Sensitivity analysis, dominant factors, and robustness of the ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5 occupational exposure models

Occupational exposure modeling is widely used in the context of the E.U. regulation on the registration, evaluation, authorization, and restriction of chemicals (REACH). First tier tools, such as European Centre for Ecotoxicology and TOxicology of Chemicals (ECETOC) targeted risk assessment (TRA) or Stoffenmanager, are used to screen a wide range of substances. Those of concern are investigated further using second tier tools, e.g., Advanced REACH Tool (ART). Local sensitivity analysis (SA) methods are used here to determine dominant factors for three models commonly used within the REACH framework: ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5. Based on the results of the SA, the robustness of the models is assessed. For ECETOC, the process category (PROC) is the most important factor. A failure to identify the correct PROC has severe consequences for the exposure estimate. Stoffenmanager is the most balanced model and decision making uncertainties in one modifying factor are less severe in Stoffenmanager. ART requires a careful evaluation of the decisions in the source compartment since it constitutes ∼75% of the total exposure range, which corresponds to an exposure estimate of 20-22 orders of magnitude. Our results indicate that there is a trade off between accuracy and precision of the models. Previous studies suggested that ART may lead to more accurate results in well-documented exposure situations. However, the choice of the adequate model should ultimately be determined by the quality of the available exposure data: if the practitioner is uncertain concerning two or more decisions in the entry parameters, Stoffenmanager may be more robust than ART.

Identification-commitment inventory (ICI-Model): confirmatory factor analysis and construct validity

The aim of this study is to confirm the factorial structure of the Identification-Commitment Inventory (ICI) developed within the frame of the Human System Audit (HSA) (Quijano et al. in Revist Psicol Soc Apl 10(2):27-61, 2000; Pap Psicól Revist Col Of Psicó 29:92-106, 2008). Commitment and identification are understood by the HSA at an individual level as part of the quality of human processes and resources in an organization; and therefore as antecedents of important organizational outcomes, such as personnel turnover intentions, organizational citizenship behavior, etc. (Meyer et al. in J Org Behav 27:665-683, 2006). The theoretical integrative model which underlies ICI Quijano et al. (2000) was tested in a sample (N = 625) of workers in a Spanish public hospital. Confirmatory factor analysis through structural equation modeling was performed. Elliptical least square solution was chosen as estimator procedure on account of non-normal distribution of the variables. The results confirm the goodness of fit of an integrative model, which underlies the relation between Commitment and Identification, although each one is operatively different.

Falsification and corroboration of conceptual hydrological models using geophysical data

Geophysical data may provide crucial information about hydrological properties, states, and processes that are difficult to obtain by other means. Large data sets can be acquired over widely different scales in a minimally invasive manner and at comparatively low costs, but their effective use in hydrology makes it necessary to understand the fidelity of geophysical models, the assumptions made in their construction, and the links between geophysical and hydrological properties. Geophysics has been applied for groundwater prospecting for almost a century, but it is only in the last 20 years that it is regularly used together with classical hydrological data to build predictive hydrological models. A largely unexplored venue for future work is to use geophysical data to falsify or rank competing conceptual hydrological models. A promising cornerstone for such a model selection strategy is the Bayes factor, but it can only be calculated reliably when considering the main sources of uncertainty throughout the hydrogeophysical parameter estimation process. Most classical geophysical imaging tools tend to favor models with smoothly varying property fields that are at odds with most conceptual hydrological models of interest. It is thus necessary to account for this bias or use alternative approaches in which proposed conceptual models are honored at all steps in the model building process.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Tests of International Asset Pricing Models in Scandinavian Stock Markets

This thesis examines whether global, local and exchange risks are priced in Scandinavian countries’ equity markets by using conditional international asset pricing models. The employed international asset pricing models are the world capital asset pricing model, the international asset pricing model augmented with the currency risk, and the partially segmented model augmented with the currency risk. Moreover, this research traces estimated equity risk premiums for the Scandinavian countries. The empirical part of the study is performed using generalized method of moments approach. Monthly observations from February 1994 to June 2007 are used. Investors’ conditional expectations are modeled using several instrumental variables. In order to keep system parsimonious the prices of risk are assumed to be constant whereas expected returns and conditional covariances vary over time. The empirical findings of this thesis suggest that the prices of global and local market risk are priced in the Scandinavian countries. This indicates that the Scandinavian countries are mildly segmented from the global markets. Furthermore, the results show that the exchange risk is priced in the Danish and Swedish stock markets when the partially segmented model is augmented with the currency risk factor.

