Mapping carcass and meat quality QTL on Sus Scrofa chromosome 2 in commercial finishing pigs

Quantitative trait loci (QTL) affecting carcass and meat quality located on SSC2 were identified using variance component methods. A large number of traits involved in meat and carcass quality was detected in a commercial crossbred population: 1855 pigs sired by 17 boars from a synthetic line, which where homozygous (A/A) for IGF2. Using combined linkage and linkage disequilibrium mapping (LDLA), several QTL significantly affecting loin muscle mass, ham weight and ham muscles (outer ham and knuckle ham) and meat quality traits, such as Minolta-L* and -b*, ultimate pH and Japanese colour score were detected. These results agreed well with previous QTL-studies involving SSC2. Since our study is carried out on crossbreds, different QTL may be segregating in the parental lines. To address this question, we compared models with a single QTL-variance component with models allowing for separate sire and dam QTL-variance components. The same QTL were identified using a single QTL variance component model compared to a model allowing for separate variances with minor differences with respect to QTL location. However, the variance component method made it possible to detect QTL segregating in the paternal line (e.g. HAMB), the maternal lines (e.g. Ham) or in both (e.g. pHu). Combining association and linkage information among haplotypes improved slightly the significance of the QTL compared to an analysis using linkage information only

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Knotty-Centrality: Finding the Connective Core of a Complex Network

A network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph’s nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed

Comprehensive Gene-Expression Survey Identifies Wif1 as a Modulator of Cardiomyocyte Differentiation

During chicken cardiac development the proepicardium (PE) forms the epicardium (Epi), which contributes to several non-myocardial lineages within the heart. In contrast to Epi-explant cultures, PE explants can differentiate into a cardiomyocyte phenotype. By temporal microarray expression profiles of PE-explant cultures and maturing Epi cells, we identified genes specifically associated with differentiation towards either of these lineages and genes that are associated with the Epi-lineage restriction. We found a central role for Wnt signaling in the determination of the different cell lineages. Immunofluorescent staining after recombinant-protein incubation in PE-explant cultures indicated that the early upregulated Wnt inhibitory factor-1 (Wif1), stimulates cardiomyocyte differentiation in a similar manner as Wnt stimulation. Concordingly, in the mouse pluripotent embryogenic carcinoma cell line p19cl6, early and late Wif1 exposure enhances and attenuates differentiation, respectively. In ovo exposure of the HH12 chicken embryonic heart to Wif1 increases the Tbx18-positive cardiac progenitor pool. These data indicate that Wif1 enhances cardiomyogenesis

Herbivore-Specific, Density-Dependent Induction of Plant Volatiles: Honest or “Cry Wolf” Signals?

Plants release volatile chemicals upon attack by herbivorous arthropods. They do so commonly in a dose-dependent manner: the more herbivores, the more volatiles released. The volatiles attract predatory arthropods and the amount determines the probability of predator response. We show that seedlings of a cabbage variety (Brassica oleracea var. capitata, cv Shikidori) also show such a response to the density of cabbage white (Pieris rapae) larvae and attract more (naive) parasitoids (Cotesia glomerata) when there are more herbivores on the plant. However, when attacked by diamondback moth (Plutella xylostella) larvae, seedlings of the same variety (cv Shikidori) release volatiles, the total amount of which is high and constant and thus independent of caterpillar density, and naive parasitoids (Cotesia vestalis) of diamondback moth larvae fail to discriminate herbivore-rich from herbivore-poor plants. In contrast, seedlings of another cabbage variety of B. oleracea (var. acephala: kale) respond in a dose-dependent manner to the density of diamondback moth larvae and attract more parasitoids when there are more herbivores. Assuming these responses of the cabbage cultivars reflect behaviour of at least some genotypes of wild plants, we provide arguments why the behaviour of kale (B. oleracea var acephala) is best interpreted as an honest signaling strategy and that of cabbage cv Shikidori (B. oleracea var capitata) as a “cry wolf” signaling strategy, implying a conflict of interest between the plant and the enemies of its herbivores: the plant profits from being visited by the herbivore's enemies, but the latter would be better off by visiting other plants with more herbivores. If so, evolutionary theory on alarm signaling predicts consequences of major interest to students of plant protection, tritrophic systems and communication alike

Targeting pathogen metabolism without collateral damage to the host

The development of drugs that can inactivate disease-causing cells (e.g. cancer cells or parasites) without causing collateral damage to healthy or to host cells is complicated by the fact that many proteins are very similar between organisms. Nevertheless, due to subtle, quantitative differences between the biochemical reaction networks of target cell and host, a drug can limit the flux of the same essential process in one organism more than in another. We identified precise criteria for this â €network-based' drug selectivity, which can serve as an alternative or additive to structural differences. We combined computational and experimental approaches to compare energy metabolism in the causative agent of sleeping sickness, Trypanosoma brucei, with that of human erythrocytes, and identified glucose transport and glyceraldehyde-3-phosphate dehydrogenase as the most selective antiparasitic targets. Computational predictions were validated experimentally in a novel parasite-erythrocytes co-culture system. Glucose-transport inhibitors killed trypanosomes without killing erythrocytes, neurons or liver cells

Physician–Patient Communication About Prescription Medication Nonadherence: A 50-state Study of America’s Seniors

CONTEXT: Understanding and improving the quality of medication management is particularly important in the context of the Medicare prescription drug benefit that took effect last January 2006. OBJECTIVE: To determine the prevalence of physician–patient dialogue about medication cost and medication adherence among elderly adults nationwide. DESIGN: Cross-sectional survey. PARTICIPANTS: National stratified random sample of community-dwelling Medicare beneficiaries aged 65 and older. MAIN OUTCOME MEASURES: Rates of physician–patient dialogue about nonadherence and cost-related medication switching. RESULTS: Forty-one percent of seniors reported taking five or more prescription medications, and more than half has 2 or more prescribing physicians. Thirty-two percent overall and 24% of those with 3 or more chronic conditions reported not having talked with their doctor about all their different medicines in the last 12 months. Of seniors reporting skipping doses or stopping a medication because of side effects or perceived nonefficacy, 27% had not talked with a physician about it. Of those reporting cost-related nonadherence, 39% had not talked with a physician about it. Thirty-eight percent of those with cost-related nonadherence reported switching to a lower priced drug, and in a multivariable model, having had a discussion about drug cost was significantly associated with this switch (odds ratio [OR] 5.04, 95% confidence interval [CI] 4.28–5.93, P < .001). CONCLUSIONS: We show that there is a communication gap between seniors and their physicians around prescription medications. This communication problem is an important quality and safety issue, and takes on added salience as physicians and patients confront new challenges associated with coverage under new Medicare prescription drug plans. Meeting these challenges will require that more attention be devoted to medication management during all clinical encounters