Desarrollos estadísticos para el procesamiento de información genómica masiva en agro-biotecnología. Statistical developments for processing massive genomic information in agro-biotechnology.

Nuevas biotecnologías, como los marcadores de la molécula de ADN, permiten caracterizar el genoma vegetal. El uso de la información genómica producida para cientos o miles de posiciones cromosómicas permite identificar genotipos superiores en menos tiempo que el requerido por la selección fenotípica tradicional. La mayoría de los caracteres de las especies vegetales cultivadas de importancia agronómica y económica, son controlados por poli-genes causantes de un fenotipo con variación continua, altamente afectados por el ambiente. Su herencia es compleja ya que resulta de la interacción entre genes, del mismo o distinto cromosoma, y de la interacción del genotipo con el ambiente, dificultando la selección. Estas biotecnologías producen bases de datos con gran cantidad de información y estructuras complejas de correlación que requieren de métodos y modelos biométricos específicos para su procesamiento. Los modelos estadísticos focalizados en explicar el fenotipo a partir de información genómica masiva requieren la estimación de un gran número de parámetros. No existen métodos, dentro de la estadística paramétrica capaces de abordar este problema eficientemente. Además los modelos deben contemplar no-aditividades (interacciones) entre efectos génicos y de éstos con el ambiente que son también dificiles de manejar desde la concepción paramétrica. Se hipotetiza que el análisis de la asociación entre caracteres fenotípicos y genotipos moleculares, caracterizados por abundante información genómica, podría realizarse eficientemente en el contexto de los modelos mixtos semiparamétricos y/o de métodos no-paramétricos basados en técnicas de aprendizaje automático. El objetivo de este proyecto es desarrollar nuevos métodos para análisis de datos que permitan el uso eficiente de información genómica masiva en evaluaciones genéticas de interés agro-biotecnológico. Los objetivos específicos incluyen la comparación, respecto a propiedades estadísticas y computacionales, de estrategias analíticas paramétricas con estrategias semiparamétricas y no-paramétricas. Se trabajará con aproximaciones por regresión del análisis de loci de caracteres cuantitativos bajo distintas estrategias y escenarios (reales y simulados) con distinto volúmenes de datos de marcadores moleculares. En el área paramétrica se pondrá especial énfasis en modelos mixtos, mientras que en el área no paramétrica se evaluarán algoritmos de redes neuronales, máquinas de soporte vectorial, filtros multivariados, suavizados del tipo LOESS y métodos basados en núcleos de reciente aparición. La propuesta semiparamétrica se basará en una estrategia de análisis en dos etapas orientadas a: 1) reducir la dimensionalidad de los datos genómicos y 2) modelar el fenotipo introduciendo sólo las señales moleculares más significativas. Con este trabajo se espera poner a disposición de investigadores de nuestro medio, nuevas herramientas y procedimientos de análisis que permitan maximizar la eficiencia en el uso de los recursos asignados a la masiva captura de datos genómicos y su aplicación en desarrollos agro-biotecnológicos.

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.

DESARROLLO DE APLICACIONES ESTADÍSTICAS PARA LA AGRICULTURA DE PRECISIÓN. STATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE

A partir de las últimas décadas se ha impulsado el desarrollo y la utilización de los Sistemas de Información Geográficos (SIG) y los Sistemas de Posicionamiento Satelital (GPS) orientados a mejorar la eficiencia productiva de distintos sistemas de cultivos extensivos en términos agronómicos, económicos y ambientales. Estas nuevas tecnologías permiten medir variabilidad espacial de propiedades del sitio como conductividad eléctrica aparente y otros atributos del terreno así como el efecto de las mismas sobre la distribución espacial de los rendimientos. Luego, es posible aplicar el manejo sitio-específico en los lotes para mejorar la eficiencia en el uso de los insumos agroquímicos, la protección del medio ambiente y la sustentabilidad de la vida rural. En la actualidad, existe una oferta amplia de recursos tecnológicos propios de la agricultura de precisión para capturar variación espacial a través de los sitios dentro del terreno. El óptimo uso del gran volumen de datos derivado de maquinarias de agricultura de precisión depende fuertemente de las capacidades para explorar la información relativa a las complejas interacciones que subyacen los resultados productivos. La covariación espacial de las propiedades del sitio y el rendimiento de los cultivos ha sido estudiada a través de modelos geoestadísticos clásicos que se basan en la teoría de variables regionalizadas. Nuevos desarrollos de modelos estadísticos contemporáneos, entre los que se destacan los modelos lineales mixtos, constituyen herramientas prometedoras para el tratamiento de datos correlacionados espacialmente. Más aún, debido a la naturaleza multivariada de las múltiples variables registradas en cada sitio, las técnicas de análisis multivariado podrían aportar valiosa información para la visualización y explotación de datos georreferenciados. La comprensión de las bases agronómicas de las complejas interacciones que se producen a la escala de lotes en producción, es hoy posible con el uso de éstas nuevas tecnologías. Los objetivos del presente proyecto son: (l) desarrollar estrategias metodológicas basadas en la complementación de técnicas de análisis multivariados y geoestadísticas, para la clasificación de sitios intralotes y el estudio de interdependencias entre variables de sitio y rendimiento; (ll) proponer modelos mixtos alternativos, basados en funciones de correlación espacial de los términos de error que permitan explorar patrones de correlación espacial de los rendimientos intralotes y las propiedades del suelo en los sitios delimitados. From the last decades the use and development of Geographical Information Systems (GIS) and Satellite Positioning Systems (GPS) is highly promoted in cropping systems. Such technologies allow measuring spatial variability of site properties including electrical conductivity and others soil features as well as their impact on the spatial variability of yields. Therefore, site-specific management could be applied to improve the efficiency in the use of agrochemicals, the environmental protection, and the sustainability of the rural life. Currently, there is a wide offer of technological resources to capture spatial variation across sites within field. However, the optimum use of data coming from the precision agriculture machineries strongly depends on the capabilities to explore the information about the complex interactions underlying the productive outputs. The covariation between spatial soil properties and yields from georeferenced data has been treated in a graphical manner or with standard geostatistical approaches. New statistical modeling capabilities from the Mixed Linear Model framework are promising to deal with correlated data such those produced by the precision agriculture. Moreover, rescuing the multivariate nature of the multiple data collected at each site, several multivariate statistical approaches could be crucial tools for data analysis with georeferenced data. Understanding the basis of complex interactions at the scale of production field is now within reach the use of these new techniques. Our main objectives are: (1) to develop new statistical strategies, based on the complementarities of geostatistics and multivariate methods, useful to classify sites within field grown with grain crops and analyze the interrelationships of several soil and yield variables, (2) to propose mixed linear models to predict yield according spatial soil variability and to build contour maps to promote a more sustainable agriculture.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Statistical Characteristics of Flash Flood in Georgia

Results are presented from the analysis of observations data on flash flood in Georgia over a period of 45 years, from 1961 to 2005, provided of the of Hydro-meteorology Service of Georgia.

The Statistical Structure of Visibility in Tbilisi

შესწავლილია ხილვადობის სიშორის სტატისტიკური სტრუქტურა თბილისში 1980-დან 2008 წლამდე პერიოდისათვის. გამოყენებულია საქართველოს ჰიდრომეტეოროლოგიური დეპარტამენტის მონაცემები ხილვადობის სხვადასხვა ბალიანობის მქონე დღეების რიცხვის შესახებ წელიწადში 9, 12 და 15 საათზე დაკვირვებებისათვის.

Analyse der Informationsverarbeitung in CHL1-defizienten Mäusen mittels metabolischer Markierung, Expressionsstudien der Immediate-Early-Gene c-fos und arg3.1/arc sowie verhaltensbiologischer Tests

Close homolog of L1, neural cell recognition molecules, c-fos, arg3.1, arc, immediat early genes, novelty, information processing, behavioral tests

High-Sensitivity C-Reactive Protein as a Predictor of Cardiovascular Events after ST-Elevation Myocardial Infarction

Background: The association between high-sensitivity C-reactive protein and recurrent major adverse cardiovascular events (MACE) in patients with ST-elevation myocardial infarction who undergo primary percutaneous coronary intervention remains controversial. Objective: To investigate the potential association between high-sensitivity C-reactive protein and an increased risk of MACE such as death, heart failure, reinfarction, and new revascularization in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention. Methods: This prospective cohort study included 300 individuals aged >18 years who were diagnosed with ST-elevation myocardial infarction and underwent primary percutaneous coronary intervention at a tertiary health center. An instrument evaluating clinical variables and the Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) risk scores was used. High-sensitivity C-reactive protein was determined by nephelometry. The patients were followed-up during hospitalization and up to 30 days after infarction for the occurrence of MACE. Student's t, Mann-Whitney, chi-square, and logistic regression tests were used for statistical analyses. P values of ≤0.05 were considered statistically significant. Results: The mean age was 59.76 years, and 69.3% of patients were male. No statistically significant association was observed between high-sensitivity C-reactive protein and recurrent MACE (p = 0.11). However, high-sensitivity C-reactive protein was independently associated with 30-day mortality when adjusted for TIMI [odds ratio (OR), 1.27; 95% confidence interval (CI), 1.07-1.51; p = 0.005] and GRACE (OR, 1.26; 95% CI, 1.06-1.49; p = 0.007) risk scores. Conclusion: Although high-sensitivity C-reactive protein was not predictive of combined major cardiovascular events within 30 days after ST-elevation myocardial infarction in patients who underwent primary angioplasty and stent implantation, it was an independent predictor of 30-day mortality.

