Right Superior Temporal Gyrus Altered Functional Activity in Patients With Definite Vestibular Migraine

The study aimed to investigate resting-state functional brain activity alterations in patients with definite vestibular migraine (dVM). Seventeen patients with dVM, 8 patients with migraine, 17 health controls (HCs) were recruited. The amplitude of low frequency fluctuation (ALFF), fractional ALFF (fALFF) and regional homogeneity (ReHo) were calculated to observe the changes in spontaneous brain activity. Then brain regions with altered fALFF were selected for seed-based functional connectivity analysis. Compared with HCs, VM patients showed significantly increased ALFF values in the right temporal lobe (Cluster size = 91 voxels, P=0.002, FWE corrected), and significantly increased ReHo values in the right superior temporal gyrus (STG), middle temporal gyrus (MTG) and inferior temporal gyrus (ITG) (Cluster size = 136 voxels, P=0.013, FWE corrected). Compared with patients with migraine, patients with VM showed significantly increased fALFF values in the right parietal lobe (Cluster size = 43 voxels, P=0.011, FWE corrected) and right frontal lobe (Cluster size =36 voxels, P=0.026, FWE corrected), significantly increased ReHo values in the right thalamus (Cluster size = 92 voxels, P=0.043, FWE corrected). Our findings documented that patients with VM showed enhanced spontaneous functional activity in the right temporal lobe (STG, MTG, and ITG) compared with HCs, and increased spontaneous activity in the right parietal lobe-frontal lobe-thalamus compared with patients with migraine. Patients with VM and migraine both had altered brain function, but the regions involved are different.

Perturbation based Fuzzified k-Mode Clustering Method for Privacy Preserving Recommender System

Recommender systems are extensively used today to ease out the problem of information overload and facilitate the product selection by users in e-commerce market. Both privacy and security are two major concerns of the user in these systems. For the protection of the user’s rating, there are several existing works on the basis of encryption or randomization methodologies. This paper proposes a methodology that not only protects the privacy of ratings but also provides better accuracy. After applying fuzzification on the user ratings, random rotation and perturbation methods are used before being fed to the collaborative filtering system. In this process, similar users are grouped into clusters by which recommendation is made. By considering different cluster size on four different datasets, the proposed fuzzified k-Mode clustering method provides less MAE and RMSE value as compared to other k-Means and k-Mode clustering approach and also achieves the better privacy than randomized perturbation method by obtaining IVDM value i.e. 0.67, 0.61, 0.55 and 0.7.

Quantum chemical modeling of the structure and properties of SnO2 nanoclusters

By the method of density functional theory with exchange-correlation functional B3LYP and basis set 3‑21G (d), the structural and energy characteristics have been considered of the molecular models of SnO2 nanoclusters of different size and composition with the number of Sn atoms from 1 to 10. Incompletely coordinated surface tin atoms were terminated by hydroxyl groups. It has been shown that the Sn–O bond length in nanoclusters does not depend on the cluster size and on the coordination number of Sn atoms, but is determined by the coordination type of neighboring oxygen atoms. Namely, the bond length Sn–O(3) (@ 2.10 Å) is greater than that of Sn–O (2) (@ 1.98 Å). The calculated values of Sn–O (3) bond lengths agree well with the experimental ones for crystalline SnO 2 samples (2.05 Å). The theoretically calculated width of the energy gap decreases naturally with increasing cluster size (from 6.14 to 3.46 eV) and approaches the experimental value of the band gap of the SnO 2 crystal (3.6 eV). The principle of additivity was used to analyze the energy characteristics of the considered models and to estimate the corresponding values for a cassiterite crystal. According to this principle, a molecular model can be represented as a set of atoms or atomic groups of several types that differ in the coordination environment and, therefore, make different contributions to the total energy of the system. The calculated value of the atomization energy for SnO2 is 1661 kJ/mol and corresponds satisfactorily to the experimentally measured specific atomization energy of crystalline SnO2 (1381 kJ/mol). It has been shown that a satisfactory reproduction of the experimental characteristics of crystalline tin dioxide is possible when using clusters containing at least 10 state atoms, for example, (SnO2)10×14H2O.

