Prediction of apparent protein digestibility of ingredients and diets by in vitro pH-stat degree of protein hydrolysis with species-specific enzymes for juvenile Pacific white shrimp Litopenaeus vannamei

Aquafeed production faces global issues related to availability of feed ingredients. Feed manufacturers require greater flexibility in order to develop nutritional and cost-effective formulations that take into account nutrient content and availability of ingredients. The search for appropriate ingredients requires detailed screening of their potential nutritional value and variability at the industrial level. In vitro digestion of feedstuffs by enzymes extracted from the target species has been correlated with apparent protein digestibility (APD) in fish and shrimp species. The present study verified the relationship between APD and in vitro degree of protein hydrolysis (DH) with Litopenaeus vannamei hepatopancreas enzymes in several different ingredients (n = 26): blood meals, casein, corn gluten meal, crab meal, distiller`s dried grains with solubles, feather meal, fish meals, gelatin, krill meals, poultry by-product meal, soybean meals, squid meals and wheat gluten. The relationship between APD and DH was further verified in diets formulated with these ingredients at 30% inclusion into a reference diet. APD was determined in vivo (30.1 +/- 0.5 degrees C, 32.2 +/- 0.4%.) with juvenile L vannamei (9 to 12 g) after placement of test ingredients into a reference diet (35 g kg(-1) CP: 8.03 g kg(-1) lipid; 2.01 kcal g(-1)) with chromic oxide as the inert marker. In vitro DH was assessed in ingredients and diets with standardized hepatopancreas enzymes extracted from pond-reared shrimp. The DH of ingredients was determined under different assay conditions to check for the most suitable in vitro protocol for APD prediction: different batches of enzyme extracts (HPf5 or HPf6), temperatures (25 or 30 degrees C) and enzyme activity (azocasein): crude protein ratios (4 U: 80 mg CP or 4 U: 40 mg CP). DH was not affected by ingredient proximate composition. APD was significantly correlated to DH in regressions considering either ingredients or diets. The relationships between APD and DH of the ingredients could be suitably adjusted to a Rational Function (y = (a + bx)/(1 + cx + dx2), n = 26. Best in vitro APD predictions were obtained at 25 degrees C, 4 U: 80 mg CP both for ingredients (R(2) = 0.86: P = 0.001) and test diets (R(2) = 0.96; P = 0.007). The regression model including all 26 ingredients generated higher prediction residuals (i.e., predicted APD - determined APD) for corn gluten meal, feather meal. poultry by-product meal and krill flour. The remaining test ingredients presented mean prediction residuals of 3.5 points. A model including only ingredients with APD>80% showed higher prediction precision (R(2) = 0.98: P = 0.000004; n = 20) with average residual of 1.8 points. Predictive models including only ingredients from the same origin (e.g., marine-based, R(2) = 0.98; P = 0.033) also displayed low residuals. Since in vitro techniques have been usually validated through regressions against in vivo APD, the DH predictive capacity may depend on the consistency of the in vivo methodology. Regressions between APD and DH suggested a close relationship between peptide bond breakage by hepatopancreas digestive proteases and the apparent nitrogen assimilation in shrimp, and this may be a useful tool to provide rapid nutritional information. (C) 2009 Elsevier B.V. All rights reserved.

Subcritical crack growth and in vitro lifetime prediction of resin composites with different filler distributions

Objectives. Verify the influence of different filler distributions on the subcritical crack growth (SCG) susceptibility, Weibull parameters (m and sigma(0)) and longevity estimated by the strength-probability-time (SPT) diagram of experimental resin composites. Methods. Four composites were prepared, each one containing 59 vol% of glass powder with different filler sizes (d(50) = 0.5; 0.9; 1.2 and 1.9 mu m) and distributions. Granulometric analyses of glass powders were done by a laser diffraction particle size analyzer (Sald-7001, Shimadzu, USA). SCG parameters (n and sigma(f0)) were determined by dynamic fatigue (10(-2) to 10(2) MPa/s) using a biaxial flexural device (12 x 1.2 mm; n = 10). Twenty extra specimens of each composite were tested at 10(0) MPa/s to determine m and sigma(0). Specimens were stored in water at 37 degrees C for 24 h. Fracture surfaces were analyzed under SEM. Results. In general, the composites with broader filler distribution (C0.5 and C1.9) presented better results in terms of SCG susceptibility and longevity. C0.5 and C1.9 presented higher n values (respectively, 31.2 +/- 6.2(a) and 34.7 +/- 7.4(a)). C1.2 (166.42 +/- 0.01(a)) showed the highest and C0.5 (158.40 +/- 0.02(d)) the lowest sigma(f0) value (in MPa). Weibull parameters did not vary significantly (m: 6.6 to 10.6 and sigma(0): 170.6 to 176.4 MPa). Predicted reductions in failure stress (P-f = 5%) for a lifetime of 10 years were approximately 45% for C0.5 and C1.9 and 65% for C0.9 and C1.2. Crack propagation occurred through the polymeric matrix around the fillers and all the fracture surfaces showed brittle fracture features. Significance. Composites with broader granulometric distribution showed higher resistance to SCG and, consequently, higher longevity in vitro. (C) 2012 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors. (C) 2011 Elsevier By. All rights reserved.

The use of body circumferences for the prediction of intra-abdominal fat in obese women with polycystic ovary syndrome

Introduction: Computerizd tomography (CT) is the gold standard for the evaluation of intra- (IAF) and total (TAF) abdominal fat; however, the high cost of the procedure and exposure to radiation limit its routine use. Objective: To develop equations that utilize anthropometric measures for the estimate of IAF and TAF in obese women with polycystic ovary syndrome (PCOS). Methods: The weight, height, BMI, and abdominal (AC), waist (WC), chest (CC), and neck (NC) circumferences of thirty obese women with PCOS were measured, and their IAF and TAF were analyzed by CT. Results: The anthropometric variables AC, CC, and NC were chosen for the TAF linear regression model because they were better correlated with the fat deposited in this region. The model proposed for TAF (predicted) was: 4.63725 + 0.01483 x AC - 0.00117 x NC - 0.00177 x CC (R-2 = 0.78); and the model proposed for IAF was: IAF (predicted) = 1.88541 + 0.01878 x WC + 0.05687 x NC - 0.01529 x CC (R-2 = 0.51). AC was the only independent predictor of TAF (p < 0.01). Conclusion: The equations proposed showed good correlation with the real value measured by CT, and can be used in clinical practice. (Nutr Hosp. 2012;27:1662-1666) DOI:10.3305/nh.2012.27.5.5933