Fibroblast Growth Factor 8 and its Receptors in The Growth and Angiogenesis of Experimental Breast Cancer . With Special Reference to Thrombospondin-1 as a Novel Target Gene for FGF-8

The growth of breast cancer is regulated by hormones and growth factors. Recently, aberrant fibroblast growth factor (FGF) signalling has been strongly implicated in promoting the progression of breast cancer and is thought to have a role in the development of endocrine resistant disease. FGFs mediate their auto- and paracrine signals through binding to FGF receptors 1-4 (FGFR1-4) and their isoforms. Specific targets of FGFs in breast cancer cells and the differential role of FGFRs, however, are poorly described. FGF-8 is expressed at elevated levels in breast cancer, and it has been shown to act as an angiogenic, growth promoting factor in experimental models of breast cancer. Furthermore, it plays an important role in mediating androgen effects in prostate cancer and in some breast cancer cell lines. We aimed to study testosterone (Te) and FGF-8 regulated genes in Shionogi 115 (S115) breast cancer cells, characterise FGF-8 activated intracellular signalling pathways and clarify the role of FGFR1, -2 and -3 in these cells. Thrombospondin-1 (TSP-1), an endogenous inhibitor of angiogenesis, was recognised as a Te and FGF-8 regulated gene. Te repression of TSP-1 was androgen receptor (AR)-dependent. It required de novo protein synthesis, but it was independent of FGF-8 expression. FGF-8, in turn, downregulated TSP-1 transcription by activating the ERK and PI3K pathways, and the effect could be reversed by specific kinase inhibitors. Differential FGFR1-3 action was studied by silencing each receptor by shRNA expression in S115 cells. FGFR1 expression was a prerequisite for the growth of S115 tumours, whereas FGFR2 expression alone was not able to promote tumour growth. High FGFR1 expression led to a growth advantage that was associated with strong ERK activation, increased angiogenesis and reduced apoptosis, and all of these effects could be reversed by an FGFR inhibitor. Taken together, the results of this thesis show that FGF-8 and FGFRs contribute strongly to the regulation of the growth and angiogenesis of experimental breast cancer and support the evidence for FGF-FGFR signalling as one of the major players in breast cancers.

CFD Modelling of Direct Contact Condensation in Suppression Pools by Applying Condensation Models of Separated Flow

The condensation rate has to be high in the safety pressure suppression pool systems of Boiling Water Reactors (BWR) in order to fulfill their safety function. The phenomena due to such a high direct contact condensation (DCC) rate turn out to be very challenging to be analysed either with experiments or numerical simulations. In this thesis, the suppression pool experiments carried out in the POOLEX facility of Lappeenranta University of Technology were simulated. Two different condensation modes were modelled by using the 2-phase CFD codes NEPTUNE CFD and TransAT. The DCC models applied were the typical ones to be used for separated flows in channels, and their applicability to the rapidly condensing flow in the condensation pool context had not been tested earlier. A low Reynolds number case was the first to be simulated. The POOLEX experiment STB-31 was operated near the conditions between the ’quasi-steady oscillatory interface condensation’ mode and the ’condensation within the blowdown pipe’ mode. The condensation models of Lakehal et al. and Coste & Lavi´eville predicted the condensation rate quite accurately, while the other tested ones overestimated it. It was possible to get the direct phase change solution to settle near to the measured values, but a very high resolution of calculation grid was needed. Secondly, a high Reynolds number case corresponding to the ’chugging’ mode was simulated. The POOLEX experiment STB-28 was chosen, because various standard and highspeed video samples of bubbles were recorded during it. In order to extract numerical information from the video material, a pattern recognition procedure was programmed. The bubble size distributions and the frequencies of chugging were calculated with this procedure. With the statistical data of the bubble sizes and temporal data of the bubble/jet appearance, it was possible to compare the condensation rates between the experiment and the CFD simulations. In the chugging simulations, a spherically curvilinear calculation grid at the blowdown pipe exit improved the convergence and decreased the required cell count. The compressible flow solver with complete steam-tables was beneficial for the numerical success of the simulations. The Hughes-Duffey model and, to some extent, the Coste & Lavi´eville model produced realistic chugging behavior. The initial level of the steam/water interface was an important factor to determine the initiation of the chugging. If the interface was initialized with a water level high enough inside the blowdown pipe, the vigorous penetration of a water plug into the pool created a turbulent wake which invoked the chugging that was self-sustaining. A 3D simulation with a suitable DCC model produced qualitatively very realistic shapes of the chugging bubbles and jets. The comparative FFT analysis of the bubble size data and the pool bottom pressure data gave useful information to distinguish the eigenmodes of chugging, bubbling, and pool structure oscillations.

