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

Hybrid Meta-heuristics with VNS and Exact Methods: Application to Large Unconditional and Conditional Vertex p-Centre Problems

Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex p-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems

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

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

<p>Abstract</p> <p>Background</p> <p>In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called <it>biclustering</it>. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.</p> <p>Methods</p> <p>We introduce <it>BiMine</it>, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, <it>BiMine </it>relies on a new evaluation function called <it>Average Spearman's rho </it>(ASR). Second, <it>BiMine </it>uses a new tree structure, called <it>Bicluster Enumeration Tree </it>(BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, <it>BiMine </it>introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.</p> <p>Results</p> <p>The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that <it>BiMine </it>competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.</p

A Cell Permeable Peptide Inhibitor of NFAT Inhibits Macrophage Cytokine Expression and Ameliorates Experimental Colitis

Nuclear factor of activated T cells (NFAT) plays a critical role in the development and function of immune and non-immune cells. Although NFAT is a central transcriptional regulator of T cell cytokines, its role in macrophage specific gene expression is less defined. Previous work from our group demonstrated that NFAT regulates Il12b gene expression in macrophages. Here, we further investigate NFAT function in murine macrophages and determined the effects of a cell permeable NFAT inhibitor peptide 11R-VIVIT on experimental colitis in mice. Treatment of bone marrow derived macrophages (BMDMs) with tacrolimus or 11R-VIVIT significantly inhibited LPS and LPS plus IFN-γ induced IL-12 p40 mRNA and protein expression. IL-12 p70 and IL-23 secretion were also decreased. NFAT nuclear translocation and binding to the IL-12 p40 promoter was reduced by NFAT inhibition. Experiments in BMDMs from IL-10 deficient (Il10−/−) mice demonstrate that inhibition of IL-12 expression by 11R-VIVIT was independent of IL-10 expression. To test its therapeutic potential, 11R-VIVIT was administered systemically to Il10−/− mice with piroxicam-induced colitis. 11R-VIVIT treated mice demonstrated significant improvement in colitis compared to mice treated with an inactive peptide. Moreover, decreased spontaneous secretion of IL-12 p40 and TNF in supernatants from colon explant cultures was demonstrated. In summary, NFAT, widely recognized for its role in T cell biology, also regulates important innate inflammatory pathways in macrophages. Selective blocking of NFAT via a cell permeable inhibitory peptide is a promising therapeutic strategy for the treatment of inflammatory bowel diseases

Are early somatic embryos of the norway spruce (Picea abies (L.) Karst.) organised?

Background Somatic embryogenesis in conifer species has great potential for the forestry industry. Hence, a number of methods have been developed for their efficient and rapid propagation through somatic embryogenesis. Although information is available regarding the previous process-mediated generation of embryogenic cells to form somatic embryos, there is a dearth of information in the literature on the detailed structure of these clusters. Methodology/Principal Findings The main aim of this study was to provide a more detailed structure of the embryogenic tissue clusters obtained through the in vitro propagation of the Norway spruce (Picea abies (L.) Karst.). We primarily focused on the growth of early somatic embryos (ESEs). The data on ESE growth suggested that there may be clear distinctions between their inner and outer regions. Therefore, we selected ESEs collected on the 56th day after sub-cultivation to dissect the homogeneity of the ESE clusters. Two colourimetric assays (acetocarmine and fluorescein diacetate/propidium iodide staining) and one metabolic assay based on the use of 2,3,5-triphenyltetrazolium chloride uncovered large differences in the metabolic activity inside the cluster. Next, we performed nuclear magnetic resonance measurements. The ESE cluster seemed to be compactly aggregated during the first four weeks of cultivation; thereafter, the difference between the 1H nuclei concentration in the inner and outer clusters was more evident. There were clear differences in the visual appearance of embryos from the outer and inner regions. Finally, a cluster was divided into six parts (three each from the inner and the outer regions of the embryo) to determine their growth and viability. The innermost embryos (centripetally towards the cluster centre) could grow after sub-cultivation but exhibited the slowest rate and required the longest time to reach the common growth rate. To confirm our hypothesis on the organisation of the ESE cluster, we investigated the effect of cluster orientation on the cultivation medium and the influence of the change of the cluster’s three-dimensional orientation on its development. Maintaining the same position when transferring ESEs into new cultivation medium seemed to be necessary because changes in the orientation significantly affected ESE growth. Conclusions and Significance This work illustrated the possible inner organisation of ESEs. The outer layer of ESEs is formed by individual somatic embryos with high metabolic activity (and with high demands for nutrients, oxygen and water), while an embryonal group is directed outside of the ESE cluster. Somatic embryos with depressed metabolic activity were localised in the inner regions, where these embryonic tissues probably have a very important transport function