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Institution

University of Minnesota

EducationMinneapolis, Minnesota, United States
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.


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Journal ArticleDOI
TL;DR: The magnitude of species-driven differences is much larger than previously thought and greater than climate-driven variation, and the decomposability of a species' litter is consistently correlated with that species' ecological strategy within different ecosystems globally, representing a new connection between whole plant carbon strategy and biogeochemical cycling.
Abstract: Worldwide decomposition rates depend both on climate and the legacy of plant functional traits as litter quality. To quantify the degree to which functional differentiation among species affects their litter decomposition rates, we brought together leaf trait and litter mass loss data for 818 species from 66 decomposition experiments on six continents. We show that: (i) the magnitude of species-driven differences is much larger than previously thought and greater than climate-driven variation; (ii) the decomposability of a species' litter is consistently correlated with that species' ecological strategy within different ecosystems globally, representing a new connection between whole plant carbon strategy and biogeochemical cycling. This connection between plant strategies and decomposability is crucial for both understanding vegetation-soil feedbacks, and for improving forecasts of the global carbon cycle.

1,935 citations

16 Aug 1996
TL;DR: This paper showed that primary productivity in more diverse plant communities is more resistant to, and recovers more fully from, a major drought and that each additional species lost from our grasslands had a progressively greater impact on drought resistance.
Abstract: One of the ecological tenets justifying conservation of biodiversity is that diversity begets stability. Impacts of biodiversity on population dynamics and ecosystem functioning have long been debated1–7, however, with many theoretical explorations2–6,8–11 but few field studies12–15. Here we describe a long-term study of grasslands16,17 which shows that primary productivity in more diverse plant communities is more resistant to, and recovers more fully from, a major drought. The curvilinear relationship we observe suggests that each additional species lost from our grasslands had a progressively greater impact on drought resistance. Our results support the diversity—stability hypothesis5,6,18,19, but not the alternative hypothesis that most species are functionally redundant19–21. This study implies that the preservation of biodiversity is essential for the maintenance of stable productivity in ecosystems.

1,932 citations

Journal ArticleDOI
TL;DR: The focus is on regression problems, which are those in which one of the measures, the dependent Variable, is of special interest, and the authors wish to explore its relationship with the other variables.
Abstract: Model fitting is an important part of all sciences that use quantitative measurements. Experimenters often explore the relationships between measures. Two subclasses of relationship problems are as follows: • Correlation problems: those in which we have a collection of measures, all of interest in their own right, and wish to see how and how strongly they are related. • Regression problems : those in which one of the measures, the dependent Variable, is of special interest, and we wish to explore its relationship with the other variables. These other variables may be called the independent Variables, the predictor Variables, or the coVariates. The dependent variable may be a continuous numeric measure such as a boiling point or a categorical measure such as a classification into mutagenic and nonmutagenic. We should emphasize that using the words ‘correlation problem’ and ‘regression problem’ is not meant to tie these problems to any particular statistical methodology. Having a ‘correlation problem’ does not limit us to conventional Pearson correlation coefficients. Log-linear models, for example, measure the relationship between categorical variables in multiway contingency tables. Similarly, multiple linear regression is a methodology useful for regression problems, but so also are nonlinear regression, neural nets, recursive partitioning and k-nearest neighbors, logistic regression, support vector machines and discriminant analysis, to mention a few. All of these methods aim to quantify the relationship between the predictors and the dependent variable. We will use the term ‘regression problem’ in this conceptual form and, when we want to specialize to multiple linear regression using ordinary least squares, will describe it as ‘OLS regression’. Our focus is on regression problems. We will use y as shorthand for the dependent variable and x for the collection of predictors available. There are two distinct primary settings in which we might want to do a regression study: • Prediction problems:We may want to make predictions of y for future cases where we know x but do not knowy. This for example is the problem faced with the Toxic Substances Control Act (TSCA) list. This list contains many tens of thousands of compounds, and there is a need to identify those on the list that are potentially harmful. Only a small fraction of the list however has any measured biological properties, but all of them can be characterized by chemical descriptors with relative ease. Using quantitative structure-activity relationships (QSARs) fitted to this small fraction to predict the toxicities of the much larger collection is a potentially cost-effective way to try to sort the TSCA compounds by their potential for harm. Later, we will use a data set for predicting the boiling point of a set of compounds on the TSCA list from some molecular descriptors. • Effect quantification:We may want to gain an understanding of how the predictors enter into the relationship that predicts y. We do not necessarily have candidate future unknowns that we want to predict, we simply want to know how each predictor drives the distribution of y. This is the setting seen in drug discovery, where the biological activity y of each in a collection of compounds is measured, along with molecular descriptors x. Finding out which descriptors x are associated with high and which with low biological activity leads to a recipe for new compounds which are high in the features associated positively with activity and low in those associated with inactivity or with adverse side effects. These two objectives are not always best served by the same approaches. ‘Feature selection’ skeeping those features associated withy and ignoring those not associated with y is very commonly a part of an analysis meant for effect quantification but is not necessarily helpful if the objective is prediction of future unknowns. For prediction, methods such as partial least squares (PLS) and ridge regression (RR) that retain all features but rein in their contributions are often found to be more effective than those relying on feature selection. What Is Overfitting? Occam’s Razor, or the principle of parsimony, calls for using models and procedures that contain all that is necessary for the modeling but nothing more. For example, if a regression model with 2 predictors is enough to explainy, then no more than these two predictors should be used. Going further, if the relationship can be captured by a linear function in these two predictors (which is described by 3 numbers sthe intercept and two slopes), then using a quadratic violates parsimony. Overfitting is the use of models or procedures that violate parsimonysthat is, that include more terms than are necessary or use more complicated approaches than are necessary. It is helpful to distinguish two types of overfitting: • Using a model that is more flexible than it needs to be. For example, a neural net is able to accommodate some curvilinear relationships and so is more flexible than a simple linear regression. But if it is used on a data set that conforms to the linear model, it will add a level of complexity without * Corresponding author e-mail: doug@stat.umn.edu. 1 J. Chem. Inf. Comput. Sci. 2004,44, 1-12

1,931 citations

Journal ArticleDOI
TL;DR: In patients with heart failure and a preserved ejection fraction, treatment with spironolactone did not significantly reduce the incidence of the primary composite outcome of death from cardiovascular causes, aborted cardiac arrest, or hospitalization for the management of heart failure.
Abstract: Background Mineralocorticoid-receptor antagonists improve the prognosis for patients with heart failure and a reduced left ventricular ejection fraction. We evaluated the effects of spironolactone in patients with heart failure and a preserved left ventricular ejection fraction. Methods In this randomized, double-blind trial, we assigned 3445 patients with symptomatic heart failure and a left ventricular ejection fraction of 45% or more to receive either spironolactone (15 to 45 mg daily) or placebo. The primary outcome was a composite of death from cardiovascular causes, aborted cardiac arrest, or hospitalization for the management of heart failure. Results With a mean follow-up of 3.3 years, the primary outcome occurred in 320 of 1722 patients in the spironolactone group (18.6%) and 351 of 1723 patients in the placebo group (20.4%) (hazard ratio, 0.89; 95% confidence interval [CI], 0.77 to 1.04; P = 0.14). Of the components of the primary outcome, only hospitalization for heart failure had a significantly lower incidence in the spironolactone group than in the placebo group (206 patients [12.0%] vs. 245 patients [14.2%]; hazard ratio, 0.83; 95% CI, 0.69 to 0.99, P = 0.04). Neither total deaths nor hospitalizations for any reason were significantly reduced by spironolactone. Treatment with spiron olactone was associated with increased serum creatinine levels and a doubling of the rate of hyperkalemia (18.7%, vs. 9.1% in the placebo group) but reduced hypokalemia. With frequent monitoring, there were no significant differences in the incidence of serious adverse events, a serum creatinine level of 3.0 mg per deciliter (265 μmol per liter) or higher, or dialysis. Conclusions In patients with heart failure and a preserved ejection fraction, treatment with spironolactone did not significantly reduce the incidence of the primary composite outcome of death from cardiovascular causes, aborted cardiac arrest, or hospitalization for the management of heart failure. (Funded by the National Heart, Lung, and Blood Institute; TOPCAT ClinicalTrials.gov number, NCT00094302.)

1,930 citations

Journal ArticleDOI
01 Oct 2010-Genetics
TL;DR: A new class of sequence-specific nucleases created by fusing transcription activator-like effectors (TALEs) to the catalytic domain of the FokI endonuclease is reported.
Abstract: Engineered nucleases that cleave specific DNA sequences in vivo are valuable reagents for targeted mutagenesis. Here we report a new class of sequence-specific nucleases created by fusing transcription activator-like effectors (TALEs) to the catalytic domain of the FokI endonuclease. Both native and custom TALE-nuclease fusions direct DNA double-strand breaks to specific, targeted sites.

1,928 citations


Authors

Showing all 118112 results

NameH-indexPapersCitations
Walter C. Willett3342399413322
David J. Hunter2131836207050
David Miller2032573204840
Mark I. McCarthy2001028187898
Dennis W. Dickson1911243148488
David H. Weinberg183700171424
Eric Boerwinkle1831321170971
John C. Morris1831441168413
Aaron R. Folsom1811118134044
H. S. Chen1792401178529
Jie Zhang1784857221720
Jasvinder A. Singh1762382223370
Feng Zhang1721278181865
Gang Chen1673372149819
Hongfang Liu1662356156290
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023200
20221,176
202111,903
202011,807
201910,984
201810,367