Evaluation of roof slope and exposure with different roofing materials in reduced models of animal production facilities in spring and summer

The present study was conducted at the Department of Rural Engineering and the Department of Animal Morphology and Physiology of FCAV/Unesp, Jaboticabal, SP, Brazil. The objective was to verify the influence of roof slope, exposure and roofing material on the internal temperature of reduced models of animal production facilities. For the development of the research, 48 reduced and dissemble models with dimensions 1.00 × 1.00 × 0.50 m were used. The roof was shed-type, and the models faced to the North or South directions, with 24 models for each side of exposure. Ceramic, galvanized-steel and fibro tiles were used to build the roofs. Slopes varied between 20, 30, 40 and 50% for the ceramic tile and 10, 30, 40 and 50% for the other two. Inside the models, temperature readings were performed at every hour, for 12 months. The results were evaluated in a general linear model in a nested 3 × 4 × 2 factorial arrangement, in which the effects of roofing material and exposure were nested on the factor Slope. Means were compared by the Tukey test at 5% of probability. After analyzing the data, we observed that with the increase in the slope and exposure to the South, there was a drop in the internal temperature within the model at the geographic coordinates of Jaboticabal city (SP/Brazil).

The Effects of Fibroblast Growth Factor 8b on Reproductive Organs and Prostate Tumorigenesis

Fibroblast growth factors (FGFs) are involved in the development and homeostasis of the prostate and other reproductive organs. FGF signaling is altered in prostate cancer. Fibroblast growth factor 8 (FGF8) is a mitogenic growth factor and its expression is elevated in prostate cancer and in premalignant prostatic intraepithelial neoplasia (PIN) lesions. FGF8b is the most transforming isoform of FGF8. Experimental models show that FGF8b promotes several phases of prostate tumorigenesis - including cancer initiation, tumor growth, angiogenesis, invasion and development of bone metastasis. The mechanisms activated by FGF8b in the prostate are unclear. In the present study, to examine the tumorigenic effects of FGF8b on the prostate and other FGF8b expressing organs, an FGF8b transgenic (TG) mouse model was generated. The effect of estrogen receptor beta (ERβ) deficiency on FGF8binduced prostate tumorigenesis was studied by breeding FGF8b-TG mice with ERβ knockout mice (BERKOFVB). Overexpression of FGF8b caused progressive histological and morphological changes in the prostate, epididymis and testis of FGF8b-TG-mice. In the prostate, hyperplastic, preneoplastic and neoplastic changes, including mouse PIN (mPIN) lesions, adenocarcinomas, sarcomas and carcinosarcomas were present in the epithelium and stroma. In the epididymis, a highly cancer-resistant tissue, the epithelium contained dysplasias and the stroma had neoplasias and hyperplasias with atypical cells. Besides similar histological changes in the prostate and epididymis, overexpression of FGF8b induced similar changes in the expression of genes such as osteopontin (Spp1), connective tissue growth factor (Ctgf) and FGF receptors (Fgfrs) in these two tissues. In the testes of the FGF8b-TG mice, the seminiferous epithelium was frequently degenerative and the number of spermatids was decreased. A portion of the FGF8b-TG male mice was infertile. Deficiency of ERβ did not accelerate prostate tumorigenesis in the FGF8b-TG mice, but increased significantly the frequency of mucinous metaplasia and slightly the frequency of inflammation in the prostate. This suggests putative differentiation promoting and anti-inflammatory roles for ERβ. In summary, these results underscore the importance of FGF signaling in male reproductive organs and provide novel evidence for a role of FGF8b in stromal activation and prostate tumorigenesis.

