Application of graph theoretical methods to the functional connectome of human brain.

During the past decade, there has been a great interest in creating mathematical models to describe the properties of connectivity in the human brain. One of the established tools to describe these interactions among regions of the brain is graph theory. However, graph theoretical methods were mainly designed for the analysis of single network which is problematic for neuroscientists wishing to study groups of subjects. Specifically, studies using the Rich Club (RC) graph measure require cumbersome methods to make statistical inferences. In the first part of this work, we propose a framework to analyse the inter-subject variability in Rich Club organisation. The proposed framework is used to identify the changes in RC coefficient and RC organisation in patients with schizophrenia relative to healthy control. We follow this work by proposing a novel method, named Rich Block (RB), which is a combination of the tradition Rich Club and Stochastic Block Models (SBM). We show that using RBs can not only facilitate an inter-subject statistical inference, it can also account for differences in profile of connectivity, and control for subject-level covariates. We validate the Rich Block approach by simulating networks of different size and structure. We find that RB accurately estimates RC coefficients and RC organisations, specifically, in network with large number of nodes and blocks. With real data we use RB to identify changes in coefficient and organisation of highly connected sub-graphs of hub blocks in schizophrenia. In the final portion of this work, we examine the methods used to define each edge in networks formed from resting-state functional magnetic resonance imaging (rs-fMRI). The standard approach in rs-fMRI is to divide the brain into regions, extract time series, and compute the temporal correlation between each region. These correlations are assumed to follow standard results, when in fact serial autocorrelation in the time series can corrupt these results. While some authors have proposed corrections to account for autocorrelation, they are poorly documented and always assume homogeneity of autocorrelation over brain regions. Thus we propose a method to account for bias in interregion correlation estimates due to autocorrelation. We develop an exact method and an approximate, more computationally efficient method that adjusts for the sampling variability in the correlation coefficient. We use inter-subject scrambled real-data to validate the proposed methods under a null setting, and intact real-data to examine the impact of our method on graph theoretical measures. We find that the standard methods fail to practically correct the sensitivity and specificity level due to over-simplifying the temporal structure of BOLD time series, while even our approximate method is substantially more accurate

Hybrid feature selection based on principal component analysis and grey wolf optimizer algorithm for Arabic news article classification

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a hybrid filter-wrapper method based on Principal Component Analysis (PCA) as a filter approach to select an appropriate and informative subset of features and Grey Wolf Optimizer (GWO) as wrapper approach (PCA-GWO) to select further informative features. Logistic Regression (LR) is used as an elevator to test the classification accuracy of candidate feature subsets produced by GWO. Three Arabic datasets, namely Alkhaleej, Akhbarona, and Arabiya, are used to assess the efficiency of the proposed method. The experimental results confirm that the proposed method based on PCA-GWO outperforms the baseline classifiers with/without feature selection and other feature selection approaches in terms of classification accuracy

Traitement continu des requêtes dépendantes de la localisation dans des environnements intérieurs

Cet article développe une représentation de données spatiales d’un environnement intérieur dit “indoor” qui tient compte des dimensions contextuelles centrées sur l’utilisateur et aborde les enjeux de gestion de données mobiles. Un modèle de données “indoor” hiérarchique et sensible au contexte est proposé. Cette conception hiérarchique favorise un traitement adaptatif et efficace des requêtes dépendantes de la localisation. Un langage de requêtes continues est développé et illustré par des exemples de requêtes. Cette approche de modélisation est complétée par le développement d’algorithmes de traitement continu des requêtes de recherche de chemin hiérarchique et des requêtes de zone sur des objets mobiles en “indoor”. Une étude expérimentale des solutions développées a été menée pour évaluer la performance et le passage à l’échelle à l’égard des propriétés intrinsèques des solutions proposées

Confound modelling in UK Biobank brain imaging

© 2020 Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds

Prediction of the intention to use a smartwatch : a comparative approach using machine learning and partial least squares structural equation modeling

