Carbon Nanotubes for Confinement-Induced Energetic Nanomaterials.

Oxidized carbon nanotubes obtained by catalytic chemical vapor deposition were filled with an aqueous solution of nano-energetic materials using a very simple impregnation method. The work compares different energetic materials but focuses especially on an inorganic compound belonging to the Werner complexes, [Co(NH3)6][NO3]3. Our results show a large increase in released energy upon heating, which we demonstrate to be related to the confinement of the nano-energetic material either directly by filling of the inner channel of carbon nanotubes or to insertion in the triangular channels between adjacent nanotubes when they form bundles.

Fully 3D-printed soft robots with integrated fluidic circuitry.

The emergence of soft robots has presented new challenges associated with controlling the underlying fluidics of such systems. Here, we introduce a strategy for additively manufacturing unified soft robots comprising fully integrated fluidic circuitry in a single print run via PolyJet three-dimensional (3D) printing. We explore the efficacy of this approach for soft robots designed to leverage novel 3D fluidic circuit elements-e.g., fluidic diodes, "normally closed" transistors, and "normally open" transistors with geometrically tunable pressure-gain functionalities-to operate in response to fluidic analogs of conventional electronic signals, including constant-flow ["direct current (DC)"], "alternating current (AC)"-inspired, and preprogrammed aperiodic ("variable current") input conditions. By enabling fully integrated soft robotic entities (composed of soft actuators, fluidic circuitry, and body features) to be rapidly disseminated, modified on demand, and 3D-printed in a single run, the presented design and additive manufacturing strategy offers unique promise to catalyze new classes of soft robots.

Slipstreaming Mother Machine: A Microfluidic Device for Single-Cell Dynamic Imaging of Yeast.

The yeast Saccharomyces cerevisiae is one of the most basic model organisms for studies of aging and other phenomena such as division strategies. These organisms have been typically studied with the use of microfluidic devices to keep cells trapped while under a flow of fresh media. However, all of the existing devices trap cells mechanically, subjecting them to pressures that may affect cell physiology. There is evidence mechanical pressure affects growth rate and the movement of intracellular components, so it is quite possible that it affects other physiological aspects such as aging. To allow studies with the lowest influence of mechanical pressure, we designed and fabricated a device that takes advantage of the slipstreaming effect. In slipstreaming, moving fluids that encounter a barrier flow around it forming a pressure gradient behind it. We trap mother cells in this region and force daughter cells to be in the negative pressure gradient region so that they are taken away by the flow. Additionally, this device can be fabricated using low resolution lithography techniques, which makes it less expensive than devices that require photolithography masks with resolution under 5 µm. With this device, it is possible to measure some of the most interesting aspects of yeast dynamics such as growth rates and Replicative Life Span. This device should allow future studies to eliminate pressure bias as well as extending the range of labs that can do these types of measurements.

A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces.

Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencephalogram (EEG). In order to transfer laboratory-tested BCIs into systems that can be used by at homes, it is relevant to investigate if it is possible to select a limited set of EEG channels that work for most subjects and across different sessions without a significant decrease in performance. In this work, two strategies for channel selection for a single-trial P300 brain computer interface were evaluated and compared. The first strategy was tailored specifically for each subject, whereas the second strategy aimed at finding a subject-independent set of channels. In both strategies, genetic algorithms (GAs) and recursive feature elimination algorithms were used. The classification stage was performed using a linear discriminant. A dataset of EEG recordings from 18 healthy subjects was used test the proposed configurations. Performance indexes were calculated to evaluate the system. Results showed that a fixed subset of four subject-independent EEG channels selected using GA provided the best compromise between BCI setup and single-trial system performance.

3D microfluidics via cyclic olefin polymer-based in situ direct laser writing.

In situ direct laser writing (isDLW) strategies that facilitate the printing of three-dimensional (3D) nanostructured components directly inside of, and fully sealed to, enclosed microchannels are uniquely suited for manufacturing geometrically complex microfluidic technologies. Recent efforts have demonstrated the benefits of using micromolding and bonding protocols for isDLW; however, the reliance on polydimethylsiloxane (PDMS) leads to limited fluidic sealing (e.g., operational pressures <50-75 kPa) and poor compatibility with standard organic solvent-based developers. To bypass these issues, here we explore the use of cyclic olefin polymer (COP) as an enabling microchannel material for isDLW by investigating three fundamental classes of microfluidic systems corresponding to increasing degrees of sophistication: (i) "2.5D" functionally static fluidic barriers (10-100 µm in height), which supported uncompromised structure-to-channel sealing under applied input pressures of up to 500 kPa; (ii) 3D static interwoven microvessel-inspired structures (inner diameters < 10 µm) that exhibited effective isolation of distinct fluorescently labelled microfluidic flow streams; and (iii) 3D dynamically actuated microfluidic transistors, which comprised bellowed sealing elements (wall thickness = 500 nm) that could be actively deformed via an applied gate pressure to fully obstruct source-to-drain fluid flow. In combination, these results suggest that COP-based isDLW offers a promising pathway to wide-ranging fluidic applications that demand significant architectural versatility at submicron scales with invariable sealing integrity, such as for biomimetic organ-on-a-chip systems and integrated microfluidic circuits.

Gait event detection during stair walking using a rate gyroscope.

Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs.

DETECCIÓN DE POTENCIALES EVOCADOS RELACIONADOS A EVENTOS EN INTERFACES CEREBRO-COMPUTADORA MEDIANTE TRANSFORMADA WAVELET / DETECTION OF EVENT-RELATED POTENTIALS IN BRAIN-COMPUTER INTERFACES USING THE WAVELET TRANSFORM / DETECÇÃO DE POTENCIAIS EVOCADOS RELACIONADOS A EVENTOS EM INTERFACES CÉREBRO-COMPUTADOR USANDO TRANSFORMADA WAVELET

Una interfaz cerebro computadora (ICC) es un sistema que provee una forma de comunicación directa entre el cerebro de una persona y el mundo exterior. Para el presente trabajo se utilizaron ICC basadas en EEG utilizando el paradigma de potenciales evocados relacionados con eventos (PRE). El objetivo de este trabajo es resolver en forma eficiente el problema de clasificación, en el cual se tienen dos clases posibles: registros con respuesta (PRE) y registros sin respuesta. Para esto se propone evaluar el desempeño de una ICC utilizando la transformada wavelet diádica discreta (DDWT, del inglés Dyadic Discrete Wavelet Transform) y la transformada wavelet packet (WPT, del inglés Wavelet Packet Transform) como métodos de extracción de características para la detección de la señal de PRE. La base de datos utilizada posee registros de EEG de época única de diez sujetos sanos. A partir de los patrones temporales (registros sin post-procesamiento) se generaron cinco conjuntos de patrones wavelet luego de aplicar la DDWT y WPT mediante diferentes técnicas. Se evaluó el desempeño de cada conjunto de patrones wavelet y de los patrones temporales mediante un clasificador lineal de Fisher. Se encontró que los patrones DDWT filtrados a 16 Hz presentan resultados de clasificación superiores a los patrones temporales. De esta manera al mejorar la etapa de extracción de características se mejora la clasificación, y consecuentemente, el desempeño del sistema completo de una ICC.

A brain-computer interface (BCI) is a system that provides a direct communication between the brain of a person and the outside world. For the present work we used an EEG-based event-related evoked potentials BCI. This paper aims to efficiently solve the problem of classification, which has two possible classes: recordings with evoked-potentials (ERP) and recordings without them. We proposed to evaluate the performance of a BCI using the discrete dyadic wavelet transform (DDWT) and the wavelet packet transform (WPT) as feature extraction methods for ERP signal detection. The database consisted of single-epoch EEG recordings from ten healthy subjects. From temporal patterns (recordings without any post-processing), five wavelet patterns were generated after applying DDWT and WPT via different techniques. The performance of the wavelet and temporal patterns were analyzed with the Fisher linear classifier finding that DDWT patterns, filtered at 16 Hz, presented better classification results than temporal patterns. This means that improving the feature extraction step, improves classification, and consequently, the performance of the entire BCI system.

Uma interface cérebro-computador (BCI) é um sistema que fornece uma forma de comunicação direta entre o cérebro de uma pessoa e o mundo exterior. Para este trabalho foram utilizados ICC baseado EEG evocados usando o paradigma de potenciais relacionados a eventos (ERP). O objetivo deste trabalho é resolver de forma eficiente o problema de classificação, em que há duas classes possíveis: registros Respondidas (PRE) e registros sem resposta. Para isso é avaliar o desempenho de uma ICC usando a wavelet diádica transformada discreta (DDWT, Discrete Wavelet Diádica Inglês Transform) e transformar pacote wavelet (WPT Transformada Wavelet Packet Inglês) como métodos de extração de características para a detecção de sinal PRE. A base de dados utilizada tem apenas EEG registra o tempo de dez indivíduos saudáveis. A partir dos padrões temporais (sem registros de pósprocessamento), cinco conjuntos de padrões após a aplicação wavelet e WPT DDWT gerado por várias técnicas. O desempenho de cada conjunto de padrões de wavelet e padrões temporais usando um classificador linear Fisher foi avaliado. Descobrimos que os padrões DDWT filtrados para 16 Hz apresentaram resultados acima da classificação padrões temporais. Assim, para melhorar a classificação de estágio de extração de características é melhorada, e, consequentemente, o desempenho de todo o sistema no ICC.