Endothelial cells, tissue factor and infectious diseases

Tissue factor is a transmembrane procoagulant glycoprotein and a member of the cytokine receptor superfamily. It activates the extrinsic coagulation pathway, and induces the formation of a fibrin clot. Tissue factor is important for both normal homeostasis and the development of many thrombotic diseases. A wide variety of cells are able to synthesize and express tissue factor, including monocytes, granulocytes, platelets and endothelial cells. Tissue factor expression can be induced by cell surface components of pathogenic microorganisms, proinflammatory cytokines and membrane microparticles released from activated host cells. Tissue factor plays an important role in initiating thrombosis associated with inflammation during infection, sepsis, and organ transplant rejection. Recent findings suggest that tissue factor can also function as a receptor and thus may be important in cell signaling. The present minireview will focus on the role of tissue factor in the pathogenesis of septic shock, infectious endocarditis and invasive aspergillosis, as determined by both in vivo and in vitro models.

Lipolysis of emulsion models of triglyceride-rich lipoproteins is altered in male patients with abdominal aorta aneurysm

Disorders of the lipid metabolism may play a role in the genesis of abdominal aorta aneurysm. The present study examined the intravascular catabolism of chylomicrons, the lipoproteins that carry the dietary lipids absorbed by the intestine in the circulation in patients with abdominal aorta aneurysm. Thirteen male patients (72 ± 5 years) with abdominal aorta aneurysm with normal plasma lipid profile and 13 healthy male control subjects (73 ± 5 years) participated in the study. The method of chylomicron-like emulsions was used to evaluate this metabolism. The emulsion labeled with 14C-cholesteryl oleate and ³H-triolein was injected intravenously in both groups. Blood samples were taken at regular intervals over 60 min to determine the decay curves. The fractional clearance rate (FCR) of the radioactive labels was calculated by compartmental analysis. The FCR of the emulsion with ³H-triolein was smaller in the aortic aneurysm patients than in controls (0.025 ± 0.017 vs 0.039 ± 0.019 min-1; P < 0.05), but the FCR of14C-cholesteryl oleate of both groups did not differ. In conclusion, as indicated by the triglyceride FCR, chylomicron lipolysis is diminished in male patients with aortic aneurysm, whereas the remnant removal which is traced by the cholesteryl oleate FCR is not altered. The results suggest that defects in the chylomicron metabolism may represent a risk factor for development of abdominal aortic aneurysm.

Dimensionality of the premenstrual syndrome: confirmatory factor analysis of premenstrual dysphoric symptoms among college students

Premenstrual syndrome and premenstrual dysphoric disorder (PMDD) seem to form a severity continuum with no clear-cut boundary. However, since the American Psychiatric Association proposed the research criteria for PMDD in 1994, there has been no agreement about the symptomatic constellation that constitutes this syndrome. The objective of the present study was to establish the core latent structure of PMDD symptoms in a non-clinical sample. Data concerning PMDD symptoms were obtained from 632 regularly menstruating college students (mean age 24.4 years, SD 5.9, range 17 to 49). For the first random half (N = 316), we performed principal component analysis (PCA) and for the remaining half (N = 316), we tested three theory-derived competing models of PMDD by confirmatory factor analysis. PCA allowed us to extract two correlated factors, i.e., dysphoric-somatic and behavioral-impairment factors. The two-dimensional latent model derived from PCA showed the best overall fit among three models tested by confirmatory factor analysis (c²53 = 64.39, P = 0.13; goodness-of-fit indices = 0.96; adjusted goodness-of-fit indices = 0.95; root mean square residual = 0.05; root mean square error of approximation = 0.03; 90%CI = 0.00 to 0.05; Akaike's information criterion = -41.61). The items "out of control" and "physical symptoms" loaded conspicuously on the first factor and "interpersonal impairment" loaded higher on the second factor. The construct validity for PMDD was accounted for by two highly correlated dimensions. These results support the argument for focusing on the core psychopathological dimension of PMDD in future studies.

