Predicting Alzheimer's risk: why and how?

Because the pathologic processes that underlie Alzheimer's disease (AD) appear to start 10 to 20 years before symptoms develop, there is currently intense interest in developing techniques to accurately predict which individuals are most likely to become symptomatic. Several AD risk prediction strategies - including identification of biomarkers and neuroimaging techniques and development of risk indices that combine traditional and non-traditional risk factors - are being explored. Most AD risk prediction strategies developed to date have had moderate prognostic accuracy but are limited by two key issues. First, they do not explicitly model mortality along with AD risk and, therefore, do not differentiate individuals who are likely to develop symptomatic AD prior to death from those who are likely to die of other causes. This is critically important so that any preventive treatments can be targeted to maximize the potential benefit and minimize the potential harm. Second, AD risk prediction strategies developed to date have not explored the full range of predictive variables (biomarkers, imaging, and traditional and non-traditional risk factors) over the full preclinical period (10 to 20 years). Sophisticated modeling techniques such as hidden Markov models may enable the development of a more comprehensive AD risk prediction algorithm by combining data from multiple cohorts. As the field moves forward, it will be critically important to develop techniques that simultaneously model the risk of mortality as well as the risk of AD over the full preclinical spectrum and to consider the potential harm as well as the benefit of identifying and treating high-risk older patients

Distribution and Character of Naleds in Northeastern Alaska

Satellite imagery and high- and low-altitude aerial photography of the North Slope of Alaska indicate that naleds (features formed during river icing) are widespread east of the Colville River but less abundant to its west. Where naleds occur, stream channels are wide and often braided. Their distribution can be related to changes in stream gradient and to the occurrence of springs. Large naleds, such as occur on the Kongakut River, often survive the summer melt season to form the nucleus of icing in the succeeding winter. Major naleds also are likely to significantly influence the nature of permafrost in their immediate vicinity. A map of naleds may serve as a guide to sources of perennially flowing water

A literature review on community-acquired methicillin-resistant Staphylococcus aureus in the United States: Clinical information for primary care nurse practitioners

Purpose: To analyze the state of the science of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) in the United States to support the integration of current knowledge for primary care nurse practitioners’ (PCNP) practice. Data sources: Published research limited to U.S. studies in MEDLINE, CINAHL, and Cochrane Review from 1950 to the week of September 4, 2008. Investigations were identified through electronic search engines and databases. Manual searches were done of hard copy references in journal articles. Citations and reference lists for English language research studies of CA-MRSA in the United States were reviewed to identify additional research that fit evaluation criteria for this analysis. Conclusions: Until the late 1990s, healthcare-associated MRSA (HA-MRSA) was the predominant cause of serious infections. Recently, CA-MRSA has caused infections in previously healthy nonhospitalized people. Major demographic and epidemiological differences exist between the two types of resistant bacteria; the emergence of CA-MRSA suggests new implications for primary care. Implications for practice: PCNPs will undoubtedly treat MRSA infections and need a comprehensive understanding of the pathogenicity, diagnosis, and management of CA-MRSA to ensure expedient and appropriate treatment. This will help to prevent invasive disease as a result of improperly treated infections.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79099/1/j.1745-7599.2010.00571.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/79099/2/JAAN_571_sm_Tables1.pd

Lewis L. Sims and Jim Barnes in a Junior Recital

This is the program for the junior voice recital of baritone, Lewis Sims, accompanied by Deborah Mashburn on piano, and the junior piano recital of Jim Barnes. The recital was held on October 7, 1966, in Mitchell Hall Auditorium

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation