Dynamic Decomposition of Spatiotemporal Neural Signals

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals

A New Pseudopolymorph of Hexakis-(4-cynaophenyl)benzene

The title compound (systematic name: benzene-4,4′,4′′,4′′′,-4′′′′,4′′′′′-hexaylhexabenzonitrile dichloromethane disolvate), C48H24N6•2CH2Cl2, crystallizes as an inclusion compound during the slow diffusion of methanol into a solution of hexakis(4-cyanophenyl)benzene in CH2Cl2. The hexakis(4- cyanophenyl)benzene molecule lies on an axis of twofold rotation in the space group Pbcn. Weak C—H•••N interactions between hexakis(4-cyanophenyl)benzene molecules define an open network with space for including guests. The resulting structure is a new pseudopolymorph of hexakis-(4-cyanophenyl)benzene. The eight known pseudopolymorphs have few shared architectural features, in part because none of the intermolecular interactions that are present plays a dominant role or forces neighboring molecules to assume particular relative orientations

Adjunctive Use of Ceftriaxone and Sodium Valproate in the Management of Tetanus: A Case Report and Literature Review

Since the introduction of active immunisation against tetanus in 1923, there has been a dramatic decline in cases of tetanus all over the world. However, cases of tetanus are still being reported in developing and developed economies; with more cases reported from Africa than from Europe. The three planks in the management of tetanus are the elimination of toxin production, neutralisation of unbound toxin and control of spasms. In this report of a fifteen-year old Nigerian female patient with Ablett II tetanus, elimination of toxin production was by the combination of metronidazole and the glutamate transporter I (GLT I) enhancer, ceftriaxone. Neutralisation of unbound toxin was by the use of anti-tetanus serum due to the non-availability of human tetanus immune globulin. Control of spasms was mainly by the use of the GABAergic-enhancing drugs diazepam and sodium valproate combined with the use of the anti-excitotoxics magnesium sulphate and ketamine. The introduction of valproate hastened the disappearance of opisthotonus and residual rigidity and deserves to be added to drug combination regimens for tetanus. Keywords: tetanus, ceftriaxone, diazepam, sodium valproate, magnesium sulphate, ketamin

Correlación entre rasgos de personalidad y mediciones acústicas en el habla de individuos hispanoparlantes del río de la plata

The background of this study is presented in three sections: a) Motivation Theories and their relationship with brain structures, b) Psychometric studies of Primary Personality Factors (PPF) and c) The role of brain structures in the production of prosodic aspects of language. Its objective is to evaluate the combination of parameters and acoustic characteristics found in the speech of normal individuals (n = 23), previously grouped according to a psychometric study of their personality in order to sort out Primary Personality Factors (PPF) and improve our knowledge on Human Temperaments. Acoustic parameters under study were: fundamental frequency (F0), total maximum energy and total duration, normal constituents of prosodic information. A three-syllable same phrase was analyzed in all the cases. The subject´s emissions were registered on a Sony (WM – 6 × 320) recorder and the phrase studied for each individual (N = 22) was processed with the Anagraf speech acoustic analysis program. The acoustic characteristics more useful to discriminate between PPF groups are: a) F0 rank and b) energy differences between stressed syllables and those not stressed. Acoustic studies can complement psychometric ones in order to differentiate each PPF group.El fundamento de este estudio se presenta en 3 secciones: a) Teorías de la Motivación y su relación con estructuras cerebrales, b) estudios psicométricos para determinar Factores Primarios de la Personalidad (FPP) y c) el rol de las estructuras cerebrales en la producción de los aspectos prosódicos del lenguaje. Su objetivo es evaluar la combinación de parámetros y características acústicas encontradas en el habla de individuos normales (n = 23), previamente agrupados en base a estudios psicométricos de la personalidad y aumentar el conocimiento acerca de los temperamentos humanos. Los parámetros acústicos estudiados incluyeron: frecuencia fundamental, energía total y duración, constituyentes normales de la información prosódica. Una misma frase de tres sílabas fue analizada en todos los casos. Las emisiones vocales se registraron en un grabador Sony (WM – 6 × 320) y la frase estudiada en cada individuo (N = 22) fue procesada con Anagraf, programa de análisis acústico del habla. El análisis de las varianzas en los resultados obtenidos mostró que las características acústicas que más probablemente pueden discriminar cada uno de los grupos de FPP son dos: a) el rango de la frecuencia fundamental, y b) la energía individual usada en las sílabas acentuadas respecto de aquellas que no lo son. Los estudios acústicos pueden complementar a los psicométricos para diferenciar los individuos que corresponden a cada grupo según los FPP

Wasserstein Variational Inference

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein variational loss are obtained by backpropagating through the Sinkhorn iterations. This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs. Using the Wasserstein variational inference framework, we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques.Comment: 8 pages, 1 figur