We discuss the intriguing ability of minimal kinetic theory to describe a broad variety of complex non-equilibrium flows across scales of motion. It is argued that, besides major computational progress, minimal kinetic theory also provides a new conceptual framework to investigate the complexities of flowing matter far from equilibrium.

Nature routinely presents us with spectacular demonstrations of organization and orchestrated motion in living species. Efficient information transfer among the individuals is known to be instrumental to the emergence of spatial patterns (e.g. V-shaped formations for birds or diamond-like shapes for fishes), responding to a specific functional goal such as predatory avoidance or energy savings. Such functional patterns materialize whenever individuals appoint one of them as a leader with the task of guiding the group towards a prescribed target destination. It is here shown that, under specific conditions, the surrounding hydrodynamics plays a critical role in shaping up a successful group dynamics to reach the desired target.

New method: We computed the lead-field matrix by using a novel routine provided by the OpenMEEG software. We performed an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. We applied a Linear Constraint Minimum Variance (LCMV) beamformer to both synthetic data and a set of real measurements recorded during a rapid visual categorization task. 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.

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. The proposed package is largely based on the NiPype framework and the MNE-Python software and benefits from standard Python packages such as NumPy and SciPy. It also incorporates several existing wrappers, such as a Freesurfer Python-wrapper for multi-subject MRI segmentation. The NeuroPype project includes three different packages: I Neuropype-ephy includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses II Neuropype-graph: functional connectivity exploiting graph-theoretical metrics including modular partitions III Neuropype-gui: a graphical interface wrapping the definition of parameters. NeuroPype provides a common and fast framework to develop workflows for advanced MEG/EEG analyses (but also fMRI and iEEG). Several pipelines have already been developed with NeuroPype to analyze different MEG and EEG datasets: e.g. EEG sleep data, MEG resting state measurements and MEG recordings in Autism. NeuroPype will be be made available via Github. Current developments will increase its compatibility with existing Python packages of interest such as machine learning tools.

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. Here, we exploreand compare brain signals recorded with magnetoencephalography (MEG) during (a)resting state, (b) focused-attention meditation (FAM) and (c) open-monitoringmeditation (OMM) in a group of expert meditators (12 monks).To this end, weestimated MEG source time courses using a minimum-norm solution and computed (1)spectral power in multiple frequency bands (delta, theta, alpha, beta andgamma), (2) graph theoretical measures, (3) long-range coupling using imaginarycoherence and weighed phase-lag index and (4) multifractal scaling parameters using Wavelet Leader-based Multifractal formalism. We compared all the measures in the three conditions(OMM, FAM and resting state) and tested for statistical significance using permutationtest (paired t-test) corrected by maximum statistics. We also used a machinelearning framework in order to see which features provide the highestclassification across conditions. Our findings reveal several differencesbetween FAM, OMM and the resting-state condition. Compared to OMM, FAM isassociated with an increase in power in regions involved in attention andperformance monitoring. In OMM, increases in activity were observed in regionsinvolved in memory and emotion processing. Moreover, OMM seems to have strongestand more connections, while resting state have connections that are weaker andfewer in number compared to OMM and FAM. We discuss these results in thecontext of previous cognitive neuroimaging studies of meditation and paths forfuture research are proposed.

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 computed the lead-field matrix which maps the neural currents onto the sensors space by a novel routine provided by the OpenMEEG framework [3]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability: we performed an analysis of the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we provided quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer [4]. The validation of the effectiveness of beamforming in ECoG was performed both with synthetic data and with experimental data recorded during a rapid visual categorization task. Results. For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. References: 1. Dumpelmann et al., (2012), Human brain mapping, 33(5), 1172-1188 2. Pascarella et al. (2016), Journal of Neuroscience Methods, 263(5), 134-144 3. Kybic et al., (2005), Medical Imaging, IEEE Transactions on, 24(1), 12-28 4. Van Veen et al., (1997), Biomedical Engineering, IEEE Transactions on, 44(9), 867-880

In the present study, we revisit the MEG inverse problem, regularization and depth weighting from a Bayesian hierarchical point of view: the primary unknown is the discretized current density and each dipole has a preferred direction extracted from the MRI of the subject and encoded in the prior distribution. The variance of each dipole is described by its hyperprior density: this hypermodel is used to build the Iterative Alternating Sequential (IAS) algorithm with the novel feature that the parameters are determined using an empirical Bayes approach. We test the performance of the IAS algorithm against synthetic but realistic data. We simulate the neural activity generated by cortical patches located in several cerebral regions including deep regions as Insula, posterior Cingulate, Cerebellum and Hippocampus. Then, we reconstruct the activity by the IAS method with and without the physiological prior. The tests show that the physiological prior significantly improves the localization of the activity also in the case when the neural sources are located in deep regions. We compare the performance of the IAS method against the results obtained using two of the most popolar inversion methods: wMNE and dSPM. A measure based on Bayesian factors is used to quantify the reliability of the reconstructions. Finally, the three inversion methods are applied to a set of auditory real data. The Bayesian hierarchical model provides a very natural interpretation for sensitivity weighting, and the parameters in the hyperprior provide a tool for controlling the quality of the solution in terms of focality, thus leading to a flexible algorithm that can handle both sparse and distributed sources. References 1. Calvetti D, Pitolli F, Somersalo E and Vantaggi B(2015) ArXiv:1503.06844 2. Calvetti D, Pascarella A, Pitolli F, Somersalo E and Vantaggi B(2015) Inverse Problems 31(12) 3. Lin FH et al.(2006) Neuroimage 31 160-171 4. Tadel et al.(2011) Computational intelligence and neuroscience, 2011:8

Motor learning induces changes in resting-state (RS) network properties in fronts-parietal (Albert et al, 2009) and sensorimotor (Taubert et al, 2011) networks. This study explores the putative modulations of spontaneous resting-state oscillations following a sensori-motor learning task. The task consisted in lifting a load with the right hand, which triggered the unloading of a load suspended to the left forearm (Paulignan et al., 1989). Because learning stabilizes quickly, a temporal delay was implemented, hence placing the subject in a dynamic learning state. Sixteen adults performed a resting state sessions in which they fixated a grey crosshair on a white background before and after two motor learning conditions: The subjects were instructed to lift with their right arm a load (800 g) placed on the ipsilateral haptic space. In the LEARNED condition, voluntary lifting of the object with the right arm instantaneously triggered the unloading of the load placed on the left arm. In the DYNAMIC LEARNING condition, a time delay was implemented per block between lifting and the resulting unloading. MEG signals were recorded using a 275-channel MEG CTF system. The performance was constant in the LEARNED condition, while postural stabilization increased during the DYNAMIC LEARNING condition (p<.001). Minimum-norm estimation revealed that alpha power (8-12 Hz) generators were located bilaterally within the pre-central gyri, the post-central gyri, the inferior parietal gyri and the superior parietal gyri. Most importantly, comparison of RS power pre and post learning revealed a significant increase of sensori-motor alpha power contralateral to postural side, only after the DYNAMIC LEARNING condition (p<.05). Our RS MEG connectivity and graph theoretical analyses also showed significant changes following motor learning. The RS oscillatory network modulations we observed following dynamic motor learning could be specifically related to sustained sensori-motor learning processes, distinct from novel skill acquisition.

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). To this end, we estimated MEG source time courses using minimum-norm and computed spectral power in multiple frequency bands (delta, theta, alpha, beta and gamma), graph theoretical measures and multifractal scaling parameters in both conditions. Preliminary findings reveal several differences between FAM and OMM. Interestingly, OMM was associated with higher theta power in the right temporal pole. We discuss these results in the context of previous cognitive neuroimaging studies of meditation and paths for future research are proposed.

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. Through the efficient combination of multiple neuroimaging and MEG/EEG packages, NeuroPype provides a common and fast framework for advanced MEG/EEG analyses. The highlights of the pipeline, include data pre-processing and cleaning, sensor or source-level connectivity analyses (Imaginary and standard coherence, phase-lag index, phase-locking, etc.), and graph-theoretical metrics (including modular partitions). The pipeline design, data structure and analysis workflow is described and future additions will be discussed.

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.

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 studied different aspects of them: comparison with nullomers of random sequences, CpG distribution and mean helical rise. In agreement with previous results we found that the number of nullomers in the human genome is much larger than expected by chance. Nevertheless antithetical results were found when considering a random DNA sequence preserving dinucleotide frequencies. The analysis of CpG frequencies in nullomers and high order nullomers revealed, as expected, a high CpG content but it also highlighted a strong dependence of CpG frequencies on the dinucleotide position, suggesting that nullomers have their own peculiar structure and are not simply sequences whose CpG frequency is biased. Furthermore, phylogenetic trees were built on eleven species based on both the similarities between the dinucleotide frequencies and the number of nullomers two species share, showing that nullomers are fairly conserved among close species. Finally the study of mean helical rise of nullomers sequences revealed significantly high mean rise values, reinforcing the hypothesis that those sequences have some peculiar structural features. The obtained results show that nullomers are the consequence of the peculiar structure of DNA (also including biased CpG frequency and CpGs islands), so that the hypermutability model, also taking into account CpG islands, seems to be not sufficient to explain nullomer phenomenon. Finally, high order nullomers could emphasize those features that already make simple nullomers useful in several applications.

