The clothing is the closest envelope of the human body, and hence, has the primary im-pact on thermal comfort, physiological response of the human body and environmental strain. On the other hand, the clothing microenvironment is affected by physiological reactions (sweating, temperature distribution, body movement). Nowadays, thermal sweating manikins used to study the interactions of the body-clothing-environment system are unable to simulate adequately the spatial and transient thermal behaviour of the human body. Ideally, a human simulator should ‘feel’ and re-spond dynamically to the thermal environment as real humans do. In this work thermal sweating devices were coupled with the iesd-Fiala multi-node model of human physiology and thermal comfort. The coupling procedure was first de-veloped for the iesd-Fiala model and a single-sector cylinder Torso. A new single-sector thermophysiological human simulator reproduced adequately the overall physiological response of the average human, which was proved by comparison with results of human subject tests for a wide range of environmental conditions. In the next step, the elaborated coupling method was applied to the multi-sector, ana-tomically-shaped thermal sweating manikin SAM. The multi-sector thermophysiologi-cal human simulator with homogenous surface temperature distribution reproduced the thermal behaviour observed in human subject tests with good accuracy. However, an attempt to advance this human simulator to one with a heterogeneously distributed sur-face temperature was unsuccessful, as the results predicted by the simulator differed greatly from those obtained from human subject tests. The single-sector physiological simulator has been shown to perform well in the valida-tion tests with use of clothing ensembles. Time saving testing, repeatability of the measurement of the physiological response of an average individual and the ability of testing in conditions unsafe for humans are major advantages of this human simulator.

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism. PBPK models, using both compound and physiologic inputs, are used to predict a drug’s pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), a key PBPK model input, define the steady state concentration differential between the tissue and plasma and are used to predict the volume of distribution. Experimental determination of these parameters once limited the development of PBPK models however in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy and none are considered standard, warranting further research. Chapter 2 presents a newly developed Kp prediction algorithm that requires only readily available input parameters. Using a test dataset, this Kp prediction algorithm demonstrated good prediction accuracy and greater prediction accuracy than preexisting algorithms. Chapter 3 introduced a decision tree based Kp prediction method. In this novel approach, six previously published algorithms, including the one developed in Chapter 2, were utilized. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physico-chemical space. Three versions of tissue specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy as compared to the use of any single Kp prediction algorithm for all tissues; the current mode of use in PBPK model building. With built-in estimation equations for those input parameters not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The two presented innovative methods will improve tissue distribution prediction accuracy thus enhancing the confidence in PBPK modeling outputs.

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism. PBPK models, using both compound and physiologic inputs, are used to predict a drug’s pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), a key PBPK model input, define the steady state concentration differential between the tissue and plasma and are used to predict the volume of distribution. Experimental determination of these parameters once limited the development of PBPK models however in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy and none are considered standard, warranting further research. Chapter 2 presents a newly developed Kp prediction algorithm that requires only readily available input parameters. Using a test dataset, this Kp prediction algorithm demonstrated good prediction accuracy and greater prediction accuracy than preexisting algorithms. Chapter 3 introduced a decision tree based Kp prediction method. In this novel approach, six previously published algorithms, including the one developed in Chapter 2, were utilized. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physico-chemical space. Three versions of tissue specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy as compared to the use of any single Kp prediction algorithm for all tissues; the current mode of use in PBPK model building. With built-in estimation equations for those input parameters not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The two presented innovative methods will improve tissue distribution prediction accuracy thus enhancing the confidence in PBPK modeling outputs.

During flight missions, fighter pilots are in general exposed to vast amounts of stress including mild hypoxia, vibrations, high accelerations, and thermal discomfort. It is interesting to predict potential risks with a certain mission or flight case due to these stresses to increase safety for fighter pilots. The most predominant risk is typically thermal discomfort which can lead to serious health concerns. Extensive exposure to high or low temperature in combination with a demanding work situation weakens the physical and mental state of the pilot and can eventually lead to life-threatening conditions. One method to estimate the physical and mental state of a person is to measure the body core temperature. The body core temperature cannot be measured continuously during flight and needs to be estimated by using for instance a human thermoregulatory model. In this study, a model of the human thermoregulatory system and the cockpit environment is developed. Current thermoregulatory models are not customized for fighter pilots but a model developed by Fiala et al. in 2001, which has previously shown good performance in both cold and warm environments as well as for various activation levels for the studied person, is used as a theoretical foundation. Clothing layers are implemented in the model corresponding to clothes used by pilots in the Swedish air force flying the fighter aircraft Gripen E in warm outside conditions. Cooling garments and air conditioning systems as well as avionics, canopy, and cockpit air are included in the model to get a realistic description of the cockpit environment. Input to the model is a flight case containing data with altitude and velocity of the fighter during a mission. human heat transfer; body temperature regulation; physiological model;cooling garment; cockpit modeling

