In this paper, we show that the seminal, biologicallyinspired saliency model by Itti et al. [21] is still competitive with current state-of-the-art methods for salient object segmentation if some important adaptions are made. We show which changes are necessary to achieve high performance, with special emphasis on the scale-space: we introduce a twin pyramid for computing Difference-of-Gaussians, which enables a flexible center-surround ratio. The resulting system, called VOCUS2, is elegant and coherent in structure, fast, and computes saliency at the pixel level. It is not only suitable for images with few objects, but also for complex scenes as captured by mobile devices. Furthermore, we integrate the saliency system into an object proposal generation framework to obtain segment-based saliency maps and boost the results for salient object segmentation. We show that our system achieves state-of-theart performance on a large collection of benchmark data.
Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. Standard techniques measure changes in total cellular poly(A) mRNA levels. The assumption that changes in gene expression as measured by these techniques are directly and well correlated with changes in rates of new gene synthesis form the basis of attempts to connect coordinated changes in gene expression with shared transcription regulatory elements. Yet systematic attempts at this approach remain difficult to demonstrate convincingly. One reason for this difficulty may result from the intricate convergence of both transcriptional and mRNA turnover events which, together, directly influence steady-state mRNA levels. Recent technical advances have led to the successful scale-up and application of nuclear run-on procedures directly to microarrays. This development has allowed a gene-by-gene comparison between new gene synthesis in the nucleus and measured changes in total cellular polyA mRNA. Results from these studies have begun to challenge the strict interpretation of changes in gene expression measured by conventional microarrays as being closely correlated with changes in mRNA transcription rate, but rather they tend to support the significant expansion of the role played by changes in mRNA stability regulation to standard analyses of gene expression. Gene expression profiles obtained from both polyA mRNA (whole-cell) and nuclear run-on (newly transcribed) RNA across a time course of one hour following the activation of human Jurkat T cells with PMA plus ionomycin revealed that regulation of mRNA stability may account for as much as 50% of all measurements of changes in total cellular polyA mRNA in this system. Stability regulation was inferred by the absence of corresponding regulation of nuclear gene transcription activity for groups of genes strongly regulated at the whole cell level and which were also resistant to inhibition by Actinomycin D pre-treatment. Consistent patterns across the time course were observed for both transcribed and stability-regulated genes. It is proposed that the regulation of mRNA stability in response to external stimuli contributes significantly to observed changes in gene expression as measured by high throughput systems.
BackgroundDiabetic nephropathy (DN) is a complex and chronic metabolic disease that evolves into a progressive fibrosing renal disorder. Effective transcriptomic profiling of slowly evolving disease processes such as DN can be problematic. The changes that occur are often subtle and can escape detection by conventional oligonucleotide DNA array analyses.Methodology/Principal FindingsWe examined microdissected human renal tissue with or without DN using Affymetrix oligonucleotide microarrays (HG-U133A) by standard Robust Multi-array Analysis (RMA). Subsequent gene ontology analysis by Database for Annotation, Visualization and Integrated Discovery (DAVID) showed limited detection of biological processes previously identified as central mechanisms in the development of DN (e.g. inflammation and angiogenesis). This apparent lack of sensitivity may be associated with the gene-oriented averaging of oligonucleotide probe signals, as this includes signals from cross-hybridizing probes and gene annotation that is based on out of date genomic data. We then examined the same CEL file data using a different methodology to determine how well it could correlate transcriptomic data with observed biology. ChipInspector (CI) is based on single probe analysis and de novo gene annotation that bypasses probe set definitions. Both methods, RMA and CI, used at default settings yielded comparable numbers of differentially regulated genes. However, when verified by RT-PCR, the single probe based analysis demonstrated reduced background noise with enhanced sensitivity and fewer false positives.Conclusions/SignificanceUsing a single probe based analysis approach with de novo gene annotation allowed an improved representation of the biological processes linked to the development and progression of DN. The improved analysis was exemplified by the detection of Wnt signaling pathway activation in DN, a process not previously reported to be involved in this disease.
Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.
Expression of the proto-oncogene c-myc is tightly regulated in vivo. Transcription of c-myc is assumed to be controlled by a number of positive and negative cis-acting control elements located upstream or within exon 1 and intron 1. However, these regulatory elements are not sufficient for c-myc expression after stable transfection or in transgenic mice. Transcription of c-myc in vivo thus requires additional control elements located outside the tested HindIII-EcoRI gene fragment. In order to identify these putative additional control elements, we mapped DNase I hypersensitive sites around the human c-myc gene in nine different tumor cell lines and in primary lymphocytes. Within the coding and 5' region of the gene, an almost identical pattern of DNase I hypersensitive sites was detected in the various cells. In contrast, chromatin analysis of the c-myc 3' region revealed a complex pattern of constitutive and tissue-specific DNase I hypersensitive sites. In enhancer trap experiments we identified two cis-acting control elements, both co-localizing with DNase I hypersensitive sites, that stimulated c-myc transcription after transient transfection in Raji or HeLa cells. Both regulatory elements exerted their enhancer activity in either orientation and regardless of their location within the plasmids. Both elements also conferred activation on a heterologous promoter. The association of these enhancers with DNase I hypersensitive sites, indicating their functional activity in vivo, make them potential candidates for the postulated regulatory control element(s) required for c-myc expression in vivo.
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