Physicochemical properties and mineral and protein content of honey samples from Ceará state, Northeastern Brazil

This study evaluated the physicochemical properties and protein and mineral content of honey samples from Ceará State, Northeastern Brazil, one of the major honey exporters in the country. Nutritional importance of the minerals detected was also analyzed. Physicochemical properties were examined according to the AOAC and CAC official methods; the protein content was determined using the Bradford method, and the minerals were analyzed by atomic absorption spectrometry. All analyses were performed in triplicate. The levels of macrominerals sodium (Na), potassium (K), calcium (Ca), and magnesium (Mg) varied from 1.80-47.20, 21.30-1513.30, 14.58-304.82, and 2.48-28.33 mg/kg, respectively, and the trace elements iron (Fe), copper (Cu), manganese (Mn), zinc (Zn), selenium (Se), and chromium (Cr) varied from 0.12-8.76, 0.07-1.29, 0.06-1.96, 0.07-1.85 mg/kg, 0.36 × 10-3-62.00 × 10-3 and 22.50 × 10-3-170.33 × 10-3 µg/kg, respectively. Myracrodruon urundeuva honey sample had high contents of macrominerals (Na, K, Ca, and Mg). Protein content of the Anacardium occidentale honey sample was the highest (1121.00 µg/g) among the samples analyzed. Among the minerals detected in the honey samples, K showed the highest concentration, followed by Ca, Na, and Mg. The presence of trace elements can show environmental contamination. The honey samples studied were free of trace elements contamination, except for Mn; the Piptadenia moniliformis was the only honey sample that was in compliance with the law requirements. The variations of the chemical constituents in the honey samples are probably related to differences in the floral origin and mineral and protein contents and confirm the nutritional importance of Ceará State honey.

The TTIP, ISDS and the shift towards transparency in investor-state arbitration

The negotiations between the EU and the US over the Transatlantic Trade and Investment Partnership (TTIP) have generated a lot of discussion about investor-state dispute settlement (ISDS). This discussion provided the inspiration for this thesis, with the TTIP in the background, setting the scene. In this thesis I study the nature of ISDS and the principle of transparency within investor-state arbitration. I aim to determine whether the use of ISDS is restricted to international arbitration and whether ISDS can be considered to constitute a system or regime. Furthermore, I consider whether the introduction of the UNCITRAL Rules on Transparency in Treaty-based Investor-State Arbitration (2014, the UNCITRAL Transparency Rules) changes investor-state arbitration in relation to transparency. To achieve this, I examine ISDS provisions in several different international investment agreements (IIAs) and evaluate the ways in which transparency is incorporated into investment law. Moreover, I compare the provisions on transparency and confidentiality in institutional arbitration rules with the UNCITRAL Transparency Rules. I have formed several conclusions, including that the ISDS provisions may contain methods other than international arbitration and that ISDS does not constitute a system. Furthermore, the UNCITRAL Transparency Rules do change – theoretically, at least – investor-state arbitration to become more transparent. Whether the UNCITRAL Transparency Rules will make investor-state arbitration fully transparent depends on the actions of the contracting state parties when negotiating new IIAs and whether they choose to incorporate the UNCITRAL Transparency Rules in the IIAs already concluded.

Application of Reptation Quantum Monte Carlo and Related Methods to LiH

This work investigates mathematical details and computational aspects of Metropolis-Hastings reptation quantum Monte Carlo and its variants, in addition to the Bounce method and its variants. The issues that concern us include the sensitivity of these algorithms' target densities to the position of the trial electron density along the reptile, time-reversal symmetry of the propagators, and the length of the reptile. We calculate the ground-state energy and one-electron properties of LiH at its equilibrium geometry for all these algorithms. The importance sampling is performed with a single-determinant large Slater-type orbitals (STO) basis set. The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software. Using the Bounce method in the calculation of non-energy-related properties, those represented by operators that do not commute with the Hamiltonian, is a novel work. We found that the unmodified Bounce gives good ground state energy and very good one-electron properties. We attribute this to its favourable time-reversal symmetry in its target density's Green's functions. Breaking this symmetry gives poorer results. Use of a short reptile in the Bounce method does not alter the quality of the results. This suggests that in future applications one can use a shorter reptile to cut down the computational time dramatically.

