Efeito de marcadores moleculares sobre características de crescimento e reprodutivas de bovinos da raça Canchim

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelo de avaliação de maturidade em gerenciamento de projetos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Metodologia baseada em sistema fuzzy intervalar do tipo-2 para detecção e identificação de faltas de incipientes em motores de indução

Desde a incorporação da automação no processo produtivo, a busca por sistemas mais eficientes, objetivando o aumento da produtividade e da qualidade dos produtos e serviços, direcionou os estudos para o planejamento de estratégias que permitissem o monitoramento de sistemas com o intuito principal de torna-los mais autônomos e robustos. Por esse motivo, as pesquisas envolvendo o diagnóstico de faltas em sistemas industriais tornaram-se mais intensivas, visto a necessidade da incorporação de técnicas para monitoramente detalhado de sistemas. Tais técnicas permitem a verificação de perturbações, falta ou mesmo falhas. Em vista disso, essa trabalho investiga técnicas de detecção e diagnostico de faltas e sua aplicação em motores de indução trifásicos, delimitando o seu estudo em duas situações: sistemas livre de faltas, e sobre atuação da falta incipiente do tipo curto-circuitoparcial nas espiras do enrolamento do estator. Para a detecção de faltas, utilizou-se analise paramétrica dos parâmetros de um modelo de tempo discreto, de primeira ordem, na estrutura autoregressivo com entradas exógenas (ARX). Os parâmetros do modelo ARX, que trazem informação sobre a dinâmica dominante do sistema, são obtidos recursivamente pela técnica dos mínimos quadrados recursivos (MQR). Para avaliação da falta, foi desenvolvido um sistema de inferência fuzzy (SIF) intervala do tipo-2, cuja mancha de incerteza ou footprint of uncertainty (FOU), características de sistema fuzzy tipo-2, é ideal como forma de representar ruídos inerentes a sistemas reais e erros numéricos provenientes do processo de estimação paramétrica. Os parâmetros do modelo ARX são entradas para o SIF. Algoritmos genéricos (AG’s) foram utilizados para otimização dos SIF intervalares tipo-2, objetivando reduzir o erro de diagnóstico da falta identificada na saída desses sistemas. Os resultados obtidos em teste de simulação computacional demonstram a efetividade da metodologia proposta.

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema baseado em regras fuzzy do tipo Takagi-Sugeno aplicado a ecos de radares meteorológicos

Pós-graduação em Ciências Ambientais - Sorocaba

Hollow-fiber liquid-phase microextraction of amphetamine-type stimulants in human hair samples

A fast method was optimized and validated in order to quantify amphetamine-type stimulants (amphetamine, AMP; methamphetamine, MAMP; fenproporex, FPX; 3,4-methylenedioxymethamphetamine, MDMA; and 3,4-methylenedioxyamphetamine, MDA) in human hair samples. The method was based in an initial procedure of decontamination of hair samples (50 mg) with dichloromethane, followed by alkaline hydrolysis and extraction of the amphetamines using hollow-fiber liquid-phase micro extraction (HF-LPME) in the three-phase mode. Gas chromatography-mass spectrometry (GC-MS) was used for identification and quantification of the analytes. The LoQs obtained for all amphetamines (around 0.05 ng/mg) were below the cut-off value (0.2 ng/mg) established by the Society of Hair Testing (SoHT). The method showed to be simple and precise. The intra-day and inter-day precisions were within 10.6% and 11.4%, respectively, with the use of only two deuteratecl internal standards (AMP-d5 and MDMA-d5). By using the weighted least squares linear regression (1/x(2)), the accuracy of the method was satisfied in the lower concentration levels (accuracy values better than 87%). Hair samples collected from six volunteers who reported regular use of amphetamines were submitted to the developed method. Drug detection was observed in all samples of the volunteers. (c) 2012 Elsevier B.V. All rights reserved.

Determination of dimethyltryptamine and beta-carbolines (ayahuasca alkaloids) in plasma samples by LC-MS/MS

Background: Ayahuasca is a psychoactive plant beverage originally used by indigenous people throughout the Amazon Basin, long before its modern use by syncretic religious groups established in Brazil, the USA and European countries. The objective of this study was to develop a method for quantification of dimethyltryptamine and beta-carbolines in human plasma samples. Results: The analytes were extracted by means of C18 cartridges and injected into LC-MS/MS, operated in positive ion mode and multiple reaction monitoring. The LOQs obtained for all analytes were below 0.5 ng/ml. By using the weighted least squares linear regression, the accuracy of the analytical method was improved at the lower end of the calibration curve (from 0.5 to 100 ng/ml; r(2)> 0.98). Conclusion: The method proved to be simple, rapid and useful to estimate administered doses for further pharmacological and toxicological investigations of ayahuasca exposure.