Among tools that are used to fill knowledge gaps on natural systems, ecological modelling has been widely applied during the last two decades. Ecological models are simple representations of a complex reality. They allow to highlight environmental drivers of species ecological niche and better understand species responses to environmental changes. However, applying models to Southern Ocean benthic organisms raises several methodological challenges. Species presence datasets are often aggregated in time and space nearby research stations or along main sailing routes. Data are often limited in number to correctly describe species occupied space and physiology. Finally, environmental datasets are not precise enough to accurately represent the complexity of marine habitats. Can we thus generate performant and accurate models at the scale of the Southern Ocean ?What are the limits of such approaches ?How could we improve methods to build more relevant models ?In this PhD thesis, three different model categories have been studied and their performance evaluated. (1) Mechanistic physiological models (Dynamic Energy Budget models, DEB) simulate how the abiotic environment influences individual metabolism and represent the species fundamental niche. (2) Species distribution models (SDMs) predict species distribution probability by studying the relationship between species presences and the environment. They represent the species realised niche. (3) Dispersal lagrangian models predict the drift of propagules in water masses. Results show that physiological models can be developed for marine Southern Ocean species to simulate the metabolic variations in link with the environment and predict population dynamics. However, more data are necessary to highlight detailed physiological contrasts between populations and to accurately evaluate models. Results obtained for SDMs suggest that models generated at the scale of the Southern Ocean and future simulations are not relevant, given the lack of data available to characterise species occupied space, the lack of precision and accuracy of future climate scenarios and the impossibility to evaluate models. Moreover, model extrapolate on a large proportion of the projected area. Adding information on species physiological limits (observations, results from experiments, physiological model outputs) was shown to reduce extrapolation and to improve the capacity of models to estimate the species realised niche. Spatial aggregation of occurrence data, which influenced model predictions and evaluation was also succefully corrected. Finally, dispersal models showed an interesting potential to highlight the role of geographic barriers or conversely of spatial connectivity and also the link between species distribution, physiology and phylogeny history. This PhD thesis provides several methodological advice, annoted codes and tutorials to help implement future modelling works applied to Southern Ocean marine species. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Les substances poly- et per-fluorées (PFAS), exclusivement d’origine anthropique, sont de nos jours largement utilisées dans diverses applications industrielles et domestiques. La synthèse des PFAS engendre leurs rejets dans l’environnement, où ces composés se retrouvent aussi bien dans l’atmosphère que dans les milieux terrestres et aquatiques. Les études de distribution environnementale, relativement récentes, ont montré la bioaccumulation de certains PFAS chez les vertébrés aquatiques. La toxicocinétique (TK) des PFAS est particulière en raison de leurs propriétés physico-chimiques. Même si les études de TK de ces composés sont de plus en plus nombreuses, quelques verrous mécanistiques restent encore à lever chez les vertébrés aquatiques, notamment concernant les processus d’absorption, de distribution et d’élimination. Dans l’optique d’améliorer ces connaissances, un modèle toxicocinétique à base physiologique (PBTK) a été développé pour décrire le devenir de trois PFAS appartenant à la famille des perfluoroalkyles : le perfluorooctane sulfonate (PFOS), le perfluorohexane sulfonate (PFHxS) et l’acide perfluorononanoïque (PFNA). Ce modèle PBTK s’appuie sur les paramètres physiologiques de la truite arc-en-ciel (Oncorhynchus mykiss) et a permis de tester plusieurs hypothèses mécanistiques de la bioaccumulation des PFAS, utile à l’évaluation du risque engendré par l’exposition à ces substances. La croissance et la température de l’eau, facteurs clés dans la TK des poïkilothermes, ont également été intégrées dans le modèle. Les trois expériences d’exposition aux PFOS, PFHxS et PFNA par voie alimentaire à différentes températures (7°C, 11°C et 19°C) réalisées au cours de cette thèse ont permis (i) de mesurer les concentrations dans les organes d’intérêt et (ii) de calibrer et évaluer les prédictions du modèle / Poly- and per-fluorinated substances (PFAS), exclusively derived from anthropogenic activity, are nowadays widely used for industrial and domestic purposes. During their synthesis, PFAS are released in the atmosphere as well as in aquatic and terrestrial compartments. Environmental distribution studies are relatively recent and have shown the bioaccumulation of some PFAS in aquatic vertebrates. Physico-chemical properties of PFAS lead to a specific toxicokinetic (TK) profile. Although TK studies on these compounds are becoming more and more abundant, some mechanistic challenges still need to be solved for aquatic vertebrates, particularly for absorption, distribution and elimination processes. In order to improve this knowledge, a physiologically based toxicokinetic (PBTK) model has been developed to describe the fate of three PFAS belonging to the perfluoroalkyl family: perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS) and perfluoronanoic acid (PFNA). This PBTK model was parametrized with rainbow trout (Oncorhynchus mykiss) physiological parameters. Next, it was used to test several mechanistic hypotheses about PFAS bioaccumulation, useful for improving the risk assessment of these chemicals. Fish growth and water temperature are key factors in the TK for poikilotherms. So, they have been integrated in the model. Three experiments of dietary exposure to PFOS, PFHxS and PFNA at different temperatures (7°C, 11°C and 19°C) have been performed. They allowed (i) to measure concentrations in organs of interest and (ii) to calibrate and evaluate the model predictions

abstract: A medical control system, a real-time controller, uses a predictive model of human physiology for estimation and controlling of drug concentration in the human body. Artificial Pancreas (AP) is an example of the control system which regulates blood glucose in T1D patients. The predictive model in the control system such as Bergman Minimal Model (BMM) is based on physiological modeling technique which separates the body into the number of anatomical compartments and each compartment's effect on body system is determined by their physiological parameters. These models are less accurate due to unaccounted physiological factors effecting target values. Estimation of a large number of physiological parameters through optimization algorithm is computationally expensive and stuck in local minima. This work evaluates a machine learning(ML) framework which has an ML model guided through physiological models. A support vector regression model guided through modified BMM is implemented for estimation of blood glucose levels. Physical activity and Endogenous glucose production are key factors that contribute in the increased hypoglycemia events thus, this work modifies Bergman Minimal Model ( Bergman et al. 1981) for more accurate estimation of blood glucose levels. Results show that the SVR outperformed BMM by 0.164 average RMSE for 7 different patients in the free-living scenario. This computationally inexpensive data driven model can potentially learn parameters more accurately with time. In conclusion, advised prediction model is promising in modeling the physiology elements in living systems. / Dissertation/Thesis / Masters Thesis Computer Science 2017