(Table 2) Mercury and selenium concentration in the brain stem of wild polar bears (Ursus maritimus), east Greenland

Polar bears (Ursus maritimus) are exposed to high concentrations of mercury because they are apex predators in the Arctic ecosystem. Although mercury is a potent neurotoxic heavy metal, it is not known whether current exposures are of neurotoxicological concern to polar bears. We tested the hypotheses that polar bears accumulate levels of mercury in their brains that exceed the estimated lowest observable adverse effect level (20 µg/g dry wt) for mammalian wildlife and that such exposures are associated with subtle neurological damage, as determined by measuring neurochemical biomarkers previously shown to be disrupted by mercury in other high-trophic wildlife. Brain stem (medulla oblongata) tissues from 82 polar bears subsistence hunted in East Greenland were studied. Despite surprisingly low levels of mercury in the brain stem region (total mercury = 0.36 ± 0.12 µg/g dry wt), a significant negative correlation was measured between N-methyl-D-aspartate (NMDA) receptor levels and both total mercury (r = -0.34, p < 0.01) and methylmercury (r = -0.89, p < 0.05). No relationships were observed among mercury, selenium, and several other neurochemical biomarkers (dopamine-2, gamma-aminobutyric acid type A, muscarinic cholinergic, and nicotinic cholinergic receptors; cholinesterase and monoamine oxidase enzymes). These data show that East Greenland polar bears do not accumulate high levels of mercury in their brain stems. However, decreased levels of NMDA receptors could be one of the most sensitive indicators of mercury's subclinical and early effects.

The evolution of the brain, the human nature of cortical circuits and intellectual creativity

The tremendous expansion and the differentiation of the neocortex constitute two major events in the evolution of the mammalian brain. The increase in size and complexity of our brains opened the way to a spectacular development of cognitive and mental skills. This expansion during evolution facilitated the addition of microcircuits with a similar basic structure, which increased the complexity of the human brain and contributed to its uniqueness. However, fundamental differences even exist between distinct mammalian species. Here, we shall discuss the issue of our humanity from a neurobiological and historical perspective.

An architectural model for software testing lesson learned systems

Software testing is a key aspect of software reliability and quality assurance in a context where software development constantly has to overcome mammoth challenges in a continuously changing environment. One of the characteristics of software testing is that it has a large intellectual capital component and can thus benefit from the use of the experience gained from past projects. Software testing can, then, potentially benefit from solutions provided by the knowledge management discipline. There are in fact a number of proposals concerning effective knowledge management related to several software engineering processes. Objective: We defend the use of a lesson learned system for software testing. The reason is that such a system is an effective knowledge management resource enabling testers and managers to take advantage of the experience locked away in the brains of the testers. To do this, the experience has to be gathered, disseminated and reused. Method: After analyzing the proposals for managing software testing experience, significant weaknesses have been detected in the current systems of this type. The architectural model proposed here for lesson learned systems is designed to try to avoid these weaknesses. This model (i) defines the structure of the software testing lessons learned; (ii) sets up procedures for lesson learned management; and (iii) supports the design of software tools to manage the lessons learned. Results: A different approach, based on the management of the lessons learned that software testing engineers gather from everyday experience, with two basic goals: usefulness and applicability. Conclusion: The architectural model proposed here lays the groundwork to overcome the obstacles to sharing and reusing experience gained in the software testing and test management. As such, it provides guidance for developing software testing lesson learned systems.