Survival and Predictive Factors of Lethality in Hemodyalisis: D/I Polymorphism of The Angiotensin I-Converting Enzyme and of the Angiotensinogen M235T Genes

Background: End-stage kidney disease patients continue to have markedly increased cardiovascular disease morbidity and mortality. Analysis of genetic factors connected with the renin-angiotensin system that influences the survival of the patients with end-stage kidney disease supports the ongoing search for improved outcomes. Objective: To assess survival and its association with the polymorphism of renin-angiotensin system genes: angiotensin I-converting enzyme insertion/deletion and angiotensinogen M235T in patients undergoing hemodialysis. Methods: Our study was designed to examine the role of renin-angiotensin system genes. It was an observational study. We analyzed 473 chronic hemodialysis patients in four dialysis units in the state of Rio de Janeiro. Survival rates were calculated by the Kaplan-Meier method and the differences between the curves were evaluated by Tarone-Ware, Peto-Prentice, and log rank tests. We also used logistic regression analysis and the multinomial model. A p value ≤ 0.05 was considered to be statistically significant. The local medical ethics committee gave their approval to this study. Results: The mean age of patients was 45.8 years old. The overall survival rate was 48% at 11 years. The major causes of death were cardiovascular diseases (34%) and infections (15%). Logistic regression analysis found statistical significance for the following variables: age (p = 0.000038), TT angiotensinogen (p = 0.08261), and family income greater than five times the minimum wage (p = 0.03089), the latter being a protective factor. Conclusions: The survival of hemodialysis patients is likely to be influenced by the TT of the angiotensinogen M235T gene.

Statistical analysis in multivariate sampling

Magdeburg, Univ., Fak. für Mathematik, Diss., 2011

Association between Angiotensin-Converting Enzyme Inhibitors and Troponin in Acute Coronary Syndrome

Background:Cardiovascular disease is the leading cause of mortality in the western world and its treatment should be optimized to decrease severe adverse events.Objective:To determine the effect of previous use of angiotensin-converting enzyme inhibitors on cardiac troponin I measurement in patients with acute coronary syndrome without ST-segment elevation and evaluate clinical outcomes at 180 days.Methods:Prospective, observational study, carried out in a tertiary center, in patients with acute coronary syndrome without ST-segment elevation. Clinical, electrocardiographic and laboratory variables were analyzed, with emphasis on previous use of angiotensin-converting enzyme inhibitors and cardiac troponin I. The Pearson chi-square tests (Pereira) or Fisher's exact test (Armitage) were used, as well as the non-parametric Mann-Whitney's test. Variables with significance levels of <10% were submitted to multiple logistic regression model.Results:A total of 457 patients with a mean age of 62.1 years, of whom 63.7% were males, were included. Risk factors such as hypertension (85.3%) and dyslipidemia (75.9%) were the most prevalent, with 35% of diabetics. In the evaluation of events at 180 days, there were 28 deaths (6.2%). The statistical analysis showed that the variables that interfered with troponin elevation (> 0.5 ng / mL) were high blood glucose at admission (p = 0.0034) and ST-segment depression ≥ 0.5 mm in one or more leads (p = 0.0016). The use of angiotensin-converting inhibitors prior to hospitalization was associated with troponin ≤ 0.5 ng / mL (p = 0.0482). The C-statistics for this model was 0.77.Conclusion:This study showed a correlation between prior use of angiotensin-converting enzyme inhibitors and reduction in the myocardial necrosis marker troponin I in patients admitted for acute coronary syndrome without ST-segment elevation. However, there are no data available yet to state that this reduction could lead to fewer severe clinical events such as death and re-infarction at 180 days.

