Multivariable relationships between autonomic nervous system related indices in hyperbaric environments

The main aim of this work is to model the relationships between parameters extracted from the heart rate variability (HRV) signal, which is derived from the electrocardiogram (ECG), at different stages of a simulated immersion in a hyperbaric chamber. The response of the Autonomic Nervous System is known to be affected by changes in atmospheric pressure, reflected in changes in the HRV signal. A dataset consisting of ECG signals from 17 subjects exposed to a controlled hyperbaric environment, simulating depths from 0 m to 40 m, was used. Both linear and nonlinear dependences of HRV parameters were analysed using linear regression and Mutual Information (entropy-based) techniques. Furthermore, relationships between parameters of the HRV signals, biophysical variables of the subjects, and atmospheric pressure changes were characterized by artificial neural networks. In particular, self-organizing maps (SOM) were trained for modelling and clustering all the data. In the mid-term, these models could be the basis to create predictive models of HRV parameters at high depths in order to increase the safety for divers by warning them if some abnormal body response could be expected just by processing the ECG signal at sea level before immersion

Photoplethysmographic Waveform in Hyperbaric Environment

The objective of this work is the identification of significant variations of morphological parameters of the photoplethysmographic (PPG) signal when the subjects are exposed to an increase in atmospheric pressure. To achieve this goal, the PPG signal of 26 subjects, exposed to a hyperbaric environment whose pressure increases up to 5 atm, has been recorded. From this record, segments of 4 minutes have been processed at 1 atm, 3 atm and 5 atm, both in the descending (D) and ascending (A) periods of the immersion. In total, four states (3D, 5, 3A and 1A) normalized to the basal state (1D) have been considered. In these segments, six morphological parameters of the PPG signal were studied. The width, the amplitude, the widths of the anacrotic and catacrotic phases, and the upward and downward slopes of each PPG pulse were extracted. The results showed significant increases in the three parameters related to the pulse width. This increase is significant in the four states analysed for the anacrotic phase width. Furthermore, a significant decrease in the amplitude and in both slopes (in the states 1A) was observed. These results show that the PPG width responds rapidly to the increase in pressure, indicating an activation of the sympathetic system, while amplitude and pulse slopes are decreased when the subjects are exposed to the hyperbaric environment for a considerable period of time

SISTEMA INTEGRADO EN UN UAV PARA RECONOCIMIENTOS TECNICOS DE INGENIEROS

La evolución rápida de la tecnología ha permitido utilizar los UAV como una herramienta cotidiana de trabajo. Un ejemplo de esta revolución es que en un futuro cercano la Fuerza Aérea de los Estados Unidos, prevé que un tercio de las aeronaves militares de uso operativo serán no tripuladas. Al principio, se consideró una herramienta para captar imágenes, pero ha pasado a utilizarse en múltiples ámbitos profesionales en temas tan diversos como entregas de carga, seguridad, agricultura de precisión, inspección de instalaciones, gestión en la construcción, protección civil, salvamento marítimo o transporte de mercancías entre otros. Desarrollando así, un papel fundamental en la vida militar por la ventaja táctica, operacional y estratégica que aporta. En las Unidades de Ingenieros, la utilización de estos medios es vital debido a que pueden operar en zonas remotas peligrosas, de forma activa o pasiva, para la adquisición de información enviando imágenes en tiempo real que alimenta la inteligencia y sirvan de asesoramiento en la toma de decisiones del puesto de Mando. Para conseguir la información precisa de estos reconocimientos es necesario un sistema que permita su obtención de manera óptima. Uno de los objetivos de este trabajo es la selección de aquellos sensores que cubran las necesidades de la misión en las Unidades de Ingenieros. Los sensores seleccionados fueron: sensor multiespectral, permite observar radiaciones invisibles al ojo humano, sensor termográfico, la cual crea imágenes gracias al calor desprendido por los cuerpos, y el sistema Lidar, para generar imágenes en 3D del terreno, aumentando así las capacidades de las Unidades de Ingenieros. Estos sensores acoplados a un Vehículo Aéreo No Tripulado (UAV - Unmanned Aerial Vehicle) reduciría el riesgo del personal al poder detectar la amenaza con antelación. El sistema UAV más sensor, permitiría la observación de puntos sensibles, sin arriesgar vidas humanas, contribuyendo al logro de objetivos a nivel táctico, operacional o estratégicos. Todas estas ventajas permiten que se incremente la velocidad con la que se cumple la misión, logrando realizar el ciclo OODA, observe, orient, decide, act, más rápido que el oponente, obteniendo así una clara ventaja. Uno de los riesgos de seguridad en las Unidades de Ingenieros es la de sufrir una explosión de artefactos explosivos en rutas, por ello es fundamental integrar e incorporar estos sistemas (UAV más sensor) que permitirán minimizar dicho riesgo y una mayor flexibilidad operativa. Para la selección del sensor y el UAV más adecuados a las necesidades de la unidad, se siguió un procedimiento analítico jerárquico (AHP) cuyos resultados recomiendan la utilización por parte de las unidades del UAV Alpha800. Gracias a este vehículo e incorporando el sensor seleccionado, se consiguen tres beneficios: • Se estudiará el terreno para la reconstrucción y habilitación de vías de comunicación. • Se obtendrá capacidad de apoyo al despliegue mediante el levantamiento topográfico para la construcción de campamentos y fortificaciones. • Estudio del terreno para el restablecimiento de servicios esenciales (aguadas y servicios eléctricos) o de vías de ferrocarriles, es decir, actividades propias de un reconocimiento de ingenieros. <br /

