3,334 research outputs found

    Discriminative segmentation-based evaluation through shape dissimilarity.

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    Segmentation-based scores play an important role in the evaluation of computational tools in medical image analysis. These scores evaluate the quality of various tasks, such as image registration and segmentation, by measuring the similarity between two binary label maps. Commonly these measurements blend two aspects of the similarity: pose misalignments and shape discrepancies. Not being able to distinguish between these two aspects, these scores often yield similar results to a widely varying range of different segmentation pairs. Consequently, the comparisons and analysis achieved by interpreting these scores become questionable. In this paper, we address this problem by exploring a new segmentation-based score, called normalized Weighted Spectral Distance (nWSD), that measures only shape discrepancies using the spectrum of the Laplace operator. Through experiments on synthetic and real data we demonstrate that nWSD provides additional information for evaluating differences between segmentations, which is not captured by other commonly used scores. Our results demonstrate that when jointly used with other scores, such as Dices similarity coefficient, the additional information provided by nWSD allows richer, more discriminative evaluations. We show for the task of registration that through this addition we can distinguish different types of registration errors. This allows us to identify the source of errors and discriminate registration results which so far had to be treated as being of similar quality in previous evaluation studies. © 2012 IEEE

    XmoNet:a Fully Convolutional Network for Cross-Modality MR Image Inference

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    Magnetic resonance imaging (MRI) can generate multimodal scans with complementary contrast information, capturing various anatomical or functional properties of organs of interest. But whilst the acquisition of multiple modalities is favourable in clinical and research settings, it is hindered by a range of practical factors that include cost and imaging artefacts. We propose XmoNet, a deep-learning architecture based on fully convolutional networks (FCNs) that enables cross-modality MR image inference. This multiple branch architecture operates on various levels of image spatial resolutions, encoding rich feature hierarchies suited for this image generation task. We illustrate the utility of XmoNet in learning the mapping between heterogeneous T1- and T2-weighted MRI scans for accurate and realistic image synthesis in a preliminary analysis. Our findings support scaling the work to include larger samples and additional modalities

    EGFR inhibitors identified as a potential treatment for chordoma in a focused compound screen.

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    Chordoma is a rare malignant bone tumour with a poor prognosis and limited therapeutic options. We undertook a focused compound screen (FCS) against 1097 compounds on three well-characterized chordoma cell lines; 154 compounds were selected from the single concentration screen (1 µm), based on their growth-inhibitory effect. Their half-maximal effective concentration (EC50 ) values were determined in chordoma cells and normal fibroblasts. Twenty-seven of these compounds displayed chordoma selective cell kill and 21/27 (78%) were found to be EGFR/ERBB family inhibitors. EGFR inhibitors in clinical development were then studied on an extended cell line panel of seven chordoma cell lines, four of which were sensitive to EGFR inhibition. Sapitinib (AstraZeneca) emerged as the lead compound, followed by gefitinib (AstraZeneca) and erlotinib (Roche/Genentech). The compounds were shown to induce apoptosis in the sensitive cell lines and suppressed phospho-EGFR and its downstream pathways in a dose-dependent manner. Analysis of substituent patterns suggested that EGFR-inhibitors with small aniline substituents in the 4-position of the quinazoline ring were more effective than inhibitors with large substituents in that position. Sapitinib showed significantly reduced tumour growth in two xenograft mouse models (U-CH1 xenograft and a patient-derived xenograft, SF8894). One of the resistant cell lines (U-CH2) was shown to express high levels of phospho-MET, a known bypass signalling pathway to EGFR. Neither amplifications (EGFR, ERBB2, MET) nor mutations in EGFR, ERBB2, ERBB4, PIK3CA, BRAF, NRAS, KRAS, PTEN, MET or other cancer gene hotspots were detected in the cell lines. Our findings are consistent with the reported (p-)EGFR expression in the majority of clinical samples, and provide evidence for exploring the efficacy of EGFR inhibitors in the treatment of patients with chordoma and studying possible resistance mechanisms to these compounds in vitro and in vivo. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

    Species-level functional profiling of metagenomes and metatranscriptomes.

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    Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

    Extensive Copy-Number Variation of Young Genes across Stickleback Populations

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    MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

    Metagenomic and Metatranscriptomic Analysis of Microbial Community Structure and Gene Expression of Activated Sludge

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    The present study applied both metagenomic and metatranscriptomic approaches to characterize microbial structure and gene expression of an activated sludge community from a municipal wastewater treatment plant in Hong Kong. DNA and cDNA were sequenced by Illumina Hi-seq2000 at a depth of 2.4 Gbp. Taxonomic analysis by MG-RAST showed bacteria were dominant in both DNA and cDNA datasets. The taxonomic profile obtained by BLAST against SILVA SSUref database and annotation by MEGAN showed that activated sludge was dominated by Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia phyla in both DNA and cDNA datasets. Global gene expression annotation based on KEGG metabolism pathway displayed slight disagreement between the DNA and cDNA datasets. Further gene expression annotation focusing on nitrogen removal revealed that denitrification-related genes sequences dominated in both DNA and cDNA datasets, while nitrifying genes were also expressed in relative high levels. Specially, ammonia monooxygenase and hydroxylamine oxidase demonstrated the high cDNA/DNA ratios in the present study, indicating strong nitrification activity. Enzyme subunits gene sequences annotation discovered that subunits of ammonia monooxygenase (amoA, amoB, amoC) and hydroxylamine oxygenase had higher expression levels compared with subunits of the other enzymes genes. Taxonomic profiles of selected enzymes (ammonia monooxygenase and hydroxylamine oxygenase) showed that ammonia-oxidizing bacteria present mainly belonged to Nitrosomonas and Nitrosospira species and no ammonia-oxidizing Archaea sequences were detected in both DNA and cDNA datasets

    Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

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    One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis
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