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Background/Objectives: Thus far, no studies have examined the relationship between fruit and vegetable (F and V) intake, urinary metabolite quantities, and weight change. Therefore, the aim of the current study was to explore changes in urinary metabolomic profiles during and after a 10-week weight loss intervention where participants were prescribed a high F and V diet (7 servings daily). Methods: Adults with overweight and obesity (n = 34) received medical nutrition therapy counselling to increase their F and V intakes to national targets (7 servings a day). Data collection included weight, dietary intake, and urine samples at baseline at week 2 and week 10. Urinary metabolite profiles were quantified using 1H NMR spectroscopy. Machine learning statistical approaches were employed to identify novel urine-based metabolite biomarkers associated with high F and V diet patterns at weeks 2 and 10. Metabolic changes appearing in urine in response to diet were quantified using Metabolite Set Enrichment Analysis (MSEA). Results: Energy intake was significantly lower (p = 0.02) at week 10 compared with baseline. Total F and V intake was significantly higher at week 2 and week 10 (p < 0.05). In total, 123 urinary metabolites were quantified. At week 10, 21 metabolites showed significant changes relative to baseline. Of these, 11 metabolites also significantly changed at week 2. These overlapping metabolites were acetic acid, dimethylamine, choline, fumaric acid, glutamic acid, L-tyrosine, histidine, succinic acid, uracil, histamine, and 2-hydroxyglutarate. Ridge Classifier and Linear Discriminant Analysis provided best prediction accuracy values of 0.96 when metabolite level of baseline was compared to week 10. Conclusions: Urinary metabolites quantified represent potential candidate biomarkers of high F and V intake, associated with a reduction in energy intake. Further studies are needed to validate these findings in larger population studies. [ABSTRACT FROM AUTHOR]

Computational progressive failure analysis (PFA) is vital for the analysis of carbon fiber reinforced polymer (CFRP) composites. The damage initiation criterion is one of the essential components of a PFA code to determine the transition of a material's state from pristine or microscopically damaged to macroscopically damaged. In this paper, data‐driven models are developed to determine the matrix damage initiation with the objective to save computation time. For 2D plane stress states, the computational cost for determining damage initiation can be dramatically reduced by implementing a binary search (BS) algorithm and predictive machine learning models. Machine learning models are evaluated against a regression problem of predicting damage crack angle as well as against a classification problem for predicting damage initiation outright. It is found that regression models perform much better for plane stress and 3D stress states when generating failure envelopes. In both cases, a neural network is able to produce a failure envelope that is over 99% accurate while reducing computational cost by over 90%. [ABSTRACT FROM AUTHOR]

Objective: To identify maternal second and third trimester urine metabolomic biomarkers for the detection of fetal congenital heart defects (CHDs). Study design: This was a prospective study. Metabolomic analysis of randomly collected maternal urine was performed, comparing pregnancies with isolated, non-syndromic CHDs versus unaffected controls. Mass spectrometry (liquid chromatography and direct injection and tandem mass spectrometry, LC–MS–MS) as well as nuclear magnetic resonance spectrometry, 1H NMR, were used to perform the analyses between 14 0/7 and 37 0/7 weeks gestation. A total of 36 CHD cases and 41 controls were compared. Predictive algorithms using urine markers alone or combined with, clinical and ultrasound (US) (four-chamber view) predictors were developed and compared. Results: A total of 222 metabolites were identified, of which 16 were overlapping between the two platforms. Twenty-three metabolite concentrations were found in significantly altered in CHD gestations on univariate analysis. The concentration of methionine was most significantly altered. A predictive algorithm combining metabolites (histamine, choline, glucose, formate, methionine, and carnitine) plus US four-chamber view achieved an AUC = 0.894; 95% CI, 0814–0.973 with a sensitivity of 83.8% and specificity of 87.8%. Enrichment pathway analysis identified several lipid related pathways that are dysregulated in CHD, including phospholipid biosynthesis, phosphatidylcholine biosynthesis, phosphatidylethanolamine biosynthesis, and fatty acid metabolism. This could be consistent with the increased risk of CHD in diabetic pregnancies. Conclusions: We report a novel, noninvasive approach, based on the analysis of maternal urine for isolated CHD detection. Further, the dysregulation of lipid- and folate metabolism appears to support prior data on the mechanism of CHD. [ABSTRACT FROM AUTHOR]