Development of glycopeptide-intermediate resistance by Staphylococcus aureus leads to attenuated infectivity in a rat model of endocarditis.

Glycopeptide-intermediate resistant Staphylococcus aureus (GISA) are characterized by multiple changes in the cell wall and an altered expression of global virulence regulators. We investigated whether GISA are affected in their infectivity in a rat model of experimental endocarditis. The glycopeptide-susceptible, methicillin-resistant S. aureus M1V2 and its laboratory-derived GISA M1V16 were examined for their ability to (i) adhere to fibrinogen and fibronectin in vitro, (ii) persist in the bloodstream after intravenous inoculation, (iii) colonize aortic vegetations in rats, and (iv) compete for valve colonization by co-inoculation. Both GISA M1V16 and M1V2 adhered similarly to fibrinogen and fibronectin in vitro. In rats, GISA M1V16 was cleared faster from the blood (P < 0.05) and required 100-times more bacteria than parent M1V2 (10(6) versus 10(4)CFU) to infect 90% of vegetations. GISA M1V16 also had 100 to 1000-times lower bacterial densities in vegetations. Moreover, after co-inoculation with GISA M1V16 and M1V2Rif, a rifampin-resistant variant of M1V2 to discriminate them in organ cultures, GISA M1V16 was out-competed by the glycopeptide-susceptible counterpart. Thus, in rats with experimental endocarditis, GISA showed an attenuated virulence, likely due to a faster clearance from the blood and a reduced fitness in cardiac vegetations. The GISA phenotype appeared globally detrimental to infectivity.

An introduction to model-independent diffusion magnetic resonance imaging.

ABSTRACT: q-Space-based techniques such as diffusion spectrum imaging, q-ball imaging, and their variations have been used extensively in research for their desired capability to delineate complex neuronal architectures such as multiple fiber crossings in each of the image voxels. The purpose of this article was to provide an introduction to the q-space formalism and the principles of basic q-space techniques together with the discussion on the advantages as well as challenges in translating these techniques into the clinical environment. A review of the currently used q-space-based protocols in clinical research is also provided.

Schistosomiasis protects against multiple sclerosis

The incidences of schistosomiasis and multiple sclerosis (MS) are mutually exclusive worldwide suggesting that schistosomiasis may offer protection against the induction of the immune-mediated disease, MS. Recent studies using the mouse model of MS, experimental autoimmune encephalomyelitis, support a direct suppression of the onset of MS by chronic Schistosoma mansoni infection. Self-reactive Th1 but not Th2 responses develop in infected mice immunized with myelin oligodendrocyte glycoprotein albeit at reduced levels indicating that the induction of auto-reactive T cells is not abolished nor phenotypically altered. CNS infiltration by inflammatory cells, particularly macrophages, is significantly reduced in S. mansoni-infected, immunized mice compared to uninfected, immunized mice. Because activated macrophages are crucial to the induction of clinical disease, these findings support the hypothesis that differences in macrophage activation may contribute to the reduced incidence and delayed progression of experimental autoimmune encephalomyelitis during schistosomiasis.

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

The aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.

Coherent forecasting of multiple-decrement life tables: a test using Japanese cause of death data

Planners in public and private institutions would like coherent forecasts of the components of age-specic mortality, such as causes of death. This has been di cult toachieve because the relative values of the forecast components often fail to behave ina way that is coherent with historical experience. In addition, when the group forecasts are combined the result is often incompatible with an all-groups forecast. It hasbeen shown that cause-specic mortality forecasts are pessimistic when compared withall-cause forecasts (Wilmoth, 1995). This paper abandons the conventional approachof using log mortality rates and forecasts the density of deaths in the life table. Sincethese values obey a unit sum constraint for both conventional single-decrement life tables (only one absorbing state) and multiple-decrement tables (more than one absorbingstate), they are intrinsically relative rather than absolute values across decrements aswell as ages. Using the methods of Compositional Data Analysis pioneered by Aitchison(1986), death densities are transformed into the real space so that the full range of multivariate statistics can be applied, then back-transformed to positive values so that theunit sum constraint is honoured. The structure of the best-known, single-decrementmortality-rate forecasting model, devised by Lee and Carter (1992), is expressed incompositional form and the results from the two models are compared. The compositional model is extended to a multiple-decrement form and used to forecast mortalityby cause of death for Japan

Multiple sclerosis risk variant HLA-DRB1*1501 associates with high expression of DRB1 gene in different human populations.

