Stochastische Modelle für nichtlineare Antwortfunktionen in ungeordneten Systemen

In dieser Arbeit werden nichtlineare Experimente zur Untersuchung der Dynamik in amorphen Festkörpern im Rahmen von Modellrechnungen diskutiert. Die Experimente beschäftigen sich mit der Frage nach dynamischen Heterogenitäten, worunter man das Vorliegen dynamischer Prozesse auf unterschiedlichen Zeitskalen versteht. Ist es möglich, gezielt 'langsame' oder 'schnelle' Dynamik in der Probe nachzuweisen, so ist die Existenz von dynamischen Heterogenitäten gezeigt. Ziel der Experimente sind deshalb sogenannte frequenzselektive Anregungen des Systems. In den beiden diskutierten Experimenten, zum einen nichtresonantes Lochbrennen, zum anderen ein ähnliches Experiment, das auf dem dynamischen Kerreffekt beruht, werden nichtlineare Antwortfunktionen gemessen. Um eine Probe in frequenzselektiver Weise anzuregen, werden zunächst einer oder mehrere Zyklen eines oszillierenden elektrischen Feldes an die Probe angelegt. Die Experimente werden zunächst im Terahertz-Bereich untersucht. Auf dieser Zeitskala findet man phonon-ähnliche kollektive Schwingungen in Gläsern. Diese Schwingungen werden durch (anharmonische) Brownsche Oszillatoren beschrieben. Der zentrale Befund der Modellrechnungen ist, daß eine frequenzselektive Anregung im Terahertz-Bereich möglich ist. Ein Nachweis dynamischer Heterogenitäten im Terahertz-Bereich ist somit durch beide Experimente möglich. Anschliessend wird das vorgestellte Kerreffekt-Experiment im Bereich wesentlich kleinerer Frequenzen diskutiert. Die langsame Reorientierungsdynamik in unterkühlten Flüssigkeiten wird dabei durch ein Rotationsdiffusionsmodell beschrieben. Es werden zum einen ein heterogenes und zum anderen ein homogenes Szenario zugrundegelegt. Es stellt sich heraus, daß wie beim Lochbrennen eine Unterscheidung durch das Experiment möglich ist. Das Kerreffekt-Experiment wird somit als eine relativ einfache Alternative zur Technik des nichtresonanten Lochbrennens vorgeschlagen.

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.

Soil fragmentation study applying different tillage systems

Runoff generation depends on rainfall, infiltration, interception, and surface depressional storage. Surface depressional storage depends on surface microtopography, usually quantified trough soil surface roughness (SSR). SSR is subject to spatial and temporal changes that create a high variability. In an agricultural environment, tillage operations produce abrupt changes in roughness. Subsequent rainfall gradually decreases roughness. Beside it, local variation in soil properties and hydrology cause its SSR to vary spatially at different scales. The methods commonly used to measure it involve collecting point elevations in regular grids using laser profilers or scanners, digital close range stereo-photogrammetry and terrestrial laser scanning or LIDAR systems. In this case, a laser-scanning instrument was used to obtain representative digital elevation models (DEMs) at a grid resolution of 7.2x7.2mm that cover an area of 0.9x0.9m. The DEMs were obtained from two study sites with different soils. The first study site was an experimental field on which five conventional tillage methods were applied. The second study site was a large olive orchard with trees planted at 7.5x5.0m and bare soils between rows. Here, three tillage treatments were applied. In this work we have evaluated the spatial variability of SSR at several scales studying differences in height calculated from points separated by incremental distances h were raised to power values q (from 0 to 4 in steps of 0.1). The q = 2 data were studied as a semivariogram model. The logarithm of average differences plotted vs. log h were characterized by their slope, ?(q). Structure functions [?(q) vs. q] were fitted showing that data had nonlinear structure functions typical of multiscale phenomena. Comparisson of the two types of soil in their respective structure functions are shown.

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.

Thyroid hormone receptor-associated proteins and general positive cofactors mediate thyroid hormone receptor function in the absence of the TATA box-binding protein-associated factors of TFIID

Coactivators previously implicated in ligand-dependent activation functions by thyroid hormone receptor (TR) include p300 and CREB-binding protein (CBP), the steroid receptor coactivator-1 (SRC-1)-related family of proteins, and the multicomponent TR-associated protein (TRAP) complex. Here we show that two positive cofactors (PC2 and PC4) derived from the upstream stimulatory activity (USA) cofactor fraction act synergistically to mediate thyroid hormone (T3)-dependent activation either by TR or by a TR-TRAP complex in an in vitro system reconstituted with purified factors and DNA templates. Significantly, the TRAP-mediated enhancement of activation by TR does not require the TATA box-binding protein-associated factors of TFIID. Furthermore, neither the pleiotropic coactivators CBP and p300 nor members of the SRC-1 family were detected in either the TR-TRAP complex or the other components of the in vitro assay system. These results show that activation by TR at the level of naked DNA templates is enhanced by cooperative functions of the TRAP coactivators and the general coactivators PC2 and PC4, and they further indicate a potential functional redundancy between TRAPs and TATA box-binding protein-associated factors in TFIID. In conjunction with earlier studies on other nuclear receptor-interacting cofactors, the present study also suggests a multistep pathway, involving distinct sets of cofactors, for activation of hormone responsive genes.

Analysis of estrogen receptor transcriptional enhancement by a nuclear hormone receptor coactivator.

The estrogen receptor (ER), a member of a large superfamily of nuclear hormone receptors, is a ligand-inducible transcription factor that regulates the expression of estrogen-responsive genes. The ER, in common with other members of this superfamily, contains two transcription activation functions (AFs)--one located in the amino-terminal region (AF-1) and the second located in the carboxyl-terminal region (AF-2). In most cell contexts, the synergistic activity of AF-1 and AF-2 is required for full estradiol (E2)-stimulated activity. We have previously shown that a ligand-dependent interaction between the two AF-containing regions of ER was promoted by E2 and the antiestrogen trans-hydroxytamoxifen (TOT). This interaction, however, was transcriptionally productive only in the presence of E2. To explore a possible role of steroid receptor coactivators in transcriptional synergism between AF-1 and AF-2, we expressed the amino terminal (AF-1-containing) and carboxyl-terminal (AF-2-containing) regions of ER as separate polypeptides in mammalian cells, along with the steroid receptor coactivator-1 protein (SRC-1). We demonstrate that SRC-1, which has been shown to significantly increase ER transcriptional activity, enhanced the interaction, mediated by either E2 or TOT, between the AF-1-containing and AF-2-containing regions of the ER. However, this enhanced interaction resulted in increased transcriptional effectiveness only with E2 and not with TOT, consistent with the effects of SRC-1 on the full-length receptor. Our results suggest that after ligand binding, SRC-1 may act, in part, as an adapter protein that promotes the integration of amino- and carboxyl-terminal receptor functions, allowing for full receptor activation. Potentially, SRC-1 may be capable of enhancing the transcriptional activity of related nuclear receptor superfamily members by facilitating the productive association of the two AF-containing regions in these receptors.

Ligand-dependent, transcriptionally productive association of the amino- and carboxyl-terminal regions of a steroid hormone nuclear receptor.

The estrogen receptor (ER), a 66-kDa protein that mediates the actions of estrogens in estrogen-responsive tissues, is a member of a large superfamily of nuclear hormone receptors that function as ligand-activated transcription factors. ER shares a conserved structural and functional organization with other members of this superfamily, including two transcriptional activation functions (AFs), one located in its amino-terminal region (AF-1) and the second located in its carboxyl-terminal, ligand-binding region (AF-2). In most promoter contexts, synergism between AF-1 and AF-2 is required for full ER activity. In these studies, we demonstrate a functional interaction of the two AF-containing regions of ER, when expressed as separate polypeptides in mammalian cells, in response to 17 beta-estradiol (E2) and antiestrogen binding. The interaction was transcriptionally productive only in response to E2, and was eliminated by point or deletion mutations that destroy AF-1 or AF-2 activity or E2 binding. Our results suggest a definitive mechanistic role for E2 in the activity of ER--namely, to alter receptor conformation to promote an association of the amino- and carboxyl-terminal regions, leading to transcriptional synergism between AF-1 and AF-2. The productive re assembly of two portions of ER expressed in cells as separate polypeptides demonstrates the evolutionarily conserved modular structural and functional organization of the nuclear hormone receptors. The ligand-dependent interaction of the two AF-containing regions of ER allows for the assembly of a complete activation function from two distinct regions within the same protein, providing a mechanism for hormonally regulated transcription.

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Semi-implicit finite strain constitutive integration and mixed strain/stress control based on intermediate configurations

A new semi-implicit stress integration algorithm for finite strain plasticity (compatible with hyperelas- ticity) is introduced. Its most distinctive feature is the use of different parameterizations of equilibrium and reference configurations. Rotation terms (nonlinear trigonometric functions) are integrated explicitly and correspond to a change in the reference configuration. In contrast, relative Green–Lagrange strains (which are quadratic in terms of displacements) represent the equilibrium configuration implicitly. In addition, the adequacy of several objective stress rates in the semi-implicit context is studied. We para- metrize both reference and equilibrium configurations, in contrast with the so-called objective stress integration algorithms which use coinciding configurations. A single constitutive framework provides quantities needed by common discretization schemes. This is computationally convenient and robust, as all elements only need to provide pre-established quantities irrespectively of the constitutive model. In this work, mixed strain/stress control is used, as well as our smoothing algorithm for the complemen- tarity condition. Exceptional time-step robustness is achieved in elasto-plastic problems: often fewer than one-tenth of the typical number of time increments can be used with a quantifiable effect in accuracy. The proposed algorithm is general: all hyperelastic models and all classical elasto-plastic models can be employed. Plane-stress, Shell and 3D examples are used to illustrate the new algorithm. Both isotropic and anisotropic behavior is presented in elasto-plastic and hyperelastic examples.

Galerkin Method and Weighting Functions Applied to Nonlinear H(infinity) Control with Output Feedback

This paper considers two aspects of the nonlinear H(infinity) control problem: the use of weighting functions for performance and robustness improvement, as in the linear case, and the development of a successive Galerkin approximation method for the solution of the Hamilton-Jacobi-Isaacs equation that arises in the output-feedback case. Design of nonlinear H(infinity) controllers obtained by the well-established Taylor approximation and by the proposed Galerkin approximation method applied to a magnetic levitation system are presented for comparison purposes.

Comparison of different nonlinear functions to describe Nelore cattle growth

This work aims to compare different nonlinear functions for describing the growth curves of Nelore females. The growth curve parameters, their (co) variance components, and environmental and genetic effects were estimated jointly through a Bayesian hierarchical model. In the first stage of the hierarchy, 4 nonlinear functions were compared: Brody, Von Bertalanffy, Gompertz, and logistic. The analyses were carried out using 3 different data sets to check goodness of fit while having animals with few records. Three different assumptions about SD of fitting errors were considered: constancy throughout the trajectory, linear increasing until 3 yr of age and constancy thereafter, and variation following the nonlinear function applied in the first stage of the hierarchy. Comparisons of the overall goodness of fit were based on Akaike information criterion, the Bayesian information criterion, and the deviance information criterion. Goodness of fit at different points of the growth curve was compared applying the Gelfand`s check function. The posterior means of adult BW ranged from 531.78 to 586.89 kg. Greater estimates of adult BW were observed when the fitting error variance was considered constant along the trajectory. The models were not suitable to describe the SD of fitting errors at the beginning of the growth curve. All functions provided less accurate predictions at the beginning of growth, and predictions were more accurate after 48 mo of age. The prediction of adult BW using nonlinear functions can be accurate when growth curve parameters and their (co) variance components are estimated jointly. The hierarchical model used in the present study can be applied to the prediction of mature BW in herds in which a portion of the animals are culled before adult age. Gompertz, Von Bertalanffy, and Brody functions were adequate to establish mean growth patterns and to predict the adult BW of Nelore females. The Brody model was more accurate in predicting the birth weight of these animals and presented the best overall goodness of fit.

Neutrophil activation induced by ArtinM: Release of inflammatory mediators and enhancement of effector functions

The D-mannose binding lectin ArtinM from Artocarpus integrifolia, previously known as KM+ and artocarpin. is considered a stimulant of Th1-type immunity, which is able to confer resistance to some intracellular pathogens. In addition, ArtinM induces neutrophil migration by haptotaxis through simultaneous interactions of its carbohydrate recognition domains (CRDs) with glycans expressed on the extracellular matrix and the neutrophil surface. In the present study, we have expanded the characterization of ArtinM as a neutrophil activator. Exposure of neutrophils to ArtinM for 15 min resulted in tyrosine phosphorylation of intracellular proteins, a process that was selectively inhibited by D-mannose or mannotriose. Shortly after stimulation, neutrophils secreted high levels of LTB(4) and underwent shedding of L-selectin from their surface. Exposure to ArtinM enhanced neutrophil functions, such as respiratory burst and zymozan and Listeria monocytogenes phagocytosis. In addition, ArtinM-stimulated neutrophils displayed increased CXCL-8 secretion and TLR2 gene transcription. These results demonstrate that ArtinM is able to induce potent neutrophil activation, a feature that should be strongly considered in the assessment of the lectin capacity to confer resistance against infections. (C) 2009 Elsevier B.V. All rights reserved.