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.

Control of leaf morphogenesis by microRNAs

Abstract: Plants with altered microRNA metabolism have pleiotropic developmental defects, but direct evidence for microRNAs regulating specific aspects of plant morphogenesis has been lacking In a genetic screen, we identified the JAW locus, which produces a microRNA that can guide messenger RNA cleavage of several TCP genes controlling leaf development MicroRNA-guided cleavage of TCP4 mRNA is necessary to prevent aberrant activity of the TCP4 gene expressed from its native promoter In addition, overexpression of wild-type and microRNA-resistant TCP variants demonstrates that mRNA cleavage is largely sufficient to restrict TCP function to its normal domain of activity TCP genes with microRNA target sequences are found in a wide range of species, indicating that microRNA-mediated control of leaf morphogenesis is conserved in plants with very different leaf forms

Linking crystallographic model and data quality.

Abstract: In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.

Tea catechins and polyphenols: health effects, metabolism, and antioxidant functions.

Abstract: Increasing interest in the health benefits of tea has led to the inclusion of tea extracts in dietary supplements and functional foods. However, epidemiologic evidence regarding the effects of tea consumption on cancer and cardiovascular disease risk is conflicting. While tea contains a number of bioactive chemicals, it is particularly rich in catechins, of which epigallocatechin gallate (EGCG) is the most abundant. Catechins and their derivatives are thought to contribute to the beneficial effects ascribed to tea. Tea catechins and polyphenols are effective scavengers of reactive oxygen species in vitro and may also function indirectly as antioxidants through their effects on transcription factors and enzyme activities. The fact that catechins are rapidly and extensively metabolized emphasizes the importance of demonstrating their antioxidant activity in vivo. In humans, modest transient increases in plasma antioxidant capacity have been demonstrated following the consumption of tea and green tea catechins. The effects of tea and green tea catechins on biomarkers of oxidative stress, especially oxidative DNA damage, appear very promising in animal models, but data on biomarkers of in vivo oxidative stress in humans are limited. Larger human studies examining the effects of tea and tea catechin intake on biomarkers of oxidative damage to lipids, proteins, and DNA are needed.

Working With Missing Values

Abstract: Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustrated for a linear model, and a series of recommendations is provided. When missing values cannot be avoided, multiple imputation and full information methods offer substantial improvements over traditional approaches. Selected results using SPSS, NORM, Stata (mvis/micombine), and Mplus are included as is a table of available software and an appendix with examples of programs for Stata and Mplus.