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Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. [http://www.allinno.com/product/polysaccharides/643.html seebio Polysucrose 400 Food additive] focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant's survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease.<br /><br /> Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In [http://en.wikipedia.org/wiki/Ficoll Polysucrose 400 Food additive] , the lesion subtype recognition model achieves accuracies of 11%, 01 and 04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline commercial or financial relationships that could be construed as a potential Colorectal cancer (CRC) is one of the most common cancers worldwide and the consumption of a high-calorie diet is one of its risk factors. Calorie restriction (CR) slows tumor growth in a variety of cancers, including colorectal cancer; however, the mechanism behind this remains unknown. In the present study, nude mouse model. In addition, tumor immunohistochemistry revealed that the CR group had significantly higher expression of Bax (P&lt;001) and significantly lower levels of Bcl2 (P&lt;0001) and Ki67 (P&lt;001) compared with control group.<br /><br /> Furthermore, data from 16S ribosomal (r)RNA sequencing implied that CR was able to reprogram the microbiota structure, characterized by increased Lactobacillus constituent ratio (P&lt;05), with amelioration of microbial dysbiosis caused by CRC. Further receiver operating characteristic curves demonstrated that the bacteria Bacteroides [area under the curve (AUC)=800], Lactobacillus (AUC=760) and Roseburia (AUC=720) served key roles in suppression of CRC in the mouse model. The functional prediction of intestinal flora indicated 'cyanoamino acid metabolism' (P&lt;01), 'replication initiation protein REP (rolling circle plasmid replication)' (P&lt;01), 'tRNA G10 N-methylase Trm11' (P&lt;01) and 'uncharacterized protein with cyclophilin fold, contains DUF369 domain' (P&lt;05) were downregulated in CR group. These findings implied that CR suppressed CRC in mice and altered the gut microbiota. This work was performed to determine the pharmacological effects of Bufei Jianpi granules on chronic obstructive pulmonary disease and its metabolism in rats. Chronic obstructive pulmonary disease (COPD), ranked as the third leading cause of death worldwide, is seriously endangering human health. At present, the pathogenesis of COPD is complex and unclear, and the drug treatment mainly aims to alleviate and improve symptoms; however, they cannot achieve the purpose of eradicating the disease.<br /><br /> Bufei Jianpi granule (BJG) is a Chinese medicine Chinese Medicine for treating COPD. This study focuses on the pharmacological effects of BJG on COPD and its metabolism in rats, aiming to provide a scientific basis for developing BJG against COPD. A total of 72 Sprague-Dawley (SD) rats were divided into the blank group, model group, positive control group, and BJG groups (36, 18, and 59&nbsp;g/kg). Except for the blank group, rats in other groups were administered lipopolysaccharide (LPS) combined with smoking for 6 weeks to establish the COPD model. After another 6 weeks of treatment, the therapeutic effect of BJG on COPD rats was evaluated. In the BJG (36&nbsp;g/kg) group, the cough condition of rats was significantly relieved and the body weight was close to that of the blank group. Compared with the mortality of 7% in the model group, no deaths occurred in the BJG (36&nbsp;g/kg) and (18&nbsp;g/kg) groups.<br /><br /> The lung tissue damage in the BJG groups was less than that in the COPD group. Compared with the model group, MV, PIF, PEF, and EF50 in the BJG groups were observably increased in a dose-dependent manner, while sRaw, Raw, and FRC were obviously decreased. Also, the contents of IL-6, IL-8, TNF-α, PGE2, MMP-9, and NO in the serum and BALF were lowered dramatically in all BJG groups. All indicators present an obvious dose-effect relationship. On this basis, the UPLC-QTOF-MS/MS technology was used to analyze characteristic metabolites in rats under physiological and pathological conditions. A total of 17 prototype and 7 metabolite components were detected, and the concentration of most components was increased in the COPD pathologic state.