Exploring the Current State of Grades 4 to 8 Science Education in Ontario

This study sought to explore the current state of Grades 4 to 8 science education in Ontario from the perspective of Junior/Intermediate (J/I) teachers. The study’s methodology was a sequential 2-phased mixed methods explanatory design denoted as QUAN (qual)  qual. Data were collected from an online survey and follow-up interviews. J/I teachers (N = 219) from 48 school boards in Ontario completed a survey that collected both quantitative and qualitative data. Interviewees were selected from the survey participant population (n = 6) to represent a range of teaching strategies, attitudes toward teaching science, and years of experience. Survey and interview questions inquired about teacher attitudes toward teaching science, academic and professional experiences, teaching strategies, support resources, and instructional time allotments. Quantitative data analyses involved the descriptive statistics and chi-square tests. Qualitative data was coded inductively and deductively. Academic background in science was found to significantly influence teachers’ reported level of capability to teach science. The undergraduate degrees held by J/I science teachers were found to significantly influence their reported levels of capability to teach science. Participants identified a lack of time allocated for science instruction and inadequate equipment and facilities as major limitations on science instruction. Science in schools was reported to be of a “second-tiered” value to language and mathematics. Implications of this study include improving undergraduate and preservice experiences of elementary teachers by supporting their science content knowledge and pedagogical content knowledge.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Efficient estimation using the characteristic function : theory and applications with high frequency data

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Estimation of State Space Models and Stochastic Volatility

Ma thèse est composée de trois chapitres reliés à l'estimation des modèles espace-état et volatilité stochastique. Dans le première article, nous développons une procédure de lissage de l'état, avec efficacité computationnelle, dans un modèle espace-état linéaire et gaussien. Nous montrons comment exploiter la structure particulière des modèles espace-état pour tirer les états latents efficacement. Nous analysons l'efficacité computationnelle des méthodes basées sur le filtre de Kalman, l'algorithme facteur de Cholesky et notre nouvelle méthode utilisant le compte d'opérations et d'expériences de calcul. Nous montrons que pour de nombreux cas importants, notre méthode est plus efficace. Les gains sont particulièrement grands pour les cas où la dimension des variables observées est grande ou dans les cas où il faut faire des tirages répétés des états pour les mêmes valeurs de paramètres. Comme application, on considère un modèle multivarié de Poisson avec le temps des intensités variables, lequel est utilisé pour analyser le compte de données des transactions sur les marchés financières. Dans le deuxième chapitre, nous proposons une nouvelle technique pour analyser des modèles multivariés à volatilité stochastique. La méthode proposée est basée sur le tirage efficace de la volatilité de son densité conditionnelle sachant les paramètres et les données. Notre méthodologie s'applique aux modèles avec plusieurs types de dépendance dans la coupe transversale. Nous pouvons modeler des matrices de corrélation conditionnelles variant dans le temps en incorporant des facteurs dans l'équation de rendements, où les facteurs sont des processus de volatilité stochastique indépendants. Nous pouvons incorporer des copules pour permettre la dépendance conditionnelle des rendements sachant la volatilité, permettant avoir différent lois marginaux de Student avec des degrés de liberté spécifiques pour capturer l'hétérogénéité des rendements. On tire la volatilité comme un bloc dans la dimension du temps et un à la fois dans la dimension de la coupe transversale. Nous appliquons la méthode introduite par McCausland (2012) pour obtenir une bonne approximation de la distribution conditionnelle à posteriori de la volatilité d'un rendement sachant les volatilités d'autres rendements, les paramètres et les corrélations dynamiques. Le modèle est évalué en utilisant des données réelles pour dix taux de change. Nous rapportons des résultats pour des modèles univariés de volatilité stochastique et deux modèles multivariés. Dans le troisième chapitre, nous évaluons l'information contribuée par des variations de volatilite réalisée à l'évaluation et prévision de la volatilité quand des prix sont mesurés avec et sans erreur. Nous utilisons de modèles de volatilité stochastique. Nous considérons le point de vue d'un investisseur pour qui la volatilité est une variable latent inconnu et la volatilité réalisée est une quantité d'échantillon qui contient des informations sur lui. Nous employons des méthodes bayésiennes de Monte Carlo par chaîne de Markov pour estimer les modèles, qui permettent la formulation, non seulement des densités a posteriori de la volatilité, mais aussi les densités prédictives de la volatilité future. Nous comparons les prévisions de volatilité et les taux de succès des prévisions qui emploient et n'emploient pas l'information contenue dans la volatilité réalisée. Cette approche se distingue de celles existantes dans la littérature empirique en ce sens que ces dernières se limitent le plus souvent à documenter la capacité de la volatilité réalisée à se prévoir à elle-même. Nous présentons des applications empiriques en utilisant les rendements journaliers des indices et de taux de change. Les différents modèles concurrents sont appliqués à la seconde moitié de 2008, une période marquante dans la récente crise financière.

