Modelling inflation in China – a regional perspective

We model provincial inflation in China during the reform period. In particular, we are interested in the ability of the hybrid New Keynesian Phillips Curve (NKPC) to capture the inflation process at the provincial level. The study highlights differences in inflation formation and shows that the NKPC provides a reasonable description of the inflation process only for the coastal provinces. A probit analysis suggests that the forward-looking inflation component and the output gap are important inflation drivers in provinces that have advanced most in marketisation of the economy and have most likely experienced excess demand pressures. These results have implications for the relative effectiveness of monetary policy across the Chinese provinces.China; inflation; regional; New Keynesian Philips Curve; GMM

Modelling inflation in China - a regional perspective

We model provincial inflation in China during the reform period. In particular, we are interested in the ability of the hybrid New Keynesian Phillips Curve (NKPC) to capture the inflation process at the provincial level. The study highlights differences in inflation formation and shows that the NKPC provides a reasonable description of the inflation process only for the coastal provinces. A probit analysis suggests that the forwardlooking inflation component and the output gap are important inflation drivers in provinces that have advanced most in marketisation of the economy and have most likely experienced excess demand pressures. These results have implications for the relative effectiveness of monetary policy across the Chinese provinces. JEL Classification: E31, C22China, GMM, inflation, New Keynesian Phillips curve, Regional

Imports and profitability in the euro area manufacturing sector: the role of emerging market economies

The paper analyses the impact of import penetration on firms’ profitability in 15 manufacturing industries in 10 euro area countries during 1995-2004, focusing on the role of emerging market economies. Our results indicate that import competition from emerging market economies has had an overall negative impact on companies’ profitability in the euro area manufacturing sector, especially for imports coming from China and Russia. However, similar negative effects are also estimated for imports from the United States. In contrast, imports from Latin America are estimated to be positively correlated with profitability. Finally, we find asymmetric effects on profitability across euro area countries and sectors. JEL Classification: L11, L13, F12, C23emerging markets, euro area, Globalisation, import penetration, Profitability

Sectoral specialisation in the EU a macroeconomic perspective

This paper analyses trends in sectoral specialisation in the EU and concludes the following: 1) The European production structure appears more homogenous than that of the US. 2) While sectoral specialisation has shown a slight increase in some smaller euro area countries towards the end-1990s, it is too early to detect any potential impact of EMU. 3) Despite some changes in sectoral composition, the business cycles of euro area countries became more synchronised over the 1990s, which may be seen as reassuring from the point of view of the single monetary policy. 4) Sectoral re-allocation accounts for as much as 50% of the increase in labour productivity growth in business sector services in the euro area. 5) The slowdown of European labour productivity growth relative to the US since the mid-1990s is explained by a stronger performance in the US wholesale and retail trade, financial intermediation and high-tech manufacturing sectors.

Predicting sepsis severity at first clinical presentation:The role of endotypes and mechanistic signatures

BACKGROUND: Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use. METHODS: Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms. FINDINGS: Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts. INTERPRETATION: The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients

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

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

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