University of Iceland

About: University of Iceland is a education organization based out in Reykjavik, Suðurnes, Iceland. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 5423 authors who have published 16199 publications receiving 694762 citations. The organization is also known as: Háskóli Íslands.

Standardizing assessment of elderly people in acute care: the interRAI Acute Care instrument.

Abstract: OBJECTIVES: To examine the frequency distributions and interrater reliability of individual items of the interRAI Acute Care instrument DESIGN: Observational study of a representative sample of older inpatients; duplicate assessments conducted on a subsample by independent assessors to examine interrater reliability SETTING: Acute medical, acute geriatric and orthopedic units in 13 hospitals in nine countries PARTICIPANTS: Five hundred thirty-three patients aged 70 and older (mean age 824, range 70–102) with an anticipated stay of 48 hours or longer of whom 161 received duplicate assessments MEASUREMENTS: Sixty-two clinical items across 11 domains Premorbid (3-day observation period before onset of the acute illness) and admission (the first 24 hours of hospital stay) assessments were conducted RESULTS: The frequency of deficits exceeded 30% for most items, ranging from 1% for physically abusive behavior to 86% for the need for support in activities of daily living after discharge Common deficits were in cognitive skills for daily decision-making (38% premorbid, 54% at admission), personal hygiene (37%, 65%), and walking (39%, 71%) Interrater reliability was substantial in the premorbid period (average κ=061) and admission period (average κ=066) Of the 69 items tested, less than moderate agreement (κ 08) for nine (13%) CONCLUSION: Initial assessment of the psychometric properties of the interRAI Acute Care instrument provided evidence that item selection and interrater reliability are appropriate for clinical application Further studies are required to examine the validity of embedded scales, diagnostic algorithms, and clinical protocols

NWChem: Past, present, and future

Abstract: Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

Abstract: Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a powerful tool able to model spatial information (e.g., contextual relations) of the image. However, due to the shortcomings of using the morphological profiles, many variants, extensions, and refinements of its definition have appeared stating that the morphological profile is still under continuous development. In this case, recently introduced theoretically sound attribute profiles (APs) can be considered as a generalization of the morphological profile, which is a powerful tool to model spatial information existing in the scene. Although the concept of the AP has been introduced in remote sensing only recently, an extensive literature on its use in different applications and on different types of data has appeared. To that end, the great amount of contributions in the literature that address the application of the AP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, and hyperspectral) proves how the AP is an effective and modern tool. The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works.

Hyperspectral Image Classification With Independent Component Discriminant Analysis

Abstract: In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses IC Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. When the data are projected in an independent space, the estimates of their multivariate density function can be computed in a much easier way as the product of univariate densities. A nonparametric kernel density estimator is used to compute the density functions of each IC. Finally, the Bayes rule is applied for the classification assignment. In this paper, we investigate the possibility of using ICDA for the classification of hyperspectral images. We study the influence of the algorithm used to enforce independence and of the number of IC retained for the classification, proposing an effective method to estimate the most suitable number. The proposed method is applied to several hyperspectral images, in order to test different data set conditions (urban/agricultural area, size of the training set, and type of sensor). Obtained results are compared with one of the most commonly used classifier of hyperspectral images (support vector machines) and show the comparative effectiveness of the proposed method in terms of accuracy.

Mutations in BRIP1 confer high risk of ovarian cancer

Abstract: Ovarian cancer causes more deaths than any other gynecologic malignancy in developed countries Sixteen million sequence variants, identified through whole-genome sequencing of 457 Icelanders, were imputed to 41,675 Icelanders genotyped using SNP chips, as well as to their relatives Sequence variants were tested for association with ovarian cancer (N of affected individuals = 656) We discovered a rare (041% allelic frequency) frameshift mutation, c2040_2041insTT, in the BRIP1 (FANCJ) gene that confers an increase in ovarian cancer risk (odds ratio (OR) = 813, P = 28 × 10(-14)) The mutation was also associated with increased risk of cancer in general and reduced lifespan by 36 years In a Spanish population, another frameshift mutation in BRIP1, c1702_1703del, was seen in 2 out of 144 subjects with ovarian cancer and 1 out of 1,780 control subjects (P = 0016) This allele was also associated with breast cancer (seen in 6/927 cases; P = 00079) Ovarian tumors from heterozygous carriers of the Icelandic mutation show loss of the wild-type allele, indicating that BRIP1 behaves like a classical tumor suppressor gene in ovarian cancer