Research and application of heart sound alignment and descriptor.

The research and application of heart sound (HS) analysis for cardiovascular disease (CVD) diagnosis has attracted more attention recently. Unlike other relevant HS analysis research, such as HS detection/component segmentation, HS feature extraction/classification etc., the proposed research treats HS as a whole and focuses mainly on comparing the similarity of acoustical characteristics reflecting pathological condition between two HSs, one of which is HS under test and another is the HS with known CVD. The concrete procedure refers to alignment of the HS into sequence and evaluating the similarity index through complexity and similarity analysis. In accordance with specific characteristics of HS, several relevant technologies such as musical instrument digital interface (MIDI), binary coding, N-gram, Lempel-Ziv (L-Z) complexity as well as super-symmetric comparison distance (SCD) similarity metric etc. are researched to be adapted and cascaded to realize the aforementioned target successfully. The contribution lies in that the aligning schemes including binary and N-gram are thoroughly investigated and then testing results witnessing the superiority of using N-gram in proposed approach are presented. The success of such a novel approach would not only assign a the new life to the traditional auscultation CVD diagnosis, but also simplify CVD diagnosis greatly leading to extensive application of such an efficient non-invasive physical diagnostic method in e-home healthcare.

Selection of bulls for progeny testing using pedigree indices and characteristics of potential bull-dams' herds.

A total of 209 bulls selected from herds in the northeastern US by Eastern AI Coop., Inc. from 1978 to 1981 were identified. The DHI data were obtained for the 145 herds from which these bulls were sampled. Also acquired were evaluations from both Modified Contemporary Comparison and animal model on these bulls and their ancestors and on cows and their sires in the bull-dam herds. From evaluation by animal model, animals appeared to have contributed information to each other effectively through relationship matrix, and thus the accuracy of cow evaluation has been improved. Bulls selected from herds of high genetic level were genetically superior to those from herds of low genetic level. However, there was no evidence that bulls from low intraherd milk variation herds were superior to those from high variation herds in the northeastern population, as was the case in Michigan herds. Parent indices were greater than bull PTA in herds of lower genetic level but less than bull PTA in herds of higher genetic level. The correlation between herd yield average and herd genetic level and that between herd yield average and intraherd yield SD were moderate but significantly different from zero. Other correlations between phenotypic and genetic measures of bull-dam herds were negligible. None of the herd characteristics showed promise in characterizing herds that were more successful in having their sampled bulls returned by AI organization after progency test.

Correlations among first and second lactation milk yield and calving interval.

Estimates of genetic correlations were .17 between first lactation milk yield and concurrent calving interval, .10 between second lactation milk yield and first calving interval, and .82 between first and second milk yields. Corresponding phenotypic correlations were .27, .16, and .58. Heritability estimates were .27 and .25 for first and second lactations and .15 for calving interval. Estimates were averages of two samples of 15 New York State herds averaging 144 AI-sired Holstein cows and 30 sires. Milk yields were 305-d, mature equivalent. Calving interval was days between first and second freshening. First milk records without a second freshening were included. Multiple-trait animal model included separate herd-year-season effects for first and second milk yields and calving interval. Numerator relationships among animals within herd, except for daughter-dam relationships, were included. The REML with the expectation-maximization algorithm was used to estimate (co)variance matrices among genetic values and environmental effects for the three traits. Results indicate a need to adjust milk records for the phenotypic effects of current and previous calving interval. The genetic association, however, between fertility and milk yield appears small. Genetic improvement of 450 kg of milk yield may result in 2 added d to first calving interval.

Estimates of genetic and environmental (co)variances for first lactation on milk yield, survival, and calving interval.

Variance and covariance components for milk yield, survival to second freshening, calving interval in first lactation were estimated by REML with the expectation and maximization algorithm for an animal model which included herd-year-season effects. Cows without calving interval but with milk yield were included. Each of the four data sets of 15 herds included about 3000 Holstein cows. Relationships across herds were ignored to enable inversion of the coefficient matrix of mixed model equations. Quadratics and their expectations were accumulated herd by herd. Heritability of milk yield (.32) agrees with reports by same methods. Heritabilities of survival (.11) and calving interval(.15) are slightly larger and genetic correlations smaller than results from different methods of estimation. Genetic correlation between milk yield and calving interval (.09) indicates genetic ability to produce more milk is lightly associated with decreased fertility.