A validated model of passive skeletal muscle to predict force and intramuscular pressure

The passive properties of skeletal muscle are often overlooked in muscle studies, yet they play a key role in tissue function in vivo. Studies analyzing and modeling muscle passive properties, while not uncommon, have never investigated the role of fluid content within the tissue. Additionally, intramuscular pressure (IMP) has been shown to correlate with muscle force in vivo and could be used to predict muscle force in the clinic. In this study, a novel model of skeletal muscle was developed and validated to predict both muscle stress and IMP under passive conditions for the New Zealand White Rabbit tibialis anterior. This model is the first to include fluid content within the tissue and uses whole muscle geometry. A nonlinear optimization scheme was highly effective at fitting model stress output to experimental stress data (normalized mean square error or NMSE fit value of 0.993) and validation showed very good agreement to experimental data (NMSE fit values of 0.955 and 0.860 for IMP and stress, respectively). While future work to include muscle activation would broaden the physiological application of this model, the passive implementation could be used to guide surgeries where passive muscle is stretched.     

Linkage disequilibrium studies in multiple endocrine neoplasia type 1 (MEN1)

Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterised by tumours of the parathyroids, pancreas and anterior pituitary. The MEN1 gene has been localised to a 2-Mb region of chromosome 11q13 by meiotic mapping studies in MEN1 families. Such studies may have a limited resolution of approximately 1 cM (i.e. 1 Mb) and we have therefore investigated 96 MEN1 families (40 British, 17 French, 12 Finnish, 7 Swedish, 7 Dutch, 7 North American, 2 Australian, 1 New Zealand, 1 German, 1 Spanish and 1 Danish) for linkage disequilibrium, in order to facilitate a finer mapping resolution. We have utilised five microsatellite DNA sequence polymorphisms from the candidate region and have accurately determined their allele sizes, which ranged from 161 bp to 272 bp. The heterozygosity and number of alleles (given in brackets), respectively, at the loci were: D11S1883 (76%, 11), D11S457 (55%, 5), PYGM (94%, 18), D11S1783 (10%, 4) and D11S449 (87%, 16). Allelic association was assessed by Chi-square 2 ×n contingency tables, by Fisher exact 2 ×n contingency tables and by a likelihood-based approach. The results of haplotype analysis revealed 91 different affected haplotypes in the 96 families, an identical affected haplotype being observed in no more than two families. These results indicate the absence of an ancestral affected haplotype. Significant linkage disequilibrium (P < 0.005) could be established amongst the microsatellite loci but not between the loci and MEN1 in either the total population or in any of the geographical sub-populations. The absence of linkage disequilibrium between MEN1 and the polymorphic loci is probably the result of the occurrence of multiple different disease-causing mutations in MEN1. Received: 1 April 1997 / Accepted: 25 June 1997     

How reliable are the Powell–Wetherall plot method and the maximum-length approach? Implications for length-based studies of growth and mortality

Length-based methods are the cornerstone of many population studies and stock assessments. This study tested two widely used methods: the Powell–Wetherall (P–W) plot and the L_max approach (i.e., estimating L_∞ directly from L_max). In most simulations, P–W estimates of the ratio total mortality/growth (Z/K ratio) were biased beyond acceptable limits (bias?>?30%). Bias in Z/K showed a complex behavior, without possible corrections. Estimates of asymptotic length (L_∞) were less biased than Z/K, but were very sensitive to intra-cohort variability in growth and to changes in the occurrence of large individuals in the sample. Exclusion of the largest size classes during the regression procedure or weighing by abundance does not solve these issues. Perfect linearization of the data and extremely narrow confidence intervals for Z/K will lead users to erroneous overconfidence in outputs. Clearly, the P–W method is not suitable for the assessment of Z/K ratios of natural populations. Estimation of L_∞ may be tentatively possible under very specific conditions, with necessary external verifications. Also, this study demonstrates that there is no way to estimate L_∞ directly from L_max, since there is no particular relationship to expect a priori between L_∞ and L_max. Errors in estimating L_∞ directly affect the estimate of the growth constant K and all other subsequent calculations in population studies, stock assessments and ecosystem models. New approaches are urgently needed for length-based studies of body growth (e.g., unconstrained curve fit with subsequent bootstrapping), that consider the inherent uncertainty regarding the underlying data and processes.     

Capturing pair-wise epistatic effects associated with three agronomic traits in barley

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.     

Exotic grass invasion impacts fitness of an endangered prairie butterfly, <Emphasis Type="Italic">Icaricia icarioides fenderi</Emphasis>

Fender’s blue butterfly is an endangered species restricted to fragmented, grassland remnants that are becoming increasingly dominated by tall, invasive grasses in western Oregon, USA. I performed a removal experiment to assess the impacts of structural degradation accompanying the invasion of Arrhenatherum elatius, tall oat grass, on butterfly fitness and fitness related behaviors. Clipping of A. elatius to native grass sward height resulted in 2.5–5 times as many eggs laid per leaf of host plant. Both male and female butterflies basked more frequently in areas removed of A. elatius inflorescences and upon encountering the treatment edge butterflies had a high rate of return into a large area removed of the grass inflorescences. Although butterfly behavior appeared to be affected by the change in sward height on the treatment edge, there was no evidence for the edge causing a disproportionate egg load. Invasion and dominance by A. elatius appeared to diminish host plant apparency which may result in overloading of eggs on conspicuous host plants, increased incidence of emigration, and a decrease in the likelihood of colonization because female butterflies appeared indifferent to larval resources beneath A. elatius inflorescences. Dominance of natural shortgrass prairies by tall stature grasses like A. elatius may be an insidious form of habitat degradation for grassland Lepidoptera worldwide, but it may go largely unnoticed because larval and adult resources can persist under the unnaturally tall grass canopy.     

Multi-model recursive identification for nonlinear systems with non-uniformly sampling

A recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.