Structure of Employment in South Dakota\u27s Manufacturing and Processing Sector

The descriptive research reported in this publication was part of a larger study carried out in South Dakota State University\u27s Economics Department from 1978 through 1982 on rural industrial development in South Dakota. Factors influencing rural industrial development at the local level were reported in Goeken and Goeken and Dobbs. A Master of Science Research Paper in Sociology by Au Yeung drew and reported on some of the data contained in the present publication. The Sociology Research Paper also contained an examination of the effects of industrial development on local population changes in South Dakota

An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska

Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues

Civil conflict, federalism and strategic delegation of leadership

This article analyzes negative externalities that policymakers in one region or group may impose upon the citizens of neighboring regions or groups. These externalities may be material, but they may also be psychological (in the form of envy). The latter form of externality may arise from the production of 'conspicuous' public goods. As a result, decentralized provision of conspicuous public goods may be too high. Potentially, a centralized legislature may internalize negative externalities. However, in a model with strategic delegation, we argue that the median voter in each jurisdiction may anticipate a reduction in local public goods supply and delegate to a policymaker who cares more for public goods than she does herself. This last effect mitigates the expected benefits of policy centralization. The authors' theory is then applied to the setting of civil conflict, where they discuss electoral outcomes in Northern Ireland and Yugoslavia before and after significant institutional changes that affected the degree of centralization. These case studies provide support for the authors' theoretical predictions

Dr1 (NC2) is present at tRNA genes and represses their transcription in human cells

Dr1 (also known as NC2{beta}) was identified as a repressor of RNA polymerase (pol) II transcription. It was subsequently shown to inhibit pol III transcription when expressed at high levels in vitro or in yeast cells. However, endogenous Dr1 was not detected at pol III-transcribed genes in growing yeast. In contrast, we demonstrate that endogenous Dr1 is present at pol III templates in human cells, as is its dimerization partner DRAP1 (also called NC2{alpha}). Expression of tRNA by pol III is selectively enhanced by RNAi-mediated depletion of endogenous human Dr1, but we found no evidence that DRAP1 influences pol III output in vivo. A stable association was detected between endogenous Dr1 and the pol III-specific transcription factor Brf1. This interaction may recruit Dr1 to pol III templates in vivo, as crosslinking to these sites increases following Brf1 induction. On the basis of these data, we conclude that the physiological functions of human Dr1 include regulation of pol III transcription

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor