Mechanisms underlying activation of neural stem cells in the adult central nervous system

À la fin du 19e siècle, Dr. Ramón y Cajal, un pionnier scientifique, a découvert les éléments cellulaires individuels, appelés neurones, composant le système nerveux. Il a également remarqué la complexité de ce système et a mentionné l’impossibilité de ces nouveaux neurones à être intégrés dans le système nerveux adulte. Une de ses citations reconnues : “Dans les centres adultes, les chemins nerveux sont fixes, terminés, immuables. Tout doit mourir, rien ne peut être régénérer” est représentative du dogme de l’époque (Ramón y Cajal 1928). D’importantes études effectuées dans les années 1960-1970 suggèrent un point de vue différent. Il a été démontré que les nouveaux neurones peuvent être générés à l’âge adulte, mais cette découverte a créé un scepticisme omniprésent au sein de la communauté scientifique. Il a fallu 30 ans pour que le concept de neurogenèse adulte soit largement accepté. Cette découverte, en plus de nombreuses avancées techniques, a ouvert la porte à de nouvelles cibles thérapeutiques potentielles pour les maladies neurodégénératives. Les cellules souches neurales (CSNs) adultes résident principalement dans deux niches du cerveau : la zone sous-ventriculaire des ventricules latéraux et le gyrus dentelé de l’hippocampe. En condition physiologique, le niveau de neurogenèse est relativement élevé dans la zone sous-ventriculaire contrairement à l’hippocampe où certaines étapes sont limitantes. En revanche, la moelle épinière est plutôt définie comme un environnement en quiescence. Une des principales questions qui a été soulevée suite à ces découvertes est : comment peut-on activer les CSNs adultes afin d’augmenter les niveaux de neurogenèse ? Dans l’hippocampe, la capacité de l’environnement enrichi (incluant la stimulation cognitive, l’exercice et les interactions sociales) à promouvoir la neurogenèse hippocampale a déjà été démontrée. La plasticité de cette région est importante, car elle peut jouer un rôle clé dans la récupération de déficits au niveau de la mémoire et l’apprentissage. Dans la moelle épinière, des études effectuées in vitro ont démontré que les cellules épendymaires situées autour du canal central ont des capacités d’auto-renouvellement et de multipotence (neurones, astrocytes, oligodendrocytes). Il est intéressant de noter qu’in vivo, suite à une lésion de la moelle épinière, les cellules épendymaires sont activées, peuvent s’auto-renouveller, mais peuvent seulement ii donner naissance à des cellules de type gliale (astrocytes et oligodendrocytes). Cette nouvelle fonction post-lésion démontre que la plasticité est encore possible dans un environnement en quiescence et peut être exploité afin de développer des stratégies de réparation endogènes dans la moelle épinière. Les CSNs adultes jouent un rôle important dans le maintien des fonctions physiologiques du cerveau sain et dans la réparation neuronale suite à une lésion. Cependant, il y a peu de données sur les mécanismes qui permettent l'activation des CSNs en quiescence permettant de maintenir ces fonctions. L'objectif général est d'élucider les mécanismes sous-jacents à l'activation des CSNs dans le système nerveux central adulte. Pour répondre à cet objectif, nous avons mis en place deux approches complémentaires chez les souris adultes : 1) L'activation des CSNs hippocampales par l'environnement enrichi (EE) et 2) l'activation des CSNs de la moelle épinière par la neuroinflammation suite à une lésion. De plus, 3) afin d’obtenir plus d’information sur les mécanismes moléculaires de ces modèles, nous utiliserons des approches transcriptomiques afin d’ouvrir de nouvelles perspectives. Le premier projet consiste à établir de nouveaux mécanismes cellulaires et moléculaires à travers lesquels l’environnement enrichi module la plasticité du cerveau adulte. Nous avons tout d’abord évalué la contribution de chacune des composantes de l’environnement enrichi à la neurogenèse hippocampale (Chapitre II). L’exercice volontaire promeut la neurogenèse, tandis que le contexte social augmente l’activation neuronale. Par la suite, nous avons déterminé l’effet de ces composantes sur les performances comportementales et sur le transcriptome à l’aide d’un labyrinthe radial à huit bras afin d’évaluer la mémoire spatiale et un test de reconnaissante d’objets nouveaux ainsi qu’un RNA-Seq, respectivement (Chapitre III). Les coureurs ont démontré une mémoire spatiale de rappel à court-terme plus forte, tandis que les souris exposées aux interactions sociales ont eu une plus grande flexibilité cognitive à abandonner leurs anciens souvenirs. Étonnamment, l’analyse du RNA-Seq a permis d’identifier des différences claires dans l’expression des transcripts entre les coureurs de courte et longue distance, en plus des souris sociales (dans l’environnement complexe). iii Le second projet consiste à découvrir comment les cellules épendymaires acquièrent les propriétés des CSNs in vitro ou la multipotence suite aux lésions in vivo (Chapitre IV). Une analyse du RNA-Seq a révélé que le transforming growth factor-β1 (TGF-β1) agit comme un régulateur, en amont des changements significatifs suite à une lésion de la moelle épinière. Nous avons alors confirmé la présence de cette cytokine suite à la lésion et caractérisé son rôle sur la prolifération, différentiation, et survie des cellules initiatrices de neurosphères de la moelle épinière. Nos résultats suggèrent que TGF-β1 régule l’acquisition et l’expression des propriétés de cellules souches sur les cellules épendymaires provenant de la moelle épinière.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

Implication of GluR2 subunit of AMPA receptor in RGS14(414)-mediated memory enhancement

Ongoing quest for finding treatment against memory loss seen in aging and in many neurological and neurodegenerative diseases, so far has been unsuccessful and memory enhancers are seen as a potential remedy against this brain dysfunction. Recently, we showed that gene corresponding to a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414) is a robust memory enhancer (Lopez-Aranda et al. 2009: Science). RGS14414-treatment in area V2 of visual cortex caused memory enhancement to such extent that it converted short-term object recognition memory (ORM) of 45min into long lasting long-term memory that could be traced even after many months. Now, through targeting of multiple receptors and molecules known to be involved in memory processing, we found that GluR2 subunit of AMPA receptor might be key to memory enhancement in RGS-animals. RGS14-animals showed a progressive increase in GluR2 protein expression while processing an object information which reached to highest level after 60min of object exposure, a time period required for conversion of short-term ORM into long-term memory in our laboratory set up. Normal rats could retain an object information in brain for 45min (short-term) and not for 60min. However, RGS-treated rats are able to retain the same information for 24h or longer (long-term). Therefore, highest expression of GluR2 subunit seen at 60min suggests that this protein might be key in memory enhancement and conversion to long-term memory in RGS-animals. In addition, we will also discuss the implication of Hebbian plasticity and interaction of brain circuits in memory enhancement.

Behavioral traits predicting cocaine-conditioned place reference in mice: role of anxiety adn the basolateral amygdala

Aims. The individual susceptibility to cocaine addiction, a factor of interest in the understanding and prevention of this disorder, may be predicted by certain behavioral traits. However, these are not usually taken into account in research, making it difficult to identify whether they are a cause or a consequence of drug use. Methods. Male C57BL/6J mice underwent a battery of behavioral tests (elevated plus maze, hole-board, novelty preference in the Y maze, episodic-like object recognition memory and forced swimming test), followed by a cocaine-conditioned place preference (CPP) training to assess the reinforcing effect of the drug. In a second study, we aimed to determine the existence of neurobiological differences between the mice expressing high or low CPP by studying the number of neurons in certain addiction-related structures: the medial prefrontal cortex, the basolateral amygdala and the ventral tegmental area. Results. Anxiety-like behaviors in the elevated plus maze successfully predicted the cocaine-CPP behavior, so that the most anxious mice were also more likely to search for cocaine in a CPP paradigm. In addition, these mice exhibited an increased number of neurons in the basolateral amygdala, a key structure in emotional response including anxiety expression, without differences in the others regions analyzed. Conclusions. Our results suggest a relevant role of anxiety as a psychological risk factor for cocaine vulnerability, with the basolateral amygdala as potential common neural center for both anxiety and addiction.

Characterization of the behavioural phenotype of calpain-knockout mice

Dissertação de Mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2016

Cultural Heritage and Mobile Robotics Technologies

This paper reviews current research works at the authors’ Institutions to illustrate how mobile robotics and related technologies can be used to enhance economical fruition, control, protection and social impact of the cultural heritage. Robots allow experiencing on-line, from remote locations, tours at museums, archaeological areas and monuments. These solutions avoid travelling costs, increase beyond actual limits the number of simultaneous visitors, and prevent possible damages that can arise by over-exploitation of fragile environments. The same tools can be used for exploration and monitoring of cultural artifacts located in difficult to reach or dangerous areas. Examples are provided by the use of underwater robots in the exploration of deeply submerged archaeological areas. Besides, technologies commonly employed in robotics can be used to help exploring, monitoring and preserving cultural artifacts. Examples are provided by the development of procedures for data acquisition and mapping and by object recognition and monitoring algorithms.

