Estrategias estadístico-computacionales avanzadas para análisis de expresión de proteínas/genes en cáncer. Advanced statistical and computational strategies for protein/gene expression analysis in cancer.

El objetivo de este proyecto, enmarcado en el área de metodología de análisis en bioingeniería-biotecnología aplicadas al estudio del cancer, es el análisis y caracterización a través modelos estadísticos con efectos mixtos y técnicas de aprendizaje automático, de perfiles de expresión de proteínas y genes de las vías metabolicas asociadas a progresión tumoral. Dicho estudio se llevará a cabo mediante la utilización de tecnologías de alto rendimiento. Las mismas permiten evaluar miles de genes/proteínas en forma simultánea, generando así una gran cantidad de datos de expresión. Se hipotetiza que para un análisis e interpretación de la información subyacente, caracterizada por su abundancia y complejidad, podría realizarse mediante técnicas estadístico-computacionales eficientes en el contexto de modelos mixtos y técnias de aprendizaje automático. Para que el análisis sea efectivo es necesario contemplar los efectos ocasionados por los diferentes factores experimentales ajenos al fenómeno biológico bajo estudio. Estos efectos pueden enmascarar la información subycente y así perder informacion relavante en el contexto de progresión tumoral. La identificación de estos efectos permitirá obtener, eficientemente, los perfiles de expresión molecular que podrían permitir el desarrollo de métodos de diagnóstico basados en ellos. Con este trabajo se espera poner a disposición de investigadores de nuestro medio, herramientas y procedimientos de análisis que maximicen la eficiencia en el uso de los recursos asignados a la masiva captura de datos genómicos/proteómicos que permitan extraer información biológica relevante pertinente al análisis, clasificación o predicción de cáncer, el diseño de tratamientos y terapias específicos y el mejoramiento de los métodos de detección como así tambien aportar al entendimieto de la progresión tumoral mediante análisis computacional intensivo.

REVISANDO LOS CONTENIDOS EN LENGUA: Hacia una semántica integradora. Transferencia y capacitación. REVIEWING CONTENTS IN LANGUAGE: Toward an integrative semantics. Transference and training.

En el trabajo se sistematiza la información semántica a fin de que ella sea la articuladora y eje de otros contenidos abordados en la enseñanza de la lengua materna. Así, en primer lugar se sistematizarán conceptos y perspectivas semánticas, para luego establecer relaciones con otros niveles lingüísticos, como lo son la Morfología y la Sintaxis, sin descuidar los aportes de la semántica a los procesos de producción y comprensión textuales. Realizaremos estudios para sistematizar y asignar al contenido un lugar dominante e integrador en el proceso de enseñanza-aprendizaje de la lengua materna, sin pretender desdibujar otras temáticas. Planteamos tareas cuyo punto culminante es la capacitación de docentes y experimentación de las propuestas didácticas en tres cursos de lengua en una institución educativa de la ciudad de Córdoba, cuyo examen, de ser positivo, permitirá la replicación y transferencia de esas prácticas a otras instituciones. Como objetivos generales, destacamos colaborar con la integración de los contenidos que se dictan en Lengua Materna, tomando como eje la naturaleza semántica del lenguaje; contribuir con la formación de los estudiantes en las dimensiones de la comprensión y producción textuales; promover la reflexión y el incremento del caudal léxico -y del lenguaje en general-, por parte de los estudiantes; contribuir con la capacitación y formación de los docentes en las temáticas abordadas, a través de acciones concretas; corroborar que la formación de los alumnos podrá tornarse más significativa en la medida en que se trabajen 'formas' lingüísticas atravesadas por contenidos. El marco teórico está constituido por los aportes de diferentes líneas, los que, compatibilizados, permitirán acceder a un abordaje integral del 'contenido' lingüístico. Más precisamente, consideramos las conceptualizaciones de autores tales como Coseriu (1986) para la delimitación semántica, sus conceptos y operaciones, al que completamos con la perspectiva de Lyons (1986, 1997); para las relaciones entre Morfología, seguimos a Ramírez Sáinz (2008) ; el vínculo sintaxis- semántica será abordado desde la perspectiva de la Gramática Generativa (Demonte,V. 1991; D'Introno, 2001; Fernández Lagunilla, M y Anula Rebollo,A, 1995; los aportes de la comprensión serán considerados desde De Beaugrande-Dresller (1997). Otros autores de referencia son Lakoff y Jhonsson (1998).

