Is a National Monetary Policy Optimal?

Monetary policy has differential effects throughout the United States. When setting monetary policy, central banks must consider how national and regional economic goals are being achieved. In this study, the methods and evidence are focused on using structural VAR analysis, assuming that the United States has an interest rate channel of monetary policy. The methods estimate the symmetry and magnitude of monetary shocks on income, unemployment and prices in major metropolitan statistical areas (MSAs) of the United States as compared to the national effects. As in Carlino and Defina (1998) and Florio (2005), differential regional effects connect to optima currency areas (OCA) literature, the advent of the Euro, increased regionalism, and the possibility of more monetary unions forming worldwide. Events in early 2010 concerning the Euro's stability show the importance of monitoring regions and their reactions to policy.E52 , E61, E37, R12

Is a National Monetary Policy Optimal?

Monetary policy has differential effects throughout the United States. When setting monetary policy, central banks must consider how national and regional economic goals are being achieved. In this study, the methods and evidence are focused on using structural VAR analysis, assuming that the United States has an interest rate channel of monetary policy. The methods estimate the symmetry and magnitude of monetary shocks on income, unemployment and prices in major metropolitan statistical areas (MSAs) of the United States as compared to the national effects. As in Carlino and Defina (1998) and Florio (2005), differential regional effects connect to optima currency areas (OCA) literature, the advent of the Euro, increased regionalism, and the possibility of more monetary unions forming worldwide. Events in early 2010 concerning the Euro's stability show the importance of monitoring regions and their reactions to policy

Internal Defect Detection in Hardwood Logs With Fast Magnetic Resonance Imaging.

Identification of defects such as knots in logs before the cutting operation would allow lumber mills to maximize the value of lumber from each log. This dissertation presented images obtained from scanning an oak log with magnetic resonance imaging (MRI). The unique characteristics of MRI images of hardwood logs were noted and were used to derive a quick algorithm to isolate defects. Defect regions had some pixels that varied considerably in intensity from their neighborhood, providing a seed for initiating the defect region. There was an overlap between the pixel gray level of the defects and clear wood. Therefore, traditional thresholding techniques did not cleanly separate these regions. In this study, region-growing methods were used to extract the defects. The algorithm grew the defect region seed until the border-pixel gray levels approached the average level of the neighborhood. The region-growing methods obtained more accurate defect regions than thresholding methods because of the simultaneous consideration of gray level and adjacency information. Two methods of MRI imaging were considered: spin-echo and echo-planar. Spin-echo imaging provided clear, detailed images but required about 20 seconds of acquisition time, which was too slow to be used in a production environment. Echo-planar images could be acquired in about 1/2 second, which was fast enough for production, but the images were fuzzy and noisy. The dissertation presented an algorithm that found the defect regions in spin-echo images. Region-growing methods use a number of parameters and the best parameters were unique for each image. However, common image statistics could be used to predict the proper parameters. The dissertation also presented an algorithm that found most of the defect regions in echo-planar images. Enhancing the echo-planar images using common general-purpose image-enhancement techniques failed because the lack of discrimination allowed the process to smooth image structures as well as noise. By taking advantage of the structure of a tree, smoothing between MRI frames accomplished the goal of smoothing along homogeneous areas and not across image structures. This z-axis smoothing enhanced the echo-planar image visually and reduced the number of false alarm defect regions

Does Size Matter? An Empirical Investigation into the Competitive Strategies of the Small Firm

This paper examines whether different strategies are associated with different sized firms in a focal industry dominated by small family-owned businesses. In an effort to shed light on how strategic choice is determined, a well-defined and geographically concentrated industry, i.e., the Northern California wine industry, is selected to minimize environmental noise. Factor analysis is applied to fourteen strategic elements to extract a parsimonious set of five primary competitive strategies: new product/market development, consolidation, niche focus, proprietary processes, and flexibility. The factors, new product/market development and consolidation as well as the control variable, age, are found to be significantly  associated with firm  size.

The renaissance strategist

Abstract The innovation practice of small family firms is an important and growing field of study. Top management teams' styles, their strategic orientations, and perceptions of external environment promote or deter innovation. This exploratory research proposes a model depicting the extent to which location impacts these variables. Ten Tuscan and ten Californian family wineries are investigated via questionnaire and in-person interviews to develop longitudinal case studies describing barriers or incentives to innovate. Changes in processes, products, or market orientations are subordinated to how top managers perceive internal and external pressure to implement innovation. Key Words: Strategy, Innovation, Location, Wine Business Desperately Seeking Serendipity: Exploring the Impact of Country Location on Innovation in the Wine Industry There is no strategy without surprise, no surprise without creativity, and true surprise and creativity are rare. Seeking strategic insight is the first key to obtaining these rare commodities, recognizing the importance of those insights when they arise is the second. Looking backward, many strategic insights sound trivial; looking forward, they are an incredible challenge to develop, recognize, and implement. The relentless search for insights is what keeps innovative companies and courageous entrepreneurs busy day and night

A Gene-Based Association Method for Mapping Traits Using Reference Transcriptome Data

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates ‘imputed’ gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations

Street connectivity and obesity in Glasgow, Scotland : impact of age, sex and socioeconomic position

This study investigated associations of street connectivity with body mass index (BMI), and whether these associations varied by sex, age and socioeconomic position, amongst adults in Glasgow, Scotland. Data on socio-demographic variables, height and weight were collected from 1062 participants in the Greater Glasgow Health and Well-being Study, and linked with neighbourhood-level census and geo-referenced data on area level deprivation and street connectivity. Results of multilevel models showed that, after adjustment for individual level covariates, street connectivity was not significantly associated with either BMI or BMI category; nor were there any significant interactions between age, sex or socioeconomic position and street connectivity

First-Year Spectroscopy for the SDSS-II Supernova Survey

This paper presents spectroscopy of supernovae discovered in the first season of the Sloan Digital Sky Survey-II Supernova Survey. This program searches for and measures multi-band light curves of supernovae in the redshift range z = 0.05 - 0.4, complementing existing surveys at lower and higher redshifts. Our goal is to better characterize the supernova population, with a particular focus on SNe Ia, improving their utility as cosmological distance indicators and as probes of dark energy. Our supernova spectroscopy program features rapid-response observations using telescopes of a range of apertures, and provides confirmation of the supernova and host-galaxy types as well as precise redshifts. We describe here the target identification and prioritization, data reduction, redshift measurement, and classification of 129 SNe Ia, 16 spectroscopically probable SNe Ia, 7 SNe Ib/c, and 11 SNe II from the first season. We also describe our efforts to measure and remove the substantial host galaxy contamination existing in the majority of our SN spectra.Comment: Accepted for publication in The Astronomical Journal(47pages, 9 figures

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Integration of Sequence Data from a Consanguineous Family with Genetic Data from an Outbred Population Identifies PLB1 as a Candidate Rheumatoid Arthritis Risk Gene

Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2×10−6). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted