood prevention and emergency response. In this study, the evolution characteristics of dam break floods were investigated in a cascading reservoir system, focusing on different break speeds of the upstream dam. The results indicate that the dam break speed determines the concavity or convexity of the water level curve changes in the upstream reservoir. Accordingly, dam breaks are classified into three modes: instant dam break, fast dam break, and slow dam break. An approximate critical speed has been identified to differentiate between the fast dam break and slow dam break. Further investigation into the evolution patterns of dam break floods in downstream reservoirs under different break modes was conducted. Correspondingly, the flood peak discharge and peak arrival time of the dam break floods vary differently with break speed under different break modes. Finally, a theoretical analysis for the flood peak discharge at the dam site during gradual dam break at a certain speed was established, which is able to predict the over-dam flood peak discharge in fast and slow dam break modes. This study is based on a combination of laboratory flume experiments and three-dimensional numerical simulations. This study has theoretical significance for the reinforcement of public infrastructure safety and the prevention of natural disasters.

Ephedra sinica (E. sinica) holds significant economic and medicinal importance and is predominantly found in arid areas. Due to the limitations of environmental variables, growth habits, and human activities, the production and suitability areas of E. sinica have significantly decreased, especially in the five northwestern provinces of China. In this study, 212 distribution points of E. sinica and 40 environmental variables were obtained to project the habitat suitability of E. sinica under different emission scenarios in the future. It identified precipitation in the wettest month, monthly mean of the diurnal temperature difference, and solar radiation intensity in April and July as the primary environmental factors affecting the suitability of E. sinica in the region. The areas of high, medium, and low suitability in the region cover 103,000 km2, 376,500 km2, and 486,800 km2. Under future scenarios, the suitability areas from 2021 to 2100 will decrease by 20%, with high suitability areas decreasing by 65% to 85% particularly. With comprehensive environmental variables, the suitability areas of E. sinica from 2021 to 2100 are projected, filling the gap in the projection of E. sinica suitability areas in the five northwestern provinces of China over long time period. The suitability areas show a significant decreasing trend. This research provides valuable insights into the suitability areas and crucial environmental factors, offering theoretical support for future protection and management efforts for E. sinica.

Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT.

Background: Salmonella is an important zoonotic pathogen, of which poultry products are important reservoirs. This study analyzed the prevalence, antimicrobial resistance, and characterization of Salmonella from broiler and laying hen sources in China. Methods: A total of 138 (12.27%) strains of Salmonella were isolated from 1125 samples from broiler slaughterhouses (20.66%, 44/213), broiler farms (18.21%, 55/302), and laying hen farms (6.39%, 39/610). Multiplex PCR was used to identify the serotypes. Antibiotic susceptibility testing to a set of 21 antibiotics was performed and all strains were screened by PCR for 24 selected antimicrobial resistance genes (ARGs). In addition, 24 strains of Salmonella were screened out by whole-genome sequencing together with 65 released Salmonella genomes to evaluate phylogenetic characteristics, multilocus sequence typing (MLST), and plasmid carriage percentages. Results: A total of 11 different serotypes were identified, with the dominance of S. Enteritidis (43/138, 31.16%), S. Newport (30/138, 21.74%), and S. Indiana (19/138, 13.77%). The results showed that S. Enteritidis (34.34%, 34/99) and S. Newport (51.28%, 20/39) were the dominant serotypes of isolates from broilers and laying hens, respectively. The 138 isolates showed the highest resistance to sulfisoxazole (SXZ, 100%), nalidixic acid (NAL, 54.35%), tetracycline (TET, 47.83%), streptomycin (STR, 39.86%), ampicillin (AMP, 39.13%), and chloramphenicol (CHL, 30.43%), while all the strains were sensitive to both tigacycline (TIG) and colistin (COL). A total of 45.65% (63/138) of the isolates were multidrug-resistant (MDR) strains, and most of them (61/63, 96.83%) were from broiler sources. The results of PCR assays revealed that 63.77% of the isolates were carrying the quinolone resistance gene qnrD, followed by gyrB (58.70%) and the trimethoprim resistance gene dfrA12 (52.17%). Moreover, a total of thirty-four ARGs, eighty-nine virulence genes, and eight plasmid replicons were detected in the twenty-four screened Salmonella strains, among which S. Indiana was detected to carry the most ARGs and the fewest plasmid replicons and virulence genes compared to the other serotypes. Conclusions: This study revealed a high percentage of multidrug-resistant Salmonella from poultry sources, stressing the importance of continuous monitoring of Salmonella serotypes and antimicrobial resistance in the poultry chain, and emergency strategies should be implemented to address this problem.

Estimating leaf chlorophyll content (LCC) in a timely manner and accurately is of great significance for the precision management of rape. The spectral index derived from UAV images has been adopted as a non-destructive and efficient way to map LCC. However, soil background impairs the performance of UAV-based LCC estimation, limiting the accuracy and applicability of the LCC estimation model, and this issue remains to be addressed. Thus, this research was conducted to study the influence of soil pixels in UAV RGB images on LCC estimation. UAV campaigns were conducted from overwintering to flowering stages to cover the process of soil background being gradually covered by rapeseed plants. Three planting densities of 11.25, 18.75, and 26.26 g/m2 were chosen to further enrich the different soil background percentage levels, namely, the rape fractional vegetation coverage (FVC) levels. The results showed that, compared to the insignificant difference observed for the ground measured LCC at a certain growth stage, a significant difference was found for most of the spectral indices extracted without soil background removal, indicating the influence of soil background. Removing soil background during the extraction of the spectral index enhanced the LCC estimation accuracy, with the coefficient of determination (R2) increasing from 0.58 to 0.68 and the root mean square error (RMSE) decreasing from 5.19 to 4.49. At the same time, the applicability of the LCC estimation model for different plant densities (FVC levels) was also enhanced. The lower the planting density, the greater the enhancement. R2 increased from 0.53 to 0.70, and the RMSE decreased from 5.30 to 4.81 under a low planting density of 11.25 g/m2. These findings indicate that soil background removal significantly enhances the performance of UAV-based rape LCC estimation, particularly under various FVC conditions.

A dynamic LC trajectory planning algorithm based on the modified driving risk field is proposed to address the issue of dynamic changes during the lane-changing (LC) process. First, a modified driving risk field (MDRF) model is constructed for LC scenarios. Then, according to the state of the target vehicle and discrete sampling points, a series of LC candidate trajectories were generated based on the quintic polynomial. After eliminating candidate trajectories that do not meet the constraints, the MDRF was utilized as a safety evaluation function. Additionally, comfort and smoothness evaluation functions were combined to evaluate candidate LC trajectories in order to obtain the optimal LC reference trajectory. Then, this paper proposes a dynamic LC trajectory planning algorithm, addressing the challenges of complex traffic scenarios and dynamic changes in adjacent vehicle states. Utilizing the optimal reference trajectory as a basis, a dynamic segmented algorithm is applied to the x–t and y–x curves, constructing an optimized objective function that considers the MDRF. Under multiple constraints, including the continuity and smoothness of the lateral and longitudinal trajectories, the penalty function approach is employed to solve the optimization objective function, yielding the optimal LC trajectory adapted to real-time changes in the traffic state. Finally, the proposed dynamic LC trajectory planning algorithm was validated under four different scenarios by using MATLAB 2023b. The simulation results indicate the safety, continuity, and dynamic feasibility of the proposed algorithm. Moreover, it demonstrates strong adaptability and flexibility in challenging dynamic LC scenarios.