Flame simulation in rotary kilns using computational fluid dynamics

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2011

Analysis of complex microwave structures using suitable computational electromagnetic techniques

Complex Microwave Structures Wake Field Computatation PETRA III Generalized Multipole Technique Antenna Antennen Wakefelder Berechnung

Forearm surface EMG signals recognition and muscoloskeletal system dynamics identification using intelligent computational methods

Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph signals recognition, Pattern classification, Fuzzy logic and neural networks methods

Optimization of radial jets mixing in cross-flow of combustion chambers using computational fluid dynamics

Cross-Flow, Radial Jets Mixing, Temperature Homogenization, Optimization, Combustion Chamber, CFD

Computational methods for model reduction of large-scale sparse structured descriptor systems

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Consumers' behavior and the Bertrand paradox: an ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.

User-friendly parallel computations with econometric examples

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

Una estimación del coste marginal en bienestar del sistema impositivo en España

Actual tax systems do not follow the normative recommendations of yhe theory of optimal taxation. There are two reasons for this. Firstly, the informational difficulties of knowing or estimating all relevant elasticities and parameters. Secondly, the political complexities that would arise if a new tax implementation would depart too much from current systems that are perceived as somewhat egalitarians. Hence an ex-novo overhaul of the tax system might just be non-viable. In contrast, a small marginal tax reform could be politically more palatable to accept and economically more simple to implement. The goal of this paper is to evaluate, as a step previous to any tax reform, the marginal welfare cost of the current tax system in Spain. We do this by using a computational general equilibrium model calibrated to a point-in-time micro database. The simulations results show that the Spanish tax system gives rise to a considerable marginal excess burden. Its order of magnitude is of about 0.50 money units for each additional money unit collected through taxes.

Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints

In this paper we explore the effect of bounded rationality on the convergence of individual behavior toward equilibrium. In the context of a Cournot game with a unique and symmetric Nash equilibrium, firms are modeled as adaptive economic agents through a genetic algorithm. Computational experiments show that (1) there is remarkable heterogeneity across identical but boundedly rational agents; (2) such individual heterogeneity is not simply a consequence of the random elements contained in the genetic algorithm; (3) the more rational agents are in terms of memory abilities and pre-play evaluation of strategies, the less heterogeneous they are in their actions. At the limit case of full rationality, the outcome converges to the standard result of uniform individual behavior.

Accuracy vs. simplicity: a complex trade-off

Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the predictions of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that a certain phenomenon never happens, or that certain conditions necessitate another. For all types of rules, there is a trade-off between the rule's accuracy and its simplicity. Thus rule selection can be viewed as a choice problem, among pairs of degree of accuracy and degree of complexity. However, one cannot in general tell what is the feasible set in the accuracy-complexity space. Formally, we show that finding out whether a point belongs to this set is computationally hard. In particular, in the context of linear regression, finding a small set of variables that obtain a certain value of R2 is computationally hard. Computational complexity may explain why a person is not always aware of rules that, if asked, she would find valid. This, in turn, may explain why one can change other people's minds (opinions, beliefs) without providing new information.

Giving SBML support to ByoDyn

Report for the scientific stay at the California Institute of Technology during the summer of 2005. ByoDyn is a tool for simulating the dynamical expression of gene regulatory networks (GRNs) and for parameter estimation in uni- and multicellular models. A software support was carried out describing GRNs in the Systems Biology Markup Language (SBML). This one is a computer format for representing and storing computational models of biochemical pathways in software tools and databases. Supporting this format gives ByoDyn a wide range of possibilities to study the dynamical properties of multiple regulatory pathways.

Catalytic tuning of phosphinoethane ligand. An improved catalyst for hydrocarbon

Report for the scientific sojourn carried out at the Department of Chemistry University of North Texas (USA) from September until November 2006. It includes the performance of two computational chemistry studies: an experimental and computational study toward the intra- and intermolecular hydroarylation of isonitriles and the development of an improved catalyst for hydrocarbon functionalization.