Elastic behaviour of polyvinyl chloride melts

A study has been made of the elastic behaviour of Polyvinylchloride commercial samples at high shear stresses, under similar conditions to those used in the processing industry, by means of an extrusion capillary rheometer.The influence in the die swell of variables as molecular weight, temperature and shear rate has been studied. Results are in agreement with general literature data.Smooth extrudates are obtained only in the zone of intermediate shear rates. In this zone it is observed that the die swell increases with shear rate.     

A formulation to model the nonlinear viscoelastic properties of the vascular tissue

Nearly all soft tissues, including the vascular tissue, present a certain degree of viscoelastic material response, which becomes apparent performing multiple relaxation tests over a wide range of strain levels and plotting the resulting stress relaxation curves, nonlinear viscoelasticity of the tissue. Changes in relaxation rate at each strain may occur at multiple strain levels. A constitutive formulation considering the particular features of the vascular tissue, such as anisotropy, together with these nonlinear viscoelastic phenomena is here presented and used to fit stress?Cstretch curves from experimental relaxation tests. This constitutive model was used to fit several data set of in vitro experimental stress relaxation tests performed on ovine and porcine aorta. The good fitting of the experimental data shows the capability of the model to reproduce the viscoelastic response of the vascular tissue.     

Modeling microwave power structures based on k-furcated waveguides arbitrarily filled with materials by modal techniques.

Waveguide structures are very popular in the microwave power industry due to their power handling capabilities. Modal expansion of the waveguide fields and application of the circuit theory allow for the division of a complex device into several simpler sections which can be analyzed separately with the best suited method. The modal techniques can be divided into two groups--those which analyze junctions or discontinuities and those which examine propagation characteristics. In this paper, a review of modal techniques for high power applications is given. Modal expansion of the fields in the waveguides is then performed and applied to modeling of k-furcated waveguides. The modal analysis based on the Coupled Mode Method is described for the waveguides partially filled with isotropic materials. A hybrid modal analysis coupled with Finite Element Method suitable for more complex waveguide structures is also described. Computational results obtained for some real-life microwave devices are presented. Excellent agreement was found when comparing the results with those generated with a commercial FDTD simulator demonstrates the validity and reliability of the proposed method.     

Genomic merit for reproductive traits. I: Estrous characteristics and fertility in Holstein heifers

Genetic selection of dairy cattle in the United States has included reproductive traits (daughter pregnancy rate, DPR; heifer conception rate, HCR), which is believed to have partly contributed to halting the decline in reproductive performance. The objectives of the current study were to evaluate the association among genomic merit for DPR (GDPR) and HCR (GHCR) with estrous characteristics measured by an automated device. Holstein heifers (n = 1,005) were genotyped at 2 mo of age and were classified into quartiles (Q1 = lowest, Q4 = highest) according to the GDPR and GHCR values of the study population. At 10 to 11 mo of age, heifers were fitted with a collar that recorded activity and rumination and determined the occurrence of estrus according to changes in activity and rumination compared with the individual's baseline values. Estrous characteristics of spontaneous estruses (SPE) and PGF_2α-synchronized estruses (PGSE) were recorded. Heifers had their estrous cycle synchronized with PGF_2α and following detection of estrus received either artificial insemination or embryo transfer according to the herd's genetic selection program. Heifers in Q2 (17.7 ± 0.3 h) of GHCR tended to have longer SPE than heifers in Q4 (16.7 ± 0.3 h). The interaction between GDPR and GHCR was associated with the likelihood of activity peak (0 = no estrus, 100 = maximum activity) ≥80 at SPE because, among heifers in Q3 and Q4 of GHCR, those in Q1 of GDPR were less likely to have an activity peak ≥80. Heifers in Q1 and Q2 of GDPR had reduced hazard of estrus within 7 d of the first PGF_2α treatment compared with heifers in Q4 of GDPR. Heifers in Q1 (16.1 ± 0.4 h) of GDPR had shorter PGSE than heifers in Q2 (17.6 ± 0.4 h) and Q4 (17.4 ± 0.4 h) and tended to have shorter PGSE than heifers in Q3 (17.4 ± 0.4 h). Rumination nadir on the day of PGSE was greater for heifers in Q1 (?30.1 ± 0.9 min/d) of GDPR compared with heifers in Q4 (?33.7 ± 0.9 min/d). Among heifers receiving only artificial insemination, those in Q1 of GHCR (adjusted hazard ratio = 0.65; 95% confidence interval = 0.48–0.88) became pregnant at a slower rate than heifers in Q4. Genomic merit for HCR was negatively associated with SPE but tended to be positively associated with hazard of pregnancy, whereas GDPR was positively associated with PGSE and hazard of estrus. Selection of dairy cattle for DPR and HCR may improve reproductive performance through different pathways, namely estrous characteristics and pregnancy establishment.     

