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

NFIL3 Is a Regulator of IL-12 p40 in Macrophages and Mucosal Immunity

Regulation of innate inflammatory responses against the enteric microbiota is essential for the maintenance of intestinal homeostasis. Key participants in innate defenses are macrophages. In these studies, the basic leucine zipper protein, NFIL3, is identified as a regulatory transcription factor in macrophages, controlling IL-12 p40 production induced by bacterial products and the enteric microbiota. Exposure to commensal bacteria and bacterial products induced NFIL3 in cultured macrophages and in vivo. The Il12b promoter has a putative DNA-binding element for NFIL3. Basal and LPS-activated NFIL3 binding to this site was confirmed by chromatin immunoprecipitation. LPS-induced Il12b promoter activity was inhibited by NFIL3 expression and augmented by NFIL3-short hairpin RNA in an Il12b-bacterial artificial chromosome-GFP reporter macrophage line. Il12b inhibition by NFIL3 does not require IL-10 expression, but a C-terminal minimal repression domain is necessary. Furthermore, colonic CD11b+ lamina propria mononuclear cells from Nfil3−/− mice spontaneously expressed Il12b mRNA. Importantly, lower expression of NFIL3 was observed in CD14+ lamina propria mononuclear cells from Crohn’s disease and ulcerative colitis patients compared with control subjects. Likewise, no induction of Nfil3 was observed in colons of colitis-prone Il10−/− mice transitioned from germ-free to a conventional microbiota. In conclusion, these experiments characterize NFIL3 as an Il12b transcriptional inhibitor. Interactions of macrophages with the enteric microbiota induce NFIL3 to limit their inflammatory capacity. Furthermore, altered intestinal NFIL3 expression may have implications for the pathogenesis of experimental and human inflammatory bowel diseases

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

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance

Neighbourhood Reduction in Global and Combinatorial Optimization: The Case of the p-Centre Problem

Neighbourhood reductions for a class of location problems known as the vertex (or discrete) and planar (or continuous) p-centre problems are presented. A brief review of these two forms of the p-centre problem is first provided followed by those respective reduction schemes that have shown to be promising. These reduction schemes have the power of transforming optimal or near optimal methods such as metaheuristics or relaxation-based procedures, which were considered relatively slow, into efficient and exciting ones that are now able to find optimal solutions or tight lower/upper bounds for larger instances. Research highlights of neighbourhood reduction for global and combinatorial optimisation problems in general and for related location problems in particular are also given

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