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In Vietnam, diarrhea, especially persistent diarrhea, is one of the most common diseases in children, while a significant proportion of cases are negative with pathogens; thus, there is an urgent need to understand gut bacterial dysbiosis. In this study, bacteria in the fecal samples of five healthy and ten diarrheal children were separated from other residues, then adopted to extract their metagenomic DNA for evaluating their diversity based on V3 and V6–V8 regions and the 16S rRNA gene by PCR-RFLP and PCR-DGGE. As a result, bacterial metagenomic DNAs with high quality, quantity and diversity were successfully extracted using a GeneJET kit and a chemical protocol. A sequence analysis of 73 representative DNA fragments from gels indicated a remarkable bacterial dysbiosis in all groups of diarrhea. Viral diarrhea was characterized by extremely reduced bacterial diversity with the blossom of Bifidobacterium and Streptococcus. Streptococcus was also the most abundant in persistent diarrhea. Beneficial bacteria that may play a role in the self- rebalance in intestinal bacterial communities, such as Bifidobacterium, Lactobacillus, and Enterococcus, were seen in all diarrheal groups, while Bacteroides and Akkermansia muciniphila were seen in the healthy group but absent in the diarrheal groups. This study provides additional evidence for a relationship between intestinal bacterial dysbiosis and diarrhea in children, emphasizing an increase in Streptococcus. [ABSTRACT FROM AUTHOR]

Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption. [ABSTRACT FROM AUTHOR]