Carcass characteristics, fatty acid composition, and meat quality of Criollo Argentino and Braford steers raised on forage in a semi-tropical region of Argentina

The purpose of this study was to characterize and compare the carcass characteristics, cholesterol concentration, fatty acid composition of intramuscular fat and subcutaneous fat, and meat quality of Criollo Argentino and Braford steers reared in an extensive system, without supplementation, and slaughtered at approximately 400 kg live weight. The Braford steers had greater (P < 0.05) carcass weight, yield, conformation score, marbling degree, fat thickness and fatness score than Criollo Argentino steers. The tissue composition of the 10th rib was: 68.1% vs. 63.6% muscle, 23.9% vs. 20.4% bone and 8.2% vs. 16.3% fat for the Criollo Argentino and Braford breeds, respectively. The meat of Longissimus muscle from Braford steers was lighter, redder, yellower and more tender than that from Criollo Argentino steers. The meat of Longissimus muscle from Braford steers had a higher fat content, similar protein and ash contents and a lower (P ? 0.001) cholesterol concentration than that from Criollo Argentino steers.     

Effect of moisture on salmonella spp. heat resistance in cocoa and hazelnut shells

The heat resistance of food-poisoning outbreak and non-outbreak associated strains of Salmonella (S. Enteritidis, S. Montevideo, S. Napoli, S. Oranienburg, S. Poona, S. Senftenberg and S. Typhimurium) was established in confectionery-related materials such as crushed cocoa bean and hazelnut shells at low moisture contents (≤ 4% w/w). The two most heat resistant strains in cocoa and hazelnut shells at ca. 4% w/w moisture were S. Oranienburg and S. Enteritidis PT30. Both strains were associated with outbreaks from dried materials. Their D_100°C values were ca. 2.5 min in crushed cocoa bean shells and 7–11 min in crushed hazelnut shells. Addition of moisture to ca. 7% w/w markedly reduced D-values (D_80°C of 2–4.5 min) for both strains in the two matrices.     

Predicting bull fertility using genomic data and biological information

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Effects of weight at slaughter and sex on the carcass characteristics of Florida suckling kids

The effect of slaughter weight and sex on some carcass traits of suckling kids of the Florida breed was evaluated. A total of 60 kids (30 male and 30 female), fed exclusively on milk replacers, were slaughtered at 7-8kg (group 1), 10-11kg (group 2) or 14-15kg (group 3) of liveweight (mean weights of 7.6kg, 10.8kg and 14.4kg, respectively). Higher slaughter weights decreased the percentage of subproducts (blood, skin, head, feet) and internal organs (lungs+traquea, heart, liver, spleen, thymus) but significantly increased the percentage of intestine and fat depots (omental fat and mesenteric fat). Higher slaughter weights also increased carcass measures (L 40.5 vs 49.1; F 22.5 vs 25.9; G 10.4 vs 14.2; Wr 10.1 vs 13.9; Wth 8.0 vs 10.5; Th 16.5 vs 199; B 32.3 vs 42.4; PT 41.5 vs 50.8), compactness carcass index (96.6 vs 152.3) and compactness leg index (27.5 vs 44.1). Sex only significantly affected the percentages of feet, internal organs, omental fat, measure L, carcass compactness index and hind limb compactness index. The meat colour and fat colour were mainly scored as pale and white respectively in the carcasses of the lightest animals, whereas heavier kids were scored as pink and cream. Slaughter weight also influenced significantly the carcass fatness (score 1 in lightest kids and 2 or 3 in heavier ones). There were no significant (p>0.05) differences between slaughter weight group and sex in dressing percentages. Percentages corresponding to the long leg, back and neck (30-33%, 18-19% and 8-10%, respectively) decreased when the slaughter weight increased, whereas the ribs (23-25%) and the flank (10-11%) increased slightly. The carcasses comprised 57-58% muscle, 22-25% bone, 5-6% subcutaneous fat and 9-12% intermuscular fat. The percentage muscle stayed the same with increasing slaughter weight, whereas the bone decreased and the fat increased. The carcasses of the heavier females contained less lean and more fat than the males. The bone percentage was significantly (p<0.05) lower in the females and the carcass fat percentage was significantly (p<0.05) higher than in the males.