Domestic Value Added and Employment Generated by Chinese Exports: A Quantitative Estimation

We develop an input-output methodology to estimate how Chinese exports affect the country’s total domestic value added (DVA) and employment for 1995 and 2002. Total DVA generated by exports is obtained by subtracting all direct and indirect imported intermediate goods from the gross value of exports, and total employment is obtained by adding all direct and indirect employment generated by exports. To implement these estimations, we use hitherto unpublished Chinese government data to construct several completely new datasets, including an input-output table with separate input-output and employment-output coefficients for processing and non-processing exports. In 2002 (1995), for every US$1,000 dollar of Chinese exports, DVA and employment are estimated to be US$466 (US$545) and 0.242 (0.375) person-year, respectively.Input-output tables, Chinese exports, employment, domestic value added

Domestic Value Added and Employment Generated by Chinese Exports: A Quantitative Estimation

We develop an input-output methodology to estimate how Chinese exports affect the country’s total domestic value added (DVA) and employment for 1995 and 2002. Total DVA generated by exports is obtained by subtracting all direct and indirect imported intermediate goods from the gross value of exports, and total employment is obtained by adding all direct and indirect employment generated by exports. To implement these estimations, we use hitherto unpublished Chinese government data to construct several completely new datasets, including an input-output table with separate input-output and employment-output coefficients for processing and non-processing exports. In 2002 (1995), for every US$1,000 dollar of Chinese exports, DVA and employment are estimated to be US$466 (US$545) and 0.242 (0.375) person-year, respectively

Selection of Burst-like Transients and Stochastic Variables Using Multi-band Image Differencing in the PAN-STARRS1 Medium-deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands g P1, r P1, i P1, and z P1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic survey

A population of highly energetic transient events in the centres of active galaxies

Recent all-sky surveys have led to the discovery of new types of transients. These include stars disrupted by the central supermassive black hole, and supernovae that are 10–100 times more energetic than typical ones. However, the nature of even more energetic transients that apparently occur in the innermost regions of their host galaxies is hotly debated1,2,3. Here we report the discovery of the most energetic of these to date: PS1-10adi, with a total radiated energy of ~2.3 × 1052 erg. The slow evolution of its light curve and persistently narrow spectral lines over ∼ 3 yr are inconsistent with known types of recurring black hole variability. The observed properties imply powering by shock interaction between expanding material and large quantities of surrounding dense matter. Plausible sources of this expanding material are a star that has been tidally disrupted by the central black hole, or a supernova. Both could satisfy the energy budget. For the former, we would be forced to invoke a new and hitherto unseen variant of a tidally disrupted star, while a supernova origin relies principally on environmental effects resulting from its nuclear location. Remarkably, we also discover that PS1-10adi is not an isolated case. We therefore surmise that this new population of transients has previously been overlooked due to incorrect association with underlying central black hole activity

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

The Drosophila melanogaster Genetic Reference Panel

A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics

Size Doesn't Matter: Towards a More Inclusive Philosophy of Biology

notes: As the primary author, O’Malley drafted the paper, and gathered and analysed data (scientific papers and talks). Conceptual analysis was conducted by both authors.publication-status: Publishedtypes: ArticlePhilosophers of biology, along with everyone else, generally perceive life to fall into two broad categories, the microbes and macrobes, and then pay most of their attention to the latter. ‘Macrobe’ is the word we propose for larger life forms, and we use it as part of an argument for microbial equality. We suggest that taking more notice of microbes – the dominant life form on the planet, both now and throughout evolutionary history – will transform some of the philosophy of biology’s standard ideas on ontology, evolution, taxonomy and biodiversity. We set out a number of recent developments in microbiology – including biofilm formation, chemotaxis, quorum sensing and gene transfer – that highlight microbial capacities for cooperation and communication and break down conventional thinking that microbes are solely or primarily single-celled organisms. These insights also bring new perspectives to the levels of selection debate, as well as to discussions of the evolution and nature of multicellularity, and to neo-Darwinian understandings of evolutionary mechanisms. We show how these revisions lead to further complications for microbial classification and the philosophies of systematics and biodiversity. Incorporating microbial insights into the philosophy of biology will challenge many of its assumptions, but also give greater scope and depth to its investigations