Search Results (2,324)

The grass carp (Ctenopharyngodon idella) is highly susceptible to infections caused by Aeromonas species, particularly A. hydrophila and A. veronii. However, the immunological mechanisms underlying co-infection by these pathogens remain largely uncharted. This study investigated the pathogenesis and host immune response in grass carp following concurrent infection with A. hydrophila and A. veronii. Mortality was observed as early as 24 h post-infection, with cumulative mortality reaching 68%. Quantitative analysis demonstrated significantly elevated bacterial loads in hepatic tissue at 3 days post-infection (dpi). Histopathological evaluation revealed severe hepatic lesions characterized by cellular necrosis, cytoplasmic vacuolization, and hemorrhagic manifestations. Comparative transcriptomic analysis of hepatic tissues between co-infected and control specimens identified 868 and 411 differentially expressed genes (DEGs) at 1 and 5 dpi, respectively. Gene ontology and KEGG pathway analyses revealed significant enrichment of immune-related genes primarily associated with Toll-like receptor signaling and TNF signaling cascades. Notably, metabolic pathways showed substantial suppression while immune responses were significantly activated after infected. These findings provide novel insights into the host–pathogen interactions during Aeromonas co-infection in grass carp, which may facilitate the development of effective prevention and control strategies. Full article

In this paper, we propose a Linux-based operating system, namely, DicomOS, tailored for medical imaging and enhanced interoperability, addressing user-friendly functionality and the main critical needs in radiology workflows. Traditional operating systems in clinical settings face limitations, such as fragmented software ecosystems and platform-specific restrictions, which disrupt collaborative workflows and hinder diagnostic efficiency. Built on Ubuntu 22.04 LTS, DicomOS integrates essential DICOM functionalities directly into the OS, providing a unified, cohesive platform for image visualization, annotation, and sharing. Methods include custom configurations and the development of graphical user interfaces (GUIs) and command-line tools, making them accessible to medical professionals and developers. Key applications such as ITK-SNAP and 3D Slicer are seamlessly integrated alongside specialized GUIs that enhance usability without requiring extensive technical expertise. As preliminary work, DicomOS demonstrates the potential to simplify medical imaging workflows, reduce cognitive load, and promote efficient data sharing across diverse clinical settings. However, further evaluations, including structured clinical tests and broader deployment with a distributable ISO image, must validate its effectiveness and scalability in real-world scenarios. The results indicate that DicomOS provides a versatile and adaptable solution, supporting radiologists in routine tasks while facilitating customization for advanced users. As an open-source platform, DicomOS has the potential to evolve alongside medical imaging needs, positioning it as a valuable resource for enhancing workflow integration and clinical collaboration. Full article

The fall armyworm, Spodoptera frugiperda, is one of the most notorious pest insects, causing damage to more than 350 plant species, and is feared worldwide as an invasive pest species since it exhibits high adaptivity against environmental stress. Here, we therefore investigated its transcriptome responses to four different types of stresses, namely cold, heat, no water and no food. We used brain samples as our interest was in the neuroendocrine responses, while previous studies used whole bodies of larvae or moths. In general, the responses were complex and encompassed a vast array of neuropeptides (NPs) and biogenic amines (BAs). The NPs were mainly involved in ion homeostasis regulation (ITP and ITPL) and metabolic pathways (AKH, ILP), and this was accompanied by changes in BA (DA, OA) biosynthesis. Cold and no-water stress changed the NP gene expression with the same patterns of expression but clearly separated from each other, and the most divergent pattern of expression was shown after no-food stress. In conclusion, our data provide a foundation in an important model and pest insect with candidate NPs and BAs and other marker candidate genes in response to environmental stress, and also potential new targets to manage pest insects. Full article

