Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Background: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as "how old the brain looks", and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1673). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0.46, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.24, p<.001) and a significant weight (-0.25, p=0.002) in a multivariate regression equation with age. Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health

A Randomized Real-Valued Negative Selection Algorithm

This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. The proposed method is a randomized algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance. © Springer-Verlag Berlin Heidelberg 2003

Magnetic Coordinate Systems

Geospace phenomena such as the aurora, plasma motion, ionospheric currents and associated magnetic field disturbances are highly organized by Earth's main magnetic field. This is due to the fact that the charged particles that comprise space plasma can move almost freely along magnetic field lines, but not across them. For this reason it is sensible to present such phenomena relative to Earth's magnetic field. A large variety of magnetic coordinate systems exist, designed for different purposes and regions, ranging from the magnetopause to the ionosphere. In this paper we review the most common magnetic coordinate systems and describe how they are defined, where they are used, and how to convert between them. The definitions are presented based on the spherical harmonic expansion coefficients of the International Geomagnetic Reference Field (IGRF) and, in some of the coordinate systems, the position of the Sun which we show how to calculate from the time and date. The most detailed coordinate systems take the full IGRF into account and define magnetic latitude and longitude such that they are constant along field lines. These coordinate systems, which are useful at ionospheric altitudes, are non-orthogonal. We show how to handle vectors and vector calculus in such coordinates, and discuss how systematic errors may appear if this is not done correctly

Validating Gene Clusterings by Selecting Informative Gene Ontology Terms with Mutual Information

We propose a method for global validation of gene clusterings. The method selects a set of informative and non-redundant GO terms through an exploration of the Gene Ontology structure guided by mutual information. Our approach yields a global assessment of the clustering quality, and a higher level interpretation for the clusters, as it relates GO terms with specific clusters. We show that in two gene expression data sets our method offers an improvement over previous approaches

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results