The goal of this thesis is introduce the theory of generalized linear models, namely probit and logit model. This models are especially used for medical data processing. In our concrete case these mentioned models are applied to data file obtained in teaching hospital Brno. The aim is statically analyzed immune response of child patients in dependence of twelve selected types of genes and find out which combinations of these genes influence septic state of patients.

Mechanism underlying nanotoxicity has remained elusive. Hence, efforts to understand whether nanoparticle properties might explain its toxicity are ongoing. Considering three different types of organo-coated silver nanoparticles (AgNPs): citrate-coated AgNP, polyvinylpyrrolidone-coated AgNP, and branched polyethyleneimine-coated AgNP, with different surface charge scenarios and core particle sizes, herein we systematically evaluate the potential role of particle size and surface charge on the toxicity of the three types of AgNPs against two model organisms, Escherichia coli and Daphnia magna. We find particle size, surface charge, and concentration dependent toxicity of all the three types of AgNPs against both the test organisms. Notably, Ag+ (as added AgNO3) toxicity is greater than each type of AgNPs tested and the toxicity follows the trend: AgNO3>BPEI-AgNP>Citrate-AgNP>PVP-AgNP. Modeling particle properties using the general linear model (GLM), a significant interaction effect of primary particle size and surface charge emerges that can explain empirically-derived acute toxicity with great precision. The model explains 99.9% variation of toxicity in E. coli and 99.8% variation of toxicity in D. magna, revealing satisfactory predictability of the regression models developed to predict the toxicity of the three organo-coated AgNPs. We anticipate that the use of GLM to satisfactorily predict the toxicity based on nanoparticle physico-chemical characteristics could contribute to our understanding of nanotoxicology and underscores the need to consider potential interactions among nanoparticle properties to explaining nanotoxicity.

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Forests in the southern United States with diverse forest ownership entities are facing threats associated with climate change and natural disturbances. This study represented the relationship between climate and species dominance, predicted future species distribution probability under a changing climate, and projected forest dynamics under ownership-based management regimes. Correlative statistics and mechanistic modeling approaches are implemented. Temporal scale includes the recent past 40 years and the future 60 years; spatial scale downscaled from southern United States to the coastal region of the northern Gulf of Mexico. In the southern United States, dominance of four major pine species experienced shifts from 1970 to 2000; quantile regression models built on the relationships among pine dominance and climatic variables can be used to predict future southern pine dominance. Furthermore, multiple climate envelope models (CEMs) were constructed for nineteen native and one invasive tree species (Chinese tallow, Triadica sebifera) to predict species establishment probabilities (SEPs) on the various land types from 2010 to 2070. CEMs achieved both predictive consistency and ecological conformity in estimating SEPs. Chinese tallow was predicted to have the highest invasionability in longleaf/slash pine and oak/gum/cypress forests during the next 60 years. Forest dynamics, in the coastal region, was projected by linking CEMs and forest landscape model (LANDIS) to evaluate ownership-based management regimes under climate change and natural disturbances. The dominance of forest species will diminish due to climate change and natural disturbances at both spatial scales—in the coastal region and non-industrial private forest (NIPF). No management on NIPF land was predicted to substantially increase the ratio of occupancy area between pines and oaks, but moderate and intensive management regimes were not significantly different. Pines are expected to be more resistant than oaks by maintaining stable age structures, which matched the forest inventory records. Overall, this study projected a future of southern forests on climate-species relationship, invasion risks, and forest community dynamics under multiple scenarios in the United States. Such knowledge could assist forest managers and landowners in foreseeing the future and making effective management prescriptions to mitigate potential threats.

Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.

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The periodic repetitions of laryngeal adduction and abduction gestures were uttered by 16 subjects. The movement of the cuneiform tubercles was tracked over time in the laryngoscopic recordings of these utterances. The adduction velocity and abduction velocity were determined objectively by means of a piecewise linear model fitted to the cuneiform tubercle trajectories. The abduction was found to be significantly faster than the adduction. This was interpreted in terms of the biomechanics and active control by the nervous system. The biomechanical properties could be responsible for a velocity of abduction that is up to 51% higher compared to the velocity of adduction. Additionally, the adduction velocity may be actively limited to prevent an overshoot of the intended adduction degree when the vocal folds are approximated to initiate phonation.

No / Since batch reactors are generally applied to produce a wide variety of specialty products, there is a great deal of interest to enhance batch operation to achieve high quality and purity product while minimizing the conversion of undesired by-product. The use of process optimization in the control of batch reactors presents a useful tool for operating batch reactors efficiently and optimally. In this work, we develop an approach, based on an on-line dynamic optimization strategy, to modify optimal temperature set point profile for batch reactors. Two different optimization problems concerning batch operation: maximization of product concentration and minimization of batch time, are formulated and solved using a sequential optimization approach. An Extended Kalman Filter (EKF) is incorporated into the proposed approach in order to update current states from their delayed measurement and to estimate unmeasurable state variables. A nonlinear model-based controller: generic model control algorithm (GMC) is applied to drive the temperature of the batch reactor to follow the desired profile. A batch reactor with complex exothermic reaction scheme is used to demonstrate the effectiveness of the proposed approach. The simulation results indicate that with the proposed strategy, large improvement in batch reactor performance, in term of the amount of a desired product and batch operation time, can be achieved compared to the method where the optimal temperature set point is pre-determined.

Yes / The need for the development of clean but still profitable processes and the study of low environmental impact and economically convenient management policies for them are two challenges for the years to come. This paper tries to give a first answer to the second of these needs, limited to the area of discontinuous productions. It deals with the development of a robust methodology for the profitable and clean management of (fed-)batch units under uncertainty, which can be referred to as a robust sustainability-oriented model-based optimization & control strategy. This procedure is specifically designed to ensure elevated process performances along with low-cost utilities usage reduction in real-time, simultaneously allowing for the effect of any external perturbation. In this way, conventional offline methods for process sustainable optimization can be easily overcome since the most suitable management policy, aimed at process sustainability, can be dynamically determined and applied in any operating condition. This leads to a significant step forward with respect to the nowadays options in terms of sustainable process management, that drives towards a cleaner and more energy-efficient future. The proposed theoretical framework is validated and tested on a case study based on the well-known fed-batch version of the Williams-Otto process to demonstrate its tangible benefits. The results achieved in this case study are promising and show that the framework is very effective in case of typical process operation while it is partially effective in case of unusual/unlikely critical process disturbances. Future works will go towards the removal of this weakness and further improvement in the algorithm robustness.

Beef cattle are continuously selected for different traits and the success in improving these traits has been remarkable. However, for certain traits, it is essential not only to improve the average performance, but also to control the variation around the mean. There is evidence that residual variance may be under genetic control, which opens the possibility of selecting for uniformity. In this sense, the objectives of the present dissertation were: 1) to investigate the extent of genetic heterogeneity of residual variance at the pedigree level in birth weight (BW), weaning weight (WW), yearling weight (YW), foot angle (FA), and claw set (CS) in American Angus cattle; 2) to compare the results of different genetic heterogeneity models; 3) to evaluate the effectiveness of Box-Cox transformation in continuous traits; and 4) to address limitations and explore alternative solutions for implementing genetic parameters for residual variance in genetic evaluations. The first study investigated the genetic heterogeneity of residual variances for BW, WW, and YW. Three models were compared: a homoscedastic residual variance model (M1), a double hierarchical generalized linear model (DHGLM, M2), and a genetically structured environmental variance model (MCMC, M3). The results showed significant genetic heterogeneity of residual variances in growth traits, suggesting the possibility of selection for uniformity. The genetic coefficient of variation for residual variance ranged from 0.90 to 0.92 in M2 and 0.31 to 0.38 in M3 for BW, 0.64 in M2 and 0.01 to 0.29 in M3 for WW, and 0.67 to 0.63 in M2 and 0.25 to 0.31 in M3 for YW. Low heritability estimates for residual variance were found, particularly in M2 (0.08 for BW, 0.06 for WW, and 0.09 for YW). The study identified both negative and positive genetic correlations between mean and residual variance, depending on the trait and data transformation. Negative correlations suggest the potential to increase trait means while decreasing residual variance. However, positive correlations indicate that the genetic response to selection for uniformity may be limited unless a selection index is used. Data transformation reduced skewness but did not eliminate genetic heterogeneity of residual variances. The Bayesian approach provided higher estimates of additive genetic variance for residual variance compared to DHGLM. Overall, the findings indicate the potential to reduce variability through selection and lay the groundwork for incorporating uniformity of growth traits into breeding goals. The second study focused on the genetic heterogeneity of residual variance for two foot conformation traits, FA and CS. Using 45,667 phenotypic records collected between 2009 and 2021, three models were compared: a traditional homoscedastic residual variance model (M1), a DHGLM (M2), and a genetically structured environmental variance model (M3). Results showed that heritability estimates for FA and CS means were within expected ranges, although lower in M2. Despite low heritability estimates for residual variance (0.07 for FA and 0.05 for CS in M2), significant genetic coefficients of variation were found, suggesting that selection on trait mean would also influence residual variance. Positive genetic correlations between mean and residual variance in M2 and M3 indicate that selection for uniformity is feasible, but may require additional strategies such as selection indices. The study highlights the potential of FA and CS as indicators for breeding programs aimed at improving production uniformity in beef cattle. Our findings suggest that selection for uniformity in growth and foot score traits in beef cattle may be limited by low heritability of residual variance and moderate to high positive genetic correlations between mean and residual variance. This was observed for most of the traits studied. To overcome these challenges, further research is needed, particularly to explore genomic information to improve the prediction accuracy of estimated breeding values (EBV) for residual variance. Although studies of uniformity using genomic data are limited, they have shown improved EBV accuracy for residual variance. Additionally, alternative methods for measuring uniformity, such as different uniformity or resilience indicators, should be considered, especially with advances in digital phenotyping. Precision livestock farming technologies that allow for extensive data collection on various production traits should be integrated into the development of new uniformity indicators. This dissertation provides valuable insights into the genetic heterogeneity of residual variance in American Angus cattle and highlights the complexity of selecting for uniformity while improving mean traits. Continued research with larger data sets, genomic information, and further methodological refinement will be critical to advance these findings to improve uniformity and productivity in beef cattle breeding. / Doctor of Philosophy / Uniformity in livestock breeding refers to the goal of reducing variability in certain traits within a livestock population to achieve more consistent and predictable outcomes. This is particularly important for traits that affect productivity, economic efficiency, animal welfare, and product quality. By achieving greater uniformity, producers can optimize management practices, improve marketability, and enhance the overall efficiency of animal production systems. Residual variance refers to the variation in traits that is not explained by known genetic or environmental factors. Recent research suggests that residual variance may be under genetic control, meaning that it is possible to select animals that not only have desirable traits, but also have less variability in those traits. Therefore, this dissertation investigates the genetic control of residual variance that may allow selection for uniformity in traits. The research focused on American Angus cattle and aimed to 1) investigate genetic heterogeneity of residual variance in traits, such as birth weight, weaning weight, yearling weight, foot angle, and claw set; 2) compare different genetic models; 3) evaluate the effectiveness of data transformations; and 4) address limitations in genetic evaluations. The first study examined genetic heterogeneity in growth traits using three models. It revealed significant genetic variability, suggesting the potential for selection for uniformity. The study found both positive and negative genetic correlations between trait means and residual variance, indicating varying potential for reducing variance while improving trait means. Data transformations reduced skewness but did not eliminate genetic heterogeneity. A Bayesian approach provided higher estimates of genetic variance than other methods. The second study focused on foot conformation traits with over 45,000 records. The study showed that despite low heritability for residual variance, there was significant genetic variation, indicating the possibility of altering residual variance through selection. Positive genetic correlations suggested that additional strategies, such as selection indices, may be needed to achieve uniformity in practice. Overall, the findings highlight the complexity of selecting for uniformity while improving average traits and underscore the need for further research, particularly using genomic data, to improve prediction accuracy. Integrating precision livestock farming technologies could help develop new indicators of uniformity, improving productivity and uniformity in beef cattle breeding.