MODELING AND DESIGN TO DETECT INTERACTION OF INSECTICIDES, HERBICIDES AND OTHER SIMILAR COMPOUNDS

This paper discusses model and experimental design aspects of agricultural studies aimed at discerning antagonism or synergy between two or more insecticides, herbicides, or other similar compounds. The developed methods involve a broad class of generalised nonlinear models, which are easily fitted to data using popular statistical packages such as the NLMIXED procedure in SAS® software. Sample computer code is given in the Appendix

Dependent Berkson errors in linear and nonlinear models

Often predictor variables in regression models are measured with errors. This is known as an errors-in-variables (EIV) problem. The statistical analysis of the data ignoring the EIV is called naive analysis. As a result, the variance of the errors is underestimated. This affects any statistical inference that may subsequently be made about the model parameter estimates or the response prediction. In some cases (e.g. quadratic polynomial models) the parameter estimates and the model prediction is biased. The errors can occur in different ways. These errors are mainly classified into classical (i.e. occur in observational studies) or Berkson type (i.e. occur in designed experiments). This thesis addresses the problem of the Berkson EIV and their effect on the statistical analysis of data fitted using linear and nonlinear models. In particular, the case when the errors are dependent and have heterogeneous variance is studied. Both analytical and empirical tools have been used to develop new approaches for dealing with this type of errors. Two different scenarios are considered: mixture experiments where the model to be estimated is linear in the parameters and the EIV are correlated; and bioassay dose-response studies where the model to be estimated is nonlinear. EIV following Gaussian distribution, as well as the much less investigated non-Gaussian distribution are examined. When the errors occur in mixture experiments both analytical and empirical results showed that the naive analysis produces biased and inefficient estimators for the model parameters. The magnitude of the bias depends on the variances of the EIV for the mixture components, the model and its parameters. First and second Scheffé polynomials are used to fit the response. To adjust for the EIV, four different approaches of corrections are proposed. The statistical properties of the estimators are investigated, and compared with the naive analysis estimators. Analytical and empirical weighted regression calibration methods are found to give the most accurate and efficient results. The approaches require the error variance to be known prior to the analysis. The robustness of the adjusted approaches for misspecified variance was also examined. Different error scenarios of EIV in the settings of concentrations in bioassay dose-response studies are studied (i.e. dependent and independent errors). The scenarios are motivated by real-life examples. Comparisons between the effects of the errors are illustrated using the 4-prameter Hill model. The results show that when the errors are non-Gaussian, the nonlinear least squares approach produces biased and inefficient estimators. An extension of the well-known simulation-extrapolation (SIMEX) method is developed for the case when the EIV lead to biased model parameters estimators, and is called Berkson simulation-extrapolation (BSIMEX). BSIMEX requires the error variance to be known. The robustness of the adjusted approach for misspecified variance is examined. Moreover, it is shown that BSIMEX performs better than the regression calibration methods when the EIV are dependent, while the regression calibration methods are preferable when the EIV are independent.EThOS - Electronic Theses Online ServiceSaudi Ministry of Higher EducationGBUnited Kingdo

Bayesian Design in Clinical Trials

In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented

Modeling of combination therapy to support drug discovery in oncology

Mathematical models based on ordinary differential equations, with impulses, are used to describe tumor growth after different treatment combinations, including chemicals as well as radiation. The models are calibrated, using a nonlinear mixed-effects framework, based on time series data of tumor volume from animal experiments. Important features incorporated into the models include natural cell death, and short-term as well as long-term response to radiation treatment, with or without co-treatment with a radiosensitizing compound. Tumor Static Exposure, defined as the treatment combinations that yield stability of the trivial solution to the system model, is introduced as a prediction tool that can also be used to compare and optimize combination therapies. The Tumor Static Exposure concept is illustrated practically, using calibrated models and data from animal experiments, as well as theoretically, using a linear cell cycle model to describe cancer growth subject to treatment with an arbitrary number of anticancer compounds

SOIL MICROBIAL AND ECOSYSTEM SERVICE RESPONSES TO METAL MIXTURES IN CANADIAN SOILS

Metal contamination is a major environmental concern especially in metal mining countries like Canada. The assessment and cleanup of soils with elevated metal concentrations is an area that has been widely studied. A major challenge faced by environmental scientists when assessing metal toxicity in soils is the wide difference in toxic effects between laboratory spiked soils and field contaminated soils. Also, since contamination occur as mixtures, researchers are faced with understanding metal mixture interactions in soil to help quantify risks associated with metal mixture contamination. When assessing the toxic effects of metals in a laboratory setting, it is recommended to use fixed ratio rays, but maintaining desired metal ratios in soils is challenging because of metal loss from leaching metal salt-spiked soils. To eliminate leaching, which is a required step, two alternative metal types (metal oxides and spinel minerals) were evaluated. The main objectives of the thesis were to investigate the differences in toxicity of three metal types found in contaminated soils and to test the adherence of mixture toxicity to additivity models using the activity of soil enzymes as model toxicity endpoints. I also extended our understanding of the effects of metal mixtures on the quality of ecosystem services using soil properties as predictors. First, the toxicity of metal salts, metal oxides and spinel minerals were assessed using acid phosphatases (ACP) and ammonia monooxygenases (AMO) as model processes in three Canadian soils. The activity of both enzymes in the soils were determined in leached and non-leached soils, as well as soils spiked with mixtures containing Pb, Cu, Ni, Co, and Zn in five fixed ratio rays. The results showed that the activity of AMO was inhibited when soils were leached with artificial rainwater. Generally, metal salts were the most toxic, while the spinel minerals were the least toxic. Two extractants, CaCl2 and Diethylenetriamine Pentaacetic Acid (DTPA), were evaluated for their ability to link toxicity to metals across all three metal forms. Salt toxicity was closely linked to CaCl2 extractable concentrations but DTPA was the most appropriate for oxides. I determined that combining fixed ratio rays with metal oxides for metal mixture studies was more appropriate for conducting mixture studies since soil ratios created using oxides were more precise and required less experimental effort compared to salts and spinel minerals. Following the investigation into the differences in toxicity of metal mixture types, I evaluated the adherence of metal mixture toxicity to the concentration addition (CA) and response addition (RA) models. I assessed mixture toxicity using metal oxides (Cu, Co, Pb, Zn, and Ni) in two Canadian soils. The additivity models were used because current risk assessment is conducted assuming metals are non-interactive and have similar modes of action. I investigated the sensitivity of the carbon (C) and phosphorus (P) cycles to the mixtures using two soil enzymes, beta glucosidases (BGD) and ACP as model processes. In general, P cycling (ACP) was a more sensitive enzyme to both single and metal mixtures compared to C cycling (BGD). Upon exposure to quinary mixtures, both synergistic and antagonistic deviations from both reference models were observed. The antagonistic deviations were observed across all concentrations, thus from low to high, but synergism was only observed at lower concentrations for both additivity models. The results indicate that, the effects of metal mixtures are greater than singles at lower concentrations which is important in the risk assessment of metal mixtures. I also observed that Cu, an essential metal, may be protecting biogeochemical cycles from mixture toxicity. In the third chapter, I developed adverse ecosystem service pathway (AESP) models to study the soil ecosystem’s response to a metal mixture containing Cu, Pb, Zn, Co, and Ni. I assessed the effects using the relationships between soil properties and ecosystem services (ES) in the presence and absence of the metal mixtures. Forty-seven (47) soils were sampled and 15 soil processes that represented five ES including food production and water purification were measured. Using a Pearson bivariate correlation matrix, I confirmed that ecosystem services were closely linked to soil properties, especially cation exchange capacity and organic carbon. Results from t-tests also showed that, except for the three soil enzyme activities measured (p < 0.05), the processes underlying ecosystem services are significantly reduced in metal-impacted soils. Using soil properties as the main predictors of ecosystem services, I built two AESP models: one for metal-impacted soils and another for control soils. These models showed adverse effects to ecosystem services in metal-impacted soils, depicted as changes in partial correlation coefficients. An AESP model, therefore, can be an important tool to better understand complex ecosystems and improve site specific risk assessment and natural resource management

