Vascular alterations upon activation of TGFβ signaling in fibroblasts - implications for systemic sclerosis

Tissue fibrosis and vascular disease are hallmarks of systemic sclerosis (SSc). Transforming growth factor β (TGFβ) is a key-player in fibroblast activation and tissue fibrosis in SSc. In contrast to fibrosis, evidence for a role of TGFβ in vascular disease of SSc is scarce. Using a transgenic mouse model with fibroblast-specific expression of a kinase-deficient TGFβ receptor type II, Derrett-Smith and colleagues demonstrate that aberrant TGFβ signaling in fibroblasts might result in activation of vascular smooth muscle cells and architectural changes of the vessel wall of the aorta

k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Most convex and nonconvex clustering algorithms come with one crucial parameter: the $k$ in $k$-means. To this day, there is not one generally accepted way to accurately determine this parameter. Popular methods are simple yet theoretically unfounded, such as searching for an elbow in the curve of a given cost measure. In contrast, statistically founded methods often make strict assumptions over the data distribution or come with their own optimization scheme for the clustering objective. This limits either the set of applicable datasets or clustering algorithms. In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution? To this end, we propose a bound on the probability that two clusters originate from the distribution of the unified cluster, specified only by the sample mean and variance. Our method is applicable as a simple wrapper to the result of any clustering method minimizing the objective of $k$-means, which includes Gaussian mixtures and Spectral Clustering. We focus in our experimental evaluation on an application for nonconvex clustering and demonstrate the suitability of our theoretical results. Our \textsc{SpecialK} clustering algorithm automatically determines the appropriate value for $k$, without requiring any data transformation or projection, and without assumptions on the data distribution. Additionally, it is capable to decide that the data consists of only a single cluster, which many existing algorithms cannot

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Autonomy support, basic need satisfaction and the optimal functioning of adult male and female sport participants: A test of basic needs theory

Grounded in Basic Needs Theory (BNT; Ryan and Deci, American Psychologist, 55, 68–78, 2000a), the present study aimed to: (a) test a theoretically-based model of coach autonomy support, motivational processes and well-/ill being among a sample of adult sport participants, (b) discern which basic psychological need(s) mediate the link between autonomy support and well-/ill-being, and (c) explore gender invariance in the hypothesized model. Five hundred and thirty nine participants (Male = 271;Female = 268; Mage = 22.75) completed a multi-section questionnaire tapping the targeted variables. Structural Equation Modeling (SEM) analysis revealed that coach autonomy support predicted participants’ basic need satisfaction for autonomy, competence and relatedness. In turn, basic need satisfaction predicted greater subjective vitality when engaged in sport. Participants with low levels of autonomy were more susceptible to feeling emotionally and physically exhausted from their sport investment. Autonomy and competence partially mediated the path from autonomy support to subjective vitality. Lastly, the results supported partial invariance of the model with respect to gender

Molecular evidence of Ureaplasma urealyticum and Ureaplasma parvum colonization in preterm infants during respiratory distress syndrome

BACKGROUND: Ureaplasma urealyticum and U. parvum have been associated with respiratory diseases in premature newborns, but their role in the pathogenesis of the respiratory distress syndrome (RDS) is unclear. The aim of this study was to detect, using molecular techniques, the role of Mycoplasma spp. and Ureaplasma spp. in respiratory secretion and blood specimens of preterm newborns with or without RDS and to evaluate the prevalence of perinatal U. urealyticum or U. parvum infection. The influence of chemotherapy on the clinical course was also evaluated. METHODS: Tracheal aspirate or nasopharingeal fluid samples from 50 preterm babies with (24) or without RDS (26) were analysed for detection of U. urealyticum and U. parvum by culture identification assay and PCR. Sequencing analysis of amplicons allowed us to verify the specificity of methods. Clarithromycin (10 mg kg(-1 )twice a day) was administered in ureaplasma-positive patients who presented clinical signs of RDS. RESULTS: 15/24 neonates with RDS (p < 0.001) and 4/26 without RDS were found PCR-positive for U. urealyticum or U. parvum. Culture identification assay was positive in 5/50 newborns, three of which with RDS. Sequencing analyses confirmed the specificity of these methods. Association of patent ductus arteriosus with ureaplasma colonization was more statistically significant (p = 0.0004) in patients with RDS than in those without RDS. CONCLUSION: Colonization of the lower respiratory tract by Ureaplasma spp. and particularly by U. parvum in preterm newborns was related to RDS. The routine use of molecular methods could be useful to screen candidate babies for etiologic therapy

Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method

BACKGROUND: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. RESULTS: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genome-wide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. CONCLUSION: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches

A survey of integral α-helical membrane proteins

Membrane proteins serve as cellular gatekeepers, regulators, and sensors. Prior studies have explored the functional breadth and evolution of proteins and families of particular interest, such as the diversity of transport-associated membrane protein families in prokaryotes and eukaryotes, the composition of integral membrane proteins, and family classification of all human G-protein coupled receptors. However, a comprehensive analysis of the content and evolutionary associations between membrane proteins and families in a diverse set of genomes is lacking. Here, a membrane protein annotation pipeline was developed to define the integral membrane genome and associations between 21,379 proteins from 34 genomes; most, but not all of these proteins belong to 598 defined families. The pipeline was used to provide target input for a structural genomics project that successfully cloned, expressed, and purified 61 of our first 96 selected targets in yeast. Furthermore, the methodology was applied (1) to explore the evolutionary history of the substrate-binding transmembrane domains of the human ABC transporter superfamily, (2) to identify the multidrug resistance-associated membrane proteins in whole genomes, and (3) to identify putative new membrane protein families

Accurate peak list extraction from proteomic mass spectra for identification and profiling studies

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which contains the mass, the charge and the intensity of every detected biomolecule. The main steps of the peak list extraction procedure are usually preprocessing, peak detection, peak selection, charge determination and monoisotoping operation.</p> <p>Results</p> <p>This paper describes an original algorithm for peak list extraction from low and high resolution mass spectra. It has been developed principally to improve the precision of peak extraction in comparison to other reference algorithms. It contains many innovative features among which a sophisticated method for managing the overlapping isotopic distributions.</p> <p>Conclusions</p> <p>The performances of the basic version of the algorithm and of its optional functionalities have been evaluated in this paper on both SELDI-TOF, MALDI-TOF and ESI-FTICR ECD mass spectra. Executable files of MassSpec, a MATLAB implementation of the peak list extraction procedure for Windows and Linux systems, can be downloaded free of charge for nonprofit institutions from the following web site: <url>http://aimed11.unipv.it/MassSpec</url></p

Observation of the Baryonic Flavor-Changing Neutral Current Decay Lambda_b -> Lambda mu+ mu-

We report the first observation of the baryonic flavor-changing neutral current decay Lambda_b -> Lambda mu+ mu- with 24 signal events and a statistical significance of 5.8 Gaussian standard deviations. This measurement uses ppbar collisions data sample corresponding to 6.8fb-1 at sqrt{s}=1.96TeV collected by the CDF II detector at the Tevatron collider. The total and differential branching ratios for Lambda_b -> Lambda mu+ mu- are measured. We find B(Lambda_b -> Lambda mu+ mu-) = [1.73+-0.42(stat)+-0.55(syst)] x 10^{-6}. We also report the first measurement of the differential branching ratio of B_s -> phi mu+ mu- using 49 signal events. In addition, we report branching ratios for B+ -> K+ mu+ mu-, B0 -> K0 mu+ mu-, and B -> K*(892) mu+ mu- decays.Comment: 8 pages, 2 figures, 4 tables. Submitted to Phys. Rev. Let