Genetic parameters and predicted response to selection for lean growth in an Australian commercial nucleus pig herd

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.

Confounding physiologic parameters in pulse transit time monitoring of children

Pulse Transit Time (PTT) measurement has showed potential in non-invasive monitoring of changes in blood pressure. In children, the common peripheral sites used for these studies are a finger or toe. Presently, there are no known studies conducted to investigate any possible physiologic parameters affecting PTT measurement at these sites for children. In this study, PTT values of both peripheral sites were recorded from 64 children in their sitting posture. Their mean age with standard deviation (SD) was 8.2 2.6years (ranged 3 to 12years). Subjects' peripheries path length, heart rate (HR), systolic (SBP) and diastolic blood pressure (DBP) were measured to investigate any contributions to PTT measurement. The peripheral pulse timing characteristic measured by photoplethysmography (PPG) shows a 59.5 8.5ms (or 24.8 0.4%) difference between the two peripheries (p

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.