The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Paul Linkowski - One of the best experts on this subject based on the ideXlab platform.
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EEG sleep patterns in twins.
Journal of Sleep Research, 1999Co-Authors: Paul LinkowskiAbstract:The investigation of sleep in twins represents one of the major methods for measuring the genetic contributions to sleep in humans. This paper reviews two twin studies in which the sleep EEG was recorded during three consecutive nights in young monozygotic (MZ) and dizygotic (DZ) male twins. The analyses, based on average values of repeated sleep recordings, indicate that a significant Proportion of Variance in stages 2, 4, and delta sleep appears to be genetically determined. Genetic influences on rapid-eye-movement sleep were found inconclusive, but this conclusion is limited by the relatively small size of the sample studied.
Heather A. Mckay - One of the best experts on this subject based on the ideXlab platform.
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Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction
Osteoporosis International, 2009Co-Authors: S. L. Manske, Teresa Liu-ambrose, D.m.l. Cooper, Saija A. Kontulainen, Pierre Guy, Bruce B. Forster, Heather A. MckayAbstract:We examined the contributions of femoral neck cortical and trabecular bone to proximal femur failure load. We found that trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for total bone size and cortical bone mineral content or cortical area. Introduction The relative contribution of femoral neck trabecular and cortical bone to proximal femur failure load is unclear. Objectives Our primary objective was to determine whether trabecular bone mineral density (TbBMD) contributes to proximal femur failure load after accounting for total bone size and cortical bone content. Our secondary objective was to describe regional differences in the relationship among cortical bone, trabecular bone, and failure load within a cross-section of the femoral neck. Materials and methods We imaged 36 human cadaveric proximal femora using quantitative computed tomography (QCT). We report total bone area (ToA), cortical area (CoA), cortical bone mineral content (CoBMC), and TbBMD measured in the femoral neck cross-section and eight 45° regions. The femora were loaded to failure. Results and observations Trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for ToA and then either CoBMC or CoA respectively. CoBMC contributed significantly to failure load in all regions of the femoral neck except the posterior region. TbBMD contributed significantly to failure load in all regions of the femoral neck except the inferoanterior, superoposterior, and the posterior regions. Conclusion Both cortical and trabecular bone make significant contributions to failure load in ex vivo measures of bone strength.
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Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteopor, 2008Co-Authors: S. L. Manske, Teresa Liu-ambrose, D.m.l. Cooper, Saija A. Kontulainen, Pierre Guy, Bruce B. Forster, Heather A. MckayAbstract:Summary We examined the contributions of femoral neck cortical and trabecular bone to proximal femur failure load. We found that trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for total bone size and cortical bone mineral content or cortical area.
S. L. Manske - One of the best experts on this subject based on the ideXlab platform.
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Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction
Osteoporosis International, 2009Co-Authors: S. L. Manske, Teresa Liu-ambrose, D.m.l. Cooper, Saija A. Kontulainen, Pierre Guy, Bruce B. Forster, Heather A. MckayAbstract:We examined the contributions of femoral neck cortical and trabecular bone to proximal femur failure load. We found that trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for total bone size and cortical bone mineral content or cortical area. Introduction The relative contribution of femoral neck trabecular and cortical bone to proximal femur failure load is unclear. Objectives Our primary objective was to determine whether trabecular bone mineral density (TbBMD) contributes to proximal femur failure load after accounting for total bone size and cortical bone content. Our secondary objective was to describe regional differences in the relationship among cortical bone, trabecular bone, and failure load within a cross-section of the femoral neck. Materials and methods We imaged 36 human cadaveric proximal femora using quantitative computed tomography (QCT). We report total bone area (ToA), cortical area (CoA), cortical bone mineral content (CoBMC), and TbBMD measured in the femoral neck cross-section and eight 45° regions. The femora were loaded to failure. Results and observations Trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for ToA and then either CoBMC or CoA respectively. CoBMC contributed significantly to failure load in all regions of the femoral neck except the posterior region. TbBMD contributed significantly to failure load in all regions of the femoral neck except the inferoanterior, superoposterior, and the posterior regions. Conclusion Both cortical and trabecular bone make significant contributions to failure load in ex vivo measures of bone strength.
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Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteopor, 2008Co-Authors: S. L. Manske, Teresa Liu-ambrose, D.m.l. Cooper, Saija A. Kontulainen, Pierre Guy, Bruce B. Forster, Heather A. MckayAbstract:Summary We examined the contributions of femoral neck cortical and trabecular bone to proximal femur failure load. We found that trabecular bone mineral density explained a significant Proportion of Variance in failure load after accounting for total bone size and cortical bone mineral content or cortical area.