Influence of chest wall on lung mechanics during inspiration

RATIONALE: The interaction between lungs and chest wall influences lung volume, that determines lung history during respiration cycle. In this study, the influence of chest wall mechanics on respiratory system is assessed by the evaluation of inspiration pressure-volume curve (PV curve) under three different situations: closed-chest, open-chest and isolated lung. The PV curve parameters in each situation allow us to further understand the role played by different chest wall elements in the respiratory function. Methods: Twenty-four male Wistar rats (236 ± 29 g) were used. The animals were weighted and then anesthetized with xylazine 2% (O,SmL/kg) and ketamine 10% (0,9mL/kg), exsanguinated and later tracheostomies with a metallic cannula (14 gauge).The cannula was connected to an automatic small animal insufflator. This setup was connected to a pressure transducer (32 samples/s). The 24 animals were randomly separated in three groups:(i) closed chest,(ii) open chest and (iii) isolated lung. The rats were insufflated with 20mL quasi-statically (constant speed of 0,1mUs). lnsufflated volume and measured pressure data were kept and PV curves were obtained for all animals. The PV curves were fitted (non-linear least squares) against the sigmoid equation (1) to obtain the sigmoid equation parameters (a,b,c,d). Elastance measurements were obtained from linear regression of pressure/volume measurements in a 0,8s interval before and after the calculated point. Results: The parameters a,b and c showed no significant change, but the parameter d showed a significant variation among the three groups. The initial elastance also varied between open and closed chest, indicating the need of a higher pressure for the lung expansion, as can be seen in Table 1. Conclusion: A supporting effect of the chest wall was observed at the initial moments of inspiration, observed as a higher initial elastance in open chest situations than in closed chest situations (p=0,00001). The similar initial elastance for the isolated lung and closed chest may be explained by the specific method used for the isolated lung experiment. As the isolated lung is supported by the trachea vertically, the weight of the tissue may have a similar effect of the residual negative pressure in the thorax, responsible for maintaining the residual volume.

Influence of chest wall on lung mechanics during inspiration.

RATIONALE: The interaction between lungs and chest wall influences lung volume, that determines lung history during respiration cycle. In this study, the influence of chest wall mechanics on respiratory system is assessed by the evaluation of inspiration pressure-volume curve (PV curve) under three different situations: closed-chest, open-chest and isolated lung. The PV curve parameters in each situation allow us to further understand the role played by different chest wall elements in the respiratory function. Methods: Twenty-four male Wistar rats (236 ± 29 g) were used. The animals were weighted and then anesthetized with xylazine 2% (0,5mL/kg) and ketamine 10% (0,9mL/kg), exsanguinated and later tracheostomized with a metallic cannula (14 gauge). The cannula was connected to an automatic small animal insufflator. This setup was connected to a pressure transducer (32 samples/s). The 24 animals were randomly separated in three groups: (i) closed chest, (ii) open chest and (iii) isolated lung. The rats were insufflated with 20mL quasi-statically (constant speed of 0,1mL/s). Insufflated volume and measured pressure data were kept and PV curves were obtained for all animals. The PV curves were fitted (non-linear least squares) against the sigmoid equation (1) to obtain the sigmoid equation parameters (a,b,c,d). Elastance measurements were obtained from linear regression of pressure/volume measurements in a 0,8s interval before and after the calculated point. Results: The parameters a, b and c showed no significant change, but the parameter d showed a significant variation among the three groups. The initial elastance also varied between open and closed chest, indicating the need of a higher pressure for the lung expansion, as can be seen in Table 1. Table 1: Mean and Standard Deviation of parameters obtained for each protocol. Protocol: Closed Chest – a (mL) -0.35±0.33; b (mL) 13.93±0.89; c (cm H2O) 21.28±2.37; d (cm H2O) 6.17±0.84; r²** (%) 99.4±0.14; Initial Elastance* (cm H2)/mL) 12.72±6.66; Weight (g) 232.33±5.72. Open Chest - a (mL) 0.01±0.28; b (mL) 14.79±0.54; c (cm H2O) 19.47±1.41; d (cm H2O) 3.50±0.28; r²** (%) 98.8±0.34; Initial Elastance* (cm H2)/mL) 28.68±2.36; Weight (g) 217.33±7.97. Isolated Lung - a (mL) -0.09±0.46; b (mL) 14.22±0.75; c (cm H2O) 21.76±1.43; d (cm H2O) 4.24±0.50; r²** (%) 98.9±0.19; Initial Elastance* (cm H2)/mL) 7.13±8.85; Weight (g) 224.33±16.66. * Elastance measures in the 0-0,1 mL range. ** Goodness of sigmoid fit versus measured data Conclusion: A supporting effect of the chest wall was observed at the initial moments of inspiration, observed as a higher initial elastance in open chest situations than in closed chest situations (p=0,00001). The similar initial elastance for the isolated lung and closed chest may be explained by the specific method used for the isolated lung experiment. As the isolated lung is supported by the trachea vertically, the weight of the tissue may have a similar effect of the residual negative pressure in the thorax, responsible for maintaining the residual volume.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

