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.

The effects of a smoking cessation intervention on 14.5-year mortality: a randomized clinical trial.

Abstract: Background Randomized clinical trials have not yet demonstrated the mortality benefit of smoking cessation. Objective To assess the long-term effect on mortality of a randomly applied smoking cessation program. Design The Lung Health Study was a randomized clinical trial of smoking cessation. Special intervention participants received the smoking intervention program and were compared with usual care participants. Vital status was followed up to 14.5 years. Setting 10 clinical centers in the United States and Canada. Patients 5887 middle-aged volunteers with asymptomatic airway obstruction. Measurements All-cause mortality and mortality due to cardiovascular disease, lung cancer, and other respiratory disease. Intervention The intervention was a 10-week smoking cessation program that included a strong physician message and 12 group sessions using behavior modification and nicotine gum, plus either ipratropium or a placebo inhaler. Results At 5 years, 21.7% of special intervention participants had stopped smoking since study entry compared with 5.4% of usual care participants. After up to 14.5 years of follow-up, 731 patients died: 33% of lung cancer, 22% of cardiovascular disease, 7.8% of respiratory disease other than cancer, and 2.3% of unknown causes. All-cause mortality was significantly lower in the special intervention group than in the usual care group (8.83 per 1000 person-years vs. 10.38 per 1000 person-years; P = 0.03). The hazard ratio for mortality in the usual care group compared with the special intervention group was 1.18 (95% CI, 1.02 to 1.37). Differences in death rates for both lung cancer and cardiovascular disease were greater when death rates were analyzed by smoking habit. Limitations Results apply only to individuals with airway obstruction. Conclusion Smoking cessation intervention programs can have a substantial effect on subsequent mortality, even when successful in a minority of participants.

A Systematic Screening of Total Antioxidants in Dietary Plants

Abstract: A predominantly plant-based diet reduces the risk for development of several chronic diseases. It is often assumed that antioxidants contribute to this protection, but results from intervention trials with single antioxidants administered as supplements quite consistently do not support any benefit. Because dietary plants contain several hundred different antioxidants, it would be useful to know the total concentration of electron-donating antioxidants (i.e., reductants) in individual items. Such data might be useful in the identification of the most beneficial dietary plants. We have assessed systematically total antioxidants in a variety of dietary plants used worldwide, including various fruits, berries, vegetables, cereals, nuts and pulses. When possible, we analyzed three or more samples of dietary plants from three different geographic regions in the world. Total antioxidants was assessed by the reduction of Fe(3+) to Fe(2+) (i.e., the FRAP assay), which occurred rapidly with all reductants with half-reaction reduction potentials above that of Fe(3+)/Fe(2+). The values, therefore, expressed the corresponding concentration of electron-donating antioxidants. Our results demonstrated that there is more than a 1000-fold difference among total antioxidants in various dietary plants. Plants that contain most antioxidants included members of several families, such as Rosaceae (dog rose, sour cherry, blackberry, strawberry, raspberry), Empetraceae (crowberry), Ericaceae (blueberry), Grossulariaceae (black currant), Juglandaceae (walnut), Asteraceae (sunflower seed), Punicaceae (pomegranate) and Zingiberaceae (ginger). In a Norwegian diet, fruits, berries and cereals contributed 43.6%, 27.1% and 11.7%, respectively, of the total intake of plant antioxidants. Vegetables contributed only 8.9%. The systematic analysis presented here will facilitate research into the nutritional role of the combined effect of antioxidants in dietary plants.

Gaussian predictive process models for large spatial data sets

Abstract: With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets. This computational burden is exacerbated in multivariate settings with several spatially dependent response variables. It is also aggravated when data are collected at frequent time points and spatiotemporal process models are used. With regard to this challenge, our contribution is to work with what we call predictive process models for spatial and spatiotemporal data. Every spatial (or spatiotemporal) process induces a predictive process model (in fact, arbitrarily many of them). The latter models project process realizations of the former to a lower dimensional subspace, thereby reducing the computational burden. Hence, we achieve the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets. We discuss attractive theoretical properties of these predictive processes. We also provide a computational template encompassing these diverse settings. Finally, we illustrate the approach with simulated and real data sets.