We present a solution based on a suitable combination of heuristics and parallel processing techniques for finding the best allocation of the financial assets of a pension fund, taking into account all the specific rules of the fund. We compare the values of an objective function computed with respect to a large set (thousands) of possible scenarios for the evolution of the Net Asset Value (NAV) of the share of each asset class in which the financial capital of the fund is invested. Our approach does not depend neither on the model used for the evolution of the NAVs nor on the objective function. In particular, it does not require any linearization or similar approximations of the problem. Although we applied it to a situation in which the number of possible asset classes is limited to few units (six in the specific case), the same approach can be followed also in other cases by grouping asset classes according to their features.

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.

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. We simulate a reactive grand canonical ensemble in a box of fixed transverse area A, with a fixed number of grafted filaments Nf, at temperature T and monomer chemical potential m 1. For a single filament case (Nf = 1) and for a bundle of Nf = 8 filaments, we analyze the structural and dynamical properties at equilibrium where the external load compensates the average force exerted by the bundle. The dynamics of the bundle-moving-wall unit are characteristic of an over-damped Brownian oscillator in agreement with recent in vitro experiments by an optical trap setup. We analyze the influence of the pressing wall on the kinetic rates of (de) polymerization events for the filaments. Both static and dynamic results compare reasonably well with recent theoretical treatments of the same system. Thus, we consider the proposed model as a good tool to investigate the properties of a bundle of living filaments.

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. The force constant is denoted as the trap strength while the distance between the two walls as the trap length to make contact with the experimental optical trap apparatus. For an ideal solution of reactive filaments and free monomers at fixed free monomer chemical potential mu(1), we obtain the general expression for the grand potential from which we derive averages and distributions of relevant physical quantities, namely, the obstacle position, the bundle polymerization force, and the number of filaments in direct contact with the wall. The grafted living filaments are modeled as discrete Wormlike chains, with F-actin persistence length l(p), subject to discrete contour length variations +/- d (the monomer size) to model single monomer (de) polymerization steps. Rigid filaments (l(p) = infinity), either isolated or in bundles, all provide average values of the stalling force in agreement with Hill's predictions F-s(H) = N(f)k(B)T ln(rho(1)/rho(1c))/d, independent of the average trap length. Here rho(1) is the density of free monomers in the solution and rho(1c) its critical value at which the filament does not grow nor shrink in the absence of external forces. Flexible filaments (l(p) < infinity) instead, for values of the trap strength suitable to prevent their lateral escape, provide an average bundle force and an average trap length slightly larger than the corresponding rigid cases (few percents). Still the stalling force remains nearly independent on the average trap length, but results from the product of two strongly L-dependent contributions: the fraction of touching filaments proportional to (< L >(O.T.))(2) and the single filament buckling force proportional to (< L >(O.T.))

We present the latest version of OpenCAPWAP, our open-source implementation of the Internet Engineering Task Force control and provisioning of wireless access point (CAPWAP) protocol. The CAPWAP protocol is designed to support centralized management of large-scale and heterogeneous wireless networks, with a special focus on IEEE 802.11-based networks. The implementation presented in this paper improves substantially on the previous version, adding full support for the Split MAC architecture and decoupling completely the implementation from a specific driver solution. Although the main goal of the paper is to describe the technical solutions we adopted in the new implementation rather than present a complete performance analysis, we report, besides a wide set of experimental tests that validates the new implementation of OpenCAPWAP in terms of its functionalities and stability, also performance figures that show how OpenCAPWAP does not introduce any significant overhead with respect to commercial proprietary solutions. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Searching for words or sentences within large sets of textual documents can be very challenging unless an index of the data has been created in advance. However, indexing can be very time consuming especially if the text is not readily available and has to be extracted from files stored in different formats. Several solutions, based on the MapReduce paradigm, have been proposed to accelerate the process of index creation. These solutions perform well when data are already distributed across the hosts involved in the elaboration. On the other hand, the cost of distributing data can introduce noticeable overhead. We propose ISODAC, a new approach aimed at improving efficiency without sacrificing reliability. Our solution reduces to the bare minimum the number of I/O operations by using a stream of in-memory operations to extract and index text. We further improve the performance by using GPUs for the most computationally intensive tasks of the indexing procedure. ISODAC indexes heterogeneous documents up to 10.6x faster than other widely adopted solutions, such as Apache Spark. As proof-of-concept, we developed a tool to index forensic disk images that can easily be used by investigators through a web interface.