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone.

Genomic and proteomic approaches towards an understanding of sleep.

The basic functions of sleep are still unclear, however, recent advances in genomics and proteomics have begun to contribute to our understanding of both normal and pathological sleep. In this review, we focus primarily on normal sleep and wake that have been studied in model organisms such as mice. Mice have been especially valuable since many different inbred strains exist that differ in sleep-related traits, and genes can be altered by either mutagenesis or targeted approaches. Advances in QTL (Quantitative Trait Loci) analysis have also helped to identify important sleep related genes, and several other QTLs have been mapped as a first step toward finding the genes that underlie basic sleep traits. In addition to more traditional genetic approaches, the abundance of different mRNAs across sleep and wake can now be studied and compared in different brain regions much more thoroughly using microarray methods. Progress at the protein level has been more difficult, but a few studies have begun to investigate changes in proteins during sleep and wake, and we present some of our own preliminary data in this area. A knowledge of which genes and proteins control or respond to changes in sleep will not only help answer fundamental questions, but may also suggest novel drug targets for improving multiple aspects of sleep and wake.

Genome-wide association study of multiple sclerosis confirms a novel locus at 5p13.1.

Multiple Sclerosis (MS) is the most common progressive and disabling neurological condition affecting young adults in the world today. From a genetic point of view, MS is a complex disorder resulting from the combination of genetic and non-genetic factors. We aimed to identify previously unidentified loci conducting a new GWAS of Multiple Sclerosis (MS) in a sample of 296 MS cases and 801 controls from the Spanish population. Meta-analysis of our data in combination with previous GWAS was done. A total of 17 GWAS-significant SNPs, corresponding to three different loci were identified:HLA, IL2RA, and 5p13.1. All three have been previously reported as GWAS-significant. We confirmed our observation in 5p13.1 for rs9292777 using two additional independent Spanish samples to make a total of 4912 MS cases and 7498 controls (ORpooled = 0.84; 95%CI: 0.80-0.89; p = 1.36 × 10-9). This SNP differs from the one reported within this locus in a recent GWAS. Although it is unclear whether both signals are tapping the same genetic association, it seems clear that this locus plays an important role in the pathogenesis of MS.

Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

Candidate gene study of TRAIL and TRAIL receptors: association with response to interferon beta therapy in multiple sclerosis patients.

TRAIL and TRAIL Receptor genes have been implicated in Multiple Sclerosis pathology as well as in the response to IFN beta therapy. The objective of our study was to evaluate the association of these genes in relation to the age at disease onset (AAO) and to the clinical response upon IFN beta treatment in Spanish MS patients. We carried out a candidate gene study of TRAIL, TRAILR-1, TRAILR-2, TRAILR-3 and TRAILR-4 genes. A total of 54 SNPs were analysed in 509 MS patients under IFN beta treatment, and an additional cohort of 226 MS patients was used to validate the results. Associations of rs1047275 in TRAILR-2 and rs7011559 in TRAILR-4 genes with AAO under an additive model did not withstand Bonferroni correction. In contrast, patients with the TRAILR-1 rs20576-CC genotype showed a better clinical response to IFN beta therapy compared with patients carrying the A-allele (recessive model: p = 8.88×10(-4), pc = 0.048, OR = 0.30). This SNP resulted in a non synonymous substitution of Glutamic acid to Alanine in position 228 (E228A), a change previously associated with susceptibility to different cancer types and risk of metastases, suggesting a lack of functionality of TRAILR-1. In order to unravel how this amino acid change in TRAILR-1 would affect to death signal, we performed a molecular modelling with both alleles. Neither TRAIL binding sites in the receptor nor the expression levels of TRAILR-1 in peripheral blood mononuclear cell subsets (monocytes, CD4+ and CD8+ T cells) were modified, suggesting that this SNP may be altering the death signal by some other mechanism. These findings show a role for TRAILR-1 gene variations in the clinical outcome of IFN beta therapy that might have relevance as a biomarker to predict the response to IFN beta in MS.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Cells derived from the coelomic epithelium contribute to multiple gastrointestinal tissues in mouse embryos.