Ammonia is commonly used as a preservative in the production of concentrated natural rubber latex (CNRL) and latex products; however, it poses a serious risk to human health and the environment. In this study, we investigated a thioacetamide derivative (TD) as a preservative of ammonia-free CNRL and the optimization of a stabilization system comprising potassium hydroxide (KOH), lauric acid (LA), and sodium dodecyl sulfate (SDS) to enhance its preservation effect. The results revealed that an optimal amount of TD (0.03%) can effectively maintain the stability of CNRL, inhibit the increase in volatile fatty acid number (VFA number), maintain stable viscosity values, and improve the mechanical stability time (MST). However, increasing the TD dosage results in an increase in both the viscosity and VFA number and a decrease in MST. KOH was used to regulate the pH value of CNRL. It was also found that it can enhance considerably the mechanical properties of CNRL dry films and accelerates the vulcanization of vulcanized film; however, an excessive amount causes latex thickening. LA proved essential for improving the MST and reducing latex viscosity, thereby substantially enhancing the stability and processability of pre-vulcanized latex, but an excessive amount is detrimental to the curing speed and final mechanical strength. SDS can rapidly improve the MST and reduce the viscosity, but it negatively affects the surface molding of dry rubber films. In conclusion, KOH, LA, and SDS at appropriate dosages play a balancing and complementary role in the preparation of ammonia-free CNRL. Upon analyzing diverse performance metrics of CNRL, it has been determined that the optimal TD dosage ranges from 0.02 to 0.03% for maximum efficacy. The KOH dosage should be maintained within 0.1–0.15% to achieve the most favorable outcome, while the LA dosage is advisable to be kept between 0.06 and 0.1%. Full article

Background: Millet peppers have rich and diverse germplasm resources. It is of great significance to characterize their phenotypes and physicochemical indicators. Methods: 30 millet germplasms were selected to measure the fruit length and width, flesh thickness, number of ventricles, fruit stalk length, and single fruit weight, and the texture characteristics of fruit such as hardness, cohesiveness, springiness, gumminess, and chewiness were determined by a texture analyzer. At the same time, high-performance liquid chromatography (HPLC) and gas chromatography (GC) were used to determine the fruit of capsaicin, dihydrocapsaicin, nordihydrocapsaicin, fatty acids, vitamin E (VE), total phenol, total sugar, and total dietary fiber. Results: M11 showed outstanding parameters in phenotype and texture. The coefficient of variation (CV) for VE was as high as 94.943% and the highest diversity index (H’) was total soluble solid, at 1.988%. M5 and M18 contained rich and diverse fatty acids. At the same time, the content of capsaicinoids in M18 also ranks among the top, second only to M27 (with a total capsaicin content of 5623.96 μg/g). PCA analysis using phenotypic data and physicochemical data showed that the classification results were different. Further hierarchical group analysis was carried out using all the index data. The results showed that 30 millet pepper germplasms were divided into three new categories: M5, M9, M18, and M24 formed one group (C1), M10, M14, M16, M19, M20, M22, M25, M26, M28, M29, and M30 formed another cluster (C2), and the remaining germplasms formed a third cluster (C3). Among them, the abundance of fatty acids in the C1 germplasm was higher than that in the other two groups. Conclusions: Our study showed that different germplasms had significant differences in morphological traits and nutritional metabolic components and were rich in genetic diversity. This study provides a theoretical basis for the improvement of millet varieties and the development of functional food. Full article

This systematic review evaluates China’s Rural Revitalization Policy, focusing on sustainable agriculture, food security, and poverty alleviation initiatives from 2010 to 2024. The study addresses critical gaps in understanding how these combined efforts impact long-term food security and ecological sustainability in impoverished areas, moving beyond the short-term outcomes often emphasized in existing literature. Following the PRISMA 2020 guidelines, we reviewed 33 peer-reviewed publications from the Web of Science and Scopus databases, employing bibliometric analyses in RStudio to assess citation patterns, collaboration networks, and thematic evolution. Our analysis reveals significant progress across three interconnected domains. First, poverty alleviation initiatives achieved a 12.3% reduction in rural poverty through integrated agricultural modernization and targeted support programs. Second, agricultural productivity increased by 9.8% through technological integration and sustainable farming practices, strengthening food security outcomes. Third, environmental sustainability improved notably, with a 15.7% increase in clean water access, demonstrating a successful balance between economic growth and ecological protection. China emerged as the largest contributor (15.2%) to research in this field, with substantial international collaboration (42.4% of publications involving cross-border co-authorship). Despite these achievements, significant regional disparities persist, particularly between eastern and western regions, where targeted interventions are needed. The findings highlight the need for regionally tailored approaches: eastern regions require focus on sustainable intensification, western regions need fundamental infrastructure development, and central regions would benefit from strengthened urban–rural linkages. This study provides valuable insights for policymakers and researchers working on rural development initiatives while identifying areas requiring further research, particularly in long-term sustainability assessments and climate resilience strategies. Full article

