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Nuclear data, especially neutron nuclear data, are the basic data for nuclear science and engineering application. Traditional nuclear data evaluation is time-consuming and labor-intensive, and is easily influenced by human factors. Machine learning technology is expected to enhance the ability of nuclear data evaluation. In order to explore the feasibility of using machine learning method to assist nuclear reaction data evaluation, a fully connected deep neural network algorithm was used to learn neutron cross section data to obtain the trained model, and the test data were used to examine the prediction ability of the model. In order to avoid the complexity of resonant region cross section, the neutron cross sections in the fast neutron region in the general evaluation nuclear library were used as the data set, with which the neural network model was trained, verified and tested. The total neutron cross sections and elastic scattering cross sections of 12 uranium isotopes from ENDF/B-Ⅷ.0 in fast neutron region were extracted. The cross sections of 230U were used as test data to be predicted, the cross sections of 232U were used as verification data, and the cross sections of other 10 nuclides were used as training data. In order to obtain a neural network model with predictive ability, a series of neural network models were trained by using training data, and then the best model was selected by using verification data to predict test data. In order to find a suitable network structure, neural network models were constructed with a certain step size (10 neurons) within the number of hidden layers 2-10 and the number of neurons in each layer less than 100. After completing all the model training, the best model was selected by using the validation data, the cross section data of test nuclides were predicted by using the best model, and the prediction ability was evaluated by comparing with the data of evaluation library. For the total cross section, taking (A, E) as the features, satisfactory prediction results can be obtained, however, for elastic scattering cross sections, it is difficult to get satisfactory prediction results by taking (A, E) as the features. Further increasing the neutron separation energy Sn of the target nucleus as feature, satisfactory results can be obtained. Validation and testing show that the trained neural network model can well grasp the variation law of cross sections with nuclides and incident neutron energy in the evaluation library, so the model has strong predictive ability for “unknown” neutron cross sections of a nuclide. Therefore, neural network algorithm is believed to have the potential to become a new way to evaluate nuclear data.

Neutron resonance cross sections are essential in many nuclear science and application fields. Evaluated nuclear libraries usually derive neutron resonance parameters using R-matrix theory. However, determining these parameters and reconstructing the pointwise cross sections required for transport calculations is a time-consuming process. This paper presents a new method for fitting neutron resonance cross sections by using a phase shift deep neural network (PhaseDNN). The network presents resonance cross sections based on its network parameters. This approach allows for the quick calculation of cross section values from the given network parameters. Additionally, PhaseDNN can fit network parameters to experimental data to obtain a smooth resonance cross section curve. Therefore, it has the potential to be an alternative to R-matrix theory.

AIM: To investigate the effects and underlying mechanism of Toona sinensis bark extract (TAE) on the colon mucosal barrier and gut microbiota in mice with ulcerative colitis (UC) induced by dextran sulfate sodium (DSS). METHODS: Sixty C57BL/6J mice were randomly assigned to control, model, and mesalazine (0.2 g/kg) groups, as well as TAE groups (low, medium, and high-doses equal to crude drug concentrations of 2. 3, 4. 6 and 9. 2 g/ kg respectively) . The UC model was induced by drinking of 2. 5% DSS and mean while the drugs were administered for 10 days. The mice were then evaluated in terms of weight, disease activity index (DAI), colon length, spleen index, and pathological changes in the colon tissues. In addition, the level of apoptosis in colon tissues was assessed by terminal de-oxynucleotidyl transferase dUTP nick-end labeling (TUNEL) fluorescence staining, and the expression of related proteins was evaluated by Western blot, levels of inflammatory factors were determined by enzyme-linked immunosorbent assays (ELISA), and the activities of total superoxide dismutase (T-SOD) and catalase (CAT) and malondialdehyde (MDA) content were assessed by biochemical assays. Furthermore, the constitution and diversity of the gut microbiota were investigated by 16S rRNA gene sequencing. RESULTS: Compared with the control group, mice in the model group showed significantly reduced body weights (P<0. 01), and the colon length was shortened significantly (P<0. 05). Marked increases in the DAI and spleen index were observed (P<0. 01), along with severe damage to the colon mucosa (P<0. 01). Mechanistically, the level of intestinal epithelial cell apoptosis was significantly raised ( P<0. 01). The model group showed markedly reduced expression of occludin and claudin-1 (P<0. 01), the level of IL-10, and activities of T-SOD and CAT in the colon tissues (P<0. 01). While the levels of IL-6, IL-1β, TNF-α, and the MDA content were increased significantly (P<0. 05). The abundance and diversity of the gut microbiota were decreased in the model group (P<0. 05). Compared with the model group, all these indicators were ameliorated by the administration of TAE (P<0. 05). The abundance of pathogenic bacteria, including Proteobacteria and Escherichia-Shigella, was decreased remarkably (P<0. 05), while that of probiotics, including Bacteroidota and Muribaculaceae, were increased significantly (P<0. 05). The abundance and diversity of the gut microbiota were increased. CONCLUSION: Taken together, Toona sinensis bark alcohol extract can alleviate damage to the intestinal mucosa by suppressing the apoptosis of intestinal epithelial cell, reducing the inflammatory response, and mitigating oxidative stress. Treatment with TAE could also maintain the homeostasis of the gut microbiota by regulating the abundance, ultimately meliorate the function of intestinal mucosal barrier. [ABSTRACT FROM AUTHOR]

