Integration of Notch 1 and calcineurin/NFAT signaling pathways in keratinocyte growth and differentiation control.

The Notch and Calcineurin/NFAT pathways have both been implicated in control of keratinocyte differentiation. Induction of the p21(WAF1/Cip1) gene by Notch 1 activation in differentiating keratinocytes is associated with direct targeting of the RBP-Jkappa protein to the p21 promoter. We show here that Notch 1 activation functions also through a second Calcineurin-dependent mechanism acting on the p21 TATA box-proximal region. Increased Calcineurin/NFAT activity by Notch signaling involves downregulation of Calcipressin, an endogenous Calcineurin inhibitor, through a HES-1-dependent mechanism. Besides control of the p21 gene, Calcineurin contributes significantly to the transcriptional response of keratinocytes to Notch 1 activation, both in vitro and in vivo. In fact, deletion of the Calcineurin B1 gene in the skin results in a cyclic alopecia phenotype, associated with altered expression of Notch-responsive genes involved in hair follicle structure and/or adhesion to the surrounding mesenchyme. Thus, an important interconnection exists between Notch 1 and Calcineurin-NFAT pathways in keratinocyte growth/differentiation control.

A Thermodynamically Consistent Damage Model for Advanced Composites

A continuum damage model for the prediction of damage onset and structural collapse of structures manufactured in fiber-reinforced plastic laminates is proposed. The principal damage mechanisms occurring in the longitudinal and transverse directions of a ply are represented by a damage tensor that is fixed in space. Crack closure under load reversal effects are taken into account using damage variables established as a function of the sign of the components of the stress tensor. Damage activation functions based on the LaRC04 failure criteria are used to predict the different damage mechanisms occurring at the ply level. The constitutive damage model is implemented in a finite element code. The objectivity of the numerical model is assured by regularizing the dissipated energy at a material point using Bazant’s Crack Band Model. To verify the accuracy of the approach, analyses ofcoupon specimens were performed, and the numerical predictions were compared with experimental data

On Recurrent and Deep Neural Networks

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.

Apprentissage des réseaux de neurones profonds et applications en traitement automatique de la langue naturelle

En apprentissage automatique, domaine qui consiste à utiliser des données pour apprendre une solution aux problèmes que nous voulons confier à la machine, le modèle des Réseaux de Neurones Artificiels (ANN) est un outil précieux. Il a été inventé voilà maintenant près de soixante ans, et pourtant, il est encore de nos jours le sujet d'une recherche active. Récemment, avec l'apprentissage profond, il a en effet permis d'améliorer l'état de l'art dans de nombreux champs d'applications comme la vision par ordinateur, le traitement de la parole et le traitement des langues naturelles. La quantité toujours grandissante de données disponibles et les améliorations du matériel informatique ont permis de faciliter l'apprentissage de modèles à haute capacité comme les ANNs profonds. Cependant, des difficultés inhérentes à l'entraînement de tels modèles, comme les minima locaux, ont encore un impact important. L'apprentissage profond vise donc à trouver des solutions, en régularisant ou en facilitant l'optimisation. Le pré-entraînnement non-supervisé, ou la technique du ``Dropout'', en sont des exemples. Les deux premiers travaux présentés dans cette thèse suivent cette ligne de recherche. Le premier étudie les problèmes de gradients diminuants/explosants dans les architectures profondes. Il montre que des choix simples, comme la fonction d'activation ou l'initialisation des poids du réseaux, ont une grande influence. Nous proposons l'initialisation normalisée pour faciliter l'apprentissage. Le second se focalise sur le choix de la fonction d'activation et présente le rectifieur, ou unité rectificatrice linéaire. Cette étude a été la première à mettre l'accent sur les fonctions d'activations linéaires par morceaux pour les réseaux de neurones profonds en apprentissage supervisé. Aujourd'hui, ce type de fonction d'activation est une composante essentielle des réseaux de neurones profonds. Les deux derniers travaux présentés se concentrent sur les applications des ANNs en traitement des langues naturelles. Le premier aborde le sujet de l'adaptation de domaine pour l'analyse de sentiment, en utilisant des Auto-Encodeurs Débruitants. Celui-ci est encore l'état de l'art de nos jours. Le second traite de l'apprentissage de données multi-relationnelles avec un modèle à base d'énergie, pouvant être utilisé pour la tâche de désambiguation de sens.

A comparative study of multilayered and single layered neural network based predictive controllers

The authors compare the performance of two types of controllers one based on the multilayered network and the other based on the single layered CMAC network (cerebellar model articulator controller). The neurons (information processing units) in the multi-layered network use Gaussian activation functions. The control scheme which is considered is a predictive control algorithm, along the lines used by Willis et al. (1991), Kambhampati and Warwick (1991). The process selected as a test bed is a continuous stirred tank reactor. The reaction taking place is an irreversible exothermic reaction in a constant volume reactor cooled by a single coolant stream. This reactor is a simplified version of the first tank in the two tank system given by Henson and Seborg (1989).

The Thyroid Hormone Receptor (TR) beta-Selective Agonist GC-1 Inhibits Proliferation But Induces Differentiation and TR beta mRNA Expression in Mouse and Rat Osteoblast-Like Cells

Previous studies showed anabolic effects of GC-1, a triiodothyronine (T3) analogue that is selective for both binding and activation functions of thyroid hormone receptor (TR) beta 1 over TR alpha 1, on bone tissue in vivo. The aim of this study was to investigate the responsiveness of rat (ROS17/2.8) and mouse (MC3T3-E1) osteoblast-like cells to GC-1. As expected, T3 inhibited cellular proliferation and stimulated mRNA expression of osteocalcin or alkaline phosphatase in both cell lineages. Whereas equimolar doses of T3 and GC-1 equally affected these parameters in ROS17/2.8 cells, the effects of GC-1 were more modest compared to those of T3 in MC3T3-E1 cells. Interestingly, we showed that there is higher expression of TR alpha 1 than TR beta 1 mRNA in rat (similar to 20-90%) and mouse (similar to 90-98%) cell lineages and that this difference is even higher in mouse cells, which highlights the importance of TR alpha 1 to bone physiology and may partially explain the modest effects of GC-1 in comparison with T3 in MC3T3-E1 cells. Nevertheless, we showed that TR beta 1 mRNA expression increases (similar to 2.8- to 4.3-fold) as osteoblastic cells undergo maturation, suggesting a key role of TR beta 1 in mediating T3 effects in the bone forming cells, especially in mature osteoblasts. It is noteworthy that T3 and GC-1 induced TR beta 1 mRNA expression to a similar extent in both cell lineages (similar to 2- to 4-fold), indicating that both ligands may modulate the responsiveness of osteoblasts to T3. Taken together, these data show that TR beta selective T3 analogues have the potential to directly induce the differentiation and activity of osteoblasts.

Reservoir Management For Flood And Drought Protection Using Infinite Horizon Model Predictive Control

Model Predictive Control (MPC) is a control method that solves in real time an optimal control problem over a finite horizon. The finiteness of the horizon is both the reason of MPC's success and its main limitation. In operational water resources management, MPC has been in fact successfully employed for controlling systems with a relatively short memory, such as canals, where the horizon length is not an issue. For reservoirs, which have generally a longer memory, MPC applications are presently limited to short term management only. Short term reservoir management can be effectively used to deal with fast process, such as floods, but it is not capable of looking sufficiently ahead to handle long term issues, such as drought. To overcome this limitation, we propose an Infinite Horizon MPC (IH-MPC) solution that is particularly suitable for reservoir management. We propose to structure the input signal by use of orthogonal basis functions, therefore reducing the optimization argument to a finite number of variables, and making the control problem solvable in a reasonable time. We applied this solution for the management of the Manantali Reservoir. Manantali is a yearly reservoir located in Mali, on the Senegal river, affecting water systems of Mali, Senegal, and Mauritania. The long term horizon offered by IH-MPC is necessary to deal with the strongly seasonal climate of the region.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Identificação e controle de sistemas dinâmicos utilizando redes wavelets

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uso de modelo de regressão aleatória na análise de produção de leite em bubalinos

Dados referentes a 1.719 controles de produção de leite de 357 fêmeas predominantemente da raça Murrah, filhas de 110 reprodutores, com partos distribuídos entre os anos de 1974 e 2004, obtidos do Programa de Melhoramento Genético de Bubalinos (PROMEBUL) com adição de registros do rebanho pertencente à EMBRAPA Amazônia Oriental - EAO, localizada em Belém, Pará. Os registros foram usados para comparar modelos de regressão aleatória na estimação de componentes de variância e predição de valores genéticos dos reprodutores utilizando a. função polinomial de Legendre, variando de segunda à quarta ordem. O modelo de regressão aleatória incluiu os efeitos de rebanho-ano, mês de parto, coeficientes de regressão para idade da fêmea (para descrever a parte fixa da curva de lactação) e coeficientes de regressão relacionados ao efeito genético direto e de ambiente permanente. A comparação entre modelos foram realizadas por meio do Critério de Informação de Akaike. O modelo de regressão aleatória que utilizou a terceira ordem de polinômio de Legendre, com quatro classes de resíduo para o ambiente temporário, foi o que melhor descreveu a variação genética aditiva da produção de leite. A herdabilidade estimada variou entre 0,08 a 0,40. A correlação genética entre produções mais próximas foram próximas da unidade, mas em idades mais distantes a correlação foi baixa. A correlação de Spearman e de Pearson entre os valores genéticos preditos em todas as situações foram próximas da unidade.

Myc-mediated control of gene transcription in cancer cells

The Myc oncoproteins belong to a family of transcription factors composed by Myc, N-Myc and L-Myc. The most studied components of this family are Myc and N-Myc because their expressions are frequently deregulated in a wide range of cancers. These oncoproteins can act both as activators or repressors of gene transcription. As activators, they heterodimerize with Max (Myc associated X-factor) and the heterodimer recognizes and binds a specific sequence elements (E-Box) onto gene promoters recruiting histone acetylase and inducing transcriptional activation. Myc-mediated transcriptional repression is a quite debated issue. One of the first mechanisms defined for the Myc-mediated transcriptional repression consisted in the interaction of Myc-Max complex Sp1 and/or Miz1 transcription factors already bound to gene promoters. This interaction may interfere with their activation functions by recruiting co-repressors such as Dnmt3 or HDACs. Moreover, in the absence of , Myc may interfere with the Sp1 activation function by direct interaction and subsequent recruitment of HDACs. More recently the Myc/Max complex was also shown to mediate transcriptional repression by direct binding to peculiar E-box. In this study we analyzed the role of Myc overexpression in Osteosarcoma and Neuroblastoma oncogenesis and the mechanisms underling to Myc function. Myc overexpression is known to correlate with chemoresistance in Osteosarcoma cells. We extended this study by demonstrating that c-Myc induces transcription of a panel of ABC drug transporter genes. ABCs are a large family trans-membrane transporter deeply involved in multi drug resistance. Furthermore expression levels of Myc, ABCC1, ABCC4 and ABCF1 were proved to be important prognostic tool to predict conventional therapy failure. N-Myc amplification/overexpression is the most important prognostic factor for Neuroblastoma. Cyclin G2 and Clusterin are two genes often down regulated in neuroblastoma cells. Cyclin G2 is an atypical member of Cyclin family and its expression is associated with terminal differentiation and apoptosis. Moreover it blocks cell cycle progression and induces cell growth arrest. Instead, CLU is a multifunctional protein involved in many physiological and pathological processes. Several lines of evidences support the view that CLU may act as a tumour suppressor in Neuroblastoma. In this thesis I showed that N-Myc represses CCNG2 and CLU transcription by different mechanisms. • N-Myc represses CCNG2 transcription by directly interacting with Sp1 bound in CCNG2 promoter and recruiting HDAC2. Importantly, reactivation of CCNG2 expression through epigenetic drugs partially reduces N-Myc and HDAC2 mediated cell proliferation. • N-Myc/Max complex represses CLU expression by direct binding to a peculiar E-box element on CLU promoter and by recruitment of HDACs and Polycomb Complexes, to the CLU promoter. Overall our findings strongly support the model in which Myc overexpression/amplification may contribute to some aspects of oncogenesis by a dual action: i) transcription activation of genes that confer a multidrug resistant phenotype to cancer cells; ii), transcription repression of genes involved in cell cycle inhibition and cellular differentiation.

