Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests

Abstract Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.http://deepblue.lib.umich.edu/bitstream/2027.42/117372/1/12919_2007_Article_2426.pd

User guide to the Magellan synthetic aperture radar images

The Magellan radar-mapping mission collected a large amount of science and engineering data. Now available to the general scientific community, this data set can be overwhelming to someone who is unfamiliar with the mission. This user guide outlines the mission operations and data set so that someone working with the data can understand the mapping and data-processing techniques used in the mission. Radar-mapping parameters as well as data acquisition issues are discussed. In addition, this user guide provides information on how the data set is organized and where specific elements of the set can be located

An ontological approach to creating an Andean Weaving Knowledge Base

Andean textiles are products of one of the richest, oldest and continuous weaving traditions in the world. Understanding the knowledge and practice of textile production as a form of cultural heritage is particularly relevant in the Andean context due to erosion of clothing traditions, reuse of traditional textiles on commodities targeted at the tourism market, and loss of knowledge embedded in textile production. ``Weaving Communities of Practice'' was a pilot project that aimed to create a knowledge base of Andean weaving designed to contribute to curatorial practice and heritage policy. The research team gathered data on the chain of activities, instruments, resources, peoples, places and knowledge involved in the production of textiles, relating to over 700 textile samples. A major part of the project has been the modelling and representation of the knowledge of domain experts and information about the textile objects themselves in the form of an OWL ontology, and the development of a suite of search facilities to be supported by the ontology. This paper describes the research challenges faced in developing the ontology and search facilities, the methodology adopted, the design and implementation of the system, and the design and outcomes of a user evaluation of the system undertaken with a group of domain experts

Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes

Soil microbial diversity, by high-throughput sequencing data to characterise the variation in marker gene sequences, for 14 sites along a 3000 m elevation gradient in tropical forest in Peru. For bacterial community composition, the 16S rRNA gene was amplified in triplicate PCR reactions using the 515f and 806r primers. For fungal community composition, the first internal transcribed spacer region (ITS1) of the rRNA gene was amplified using the ITS1-F and ITS2 primer pair

The Contribution of the Parietal Lobes to Speaking and Writing

The left parietal lobe has been proposed as a major language area. However, parietal cortical function is more usually considered in terms of the control of actions, contributing both to attention and cross-modal integration of external and reafferent sensory cues. We used positron emission tomography to study normal subjects while they overtly generated narratives, both spoken and written. The purpose was to identify the parietal contribution to the modality-specific sensorimotor control of communication, separate from amodal linguistic and memory processes involved in generating a narrative. The majority of left and right parietal activity was associated with the execution of writing under visual and somatosensory control irrespective of whether the output was a narrative or repetitive reproduction of a single grapheme. In contrast, action-related parietal activity during speech production was confined to primary somatosensory cortex. The only parietal area with a pattern of activity compatible with an amodal central role in communication was the ventral part of the left angular gyrus (AG). The results of this study indicate that the cognitive processing of language within the parietal lobe is confined to the AG and that the major contribution of parietal cortex to communication is in the sensorimotor control of writing

Surface processes recorded by rocks and soils on Meridiani Planum, Mars: Microscopic Imager observations during Opportunity's first three extended missions

The Microscopic Imager (MI) on the Mars Exploration Rover Opportunity has returned images of Mars with higher resolution than any previous camera system, allowing detailed petrographic and sedimentological studies of the rocks and soils at the Meridiani Planum landing site. Designed to simulate a geologist's hand lens, the MI is mounted on Opportunity's instrument arm and can resolve objects 0.1 mm across or larger. This paper provides an overview of MI operations, data calibration, and analysis of MI data returned during the first 900 sols (Mars days) of the Opportunity landed mission. Analyses of Opportunity MI data have helped to resolve major questions about the origin of observed textures and features. These studies support eolian sediment transport, rather than impact surge processes, as the dominant depositional mechanism for Burns formation strata. MI stereo observations of a rock outcrop near the rim of Erebus Crater support the previous interpretation of similar sedimentary structures in Eagle Crater as being formed by surficial flow of liquid water. Well-sorted spherules dominate ripple surfaces on the Meridiani plains, and the size of spherules between ripples decreases by about 1 mm from north to south along Opportunity's traverse between Endurance and Erebus craters

Subjectivity in a context of environmental change: opening new dialogues in mental health research

In a period of unstable experimentation with challenges of globalization of associated risks, and disenchantment with ‘enduring injustice’, we bring forward a consideration of subjectivity to the study of environmental change and mental health. We begin by identifying how mainstream climate change and mental health studies are unable to explain the emergent and co-evolutionary pathways of agency. As a means of freeing these studies of their objective dimensions of linear-causation, we argue in favour of a re-positioning of subjectivity within an appreciation of recognition conflicts and beyond the over-deterministic interpretations of power centres—state, market or religion. We draw on one example of scientific research that was conducted in a region undergoing strong environmental, social and cultural changes, in the state of São Paulo/Brazil, with the aim to open mental health research to new dialogues, to which we contribute with the notion of the ‘pluriversal subject’

predictive precision medicine towards the computational challenge

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level