11th International Conference on Predictive Modelling in Food: book of abstracts

It is our great pleasure to welcome you in Bragança, Portugal, for the 11th International Conference of Predictive Modelling in Food (ICPMF11). Since 1992, ten ICPMF editions have taken place, providing a forum for the exchange of ideas, identification of research needs and novel approaches for the advancement of predictive modelling towards ensuring safety and quality of foods. Bragança is a typically-Portuguese old town (Romanic origin dates back to the 10th century), located by the Natural Park of Montesinho – one of the wildest forest zones of Europe – and the Douro Valley – the third oldest protected wine region in the world; and surrounded by traditional villages of a distinctive rustic beauty. Bragança houses several traditional industries producing a myriad of local foods, such as cheese, fermented meats, wine, chestnuts and honey, which provide substantial economic sustainability to the region. ICPMF11 reunites food researchers, stakeholders, risk assessors and users of predictive models to present recent developments and trends in modelling approaches for food quality, safety and sustainability. We succeeded to gather a significant number of delegates from over the world to participate in a comprehensive scientific programme that includes keynote lectures, oral communications and posters, allocated in sessions focusing on: . Advances in predictive microbiology modelling . Predictive modelling in innovative food processing and preservation technologies . Advances in microbial dynamics and interactions . Advances in software and database tools . Meta-analysis protocols and applications . Advances in risk assessment methods and integration of omics techniques . Advances in predictive modelling in food quality and safety . Predictive mycology . Individual cell and whole-cell modelling Apart from those, ICPMF11 features for the first time a special session dedicated to “Innovative approaches for ensuring safety of traditional foods” and the Round Table: “Assuring the Safety of Traditional Foods: A Scientific Contribution to Protecting our Cultural Heritage”. We, as food researchers based in a Mediterranean mountain region, are aware that the production of traditional foods plays a key role in the development of rural regions, since the agricultural commodities used as raw materials are generally produced locally, allowing and stimulating local commercialisation, thus contributing to a sustainable environment, and employment in rural populations. It was inspiring for us to have received many submissions from both developed and developing countries on the valorisation of traditional foods through the application of up-to-date modelling research. Besides that, one morning workshop and three afternoon tutorials were programmed during the day before the scientific programme. The workshop “How to benefit from the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP)” was organised by Matthias Filter. The parallel tutorials “Towards an integrated predictive software map: Practical examples of use of predictive microbiology software tools for food safety and quality”; “Advanced methods in predictive microbiology” and “Topics in quantitative microbial risk assessment using R” were organised by Fernando Pérez-Rodríguez, Pablo Fernández, Alberto Garre and Mariem Ellouze; by Lihan Huang, Cheng-An Hwang and Vasco Cadavez; and by Patrick Njage and Ana Sofia Ribeiro Duarte, respectively. We thank these organisers for their proposals. Abstracts, reviewed by the ICPMF11 Scientific Committee, are published in the present Book of Abstracts while peer-reviewed original research articles will be invited to be published in ICPMF11 Special Issues in the International Journal of Food Microbiology and Microbial Risk Analysis. To stimulate the participation of postgraduate students and young researchers, two kinds of awards were arranged: the Young Researcher Best Oral Presentation prizes, sponsored by Elsevier; and the Developing Scientist Best Poster prizes, sponsored by the International Committee on Food Microbiology and Hygiene (ICFMH) of the International Union of Microbiological Societies (IUMS). For the first time, this ICPMF edition gives out two awards for the Senior Researcher Best Oral Presentation, sponsored by the open-access journal Foods – MDPI. In addition to the scientific programme, we prepared an exciting social programme for delegates to appreciate the rich culture, gastronomy and traditions of Bragança, w includes welcome reception, live music, tasting of regional food and a gala dinner in the Castle of Bragança. We look forward to lively discussions, and hope that this meeting will give you the opportunity to strengthen friendship and cooperation, and build new contacts for future research endeavours.info:eu-repo/semantics/publishedVersio

Effects of Bioavailability and Accumulation of Single Metal and Mixture Metal on Toxicity to the Mite, Oppia nitens

