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.     

Glutamate 85 and glutamate 228 contribute to the pH-response of the soluble form of chloride intracellular channel 1

The chloride intracellular channel protein, CLIC1, is synthesised as a soluble monomer that can reversibly bind membranes. Soluble CLIC1 is proposed to respond to the low pH found at a membrane surface by partially unfolding and restructuring into a membrane-competent conformation. This transition is proposed to be controlled by strategically located “pH-sensor” residues that become protonated at acidic pH. In this study, we investigate the role of two conserved glutamate residues, Glu85 in the N-domain and Glu228 in the C-domain, as pH-sensors. E85L and E228L CLIC1 variants were created to reduce pH sensitivity by permanently breaking the bonds these residues form. The structure and stability of each variant was compared to the wild type at both pH 7.0 and pH 5.5. Neither substitution significantly altered the structure but both decreased the conformational stability. Furthermore, E85L CLIC1 formed a urea-induced unfolding intermediate state at both pH 7 and pH 5.5 compared to wild-type and E228L CLIC1 which only formed the intermediate at pH 5.5. We conclude that Glu85 and Glu228 are two of the five pH-sensor residues of CLIC1 and contribute to the pH-response in different ways. Glu228 lowers the stability of the native state at pH 5.5, while Glu85 contributes both to the stability of the native state and to the formation of the intermediate state. By putting these interactions into the context of the three previously described CLIC1 pH-sensor residues, we propose a mechanism for the conversion of CLIC1 from the soluble state to the pre-membrane form.     

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.     

The utility of wing morphometrics for assigning type specimens to cryptic bumblebee species

Since the beginning of taxonomy, species have been described based on morphology, but the advent of using semio-chemicals and genetics has led to the discovery of cryptic species (i.e. morphologically similar species). When a new cryptic species is described, earlier type specimens have to be re-evaluated, although this process can be challenging as only nondestructive methods ought to be used in order to preserve the integrity of the type specimens. Methods should allow comparison with recently collected specimens clustered based on chemical, ethological and/or genetic traits with old specimens (i.e. type specimens) where only morphological traits are available. Here we develop a method based on geometric morphometric analyses of wing shape for a taxonomically challenging group of bumblebees, the subgenus Alpinobombus Skorikov. We consider nine monophyletic taxa (including several cryptic species) to assess the accuracy of this method to discriminate the taxa based on their wing shape and then to attribute type specimens using a leave-one-out cross-validation procedure. We show that, for these bees, wing shape is taxon-specific, except for two sister taxa for which the species status is still debated. Moreover, for most of the taxa, type specimens were correctly attributed with high posterior probabilities of attribution, except for a few type specimens corresponding to the same two sister taxa where taxa delimitation based on wing shape was previously the subject of discussion. Our study highlights the potential of geometric morphometric analyses to help in the re-attribution of type specimens when the existence of cryptic species is revealed.     

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.