Aortic stenosis. Gender influence on left ventricular geometry and function in patients under 70 years of age

OBJECTIVE: To verify if adaptive left ventricle (LV) characteristics are also present in individuals under 70 years of age with severe aortic stenosis (AS). METHODS: The study comprised 40 consecutive patients under 70 years of age with AS and no associated coronary artery disease, referred for valve surgery. Out of the 40 patients, 22 were men and 18 women, and the mean age was 49.8±14.3 years. Cardiac symptoms, presence of systemic hypertension (SH), functional class according to the New York Heart Association (NYHA), and valve lesion etiology were considered. LV cavity dimensions, ejection fraction (EF), fractional shortening (FS), mass (MS), and relative diastolic thickness (RDT) were examined by Doppler echocardiography. RESULTS: Fourteen (63.6%) men and 11 (61.6%) women were classified as NYHA class III/IV (p=0.70). There was no difference in the frequency of angina, syncope or dyspnea between genders. The incidence of SH was greater in women than in men (10 versus 2, p=0.0044). Women had a smaller LV end-diastolic diameter index (32.1±6.5 x 36.5±5.3mm/m², p=0.027), LV end-systolic diameter index (19.9±5.9 x 26.5±6.4mm/m², p=0.0022) and LV mass index (MS) (211.4±71.1 x 270.9±74.9g/m², p=0.017) when compared with men. EF (66.2±13.4 x 52.0±14.6%, p=0.0032), FS (37.6±10.7 x 27.9±9.6%, p=0.0046) and RDT (0.58±0.22 x 0.44±0.09, p=0.0095) were significantly greater in women than in men. CONCLUSION: It is the patient gender rather than age that influences left ventricular adaptive response to AS.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Left Ventricular Diastolic Function in Essential Hypertensive Patients: Influence of Age and Left Ventricular Geometry

PURPOSE - To evaluate diastolic dysfunction (DD) in essential hypertension and the influence of age and cardiac geometry on this parameter. METHODS - Four hundred sixty essential hypertensive patients (HT) underwent Doppler echocardiography to obtain E/A wave ratio (E/A), atrial deceleration time (ADT), and isovolumetric relaxation time (IRT). All patients were grouped according to cardiac geometric patterns (NG - normal geometry; CR - concentric remodeling; CH- concentric hypertrophy; EH - eccentric hypertrophy) and to age (<40; 40 - 60; >60 years). One hundred six normotensives (NT) persons were also evaluated. RESULTS - A worsening of diastolic function in the HT compared with the NT, including HT with NG (E/A: NT - 1.38±0.03 vs HT - 1.27±0.02, p<0.01), was observed. A higher prevalence of DD occurred parallel to age and cardiac geometry also in the prehypertrophic groups (CR). Multiple regression analysis identified age as the most important predictor of DD (r²=0.30, p<0.01). CONCLUSION - DD was prevalent in this hypertensive population, being highly affected by age and less by heart structural parameters. DD is observed in incipient stages of hypertensive heart disease, and thus its early detection may help in the risk stratification of hypertensive patients.

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.

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.

Flame simulation in rotary kilns using computational fluid dynamics

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

Variations and application conditions of the data type "Image" : the foundation of computational visualistics

Visualistics, computer science, picture syntax, picture semantics, picture pragmatics, interactive pictures

Convex and toric geometry to analyze complex dynamics in chemical reaction systems

Convex cone, toric variety, graph theory, electrochemical catalysis, oxidation of formic acid, feedback-loopsbifurcations, enzymatic catalysis, Peroxidase reaction, Shil'nikov chaos

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

Ricci-flat complex geometry and its applications

Magdeburg, Univ., Fak. für Mathematik, Habil.-Schr., 2010

Adelic convex geometry of numbers

Magdeburg, Univ., Fak. für Mathematik, Diss., 2014

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

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

A Grid Supercomputing Enviroment for High Demand Computational Applications

Despite the huge increase in processor and interprocessor network performace, many computational problems remain unsolved due to lack of some critical resources such as floating point sustained performance, memory bandwidth, etc... Examples of these problems are found in areas of climate research, biology, astrophysics, high energy physics (montecarlo simulations) and artificial intelligence, among others. For some of these problems, computing resources of a single supercomputing facility can be 1 or 2 orders of magnitude apart from the resources needed to solve some them. Supercomputer centers have to face an increasing demand on processing performance, with the direct consequence of an increasing number of processors and systems, resulting in a more difficult administration of HPC resources and the need for more physical space, higher electrical power consumption and improved air conditioning, among other problems. Some of the previous problems can´t be easily solved, so grid computing, intended as a technology enabling the addition and consolidation of computing power, can help in solving large scale supercomputing problems. In this document, we describe how 2 supercomputing facilities in Spain joined their resources to solve a problem of this kind. The objectives of this experience were, among others, to demonstrate that such a cooperation can enable the solution of bigger dimension problems and to measure the efficiency that could be achieved. In this document we show some preliminary results of this experience and to what extend these objectives were achieved.