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

Epothilone-d rescues cognition and attenuates alzheimer’s disease-like pathology in APP/PS1 mice

AIMS: Cognitive decline in Alzheimer's disease (AD) patients has been linked to synaptic damage and neuronal loss. Hyperphosphorylation of tau protein destabilizes microtubules leading to the accumulation of autophagy/vesicular material and the generation of dystrophic neurites, thus contributing to axonal/synaptic dysfunction. In this study, we analyzed the effect of a microtubule-stabilizing compound in the progression of the disease in the hippocampus of APP751SL/PS1M146L transgenic model. METHODS: APP/PS1 mice (3 month-old) were treated with a weekly intraperitoneal injection of 2 mg/kg epothilone-D (Epo-D) for 3 months. Vehicle-injected animals were used as controls. Mice were tested on the Morris water maze, Y-maze and object-recognition tasks for memory performance. Abeta, AT8, ubiquitin and synaptic markers levels were analyzed by Western-blots. Hippocampal plaque, synaptic and dystrophic loadings were quantified by image analysis after immunohistochemical stainings. RESULTS: Epo-D treated mice exhibited a significant improvement in the memory tests compared to controls. The rescue of cognitive deficits was associated to a significant reduction in the AD-like hippocampal pathology. Levels of Abeta, APP and ubiquitin were significantly reduced in treated animals. This was paralleled by a decrease in the amyloid burden, and more importantly, in the plaque-associated axonal dystrophy pathology. Finally, synaptic levels were significantly restored in treated animals compared to controls. CONCLUSION: Epo-D treatment promotes synaptic and spatial memory recovery, reduces the accumulation of extracellular Abeta and the associated neuritic pathology in the hippocampus of APP/PS1 model. Therefore, microtubule stabilizing drugs could be considered therapeutical candidates to slow down AD progression. Supported by FIS-PI12/01431 and PI15/00796 (AG),FIS-PI12/01439 and PI15/00957(JV)

Altered oscillatory dynamics of CA1 parvalbumin-basket cells during theta-gamma rhythmopathies of temporal lobe epilepsy

Recent reports in human demonstrate a role of theta– gamma coupling in memory for spatial episodes and a lack of coupling in people experiencing temporal lobe epilepsy, but the mechanisms are unknown. Using multisite silicon probe recordings of epileptic rats engaged in episodic-like object recognition tasks, we sought to evaluate the role of theta– gamma coupling in the absence of epileptiform activities. Our data reveal a specific association between theta– gamma (30 – 60 Hz) coupling at the proximal stratum radiatum of CA1 and spatial memory deficits. We targeted the microcircuit mechanisms with a novel approach to identify putative interneuronal types in tetrode recordings (parvalbumin basket cells in particular) and validated classification criteria in the epileptic context with neurochemical identification of intracellularly recorded cells. In epileptic rats, putative parvalbumin basket cells fired poorly modulated at the falling theta phase, consistent with weaker inputs from Schaffer collaterals and attenuated gamma oscillations, as evaluated by theta-phase decomposition of current–source density signals. We propose that theta– gamma interneuronal rhythmopathies of the temporal lobe are intimately related to episodic memory dysfunction in this condition.

Into the looking glass: Literacy acquisition and mirror invariance in preschool and first-grade children

At what point in reading development does literacy impact object recognition and orientation processing? Is it specific to mirror images? To answer these questions, forty-six 5- to 7-year-old preschoolers and first graders performed two same–different tasks differing in the matching criterion-orientation-based versus shape-based (orientation independent)-on geometric shapes and letters. On orientation-based judgments, first graders out- performed preschoolers who had the strongest difficulty with mirrored pairs. On shape-based judgments, first graders were slower for mirrored than identical pairs, and even slower than preschoolers. This mirror cost emerged with letter knowledge. Only first graders presented worse shape-based judgments for mirrored and rotated pairs of reversible (e.g., b-d; b-q) than nonreversible (e.g., e-ә) letters, indicating readers’ difficulty in ignoring orientation contrasts relevant to letters.

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.