Herein, we report a practical example of salicylaldehyde-based cobalt-catalyzed C−H deuteriomethoxylation of benzamides using deuterated methanol, facilitated by 8-aminoquinoline as a directing group. The salicylaldehyde-based cobalt catalyst is user-friendly, and the reaction exhibits broad functional group tolerance, accommodating benzene, heterocycles, and naphthalene rings. The synthetic utility of this methodology was demonstrated through a gram-scale reaction and the subsequent removal of the 8-aminoquinoline directing group to yield deuteriomethoxylated benzoic acid. Preliminary mechanistic studies suggest that C−H activation is not the rate-determining step of the reaction. Full article

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error before stabilizing the production process. Therefore, to efficiently transform these inefficiencies related to time and cost, there is a need for real-time predictive technology for forming quality based on the position of drawbeads and the bead force. This study proposes a method for predicting formability in real-time, based on a digital twin framework that considers the position of drawbeads and holder force. A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. The accuracy of the machine learning models was demonstrated, achieving 100% accuracy in determining crack occurrence, with a mean squared error (MSE) of 0.141 for wrinkle prediction and 0.038 for crack prediction, thereby ensuring the accuracy of the forming prediction model based on drawbead applications. Based on these predictive models, a user-friendly GUI has been developed, which is expected to reduce design time and costs while facilitating real-time predictions of forming quality, such as wrinkles and cracks, on-site. Full article

Background: Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by intermittent upper airway obstruction, leading to significant health consequences. Traditional diagnostic methods, such as polysomnography, are time-consuming and resource-intensive. Objectives: This study explores the potential of proton-transfer-reaction mass spectrometry (PTR-MS) in identifying volatile organic compound (VOC) biomarkers for the non-invasive detection of OSA. Methods: Breath samples from 89 participants, including 49 OSA patients and 40 controls, were analyzed using PTR-MS. Significance analysis was performed between OSA patients and controls to identify potential biomarkers for OSA. To as-sess the differences in VOC concentrations between OSA patients and control subjects, the Wilcoxon rank-sum test was employed. partial least squares discriminant analysis (PLS-DA) analysis and heatmap plot was conducted to visualize the differentiation between OSA patients and control subjects based on their VOC profiles.In order to further investigate the correlation between identified biomarkers and the severity of OSA measured by Apnea–Hypopnea Index (AHI), regression analysis was conducted between biomarkers and AHI Index. Results: The results identified specific VOCs, including m045 (acetaldehyde), m095.950, and m097.071, which showed significant differences between OSA patients and controls. Advanced statistical analyses, including PLS-DA and correlation mapping, highlighted the robustness of these biomarkers, with m045 (acetaldehyde) specifically emerging as a potential biomarker associated with the AHI Index. Conclusions: This study underscores the potential of VOCs as biomarkers for identifying patients with severe AHI levels. The analysis of VOCs using PTR-MS presents a rapid, non-invasive, and cost-effective method that could be seamlessly integrated into clinical practice, allowing clinicians to better stratify patients based on their need for polysomnography and prioritize those requiring earlier testing. Future studies are necessary to validate these findings in larger cohorts and to explore the integration of PTR-MS with other diagnostic modalities for improved accuracy and clinical utility. Full article

In this paper, we generalize the 2 + 1-dimensional Gardner (2DG) equation to three spatial dimensions, i.e., 3 + 1 and 3 + 2 dimensions, and construct the solutions of the Cauchy problems and Lax pairs for the Gardner equation in three spatial dimensions via a novel non-local d-bar formalism. Several new long derivative operators $D_x$, $D_y$ and $D_t$ are introduced to study the initial value problems for the Gardner equation in three spatial dimensions. It follows that Propositions 1 and 3 summarize the main results of this paper. Full article