Terahertz spectroscopy systems, which integrate terahertz sources and detectors, have important applications in many fields such as materials science and security checking. Based on highly sensitive frequency up-conversion detection, large dynamic range spectral measurements in a terahertz region are reported. Our system realized the detection sensitivity at a 10 aJ level with a 2-(3-(4-hydroxystyryl)-5,5-dime-thylcyclohex-2-enylidene) malononitrile (OH1) crystal and a dynamic range up to seven orders. Based on this system, we verified the validity of the spectral measurement with tests which were conducted on monohydrate glucose, anhydrous glucose and mixed tablet samples with a thickness of 0.8 mm in 1~3 THz, respectively. Also, a mini coppery elbow tube with an inner diameter of 1 mm was used for the transmission of a terahertz wave to simulate some strip biological tissue samples. By allowing terahertz to transmit through this tube filled with 0.5 g glucose powder, we successfully obtained the absorption spectrum with a minimum transmittance at the absorption peak in the order of 10−4.

A total of 265 Salmonella Enteritidis isolates collected from retail markets and children’s hospitals in Shanghai were used to investigate the prevalence and molecular epidemiology of plasmid-mediated fosfomycin resistance genes. Nine of the isolates—7 from the 146 (4.79%) retail chicken-related samples and 2 from the 119 (1.68%) samples from clinical children—were fosfomycin-resistant (FosR). The fosA3 gene was detected in all of the nine FosR isolates, which were located on Inc F-type (8/9, 88.9%) and unknown-type (1/9, 11.1%) transferable plasmids. In total, five plasmid types, namely Inc HI2 (1/9, 11.1%), Inc I1 (3/9, 33.3%), Inc X (8/9, 88.9%), Inc FIIs (9/9, 100%), and Inc FIB (9/9, 100%), were detected in these FosR isolates, which possessed five S1 nuclease pulsed-field gel electrophoresis (S1-PFGE) profiles. The extended-spectrum β-lactamase determinant blaCTX-M-14 subtype was identified in one FosR S. Enteritidis isolate, which was located in a transferable unknown-type plasmid co-carrying fosA3 and tetR genes. Sequence homology analysis showed that this plasmid possessed high sequence similarity to previously reported blaCTX-M-14- and fosA3-positive plasmids from E. coli strains, implying that plasmids carrying the fosA3 gene might be disseminated among Enterobacterales. These findings highlight further challenges in the prevention and treatment of Enterobacteriaceae infections caused by plasmids containing fosA3.

Glioblastoma multiforme (GBM) is the most aggressive brain tumor. There is a pressing need to develop novel treatment strategies due to the poor targeting effect of current therapeutics. Here, a gold cluster coated with optimized GBM-targeting peptide is engineered, namely NA. NA can efficiently target GBM both in vitro and in vivo. Interestingly, the uptake of NA significantly sensitizes GBM cells to ferroptosis, a form of programmed cell death that can bypass the tumor resistance to apoptosis. This effect is exerted through regulating the HO-1-dependent iron ion metabolism, which is the non-canonical pathway of ferroptosis. The combined treatment of a ferroptosis inducer and NA profoundly inhibited tumor growth in both the GBM spheroid model and a syngeneic mouse model with enhanced ferroptosis levels and excellent biosafety. Importantly, the infiltration of tumoricidal lymphocytes is also significantly increased within tumor. Therefore, NA presents a potential novel nanomaterial-based strategy for GBM treatment.

Small non-coding RNAs (sRNAs) are pivotal post-transcriptional regulatory factors influencing biological activity. Studies on the rice bacterial blight pathogen Xanthomonas oryzae pathovar oryzae strain PXO99A, previously identified a virulence-associated sRNA, trans3287. A mutant strain lacking this sRNA, named SK01, resulted in markedly diminished virulence towards rice. This study aims to further elucidate the underlying bacterial virulent function of trans3287. The expression of trans3287 was quantified in virulence-inducing and standard nutritional conditions to clarify its production mechanism. The detection of virulence-associated genes revealed that trans3287 regulated the synthesis processes of extracellular polysaccharides, lipopolysaccharides, and the type III secretion system. Moreover, bioinformatics prediction and quantitative PCR indicated a potential direct target of trans3287, PXO_03470, encoding cardiolipin synthase. A dual-plasmid system fusing with GFP tag and protein immunoblotting confirmed that sRNA trans3287 negatively regulated PXO_03470. Bacterial biofilms demonstrated trans3287 regulated the disruption of biofilm integrity through cardiolipin synthase. This study provides preliminary insights into the mechanistic underpinnings of the role of sRNA trans3287 in mediating bacterial virulence through cardiolipin synthase.

Studies have argued that water risk affects corporate sustainability, but few of them have fully explored whether or not and how water resources have a direct impact on corporate finance and strategy. This study takes the listed companies in the Chinese A-share market from 2019 to 2023 as a sample to understand the threat of water vulnerability to corporate sustainability from the perspective of capital cost. This study argues that water vulnerability positively relates to corporate capital cost by increasing corporate financing constraints. Meanwhile, this study also examines the role of water regulation and water investment in the relationship between water vulnerability and corporate capital cost. Water regulation brings legitimate pressure to corporations and increases the transformation risks faced by them, so it has a positive moderating effect. Water investment can alleviate the vulnerability of local water resources and reduce the physical water risk faced by corporations, so it has a negative moderating effect. The study finds that the two measures mainly play a significant moderating effect on the cost of debt. In addition, the study finds that the positive relationship between water vulnerability and capital cost has industrial and firm-level heterogeneity, while the moderating effect of government water governance has only industrial heterogeneity.

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite rock mass is essential. It can provide significant reference and support for the design of underground engineering, engineering safety assessment, the theory of rock mechanics, and resource development. In this study, indoor experiments are combined with numerical simulations to explore the impact of fracture dip angles on the mechanical behavior of unanchored and anchored granite samples from both macroscopic and microscopic perspectives. It also investigates the evolution of the anchoring and crack-stopping effect of rock bolts on granite containing fractures with different dip angles. The results show that the load-displacement trends, displacement fields, and debris fields from indoor experiments and numerical simulations are highly similar. Additionally, it was discovered that, in comparison to the unanchored samples, the anchored samples with fractures at various angles all exhibited a higher degree of tensile failure rather than shear failure that propagates diagonally across the samples from the regions around the fracture tips. This finding verifies the effectiveness of the numerical model parameter calibration. At the same time, it was observed that the internal force chain value level in the anchored samples is higher than in the unanchored samples, indicating that the anchored samples possess greater load-bearing capacity. Furthermore, as the angle αs increases, the reinforcing and crack-stopping effects of the rock bolts become increasingly less pronounced.