Cellular and molecular assessment of rose bengal photodynamic antimicrobial therapy on keratocytes, corneal endothelium and limbal stem cell niche

Rose Bengal Photodynamic Antimicrobial Therapy (RB-PDAT) is a novel potential treatment for progressive infectious keratitis. The principle behind this therapy is using Rose Bengal as a photosensitizer that can be activated by green light and results in the production of oxygen free radicals which in turn eradicate the microorganism. Given RB-PDAT's mechanism of action and the potential cytotoxic effects, concerns regarding the safety of this technique have arisen. The purpose of this study was to evaluate the effect of RB-PDAT on keratocytes, while focusing on the safety profile that the photo-chemical reaction has on the limbal stem cell (LSC) niche and endothelial cell layer of the treated cornea. To perform RB-PDAT, Rose Bengal solution (0.1% RB in BSS) was applied to the right cornea of rabbits for 30 min and then irradiated by a custom-made green LED light source (525 nm, 6 mW/cm2) for 15 min (5.4 J/cm2). Three rabbits were sacrificed and enucleated after 24 h for evaluation. TUNEL assay and immunohistochemistry for endothelium and limbal stem cell viability were performed on whole mounts and frozen sections in treated and control eyes. LSC of both eyes were isolated and cultured to perform MTT viability and proliferation, and scratch wound healing assays under time-lapse microscopy. Interestingly, while Rose Bengal dye penetration was superficial, yet associated cellular apoptosis was evidenced in up to 1/3 of the stromal thickness on frozen sections. TUNEL assay on whole mounts showed no endothelial cell death following treatment. Immunohistochemistry on frozen sections of LSC displayed no structural difference between treated and non-treated eyes. There was no difference in LSC proliferation rates and scratch wound healing assay demonstrated adequate cell migration from treated and non-treated eyes. The current study suggests that even though penetration of the RB dye has been shown to be limited, oxidative stress produced by RB-PDAT can reach deeper into the corneal stroma. Nevertheless, our results show that performing RB-PDAT is safe on the corneal endothelium and has no effect on LSC viability or function. •Superficial penetration of the Rose Bengal dye is seen after RB-PDAT.•Cellular apoptosis was evidenced in up to 1/3 of stromal thickness.•Effects exerted by RB-PDAT penetrate deeper into the corneal stroma than the dye.•RB-PDAT is safe on the corneal endothelium.•RB-PDAT has no effect on limbal stem cell viability or function

Locally linear embedding (LLE) for MRI based Alzheimer's disease classification

Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer’s disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of locally linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with following diagnoses: Cognitive normal (CN; n= 137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found classifications using embedded MRI features generally outperformed (p < 0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p = 0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: = 0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: = 0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD