University of New Mexico

About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.

Reorganization of an arid ecosystem in response to recent climate change

Abstract: Natural ecosystems contain many individuals and species interacting with each other and with their abiotic environment. Such systems can be expected to exhibit complex dynamics in which small perturbations can be amplified to cause large changes. Here, we document the reorganization of an arid ecosystem that has occurred since the late 1970s. The density of woody shrubs increased 3-fold. Several previously common animal species went locally extinct, while other previously rare species increased. While these changes are symptomatic of desertification, they were not caused by livestock grazing or drought, the principal causes of historical desertification. The changes apparently were caused by a shift in regional climate: since 1977 winter precipitation throughout the region was substantially higher than average for this century. These changes illustrate the kinds of large, unexpected responses of complex natural ecosystems that can occur in response to both natural perturbations and human activities.

Space-scale diagrams: understanding multiscale interfaces

Abstract: Big information worlds cause big problems for interfaces. There is too much to see. They are hard to navigate. An armada of techniques has been proposed to present the many scales of information needed. Space-scale diagrams provide an analytic framework for much of this work. By representing both a spatial world and its different magnifications explicitly, the diagrams allow the direct visualization and analysis of important scale related issues for interfaces.

Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography

Abstract: Background: Risk prediction models for breast cancer can be improved by the addition of recently identifi ed risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography. Methods: There were 2 392 998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11 638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent ( P <.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fi t. All statistical tests were twosided. Results: Statistically signifi cant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically signifi cant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confi dence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women. Conclusion: Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance. [J Natl Cancer Inst 2006;98: 1204 – 14 ]