Background: JianPiYiFei (JPYF) II granules are a Chinese medicine for the treatment of chronic obstructive pulmonary disease (COPD). However, the main components and underlying mechanisms of JPYF II granules are not well understood. This study aimed to elucidate the potential mechanism of JPYF II granules in the treatment of COPD using network pharmacology, molecular docking, and molecular dynamics simulation techniques. Methods: The active compounds and corresponding protein targets of the JPYF II granules were found using the TCMSP, ETCM, and Uniport databases, and a compound–target network was constructed using Cytoscape3.9.1. The COPD targets were searched for in GEO datasets and the OMIM and GeneCards databases. The intersection between the effective compound-related targets and disease-related targets was obtained, PPI networks were constructed, and GO and KEGG enrichment analyses were performed. Then, molecular docking analysis verified the results obtained using network pharmacology. Finally, the protein–compound complexes obtained from the molecular docking analysis were simulated using molecular dynamics (MD) simulations. Results: The network pharmacological results showed that quercetin, kaempferol, and stigmasterol are the main active compounds in JPYF II granules, and AKT1, IL-6, and TNF are key target proteins. The PI3K/AKT signaling pathway is a potential pathway through which the JPYF II granules affect COPD. The results of the molecular docking analysis suggested that quercetin, kaempferol, and stigmasterol have a good binding affinity with AKT1, IL-6, and TNF. The MD simulation results showed that TNF has a good binding affinity with the compounds. Conclusions: This study identified the effective compounds, targets, and related underlying molecular mechanisms of JPYF II granules in the treatment of COPD through network pharmacology, molecular docking, and MD simulation techniques, which provides a reference for subsequent research on the treatment of COPD.

Ascorbic acid (AsA), an essential non-enzymatic antioxidant in plants, regulates development growth and responses to abiotic and biotic stresses. However, research on AsA’s role in cold tolerance remains largely unknown. Here, our study uncovered the positive role of AsA in improving cold stress tolerance in tomato seedlings. Physiological analysis showed that AsA significantly enhanced the enzyme activity of the antioxidant defense system in tomato seedling leaves and increased the contents of proline, sugar, abscisic acid (ABA), and endogenous AsA. In addition, we found that AsA is able to protect the photosynthetic system of tomato seedlings, thereby relieving the declining rate of chlorophyll fluorescence parameters. qRT-PCR analysis indicated that AsA significantly increased the expression of genes encoding antioxidant enzymes and involved in AsA synthesis, ABA biosynthesis/signal transduction, and low-temperature responses in tomato. In conclusion, the application of exogenous AsA enhances cold stress tolerance in tomato seedlings through various molecular and physiological responses. This provides a theoretical foundation for exploring the regulatory mechanisms underlying cold tolerance in tomato and offers practical guidance for enhancing cold tolerance in tomato cultivation.

The increase in atmospheric CO2 leads to global warming and ecological environment deterioration. Carbon storage modeling and assessment can promote the sustainable development of the ecological environment. This paper took Xinjiang as the study area, analyzed the spatial and temporal evolution of land use in four periods from 1990 to 2020, explored the spatial relationship of carbon stocks using the InVEST model, and coupled the GMOP model with the PLUS model to carry out multiple scenarios for the future simulation of land use in the study area. We found (1) Over time, the types with an increasing area were mainly impervious and cropland, and the types with a decreasing area were grassland, snow/ice, and barren; spatially, the types were predominantly barren and grassland, with the conversion of grassland to cropland being more evident in the south of Northern Xinjiang and north of Southern Xinjiang. (2) The evolutionary pattern of terrestrial carbon stocks is increasing and then decreasing in time, and the carbon sink areas are concentrated in the Tarim River Basin and the vicinity of the Ili River; spatially, there are differences in the aggregation between the northern, southern, and eastern borders. By analyzing the transfer in and out of various categories in Xinjiang over the past 30 years, it was obtained that the transfer out of grassland reduced the carbon stock by 5757.84 × 104 t, and the transfer out of Barren increased the carbon stock by 8586.12 × 104 t. (3) The land use layout of the sustainable development scenario is optimal under the conditions of satisfying economic and ecological development. The reduction in terrestrial carbon stocks under the 2020–2030 sustainable development scenario is 209.79 × 104 t, which is smaller than the reduction of 830.79 × 104 t in 2010–2020. Land optimization resulted in a lower loss of carbon stocks and a more rational land-use layout. Future planning in Xinjiang should be based on sustainable development scenarios, integrating land resources, and achieving sustainable economic and ecological development.

N6-methyladenosine (m6A) modification is a prevalent modification of messenger ribonucleic acid (mRNA) in eukaryote cells and is closely associated with recurrent pregnancy loss (RPL). Circular RNAs (circRNAs) play critical roles in embryo implantation, trophoblast invasion and immune balance, which are important events during pregnancy. However, how m6A modification is regulated by circRNAs and the potential regulatory mechanism of circRNAs on RPL occurrence remain largely unclassified. We displayed the expression profiles of circRNAs and mRNAs in the decidua of normal pregnancies and RPL patients based on circRNA sequencing and the Gene Expression Omnibus database. A total of 936 differentially expressed circRNAs were identified, including 509 upregulated and 427 downregulated circRNAs. Differentially expressed circRNAs were enriched in immune, metabolism, signaling and other related pathways via the analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The competitive endogenous RNA (ceRNA) network was predicted to supply the possible role of circRNAs in RPL occurrence, and we further analyzed the profiles of nine m6A regulators (seven readers, one writer and one eraser) managed by circRNAs in this network. We also showed the expression profiles of circRNAs in the serum, trying to seek a potential biomarker to help in the diagnosis of RPL. These data imply that circRNAs are involved in pathogenesis of RPL by changing immune activities, metabolism and m6A modification in the ceRNA network. Our study might provide assistance in exploring the pathogenesis and diagnosis of RPL.

