Roles of transient guidepost neurons in corpus callosum formation and guidance

RESUMENeurones transitoires jouant un rôle de cibles intermédiaires dans le guidage des axones du corps calleuxLe guidage axonal est une étape clé permettant aux neurones d'établir des connexions synaptiques et de s'intégrer dans un réseau neural fonctionnel de manière spécifique. Des cellules-cibles intermédiaires appelées « guidepost » aident les axones à parcourir de longues distances dans le cerveau en leur fournissant des informations directionnelles tout au long de leur trajet. Il a été démontré que des sous-populations de cellules gliales au niveau de la ligne médiane guident les axones du corps calleux (CC) d'un hémisphère vers l'autre. Bien qu'il fût observé que le CC en développement contenait aussi des neurones, leur rôle était resté jusqu'alors inconnu.La publication de nos résultats a montré que pendant le développement embryonnaire, le CC contient des glies mais aussi un nombre considérable de neurones glutamatergiques et GABAergiques, nécessaires à la formation du corps calleux (Niquille et al., PLoS Biology, 2009). Dans ce travail, j'ai utilisé des techniques de morphologie et d'imagerie confocale 3D pour définir le cadre neuro-anatomique de notre modèle. De plus, à l'aide de transplantations sur tranches in vitro, de co-explants, d'expression de siRNA dans des cultures de neurones primaires et d'analyse in vivo sur des souris knock-out, nous avons démontré que les neurones du CC guident les axones callosaux en partie grâce à l'action attractive du facteur de guidage Sema3C sur son récepteur Npn- 1.Récemment, nous avons étudié l'origine, les aspects dynamiques de ces processus, ainsi que les mécanismes moléculaires impliqués dans la mise en place de ce faisceau axonal (Niquille et al., soumis). Tout d'abord, nous avons précisé l'origine et l'identité des neurones guidepost GABAergiques du CC par une étude approfondie de traçage génétique in vivo. J'ai identifié, dans le CC, deux populations distinctes de neurones GABAergiques venant des éminences ganglionnaires médiane (MGE) et caudale (CGE). J'ai ensuite étudié plus en détail les interactions dynamiques entre neurones et axones du corps calleux par microscopie confocale en temps réel. Puis nous avons défini le rôle de chaque sous-population neuronale dans le guidage des axones callosaux et de manière intéressante les neurones GABAergic dérivés de la MGE comme ceux de la CGE se sont révélés avoir une action attractive pour les axones callosaux dans des expériences de transplantation. Enfin, nous avons clarifié la base moléculaire de ces mécanismes de guidage par FACS sorting associé à un large criblage génétique de molécules d'intérêt par une technique très sensible de RT-PCR et ensuite ces résultats ont été validés par hybridation in situ.Nous avons également étudié si les neurones guidepost du CC étaient impliqués dans son agénésie (absence de CC), présente dans nombreux syndromes congénitaux chez 1 humain. Le gène homéotique Aristaless (Arx) contrôle la migration des neurones GABAergiques et sa mutation conduit à de nombreuses pathologies humaines, notamment la lissencéphalie liée à IX avec organes génitaux anormaux (XLAG) et agénésie du CC. Fait intéressant, nous avons constaté qu'ARX est exprimé dans toutes les populations GABAergiques guidepost du CC et que les embryons mutant pour Arx présentent une perte drastique de ces neurones accompagnée de défauts de navigation des axones (Niquille et al., en préparation). En outre, nous avons découvert que les souris déficientes pour le facteur de transcription ciliogenic RFX3 souffrent d'une agénésie du CC associé avec des défauts de mise en place de la ligne médiane et une désorganisation secondaire des neurones glutamatergiques guidepost (Benadiba et al., submitted). Ceci suggère fortement l'implication potentielle des deux types de neurones guidepost dans l'agénésie du CC chez l'humain.Ainsi, mon travail de thèse révèle de nouvelles fonctions pour ces neurones transitoires dans le guidage axonal et apporte de nouvelles perspectives sur les rôles respectifs des cellules neuronales et gliales dans ce processus.ABSTRACTRole of transient guidepost neurons in corpus callosum development and guidanceAxonal guidance is a key step that allows neurons to build specific synaptic connections and to specifically integrate in a functional neural network. Intermediate targets or guidepost cells act as critical elements that help to guide axons through long distance in the brain and provide information all along their travel. Subpopulations of midline glial cells have been shown to guide corpus callosum (CC) axons to the contralateral cerebral hemisphere. While neuronal cells are also present in the developing corpus callosum, their role still remains elusive.Our published results unravelled that, during embryonic development, the CC is populated in addition to astroglia by numerous glutamatergic and GABAergic guidepost neurons that are essential for the correct midline crossing of callosal axons (Niquille et al., PLoS Biology, 2009). In this work, I have combined morphological and 3D confocal imaging techniques to define the neuro- anatomical frame of our system. Moreover, with the use of in vitro transplantations in slices, co- explant experiments, siRNA manipulations on primary neuronal culture and in vivo analysis of knock-out mice we have been able to demonstrate that CC neurons direct callosal axon outgrowth, in part through the attractive action of Sema3C on its Npn-1 receptor.Recently, we have studied the origin, the dynamic aspects of these processes as well as the molecular mechanisms involved in the establishment of this axonal tract (Niquille et al., submitted). First, we have clarified the origin and the identity of the CC GABAergic guidepost neurons using extensive in vivo cell fate-mapping experiments. We identified two distinct GABAergic neuronal subpopulations, originating from the medial (MGE) and caudal (CGE) ganglionic eminences. I then studied in more details the dynamic interactions between CC neurons and callosal axons by confocal time-lapse video microscopy and I have also further characterized the role of each guidepost neuronal subpopulation in callosal guidance. Interestingly, MGE- and CGE-derived GABAergic neurons are both attractive for callosal axons in transplantation experiments. Finally, we have dissected the molecular basis of these guidance mechanisms by using FACS sorting combined with an extensive genetic screen for molecules of interest by a sensitive RT-PCR technique, as well as, in situ hybridization.I have also investigated whether CC guidepost neurons are involved in agenesis of the CC which occurs in numerous human congenital syndromes. Aristaless-related homeobox gene (Arx) regulates GABAergic neuron migration and its mutation leads to numerous human pathologies including X-linked lissencephaly with abnormal genitalia (XLAG) and severe CC agenesis. Interestingly, I found that ARX is expressed in all the guidepost GABAergic neuronal populations of the CC and that Arx-/- embryos exhibit a drastic loss of CC GABAergic interneurons accompanied by callosal axon navigation defects (Niquille et al, in preparation). In addition, we discovered that mice deficient for the ciliogenic transcription factor RFX3 suffer from CC agenesis associated with early midline patterning defects and a secondary disorganisation of guidepost glutamatergic neurons (Benadiba et al., submitted). This strongly points out the potential implication of both types of guidepost neurons in human CC agenesis.Taken together, my thesis work reveals novel functions for transient neurons in axonal guidance and brings new perspectives on the respective roles of neuronal and glial cells in these processes.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Parametric Approach to Blind Deconvolution of Nonlinear Channels

