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Abstract Background Peste des petits ruminants (PPR) is a serious disease that affects goats, sheep and other small ruminants. As one of the earliest and most serious countries, PPR has seriously threatened India's animal husbandry economy. Results In this study, the spatiotemporal characteristics of the PPR in India outbreaks were analyzed. Between 2010 and 2018, the epidemic in India broke out all over the country in a cluster distribution. Epidemic clusters in northern and southern India are at higher risk, and the outbreak time of PPR has significant seasonality. The results of the analysis of the development and transmission of PPR under the natural infection conditions showed that the PPR outbreak in India reached a peak within 15 days. Finally, the quantitative risk analysis results based on scenario tree show showed that the average probability of infecting PPRV in live sheep exported from India was 1.45 × 10–4. Conclusions This study analyzed the prevalence of PPR in India. The analysis of transmission dynamics on the development of the epidemic provides a reference for the prevention and control of the epidemic. At the same time, it provides risk analysis and suggestions on trade measures for the trading countries of India.

Heavy metal pollution is a major threat to agricultural produce and it can pose potential ecological risks which subsequently impacts on human health. Strawberries are an economically important produce of China. The intrinsic link of heavy metal pollution risk in the soil-strawberry ecosystem is of concern. In this study, the pollution index of heavy metal pollutants in farmlands of different provinces were evaluated, and the results showed significantly high levels of cadmium. In addition, Nemerow integrated pollution index analysis showed that low-pollution farmlands only accounted for 14.07% of the total arable land area. Then, the transfer factors were used to calculate the migration of heavy metals from the soil into strawberries. The results showed that cadmium and nickel were relatively high in strawberries from the Guangxi province. Similar results were found for mercury in Jiangxi Province. The pollution index of single food pollution also showed that mercury in strawberries from Jiangxi Province was at a moderate pollution level. The comprehensive pollution index indicated that heavy metal pollution in strawberries in Central China may be severe. In addition, spatial clustering analysis showed that cadmium, chromium, lead, arsenic and zinc in strawberries had significant hotspot clustering in central, south and southwest China. Finally, our studies also suggested that the risk of carcinogenic and non-carcinogenic diseases was higher in the (2, 4] years age group than in other age groups. People in Yunnan Province were also found to have a higher non-carcinogenic risk than those in other provinces and cities in China. This study provides a comprehensive view of the potential risks of heavy metal contamination in strawberries, which could provide assistance in the design of regulatory and risk management programs for chemical pollutants in strawberries, thus ensuring the safety of consumption of these edible fruits.

Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q2 = 0.612 (cross-validated correlation coefficient) and r2 = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Rubusoside is a natural sweetener and the active component of Rubus suavissimus. The preventive and therapeutic effect of rubusoside on high-fat diet-induced (HFD) serum metabolite changes in golden hamsters was analyzed by 1H-NMR metabolomics to explore the underlying mechanism of lipid metabolism regulation. 1H-NMR serum metabolomics analyses revealed a disturbed amino acid-, sugar-, fat-, and energy metabolism in HFD animals. Animals supplemented with rubusoside can partly reverse the metabolism disorders induced by high-fat diet and exerted good anti-hypertriglyceridemia effect by intervening in some major metabolic pathways, involving amino acid metabolism, synthesis of ketone bodies, as well as choline and 4-hydroxyphenylacetate metabolism. This study indicates that rubusoside can interfere with and normalize high-fat diet-induced metabolic changes in serum and could provide a theoretical basis to establish rubusoside as a potentially therapeutic tool able to revert or prevent lipid metabolism disorders.