Stream programming framework for global ilumination techniques using a GPU

Los procesadores de streams están comenzando a ser una alternativa accesible para implementar técnicas de rendering asistidas por hardware que habitualmente estaban relegadas al uso offline. Nosotros elaboramos un marco de trabajo para procesamiento de streams basado en los conceptos del modelo de Stream Programming, seleccionamos el algoritmo de Photon Mapping y una GPU (Graphics Processing Unit) Nvidia para una implementación de un caso de prueba. Definimos un conjunto de clases en C++ para encapsular los componentes (kernels y streams) de este nuevo paradigma, usando OpenGL y el lenguaje Cg. Nuestra aplicación combina el método de Photon Mapping y una estructura de aceleración BVH (Bounding Volumes Hierarchy) en un pipeline de renderizado basado casi completamente en la GPU. Finalmente, evaluamos su desempeño usando un modelo de caja de Cornell.Stream processors are becoming an affordable alternative to implement hardware assisted rendering techniques which were usually relegated to offline usage. We built a stream processing framework based on the Stream Programming Model concepts, selected the Photon Mapping algorithm and an NVIDIA GPU (Graphics Processing Unit) as a test case implementation of a Global Illumination technique. We defined a set of C++ classes to encapsulate the components (kernels and streams) of this new paradigm, using OpenGL and Cg language. Our application combines the Photon Splatting method and the BVH (Bounding Volumes Hierarchy) acceleration structure into a rendering pipeline relying almost entirely on the GPU. Finally, we evaluated its performance using a Cornell Box model.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Stream programming framework for global ilumination techniques using a GPU

Los procesadores de streams están comenzando a ser una alternativa accesible para implementar técnicas de rendering asistidas por hardware que habitualmente estaban relegadas al uso offline. Nosotros elaboramos un marco de trabajo para procesamiento de streams basado en los conceptos del modelo de Stream Programming, seleccionamos el algoritmo de Photon Mapping y una GPU (Graphics Processing Unit) Nvidia para una implementación de un caso de prueba. Definimos un conjunto de clases en C++ para encapsular los componentes (kernels y streams) de este nuevo paradigma, usando OpenGL y el lenguaje Cg. Nuestra aplicación combina el método de Photon Mapping y una estructura de aceleración BVH (Bounding Volumes Hierarchy) en un pipeline de renderizado basado casi completamente en la GPU. Finalmente, evaluamos su desempeño usando un modelo de caja de Cornell.Stream processors are becoming an affordable alternative to implement hardware assisted rendering techniques which were usually relegated to offline usage. We built a stream processing framework based on the Stream Programming Model concepts, selected the Photon Mapping algorithm and an NVIDIA GPU (Graphics Processing Unit) as a test case implementation of a Global Illumination technique. We defined a set of C++ classes to encapsulate the components (kernels and streams) of this new paradigm, using OpenGL and Cg language. Our application combines the Photon Splatting method and the BVH (Bounding Volumes Hierarchy) acceleration structure into a rendering pipeline relying almost entirely on the GPU. Finally, we evaluated its performance using a Cornell Box model.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Fibronectin rescues estrogen receptor α from lysosomal degradation in breast cancer cells

Estrogen receptor α (ERα) is expressed in tissues as diverse as brains and mammary glands. In breast cancer, ERα is a key regulator of tumor progression. Therefore, understanding what activates ERα is critical for cancer treatment in particular and cell biology in general. Using biochemical approaches and superresolution microscopy, we show that estrogen drives membrane ERα into endosomes in breast cancer cells and that its fate is determined by the presence of fibronectin (FN) in the extracellular matrix; it is trafficked to lysosomes in the absence of FN and avoids the lysosomal compartment in its presence. In this context, FN prolongs ERα half-life and strengthens its transcriptional activity. We show that ERα is associated with β1-integrin at the membrane, and this integrin follows the same endocytosis and subcellular trafficking pathway triggered by estrogen. Moreover, ERα+ vesicles are present within human breast tissues, and colocalization with β1-integrin is detected primarily in tumors. Our work unravels a key, clinically relevant mechanism of microenvironmental regulation of ERα signaling.Fil: Sampayo, Rocío Guadalupe. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Toscani, Andrés Martin. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Rubashkin, Matthew G.. University of California; Estados UnidosFil: Thi, Kate. Lawrence Berkeley National Laboratory; Estados UnidosFil: Masullo, Luciano Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Violi, Ianina Lucila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Lakins, Jonathon N.. University of California; Estados UnidosFil: Caceres, Alfredo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Hines, William C.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Coluccio Leskow, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Nacional de Luján; ArgentinaFil: Stefani, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Chialvo, Dante Renato. Universidad de Buenos Aires; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; ArgentinaFil: Bissell, Mina J.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Weaver, Valerie M.. University of California; Estados UnidosFil: Simian, Marina. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment