Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Parallel Dimensionality-Varied Convolutional Neural Network for Hyperspectral Image Classification

Part 5: Perceptual IntelligenceInternational audienceMany spectral-spatial classification methods of HSI based on convolutional neural network (CNN) are proposed and achieve outstanding performance recently. However, these methods require tremendous computations with complex network and excessively large model. Moreover, single machine is obviously weak when dealing with big data. In this paper, a parallel dimensionality-varied convolutional neural network (DV-CNN) is proposed to address these issues. The dimensionalities of feature maps extracted vary with stages in DV-CNN, and DV-CNN reduces the dimensionalities of feature maps to simplify the computation and the structure of network without information loss. Besides, the parallel architecture of DV-CNN can obviously reduce the training time. The experiments compared with state-of-the-art methods are performed on Indian Pines and Pavia University scene datasets. The results of experiments demonstrate that parallel DV-CNN can obtain better classification performance, reduce the time consuming and improve the training efficiency

Structure and optical properties of SnS nanowire arrays prepared with two-step method

Single crystalline SnS nanowire arrays have been synthesized by sulfurating the Sn nanowire arrays which were prepared with the electrochemical deposition. The obtained SnS nanowire arrays are charactered with the XRD, SEM, TEM and the UV/Visible/NIR spectrophotometer. And the results indicate that the nanowires with an average diameter of 50 nm and a length of several tens micrometers, which same with the as prepared Sn nanowires. There are two absorption peaks indicate with the direct and indirect bandgaps about the orthorhombic SnS nanowire arrays

Effects of Ce doping on the properties of ZnO thin films prepared by DC reactive magnetron sputtering

The ZnO and ZnO:Ce thin films were prepared by DC reactive magnetron sputtering. The structure, surface morphology, optical and photoluminescence properties of ZnO:Ce thin films were investigated. The XRD results indicated that all the samples exhibited a hexagonal wurtzite structure. The surface morphology of the films was sensitive to the Ce concentration. All the films had a higher average transmittance (more than 85%) in the visible region and a strong absorption near the band-edge of ZnO. The photoluminescence properties of the ZnO:Ce thin films were also studied. Blue emissions were observed from the ZnO:Ce thin films. Our results indicated that the photoluminescence properties of ZnO thin films doped with low Ce concentration were related to the intrinsic transition of Ce3+ ions. However, when the Ce concentration increased, Zn-i also played an important role

Soft magnetic property of [Fe80Ni20-O/ZnO](n) multilayer thin films for high-frequency application

A series of [Fe80Ni20-O/ZnO](n) multilayer thin films with different ZnO separate layer thicknesses (t, from 0 to 3 nm) and fixed Fe80Ni20-O layer thickness (about 5 nm) have been fabricated on (100)-oriented silicon wafers and glass substrates by reactive magnetron sputtering. Microstructure analysis and static magnetic measurement results indicate that the magnetic properties of the films can be adjusted by the variation of ZnO monolayers thickness. All films reveal an evident in-plane uniaxial magnetic anisotropy (IPUMA). The values of in-plane uniaxial magnetic anisotropy fields (H-k) and resistivity (rho) can be changed from 8 to 57 Oe and 62 to 168 mu Omega.cm respectively with the t increasing. While the values of hard axis coercivity (H-ch) and easy axis coercivity (H-ce) reveal minimums of 1.5 and 3 Oe respectively at t = 1 nm