The literature on the built environment (BE) and travel has offered evidence on both short- and long-term aspects of travel behavior with a main focus on home and work neighborhoods; however, the effects of the BE at the main activity space on the modality style have remained largely unknown. Moreover, little is known about the inter-modal substitutions and how the substitution is affected by the satiation effects. Based on survey data from Beijing, a Multiple Discrete Continuous Extreme Value (MDCEV) model is adopted to reveal the effects of BE at home, work, and activity space locations on the modality style. Results show that BE features at the home, work, and main activity space neighborhoods are essential triggers of the modality style, among which home BE features play the most vital role. The satiation effects visualized from various travel modes suggest that car traveling remains the most preferred travel mode. These findings can provide refined BE planning implications according to local land-use patterns for urban planners and transport policymakers because a one-size-fits-all design is not a solution to regulate people’s travel behavior. Full article

Based on density functional theory calculations, this study analyzed the gas-sensing performance of Ti₃C₂T_x (T=O, F, OH) monolayers modified with precious metal atoms (Ag and Au) for HCHO and C₆H₆ gas molecules. Firstly, stable structures of Ag- and Au-single-atom doped Ti₃C₂T_x (T=O, F, OH) surfaces were constructed and then HCHO and C₆H₆ gas molecules were set to approach the modified structures at different initial positions. The most stable adsorption structure was selected for further analysis of the adsorption energy, adsorption distance, charge transfer, charge deformation density, total density of states, and partial density of states. The results show that the Ag and Au modifications improved the adsorption performance of Ti₃C₂O₂ for HCHO and C₆H₆. In comparison, the effect of the Au modification was better than that of Ag. For Ti₃C₂F₂, the Ag and Au doping modifications did not significantly change the adsorption effects for HCHO and C₆H₆. However, the Ag and Au doping modifications decreased the adsorption of Ti₃C₂(OH)₂ for HCHO, while there was no significant change in the gas adsorption for C₆H₆. The above results serve as a theoretical foundation for the design of new sensors for HCHO and C₆H₆. Full article

Additive manufacturing (AM) has revolutionized the design and production of complex geometries by offering unprecedented creative freedom over traditional manufacturing. Despite its growing prominence, AM lacks automated and standardized design rules tailored to specific AM processes, resulting in time-consuming and expert-dependent manual verification. To address these limitations, this research introduces a novel design for additive manufacturing (DfAM) framework consisting of two complementary models designed to automate the design process. The first model, based on a decision tree algorithm, evaluates part compliance with established AM design rules. A modified J48 classifier was implemented to enhance data mining accuracy by achieving a 91.25% classification performance accuracy. This model systematically assesses whether input part characteristics meet AM processing standards, thereby providing a robust tool for verifying design rules. The second model features an AM design rule engine developed with a Python-based graphical user interface (GUI). This engine generates specific recommendations for design adjustments based on part characteristics and machine compatibility, offering a user-friendly approach for identifying potential design issues and ensuring DfAM compliance. By linking part specifications to various AM techniques, this model supports both researchers and engineers in anticipating and mitigating design flaws. Overall, this research establishes a foundation for a comprehensive DfAM expert system. Full article

In the background of high frequency and intensity forest fires driven by future warming and a drying climate, early detection and effective control of fires are extremely important to reduce losses. Meteorological satellite imagery is commonly used for near-real-time forest fire monitoring, thanks to its high temporal resolution. To address the misjudgments and omissions caused by solely relying on changes in infrared band brightness values and a single image in forest fire early discrimination, this paper constructs the angle slope indexes ANIR, AMIR, AMNIR, ∆ANIR, and ∆AMIR based on the reflectance of the red band and near-infrared band, the brightness temperature of the mid-infrared and far-infrared band, the difference between the AMIR and ANIR, and the index difference between time-series images. These indexes integrate the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. We also used the decomposed three-dimensional OTSU (maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data to address the different discrimination thresholds caused by different time and space backgrounds. In this paper, the Himawari-8 satellite imagery was used to detect forest fires based on the angle slope indices thresholds algorithm (ASITR), and the fusion of the decomposed three-dimensional OTSU and ASITR algorithm (FDOA). Results show that, compared with ASITR, the accuracy of FDOA decreased by 3.41% (0.88 vs. 0.85), the omission error decreased by 52.94% (0.17 vs. 0.08), and the overall evaluation increased by 3.53% (0.85 vs. 0.88). The ASITR has higher accuracy, and the fusion of decomposed three-dimensional OTSU and angle slope indexes can reduce forest fire omission error and improve the overall evaluation. Full article

The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R²). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. Full article