The role of MYCN-mediated transcriptional repression in neuronal physiopathology

MYC is a transcription factor that can activate transcription of several targets by direct binding to their promoters at specific DNA sequences (E-box). Recent findings have also shown that it can exert its biological role by repressing transcription of other set of genes. C-MYC can mediate repression on its target genes through interaction with factors bound to promoter regions but not through direct recognition of typical E-Boxes. In this thesis, we investigated whether MYCN can also repress gene transcription and how this is mechanistically achieved. Moreover, expression of TRKA, P75NTR and ABCC3 is attenuated in aggressive MYCN-amplified tumors, suggesting a causal link between elevated MYCN activity and transcriptional repression of these three genes. We found that MYCN is physically associated with gene promoters in vivo in proximity of the transcriptional start sites and this association requires interactions with SP1 and/or MIZ-1. Furthermore, we show that this interaction could interfere with SP1 and MIZ-1 activation functions by recruiting co-repressors such as DNMT3a or HDACs. Studies in vitro suggest that MYCN interacts through distinct domains with SP1, MIZ-1 and HDAC1 supporting the idea that MYCN may form different complexes by interacting with different proteins. Re-expression of endogenous TRKA and P75NTR with exposure to the TSA sensitizes neuroblastoma to NGF-mediated apoptosis, whereas ectopic expression of ABCC3 decreases cell motility without interfering with growth. Finally, using shRNA whole genome library, we dissected the P75NTR repression trying to identify novel factors inside and/or outside MYCN complex for future therapeutic approaches. Overall, our results support a model in which MYCN can repress gene transcription by direct interaction with SP1 and/or MIZ-1, and provide further lines of evidence on the importance of transcriptional repression induced by Myc in tumor biology.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Transformación del espacio cinemático Laban para la evaluación del movimiento

Este proyecto tiene como objetivo la implementación de un sistema capaz de analizar el movimiento corporal a partir de unos puntos cinemáticos. Estos puntos cinemáticos se obtienen con un programa previo y se captan con la cámara kinect. Para ello el primer paso es realizar un estudio sobre las técnicas y conocimientos existentes relacionados con el movimiento de las personas. Se sabe que Rudolph Laban fue uno de sus mayores exponentes y gracias a sus observaciones se establece una relación entre la personalidad, el estado anímico y la forma de moverse de un individuo. Laban acuñó el término esfuerzo, que hace referencia al modo en que se administra la energía que genera el movimiento y de qué manera se modula en las secuencias, es una manera de describir la intención de las expresiones internas. El esfuerzo se divide en 4 categorías: peso, espacio, tiempo y flujo, y cada una de estas categorías tiene una polaridad denominada elemento de esfuerzo. Con estos 8 elementos de esfuerzo un movimiento queda caracterizado. Para poder cuantificar los citados elementos de esfuerzo se buscan movimientos que representen a alguno de ellos. Los movimientos se graban con la cámara kinect y se guardan sus valores en un archivo csv. Para el procesado de estos datos se establece que el sistema más adecuado es una red neuronal debido a su flexibilidad y capacidad a la hora de procesar entradas no lineales. Para la implementación de la misma se requiere un amplio estudio que incluye: topologías, funciones de activación, tipos de aprendizaje, algoritmos de entrenamiento entre otros. Se decide que la red tenga dos capas ocultas, para mejor procesado de los datos, que sea estática, siga un proceso de cálculo hacia delante (Feedforward) y el algoritmo por el que se rija su aprendizaje sea el de retropropagación (Backpropagation) En una red estática las entradas han de ser valores fijos, es decir, no pueden variar en el tiempo por lo que habrá que implementar un programa intermedio que haga una media aritmética de los valores. Una segunda prueba con la misma red trata de comprobar si sería capaz de reconocer movimientos que estuvieran caracterizados por más de un elemento de esfuerzo. Para ello se vuelven a grabar los movimientos, esta vez en parejas de dos, y el resto del proceso es igual. ABSTRACT. The aim of this project is the implementation of a system able to analyze body movement from cinematic data. This cinematic data was obtained with a previous program. The first step is carrying out a study about the techniques and knowledge existing nowadays related to people movement. It is known that Rudolf Laban was one the greatest exponents of this field and thanks to his observations a relation between personality, mood and the way the person moves was made. Laban coined the term effort, that refers to the way energy generated from a movement is managed and how it is modulated in the sequence, this is a method of describing the inner intention of the person. The effort is divided into 4 categories: weight, space, time and flow, and each of these categories have 2 polarities named elements of effort. These 8 elements typify a movement. We look for movements that are made of these elements so we can quantify them. The movements are recorded with the kinect camera and saved in a csv file. In order to process this data a neural network is chosen owe to its flexibility and capability of processing non-linear inputs. For its implementation it is required a wide study regarding: topology, activation functions, different types of learning methods and training algorithms among others. The neural network for this project will have 2 hidden layers, it will be static and follow a feedforward process ruled by backpropagation. In a static net the inputs must be fixed, this means they cannot vary in time, so we will have to implement an intermediate program to calculate the average of our data. A second test for our net will be checking its ability to recognize more than one effort element in just one movement. In order to do this all the movements are recorded again but this time in pairs, the rest of the process remains the same.