Financial Exchange Industrial Organization, Sports championships and Stock Market reaction, Beauty Premiums, and Machine learning

First, I examine how the forces of automation, competition, and demutualization are rapidly changing the industrial organization, ownership, and capital structure of the financial exchange industry. I propose the conditions under which demutualization becomes optimal from the perspective of mutually owned exchange owners. I then proceed to build an empirical dataset characterizing the evolution of the leading stock and derivative exchanges around the World along these dimensions. I empirically find that technology driven growth opportunities, product driven growth opportunities and increases in market concentration are the main stimulants for demutualization. In this paper I examine market reaction at the firm-level to sudden changes in investor mood. I use the results of professional sports championships in North America as our primary mood variable. To test the effect of investor mood, we analyze the return patterns of publicly traded firms headquartered geographically near those teams. This paper examines the effects of instructors’ attractiveness on student evaluations of their teaching. I build on previous studies by holding both observed and unobserved characteristics of the instructor and classes constant. Our identification strategy exploits the fact that many instructors, in addition to traditional teaching in the classroom, also teach in the online environment, where attractiveness is either unknown or less salient. I utilize multiple attractiveness measures, including facial symmetry software, subjective evaluations, and a novel, proxy methodology that resembles a “Keynesian Beauty Contest.” Also, In the aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognizing these documents for further information extraction

Assessing Heavy Episodic Drinking: A Random Survey of 18 to 34-Year-Olds in Four Cities in Four Different Continents

Background: Heavy episodic drinking (HED) can have health and social consequences. This study assesses the associations between HED and demographic, socioeconomic, motivation and effects indicators for people aged 18–34 years old living in four cities in different regions of the world. Method: Multistage random sampling was consistent across the four cities (Ilorin (Nigeria), Wuhan (China), Montevideo (Uruguay) and Moscow (Russia)). The questionnaire was forward/back translated and face-to-face interviewing was undertaken. A total of 6235 interviews were undertaken in 2014. Separate univariable and multivariable modelling was undertaken to determine the best predictors of HED. Results: HED prevalence was 9.0%. The best predictors differed for each city. The higher probability of HED in the final models included beliefs that they have reached adulthood, feeling relaxed as an effect of drinking alcohol, and forgetting problems as an effect of drinking alcohol. Lower probability of HED was associated with not being interested in alcohol as a reason for limiting alcohol, and the belief that drinking alcohol is too expensive or a waste of money. Conclusion: Although some indicators were common across the four cities, the variables included in the final models predominantly differed from city to city. The need for country-specific prevention and early intervention programs are warranted

Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects and tissue-specific enrichment of eQTLs

We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Test beam performance of a CBC3-based mini-module for the Phase-2 CMS Outer Tracker before and after neutron irradiation

The Large Hadron Collider (LHC) at CERN will undergo major upgrades to increase the instantaneous luminosity up to 5–7.5×10$^{34}$ cm$^{-2}$s$^{-1}$. This High Luminosity upgrade of the LHC (HL-LHC) will deliver a total of 3000–4000 fb-1 of proton-proton collisions at a center-of-mass energy of 13–14 TeV. To cope with these challenging environmental conditions, the strip tracker of the CMS experiment will be upgraded using modules with two closely-spaced silicon sensors to provide information to include tracking in the Level-1 trigger selection. This paper describes the performance, in a test beam experiment, of the first prototype module based on the final version of the CMS Binary Chip front-end ASIC before and after the module was irradiated with neutrons. Results demonstrate that the prototype module satisfies the requirements, providing efficient tracking information, after being irradiated with a total fluence comparable to the one expected through the lifetime of the experiment

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "delta beta" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb(-1) collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification.Peer reviewe