23 research outputs found

    Revealing a brain network endophenotype in families with idiopathic generalised epilepsy

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    Idiopathic generalised epilepsy (IGE) has a genetic basis. The mechanism of seizure expression is not fully known, but is assumed to involve large-scale brain networks. We hypothesised that abnormal brain network properties would be detected using EEG in patients with IGE, and would be manifest as a familial endophenotype in their unaffected first-degree relatives. We studied 117 participants: 35 patients with IGE, 42 unaffected first-degree relatives, and 40 normal controls, using scalp EEG. Graph theory was used to describe brain network topology in five frequency bands for each subject. Frequency bands were chosen based on a published Spectral Factor Analysis study which demonstrated these bands to be optimally robust and independent. Groups were compared, using Bonferroni correction to account for nonindependent measures and multiple groups. Degree distribution variance was greater in patients and relatives than controls in the 6-9 Hz band (p = 0.0005, p = 0.0009 respectively). Mean degree was greater in patients than healthy controls in the 6-9 Hz band (p = 0.0064). Clustering coefficient was higher in patients and relatives than controls in the 6-9 Hz band (p = 0.0025, p = 0.0013). Characteristic path length did not differ between groups. No differences were found between patients and unaffected relatives. These findings suggest brain network topology differs between patients with IGE and normal controls, and that some of these network measures show similar deviations in patients and in unaffected relatives who do not have epilepsy. This suggests brain network topology may be an inherited endophenotype of IGE, present in unaffected relatives who do not have epilepsy, as well as in affected patients. We propose that abnormal brain network topology may be an endophenotype of IGE, though not in itself sufficient to cause epilepsy

    Improved RMR rock mass classification using artificial intelligence algorithms

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    Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures

    SixC1 12xO2 alloys: A possible route to stabilize carbon-based silica-like solids?

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    Novel extended tetrahedral forms Of CO2 have been synthesized recently under high-pressure conditions. We perform ab initio density functional theory calculations to investigate whether doping with Si can extend the stability range of such tetrahedral forms Of CO2 to ambient pressure. Calculations are performed with a simple cubic cell containing eight formula units in alpha-cristobalite-like structure. Though we find that all the SiC1-xO2 structures considered by us are thermodynamically unstable with respect to decomposition into the end members at ambient pressures, the energy differences are small, suggesting that it might be possible for such phases to exist in metastable forms. At higher pressures, the heat of formation is found to be negative. The bonding between C and O atoms is more covalent than that between Si and 0 atoms. We also find indications that some C atoms may prefer three-fold coordination at low pressur
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