Atlas-free Brain Tissue Segmentation Using a Single T1-weighted MRI Acquisition

Many studies investigating the aging brain or disease-induced brain alterations rely on accurate and reproducible brain tissue segmentation. Being a preliminary processing step prior to the segmentation, reliableskull-stripping the removal ofnon-brain tissue is also crucial for all later image assessment. Typically, segmentation algorithms rely on an atlas i.e. pre-segmented template data. Brain morphology, however, differs considerably depending on age, sex and race. In addition, diseased brains may deviate significantly from the atlas information typically gained from healthy volunteers. The imposed prior atlas information can thus lead to degradation of segmentation results. The recently introduced MP2RAGE sequence provides a bias-free T1 contrast with heavily reduced T2*- and PD-weighting compared to the standard MP-RAGE [1]. To this end, it acquires two image volumes at different inversion times in one acquisition, combining them to a uniform, i.e. homogenous image. In this work, we exploit the advantageous contrast properties of the MP2RAGE and combine it with a Dixon (i.e. fat-water separation) approach. The information gained by the additional fat image of the head considerably improves the skull-stripping outcome [2]. In conjunction with the pure T1 contrast of the MP2RAGE uniform image, we achieve robust skull-stripping and brain tissue segmentation without the use of an atlas

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

Background DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity. Results Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains. Conclusions A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/ webcite.

A new label fusion method using graph cuts: application to hippocampus segmentation

The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Aplicación móvil y web para la gestión de lugares geolocalizados

El proyecto “Aplicación móvil y web para la gestión de lugares geolocalizados (www.midiez.com)” tiene como objetivo principal crear un repositorio de listas categorizadas de sitios para su uso en el ámbito personal o comercial. Tanto la aplicación web como la aplicación móvil desarrollada en Android tienen el propósito de gestionar listas de lugares de interés (Restaurantes, tiendas,..) o con propósitos específicos (Organización de viajes) o simplemente como una forma de anotar aquellos sitios que nos comentan y que nos gustaría visitar. El desarrollo de este proyecto además permitirá contrastar las distintas alternativas y la evolución de las distintas herramientas que se han ido desarrollando para la gestión del ocio en los últimos años desde el sistema Android y plataformas web. Todo el proyecto ha sido realizado usando software libre (PHP para el lenguaje web servidor y Java para la programación móvil). La principal finalidad desde el punto de vista del desarrollador es: aprovechar las sinergias de la programación móvil y la programación web de manera que las mismas capas de negocio de Datos sean usadas por ambas plataformas. Asimismo crear una aplicación distribuida y fácilmente escalable. Las herramientas que se han usado para desarrollar han sido: la SDK proporcionada por Google, una JDK de Java y un IDE de desarrollo Java como es Eclipse y otro similar para el desarrollo de la parte PHP. La BBDD elegida ha sido MySQL. El proyecto pretende mostrar el potencial de las aplicaciones móviles geolocalizadas desde el punto de vista del ocio y compararlas con el estado del arte actual. Por lo tanto la mayor parte del tiempo dedicado al proyecto ha sido empleado en el desarrollo de la aplicación web, la aplicación móvil y en la base de datos pero también he dedicado una pequeña parte del trabajo para realizar un estudio sobre las consecuencias que esta tecnología está teniendo en nuestros cerebros. ABSTRACT The project "Web and Mobile App for managing geolocation places” has as main objective managing of places lists in order to use them in the leisure time scope. Nowadays the use of GPS is being a constant in mobile applications so that is already part of our daily life. We used to know where we are always and at the same time we can find locations using the technology of our mobile phones. Now it is very difficult to get lost outside but also is difficult to explain somebody how to get to anywhere without using Google Maps. Google Maps, Geolocation, gps navigators, … all that kind of stuff are making our life easier and less complicated but also are making our brains lazier. Furthermore, the development of this project will use the potential of locate places into maps to avoid annotate every spot we would like to visit or a brand new restaurant. The project itself shows the location features of Google Maps combined with an places data base in order to create, and manage places lists and use them to get to them as well as to share those places with our contacts. Also, the main purpose from the point of view of the developer is to combine different programming languages and use the resulting synergies in a easily scalable and portable environment. The tools that have been used to develop are: the SDK provided by Google, one JDK Java and Java development IDE such as Eclipse and similar to the development of the PHP part. The DB has been chosen MySQL. Finally, this project aims to show, from an educational point of view, the use and potential of this technology. Thus, it has been devoted a large amount of time of the project (and, consequently, its documentation) on develop the Android app, the data base and the web app but also but also to highlight the consequences of using technology.

Alternative neural circuitry that might be impaired in the development of Alzheimer disease

It is well established that some individuals with normal cognitive capacity have abundant senile plaques in their brains. It has been proposed that those individuals are resilient or have compensation factors to prevent cognitive decline. In this comment, we explore an alternative mechanism through which cognitive capacity is maintained. This mechanism could involve the impairment of alternative neural circuitry. Also, the proportion of molecules such as A? or tau protein present in different areas of the brain could be important.

Constitutive and regulated α-secretase cleavage of Alzheimer’s amyloid precursor protein by a disintegrin metalloprotease

Amyloid β peptide (Aβ), the principal proteinaceous component of amyloid plaques in brains of Alzheimer’s disease patients, is derived by proteolytic cleavage of the amyloid precursor protein (APP). Proteolytic cleavage of APP by a putative α-secretase within the Aβ sequence precludes the formation of the amyloidogenic peptides and leads to the release of soluble APPsα into the medium. By overexpression of a disintegrin and metalloprotease (ADAM), classified as ADAM 10, in HEK 293 cells, basal and protein kinase C-stimulated α-secretase activity was increased severalfold. The proteolytically activated form of ADAM 10 was localized by cell surface biotinylation in the plasma membrane, but the majority of the proenzyme was found in the Golgi. These results support the view that APP is cleaved both at the cell surface and along the secretory pathway. Endogenous α-secretase activity was inhibited by a dominant negative form of ADAM 10 with a point mutation in the zinc binding site. Studies with purified ADAM 10 and Aβ fragments confirm the correct α-secretase cleavage site and demonstrate a dependence on the substrate’s conformation. Our results provide evidence that ADAM 10 has α-secretase activity and many properties expected for the proteolytic processing of APP. Increases of its expression and activity might be beneficial for the treatment of Alzheimer’s disease.

Dehydroepiandrosterone: A potential signalling molecule for neocortical organization during development

Dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEAS) are the most abundant steroids produced by the human adrenal, but no receptors have been identified for these steroids, and no function for them has been established, other than as precursors for sex steroid synthesis. DHEA and DHEAS are found in brains from many species, and we have shown that enzymes crucial for their synthesis, especially P450c17 (17α-hydroxylase/c17,20 lyase), are expressed in a developmentally regulated, region-specific fashion in the developing rodent brain. One region of embryonic expression of P450c17, the neocortical subplate, has been postulated to play a role in guiding cortical projections to their appropriate targets. We therefore determined if products of P450c17 activity, DHEA and DHEAS, regulated the motility and/or growth of neocortical neurons. In primary cultures of mouse embryonic neocortical neurons, DHEA increased the length of neurites containing the axonal marker Tau-1, and the incidence of varicosities and basket-like process formations in a dose-dependent fashion. These effects could be seen at concentrations normally found in the brain. By contrast, DHEAS had no effect on Tau-1 axonal neurites but increased the length of neurites containing the dendritic marker microtubule-associated protein-2. DHEA rapidly increased free intracellular calcium via activation of N-methyl-d-aspartate (NMDA) receptors. These studies provide evidence of mechanisms by which DHEA and DHEAS exert biological actions, show that they have specific functions other than as sex steroid precursors, mediate their effects via non-classic steroid hormone receptors, and suggest that their developmentally regulated synthesis in vivo may play crucial and different roles in organizing the neocortex.

Autoradiographic characterization of [3H]-5-HT-moduline binding sites in rodent brain and their relationship to 5-HT1B receptors

