No / The ability to predict how far a drug will penetrate into the tumour microenvironment within its pharmacokinetic (PK) lifespan would provide valuable information about therapeutic response. As the PK profile is directly related to the route and schedule of drug administration, an in silico tool that can predict the drug administration schedule that results in optimal drug delivery to tumours would streamline clinical trial design. This paper investigates the application of mathematical and computational modelling techniques to help improve our understanding of the fundamental mechanisms underlying drug delivery, and compares the performance of a simple model with more complex approaches. Three models of drug transport are developed, all based on the same drug binding model and parametrized by bespoke in vitro experiments. Their predictions, compared for a ‘tumour cord’ geometry, are qualitatively and quantitatively similar. We assess the effect of varying the PK profile of the supplied drug, and the binding affinity of the drug to tumour cells, on the concentration of drug reaching cells and the accumulated exposure of cells to drug at arbitrary distances from a supplying blood vessel. This is a contribution towards developing a useful drug transport modelling tool for informing strategies for the treatment of tumour cells which are ‘pharmacokinetically resistant’ to chemotherapeutic strategies.

Yes / The tumour vasculature and microenvironment is complex and heterogeneous, contributing to reduced delivery of cancer drugs to the tumour. We have developed an in silico model of drug transport in a tumour cord to explore the effect of different drug regimes over a 72 h period and how changes in pharmacokinetic parameters affect tumour exposure to the cytotoxic drug doxorubicin. We used the model to describe the radial and axial distribution of drug in the tumour cord as a function of changes in the transport rate across the cell membrane, blood vessel and intercellular permeability, flow rate, and the binding and unbinding ratio of drug within the cancer cells. We explored how changes in these parameters may affect cellular exposure to drug. The model demonstrates the extent to which distance from the supplying vessel influences drug levels and the effect of dosing schedule in relation to saturation of drug-binding sites. It also shows the likely impact on drug distribution of the aberrant vasculature seen within tumours. The model can be adapted for other drugs and extended to include other parameters. The analysis confirms that computational models can play a role in understanding novel cancer therapies to optimize drug administration and delivery.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Processos de produção precisam ser avaliados continuamente para que funcionem de modo mais eficaz e eficiente possível. Um conjunto de ferramentas utilizado para tal finalidade é denominado controle estatístico de processos (CEP). Através de ferramentas do CEP, o monitoramento pode ser realizado periodicamente. A ferramenta mais importante do CEP é o gráfico de controle. Nesta tese, foca-se no monitoramento de uma variável resposta, por meio dos parâmetros ou coeficientes de um modelo de regressão linear simples. Propõe-se gráficos de controle χ2 adaptativos para o monitoramento dos coeficientes do modelo de regressão linear simples. Mais especificamente, são desenvolvidos sete gráficos de controle χ2 adaptativos para o monitoramento de perfis lineares, a saber: gráfico com tamanho de amostra variável; intervalo de amostragem variável; limites de controle e de advertência variáveis; tamanho de amostra e intervalo de amostragem variáveis; tamanho de amostra e limites variáveis; intervalo de amostragem e limites variáveis e por fim, com todos os parâmetros de projeto variáveis. Medidas de desempenho dos gráficos propostos foram obtidas através de propriedades de cadeia de Markov, tanto para a situação zero-state como para a steady-state, verificando-se uma diminuição do tempo médio até um sinal no caso de desvios pequenos a moderados nos coeficientes do modelo de regressão do processo de produção. Os gráficos propostos foram aplicados a um exemplo de um processo de fabricação de semicondutores. Além disso, uma análise de sensibilidade dos mesmos é feita em função de desvios de diferentes magnitudes nos parâmetros do processo, a saber, no intercepto e na inclinação, comparando-se o desempenho entre os gráficos desenvolvidos e também com o gráfico χ2 com parâmetros fixos. Os gráficos propostos nesta tese são adequados para vários tipos de aplicações. Neste trabalho também foi considerado características de qualidade as quais são representadas por um modelo de regressão não-linear. Para o modelo de regressão não-linear considerado, a proposta é utilizar um método que divide o perfil não-linear em partes lineares, mais especificamente, um algoritmo para este fim, proposto na literatura, foi utilizado. Desta forma, foi possível validar a técnica proposta, mostrando que a mesma é robusta no sentido que permite tipos diferentes de perfis não-lineares. Aproxima-se, portanto um perfil não-linear por perfis lineares por partes, o que proporciona o monitoramento de cada perfil linear por gráficos de controle, como os gráficos de controle desenvolvidos nesta tese. Ademais apresenta-se a metodologia de decompor um perfil não-linear em partes lineares de forma detalhada e completa, abrindo espaço para ampla utilização. / Production processes need to be continually evaluated so that they are able to produce in the most effective and efficient way. Statistical process control (SPC) consists of a set of tools used for this purpose. The monitoring can be periodically performed through the SPC tools. The most important tool of SPC is the control chart. In this thesis, we focus on the monitoring of a response variable through the parameters or coefficients of a linear regression model. It is proposed adaptive χ2 control charts for monitoring the coefficients of linear regression models. More specifically, seven adaptive χ2 control charts are proposed for monitoring a simple linear regression model, being distinguished by the following properties: variable sample size; variable sampling interval; variable warning and control limits; variable sample size and sampling interval; variable sample size and limits; variable sampling interval and limits and finally, all design parameters varying. Performance measures of these charts were obtained through properties of Markov chain, for both the zero-state and the steady-state situation. It was found that the average time until a signal in the case of small to moderate shifts in the coefficients of the regression model decreased. The proposed charts were applied to an example of semiconductors manufacturing process. Moreover, a sensitivity analysis of the proposed charts is performed for different shifts magnitudes in the process parameters, namely the intercept and the slope, comparing the performance between the developed charts and also with the fixed parameter χ2 chart. The proposed charts in this thesis are suitable for several applications. In this work, it was also considered quality characteristics represented by nonlinear regression models. To the considered nonlinear regression model, the proposal is to use a method that divides the nonlinear profile in linear parts. More specifically, an algorithm for this purpose, proposed in the literature, was utilized. It approximates nonlinear profile by a set of linear profiles. It was possible to validate this technique, showing that it is robust in the sense that it allows different types of nonlinear profiles to be considered. In this way, techniques such as control charts developed here can be used to monitor each linear part. Furthermore, we present the methodology to decompose a nonlinear profile in linear parts in a detailed and complete way, allowing its widespread use.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Processos de produção precisam ser avaliados continuamente para que funcionem de modo mais eficaz e eficiente possível. Um conjunto de ferramentas utilizado para tal finalidade é denominado controle estatístico de processos (CEP). Através de ferramentas do CEP, o monitoramento pode ser realizado periodicamente. A ferramenta mais importante do CEP é o gráfico de controle. Nesta tese, foca-se no monitoramento de uma variável resposta, por meio dos parâmetros ou coeficientes de um modelo de regressão linear simples. Propõe-se gráficos de controle χ2 adaptativos para o monitoramento dos coeficientes do modelo de regressão linear simples. Mais especificamente, são desenvolvidos sete gráficos de controle χ2 adaptativos para o monitoramento de perfis lineares, a saber: gráfico com tamanho de amostra variável; intervalo de amostragem variável; limites de controle e de advertência variáveis; tamanho de amostra e intervalo de amostragem variáveis; tamanho de amostra e limites variáveis; intervalo de amostragem e limites variáveis e por fim, com todos os parâmetros de projeto variáveis. Medidas de desempenho dos gráficos propostos foram obtidas através de propriedades de cadeia de Markov, tanto para a situação zero-state como para a steady-state, verificando-se uma diminuição do tempo médio até um sinal no caso de desvios pequenos a moderados nos coeficientes do modelo de regressão do processo de produção. Os gráficos propostos foram aplicados a um exemplo de um processo de fabricação de semicondutores. Além disso, uma análise de sensibilidade dos mesmos é feita em função de desvios de diferentes magnitudes nos parâmetros do processo, a saber, no intercepto e na inclinação, comparando-se o desempenho entre os gráficos desenvolvidos e também com o gráfico χ2 com parâmetros fixos. Os gráficos propostos nesta tese são adequados para vários tipos de aplicações. Neste trabalho também foi considerado características de qualidade as quais são representadas por um modelo de regressão não-linear. Para o modelo de regressão não-linear considerado, a proposta é utilizar um método que divide o perfil não-linear em partes lineares, mais especificamente, um algoritmo para este fim, proposto na literatura, foi utilizado. Desta forma, foi possível validar a técnica proposta, mostrando que a mesma é robusta no sentido que permite tipos diferentes de perfis não-lineares. Aproxima-se, portanto um perfil não-linear por perfis lineares por partes, o que proporciona o monitoramento de cada perfil linear por gráficos de controle, como os gráficos de controle desenvolvidos nesta tese. Ademais apresenta-se a metodologia de decompor um perfil não-linear em partes lineares de forma detalhada e completa, abrindo espaço para ampla utilização. / Production processes need to be continually evaluated