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An in situ study was conducted on four rumen-cannulated wethers to determine (using 15N infusion techniques) the microbial contamination (mg bacterial DM or crude protein (CP)/100mg DM or CP) and the associated error on the effective degradability of fourteen feeds: barley and maize grains, soyabean and sunflower meals, full-fat soyabean, maize gluten feed, soyabean hulls, brewers dried grains, sugarbeet pulp, wheat bran, lucerne and vetch-oat hays, and barley and lentil straws. The DM or CP contamination in residues (M) fitted to single exponential or sigmoid curves. A general model (M=m (1???e???ft)j) was proposed to match this fit. Asymptotic values (m) varied from 2??84% to 13??3% and from 2??85% to 80??9% for DM and CP, respectively. Uncorrected results underestimated the effective degradability of both DM (P<0??05) and CP (P<0??01). For CP, this underestimation varied from 0??59% to 13??1%, with a higher but unascertainable error for barley straw. Excluding maize grain, the microbial contamination of both DM and CP, and the associated underestimation of the effective degradability of CP, were positively related to the cellulose content of the feed. The error in the effective degradability of CP was also negatively related to the CP content and its apparent effective degradability (R2 0??867). This equation allows easier and more accurate estimates of effective degradability, needed to improve protein-rationing systems. [ABSTRACT FROM PUBLISHER]

The present study was conducted to determine the effect of feed intake on the composition of the rumen contents of sheep and on their bacterial densities. Whole rumen contents were sampled after a period of continuous inter-rumen infusion of 15NH3 from four rumen-cannulated wethers successively fed on a hay???concentrate diet (2:1, / on a DM basis) at two rates of feed intake: 40 and 80 g D/g body weight0??75. Total weight and chemical composition of rumen contents, as well as the distribution by size and chemical composition of particles, were determined. The populations of bacteria associated with the liquid (liquid-associated bacteria, LAB) and solid (solid-associated bacteria, SAB) fractions of rumen digesta and the distribution of SAB according to feed particle size were also examined. The greater feed intake caused an increase in the mass of the rumen contents, while its chemical composition did not change, except for a higher content of organic matter (P=0??023). The distribution of feed particles by size was similar at both levels of intake. The concentrations of neutral- and acid-detergent fibre in feed particles decreased and those of total, dietary, and microbial N increased, both with a quadratic response (P=0??001), as particle size decreased. The proportion of LAB in the microbial biomass of rumen digesta reached only 8??0 %. This proportion and the density of LAB were unaffected by the level of feed intake, whereas an apparent reduction (10??4 %) occurred with the SAB biomass in whole rumen contents. A systematic, but not significant, reduction (mean value 11??9 %) in the level of microbial colonisation in the different particle fractions with the increase of feed intake was also observed. [ABSTRACT FROM PUBLISHER]

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice.

Objective: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers., Setting and Sample Population: Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled., Materials and Methods: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training., Results: Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane)., Conclusions: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)