Canada has some of the largest metal deposits in the world and the Canadian mining industry is one the largest employers of labour in Canada. Consequently, mining and smelting operations in Canada are one of the sources of metal level increase in the environment. Metals pollute the terrestrial environment because of fall-out from the mining industry. Soils are major sinks for metals in the terrestrial environment. It is therefore important that metal risk assessment should clearly reflect the metal contamination in the soils. The main objectives of this thesis were to generate more realistic metal toxicity data using a native Canadian invertebrate species that will help improve metal risk assessment in Canada. Firstly, toxicity of common metals (Cu, Pb, Zn, Co, Ni) found in contaminated sites in Canada was assessed on an oribatid mite, Oppia nitens which is abundant in Canadian soils. The metal toxicities were assessed as singles and as mixtures in five different soils. The metal mixture ratios were fixed such that it reflected ratios of metals found in contaminated sites. The patterns of sensitivity of the mite to metals by soils differed between single metals and metal mixtures. Nickel, which had not been tested with Oppia nitens before, was found to be the most toxic metal to the mite and zinc was less toxic. Concentration addition was protective of 53% of metal mixture toxicity due to antagonistic and concentration addition. Bioavailable metals existed as metals bound to fulvic acid. After determining the toxicity of the metals in the five soils, the multigenerational effect of one of the metals on soil mites was investigated in the most sensitive soil to single metal contamination. Continuous and pulse zinc exposure effect on O. nitens populations was assessed in three generations of the mites. Using critical-effect levels (EC50s), pulse exposed mites seemed to be tolerant and the continuous exposed mites were sensitive. However, the instantaneous population growth rate showed that both pulse and continuous exposures were more sensitive than their parents. The major finding from this study was that persistence of metals in soils can cause multigenerational adverse effects on continuously exposed mites in the soil. The last chapter of this thesis investigated the direct effect of soil habitat quality as a site-specific feature on organisms and how it influenced their response to metal contamination. For this test, forty-seven (47) soils were ranked according to their habitat qualities from one to three (high to low), using standard soil invertebrate species (Folsomia candida, Enchytraeus crypticus) fitness and plant (Elymus lanceolatus) productivity as metrics to choose habitat qualities. From the ranked 47 soils, eighteen (18) soils comprising six soils making each habitat quality was chosen in a duplicated experiment. The soils were spiked with increasing concentrations of Zn and the Zn toxicokinetics, toxicodynamics, survival and reproduction of mites were assessed. The mites in the soils of high habitat quality were less stressed than mites in the low habitat quality soils despite being exposed to the same amount of bioavailable metals. The key findings from this study were that soil habitat quality has a direct influence on how its inhabitants cope with metal stress. Therefore, habitat qualities of soils can be considered as a site-specific feature in remediation of contaminated sites

Causal and Design Issues in Clinical Trials

The first part of my dissertation focuses on post-randomization modification of intent-to-treat effects. For example, in the field of behavioral science, investigations involve the estimation of the effects of behavioral interventions on final outcomes for individuals stratified by post-randomization moderators measured during the early stages of the intervention (e.g., landmark analyses in cancer research). Motivated by this, we address several questions on the use of standard and causal approaches to assessing the modification of intent-to-treat effects of a randomized intervention by a post-randomization factor. First, we show analytically the bias of the estimators of the corresponding interaction and meaningful main effects for the standard regression model under different combinations of assumptions. Such results show that the assumption of independence between two factors involved in an interaction, which has been assumed in the literature, is not necessary for unbiased estimation. Then, we present a structural nested distribution model estimated with G-estimation equations, which does not assume that the post-randomization variable is effectively randomized to individuals. We show how to obtain efficient estimators of the parameters of the structural distribution model. Finally, we confirm with simulations the performance of these optimal estimators and further assess our approach with data from a randomized cognitive therapy trial. The second part of my dissertation is on optimal and adaptive designs for dose-finding experiments in clinical trials with multiple correlated responses. For instance, in phase I/II studies, efficacy and toxicity are often the primary endpoints which are observed simultaneously and need to be evaluated together. Accordingly, we focus on bivariate responses with one continuous and one categorical. We adopt the bivariate probit dose-response model and study locally optimal, two-stage optimal, and fully adaptive designs under different cost constraints. We assess the performance of the different designs through simulations and suggest that the two-stage designs are as efficient as and may be more efficient than the fully adaptive deigns under a moderate sample size in the initial stage. In addition, two-stage designs are easier to construct and implement, and thus can be a useful approach in practice