University of Missouri

About: University of Missouri is a education organization based out in Columbia, Missouri, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 41427 authors who have published 83598 publications receiving 2911437 citations. The organization is also known as: Mizzou & Missouri-Columbia.

Genetics of Aux/IAA and ARF action in plant growth and development.

Abstract: Dramatic advances in our understanding of auxin signal-response pathways have been made in recent years. Much of this new knowledge has come through the study of mutants in Arabidopsis thaliana. Mutations have been identified in a wide variety of auxin-response components, including auxin transporters, protein kinases and phosphatases, components of a ubiquitin-proteosome pathway, and transcriptional regulators. This review focuses on mutations that affect auxin-modulated transcription factors, in particular those in the Aux/IAA and AUXIN RESPONSE FACTOR (ARF) genes. Mutants in members of these related gene families exhibit phenotypes that indicate both unique localized functions, as well as overlapping redundant functions, throughout plant development - from embryogenesis to flowering. Effects of specific mutations on Aux/IAA and ARF protein functions at the biochemical and physiological levels will be discussed. We will also discuss potential mechanisms for interactions between auxin and light response pathways that are suggested by these mutants.

Disclosure Incentives and Effects on Cost of Capital around the World

Abstract: Prior research predicts that firms reliant on external financing are more likely to undertake a higher level of disclosure, and a higher disclosure level should, in turn, lead to a lower cost of external financing. This paper tests these predictions outside the United States where alternative legal and financial systems could mitigate the effectiveness of such disclosures and, comprehensively, examines both disclosure incentives and disclosure consequences on cost of capital for a common set of firms. Using a sample from 34 countries, we find that firms in industries with greater external financing needs have higher voluntary disclosure levels, and that an expanded disclosure policy for these firms leads to a lower cost of both debt and equity capital. Crosscountry differences in legal and financial systems affect observed disclosure levels in predicted ways. However, a surprising result in the study is that voluntary disclosure incentives appear to operate independently of country‐level factors, which su...

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Everyday Problem Solving in Engineering: Lessons for Engineering Educators

Abstract: Practicing engineers are hired, retained, and rewarded for solving problems, so engineering students should learn how to solve workplace problems Workplace engineering problems are substantively different from the kinds of problems that engineering students most often solve in the classroom; therefore, learning to solve classroom problems does not necessarily prepare engineering students to solve workplace problems These qualitative studies of workplace engineering problems identify the attributes of workplace problems Workplace problems are ill-structured and complex because they possess conflicting goals, multiple solution methods, non-engineering success standards, non-engineering constraints, unanticipated problems, distributed knowledge, collaborative activity systems, the importance of experience, and multiple forms of problem representation Some implications for designing engineering curricula and experiences that better prepare students for solving workplace problems are considered

Socioeconomic status measurement with discrete proxy variables: is principal component analysis a reliable answer?

Abstract: The last several years have seen a growth in the number of publications in economics that use principal component analysis (PCA) in the area of welfare studies. This paper explores the ways discrete data can be incorporated into PCA. The effects of discreteness of the observed variables on the PCA are reviewed. The statistical properties of the popular Filmer and Pritchett (2001) procedure are analyzed. The concepts of polychoric and polyserial correlations are introduced with appropriate references to the existing literature demonstrating their statistical properties. A large simulation study is carried out to compare various implementations of discrete data PCA. The simulation results show that the currently used method of running PCA on a set of dummy variables as proposed by Filmer and Pritchett (2001) can be improved upon by using procedures appropriate for discrete data, such as retaining the ordinal variables without breaking them into a set of dummy variables or using polychoric correlations. An empirical example using Bangladesh 2000 Demographic and Health Survey data helps in explaining the differences between procedures.