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.

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.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Modelling bird species richness with neural networks for forest landscape management in NE Spain

Para preservar la biodiversidad de los ecosistemas forestales de la Europa mediterránea en escenarios actuales y futuros de cambio global mediante una gestión forestal sostenible es necesario determinar cómo influye el medio ambiente y las propias características de los bosques sobre la biodiversidad que éstos albergan. Con este propósito, se analizó la influencia de diferentes factores ambientales y de estructura y composición del bosque sobre la riqueza de aves forestales a escala 1 × 1 km en Cataluña (NE de España). Se construyeron modelos univariantes y multivariantes de redes neuronales para respectivamente explorar la respuesta individual a las variables y obtener un modelo parsimonioso (ecológicamente interpretable) y preciso. La superficie de bosque (con una fracción de cabida cubierta superior a 5%), la fracción de cabida cubierta media, la temperatura anual y la precipitación estival medias fueron los mejores predictores de la riqueza de aves forestales. La red neuronal multivariante obtenida tuvo una buena capacidad de generalización salvo en las localidades con una mayor riqueza. Además, los bosques con diferentes grados de apertura del dosel arbóreo, más maduros y más diversos en cuanto a su composición de especies arbóreas se asociaron de forma positiva con una mayor riqueza de aves forestales. Finalmente, se proporcionan directrices de gestión para la planificación forestal que permitan promover la diversidad ornítica en esta región de la Europa mediterránea.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Advanced orbital pipe welding

Since the introduction of automatic orbital welding in pipeline application in 1961, significant improvements have been obtained in orbital pipe welding systems. Requirement of more productive welding systems for pipeline application forces manufacturers to innovate new advanced systems and welding processes for orbital welding method. Various methods have been used to make welding process adaptive, such as visual sensing, passive visual sensing, real-time intelligent control, scan welding technique, multi laser vision sensor, thermal scanning, adaptive image processing, neural network model, machine vision, and optical sensing. Numerous studies are reviewed and discussed in this Master’s thesis and based on a wide range of experiments which already have been accomplished by different researches the vision sensor are reported to be the best choice for adaptive orbital pipe welding system. Also, in this study the most welding processes as well as the most pipe variations welded by orbital welding systems mainly for oil and gas pipeline applications are explained. The welding results show that Gas Metal Arc Welding (GMAW) and its variants like Surface Tension Transfer (STT) and modified short circuit are the most preferred processes in the welding of root pass and can be replaced to the Gas Tungsten Arc Welding (GTAW) in many applications. Furthermore, dual-tandem gas metal arc welding technique is currently considered the most efficient method in the welding of fill pass. Orbital GTAW process mostly is applied for applications ranging from single run welding of thin walled stainless tubes to multi run welding of thick walled pipes. Flux cored arc welding process is faster process with higher deposition rate and recently this process is getting more popular in pipe welding applications. Also, combination of gas metal arc welding and Nd:YAG laser has shown acceptable results in girth welding of land pipelines for oil and gas industry. This Master’s thesis can be implemented as a guideline in welding of pipes and tubes to achieve higher quality and efficiency. Also, this research can be used as a base material for future investigations to supplement present finding.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.