Intraindividual relations between achievement goals and discrete achievement emotions: an experience sampling approach

Theories on the link between achievement goals and achievement emotions focus on their within-person functional relationship (i.e., intraindividual relations). However, empirical studies have failed to analyze these intraindividual relations and have instead examined between-person covariation of the two constructs (i.e., interindividual relations). Aiming to better connect theory and empirical research, the present study (N = 120 10th grade students) analyzed intraindividual relations by assessing students’ state goals and emotions using experience sampling (N = 1,409 assessments within persons). In order to replicate previous findings on interindividual relations, students’ trait goals and emotions were assessed using self-report questionnaires. Despite being statistically independent, both types of relations were consistent with theoretical expectations, as shown by multi-level modeling: Mastery goals were positive predictors of enjoyment and negative predictors of boredom and anger; performance-approach goals were positive predictors of pride; and performance-avoidance goals were positive predictors of anxiety and shame. Reasons for the convergence of intra- and interindividual findings, directions for future research, and implications for educational practice are discussed.

Métodos de bootstrap e aplicações em problemas biológicos

Pós-graduação em Matemática Universitária - IGCE

Efeitos da punição sobre respostas mantidas em diferentes sistemas econômicos (aberto e fechado) em Rattus norvegicus

Os sistemas econômicos comportamentais são definidos como diferentes relações existentes entre o consumo e a forma como o organismo o obtém. Existem tipicamente dois tipos de sistemas econômicos: a economia fechada, na qual a porção alimentar diária do sujeito só pode ser adquirida dentro da sessão experimental; e a economia aberta, na qual, além desta, o sujeito recebe uma complementação alimentar após a sessão. Este estudo teve como objetivo averiguar os efeitos da punição positiva sobre respostas mantidas em diferentes sistemas econômicos. Foram realizados dois experimentos. No Experimento 1 dois Rattus norvegicus, machos, privados de água por 24 horas, divididos entre as duas economias: A1 (aberta) e F1 (fechada). O estímulo aversivo foi um Jato de ar-quente (JAQ) por 5 segundos e contingente a cada resposta de pressão à barra (RPB). Cada sujeito passou pelas seguintes fases: Nível Operante, Modelagem da RPB, Fortalecimento em CRF, Punição e Recondicionamento. No Experimento 2 foram utilizados quatro Rattus norvegicus, Wistar, machos, privados de água por 24 horas, divididos em duas duplas: FAF (Fechada/Aberta/Fechada) e AFA (Aberta/Fechada/Aberta). O estímulo aversivo foi um choque de 1.3mA, por cinco segundos e contingente a cada RPB. Durante o experimento, ambos passaram pelas seguintes fases: Nível Operante, Modelagem da RPB, Fortalecimento em FR10, Punição (em uma economia), Recondicionamento, Punição (em uma economia diferente da anterior), outro Recondicionamento, por fim, uma sessão de Punição na economia inicial. Os dados dos dois Experimentos demonstraram uma supressão média no responder durante as fases de Punição em comparação com as fases de Fortalecimento/Recondicionamento, em ambas as economias e em todos os sujeitos: 48,7%(F1); 96,6%(A1); 99,9%, 99,9% e 89,8%(FAF1); 93,2%, 99,4% e 84,8% (FAF2); 99,8%, 83,6% e 95% (AFA1); 92,3%, 90,9% e 91,6% (AFA2). Estes resultados demonstram que tanto o choque quanto o JAQ funcionaram como estímulos aversivos, porém a diferença entre as duas economias foi maior nos sujeitos que tiveram suas respostas punidas com o JAQ.

Topics in financial econometrics

In the first chapter, we consider the joint estimation of objective and risk-neutral parameters for SV option pricing models. We propose a strategy which exploits the information contained in large heterogeneous panels of options, and we apply it to S&P 500 index and index call options data. Our approach breaks the stochastic singularity between contemporaneous option prices by assuming that every observation is affected by measurement error. We evaluate the likelihood function by using a MC-IS strategy combined with a Particle Filter algorithm. The second chapter examines the impact of different categories of traders on market transactions. We estimate a model which takes into account traders’ identities at the transaction level, and we find that the stock prices follow the direction of institutional trading. These results are carried out with data from an anonymous market. To explain our estimates, we examine the informativeness of a wide set of market variables and we find that most of them are unambiguously significant to infer the identity of traders. The third chapter investigates the relationship between the categories of market traders and three definitions of financial durations. We consider trade, price and volume durations, and we adopt a Log-ACD model where we include information on traders at the transaction level. As to trade durations, we observe an increase of the trading frequency when informed traders and the liquidity provider intensify their presence in the market. For price and volume durations, we find the same effect to depend on the state of the market activity. The fourth chapter proposes a strategy to express order aggressiveness in quantitative terms. We consider a simultaneous equation model to examine price and volume aggressiveness at Euronext Paris, and we analyse the impact of a wide set of order book variables on the price-quantity decision.

DISTRIBUTED APPROACH FOR SOLVING OPTIMAL POWER FLOW PROBLEMS IN THREE-PHASE UNBALANCED DISTRIBUTION NETWORK

The objective of this report is to study distributed (decentralized) three phase optimal power flow (OPF) problem in unbalanced power distribution networks. A full three phase representation of the distribution networks is considered to account for the highly unbalance state of the distribution networks. All distribution network’s series/shunt components, and load types/combinations had been modeled on commercial version of General Algebraic Modeling System (GAMS), the high-level modeling system for mathematical programming and optimization. The OPF problem has been successfully implemented and solved in a centralized approach and distributed approach, where the objective is to minimize the active power losses in the entire system. The study was implemented on the IEEE-37 Node Test Feeder. A detailed discussion of all problem sides and aspects starting from the basics has been provided in this study. Full simulation results have been provided at the end of the report.

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

Chemical and Sr isotopic compositions of 3 step leachates and residues of bulk samples, zeolites, and smectites from Lower Miocene sediments at ODP Site 166-1007

Massive clinoptilolite authigenesis was observed at about 1105 meters below sea floor (mbsf) in lower Miocene wellcompacted carbonate periplatform sediments from the Great Bahama Bank [Ocean Drilling Program, ODP Leg 166, Site 1007]. The diagenetic assemblage comprises abundant zeolite crystallized within foraminifer tests and sedimentary matrix, as well as Mg smectites. In carbonate-rich deposits, the formation of the zeolite requires a supply of silica. Thus, the objective of the study is to determine the origin of the silica supply, its diagenetic evolution, and consequently the related implications on interpretation of the sedimentary record, in terms of local or global paleoceanographic change. For lack of evidence for any volcaniclastic input or traces of Si-enriched deep fluids circulation, an in situ biogenic source of silica is validated by isotopic data and chemical modeling for the formation of such secondary minerals in shallow-water carbonate sequences. Geochemical and strontium isotopic data clearly establish the marine signature of the diagenetic zeolite, as well as its contemporaneous formation with the carbonate deposition (Sr model ages of 19.6-23.2 Ma). The test of saturation for the pore fluids specifies the equilibrium state of the present mineralogical assemblage. Seawater-rock modeling specifies that clinoptilolite precipitates from the dissolution of biogenic silica, which reacts with clay minerals. The amount of silica (opal-A) involved in the reaction has to be significant enough, at least 10 wt.%, to account for the observed content of clinoptilolite occurring at the most zeolite-rich level. Modeling also shows that the observed amount of clinoptilolite (~19%) reflects an in situ and short-term reaction due to the high reactivity of primary biogenic silica (opal-A) until its complete depletion. The episodic occurrence of these well-lithified zeolite-rich levels is consistent with the occurrence of seismic reflectors, particularly the P2 seismic sequence boundary located at 1115 mbsf depth and dated as 23.2 Ma. The age range of most zeolitic sedimentary levels (biostratigraphic ages of 21.5-22 Ma) correlates well with that of the early Miocene glaciation Mi-1 and Mi-1a global events. Thus, the clinoptilolite occurrence in the shallow carbonate platform environment far from volcanogenic supply, or in other sensitive marine areas, is potentially a significant new proxy for paleoproductivity and oceanic global events, such as the Miocene events, which are usually recognized in deep-sea pelagic sediments and high latitude deposits.

Improper use of formative endogenous variables

Researchers often develop and test conceptual models containing formative variables. In many cases, these formative variables are specified as being endogenous. This article provides a clarification of formative variable theory, distinguishing between the formative latent variable and the formative composite variable. When an endogenous latent variable relies on formative indicators for measurement, empirical studies can say nothing about the relationship between exogenous variables and the endogenous formative latent variable: conclusions can only be drawn regarding the exogenous variables' relationships with a composite variable. The authors also show the dangers associated with developing theory about antecedents to endogenous formative variables at the (aggregate) formative latent variable level. Modeling relationships with endogenous formative variables at the (disaggregate) indicator level informs richer theory development, and encourages more precise empirical testing. When antecedents' relationships with endogenous formative variables are modeled at the formative latent variable level rather than the formative indicator level, theory construction can verge on the superficial, and empirical findings can be ambiguous in substantive meaning.

A modelling and simulation environment for self-aware and self-expressive systems

Self-awareness and self-expression are promising architectural concepts for embedded systems to be equipped with to match them with dedicated application scenarios and constraints in the avionic and space-flight industry. Typically, these systems operate in largely undefined environments and are not reachable after deployment for a long time or even never ever again. This paper introduces a reference architecture as well as a novel modelling and simulation environment for self-aware and self-expressive systems with transaction level modelling, simulation and detailed modelling capabilities for hardware aspects, precise process chronology execution as well as fine timing resolutions. Furthermore, industrial relevant system sizes with several self-aware and self-expressive nodes can be handled by the modelling and simulation environment.

Modeling the Dynamics of Viral Evolution Considering Competition Within Individual Hosts and at Population Level: The Effects of Treatment

We consider two viral strains competing against each other within individual hosts (at cellular level) and at population level (for infecting hosts) by studying two cases. In the first case, the strains do not mutate into each other. In this case, we found that each individual in the population can be infected by only one strain and that co-existence in the population is possible only when the strain that has the greater basic intracellular reproduction number, R (0c) , has the smaller population number R (0p) . Treatment against the one strain shifts the population equilibrium toward the other strain in a complicated way (see Appendix B). In the second case, we assume that the strain that has the greater intracellular number R (0c) can mutate into the other strain. In this case, individual hosts can be simultaneously infected by both strains (co-existence within the host). Treatment shifts the prevalence of the two strains within the hosts, depending on the mortality induced by the treatment, which is, in turn, dependent upon the doses given to each individual. The relative proportions of the strains at the population level, under treatment, depend both on the relative proportions within the hosts (which is determined by the dosage of treatment) and on the number of individuals treated per unit time, that is, the rate of treatment. Implications for cases of real diseases are briefly discussed.

Modeling AIDS vaccines: the cellular level

This paper discuses current strategies for the development of AIDS vaccines wich allow immunzation to disturb the natural course of HIV at different detailed stages of its life cycle. Mathematical models describing the main biological phenomena (i.e. virus and vaccine induced T4 cell growth; virus and vaccine induced activation latently infected T4 cells; incremental changes immune response as infection progress; antibody dependent enhancement and neutralization of infection) and allowing for different vaccination strategies serve as a backgroud for computer simulations. The mathematical models reproduce updated information on the behavior of immune cells, antibody concentrations and free viruses. The results point to some controversial outcomes of an AIDS vaccine such as an early increase in virus concentration among vaccinated when compared to nonvaccinated individuals.

Mathematical modeling of level of anaesthesia from EEG measurements

Problem of modeling of anaesthesia depth level is studied in this Master Thesis. It applies analysis of EEG signals with nonlinear dynamics theory and further classification of obtained values. The main stages of this study are the following: data preprocessing; calculation of optimal embedding parameters for phase space reconstruction; obtaining reconstructed phase portraits of each EEG signal; formation of the feature set to characterise obtained phase portraits; classification of four different anaesthesia levels basing on previously estimated features. Classification was performed with: Linear and quadratic Discriminant Analysis, k Nearest Neighbours method and online clustering. In addition, this work provides overview of existing approaches to anaesthesia depth monitoring, description of basic concepts of nonlinear dynamics theory used in this Master Thesis and comparative analysis of several different classification methods.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Affiliation: Institut de recherche en immunologie et en cancérologie, Université de Montréal

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.