Lahiru Handunnetthi - One of the best experts on this subject based on the ideXlab platform.
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Regulatory genomic regions active in immune cell types explain a large Proportion of the genetic risk of multiple sclerosis.
Journal of human genetics, 2014Co-Authors: Ramyiadarsini I. Elangovan, Giulio Disanto, Antonio J. Berlanga-taylor, Sreeram V. Ramagopalan, Lahiru HandunnetthiAbstract:There is little understanding of how genetic variants discovered in recent genome-wide association studies are involved in the pathogenesis of multiple sclerosis (MS). We aimed to investigate which chromatin states and cell types explain genetic risk in MS. We used genotype data from 1854 MS patients and 5164 controls produced by the International Multiple Sclerosis Genetics Consortium and Wellcome Trust Case Control Consortium. We estimated the Proportion of phenotypic Variance between cases and controls explained by cell-specific chromatin state and DNase I hypersensitivity sites (DHSs) using the Genome-wide Complex Trait Analysis software. A large Proportion of Variance was explained by single-nucleotide polymorphisms (SNPs) in strong enhancer (SE) elements of immortalized B lymphocytes (5.39%). Three independent SNPs located within SE showed suggestive evidence of association with MS: rs12928822 (odds ratio (OR)=0.81, 95% confidence interval (CI)=0.73-0.89, P=2.48E-05), rs727263 (OR=0.75, 95% CI=0.66-0.85, P=3.26E-06) and rs4674923 (OR=0.85, 95% CI=0.79-0.92, P=1.63E-05). Genetic variants located within DHSs of CD19+ B cells explained the greatest Proportion of Variance. Genetic variants influencing the risk of MS are located within regulatory elements active in immune cells. This study also identifies a number of immune cell types likely to be involved in the causal cascade and that carry important implications for future studies of therapeutic design.
M. Wolfová - One of the best experts on this subject based on the ideXlab platform.
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Effect of service sire on litter size traits in Czech Large White and Landrace pigs.
Czech Journal of Animal Science, 2018Co-Authors: J. Wolf, M. WolfováAbstract:The Proportion of Variance for service sire effect was estimated for three litter size traits (numbers of piglets born, born alive, and weaned) in Czech Large White (89 231 litters) and Czech Landrace (28 320 litters) pigs. Each trait in the first parity was considered as one trait and that trait in the second and subsequent parities was treated as a repeated trait. Consequently, three two-trait animal models were evalu- ated for each litter size trait: (i) the service sire effect was included and the complete relationship matrix for all the animals (service sires and sows) was taken into account; (ii) the service sire effect was included as a random effect without inclusion of the relationship matrix; (iii) the service sire effect was omitted from the model. Using the residual Variance as a criterion, both models including the service sire effect were slightly better than the model without this effect. Estimates of genetic parameters were very similar for the two models including the service sire effect. The Proportion of Variance for service sire was in the range from 2 to 3% (standard error approx. 0.2%) in Czech Large White and 2% (standard error approx. 0.3%) in Czech Landrace for all three litter size traits and all models. Models without service sire effect or models including service sire as a simple random effect and without inclusion of the genetic relationship matrix are recom- mended for genetic evaluation of litter size traits.
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Genetic parameters including the service sire effect for the sow traits stillbirth and piglet losses in Czech Large White and Landrace
Czech Journal of Animal Science, 2012Co-Authors: J. Wolf, M. WolfováAbstract: Genetic parameters including the Proportion of Variance for the service sire effect were estimated for number of piglets stillborn (including piglets died until 24 h after birth) and number of piglets died from 24 h after birth until weaning in Czech Large White (89 231 litters) and Czech Landrace (28 320 litters) pigs. Both traits were considered to be traits of the sow. Two two-trait animal models were evaluated for each breed including or excluding the service sire effect. Estimates of genetic parameters were very similar for the two models. The heritability for number of stillborn piglets was 0.06 in both breeds and both models and the heritability for number of piglets died until weaning was 0.07 in Czech Large White and 0.05 to 0.06 in Czech Landrace. The Proportion of Variance due to service sire was very low (between 0.8 and 1.6%). Therefore, there is no need to include the service sire effect in models for genetic evaluation. A selection against farrowing losses is recommended though only a slow improvement of the trait can be expected. Selecting against piglets died until weaning seems to be cumbersome. Probably a selection on number of piglets weaned could be helpful in minimizing piglet losses until weaning.
