Dynamic heterogeneities in attractive colloids

We study the formation of a colloidal gel by means of Molecular Dynamics simulations of a model for colloidal suspensions. A slowing down with gel-like features is observed at low temperatures and low volume fractions, due to the formation of persistent structures. We show that at low volume fraction the dynamic susceptibility, which describes dynamic heterogeneities, exhibits a large plateau, dominated by clusters of long living bonds. At higher volume fraction, where the effect of the crowding of the particles starts to be present, it crosses over towards a regime characterized by a peak. We introduce a suitable mean cluster size of clusters of monomers connected by "persistent" bonds which well describes the dynamic susceptibility.Comment: 4 pages, 4 figure

Cluster permutation analysis for EEG series based on non-parametric Wilcoxon–Mann–Whitney statistical tests

Cluster-based permutation tests are widely used in neuroscience studies for the analysis of high-dimensional electroencephalography (EEG) and event-related potential (ERP) data as it may address the multiple comparison problem without reducing the statistical power. However, classical cluster-based permutation analysis relies on parametric t-tests, whose assumptions may not be verified in case of non-normality of the data distribution and alternative options may be considered. To overcome this limitation, here we present a new software for a cluster permutation analysis for EEG series based on non-parametric Wilcoxon–Mann–Whitney tests. We tested both t-test and non-parametric Wilcoxon implementations in two independent datasets of ERPs and EEG spectral data: while t-test-based and non-parametric Wilcoxon-based cluster analyses showed similar results in case of ERP data, the t-test implementation was not able to find clustered effects in case of spectral data. We encourage the use of non-parametric statistics for a cluster permutation analysis of EEG data, and we provide a publicly available software for this computation

A theoretical quantum study of the electronic properties of mentoxy dichloro phosphorous (C10H19OPCl2)

Indexación: Scopus.A theoretical quantum study of the organophosphorus compound with formula C10H19OPCl2 (MEPCL2) was carried out. The results of the calculations show excellent agreement between experimental and computed frequencies evaluated at the B3LYP/6-311++G(d,p) level of theory. A study of the electronic properties, such as excitation energies and wavelengths were performed employing the time-dependent DFT (TD-DFT) method. Global a chemical reactivity of MEPCL2 was analyzed through global reactivity descriptors, while its local reactivity was analyzed by mean maps of the electrostatic potential. Also, the orbital energies values suggest that a charge transfer is occurring within the molecule. © 2018 American Physical Society.https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0717-97072018000103887&lng=en&nrm=iso&tlng=e

Two channel model for optical conductivity of high mobility organic crystals

We show that the temperature dependence of conductivity of high mobility organic crystals Pentacene and Rubrene can be quantitatively described in the framework of the model where carriers are scattered by quenched local impurities and interact with phonons by Su-Schrieffer-Hegger (SSH) coupling. Within this model, we present approximation free results for mobility and optical conductivity obtained by world line Monte Carlo, which we generalize to the case of coupling both to phonons and impurities. We find fingerprints of carrier dynamics in these compounds which differ from conventional metals and show that the dynamics of carriers can be described as a superposition of a Drude term representing diffusive mobile particles and a Lorentz term associated with dynamics of localized charges.Comment: 6 pages, 5 figure

Methodological Considerations on EEG Electrical Reference: A Functional Brain-Heart Interplay Study

The growing interest in the study of functional brain-heart interplay (BHI) has motivated the development of novel methodological frameworks for its quantification. While a combination of electroencephalography (EEG) and heartbeat-derived series has been widely used, the role of EEG preprocessing on a BHI quantification is yet unknown. To this extent, here we investigate on four different EEG electrical referencing techniques associated with BHI quantifications over 4-minute resting-state in 15 healthy subjects. BHI methods include the synthetic data generation model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient (MIC). EEG signals were offline referenced under the Cz channel, common average, mastoids average, and Laplacian method, and statistical comparisons were performed to assess similarities between references and between BHI techniques. Results show a topographical agreement between BHI estimation methods depending on the specific EEG reference. Major differences between BHI methods occur with the Laplacian reference, while major differences between EEG references are with the MIC analysis. We conclude that the choice of EEG electrical reference may significantly affect a functional BHI quantification

Multivariate Pattern Analysis of Entropy estimates in Fast- and Slow-Wave Functional Near Infrared Spectroscopy: A Preliminary Cognitive Stress study

Functional near infrared spectroscopy (fNIRS) is a modality that can measure shallow cortical brain signals and also contains pulsatile oscillations that originate from heartbeat dynamics. In particular, while fNIRS slow waves (0 Hz to 0.6 Hz) refer to the standard hemodynamic signal, fast-wave (0.8 Hz to 3 Hz) fNIRS signals refer to cardiac oscillations. Using a cognitive stress experiment paradigm with mental arithmetic, the aim of this study was to assess differences in cortical activity when using slow-wave or fast-wave fNIRS signals. Furthermore, we aimed to see whether fNIRS fast and slow waves provide different information to discriminate mental arithmetic tasks from baseline. We used data from 10 healthy subjects from an open dataset performing mental arithmetic tasks and assessed fNIRS signals using mean values in the time domain, as well as complexity estimates including sample, fuzzy, and distribution entropy. A searchlight representational similarity analysis with pairwise t-test group analysis was performed to compare the representational dissimilarity matrices of each searchlight center. We found significant representational differences between fNIRS fast and slow waves for all complexity estimates, at different brain regions. On the other hand, no statistical differences were observed for mean values. We conclude that entropy analysis of fNIRS data may be more sensitive than traditional methods like mean analysis at detecting the additional information provided by fast-wave signals for discriminating mental arithmetic tasks and warrants further research