Effects of allopurinol on PG-LPS-induced increases in NOX4 by Akinaka Morii (12709672)

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The top 10 putative metabolites with the largest negative fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

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The top 10 putative metabolites with the largest positive fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

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The principal component analysis (PCA) plot utilized in the quality assurance process. by Bhuripit Saraphol (20855573)

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Diversity of the fecal microbiome in normal and thin cows according to 16S rRNA gene sequencing data. by Bhuripit Saraphol (20855573)

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Schematic illustration of the proposed role of XO and NOX4 in the heart of PG-LPS-treated mice. by Akinaka Morii (12709672)

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The numbers of normal and thin cows from each farm. by Bhuripit Saraphol (20855573)

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Multivariate and clustering analysis of metabolomic data. by Bhuripit Saraphol (20855573)

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Cardiac function assessed by echocardiography with/without allopurinol. by Akinaka Morii (12709672)

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Results of the pathway analysis performed using the STITCH bioinformatics tool. by Bhuripit Saraphol (20855573)

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Raw metabolomic data. by Bhuripit Saraphol (20855573)

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Effects of allopurinol on chronic PG-LPS-induced oxidative stress in cardiac muscle. by Akinaka Morii (12709672)

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Effects of allopurinol on cardiac myocyte apoptosis induced by chronic PG-LPS infusion. by Akinaka Morii (12709672)

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Schematic illustration of experimental procedure and comparison by Akinaka Morii (12709672)

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Stacked bar plots illustrating the relative abundance distribution of fecal microbial communities in thin and normal cows. by Bhuripit Saraphol (20855573)

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Effects of allopurinol on PG-LPS-induced fibrosis in cardiac muscle. by Akinaka Morii (12709672)

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Effects of allopurinol on PG-LPS-induced phospho-CaMKII by Akinaka Morii (12709672)

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Schematic diagram of the experimental workflow. by Bhuripit Saraphol (20855573)

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Normality test using the Shapiro-Wilk test. by Akinaka Morii (12709672)

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Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”