A tutorial on the implementations of linear image filters in CPU and GPU

This article presents an overview of the implementation of linear image filters in CPU and GPU. The main goal is to present a self contained discussion of different implementations and their background using tools from digital signal processing. First, using signal processing tools, we discuss different algorithms and estimate their computational cost. Then, we discuss the implementation of these filters in CPU and GPU. It is very common to find in the literature that GPUs can easity reduce computational times in many algorithms (straightforward implementations). In this work we show that GPU implementations not always reduce the computational time but also not all algorithms are suited for GPUs. We beleive this is a review that can help researchers and students working in this area. Although the experimental results are not meant to show which is the best implementation (in terms of running time), the main results can be extrapolated to CPUs and GPUs of different capabilities.XV Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV).Red de Universidades con Carreras en Informática (RedUNCI

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

FGFR1 and NTRK3 actionable alterations in “Wild-Type” gastrointestinal stromal tumors

BACKGROUND: About 10–15% of adult, and most pediatric, gastrointestinal stromal tumors (GIST) lack mutations in KIT, PDGFRA, SDHx, or RAS pathway components (KRAS, BRAF, NF1). The identification of additional mutated genes in this rare subset of tumors can have important clinical benefit to identify altered biological pathways and select targeted therapies. METHODS: We performed comprehensive genomic profiling (CGP) for coding regions in more than 300 cancer-related genes of 186 GISTs to assess for their somatic alterations. RESULTS: We identified 24 GIST lacking alterations in the canonical KIT/PDGFRA/RAS pathways, including 12 without SDHx alterations. These 24 patients were mostly adults (96%). The tumors had a 46% rate of nodal metastases. These 24 GIST were more commonly mutated at 7 genes: ARID1B, ATR, FGFR1, LTK, SUFU, PARK2 and ZNF217. Two tumors harbored FGFR1 gene fusions (FGFR1–HOOK3, FGFR1–TACC1) and one harbored an ETV6–NTRK3 fusion that responded to TRK inhibition. In an independent sample set, we identified 5 GIST cases lacking alterations in the KIT/PDGFRA/SDHx/RAS pathways, including two additional cases with FGFR1–TACC1 and ETV6–NTRK3 fusions. CONCLUSIONS: Using patient demographics, tumor characteristics, and CGP, we show that GIST lacking alterations in canonical genes occur in younger patients, frequently metastasize to lymph nodes, and most contain deleterious genomic alterations, including gene fusions involving FGFR1 and NTRK3. If confirmed in larger series, routine testing for these translocations may be indicated for this subset of GIST. Moreover, these findings can be used to guide personalized treatments for patients with GIST. Trial registration NCT 02576431. Registered October 12, 2015 ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-016-1075-6) contains supplementary material, which is available to authorized users

3D Object-Camera and 3D Face-Camera Pose Estimation for Quadcopter Control: Application to Remote Labs

International audienceWe present the implementation of two visual pose estimation algorithms (object-camera and face-camera) with a control system for a low cost quadcopter for an application in a remote electronic laboratory. The objective is threefold: (i) to allow the drone to inspect instruments in the remote lab, (ii) to localize a teacher and center his face in the image for student-teacher remote communication, (iii) and to return back home and land on a platform for automatic recharge of the batteries. The object-camera localization system is composed of two complementary visual approaches: (i) a visual SLAM (Simultaneous Localization And Mapping) system, and (ii) a homography-based localization system. We extend the application scenarios of the SLAM system by allowing close range inspection of a planar instrument and autonomous landing. The face-camera localization system is based on 3D modeling of the face, and a state of the art 2D facial point detector. Experiments conducted in a remote laboratory workspace are presented. They prove the robustness of the proposed object-camera visual pose system compared to the SLAM system, the performance of the face-camera visual servoing and pose estimation system in terms of real-time, robustness and accuracy

The transcription factor ZNF217 is a prognostic biomarker and therapeutic target during breast cancer progression.

UnlabelledThe transcription factor ZNF217 is a candidate oncogene in the amplicon on chromosome 20q13 that occurs in 20% to 30% of primary human breast cancers and that correlates with poor prognosis. We show that Znf217 overexpression drives aberrant differentiation and signaling events, promotes increased self-renewal capacity, mesenchymal marker expression, motility, and metastasis, and represses an adult tissue stem cell gene signature downregulated in cancers. By in silico screening, we identified candidate therapeutics that at low concentrations inhibit growth of cancer cells expressing high ZNF217. We show that the nucleoside analogue triciribine inhibits ZNF217-induced tumor growth and chemotherapy resistance and inhibits signaling events [e.g., phospho-AKT, phospho-mitogen-activated protein kinase (MAPK)] in vivo. Our data suggest that ZNF217 is a biomarker of poor prognosis and a therapeutic target in patients with breast cancer and that triciribine may be part of a personalized treatment strategy in patients overexpressing ZNF217. Because ZNF217 is amplified in numerous cancers, these results have implications for other cancers.SignificanceThis study finds that ZNF217 is a poor prognostic indicator and therapeutic target in patients with breast cancer and may be a strong biomarker of triciribine treatment efficacy in patients. Because previous clinical trials for triciribine did not include biomarkers of treatment efficacy, this study provides a rationale for revisiting triciribine in the clinical setting as a therapy for patients with breast cancer who overexpress ZNF217