5-HT-moduline is an endogenous tetrapeptide [Leu-Ser-Ala-Leu (LSAL)] that was first isolated from bovine brain tissue. To understand the physiological role of this tetrapeptide, we studied the localization of 5-HT-moduline binding sites in rat and mouse brains. Quantitative data obtained with a gaseous detector of β-particles (β-imager) indicated that [3H]-5-HT-moduline bound specifically to rat brain sections with high affinity (Kd = 0.77 nM and Bmax = 0.26 dpm/mm2). Using film autoradiography in parallel, we found that 5-HT-moduline binding sites were expressed in a variety of rat and mouse brain structures. In 5-HT1B receptor knock-out mice, the specific binding of [3H]-5-HT-moduline was not different from background labeling, indicating that 5-HT-moduline targets are exclusively located on the 5-HT1B receptors. Although the distribution of 5-HT-moduline binding sites was similar to that of 5-HT1B receptors, they did not overlap totally. Differences in distribution patterns were found in regions containing either high levels of 5-HT1B receptors such as globus pallidus and subiculum that were poorly labeled or in other regions such as dentate gyrus of hippocampus and cortex where the relative density of 5-HT-moduline binding sites was higher than that of 5-HT1B receptors. In conclusion, our data, based on autoradiographic localization, indicate that 5-HT-moduline targets are located on 5-HT1B receptors present both on 5-HT afferents and postsynaptic neurons. By interacting specifically with 5-HT1B receptors, this tetrapeptide may play a pivotal role in pathological states such as stress that involves the dysfunction of 5-HT neurotransmission.

Immunologic tolerance to myelin basic protein decreases stroke size after transient focal cerebral ischemia

Immune mechanisms contribute to cerebral ischemic injury. Therapeutic immunosuppressive options are limited due to systemic side effects. We attempted to achieve immunosuppression in the brain through oral tolerance to myelin basic protein (MBP). Lewis rats were fed low-dose bovine MBP or ovalbumin (1 mg, five times) before 3 h of middle cerebral artery occlusion (MCAO). A third group of animals was sensitized to MBP but did not survive the post-stroke period. Infarct size at 24 and 96 h after ischemia was significantly less in tolerized animals. Tolerance to MBP was confirmed in vivo by a decrease in delayed-type hypersensitivity to MBP. Systemic immune responses, characterized in vitro by spleen cell proliferation to Con A, lipopolysaccharide, and MBP, again confirmed antigen-specific immunologic tolerance. Immunohistochemistry revealed transforming growth factor β1 production by T cells in the brains of tolerized but not control animals. Systemic transforming growth factor β1 levels were equivalent in both groups. Corticosterone levels 24 h after surgery were elevated in all sham-operated animals and ischemic control animals but not in ischemic tolerized animals. These results demonstrate that antigen-specific modulation of the immune response decreases infarct size after focal cerebral ischemia and that sensitization to the same antigen may actually worsen outcome.

Responses in the brain of estrogen receptor α-disrupted mice

These studies sought to determine if neurons in the estrogen receptor-α knockout (ERαKO) mouse brain concentrated 16α-[125I]iodo-11β-methoxy-17β-estradiol (125I-estrogen), and if so, whether estrogen binding augmented the expression of progesterone receptor (PR) mRNA. Mice were injected with 125I-estrogen and cryostat sections thaw mounted onto emulsion-coated slides. After 30–90 days of exposure, cells with a nuclear uptake and retention of 125I-estrogen were observed in a number of ERαKO mouse brain regions including the preoptic nucleus and arcuate nucleus of the hypothalamus, bed nucleus of the stria terminalis, and amygdala, although the number of labeled cells and intensity of nuclear concentration was markedly attenuated when compared with wild-type littermates. Competition studies with excess 17β-estradiol, diethylstilbestrol, or moxestrol, but not with R5020 or dihydrotestosterone, prevented the nuclear concentration of 125I-estrogen. To determine if the low level of estrogen binding was capable of regulating gene expression, in situ hybridization was used to evaluate PR mRNA in the brain. ERαKO and wild-type mice were ovariectomized and treated with vehicle or 17β-estradiol, and brains were sectioned and hybridized with a PR cRNA probe. Analysis of hybridization signal revealed a similar, low level of PR mRNA in ovariectomized wild-type and homozygous mice, and a marked increase in expression after treatment of ovariectomized animals with 17β-estradiol, with the level of hybridization signal being significantly higher in wild-type animals when compared with ERαKO mice. The results demonstrate that estrogen binds in the ERαKO brain and is capable of modulating PR gene expression, thus supporting the presence and functionality of a nonclassical estrogen receptor.