University of Minnesota

About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.

Innovation in the States: A Diffusion Study

Abstract: This study focuses on a nonmonetary dimension of public policy—innovation by states in the fields of education, welfare, and civil rights. Innovation is considered equivalent to the adoption of a law by a state. From the literature on diffusion (or spread) of innovations, the explanation of user interaction is taken, and a simple model with an interaction term is constructed. The model performs fairly well when evaluated by several common criteria. The results do vary somewhat from one issue area to another; other types of supplementary analysis also indicate variation in diffusion patterns according to the issue involved. Political and economic differences among states are found to account for differences in time of adoption, and “innovativeness” is shown to be an issue- and time-specific factor.

A combined SVM and LDA approach for classification

Abstract: This paper describes a new large margin classifier, named SVM/LDA. This classifier can be viewed as an extension of support vector machine (SVM) by incorporating some global information about the data. The SVM/LDA classifier can be also seen as a generalization of linear discriminant analysis (LDA) by incorporating the idea of (local) margin maximization into standard LDA formulation. We show that existing SVM software can be used to solve the SVM/LDA formulation. We also present empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data.

Satellite remote sensing of wetlands

Abstract: To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.

Dynamic FoxO transcription factors

Abstract: Forkhead box O (FoxO) transcription factors FoxO1, FoxO3a, FoxO4 and FoxO6, the mammalian orthologs of Caenorhabditis elegans DAF-16, are emerging as an important family of proteins that modulate the expression of genes involved in apoptosis, the cell cycle, DNA damage repair, oxidative stress, cell differentiation, glucose metabolism and other cellular functions. FoxO proteins are regulated by multiple mechanisms. They undergo inhibitory phosphorylation by protein kinases such as Akt, SGK, IKK and CDK2 in response to external and internal stimuli. By contrast, they are activated by upstream regulators such as JNK and MST1 under stress conditions. Their activities are counterbalanced by the acetylases CBP and p300 and the deacetylase SIRT1. Also, whereas polyubiquitylation of FoxO1 and FoxO3a leads to their degradation by the proteasome, monoubiquitylation of FoxO4 facilitates its nuclear localization and augments its transcriptional activity. Thus, the potent functions of FoxO proteins are tightly controlled by complex signaling pathways under physiological conditions; dysregulation of these proteins may ultimately lead to disease such as cancer.

Sleep-Disordered Breathing, Glucose Intolerance, and Insulin Resistance The Sleep Heart Health Study

Abstract: Clinic-based studies suggest that sleep-disordered breathing (SDB) is associated with glucose intolerance and insulin resistance. However, in the available studies, researchers have not rigorously controlled for confounding variables to assess the independent relation between SDB and impaired glucose metabolism. The objective of this study was to determine whether SDB was associated with glucose intolerance and insulin resistance among community-dwelling subjects (n=2,656) participating in the Sleep Heart Health Study (1994-1999). SDB was characterized with the respiratory disturbance index and measurements of oxygen saturation during sleep. Fasting and 2-hour glucose levels measured during an oral glucose tolerance test were used to assess glycemic status. Relative to subjects with a respiratory disturbance index of less than 5.0 events/hour (the reference category), subjects with mild SDB (5.0-14.9 events/hour) and moderate to severe SDB (> or =15 events/hour) had adjusted odds ratios of 1.27 (95% confidence interval: 0.98, 1.64) and 1.46 (95% confidence interval: 1.09, 1.97), respectively, for fasting glucose intolerance (p for trend < 0.01). Sleep-related hypoxemia was also associated with glucose intolerance independently of age, gender, body mass index, and waist circumference. The results of this study suggest that SDB is independently associated with glucose intolerance and insulin resistance and may lead to type 2 diabetes mellitus.