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Correlations between negative-induced cerebral dynamics and idiopathic intellectual disability: an EEG study

Intellectual disability has long been associated with deficits in socio-emotional processing. However, studies investigating brain dynamics of maladaptive socio-emotional skills associated with intellectual disability are scarce. Here, we compared differences in brain activity between low intelligence quotient (I.Q.<75, N=13) and normal controls (N=15) while evaluating their subjective emotions. Positive (P) and negative (N) valenced pictures were presented one at a time to participants of both groups, at a rate of ¾. The task required that each participant evaluate their subjective emotion and press a predefined push-button when done, alternatively P and N. Electroencephalographic (EEG) signals were continuously recorded, and the 1000ms time window following each picture was analyzed offline for power in frequency domain. Alpha low (8-10Hz) and upper (10-13Hz) frequency bands were then compared for both groups and for both P and N emotions in 12 distributed scalp electrodes. The qualitative evaluation of emotions was similar between both groups, with constant longer reaction times for the low IQ participants. The EEG signal comparison shows marked power decrease in upper alpha frequency range for N emotions in low intelligence group. Otherwise no significant difference was noticed between low and normal IQ. Main findings of the present study are (1) results do not support the hypothesis that impairment in developmental intelligence roots in maladaptive emotional processing; (2) the strong alpha power suppression during negative-induced emotions suggests the involvement of an extended neural network and more effortful inhibition processes than positive ones. We call for further studies with a larger sample.

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.

Predicting chick body mass by artificial intelligence-based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Yhteiskunnallisen sisältöpalvelun sosioteknologinen tarkastelu Kohosen itseorganisoituvien karttojen avulla - Case deittipalvelu

Työn tavoitteena on tutkia deittipalvelun käyttäjien anonyymiaineistoa neuroverkko-opetuksessa segmentoituneiden piirrekarttojen (SOM, Self-Organizing Map) avulla. Näiden piirrekarttojen avulla on tarkoitus selvittää, löytyykö mahdollisesti selkeitä SMS- ja e-mail - käyttäjäryhmiä. Tutkimusta lähestytään perehtymällä ensin yrityksen tekniseen palvelualusta-arkkitehtuuriin ja myös varsinaiseen deittipalveluun käyttäjän kannalta.Tutkimus aloitettiin koodaamalla tietoaineisto SOM Toolbox-ohjelmalle käytettäväksi. Varsinaisia tutkimustuloksia analysoitiin valitsemalla otoksia neuroverkko-opetuksessa segmentoituneista piirrekartoista. Saadut tulokset osoittavat, ettäSOM-teknologia soveltuu hyvin sisältöpalveluiden sosioteknologiseen tutkimukseen ja sitä on myös mahdollista käyttää asiakkuudenhallinnassa erilaisten käyttäjäryhmien profilointiin.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.