Oregon State University

About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Climate change. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.

Gut microbiota and intestinal FXR mediate the clinical benefits of metformin.

Abstract: The anti-hyperglycemic effect of metformin is believed to be caused by its direct action on signaling processes in hepatocytes, leading to lower hepatic gluconeogenesis. Recently, metformin was reported to alter the gut microbiota community in humans, suggesting that the hyperglycemia-lowering action of the drug could be the result of modulating the population of gut microbiota. However, the critical microbial signaling metabolites and the host targets associated with the metabolic benefits of metformin remained elusive. Here, we performed metagenomic and metabolomic analysis of samples from individuals with newly diagnosed type 2 diabetes (T2D) naively treated with metformin for 3 d, which revealed that Bacteroides fragilis was decreased and the bile acid glycoursodeoxycholic acid (GUDCA) was increased in the gut. These changes were accompanied by inhibition of intestinal farnesoid X receptor (FXR) signaling. We further found that high-fat-diet (HFD)-fed mice colonized with B. fragilis were predisposed to more severe glucose intolerance, and the metabolic benefits of metformin treatment on glucose intolerance were abrogated. GUDCA was further identified as an intestinal FXR antagonist that improved various metabolic endpoints in mice with established obesity. Thus, we conclude that metformin acts in part through a B. fragilis-GUDCA-intestinal FXR axis to improve metabolic dysfunction, including hyperglycemia.

On the classification and nomenclature of baculoviruses: A proposal for revision

Abstract: Recent evidence from genome sequence analyses demands a substantial revision of the taxonomy and classification of the family Baculoviridae. Comparisons of 29 baculovirus genomes indicated that baculovirus phylogeny followed the classification of the hosts more closely than morphological traits that have previously been used for classification of this virus family. On this basis, dipteran- and hymenopteran-specific nucleopolyhedroviruses (NPV) should be separated from lepidopteran-specific NPVs and accommodated into different genera. We propose a new classification and nomenclature for the genera within the baculovirus family. According to this proposal the updated classification should include four genera: Alphabaculovirus (lepidopteran-specific NPV), Betabaculovirus (lepidopteran-specific Granuloviruses), Gammabaculovirus (hymenopteran-specific NPV) and Deltabaculovirus (dipteran-specific NPV).

Identification of nonpoint sources of fecal pollution in coastal waters by using host-specific 16S ribosomal DNA genetic markers from fecal anaerobes.

Abstract: We describe a new PCR-based method for distinguishing human and cow fecal contamination in coastal waters without culturing indicator organisms, and we show that the method can be used to track bacterial marker sequences in complex environments. We identified two human-specific genetic markers and five cow-specific genetic markers in fecal samples by amplifying 16S ribosomal DNA (rDNA) fragments from members of the genus Bifidobacterium and the Bacteroides-Prevotella group and performing length heterogeneity PCR and terminal restriction fragment length polymorphism analyses. Host-specific patterns suggested that there are species composition differences in the Bifidobacterium and Bacteroides-Prevotella populations of human and cow feces. The patterns were highly reproducible among different hosts belonging to the same species. Additionally, all host-specific genetic markers were detected in water samples collected from areas frequently contaminated with fecal pollution. Ease of detection and longer survival in water made Bacteroides-Prevotella indicators better than Bifidobacterium indicators. Fecal 16S rDNA sequences corresponding to our Bacteroides-Prevotella markers comprised closely related gene clusters, none of which exactly matched previously published Bacteroides or Prevotella sequences. Our method detected host-specific markers in water at pollutant concentrations of 2.8 × 10−5 to 2.8 × 10−7 g (dry weight) of feces/liter and 6.8 × 10−7 g (dry weight) of sewage/liter. Although our aim was to identify nonpoint sources of fecal contamination, the method described here should be widely applicable for monitoring spatial and temporal fluctuations in specific bacterial groups in natural environments.

Bedload and Size Distribution in Paved Gravel-Bed Streams

Abstract: Field data are used to study the size distribution of bedload in paved gravel-bed streams Similarity analysis yields the results that all grain size ranges are of approximately equal transportability when the critical condition for breaking the pavement is exceeded This result is only approximately correct due to deviations from similarity However, it is adequate to justify development of a method for calculating total bedload, which requires only the subpavement median grain size rather than the size distribution A method for calculating bedload size distribution that accounts for deviation from similarity is also developed

Guidelines for assessing the suitability of spatial climate data sets

Abstract: Spatial climate data are often key drivers of computer models and statistical analyses, which form the basis for scientific conclusions, management decisions, and other important outcomes. The recent availability of very high-resolution climate data sets raises important questions about the tendency to equate resolution with realism. This paper discusses the relationship between scale and spatial climate-forcing factors, and provides background and advice on assessing the suitability of data sets. Spatial climate patterns are most affected by terrain and water bodies, primarily through the direct effects of elevation, terrain-induced climate transitions, cold air drainage and inversions, and coastal effects. The importance of these factors is generally lowest at scales of 100 km and greater, and becomes greatest at less than 10 km. Except in densely populated regions of developed countries, typical station spacing is on the order of 100 km. Regions without major terrain features and which are at least 100 km from climatically important coastlines can be handled adequately by most interpolation techniques. Situations characterized by significant terrain features, but with no climatically important coastlines, no rain shadows, and a well-mixed atmosphere can be reasonably handled by methods that explicitly account for elevation effects. Regions having significant terrain features, and also significant coastal effects, rain shadows, or cold air drainage and inversions are best handled by sophisticated systems that are configured and evaluated by experienced climatologists. There is no one satisfactory method for quantitatively estimating errors in spatial climate data sets, because the field that is being estimated is unknown between data points. Perhaps the best overall way to assess errors is to use a combination of approaches, involve data that are as independent from those used in the analysis as possible, and use common sense in the interpretation of results. Data set developers are encouraged to conduct expert reviews of their draft data sets, which is probably the single most effective way to improve data set quality. Copyright  2006 Royal Meteorological Society.