Quantitative proteomics methods for the analysis of histone post-translational modifications

Les histones sont des protéines nucléaires hautement conservées chez les cellules des eucaryotes. Elles permettent d’organiser et de compacter l’ADN sous la forme de nucléosomes, ceux-ci representant les sous unités de base de la chromatine. Les histones peuvent être modifiées par de nombreuses modifications post-traductionnelles (PTMs) telles que l’acétylation, la méthylation et la phosphorylation. Ces modifications jouent un rôle essentiel dans la réplication de l’ADN, la transcription et l’assemblage de la chromatine. L’abondance de ces modifications peut varier de facon significative lors du developpement des maladies incluant plusieurs types de cancer. Par exemple, la perte totale de la triméthylation sur H4K20 ainsi que l’acétylation sur H4K16 sont des marqueurs tumoraux spécifiques a certains types de cancer chez l’humain. Par conséquent, l’étude de ces modifications et des événements determinant la dynamique des leurs changements d’abondance sont des atouts importants pour mieux comprendre les fonctions cellulaires et moléculaires lors du développement de la maladie. De manière générale, les modifications des histones sont étudiées par des approches biochimiques telles que les immuno-buvardage de type Western ou les méthodes d’immunoprécipitation de la chromatine (ChIP). Cependant, ces approches présentent plusieurs inconvénients telles que le manque de spécificité ou la disponibilité des anticorps, leur coût ou encore la difficulté de les produire et de les valider. Au cours des dernières décennies, la spectrométrie de masse (MS) s’est avérée être une méthode performante pour la caractérisation et la quantification des modifications d’histones. La MS offre de nombreux avantages par rapport aux techniques traditionnelles. Entre autre, elle permet d’effectuer des analyses reproductibles, spécifiques et facilite l’etude d’un large spectre de PTMs en une seule analyse. Dans cette thèse, nous présenterons le développement et l’application de nouveaux outils analytiques pour l’identification et à la quantification des PTMs modifiant les histones. Dans un premier temps, une méthode a été développée pour mesurer les changements d’acétylation spécifiques à certains sites des histones. Cette méthode combine l’analyse des histones intactes et les méthodes de séquençage peptidique afin de déterminer les changements d’acétylation suite à la réaction in vitro par l’histone acétyltransférase (HAT) de levure Rtt109 en présence de ses chaperonnes (Asf1 ou Vps75). Dans un second temps, nous avons développé une méthode d’analyse des peptides isomériques des histones. Cette méthode combine la LC-MS/MS à haute résolution et un nouvel outil informatique appelé Iso-PeptidAce qui permet de déconvoluer les spectres mixtes de peptides isomériques. Nous avons évalué Iso-PeptidAce avec un mélange de peptides synthétiques isomériques. Nous avons également validé les performances de cette approche avec des histones isolées de cellules humaines érythroleucémiques (K562) traitées avec des inhibiteurs d’histones désacétylases (HDACi) utilisés en clinique, et des histones de Saccharomyces cerevisiae liées au facteur d’assemblage de la chromatine (CAF-1) purifiées par chromatographie d’affinité. Enfin, en utilisant la méthode présentée précédemment, nous avons fait une analyse approfondie de la spécificité de plusieurs HATs et HDACs chez Schizosaccharomyces pombe. Nous avons donc déterminé les niveaux d’acétylation d’histones purifiées à partir de cellules contrôles ou de souches mutantes auxquelles il manque une HAT ou HDAC. Notre analyse nous a permis de valider plusieurs cibles connues des HATs et HDACs et d’en identifier de nouvelles. Nos données ont également permis de définir le rôle des différentes HATs et HDACs dans le maintien de l’équilibre d’acétylation des histones. Dans l’ensemble, nous anticipons que les méthodes décrites dans cette thèse permettront de résoudre certains défis rencontrés dans l’étude de la chromatine. De plus, ces données apportent de nouvelles connaissances pour l’élaboration d’études génétiques et biochimiques utilisant S. pombe.

Investigations on The Finite Size Effects of Some Inverse Spinels and Studies on Their Composites Based on Nitrile Rubber

Magnetism and magnetic materials have been an ever-attractive subject area for engineers and scientists alike because of its versatility in finding applications in useful devices. They find applications in a host of devices ranging from rudimentary devices like loud speakers to sophisticated gadgets like waveguides and Magnetic Random Access Memories (MRAM).The one and only material in the realm of magnetism that has been at the centre stage of applications is ferrites and in that spinel ferrites received the lions share as far as practical applications are concerned.It has been the endeavour of scientists and engineers to remove obsolescence and improve upon the existing so as to save energy and integrate in to various other systems. This has been the hallmark of material scientists and this has led to new materials and new technologies.In the field of ferrites too there has been considerable interest to devise new materials based on iron oxides and other compounds. This means synthesising ultra fine particles and tuning its properties to device new materials. There are various preparation techniques ranging from top- down to bottom-up approaches. This includes synthesising at molecular level, self assembling,gas based condensation. Iow temperature eo-precipitation, solgel process and high energy ball milling. Among these methods sol-gel process allows good control of the properties of ceramic materials. The advantage of this method includes processing at low temperature. mixing at the molecular level and fabrication of novel materials for various devices.Composites are materials. which combine the good qualities of one or more components. They can be prepared in situ or by mechanical means by the incorporation of fine particles in appropriate matrixes. The size of the magnetic powders as well as the nature of matrix affect the processability and other physical properties of the final product. These plastic/rubber magnets can in turn be useful for various applications in different devices. In applications involving ferrites at high frequencies, it is essential that the material possesses an appropriate dielectric permittivity and suitable magnetic permeability. This can be achieved by synthesizing rubber ferrite composites (RFC's). RFCs are very useful materials for microwave absorptions. Hence the synthesis of ferrites in the nanoregirne.investigations on their size effects on the structural, magnetic, and electrical properties and the incorporation of these ferrites into polymer matrixes assume significance.In the present study, nano particles of NiFe204, Li(!5Fe2S04 and Col-e-O, are prepared by sol gel method. By appropriate heat treatments, particles of different grain sizes are obtained. The structural, magnetic and electrical measurements are evaluated as a function of grain size and temperature. NiFel04 prepared in the ultrafine regime are then incorporated in nitrile rubber matrix. The incorporation was carried out according to a specific recipe and for various loadings of magnetic fillers. The cure characteristics, magnetic properties, electrical properties and mechanical properties of these elastomer blends are carried out. The electrical permittivity of all the rubber samples in the X - band are also conducted.