Genetic feature selection to optimally detect P300 in brain computer interfaces.

A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.

ESTIMACIÓN DE POTENCIALES EVOCADOS AUDITIVOS DEL TRONCO CEREBRAL MEDIANTE DESCOMPOSICIÓN MODAL EMPÍRICA

Los potenciales evocados auditivos del tronco cerebral (PEATC) son frecuentemente usados para fines diagnósticos; sin embargo, su procesamiento se hace difícil porque están inmersos en una gran cantidad de ruido proveniente no solo de fuentes externas sino también fisiológicas. Hasta ahora el método más utilizado y aceptado para obtener un registro confiable es la promediación coherente, aunque presenta algunos inconvenientes. La descomposición modal empírica (EMD) es una técnica relativamente nueva que se usa para el procesamiento de señales no estacionarias como la mayoría de señales fisiológicas. Este método separa una señal, extrayendo la energía asociada a diferentes escalas de tiempo intrínsecas, en una suma finita de modos oscilatorios. El propósito de este trabajo fue evaluar la EMD como una herramienta para mejorar el desempeño de la promediación coherente de PEATC buscando reducir la cantidad de épocas necesarias para obtener un registro confiable. Para tal fin se analizó la reconstrucción de ocho registros usando solamente los modos 2, 3 y 4 resultantes de la EMD, los estudios determinaron que una reconstrucción de 800 épocas es aceptable.

The brainstem auditory evoked potentials (BAEPs) are commonly used for diagnostic purposes; however, processing becomes difficult because they are immersed in a large amount of noise coming not only from external sources but also from other physiological sources. So far the most widely used and accepted method to obtain reliable recording is the coherent averaging, but this type of processing presents some drawbacks. The empirical mode decomposition (EMD) is a relatively new technique which is used for processing of non-stationary signal like almost physiological signals. This method separates a signal, xextracting the energy associated with various intrinsic time scales, into a finite set of oscillatory modes. The purpose of the study was to asssess the EMD as a tool for improving the performance of the averaging coherent BAEPs seeking to reduce the amount of epochs needed to obtain a reliable register. To this end, we have analyzed the reconstruction of eight registers using only modes 2, 3 and 4 resulting from the EMD, the studies found that a reconstruction of 800 epochs is acceptable.

M-wave elimination from surface electromyogram of electrically stimulated muscles using singular value decomposition: preliminary results.

The goal of this preliminary study was to investigate the feasibility of using singular value decomposition (SVD) to eliminate the M-wave from the surface electromyogram (EMG) of an electrically stimulated paretic muscle in order to extract the volitional response. An SVD-based algorithm combining the subspaces method and a subsequent filtering is presented. It was evaluated with EMG signals registered from surface of electrically stimulated muscles with simulated paresis and its performance was compared with a conventional fixed filter. The filtering strategy proposed showed a good performance in static conditions where there were no traces of the M-wave. In dynamic conditions, the SVD-based algorithm was robust but with some remaining M-wave traces. It would be as a consequence of modifications in the data matrix and, therefore, in the subspaces generator columns and the singular values. In general, the fixed filter was very sensitive to input signal disturbances. In all of these conditions there was a greater power reduction for the SVD-based filter than for the fixed filter.

Esclerosis múltiple: parámetros clínicos y humorales intrarraquídeos de valor pronóstico / Multiple sclerosis: clinical and humoral intrarhachidian parameters of prognostic value

El diagnóstico de la Esclerosis Múltiple es eminentemente clínico, en base a la anamnesis y examen físico. El análisis del líquido cefalorraquídeo (LCR) pone en evidencia una reacción inflamatoria del Sistema Nervioso Central. Los potenciales evocados pueden mostrar lesiones multifocales latentes. La RMN revela lesiones diseminadas de la sustancia blanca y excluye otras patologías. La evolución de la enfermedad es notablemente variable de un paciente a otro, especialmente en formas de empujes y remisiones. No obstante, el estudio epidemiológico de estos enfermos, junto a los sistemas funcionales afectados y el grado de perfil desmielinizante intratecal permiten definir pautas pronósticas, favorables y desfavorables

Seudolinfoma por carbamacepina / Pseudolymphoma caused by carbamacepine

Se presenta el primer caso observado y comunicado en el Uruguay y de los hasta ahora escasos mundiales. En una paciente que recibió carbamacepina aparecieron adenopatías cervicales y alteraciones del hemograma con reacción celular ganglionar de tipo proliferativa en la biopsia. El cuadro cedió totalmente con la suspensión del fármaco