This study makes use of a cohesive yet innovative research model to identify the determinants of the adoption of smart watches using constructs from the Technology Acceptance Model (TAM) and constructs of smartwatches, including effectiveness, content richness, and personal innovativeness. The chief objective of the study was to encourage the use of smartwatches for medical purposes so that the role of doctors can be made more effective and to facilitate access to patient records. Our conceptual framework highlights the association of TAM constructs (i.e., perceived usefulness and perceived ease of use) with the content richness, the construct of user satisfaction, and innovativeness. To measure the effectiveness of the smartwatch, an external factor based on the flow theory was added, which emphasizes the control over the smartwatch and the degree of involvement. The study employs data from 385 respondents involved in the field of medicine, such as doctors, patients, and nurses. The data were gathered through a survey and used for evaluation of the research model using partial least squares structural equation modeling (PLS-SEM) and machine learning (ML) models. The significance and performance of factors impacting THE adoption of smartwatches were also identified using Importance-Performance Map Analysis (IPMA). User satisfaction is the most important predictor of intention to adopt a medical smartwatch according to the ML and IPMA analyses. The fitting of the structural equation model to the sample showed a high dependence of user satisfaction on perceived usefulness and perceived ease of use. Furthermore, two critical factors, innovativeness and content richness, are demonstrated to enhance perceived usefulness. However, one should consider that perceived usefulness or behavioral intention could not be determined based on perceived ease of use. In general, the findings suggest that smartwatch usage could become critically important in the medical field as a mediator that allows doctors, patients, and other users to access essential information

Spinal Anesthesia Reduces Myocardial Ischemia-triggered Ventricular Arrhythmias by Suppressing Spinal Cord Neuronal Network Interactions in Pigs

Background: Cardiac sympathoexcitation leads to ventricular arrhythmias. Spinal anesthesia modulates sympathetic output and can be cardioprotective. However, its effect on the cardio-spinal reflexes and network interactions in the dorsal horn cardiac afferent neurons and the intermediolateral nucleus sympathetic neurons that regulate sympathetic output is not known. The authors hypothesize that spinal bupivacaine reduces cardiac neuronal firing and network interactions in the dorsal horn–dorsal horn and dorsal horn–intermediolateral nucleus that produce sympathoexcitation during myocardial ischemia, attenuating ventricular arrhythmogenesis. Methods: Extracellular neuronal signals from the dorsal horn and intermediolateral nucleus neurons were simultaneously recorded in Yorkshire pigs (n = 9) using a 64-channel high-density penetrating microarray electrode inserted at the T2 spinal cord. Dorsal horn and intermediolateral nucleus neural interactions and known markers of cardiac arrhythmogenesis were evaluated during myocardial ischemia and cardiac load–dependent perturbations with intrathecal bupivacaine. Results: Cardiac spinal neurons were identified based on their response to myocardial ischemia and cardiac load–dependent perturbations. Spinal bupivacaine did not change the basal activity of cardiac neurons in the dorsal horn or intermediolateral nucleus. After bupivacaine administration, the percentage of cardiac neurons that increased their activity in response to myocardial ischemia was decreased. Myocardial ischemia and cardiac load–dependent stress increased the short-term interactions between the dorsal horn and dorsal horn (324 to 931 correlated pairs out of 1,189 pairs, P \u3c 0.0001), and dorsal horn and intermediolateral nucleus neurons (11 to 69 correlated pairs out of 1,135 pairs, P \u3c 0.0001). Bupivacaine reduced this network response and augmentation in the interactions between dorsal horn–dorsal horn (931 to 38 correlated pairs out of 1,189 pairs, P \u3c 0.0001) and intermediolateral nucleus–dorsal horn neurons (69 to 1 correlated pairs out of 1,135 pairs, P \u3c 0.0001). Spinal bupivacaine reduced shortening of ventricular activation recovery interval and dispersion of repolarization, with decreased ventricular arrhythmogenesis during acute ischemia. Conclusions: Spinal anesthesia reduces network interactions between dorsal horn–dorsal horn and dorsal horn–intermediolateral nucleus cardiac neurons in the spinal cord during myocardial ischemia. Blocking short-term coordination between local afferent–efferent cardiac neurons in the spinal cord contributes to a decrease in cardiac sympathoexcitation and reduction of ventricular arrhythmogenesis

