Prevalence of myocardial hypertrophy in a population of asymptomatic Swedish Maine coon cats

<p>Abstract</p> <p>Background</p> <p>Maine coon cats have a familial disposition for developing hypertrophic cardiomyopathy (HCM) with evidence of an autosomal dominant mode of inheritance <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. The current mode to diagnose HCM is by use of echocardiography. However, definite reference criteria have not been established. The objective of the study was to determine the prevalence of echocardigraphic changes consistent with hypertrophic cardiomyopathy in Swedish Maine coon cats, and to compare echocardiographic measurements with previously published reference values.</p> <p>Methods</p> <p>All cats over the age of 8 months owned by breeders living in Stockholm, listed on the website of the Maine Coon breeders in Sweden by February 2001, were invited to participate in the study. Physical examination and M-mode and 2D echocardiographic examinations were performed in all cats.</p> <p>Results</p> <p>Examinations of 42 asymptomatic Maine coon cats (10 males and 32 females) were performed. The age of the cats ranged from 0,7 to 9,3 years with a mean of 4,8 ± 2,3 years. Four cats (9,5%) had a diastolic interventricular septal (IVSd) or left ventricular free wall (LVPWd) thickness exceeding 6,0 mm. In 3 of these cats the hypertrophy was segmental. Two cats (4,8%) had systolic anterior motion (SAM) of the mitral valve without concomitant hypertrophy. Five cats (11,9%) had IVSd or LVPWd exceeding 5,0 mm but less than 6,0 mm.</p> <p>Conclusion</p> <p>Depending on the reference values used, the prevalence of HCM in this study varied from 9,5% to 26,2%. Our study suggests that the left ventricular wall thickness of a normal cat is 5,0 mm or less, rather than 6,0 mm, previously used by most cardiologists. Appropriate echocardiographic reference values for Maine coon cats, and diagnostic criteria for HCM need to be further investigated.</p

Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.

As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems

Methods to Quantify Nanomaterial Association with, and Distribution across, the Blood-Brain Barrier in Vivo

The role and functional anatomy of the blood-brain barrier (BBB) is summarized to enable the investigator to appropriately address evaluation of nanomaterial interaction with, and distribution across, it into brain tissue (parenchyma). Transport mechanisms across the BBB are presented, in relation to nanomaterial physicochemical properties. Measures and test substances to assess BBB integrity/disruption/permeation are introduced, along with how they are used to interpret the results obtained with the presented methods. Experimental pitfalls and misinterpretation of results of studies of brain nanomaterial uptake are briefly summarized, that can be avoided with the methods presented in this chapter. Two methods are presented. The in situ brain perfusion technique is used to determine rate and extent of nanomaterial distribution into the brain. The capillary depletion method separates brain parenchymal tissue from the endothelial cells that contribute to the BBB. It is used to verify nanomaterial brain tissue entry. These methods are best used together, the latter refining the results obtained with the former. Details of the materials and equipment needed to conduct these methods, and description of the procedures and data interpretation, are provided

Plastidial Starch Phosphorylase in Sweet Potato Roots Is Proteolytically Modified by Protein-Protein Interaction with the 20S Proteasome

Post-translational regulation plays an important role in cellular metabolism. Earlier studies showed that the activity of plastidial starch phosphorylase (Pho1) may be regulated by proteolytic modification. During the purification of Pho1 from sweet potato roots, we observed an unknown high molecular weight complex (HX) showing Pho1 activity. The two-dimensional gel electrophoresis, mass spectrometry, and reverse immunoprecipitation analyses showed that HX is composed of Pho1 and the 20S proteasome. Incubating sweet potato roots at 45°C triggers a stepwise degradation of Pho1; however, the degradation process can be partially inhibited by specific proteasome inhibitor MG132. The proteolytically modified Pho1 displays a lower binding affinity toward glucose 1-phosphate and a reduced starch-synthesizing activity. This study suggests that the 20S proteasome interacts with Pho1 and is involved in the regulation of the catalytic activity of Pho1 in sweet potato roots under heat stress conditions

Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags

Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback (Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP–based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior