Space-time variation of ciliates related to environmental factors in 15 nearshore stations of the Gulf of Gabes

Diversity and structure of ciliate communities in the Gulf of Gabes (Tunisia) were investigated based on a survey of 15 nearshore stations along 237 Km, by monthly sampling over a 1-year. Ciliated protozoa were identified to genus and/or species level and enumerated. Statistic tools were used to explain the ciliates assemblage. High ciliates species richness from 133 taxa was recorded, including new records of 76 species. This study showed a longitudinal distribution of ciliate communities, which are organized in northern stations (from Tabia to Harbor of Gabes) and southern stations (from Zarrat to Jabiat Haj Ali). The number of taxa increased significantly in northern stations but decreased in the southern. This distribution was mainly influenced by the salinity and phytoplankton abundance. Ciliate taxa were grouped into fives size-classes: 15-30 µm, 30-50 µm, 50-100 µm, 100-200 µm and >200 µm. In terms of abundance, most abundant size groups were small ciliates (15-30 μm) accounted from 15 to 79 %, while the greatest biomass contribution came from the 50-100 μm size classes. We thus conclude high diversity of ciliates communities that showed a geographical distribution influenced by abiotic and biotic factors along the coast of Gulf of Gabes

Local and regional factors influencing zooplankton communities in the connected Kasseb Reservoir, Tunisia

Associations between zooplankton community structure and abiotic (temperature, dissolved oxygen, turbidity, nutriments) and biotic factors (chlorophyll a and phytoplankton community) were examined, in Kasseb Reservoir, northern Tunisia. Samples were taken bimonthly from July to December 2002 at 3 sampling stations (deepest station: Station 1, Brik River: Station 2 and M’Zaz Stama River: Station 3). From our results it is evident that zooplankton exhibit seasonally and spatially heterogeneous distribution. The highest density of zooplankton was recorded in September at a depth of 5 m (10.8 × 103 ind·l-1). At Station 1 cyclopoid copepods (65% of total abundance) were the most abundant group followed by Cladocera (21% of total abundance). At Station 2 (93% of total abundance) and Station 3 (98% of total abundance) cyclopoid copepodswere numerically dominant throughout the study period. Canonical correspondence analysis (CCA) was used to estimate the influence of abiotic and biotic factors in structuring the zooplankton assemblage. Zooplankton abundance was negatively correlated with turbidity (r= -0.381,

Star p-hub center problem and star p-hub median problem with bounded path lengths

We consider two problems that arise in designing two-level star networks taking into account service quality considerations. Given a set of nodes with pairwise traffic demand and a central hub, we select p hubs and connect them to the central hub with direct links and then we connect each nonhub node to a hub. This results in a star/star network. In the first problem, called the Star p-hub Center Problem, we would like to minimize the length of the longest path in the resulting network. In the second problem, Star p-hub Median Problem with Bounded Path Lengths, the aim is to minimize the total routing cost subject to upper bound constraints on the path lengths. We propose formulations for these problems and report the outcomes of a computational study where we compare the performances of our formulations. © 2012 Elsevier Ltd. All rights reserved

Early Life Stress Triggers Persistent Colonic Barrier Dysfunction and Exacerbates Colitis in Adult IL-10−/− Mice:

It has become increasingly evident that disease flares in the human inflammatory bowel diseases (IBD) are influenced by life stress. It is known that life stress can trigger disturbances in intestinal barrier function and activate proinflammatory signaling pathways, which are important contributors to intestinal inflammation and clinical disease; however, the exact mechanisms of stress-induced IBD exacerbations remain to be elucidated. Here we present a model of early life stress-induced exacerbation of colitis in IL-10-/- mice

Linear inequalities among graph invariants: Using GraPHedron to uncover optimal relationships

Optimality of a linear inequality in finitely many graph invariants is defined through a geometric approach. For a fixed number of graph vertices, consider all the tuples of values taken by the invariants on a selected class of graphs. Then form the polytope which is the convex hull of all these tuples. By definition, the optimal linear inequalities correspond to the facets of this polytope. They are finite in number, are logically independent, and generate precisely all the linear inequalities valid on the class of graphs. The computer system GraPHedron, developed by some of the authors, is able to produce experimental data about such inequalities for a "small" number of vertices. It greatly helps in conjecturing optimal linear inequalities, which are then hopefully proved for any number of vertices. Two examples are investigated here for the class of connected graphs. First, all the optimal linear inequalities for the stability number and the number of edges are obtained. To this aim, a problem of Ore (1962) related to the Turán Theorem (1941) is solved. Second, several optimal inequalities are established for three invariants: the maximum degree, the irregularity, and the diameter. © 2008 Wiley Periodicals, Inc

Recovery, assessment, and molecular characterization of minor olive genotypes in Tunisia

Olive is one of the oldest cultivated species in the Mediterranean Basin, including Tunisia, where it has a wide diversity, with more than 200 cultivars, of both wild and feral forms. Many minor cultivars are still present in marginal areas of Tunisia, where they are maintained by farmers in small local groves, but they are poorly characterized and evaluated. In order to recover this neglected germplasm, surveys were conducted in different areas, and 31 genotypes were collected, molecularly characterized with 12 nuclear microsatellite (simple sequence repeat (SSR)) markers, and compared with 26 reference cultivars present in the Tunisian National Olive collection. The analysis revealed an overall high genetic diversity of this olive’s germplasm, but also discovered the presence of synonymies and homonymies among the commercialized varieties. The structure analysis showed the presence of different gene pools in the analyzed germplasm. In particular, the marginal germplasm from Ras Jbal and Azmour is characterized by gene pools not present in commercial (Nurseries) varieties, pointing out the very narrow genetic base of the commercialized olive material in Tunisia, and the need to broaden it to avoid the risk of genetic erosion of this species in this country

SEARCHPATTOOL: a new method for mining the most specific frequent patterns for binding sites with application to prokaryotic DNA sequences

<p>Abstract</p> <p>Background</p> <p>Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limited to short ones or impose some constraints on the pattern type. Many patterns for binding sites in prokaryotic species are not well characterized but are known to be large, between 16–30 base pairs (bp) and contain at least 2 conserved bases. The length of prokaryotic species promoters (about 400 bp) and our interest in studying a small set of genes that could be a cluster of co-regulated genes from microarray experiments led to the development of a new exhaustive algorithm targeting these large patterns.</p> <p>Results</p> <p>We present Searchpattool, a new method to search for and select the most specific (conservative) frequent patterns. This method does not impose restrictions on the density or the structure of the pattern. The best patterns (motifs) are selected using several statistics, including a new application of a z-score based on the number of matching sequences. We compared Searchpattool against other well known algorithms on a <it>Bacillus subtilis </it>group of 14 input sequences and found that in our experiments Searchpattool always performed the best based on performance scores.</p> <p>Conclusion</p> <p>Searchpattool is a new method for pattern discovery relative to transcription factor binding sites for species or genes with short promoters. It outputs the most specific significant patterns and helps the biologist to choose the best candidates.</p

Quantitative proteome landscape of the NCI-60 cancer cell lines

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p