Background and purpose The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine‐learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. Methods We used a multimodal 3‐T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single‐subject level classification. Results The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. Conclusions A machine‐learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.
Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.
This paper addresses the measurement of the social dimension of cognitive trust in collaborative networks. Trust indicators are typically measured and combined in literature in order to calculate partners' trustworthiness. When expressing the result of a measurement, some quantitative indication of the quality of the result-the uncertainty of measurement-should be given. However, currently this is not taken into account for the measurement of the social dimension of cognitive trust in collaborative networks. In view of this, an innovative metrology-based approach for the measurement of social cognitive trust indicators in collaborative networks is presented. Thus, a measurement result is always accompanied by its uncertainty of measurement, as well as by information traditionally used to properly interpret the results: the sample size, and the standard deviation of the sample.
SCALA© (Sampling Campaigns for Aerosols in the Low Atmosphere) is a web-based software system that was developed in a multidisciplinary manner to integrally support the documentation and the management and analysis of atmospheric aerosol data from sampling campaigns. The software development process applied considered the prototyping and the evolutionary approaches. The software product (SCALA©) allows for the comprehensive management of the sampling campaigns’ life cycle (management of the profiles and processes involved in the start-up, development and closure of a campaign) and provides support for both intra- and inter-campaigns data analysis. The pilot deployment of SCALA© considers the Spanish Network on Environmental Differential Mobility Analysers (DMAs) (REDMAAS) and the PROACLIM project. This research project involves, among other objectives, the study of temporal and spatial variations of the atmospheric aerosol through a set of microphysical properties (size distribution, optical properties, hygroscopicity, etc.) measured in several locations in Spain. The main conclusions regarding size distribution are presented in this work. These have been have been extracted through SCALA© from the data collected in the REDMAAS 2015 and 2019 intercomparison campaigns and two years (2015 and 2016) of measurements with two Scanning Mobility Particle Sizers (SMPS) at CIEMAT (Madrid, central Spain) and UDC (A Coruña, NW of Spain) sites.
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