Pullorum disease, an intestinal disease in chickens caused by Salmonella enterica serovar pullorum (S. Pullorum), is a significant threat to the poultry industry and results in substantial economic losses. The bacteria’s transmission, both vertical and horizontal, makes it difficult to completely eliminate it. Control strategies for pullorum disease primarily involve stringent eradication programs that cull infected birds and employ antibiotics for treatment. However, eradication programs are costly, and antibiotic use is restricted. Therefore, developing alternative control strategies is essential. Increasingly, studies are focusing on modulating the gut microbiota to control intestinal diseases. Modulating the chicken gut microbiota may offer a novel strategy for preventing and controlling pullorum disease in poultry. However, the impact of S. Pullorum on the chicken gut microbiota has not been well established, prompting our exploration of the relationship between S. Pullorum and the chicken gut microbiota in this study. In this study, we initially analyzed the dynamic distribution of the gut microbiota in chickens infected with S. Pullorum. Alpha diversity analysis revealed a decrease in observed OTUs and the Shannon diversity index in the infected group, suggesting a reduction in the richness of the chicken gut microbiota due to S. Pullorum infection. Principal coordinate analysis (PCoA) showed distinct clusters between the gut microbiota of infected and uninfected groups, indicating S. Pullorum infection changed the chicken gut microbiota structure. Specifically, S. Pullorum infection enriched the relative abundance of the genera Escherichia-Shigella (65% in infected vs. 40.6% in uninfected groups) and Enterococcus (10.8% vs. 3.7%) while reducing the abundance of Lactobacillus (9.9% vs. 32%) in the chicken microbiota. Additionally, based on the observed changes in the chicken gut microbiota, we isolated microorganisms, including Bifidobacterium pseudolongum, Streptococcus equi and Lacticaseibacillus paracasei (L. paracasei), which were decreased by S. Pullorum infection. Notably, the L. paracasei Lp02 strain was found to effectively inhibit S. Pullorum proliferation in vitro and alleviate its infection in vivo. We found that S. Pullorum infection reduced the richness of the chicken gut microbiota and enriched the relative abundance of the genera Escherichia-Shigella and Enterococcus while decreasing the abundance of the anaerobic genus Lactobacillus. Furthermore, microbiota analysis enabled the isolation of several antimicrobial microorganisms from healthy chicken feces, with a L. paracasei strain notably inhibiting S. Pullorum proliferation in vitro and alleviating its infection in vivo. Overall, this research enhances our understanding of the interaction between gut microbiota and pathogen infection, as well as offers new perspectives and strategies for modulating the chicken gut microbiota to control pullorum disease.

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields.

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.

Cerebral cavernous malformations (CCMs) are abnormal expansions of brain capillaries that increase the risk of hemorrhagic strokes, with CCM1 mutations responsible for about 50% of familial cases. The disorder can cause irreversible brain damage by compromising the blood–brain barrier (BBB), leading to fatal brain hemorrhages. Studies show that progesterone and its derivatives significantly impact BBB integrity. The three CCM proteins (CCM1, CCM2, and CCM3) form the CCM signaling complex (CSC), linking classic and non-classic progesterone signaling within the CmPn network, which is crucial for maintaining BBB integrity. This study aimed to explore the relationship between CCM1 and key pathways of the CmPn signaling network using three mouse embryonic fibroblast lines (MEFs) with distinct CCM1 expressions. Omics and systems biology analysis investigated CCM1-mediated signaling within the CmPn network. Our findings reveal that CCM1 is essential for regulating cellular processes within progesterone-mediated CmPn/CmP signaling, playing a crucial role in maintaining microvessel integrity. This regulation occurs partly through gene transcription control. The critical role of CCM1 in these processes suggests it could be a promising therapeutic target for CCMs.

The advent and progression of AR (augmented reality) technology, coupled with the emergence of AR hearing aid glasses, offer a novel opportunity for people with hearing impairments (PHI). This study aims to explore the intention of this population to employ AR hearing aid glasses as their choice of hearing aid device and the specific factors influencing their preference. This study utilized the partial least squares SEM (PLS-SEM) analytical method to create structural equation model for intentions of PHI to use AR glasses as hearing aids. Data were gathered from on-site experiences across multiple locations; a total of 189 valid questionnaires from individuals with varying degrees of hearing disabilities were used for statistical analysis. According to the data analysis results, we discovered that functionality quality, perceived interaction speed, and perceived usability significantly influence communication effectiveness. Further, communication effectiveness positively influences confidence and societal perception, and the latter has a positive impact on information. Both of these factors positively influence behavioral intention. Based on these findings, this study offers design recommendations for AR hearing aid glasses to cater to the specific needs of PHI, aiming to enhance their quality of life. Furthermore, this study provides pivotal insights for the prospective growth of this emerging industry.

In the avian species, genetic modification by cell nuclear transfer is infeasible due to its unique reproductive system. The in vitro primordial germ cell modification approach is difficult and cumbersome, although it is the main method of genetic modification in chickens. In the present study, the adenoviral CRISPR/Cas9 vector was directly microinjected into the dorsal aorta of chicken embryos to achieve in vivo genetic modification. The results demonstrated that keratin 75-like 4 (KRT75L4), a candidate gene crucial for feather development, was widely knocked out, and an 8bp deletion was the predominant mutation that occurred in multiple tissues in chimeras, particularly in the gonad (2.63–11.57%). As we expected, significant modification was detected in the sperm of G0 (0.16–4.85%), confirming the potential to generate homozygous chickens and establishing this vector as a simple and effective method for genetic modification in avian species.

The endoparasitoids Chouioa cunea Yang and Tetrastichus septentrionalis Yang (Hymenoptera: Eulophidae) are both gregarious pupal parasitoids of the fall webworm, Hyphantria cunea (Drury) (Lepidoptera: Erebidae). In order to analyze the competitive interactions between both parasitoids exploiting H. cunea, we assessed both extrinsic and intrinsic competition. The search time, oviposition duration, and oviposition frequency were used as evaluation criteria for extrinsic competition. The number of survival days, female ratio, and number of parasitoids emerging from the host were used as evaluation criteria for intrinsic competition. The results indicated that both parasitoid species were able to parasitize hosts that were already parasitized by competitors. The first released species consistently emerged as the superior competitor in multiparasitized hosts. Both parasitoid release orders and time intervals between oviposition affect the competition of parasitoids and the parasitic efficiency. The results emphasize the parasitic abilities of both parasitoid species and provide a basis for future research on competition mechanisms and biological control of H. cunea.

Van der Waals (vdW) heterostructures are mainly fabricated by a classic dry transfer procedure, but the interface quality is often subject to the vdW gap, residual strains, and defect species. The realization of interface fusion and repair holds significant implications for the modulation of multiple photoelectric conversion processes. In this work, we propose a thermally mismatched strategy to trigger broad-band and high-speed photodetection performance based on a type-I heterostructure composed of black phosphorus (BP) and FePS3 (FPS) nanoflakes. The BP acts as photothermal source to promote interface fusion when large optical power is adopted. The regulation of optical power enables the device from pyroelectric (PE) and/or alternating current photovoltaic (AC–PV) mode to a mixed photovoltaic (PV)/photothermoelectric (PTE)/PE mode. The fused heterostructure device presents an extended detection range (405~980 nm) for the FPS. The maximum responsivity and detectivity are 329.86 mA/W and 6.95 × 1010 Jones, respectively, and the corresponding external quantum efficiency (EQE) approaches ~100%. Thanks to these thermally-related photoelectric conversion mechanism, the response and decay time constants of device are as fast as 290 μs and 265 μs, respectively, superior to current all FPS-based photodetectors. The robust environmental durability also renders itself as a high-speed and broad-band imaging sensor.

