With the exponential increase in data dimension and complexity, conducting state-of-the-art brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. Here we describe NeuroPype, a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on NiPype and MNE-Python and benefits from standard Python packages such as NumPy and SciPy. The pipeline also incorporates several existing wrappers, such as a Freesurfer Pyhton-wrapper for multi-subject MRI segmentation.

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).

Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method.

We consider the problem of determining quantitative information about corrosion occurring on an inaccesible part of a specimen. The data for the problem consist of prescribed current flux and voltage measurements on an accessible part of the specimen boundary. The problem is modelled by Laplace's equation with an unknown term in the boundary conditions. Our goal is recovering from the data. We prove uniqueness under certain regularity assumptions and construct a regularized numerical method for obtaining approximate solutions to the problem.

The establishment of thermal diffusion in an Ar-Kr Lennard-Jones mixture is investigated via dynamical non equilibrium molecular dynamics [G. Ciccotti, G. Jacucci, Phys. Rev. Lett. 35, 789 (1975)]. We observe, in particular, the evolution of the density and temperature fields of the system following the onset of the thermal gradient. In stationary conditions, we also compute the Soret coefficient of the mixture.

Here we describe NeuroPype, which is a free open-source Python package, we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on the Nipype framework , a tool developed in fMRI field, which facilitates data analyses by wrapping many commonly-used neuro-imaging software into a common python framework.

The phenomenologyand reported effects of meditation vary according to the technique practiced.While numerous studies have explored the cerebral mechanisms involved inmeditation, little research provides direct comparisons between the neuronalnetwork dynamics involved in different meditation techniques.

A nullomer is an oligomer that does not occur as a subsequence in a given DNA sequence, i.e. it is an absent word of that sequence. The importance of nullomers in several applications, from drug discovery to forensic practice, is now debated in the literature. Here, we investigated the nature of nullomers, whether their absence in genomes has just a statistical explanation or it is a peculiar feature of genomic sequences. We introduced an extension of the notion of nullomer, namely high order nullomers, which are nullomers whose mutated sequences are still nullomers.

We present the first analytic computation of the Detweiler-Barack-Sago gauge-invariant redshift function for a small mass in eccentric equatorial orbit around a spinning black hole. Our results give the redshift contributions that mix eccentricity and spin effects, through second order in eccentricity, second order in spin parameter, and the eight-and-a-half post-Newtonian order.

Robust Design Optimization (RDO) represents a really interesting opportunity when the specifications of the design are careful and accurate: the possibility to optimize an industrial object for the real usage situation, improving the overall performances while reducing the risk of occurrence of off-design con- ditions, strictly depends on the availability of the information about the probability of occurrence of the various operative conditions during the lifetime of the design.