COVID-19 clusters in schools: frequency, size, and transmission rates from crowdsourced exposure reports

The role of schools in the spread of the COVID-19 pandemic is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports for several jurisdictions in the US and Canada, providing estimates of mean and dispersion for cluster size, whilst factoring in imperfect ascertainment. Our parameter estimates are robust to variations in ascertainment fraction. We use these estimates in three ways: i) to explore how uneven the distribution of cases is among different clusters in different jurisdictions (that is, what fraction of cases are in the 20% largest clusters), ii) to estimate how long it will be until we see a cluster a given size in jurisdiction, and iii) to determine the distribution of instantaneous transmission rate β among different index case. We show how these latter distribution can be used in simulations of school transmission where we explore the effect of different interventions, in the context of highly variable transmission rates.

Kinematic Viscosity and Electrical Resistivity of a Multicomponent Melt due to Liquid–Liquid Structure Transition

We investigated the kinematic viscosity and electrical resistivity of the multicomponent Fe74Cu1Nb1.5Mo1.5B8.5Si13.5 melt during three heating–cooling cycles. The temperature dependence of kinematic viscosity and electrical resistivity have the anomalous zones in the same temperature range and they are associated with the liquid–liquid structure transition (LLST). The anomalies were explained by changes in the activation energy and the cluster size. As the cluster size decreases, the activation energy decreases, but the viscosity and electrical resistance increase. LLST begins with the cluster dissolution, and as a result, the Arrhenius plot becomes nonlinear in the transition temperature range. After three cycles of heating–cooling, the temperature dependences of the kinematic viscosity and electrical resistance did not qualitatively change, and this allows us to conclude that LLST is thermoreversible. With an increase in the number of thermal cycles, the activation energy of viscous flow decreases, as well as the onset temperature and temperature range of LLST.

Thermodynamic analysis of Al clusters formation over aluminum melt

The formation of the aluminum nanoparticles with the size of up to 60 atoms in a gas phase is theoretically studied. Thermodynamic modeling has been applied to investigate the effect of the synthesis conditions on the distribution of the nanoparticles. The magic numbers of the particles have been estimated and found to be consistent with the available data. Furthermore, the simulations showed that higher amounts of larger nanoparticles are obtained during condensation from the supercooled aluminum vapor. In contrast, lower amounts of smaller clusters may be formed in a gas phase over the aluminum melt. Varying the temperature and concentration of supercooled aluminum vapor in a broad range results in no significant change in cluster size distribution. This effect is governed by the equilibrium shift.

A simulation study for evaluating the performance of clustering measures in multilevel logistic regression

Background Multilevel logistic regression models are widely used in health sciences research to account for clustering in multilevel data when estimating effects on subject binary outcomes of individual-level and cluster-level covariates. Several measures for quantifying between-cluster heterogeneity have been proposed. This study compared the performance of between-cluster variance based heterogeneity measures (the Intra-class Correlation Coefficient (ICC) and the Median Odds Ratio (MOR)), and cluster-level covariate based heterogeneity measures (the 80% Interval Odds Ratio (IOR-80) and the Sorting Out Index (SOI)). Methods We used several simulation datasets of a two-level logistic regression model to assess the performance of the four clustering measures for a multilevel logistic regression model. We also empirically compared the four measures of cluster variation with an analysis of childhood anemia to investigate the importance of unexplained heterogeneity between communities and community geographic type (rural vs urban) effect in Malawi. Results Our findings showed that the estimates of SOI and ICC were generally unbiased with at least 10 clusters and a cluster size of at least 20. On the other hand, estimates of MOR and IOR-80 were less accurate with 50 or fewer clusters regardless of the cluster size. The performance of the four clustering measures improved with increased clusters and cluster size at all cluster variances. In the analysis of childhood anemia, the estimate of the between-community variance was 0.455, and the effect of community geographic type (rural vs urban) had an odds ratio (OR)=1.21 (95% CI: 0.97, 1.52). The resulting estimates of ICC, MOR, IOR-80 and SOI were 0.122 (indicative of low homogeneity of childhood anemia in the same community); 1.898 (indicative of large unexplained heterogeneity); 0.345-3.978 and 56.7% (implying that the between community heterogeneity was more significant in explaining the variations in childhood anemia than the estimated effect of community geographic type (rural vs urban)), respectively. Conclusion At least 300 clusters with sizes of at least 50 would be adequate to estimate the strength of clustering in multilevel logistic regression with negligible bias. We recommend using the SOI to assess unexplained heterogeneity between clusters when the interest also involves the effect of cluster-level covariates, otherwise, the usual intra-cluster correlation coefficient would suffice in multilevel logistic regression analyses.