so that they are able to produce in the most effective and efficient way. Statistical process control (SPC) consists of a set of tools used for this purpose. The monitoring can be periodically performed through the SPC tools. The most important tool of SPC is the control chart. In this thesis, we focus on the monitoring of a response variable through the parameters or coefficients of a linear regression model. It is proposed adaptive χ2 control charts for monitoring the coefficients of linear regression models. More specifically, seven adaptive χ2 control charts are proposed for monitoring a simple linear regression model, being distinguished by the following properties: variable sample size; variable sampling interval; variable warning and control limits; variable sample size and sampling interval; variable sample size and limits; variable sampling interval and limits and finally, all design parameters varying. Performance measures of these charts were obtained through properties of Markov chain, for both the zero-state and the steady-state situation. It was found that the average time until a signal in the case of small to moderate shifts in the coefficients of the regression model decreased. The proposed charts were applied to an example of semiconductors manufacturing process. Moreover, a sensitivity analysis of the proposed charts is performed for different shifts magnitudes in the process parameters, namely the intercept and the slope, comparing the performance between the developed charts and also with the fixed parameter χ2 chart. The proposed charts in this thesis are suitable for several applications. In this work, it was also considered quality characteristics represented by nonlinear regression models. To the considered nonlinear regression model, the proposal is to use a method that divides the nonlinear profile in linear parts. More specifically, an algorithm for this purpose, proposed in the literature, was utilized. It approximates nonlinear profile by a set of linear profiles. It was possible to validate this technique, showing that it is robust in the sense that it allows different types of nonlinear profiles to be considered. In this way, techniques such as control charts developed here can be used to monitor each linear part. Furthermore, we present the methodology to decompose a nonlinear profile in linear parts in a detailed and complete way, allowing its widespread use.

Social media has become an important means of everyday communication. It is a mechanism for “sharing” and “resharing” of information. While social network platforms provide the means to users for resharing/reblogging (aka retweeting), it remains unclear what motivates users to share. Predicting the spread of content is quite important for several purposes such as viral marketing, popular news detection, personalized message recommendation and on-line advertisement. Social content systems store all the information produced in the interactions between users. However, to turn this data into information that allows us to extract patterns, it is important to consider the different phenomena involved in these interactions. In this work, two phenomena that influence the evolution of networks are studied for Twitter: diffusion of information and communication among users. Previous studies have shown that history of interaction among users and properties of the message are good attributes to understand the retweet behavior of users. Factors like content of message and time are less investigated. We propose a prediction model for retweet actions of users. It formulates a function which ranks the users according to how receptive they are to a particular message. The function generates a confidence score for the edges joining the initiator of the message and the followers. Two different pieces of information propagate through different users in the network. We divide the task of calculating confidence score into two parts. The first part is independent of the test tweet. It models transmission rate of the tie between the initiator and the follower. We call this as ‘Pairwise Influence Estimation’. The second part incorporates the tweet properties and user activeness as per time in the ranking function. The proposed model exploits all the dimensions of information dif-fusion process-influence, content and temporal properties. We have captured local aspects of diffusion. It has been observed that users do not read all the messages on their site. This results in shortcomings in the above models. Considering this, we first study the temporal behavior of users’ activities, which directly reflects their availability pertaining to the upcoming post. Also, as it is a continuous task of predicting retweet behavior, we design a user-centric, and temporally localized incremental classification model by considering the fact that users do not read all their tweets. We have tested the effectiveness of this model by using real data from Twitter. We demonstrate that the new proposed model is more accurate in describing the information propagation in microblog compared to the existing methods. Our model works well when we consider different classes of users depending on their activity patterns. In addition, we also investigate the parameters of the model for different classes of users. We report some interesting distinguishing patterns in retweeting behavior of users.