Gut mesodermal tissues originate from the splanchnopleural mesenchyme. However, the embryonic gastrointestinal coelomic epithelium gives rise to mesenchymal cells, whose significance and fate are little known. Our aim was to investigate the contribution of coelomic epithelium-derived cells to the intestinal development. We have used the transgenic mouse model mWt1/IRES/GFP-Cre (Wt1(cre)) crossed with the Rosa26R-EYFP reporter mouse. In the gastrointestinal duct Wt1, the Wilms' tumor suppressor gene, is specific and dynamically expressed in the coelomic epithelium. In the embryos obtained from the crossbreeding, the Wt1-expressing cell lineage produces the yellow fluorescent protein (YFP) allowing for colocalization with differentiation markers through confocal microscopy and flow cytometry. Wt1(cre-YFP) cells were very abundant throughout the intestine during midgestation, declining in neonates. Wt1(cre-YFP) cells were also transiently observed within the mucosa, being apparently released into the intestinal lumen. YFP was detected in cells contributing to intestinal vascularization (endothelium, pericytes and smooth muscle), visceral musculature (circular, longitudinal and submucosal) as well as in Cajal and Cajal-like interstitial cells. Wt1(cre-YFP) mesenchymal cells expressed FGF9, a critical growth factor for intestinal development, as well as PDGFRα, mainly within developing villi. Thus, a cell population derived from the coelomic epithelium incorporates to the gut mesenchyme and contribute to a variety of intestinal tissues, probably playing also a signaling role. Our results support the origin of interstitial cells of Cajal and visceral circular muscle from a common progenitor expressing anoctamin-1 and SMCα-actin. Coelomic-derived cells contribute to the differentiation of at least a part of the interstitial cells of Cajal.

Tissue distribution of residual antimony in rats treated with multiple doses of meglumine antimoniate

Meglumine antimoniate (MA) and sodium stibogluconate are pentavalent antimony (SbV) drugs used since the mid-1940s. Notwithstanding the fact that they are first-choice drugs for the treatment of leishmaniases, there are gaps in our knowledge of their toxicological profile, mode of action and kinetics. Little is known about the distribution of antimony in tissues after SbV administration. In this study, we evaluated the Sb content of tissues from male rats 24 h and three weeks after a 21-day course of treatment with MA (300 mg SbV/kg body wt/d, subcutaneous). Sb concentrations in the blood and organs were determined by inductively coupled plasma-mass spectrometry. In rats, as with in humans, the Sb blood levels after MA dosing can be described by a two-compartment model with a fast (t1/2 = 0.6 h) and a slow (t1/2 >> 24 h) elimination phase. The spleen was the organ that accumulated the highest amount of Sb, while bone and thyroid ranked second in descending order of tissues according to Sb levels (spleen >> bone, thyroid, kidneys > liver, epididymis, lungs, adrenals > prostate > thymus, pancreas, heart, small intestines > skeletal muscle, testes, stomach > brain). The pathophysiological consequences of Sb accumulation in the thyroid and Sb speciation in the liver, thyroid, spleen and bone warrant further studies.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 differentcompositional datasets and modelled the first canonical variable using a segmented regression modelsolely based on an observation about the scatter plots. In this paper, multiple linear regressions areapplied to different datasets to confirm the validity of our proposed model. In addition to dating theunknown tephras by calibration as discussed previously, another method of mapping the unknown tephrasinto samples of the reference set or missing samples in between consecutive reference samples isproposed. The application of these methodologies is demonstrated with both simulated and real datasets.This new proposed methodology provides an alternative, more acceptable approach for geologists as theirfocus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age ofunknown tephra.Kew words: Tephrochronology; Segmented regression