The phenomenology and reported effects of meditation vary according to the technique practiced. While numerous studies have explored the cerebral mechanisms involved in meditation, little research provides direct comparisons between the neuronal network dynamics involved in different meditation techniques. Here, we explore and compare brain signals recorded with magnetoencephalography (MEG) during (a) focused-attention meditation (FAM), and (b) open-monitoring meditation (OMM) in a group of expert meditators (12 monks).

With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools which often hinders reproducibility of the experimental findings. Here we describe NeuroPype, which is a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies.

We report a coarse-grained molecular dynamics simulation study of a bundle of parallel actin filaments under supercritical conditions pressing against a loaded mobile wall using a particle-based approach where each particle represents an actin unit. The filaments are grafted to a fixed wall at one end and are reactive at the other end, where they can perform single monomer (de) polymerization steps and push on a mobile obstacle.

The problem is addressed of the maximal integrability of the gradient of solutions to quasilinear elliptic equations, with merely measurable coefficients, in two variables. Optimal results are obtained in the framework of Orlicz spaces, and in the more general setting of all rearrangement-invariant spaces. Applications to special instances are exhibited, which provide new gradient bounds, or improve certain results available in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. However, available datasets consist of a small number of samples (n patients) and a large number of gene expression data (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches have been proposed.

We study the effects of a controlled gas flow on the dynamics of electrified jets in the electrospinning process. The main idea is to model the air drag effects of the gas flow by using a nonlinear Langevin-like approach. The model is employed to investigate the dynamics of electrified polymer jets at different conditions of air drag force, showing that a controlled gas counterflow can lead to a decrease of the average diameter of electrospun fibers, and potentially to an improvement of the quality of electrospun products.

Estimates for solutions to homogeneous Dirichlet problems for a class of elliptic equations with zero order term in the form L(u) = g(x, u) + f (x),where the operator L fulfills an anisotropic elliptic condition, are established. Such estimates are obtained in terms of solutions to suitable problems with radially symmetric data, when no sign conditions on g are required.

We establish the statistical mechanics framework for a bundle of N-f living and uncrosslinked actin filaments in a supercritical solution of free monomers pressing against a mobile wall. The filaments are anchored normally to a fixed planar surface at one of their ends and, because of their limited flexibility, they grow almost parallel to each other. Their growing ends hit a moving obstacle, depicted as a second planar wall, parallel to the previous one and subjected to a harmonic compressive force.

We study sparse high dimensional additive model fitting via penalization with sparsity-smoothness penalties. We review several existing algorithms that have been developed for this problem in the recent literature, highlighting the connections between them, and present some computationally efficient algorithms for fitting such models. Furthermore, using reasonable assumptions and exploiting recent results on group LASSO-like procedures, we take advantage of several oracle results which yield asymptotic optimality of estimators for high-dimensional but sparse additive models.

Background: Electrocorticography (ECoG) measures the distribution of the electrical potentials on the cortex produced by the neural currents. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of the neural currents. This study addresses the ECoG source modeling developing a beamformer method.