Dynamic location of mobiles and obstacles for robotics applications

Usando algoritmos de procesamiento de imágenes y detección de movimientos se logró obtener la posición de dos móviles y de dos obstáculos fijos en un escenario en el cual se controlan las condiciones de color de los mismos. Para tal fin, se implementaron tres algoritmos, el primero de ellos se basa en los gradientes espacial y temporal para realizar la detección de los bordes de los objetos en movimiento dentro de la escena, el segundo es una detección en color, con la cual se identifica el color de los móviles y el tercero se basa en correlación de imágenes para realizar la búsqueda del objeto en movimiento. La detección de los obstáculos (objetos estáticos) para los tres algoritmos se realizó por medio de reconocimiento de un color característico en los obstáculos. Después de detectados los objetos de interés en la escena, se realiza su localización en la imagen por medio del centroide, para posteriormente entregar su posición y orientación en unidades de longitud.Using algorithms of processing of images and detection of movements was possible to obtain the position of two mobile objects and two fixed obstacles in a scene where they interact under controlled conditions of color. For such an end, three algorithms were implemented, the first of them is based on the spatial and temporal gradients to carry out the detection of the borders of the objects in movement inside the scene, the second is a detection in color, with which the color of the mobile objects is identified and the third is based on correlation of images to accomplish the search of the object in movement. The detection of the obstacles (static objects) for the three algorithms was carried out through recognition of a characteristic color in the obstacles. After having detected the objects of interest in the scene, is carried out their localization in the image through the centroide (center), for later on to give their position and orientation in centimeters

Localización dinámica de móviles y obstáculos para aplicaciones en robótica

Usando algoritmos de procesamiento de imágenes y detección de movimientos se logró obtener la posición de dos móviles y de dos obstáculos fijos en un escenario en el cual se controlan las condiciones de color de los mismos. Para tal fin, se implementaron tres algoritmos, el primero de ellos se basa en los gradientes espacial y temporal para realizar la detección de los bordes de los objetos en movimiento dentro de la escena, el segundo es una detección en color, con la cual se identifica el color de los móviles y el tercero se basa en correlación de imágenes para realizar la búsqueda del objeto en movimiento. La detección de los obstáculos (objetos estáticos) para los tres algoritmos se realizó por medio de reconocimiento de un color característico en los obstáculos. Después de detectados los objetos de interés en la escena, se realiza su localización en la imagen por medio del centroide, para posteriormente entregar su posición y orientación en unidades de longitud.Palabras Clave: Análisis dinámico de imágenes, color, correlación, gradientes, identificación, segmentación

Petrography and application of the rietveld method to the quantitative analysis of phases of natural clinker generated by coal spontaneous combustion

Fine-grained and mainly reddish color, compact and slightly breccious and vesicular pyrometamorphic rocks (natural clinker) are associated to the spontaneous combustion of coal seams of the Cerrejón Formation exploited by Carbones del Cerrejón Limited in La Guajira Peninsula (Caribbean Region of Colombia). These rocks constitute remaining inorganic materials derived from claystones, mudstones and sandstones originally associated with the coal and are essentially a complex mixture of various amorphous and crystalline inorganic constituents. In this paper, a petrographic characterization of natural clinker, aswell as the application of the X-ray diffraction (Rietveld method) by mean of quantitative analysis of its mineral phases were carried out. The RIQAS program was used for the refinement of X ray powder diffraction profiles, analyzing the importance of using the correct isostructural models for each of the existing phases, which were obtained from the Inorganic Crystal Structure Database (ICSD). The results obtained in this investigation show that the Rietveld method can be used as a powerful tool in the quantitative analysis of phases in polycrystalline samples, which has been a traditional problem in geology

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

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

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

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

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing