A mathematical function for the description of nutrient-response curve

Immunoregulatory and antioxidant properties of tryptophan in quail chick

A dose-response assay was carried out to investigate the effects of graded levels of dietary tryptophan (Trp) on blood variables, immunity, and meat quality in quail chicks during the last two weeks of the growing period. A total of 420 21-day-old quail chicks were randomly distributed across the seven experimental groups (i.e., 2.12, 2.25, 2.38, 2.51, 2.64, 2.77, and 2.90 g Trp/kg of diet) with four pen replicates of 15 birds each. Blood variables, including uric acid (UA), albumin (ALB), and triglycerides (TG), responded inversely to increasing dietary Trp (P < 0.001). The concentration of aspartate aminotransferase (AST) in serum, the relative weight of bursa of Fabricius (BF), immunoglobulin G (IgG), water holding capacity (WHC), and antigen production against the sheep red blood cells (SRBC) increased with increasing dietary Trp (P < 0.001). In contrast, the concentration of malondialdehyde (MDA) and drip loss in meat samples decreased with increasing dietary Trp (P < 0.001). The best models for optimal dietary Trp were identified based on a statistical merit basis known as the model accuracy index (δ). The estimated dietary Trp for optimizing ALP, UA, total protein (TP), TG, SRBC, IgG, BF, drip loss, WHC, and MDA were obtained at 2.347, 2.371, 2,372, 2.485, 2,691, 2.738, 2.306, 2.359, 2.247, and 2.500 g/kg of diet, respectively. Principal component analysis showed that UA, TG, IgG, and drip loss had a higher association with dietary Trp rather than other responses. Considering the high δ and eigenvalues of the models, the best estimation of dietary Trp level required for the optimization of the studied traits in quail chicks would be 2.738 g Trp/kg of diet, which was significantly higher than that recommended for the quail performance by NRC (1994).

Assessing the nutritional equivalency of DL-methionine and L-methionine in broiler chickens: a meta-analytical study

The purpose of this study was to perform a meta-analysis comparing the biological efficiency of DL-methionine and L-methionine (DL-Met and L-Met) in broiler nutrition. This analysis was based on a dataset comprising experiment results published in peer-reviewed papers since 2007. All experiments investigated the response of broilers (aged 0-21 d) to different dietary concentrations of DL- and L-Met, achieved by supplementing crystalline Met. A graphical analysis was conducted on the database using ellipses coverage and nonparametric density techniques. Two methods of linear and nonlinear exponential meta-regression analysis were used to determine relative bioavailability based on average daily gain (ADG) and feed conversion ratio (FCR) responses to dietary Met sources. The analyses of ADG and FCR obtained from both linear and exponential models showed a relative difference between the tested methionine sources. The results showed that both ADG and FCR were improved in L-Met compared to DL-Met supplemented diets. Linear regression analysis revealed that DL-Met was 94.97% (for ADG) and 95.63% (for FCR) as efficacious as L-Met (P < 0.01). In contrast, the analysis of the fitted nonlinear exponential model showed that the biological efficiencies of DL-Met were 91.33% and 76.57% of the values for L-Met for ADG (P = 0.01) and FCR (P = 0.09), respectively. Based on the meta-analytical results, an equivalence of relative biological efficiency of DL-Met in comparison with L-Met for young broilers could not be confirmed.

A Gaussian process regression model to predict energy contents of corn for poultry

The present study proposes a Gaussian process regression (GPR) approach to develop a model to predict true metabolizable energy corrected for nitrogen (TMEn) content of corn samples (as model output) for poultry given levels of feed chemical compositions of crude protein, ether extract, crude fiber, and ash (as model inputs). A 30 corn samples obtained from 5 origins [Brazil (n = 9), China (n = 5), Iran (n = 7), and Ukraine (n = 9)] were assayed to determine chemical composition and TMEn content using chemical analyses and bioassay technique. In addition to GPR model, data were also analyzed by multiple linear regression (MLR) model. Results revealed that corn samples of different origins differ in their gross energy and chemical composition of crude protein, crude fiber, and ash, but no differences were observed for their ether extract and TMEn contents. Based on model evaluation criteria of R² and root mean square error (RMSE), the GPR model showed satisfactory performance (R² = 0.92 and RMSE = 33.68 kcal/kg DM) in predicting TMEn and produced relatively better prediction values than those produce by MLR (R² = 0.23 and RMSE = 104.85 kcal/kg DM). The GPR model may be capable of improving our aptitude and capacity to precisely predict energy contents of feed ingredients to formulate optimal diets for poultry.

Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm

Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF₁₇ was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF₁₇, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89-0.95) than regression models (0.56-0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel^® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.