Explore the vast range of titles available.

Brucellosis, caused by a number of Brucella species, remains the most prevalent zoonotic disease worldwide. Brucella establish chronic infections within host macrophages despite triggering cytosolic innate immune sensors, including Stimulator of Interferon Genes (STING), which potentially limit infection. In this study, STING was required for control of chronic Brucella infection in vivo . However, early during infection, Brucella down-regulated STING mRNA and protein. Down-regulation occurred post-transcriptionally, required live bacteria, the Brucella type IV secretion system, and was independent of host IRE1-RNase activity. STING suppression occurred in MyD88 -/- macrophages and was not induced by Toll-like receptor agonists or purified Brucella lipopolysaccharide (LPS). Rather, Brucella induced a STING-targeting microRNA, miR-24-2, in a type IV secretion system-dependent manner. Furthermore, STING downregulation was inhibited by miR-24 anti-miRs and in Mirn23a locus-deficient macrophages. Failure to suppress STING expression in Mirn23a -/- macrophages correlated with diminished Brucella replication, and was rescued by exogenous miR-24. Mirn23a -/- mice were also more resistant to splenic colonization one week post infection. Anti-miR-24 potently suppressed replication in wild type, but much less in STING -/- macrophages, suggesting most of the impact of miR-24 induction on replication occurred via STING suppression. In summary, Brucella sabotages cytosolic surveillance by miR-24-dependent suppression of STING expression; post-STING activation “damage control” via targeted STING destruction may enable establishment of chronic infection.

With the implementation of “The Belt and Road” policy and increasing investment, there willbe a large number of large scaled construction projects (LSCP) in China. However, thecomprehensive benefit of most LSCP is not satisfactory because of concerning more about theeconomic benefits. It makes the sustainability of LSCP concerned about. In order to ensure thesustainabl e development, the evaluation of the comprehensive benefit of LSCP should becarried out. Based on comprehensive literature review and content analysis, 30 influence factorsof comprehensive benefit evaluation for LSCP are identified. The evaluation index system of 17factors containing three subsystems of social, economic and environmental benefit isestablished through factor analysis. Entropy method is used to determine weights of eachindicator, and then synthetic evaluation model is put up. This paper selects a practical case,Lieder Village reconstruction in Guangzhou, to calculate the synthetic evaluation value usingfuzzy theory. The evaluation results are satisfactory and in line with reality. It shows that theevaluation index system and synthetic evaluation model has a certain reference value foranalysis of comprehensive benefit and can help for enhancing the construction andmanagement level of LSCP and promoting the sustainable development of LSCP.

Brucellosis, caused by Brucella bacteria species, remains the most prevalent zoonotic disease worldwide. Brucella establish chronic infections within host macrophages despite triggering cytosolic innate immune sensors, including Stimulator of Interferon Genes (STING), which potentially limit infection. In this study, STING was required for control of chronic Brucella infection in vivo. However, early during infection, Brucella down-regulated STING mRNA and protein. Down-regulation occurred post-transcriptionally, required live bacteria, the Brucella type IV secretion system, and was independent of host IRE1-RNase activity. Rather, Brucella induced a STING-targeting microRNA, miR-24-2. Furthermore, STING downregulation was inhibited by miR-24 anti-miRs and in mirn23a locus-deficient macrophages. Failure to suppress STING expression in mirn23a-/- macrophages correlated with diminished Brucella replication, and was rescued by exogenous miR-24. Anti-miR-24 potently suppressed replication in wild type, but much less in STING-/- macrophages, suggesting most of the impact of miR-24 induction on replication occurred via STING suppression. In summary, Brucella sabotages innate immunity by miR-24-dependent suppression of STING expression; post-STING activation damage control via targeted STING destruction may enable establishment of chronic infection.

Ultrathin two-dimensional （2D） porous Zn（OH）2 nanosheets （PNs） were fabricated by means of one-dimensional Cu nanowires as backbones. The PNs have thickness of approximately 3.8 nm and pore size of 4-10 nm. To form ＂smart＂ porous nanosheets, DNA aptamers were covalently conjugated to the surface of PNs. These ultrathin nanosheets show good biocompatibility, effident cellular uptaker and promising pH-stimulated drug release.

Materials exhibiting long-lived, persistent luminescence in the visible spectrum are useful for applications in the display, information encryption and bioimaging sectors1–4. Herein, we report the development of several organic phosphors that provide colour-tunable, ultra-long organic phosphorescence (UOP). The emission colour can be tuned by varying the excitation wavelength, allowing dynamic colour tuning from the violet to the green part of the visible spectrum. Our experimental data reveal that these organic phosphors can have an ultra-long lifetime of 2.45 s and a maximum phosphorescence efficiency of 31.2%. Furthermore, we demonstrate the applications of colour-tunable UOP for use in a multicolour display and visual sensing of ultraviolet light in the range from 300 to 360 nm. The findings open the opportunity for the development of smart luminescent materials and sensors with dynamically controlled phosphorescence.Organic phosphors with ultra-long lifetimes and an emission colour that can be tuned by the excitation wavelength are reported.

