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

Rationality of 17 cities' public perception of SARS and predictive model of psychological behavior

This study investigated the feature of Chinese peoples' perception of SARS by surveying a stratified sample of 4231 people from 17 cities in China, and primarily proposed a risk perception centered predictive model of psychological behavior in crisis. The results indicated that, negative SARS-related information, especially information of personal interest, will arouse people's risk perception of high level, and lead to irrational nervousness or scare; but positive SARS-related information, including recovery information and that with measures taken by government, can decrease the level of risk perception. In the middle of May, people felt the highest level of risk on the SARS pathogens; the following are the physical health condition and infectivity after recovering from SARS; they are factors that need special attention. SEM result analyses supported our hypotheses in that SARS-related information affect people's coping behavior and mental health through their risk perception. the four indices of risk assessment, feeling of nervousness, coping behavior and mental health are effective presentimental indices for public psychological behavior in risky events.This study investigated the feature of Chinese peoples' perception of SARS by surveying a stratified sample of 4231 people from 17 cities in China, and primarily proposed a risk perception centered predictive model of psychological behavior in crisis. The results indicated that, negative SARS-related information, especially information of personal interest, will arouse people's risk perception of high level, and lead to irrational nervousness or scare; but positive SARS-related information, including recovery information and that with measures taken by government, can decrease the level of risk perception. In the middle of May, people felt the highest level of risk on the SARS pathogens; the following are the physical health condition and infectivity after recovering from SARS; they are factors that need special attention. SEM result analyses supported our hypotheses in that SARS-related information affect people's coping behavior and mental health through their risk perception. the four indices of risk assessment, feeling of nervousness, coping behavior and mental health are effective presentimental indices for public psychological behavior in risky events

Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai-Tibet Plateau in China- modeling the interactions of vegetation productivity, phenology, and the soil freeze-thaw process

Phenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze-thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high-altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.</p