Background: Renal angiomyolipoma (AML) without local invasion is generally considered benign. However, it may extend to the renal sinus, even the renal vein, or the inferior vena cava (IVC). In patients with non-tuberous sclerosis complex, coexistence of renal cell carcinoma (RCC) and renal AML is uncommon. Case presentation: A 72-year-old woman was incidentally found to have a solitary right renal mass with an IVC thrombus extending into the right atrium during a routine health checkup. Robot-assisted laparoscopic radical nephrectomy and thrombectomy were successfully performed through adequate preoperative examination and preparation. Two tumor lesions were found and pathologically confirmed as renal AML and RCC, and the tumor thrombus was derived from the renal AML. During the one-year follow-up period, no signs of recurrence or metastatic disease were observed. Conclusions: Renal AML with a tumor thrombus in the IVC and right atrium accompanied by RCC may occur, although rarely. In clinical practice, if preoperative manifestations differ from those of common diseases, rare diseases must be considered to avoid missed diagnoses. In addition, adequate examination and multidisciplinary discussions before making a diagnosis are necessary. For a level 4 tumor thrombus with no infringement of the venous wall, adoption of robot-assisted minimally invasive surgery, without extracorporeal circulation technology, is feasible.

The life strategies of bacterial communities determine their structure and function and are an important driver of biogeochemical cycling. However, the variations in these strategies under different soil resource conditions remain largely unknown. We explored the bacterial life strategies and changes in structure and functions between Antarctic soils and forest (temperate, subtropical, and tropical) soils. The results showed that the weighted mean rRNA operon copy number in temperate soils was 19.5% lower than that in Antarctic soils, whereas no significant differences were observed among Antarctic, subtropical, and tropical soils. An unexpected result was that bacterial communities in Antarctic soils tended to be copiotrophs, such as Actinobacteriota and Bacteroidota, whereas those in temperate soils tended to be oligotrophs, such as Acidobacteriota and Chloroflexi. Functional predictions showed that in comparison to copiotrophs in Antarctic soils, temperate-inhabiting oligotrophic bacteria exhibited an 84.2–91.1% lower abundance of labile C decomposition genes (hemicellulose, cellulose, monosaccharides, and disaccharides), whereas a 74.4% higher abundance of stable C decomposition (lignin). Genes involved in N cycling (nitrogen fixation, assimilatory nitrate reduction, and denitrification) were 24.3–64.4% lower in temperate soils than in Antarctic soils. Collectively, our study provides a framework for describing the life strategies of soil bacteria, which are crucial to global biogeochemical cycles.

Safflower yellow is an extract of the famous Chinese medicine Carthamus tinctorious L, and safflower yellow injection (SYI) is widely used clinically to treat angina pectoris. However, there are few studies on the anti-myocardial ischemia/reperfusion (I/R) injury effect of SYI, and its mechanisms are unclear. In the present study, we aimed to investigate the protective effect of SYI on myocardial I/R injury and explore its underlying mechanisms. Male Sprague Dawley rats were randomly divided into a control group, sham group, model group, and SYI group (20 mg/kg, femoral vein injection 1 h before modeling). The left anterior descending coronary artery was ligated to establish a myocardial I/R model. H9c2 cells were exposed to oxygen–glucose deprivation/reoxygenation (OGD/R) after incubation with 80 μg/mL SYI for 24 h. In vivo, TsTC, HE, and TUNEL staining were performed to evaluate myocardial injury and apoptosis. A kit was used to detect superoxide dismutase (SOD) and malondialdehyde (MDA) to assess oxidative stress. In vitro, flow cytometry was used to detect the reactive oxygen species (ROS) content and apoptosis rate. Protein levels were determined via Western blotting. Pretreatment with SYI significantly reduced infarct size and pathological damage in rat hearts and suppressed cardiomyocyte apoptosis in vivo and in vitro. In addition, SYI inhibited oxidative stress by increasing SOD activity and decreasing MDA content and ROS production. Myocardial I/R and OGD/R activate endoplasmic reticulum (ER) stress, as evidenced by increased expression of activating transcription factor 6 (ATF6), glucose-regulated protein 78 (GRP78), cysteinyl aspartate-specific proteinase caspase-12, and C/EBP-homologous protein (CHOP), which were all inhibited by SYI. SYI ameliorated myocardial I/R injury by attenuating apoptosis, oxidative damage, and ER stress, which revealed new mechanistic insights into its application.

Saccharides, being one of the fundamental molecules of life, play essential roles in the physiological and pathological functions of cells. However, their intricate structures pose challenges for detection. Nanopore technology, with its high sensitivity and capability for single-molecule-level analysis, has revolutionized the identification and structural analysis of saccharide molecules. This review focuses on recent advancements in nanopore technology for carbohydrate detection, presenting an array of methods that leverage the molecular complexity of saccharides. Biological nanopore techniques utilize specific protein binding or pore modifications to trigger typical resistive pulses, enabling the high-sensitivity detection of monosaccharides and oligosaccharides. In solid-state nanopore sensing, boronic acid modification and pH gating mechanisms are employed for the specific recognition and quantitative analysis of polysaccharides. The integration of artificial intelligence algorithms can further enhance the accuracy and reliability of analyses. Serving as a crucial tool in carbohydrate detection, we foresee significant potential in the application of nanopore technology for the detection of carbohydrate molecules in disease diagnosis, drug screening, and biosensing, fostering innovative progress in related research domains.

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions.

Acute ischemic stroke (AIS) is a cerebrovascular disease that seriously affects the physical and mental health and quality of life of patients. However, there is a lack of reliable prognostic prediction methods. The main objective of this study was to investigate the prognostic value of long non-coding RNA (lncRNA) H19 in lymphocytes of patients with AIS, and to construct a prognostic prediction model for AIS including lncRNA H19 in lymphocytes, which would provide new ideas for the prognostic evaluation of AIS. Poor prognosis was defined when the patient’s modified Rankin scale (mRS) score at 3 months after AIS onset was greater than 2. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the level of lncRNA H19 in lymphocytes. Spearman correlation analysis revealed a positive correlation between lncRNA H19 and mRS score at 3 months after AIS onset (r = 0.1977, p = 0.0032), while lncRNA H19 was negatively correlated with white blood cells counts, lymphocytes counts, and neutrophils counts. Logistic regression analysis identified lncRNA H19 as an independent predictor of poor prognosis (OR = 3.062 [1.69–5.548], p < 0.001). Moreover, a nomogram prediction model incorporating lncRNA H19 in lymphocytes demonstrated effective discrimination, calibration, and clinical applicability in predicting AIS outcomes. The findings suggest that lncRNA H19 in lymphocytes could be a valuable prognostic indicator and a potential pharmacological target for AIS patients, and might be a novel pathway for enhanced prognostic evaluation and targeted therapeutic strategies.