Accurate classification of mung bean seeds is essential for enhancing both their nutritional value and crop yields. However, current methods are limited, primarily due to the time-consuming and inaccurate classification process resulting from a lack of diverse dataset features. To overcome these challenges, this study develops a multimodal dataset that integrates Raman spectral features and image-based features through early fusion. Furthermore, the classification of mung bean seed varieties is achieved in a rapid, accurate, and non-destructive manner by optimizing a stacking ensemble learning model using the Kepler Optimization Algorithm (KOA). The multimodal dataset comprises 59 features, selected using the Competitive Adaptive Reweighted Sampling (CARS) method. Specifically, 44 key features are extracted from 700 Raman spectral data points, while 15 key features are derived from 43 image numerical features. The study also used the Kepler Optimization Algorithm to optimize the parameters of various machine learning models, including Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Backpropagation Neural Network (BPNN), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). By constructing a stacking ensemble learning model, the research effectively leverages the strengths of multiple classifiers, thereby enhancing the overall classification performance. Experimental results demonstrate that the proposed method significantly improves mung bean seed classification accuracy, with the Kepler-optimized stacking ensemble model achieving an accuracy of 90.71%. This represents a 3.24% improvement over KOA-RF and a 1.59% improvement over KOA-GBDT. In comparison to baseline models, the proposed method proves to be more efficient. This study underscores the potential of combining multimodal features with a Kepler-optimized stacking ensemble learning model for mung bean seed classification. It highlights the role of advanced artificial intelligence techniques in agricultural production and provides valuable technical support for the precise classification of mung bean seeds.

Metabolic reprogramming fuels cancer cell metastasis and remodels the immunosuppressive tumor microenvironment (TME). We report here that circPETH, a circular RNA (circRNA) transported via extracellular vesicles (EVs) from tumor-associated macrophages (TAMs) to hepatocellular carcinoma (HCC) cells, facilitates glycolysis and metastasis in recipient HCC cells. Mechanistically, circPETH-147aa, encoded by circPETH in an m6A-driven manner, promotes PKM2-catalyzed ALDOA-S36 phosphorylation via the MEG pocket. Furthermore, circPETH-147aa impairs anti-HCC immunity by increasing HuR-dependent SLC43A2 mRNA stability and driving methionine and leucine deficiency in cytotoxic CD8 + T cells. Importantly, through virtual and experimental screening, we find that a small molecule, Norathyriol, is an effective inhibitor that targets the MEG pocket on the circPETH-147aa surface. Norathyriol reverses circPETH-147aa-facilitated acquisition of metabolic and metastatic phenotypes by HCC cells, increases anti-PD1 efficacy, and enhances cytotoxic CD8 + T-cell function. Here we show that Norathyriol is a promising anti-HCC agent that contributes to attenuating the resistance of advanced HCC to immune checkpoint blocker (ICB) therapies. Tumor-associated macrophages (TAMs) are associated with poor prognosis and low responses to immunotherapy. Here, authors discover that a circular RNA, circPETH, produced by TAMs generates a metabolic reprogramming in hepatocellular carcinoma that promotes metastasis and impairs the activity of cytotoxic CD8 + T cells.

With increasing cybersecurity threats, effective intrusion detection has become critical for safeguarding networks. Although deep learning methods have advanced, two major issues persist: (1) class imbalance biases models toward normal traffic, increasing false negatives; (2) single-task frameworks limit feature representation and fail to leverage multi-task collaboration potential. To address these, we propose Memory Autoencoder with CNN-Attention Integration Network(MEMCAIN), a multi-task feature fusion deep learning method. First, MEMCAIN integrates CNN with attention mechanisms, constructing CCA Blocks through contrastive normalization to capture spatiotemporal features. These blocks are stacked to form CCANet, enabling comprehensive spatiotemporal feature extraction from traffic data. Second, a memory autoencoder is introduced to capture latent distribution features of traffic flows. Finally, an end-to-end collaborative training framework jointly optimizes CCANet (main task) and the memory autoencoder (auxiliary task). Experiments demonstrate MEMCAIN’s significant superiority over baselines across multiple datasets, with ablation studies validating each module’s efficacy for fine-grained intrusion detection in complex network environments.

Recently, Siamese-based trackers have become particularly popular. The correlation module in these trackers is responsible for fusing the feature information from the template and the search region, to obtain the response results. However, there are very rich contextual information and feature dependencies among video sequences, and it is difficult for a simple correlation module to efficiently integrate useful information. Therefore, the tracker encounters the challenges of information loss and local optimal solutions. In this work, we propose a novel attention-enhanced network with a Transformer variant for robust visual tracking. The proposed method carefully designs the local feature information association module (LFIA) and the global feature information fusion module (GFIF) based on the attention mechanism, which can effectively utilize contextual information and feature dependencies to enhance feature information. Our approach transforms the visual tracking problem into a bounding box prediction problem, using only a simple prediction network for object localization, without any prior knowledge. Ultimately, we propose a robust tracker called RANformer. Experiments show that the proposed tracker achieves state-of-the-art performance on 7 popular tracking benchmarks while meeting real-time requirements with a speed exceeding 40FPS.

Indicine cattle, also referred to as zebu ( Bos taurus indicus ), play a central role in pastoral communities across a wide range of agro-ecosystems, from extremely hot semiarid regions to hot humid tropical regions. However, their adaptive genetic changes following their dispersal into East Asia from the Indian subcontinent have remained poorly documented. Here, we characterize their global genetic diversity using high-quality whole-genome sequencing data from 354 indicine cattle of 57 breeds/populations, including major indicine phylogeographic groups worldwide. We reveal their probable migration into East Asia was along a coastal route rather than inland routes and we detected introgression from other bovine species. Genomic regions carrying morphology-, immune-, and heat-tolerance-related genes underwent divergent selection according to Asian agro-ecologies. We identify distinct sets of loci that contain promising candidate variants for adaptation to hot semi-arid and hot humid tropical ecosystems. Our results indicate that the rapid and successful adaptation of East Asian indicine cattle to hot humid environments was promoted by localized introgression from banteng and/or gaur. Our findings provide insights into the history and environmental adaptation of indicine cattle. Indicine cattle make up half of all cattle populations worldwide. Using a large genomic dataset, this study finds historic migrations and extensive introgression with domestic and wild bovine species has facilitated this species physiological adaptation to extreme environments.