The Incumbent Spending Puzzle

Objective This article seeks to explain the puzzle of why incumbents spend so much on campaigns despite most research finding that their spending has almost no effect on voters. Methods The article uses ordinary least squares, instrumental variables, and fixed-effects regression to estimate the impact of incumbent spending on election outcomes. The estimation includes an interaction term between incumbent and challenger spending to allow the effect of incumbent spending to depend on the level of challenger spending. Results The estimation provides strong evidence that spending by the incumbent has a larger positive impact on votes received the more money the challenger spends. Conclusion Campaign spending by incumbents is most valuable in the races where the incumbent faces a serious challenge. Raising large sums of money to be used in close races is thus a rational choice by incumbents.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Stereoselective pharmacokinetics of ketamine and norketamine after racemic ketamine or S-ketamine administration during isoflurane anaesthesia in Shetland ponies

BACKGROUND: The arterial pharmacokinetics of ketamine and norketamine enantiomers after racemic ketamine or S-ketamine i.v. administration were evaluated in seven gelding ponies in a crossover study (2-month interval). METHODS: Anaesthesia was induced with isoflurane in oxygen via a face-mask and then maintained at each pony's individual MAC. Racemic ketamine (2.2 mg kg(-1)) or S-ketamine (1.1 mg kg(-1)) was injected in the right jugular vein. Blood samples were collected from the right carotid artery before and at 1, 2, 4, 8, 16, 32, 64, and 128 min after ketamine administration. Ketamine and norketamine enantiomer plasma concentrations were determined by capillary electrophoresis. Individual R-ketamine and S-ketamine concentration vs time curves were analysed by non-linear least square regression two-compartment model analysis using PCNonlin. Plasma disposition curves for R-norketamine and S-norketamine were described by estimating AUC, C(max), and T(max). Pulse rate (PR), respiratory rate (R(f)), tidal volume (V(T)), minute volume ventilation (V(E)), end-tidal partial pressure of carbon dioxide (PE'(CO(2))), and mean arterial blood pressure (MAP) were also evaluated. RESULTS: The pharmacokinetic parameters of S- and R-ketamine administered in the racemic mixture or S-ketamine administered separately did not differ significantly. Statistically significant higher AUC and C(max) were found for S-norketamine compared with R-norketamine in the racemic group. Overall, R(f), V(E), PE'(CO(2)), and MAP were significantly higher in the racemic group, whereas PR was higher in the S-ketamine group. CONCLUSIONS: Norketamine enantiomers showed different pharmacokinetic profiles after single i.v. administration of racemic ketamine in ponies anaesthetised with isoflurane in oxygen (1 MAC). Cardiopulmonary variables require further investigation.

Car ownership modeling and forecasts for China

A great increase of private car ownership took place in China from 1980 to 2009 with the development of the economy. To explain the relationship between car ownership and economic and social changes, an ordinary least squares linear regression model is developed using car ownership per capita as the dependent variable with GDP, savings deposits and highway mileages per capita as the independent variables. The model is tested and corrected for econometric problems such as spurious correlation and cointegration. Finally, the regression model is used to project oil consumption by the Chinese transportation sector through 2015. The result shows that about 2.0 million barrels of oil will be consumed by private cars in conservative scenario, and about 2.6 million barrels of oil per day in high case scenario in 2015. Both of them are much higher than the consumption level of 2009, which is 1.9 million barrels per day. It also shows that the annual growth rate of oil demand by transportation is 2.7% - 3.1% per year in the conservative scenario, and 6.9% - 7.3% per year in the high case forecast scenario from 2010 to 2015. As a result, actions like increasing oil efficiency need to be taken to deal with challenges of the increasing demand for oil.

Spatial analysis of cardiovascular disease mortality and air pollution in Harris County, Texas

The association between fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) mortality was spatially analyzed for Harris County, Texas, at the census tract level. The objective was to assess how increased PM2.5 exposure related to CVD mortality in this area while controlling for race, income, education, and age. An estimated exposure raster was created for Harris County using Kriging to estimate the PM2.5 exposure at the census tract level. The PM2.5 exposure and the CVD mortality rates were analyzed in an Ordinary Least Squares (OLS) regression model and the residuals were subsequently assessed for spatial autocorrelation. Race, median household income, and age were all found to be significant (p<0.05) predictors in the model. This study found that for every one μg/m3 increase in PM2.5 exposure, holding age and education variables constant, an increase of 16.57 CVD deaths per 100,000 would be predicted for increased minimum exposure values and an increase of 14.47 CVD deaths per 100,000 would be predicted for increased maximum exposure values. This finding supports previous studies associating PM2.5 exposure with CVD mortality. This study further identified the areas of greatest PM2.5 exposure in Harris County as being the geographical locations of populations with the highest risk of CVD (i.e., predominantly older, low-income populations with a predominance of African Americans). The magnitude of the effect of PM2.5 exposure on CVD mortality rates in the study region indicates a need for further community-level studies in Harris County, and suggests that reducing excess PM2.5 exposure would reduce CVD mortality.^