Carbon Capture, Utilization, and Storage (CCUS) stands as one of the effective means to reduce carbon emissions and serves as a crucial technical pillar for achieving experimental carbon neutrality. CO2-enhanced oil recovery (CO2-EOR) represents the foremost method for CO2 utilization. CO2-EOR represents a favorable technical means of efficiently developing extra-low-permeability reservoirs. Nevertheless, the process known as the direct injection of CO2 is highly susceptible to gas scrambling, which reduces the exposure time and contact area between CO2 and the extra-low-permeability oil matrix, making it challenging to utilize CO2 molecular diffusion effectively. In this paper, a comprehensive study involving the application of a CO2 nanobubble system in extra-low-permeability reservoirs is presented. A modified nano-SiO2 particle with pro-CO2 properties was designed using the Pickering emulsion template method and employed as a CO2 nanobubble stabilizer. The suitability of the CO2 nanobubbles for use in extra-low-permeability reservoirs was evaluated in terms of their temperature resistance, oil resistance, dimensional stability, interfacial properties, and wetting-reversal properties. The enhanced oil recovery (EOR) effect of the CO2 nanobubble system was evaluated through core experiments. The results indicate that the CO2 nanobubble system can suppress the phenomena of channeling and gravity overlap in the formation. Additionally, the system can alter the wettability, thereby improving interfacial activity. Furthermore, the system can reduce the interfacial tension, thus expanding the wave efficiency of the repellent phase fluids. The system can also improve the ability of CO2 to displace the crude oil or water in the pore space. The CO2 nanobubble system can take advantage of its size and high mass transfer efficiency, among other advantages. Injection of the gas into the extra-low-permeability reservoir can be used to block high-gas-capacity channels. The injected gas is forced to enter the low-permeability layer or matrix, with the results of core simulation experiments indicating a recovery rate of 66.28%. Nanobubble technology, the subject of this paper, has significant practical implications for enhancing the efficiency of CO2-EOR and geologic sequestration, as well as providing an environmentally friendly method as part of larger CCUS-EOR.

In view of the problem of poor flowability in the production and transportation of high-wax crude oil and high-viscosity crude oil, crude oil flow improvers are commonly used to reduce their viscosity and pour point. Although polymer-based crude oil flow improvers are highly effective in improving crude oil flowability, there are still problems such as high cost and the need for a large amount of solvent dilution when used. In this work, highly dispersed organic modified nano-montmorillonite was prepared by using Na-based montmorillonite and quaternary ammonium salts, and the influencing factors on the viscosity of the crude oil were investigated. The most effective modified nano-montmorillonite (B@MMT) can reduce the viscosity by 96.7% (21 °C) and depress the pour point by 15 °C. Furthermore, it has shown a high improvement in flowability in the other four different sources of crude oil, with viscosity reduction rates of 52.2, 93.4, 79.1 and 67.4%, respectively. B@MMT was characterized by FTIR, SEM, zeta potential and contact angle. Based on DSC and wax crystal structure analysis, the mechanism of the influence of B@MMT on crude oil viscosity and pour point was explored. Finally, the cost of B@MMT was estimated, and the result shows that, compared with the crude oil flow improver in use, B@MMT has considerable commercial competitive advantages.

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R2: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

In satellite and airborne electro-optical tracking systems, there are numerous processing devices and complex data flows. To ensure the coordinated operation of the system, the multiple devices within the target system must operate under unified time control for data acquisition, computation, and output. This study introduces a multi-channel data simulator based on a time unification system. The complete simulation system includes the host computer, simulator, target system, and time reference generator. The simulator has programmable input and output interfaces for multi-channel protocols and has storage and real-time working modes. In the storage mode, the simulated data are pre-transmitted to the simulator’s storage and sent to the target system according to the time reference generator. The simulator simultaneously stores the target system results. In the real-time mode, the host computer generates simulated data based on the target system’s results and outputs the data through the simulator in real time. The main contribution of the simulator is that it achieves system-level closed-loop simulation and completes the functional and performance verification of the target system. Through experimental verification, it is found that the simulator can achieve 4.2 Gbps of simulated data transmission and 1.6 Gbps of data reception and storage, with a closed-loop delay of 39.9 µs.

Classical swine fever (CSF) is caused by the classical swine fever virus (CSFV), which poses a threat to swine production. The activation of host innate immunity through linker proteins such as tumor necrosis factor receptor (TNF-R)-associated factor (TRAF) is crucial for the induction of the NF-κB pathway. Recent research has revealed the involvement of mitochondrial antiviral-signaling protein (MAVS) in the interaction with TRAF2, 3, 5, and 6 to activate both the NF-κB and IRF3 pathways. This study revealed that CSFV infection led to the upregulation of TRAF1 mRNA and protein levels; moreover, TRAF1 overexpression inhibited CSFV replication, while TRAF1 knockdown promoted replication, highlighting its importance in the host response to CSFV infection. Additionally, the expression of RIG-I, MAVS, TRAF1, IRF1, and ISG15 were detected in PK-15 cells infected with CSFV, revealing that TRAF1 plays a role in regulating IRF1 and ISG15 within the RIG-I pathway. Furthermore, Co-IP, GST pull-down, and IFA analyses demonstrated that TRAF1 interacted with MAVS and co-localized in the cytoplasm during CSFV infection. Ultimately, TRAF1 acted as a novel member of the TRAF family, bound to MAVS as a linker molecule, and functioned as a mediator downstream of MAVS in the RIG-I/MAVS pathway against CSFV replication.

Low-light imaging capabilities are in urgent demand in many fields, such as security surveillance, night-time autonomous driving, wilderness rescue, and environmental monitoring. The excellent performance of SPAD devices gives them significant potential for applications in low-light imaging. This article presents a 64 (rows) × 128 (columns) SPAD image sensor designed for low-light imaging. The chip utilizes a three-dimensional stacking architecture and microlens technology, combined with compact gated pixel circuits designed with thick-gate MOS transistors, which further enhance the SPAD’s photosensitivity. The configurable digital control circuit allows for the adjustment of exposure time, enabling the sensor to adapt to different lighting conditions. The chip exhibits very low dark noise levels, with an average DCR of 41.5 cps at 2.4 V excess bias voltage. Additionally, it employs a denoising algorithm specifically developed for the SPAD image sensor, achieving two-dimensional grayscale imaging under 6 × 10−4 lux illumination conditions, demonstrating excellent low-light imaging capabilities. The chip designed in this paper fully leverages the performance advantages of SPAD image sensors and holds promise for applications in various fields requiring low-light imaging capabilities.