Reduction of CO\u3csub\u3e2\u3c/sub\u3e By a Masked Two-Coordinate Cobalt(I) Complex and Characterization of a Proposed Oxodicobalt(II) Intermediate

Fixation and chemical reduction of CO2 are important for utilization of this abundant resource, and understanding the detailed mechanism of C-O cleavage is needed for rational development of CO2 reduction methods. Here, we describe a detailed analysis of the mechanism of the reaction of a masked two-coordinate cobalt(i) complex, LtBuCo (where LtBu = 2,2,6,6-tetramethyl-3,5-bis[(2,6-diisopropylphenyl)imino]hept-4-yl), with CO2, which yields two products of C-O cleavage, the cobalt(i) monocarbonyl complex LtBuCo(CO) and the dicobalt(ii) carbonate complex (LtBuCo)2(μ-CO3). Kinetic studies and computations show that the κN,η6-arene isomer of LtBuCo rearranges to the κ2N,N′ binding mode prior to binding of CO2, which contrasts with the mechanism of binding of other substrates to LtBuCo. Density functional theory (DFT) studies show that the only low-energy pathways for cleavage of CO2 proceed through bimetallic mechanisms, and DFT and highly correlated domain-based local pair natural orbital coupled cluster (DLPNO-CCSD(T)) calculations reveal the cooperative effects of the two metal centers during facile C-O bond rupture. A plausible intermediate in the reaction of CO2 with LtBuCo is the oxodicobalt(II) complex LtBuCoOCoLtBu, which has been independently synthesized through the reaction of LtBuCo with N2O. The rapid reaction of LtBuCoOCoLtBu with CO2 to form the carbonate product indicates that the oxo species is kinetically competent to be an intermediate during CO2 cleavage by LtBuCo. LtBuCoOCoLtBu is a novel example of a thoroughly characterized molecular cobalt-oxo complex where the cobalt ions are clearly in the +2 oxidation state. Its nucleophilic reactivity is a consequence of high charge localization on the μ-oxo ligand between two antiferromagnetically coupled high-spin cobalt(ii) centers, as characterized by DFT and multireference complete active space self-consistent field (CASSCF) calculations

Differentiation between small hepatocellular carcinoma (<3 cm) and benign hepatocellular lesions in patients with Budd-Chiari syndrome: the role of multiparametric MR imaging

ObjectiveTo investigate the value of multiparametric MR imaging to differentiate between small hepatocellular carcinoma (s-HCC) versus benign liver lesions in patients with Budd-Chiari syndrome.Methods12 patients with benign hepatocellular lesions and 32 patients with small (&lt;3 cm) HCCs were assessed. MRI images were reviewed by two radiologists blinded to the patient background information; lesion T1 and T2 signal intensities and ADC values were compared with the background liver. Enhancement of lesion relative to hepatic parenchyma [(T1Enh-T1liver)/T1liver] in the arterial, venous, and delayed phases was also compared between the two groups. A multivariable logistic model was developed using these categorical measures; the predictive value of the model was tested using the Area Under the Receiver operating characteristic (AU-ROC) curve for logistic models. P-values &lt;0.05 were considered statistically significant.ResultsThere were consistent differences in T1lesion/T1liver, and T2lesion/T2liver, and ADClesion/ADCliver between benign hepatocellular lesions versus the sHCC group (p&lt;0.001, p&lt;0.001, p = 0.045, respectively). Lesion-to-background liver enhancement in the portal venous and delayed phases was different between the benign lesions versus sHCC (p=0.001). ROC analysis for the logistic model that included the T1 ratio, T2 ratio, and portal venous enhancement ratio demonstrated excellent discriminatory power with the area under the curve of 0.94.ConclusionMultiparametric MR imaging is a useful method to help differentiate benign liver lesions from sHCC in patients with Budd-Chiari syndrome