Biomedical subjects

R J Hines

Publications and source records attributed to R J Hines.

5 recordsLinked to original sources

PubMed2000

Increased power with modified forms of the Levene (Med) test for heterogeneity of variance.

While the conventional Levene (Med) test is a widely used and robust test for detecting heterogeneity of variance, it does not take notice of either the linear dependencies among the residuals involved or the possibility of a mean (or median)-variance relationship. This paper explores the substantial improvements in power possible by investigating the benefits both of removing such linear dependencies (structural zeros) and of modeling (even roughly) such relationships.

Analysis of Variance↗

PubMed1995

Detection of porcine reproductive and respiratory syndrome virus in boar semen by PCR.

Porcine reproductive and respiratory syndrome virus (PRRSV) causes a devastating disease in swine. The presence and transmission of PRRSV by boar semen has been demonstrated by using a swine bioassay. In this assay, 4- to 8-week-old pigs were inoculated intraperitoneally with semen from PRRSV-infected boars. Seroconversion of these piglets indicated the presence of PRRSV in semen. Seroconversion in gilts has also been demonstrated following artificial insemination with semen from PRRSV-infected boars. These methods of detecting PRRSV in boar semen are time-consuming, laborious, and expensive. The objective of this study was to develop a reliable and sensitive PCR assay to directly detect PRRSV in boar semen. Primers from open reading frames 1b and 7 of the PRRSV genome were used in nested PCRs. Virus was detected at concentrations as low as 10 infectious virions per ml in PRRSV-spiked semen. Specificity was confirmed by using a nested PCR and a 32P-labeled oligonucleotide probe. The primers did not react with related arteriviruses or other swine viruses. The PCR assay showed good correlation with the swine bioassay, and both methods were superior to virus isolation. To consistently identify PRRSV in boar semen, the cell fraction was separated by centrifugation at 600 x g for 20 min, a lysis buffer without a reducing agent (2-mercaptoethanol) was used, and nondiluted and 1:20-diluted cell fractions were evaluated by PCR. PRRSV was not reliably detected in the seminal plasma fraction of boar semen.

Animals↗

PubMed1995

Persistence of porcine reproductive and respiratory syndrome virus in serum and semen of adult boars.

Four seronegative adult boars were intranasally inoculated with porcine reproductive and respiratory syndrome virus (PRRSV) isolate VR-2332. Serum and semen were collected 2-3 times weekly for over 100 days postinoculation (DPI). Serum samples were assayed for PRRSV by virus isolation (VI) and a polymerase chain reaction (PCR) and screened for antibodies to PRRSV using the indirect fluorescent antibody (IFA) and virus neutralization (VN) tests. Semen was assayed for PRRSV RNA by PCR. Virus and viral RNA was detected in the serum of all boars within 1 DPI by Vi and/or PCR. However, VI results indicated that viremia was transient and occurred from 1 to 9 DPI. Viral RNA was detected in serum from 1 to 31 DPI. In the acute stage of the infection, PRRSV RNA was detected in serum by PCR prior to the presence of viral RNA in semen. The PRRSV RNA was detected in semen as early as 3 DPI and persisted for 25 DPI in 2 of the boars and 56 and 92 DPI in the remaining 2 boars. Detection of PRRSV RNA in semen occurred 2-8 and 28-35 days prior to the detection of antibodies by IFA and VN, respectively. PRRSV was isolated from the bulbourethral gland of the boar that shed viral RNA in semen for 92 DPI. These results suggest that PRRSV RNA can be detected by PCR in boar serum and semen, and may persist for variable periods of time. Viremia and the serologic status of the boar are not adequate indicators of when PRRSV or PRRSV RNA is being shed in the semen. Preliminary findings also indicated that neither shipping stress nor reinoculation with homologous PRRSV resulted in viremia or viral RNA shedding in semen.

Animals↗

PubMed1994

What makes folk tales unique: content familiarity, causal structure, scripts, or superstructures?

Requiring readers to re-order randomly ordered sentences into a coherent text significantly enhances recall relative to that in a read-only control condition for non-folk-tale texts but not for folk tales (Einstein, McDaniel, Owen, & Coté, 1990). Experiments 1-3 showed that embedding components of folk tales (e.g., causal structure, conventional scripts, content related to background knowledge) in non-folk-tale texts did not render sentence unscrambling ineffective for increasing recall. In Experiments 4a-4c, a folk tale was presented either as a fairy tale or as part of a newspaper article. Significant sentence unscrambling effects (in free recall) were not obtained in either presentation format, which implies that a story superstructure (a story grammar) does not contribute to the absence of the sentence unscrambling effect. It is suggested that understanding why the sentence unscrambling effect is absent for folk tales may require considering the functional role that narrative plays in socioculturally situated cognition.

Cognition↗

PubMed1993

Modelling overdispersion in toxicological mortality data grouped over time.

Toxicologists frequently conduct toxicity experiments in which different treatment conditions are applied to groups of animals and the resulting mortality in each group is measured at a number of discrete time points over the course of the experiment. Both survival analysis and generalized linear models have been proposed for analyzing this type of data. Whatever the approach taken, the model should allow for the presence of extra-multinomial variation arising from the use of groups of animals rather than individuals as the experimental units. We consider a number of models for overdispersion that can be incorporated into the generalized linear model framework for multinomial data. These models are extensions of ones proposed for binomial data by Williams (1982, Applied Statistics 31, 144-148) and Moore (1986, Biometrika 73, 583-588; 1987, Applied Statistics 36, 8-14). In addition, we examine robust asymptotic covariance matrix estimators for regression parameters, similar to those given in Liang and Zeger (1986, Biometrika 73, 13-22) and Zeger and Liang (1986, Biometrics 42, 121-130), and compare them to the model-based asymptotic estimators. Recommendations for analysis are given.

Analysis of Variance↗