Background: Adverse drug reactions (ADRs) refer to an unintended harmful reaction that occurs after the administration of a medication for therapeutic purposes, which is unrelated to the intended pharmacological action of the drug. In the United States, ADRs account for 6% of all hospital admissions annually. The cost of ADR-related illnesses in 2016 was estimated at USD 528.4 billion. Increasing the awareness of ADRs is an effective measure to prevent them. Assessing suspected drugs in adverse events helps to enhance the awareness of ADRs. Methods: In this study, a suspect drug assisted judgment model (SDAJM) is designed to identify suspected drugs in adverse events. This framework utilizes the graph isomorphism network (GIN) and an attention mechanism to extract features based on patients’ demographic information, drug information, and ADR information. Results: By comparing it with other models, the results of various tests show that this model performs well in predicting the suspected drugs in adverse reaction events. ADR signal detection was conducted on a group of cardiovascular system drugs, and case analyses were performed on two classic drugs, Mexiletine and Captopril, as well as on two classic antithyroid drugs. The results indicate that the model can accomplish the task of predicting drug ADRs. Validation using benchmark datasets from ten drug discovery domains shows that the model is applicable to classification tasks on the Tox21 and SIDER datasets. Conclusions: This study applies deep learning methods to construct the SDAJM model for three purposes: (1) identifying drugs suspected to cause adverse drug events (ADEs), (2) predicting the ADRs of drugs, and (3) other drug discovery tasks. The results indicate that this method can offer new directions for research in the field of ADRs.

With the advancement of agricultural intelligence, dairy-cow farming has become a significant industry, and the application of computer vision technology in the automated monitoring of dairy cows has also attracted much attention. However, most of the images in the conventional detection dataset are high-quality images under normal lighting, which makes object detection very challenging in low-light environments at night. Therefore, this study proposed a night-time detection framework for cows based on an improved lightweight Zero-DCE (Zero-Reference Deep Curve Estimation) image enhancement network for low-light images. Firstly, the original feature extraction network of Zero-DCE was redesigned with an upsampling structure to reduce the influence of noise. Secondly, a self-attention gating mechanism was introduced in the skip connections of the Zero-DCE to enhance the network’s attention to the cow area. Then, an improved kernel selection module was introduced in the feature fusion stage to adaptively adjust the size of the receptive field. Finally, a depthwise separable convolution was used to replace the standard convolution of Zero-DCE, and an Attentive Convolutional Transformer (ACT) module was used to replace the iterative approach in Zero-DCE, which further reduced the computational complexity of the network and speeded up the inference. Four different object-detection models, YOLOv5, CenterNet, EfficientDet, and YOLOv7-tiny, were selected to evaluate the performance of the improved network and were tested on the night-time dataset before and after enhancement. Experimental results demonstrate that the detection performance of all models is significantly improved when processing night-time image samples through the enhanced Zero-DCE model. In summary, the improved lightweight Zero-DCE low-light enhancement network proposed in this study shows excellent performance, which can ensure that various object-detection models can quickly and accurately identify targets in low-light environments at night and are suitable for real-time monitoring in actual production environments.

As a kind of proteolytic enzyme extracted from earthworms, lumbrokinase has been used as an antithrombotic drug clinically. Nevertheless, its potential in anti-cancer, especially in anti-non-small cell lung cancer (NSCLC), as a single form of treatment or in combination with other therapies, is still poorly understood. In this study, we explored the anti-tumor role and the responsive molecular mechanisms of lumbrokinase in suppressing tumor angiogenesis and chemoresistance development in NSCLC and its clinical potential in combination with bevacizumab and chemotherapeutics. Lumbrokinase was found to inhibit cell proliferation in a concentration-dependent manner and caused metastasis suppression and apoptosis induction to varying degrees in NSCLC cells. Lumbrokinase enhanced the anti-angiogenesis efficiency of bevacizumab by down-regulating BPTF expression, decreasing its anchoring at the VEGF promoter region and subsequent VEGF expression and secretion. Furthermore, lumbrokinase treatment reduced IC50 values of chemotherapeutics and improved their cytotoxicity in parental and chemo-resistant NSCLC cells via inactivating the NF-κB pathway, inhibiting the expression of COX-2 and subsequent secretion of PGE2. LPS-induced NF-κB activation reversed its inhibition on NSCLC cell proliferation and its synergy with chemotherapeutic cytotoxicity, while COX-2 inhibitor celecoxib treatment boosted such effects. Lumbrokinase combined with bevacizumab, paclitaxel, or vincristine inhibited the xenograft growth of NSCLC cells in mice more significantly than a single treatment. In conclusion, lumbrokinase inhibited NSCLC survival and sensitized NSCLC cells to bevacizumab or chemotherapeutics treatment by targeted down-regulation of BPTF/VEGF signaling and inactivation of NF-κB/COX-2 signaling, respectively. The combinational applications of lumbrokinase with bevacizumab or chemotherapeutics are expected to be developed as promising candidate therapeutic strategies to improve the efficacy of the original monotherapy in anti-NSCLC.

Calcium (Ca2+), a second messenger, plays a crucial role in plant growth and development as well as in responding to biotic and abiotic stresses. In this work, we explored the role of exogenous calcium in alleviating cold stress and examined the relationship between calcium chloride (CaCl2) and calcium channel blockers, lanthanum chloride (LaCl3), in tea plants under cold stress at the physiological and transcriptional levels. Exogenous Ca2+ partially offsets the negative impacts of cold stress which increased the tolerance of tea plants by significantly raising the photochemical efficiency of PSII, protective enzyme activities, and the ABA content, which reduced the relative electrical conductivity (REC) level and the malondialdehyde (MDA) concentration. At the transcriptome level, exogenous Ca2+ significantly enhanced the expression of key genes involved in cold response pathways. Nevertheless, LaCl3 treatment not only significantly inhibited the activities of antioxidant enzymes including superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), but also increased cold damage. This study aims to provide essential insight into the role of exogenous Ca2+ in tea plants responding to cold stress, and to better understand the molecular mechanisms that facilitate Ca-mediated cold tolerance.

Strengthening urban–rural linkages (URLs) has been proposed by UN-Habitat within the framework of ‘Sustainable Development Goals (SDGs)’ to narrow down urban–rural differences via shaping new urban–rural relationships. Like URL, the aim of urban–rural integration (URI) has been promoted by the Chinese government since 2019 to address existing urban–rural divides. This concept underlines the ‘rural revitalisation’ strategy and emphasises a two-way flow of urban–rural development factors. Introducing and upgrading ‘appropriate’ rural industries is crucial to stimulate and facilitate the circulation of urban–rural development factors. This research studied three neighbouring villages, situated in urban–rural interface areas in Huangyan-Taizhou, China, each driven by different types of small industries supported by URI. It analyses the impact of small rural industries on the flow of development factors between urban and rural areas. The results showed that small-scale rural industries have been enhanced URL by decreasing urban–rural differences by creating new job opportunities to attract an in-flow population, increasing investments, and upgrading public services and infrastructure. Indigenous industries demonstrated lower profitability but exhibited greater resilience compared to industries linked to global production chains and rural tourism. Thus, this study demonstrates the imperative to carefully consider the opportunities and potential risks associated with pursuing strategies of URI through rural industry development. By providing empirical insights from URI projects in China, this study contributes to theoretical and policy dialogues concerning the concepts of both URL and URI by exploring the localization of SDGs. Furthermore, it offers valuable practical knowledge and experience for other global regions confronting similar challenges to urban and rural development.

Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current attention-based solutions, we propose a new dehazing network combining an innovative Haze-Aware Attention Module (HAAM) with a Multiscale Frequency Enhancement Module (MFEM). The HAAM is inspired by the atmospheric scattering model, thus skillfully integrating physical principles into high-dimensional features for targeted dehazing. It picks up on latent features during the image restoration process, which gives a significant boost to the metrics, while the MFEM efficiently enhances high-frequency details, thus sidestepping wavelet or Fourier transform complexities. It employs multiscale fields to extract and emphasize key frequency components with minimal parameter overhead. Integrated into a simple U-Net framework, our Haze-Aware Attention Network (HAA-Net) for single-image dehazing significantly outperforms existing attention-based and transformer models in efficiency and effectiveness. Tested across various public datasets, the HAA-Net sets new performance benchmarks. Our work not only advances the field of image dehazing but also offers insights into the design of attention mechanisms for broader applications in computer vision.

Neuropathic pain (NP) is a common type of chronic pain caused by a lesion or disease of the somatosensory nervous system. This condition imposes a considerable economic burden on society and patients. Daphnetin (DAP) is a natural product isolated from a Chinese medicinal herb with various pharmacological activities, such as anti-inflammatory and analgesic properties. However, the underlying mechanisms of these effects are not fully understood. In the present study, we aimed to investigate DAP’s anti-inflammatory and analgesic effects and explore the underlying mechanisms of action. The NP model was established as chronic constrictive injury (CCI) of the sciatic nerve, and pain sensitivity was evaluated by measuring the mechanical withdrawal threshold (MWT) and thermal withdrawal threshold (TWT). The activation of microglia in the spinal dorsal horn was measured via immunofluorescence staining. Protein levels were measured using a western blot assay. Using a mass-spectrometry proteomics platform and an LC-MS/MS-based metabolomics platform, proteins and metabolites in spinal cord tissues were extracted and analyzed. DAP treatment ameliorated the MWT and TWT in CCI rats. The expression of IL-1β, IL-6, and TNF-α was inhibited by DAP treatment in the spinal cords of CCI rats. Moreover, the activation of microglia was suppressed after DAP treatment. The elevation in the levels of P2X4, IRF8, IRF5, BDNF, and p-P38/P38 in the spinal cord caused by CCI was inhibited by DAP. Proteomics and metabolomics results indicated that DAP ameliorated the imbalance of glycerophospholipid metabolism in the spinal cords of CCI rats. DAP can potentially ameliorate NP by regulating microglial responses and glycerophospholipid metabolism in the CCI model. This study provides a pharmacological justification for using DAP in the management of NP.

Increasingly, urban planners are adopting virtual reality (VR) in designing urban green spaces (UGS) to visualize landscape designs in immersive 3D. However, the psychological effect of green spaces from the experience in VR may differ from the actual experience in the real world. In this paper, we systematically reviewed studies in the literature that conducted experiments to investigate the psychological benefits of nature in both VR and the real world to study nature in VR anchored to nature in the real world. We separated these studies based on the type of VR setup used, specifically, 360-degree video or 3D virtual environment, and established a framework of commonly used standard questionnaires used to measure the perceived mental states. The most common questionnaires include Positive and Negative Affect Schedule (PANAS), Perceived Restorativeness Scale (PRS), and Restoration Outcome Scale (ROS). Although the results from studies that used 360-degree video were less clear, results from studies that used 3D virtual environments provided evidence that virtual nature is comparable to real-world nature and thus showed promise that UGS designs in VR can transfer into real-world designs to yield similar physiological effects.

On 5 September 2022 (BJT), Luding, located in southwestern Sichuan Province, China, experienced an MS 6.8 earthquake. This earthquake occurred within the historical rupture zone of the 1786 MS 7.75 event, part of the southern section of the Xianshui He Fault belt. Given the average 155-year recurrence interval for strong earthquakes in this area, the 236 years since the last event made this earthquake somewhat expected. However, prior to this event, we did not detect any anomalies indicating low surface b-values, which are often indicative of a high-stress state in the source area before strong earthquakes, as highlighted by numerous studies. Our research focused on the northern section of the eastern boundary of the Sichuan–Yunnan sub-block, encompassing the Xianshui He, Anning He, Zemu He, and Daliang Shan fault belts. We meticulously located earthquakes of ML ≥ 1.5 from 2009 to May 2022. The catalog was divided into two periods: 2009–2014 and 2015–May 2022. Using an AIC-constraint method, we analyzed the changes in b-values (Δb) in the latter period compared to the former. Our findings revealed a significant abnormal Δb zone (Δb < −0.3), with a radius of approximately 50 km, when ΔAIC ≥ 2 was selected. Intriguingly, the epicenter of the recent Luding MS 6.8 earthquake fell within this abnormal zone. Furthermore, we calculated the b-value cross-section for the southern section of the Xianshui He fault belt using a directory of precisely located small earthquakes. This revealed that the location, scale, and shape of the abnormally low-b-value area corresponded with the large displacement co-seismic area of the main earthquake, affirming the b-value’s effectiveness in identifying asperities. The b-value’s temporal evolution prior to the mainshock exhibited a nearly decade-long continuous decrease, signifying a long-term stress-loading process akin to that observed before many strong earthquakes. The b-value anomalies observed from different profiles before the Luding earthquake underline the necessity of a comprehensive, multi-dimensional analysis of such anomalies. Finally, our analysis indicates that nine earthquakes with MS ≥ 6.5, including the Luding MS 6.8 event, have contributed to increased Coulomb Failure Stress change (ΔCFS) in the Daofu (DF)–Kangding (KD) section of the Xianshui He fault belt and the northern section of the Anning He fault belt south of Shimian (SM), with amplitudes surpassing the 0.01 MPa threshold. This suggests the potential for strong earthquakes in these zones.

In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decision processes. To dissect the requirements of action output continuity for longitudinal and lateral control, this paper adopts the deep deterministic policy gradient algorithm (DDPG) for longitudinal velocity control and the deep Q-network algorithm (DQN) for lateral motion control. Multi-agent reinforcement learning methods are applied to the task of trajectory tracking in distributed-driven vehicle autonomous driving. By contrasting with two classical trajectory tracking control methods, the proposed approach in this paper is validated to exhibit superior trajectory tracking performance, ensuring that both longitudinal velocity deviation and lateral position deviation of the vehicle remain at lower levels. Compared with classical control methods, the maximum lateral position deviation is improved by up to 90.5% and the maximum longitudinal velocity deviation is improved by up to 97%. Furthermore, it demonstrates excellent generalization and high computational efficiency, and the running time can be reduced by up to 93.7%.