Frequency of Cardiovascular Involvement in Familial Amyloidotic Polyneuropathy in Brazilian Patients

Background:Familial amyloidotic polyneuropathy (FAP) is a rare disease diagnosed in Brazil and worldwide. The frequency of cardiovascular involvement in Brazilian FAP patients is unknown.Objective:Detect the frequency of cardiovascular involvement and correlate the cardiovascular findings with the modified polyneuropathy disability (PND) score.Methods:In a national reference center, 51 patients were evaluated with clinical examination, electrocardiography (ECG), echocardiography (ECHO), and 24-hour Holter. Patients were classified according to the modified PND score and divided into groups: PND 0, PND I, PND II, and PND > II (which included PND IIIa, IIIb, and IV). We chose the classification tree as the statistical method to analyze the association between findings in cardiac tests with the neurological classification (PND).Results:ECG abnormalities were present in almost 2/3 of the FAP patients, whereas ECHO abnormalities occurred in around 1/3 of them. All patients with abnormal ECHO also had abnormal ECG, but the opposite did not apply. The classification tree identified ECG and ECHO as relevant variables (p < 0.001 and p = 0.08, respectively). The probability of a patient to be allocated to the PND 0 group when having a normal ECG was over 80%. When both ECG and ECHO were abnormal, this probability was null.Conclusions:Brazilian patients with FAP have frequent ECG abnormalities. ECG is an appropriate test to discriminate asymptomatic carriers of the mutation from those who develop the disease, whereas ECHO contributes to this discrimination.

Serial High-Sensitivity Troponin T in Post-Primary Angioplasty Exercise Test

Abstract Background: The kinetics of high-sensitivity troponin T (hscTnT) release should be studied in different situations, including functional tests with transient ischemic abnormalities. Objective: To evaluate the release of hscTnT by serial measurements after exercise testing (ET), and to correlate hscTnT elevations with abnormalities suggestive of ischemia. Methods: Patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary angioplasty were referred for ET 3 months after infarction. Blood samples were collected to measure basal hscTnT immediately before (TnT0h), 2 (TnT2h), 5 (TnT5h), and 8 hours (TnT8h) after ET. The outcomes were peak hscTnT, TnT5h/TnT0h ratio, and the area under the blood concentration-time curve (AUC) for hscTnT levels. Log-transformation was performed on hscTnT values, and comparisons were assessed with the geometric mean ratio, along with their 95% confidence intervals. Statistical significance was assessed by analysis of covariance with no adjustment, and then, adjusted for TnT0h, age and sex, followed by additional variables (metabolic equivalents, maximum heart rate achieved, anterior wall STEMI, and creatinine clearance). Results: This study included 95 patients. The highest geometric means were observed at 5 hours (TnT5h). After adjustments, peak hscTnT, TnT5h/TnT0h and AUC were 59% (p = 0.002), 59% (p = 0.003) and 45% (p = 0.003) higher, respectively, in patients with an abnormal ET as compared to those with normal tests. Conclusion: Higher elevations of hscTnT may occur after an abnormal ET as compared to a normal ET in patients with STEMI.

Early Markers of Atherosclerotic Disease in Individuals with Excess Weight and Dyslipidemia

Abstract Background: Excessive weight is a cardiovascular risk factor since it generates a chronic inflammatory process that aggravates the endothelial function. Objective: To evaluate the endothelial function in individuals with excess weight and mild dyslipidemia using brachial artery flow-mediated dilation (BAFMD), and the association of endothelial function with anthropometric and biochemical variables. Methods: Cross-sectional study that included 74 individuals and evaluated anthropometric variables (body mass index [BMI], waist-hip ratio [WHR], waist circumference [AC], and percentage of body fat [PBF]), biochemical (blood glucose, insulinemia, ultrasensitive C-reactive protein, fibrinogen, total cholesterol, HDL-cholesterol, triglycerides, and LDL-cholesterol) and endothelial function (BAFMD, evaluated by ultrasound). The statistical analysis was performed with SPSS, version 16.0. To study the association between the variables, we used chi-square, Student's t and Mann-Whitney tests, and Pearson's correlation. Logistic regression analyzed the independent influence of the factors. Values of p < 0.05 were considered significant. Results: The participants had a mean age of 50.8 years, and 57% were female. BMI, WC, WHR, and PBF showed no significant association with BAFMD. The male gender (p = 0.02) and higher serum levels of fibrinogen (p = 0.02) were significantly and independently associated with a BAFMD below 8%. Conclusions: In individuals with excess weight and mild untreated dyslipidemia, male gender and higher levels of fibrinogen were independently associated with worse BAFMD.