Plasma concentrations and placental immunostaining of interleukin-10 and tumornecrosis factor-α as predictors of alterations in the embryo-fetal organism and the placental development of diabetic rats

Interleukin-10 (IL-10) appears to be the key cytokine for the maintenance of pregnancy and inhibits the secretion of inflammatory cytokines such as tumor necrosis factor-α (TNF-α). However, there are no studies evaluating the profile of these cytokines in diabetic rat models. Thus, our aim was to analyze IL-10 and TNF-α immunostaining in placental tissue and their respective concentrations in maternal plasma during pregnancy in diabetic rats in order to determine whether these cytokines can be used as predictors of alterations in the embryo-fetal organism and in placental development. These parameters were evaluated in non-diabetic (control; N = 15) and Wistar rats with streptozotocin (STZ)-induced diabetes (N = 15). At term, the dams (100 days of life) were killed under anesthesia and plasma and placental samples were collected for IL-10 and TNF-α determinations by ELISA and immunohistochemistry, respectively. The reproductive performance was analyzed. Plasma IL-10 concentrations were reduced in STZ rats compared to controls (7.6 ± 4.5 vs 20.9 ± 8.1 pg/mL). The placental scores of immunostaining intensity did not differ between groups (P > 0.05). Prevalence analysis showed that the IL-10 expression followed TNF-α expression, showing a balance between them. STZ rats also presented impaired reproductive performance and reduced plasma IL-10 levels related to damage during early embryonic development. However, the increased placental IL-10 as a compensatory mechanism for the deficit of maternal regulation permitted embryo development. Therefore, the data suggest that IL-10 can be used as a predictor of changes in the embryo-fetal organism and in placental development in pregnant diabetic rats.

Epidermal growth factor receptor (EGFR) mutations in lung cancer: preclinical and clinical data

Lung cancer leads cancer-related mortality worldwide. Non-small-cell lung cancer (NSCLC), the most prevalent subtype of this recalcitrant cancer, is usually diagnosed at advanced stages, and available systemic therapies are mostly palliative. The probing of the NSCLC kinome has identified numerous nonoverlapping driver genomic events, including epidermal growth factor receptor (EGFR) gene mutations. This review provides a synopsis of preclinical and clinical data on EGFR mutated NSCLC and EGFR tyrosine kinase inhibitors (TKIs). Classic somatic EGFR kinase domain mutations (such as L858R and exon 19 deletions) make tumors addicted to their signaling cascades and generate a therapeutic window for the use of ATP-mimetic EGFR TKIs. The latter inhibit these kinases and their downstream effectors, and induce apoptosis in preclinical models. The aforementioned EGFR mutations are stout predictors of response and augmentation of progression-free survival when gefitinib, erlotinib, and afatinib are used for patients with advanced NSCLC. The benefits associated with these EGFR TKIs are limited by the mechanisms of tumor resistance, such as the gatekeeper EGFR-T790M mutation, and bypass activation of signaling cascades. Ongoing preclinical efforts for treating resistance have started to translate into patient care (including clinical trials of the covalent EGFR-T790M TKIs AZD9291 and CO-1686) and hold promise to further boost the median survival of patients with EGFR mutated NSCLC.

Dynamic Performance Evaluation of Canadian Fixed-Income Funds using Markov Regime Switching Models

This thesis examines the performance of Canadian fixed-income mutual funds in the context of an unobservable market factor that affects mutual fund returns. We use various selection and timing models augmented with univariate and multivariate regime-switching structures. These models assume a joint distribution of an unobservable latent variable and fund returns. The fund sample comprises six Canadian value-weighted portfolios with different investing objectives from 1980 to 2011. These are the Canadian fixed-income funds, the Canadian inflation protected fixed-income funds, the Canadian long-term fixed-income funds, the Canadian money market funds, the Canadian short-term fixed-income funds and the high yield fixed-income funds. We find strong evidence that more than one state variable is necessary to explain the dynamics of the returns on Canadian fixed-income funds. For instance, Canadian fixed-income funds clearly show that there are two regimes that can be identified with a turning point during the mid-eighties. This structural break corresponds to an increase in the Canadian bond index from its low values in the early 1980s to its current high values. Other fixed-income funds results show latent state variables that mimic the behaviour of the general economic activity. Generally, we report that Canadian bond fund alphas are negative. In other words, fund managers do not add value through their selection abilities. We find evidence that Canadian fixed-income fund portfolio managers are successful market timers who shift portfolio weights between risky and riskless financial assets according to expected market conditions. Conversely, Canadian inflation protected funds, Canadian long-term fixed-income funds and Canadian money market funds have no market timing ability. We conclude that these managers generally do not have positive performance by actively managing their portfolios. We also report that the Canadian fixed-income fund portfolios perform asymmetrically under different economic regimes. In particular, these portfolio managers demonstrate poorer selection skills during recessions. Finally, we demonstrate that the multivariate regime-switching model is superior to univariate models given the dynamic market conditions and the correlation between fund portfolios.