Slimming grass carp is a commercial variety with good body form and meat quality, which is cultured by starving common grass carp in a clean flowing water environment. Compared to common grass carp, slimming grass carp has a far higher economic value. Until now, no molecular study has concentrated on the regulation mechanism of the muscle characteristics of slimming grass carp. This study first reported the gene expression profile of the muscle characteristics of slimming grass carp based on the level of extrachromosomal circular DNAs (eccDNAs). EccDNAs are double-stranded circular DNAs derived from genomic DNAs and play crucial roles in the functional regulation of a wide range of biological processes, none of which have been shown to occur in fish. Here, muscle eccDNAs from slimming grass carp and common grass carp were both generally sequenced, and the information, as well as the expression profile of eccDNAs, were compared and analysed. The findings reveal that 82,238 and 25,857 eccDNAs were detected from slimming grass carp and common grass carp, respectively. The length distribution of eccDNAs was in the range of 1~1000 bp, with two peaks at about 200 bp and 400 bp. When the expression profiles of eccDNAs between slimming grass carp and common grass carp were compared, 3523 up-regulated and 175 down-regulated eccDNAs were found. Enrichment analysis showed that these eccDNA genes were correlated with cellular structure and response, cell immunology, enzyme activity, etc. Certain differentially expressed eccDNAs involved in muscle characteristics were detected, which include myosin heavy chain, myosin light chain, muscle segment homeobox C, calsequestrin, calmodulin, etc., among which the majority of genes were linked to muscle structure and contraction. This indicates that during the process of cultivating from common grass carp to slimming grass carp, the treatment primarily affected muscle structure and contraction, making the meat quality of slimming grass carp different from that of common grass carp. This result provides molecular evidence and new insights by which to elucidate the regulating mechanism of muscle phenotypic characterisation in slimming grass carp and other fish. [ABSTRACT FROM AUTHOR]

Time-varying meshing stiffness has an important contribution to the vibration control in gear systems. However, the design of gears in the electric vehicles and aerospace field is more focused on lightweight. The accurate and efficient evaluation of the time-varying meshing stiffness of lightweight spur gears has become a challenging problem, such as considering the thin-walled or oblique web. Therefore, a new approach is proposed to calculate the mesh stiffness of the lightweight spur gear by combining an analytical model and the dynamic model of the gear pair. The gear model is constructed by adopting the shell element to save computer resources and model preparation time. The validity of the presented model is investigated by comparing it with the traditional analytical and finite element models. The analytical results indicate that the proposed model has the same level of computational efficiency as the analytical model while maintaining the precision of the finite element model. The advantages of the proposed method are the ability to consider the effects of thin-walled web and oblique spoke plate structures on the mesh stiffness. [ABSTRACT FROM AUTHOR]