Body weight supported treadmill training (BWSTT) is being promoted as an effective means of restoring ambulatory abilities among individuals with incomplete spinal cord injuries. The emphasis of this thesis is on the description of the metabolic demands and the EMG profiles of able-bodied persons and individuals with a spinal cord injury (SCI) while walking under the identical conditions on a body weight support (BWS) treadmill. The secondary purpose was to contrast the metabolic and muscular responses between the two groups. Two separate chapters describing the metabolic demands and EMG profiles respectively follow the review of the literature. The metabolic results indicate that raising the speed and/or decreasing the amount of BWS increase the intensity of BWS treadmill walking, with speed having a more profound effect. The SCI group was less efficient and they had greater metabolic rates of oxygen consumption than the controls for all conditions examined. This led to the conclusion that walking on the treadmill, for the SCI group can provide an effective aerobic exercise stimulus. The EMG profiles suggest that speed and BWS affect the phasic characteristics of the muscular activity while walking for both groups. Furthermore, abnormalities, omissions and inappropriate levels of activity were observed in the SCI group when compared to the controls. These irregularities suggest that the SCI participants have adopted altered motor strategies while walking, relative to the control group. Nonetheless, the SCI participants showed evidence of appropriate modulations in their EMG activity to meet the demands of the task as they changed from one condition to the next. / Thesis / Master of Science (MSc)

Doctor of Philosophy / Department of Diagnostic Medicine/Pathobiology / Harvey Morgan Scott / A randomized controlled field trial was designed to evaluate the effects of two treatment strategies on ceftiofur and tetracycline resistances in feedlot cattle. The two strategies consisted of administering ceftiofur crystalline-free acid administration (CCFA) at either one or else all of the steers within a pen, and subsequent feeding/not feeding of therapeutic doses of chlortetracycline. Both strategies were hypothesized to reduce ceftiofur resistance. The effects of treatment strategies were evaluated via metagenome-based and culture-based assays. In this 26-day study, 176 steers were allocated to 16 pens of 11 steers each. The two strategies were randomly assigned to the pens in a two-way full-factorial manner resulting in four treatment groups. The blaCMY-2, blaCTX-M, tet(A), tet(B), and 16S rRNA gene copies/g feces were quantified using qRT-PCR from fecal community DNA. Antimicrobial susceptibility profiles were determined using microbroth dilution technique from the non-type-specific (NTS) E. coli isolates (n=1,050). The NTS E. coli DNA was screened for the presence of blaCMY-2, tet(A), and tet(B) genes. Pens in which all the steers received CCFA treatment showed an increase in blaCMY-2 and blaCTX-M log10 gene copies/g feces and in the proportion of ceftiofur-resistant and blaCMY-2 positive NTS E. coli. This was in contrast to the pens where only one animal received CCFA treatment. There was a significant decrease in quantities of tetracycline genes in community DNA in pens where all animals received CCFA treatment. In contrast to metagenome-based assay results, culture-based assays indicated an increase in the proportion of tetracycline resistant NTS E. coli upon CCFA treatment. Thereafter, chlortetracycline administration led to rapid expansion both of ceftiofur (blaCMY-2, blaCTX-M) and tetracycline [tet(A) and tet(B)] log10 gene copies/g feces. Chlortetracycline treatment delayed the return of the ceftiofur resistance prevalence to baseline among NTS E. coli and thus did not lead to the hypothesized decrease in ceftiofur resistance. Our data suggest that chlortetracycline use is contraindicated when attempting to avoid expansion of resistance to critically important 3rd generation cephalosporins in feedlot cattle. Further studies are required to better establish the animal-level effects of co-housing antimicrobial-treated and non-treated animals together at varying ratios on the levels of antimicrobial resistance.

Control and monitoring networks often operate on AC/DC power systems. DC batteries and chargers are commonly used on industrial plants as auxiliary DC power systems for these control and monitoring networks. The energy demand and load profiles for these control networks differ from application to application. Proper design, sizing, and maintenance of the components that forms part of the DC control power system are therefore required. Throughout the load profile of a control and monitoring system there are various peak currents. The peak currents are classified as inrush and momentary loads. These inrush and momentary loads play a large role when calculating the required battery size for an application. This study investigates the feasibility of using supercapacitors in conjunction with batteries, in order to reduce the size of the required battery capacity. A reduction in the size of the required battery capacity not only influences the cost of the battery itself, but also influences the hydrogen emissions, the physical space requirements, and the required rectifiers and chargers. When calculating the required size batteries for an auxiliary power system, a defined load profile is required. Control and monitoring systems are used to control dynamic processes, which entails a continuous starting and stopping of equipment as the process demands. This starting and stopping of devices will cause fluctuations in the load profile. Ideally, data should be obtained from a live plant for the purpose of defining load profiles. Unfortunately, due to the economic risks involved, installing data logging equipment on a live industrial plant for the purpose of research, is not allowed. There are also no historical data available from which load profiles could be generated. In order to evaluate the influence of supercapacitors, complex load profiles are required. In this study, an alternative method of defining the load profile for a dynamic process is investigated. Load profiles for various applications are approximated using a probabilistic approach. The approximation methodology make use of plant operating philosophies as input to the Markov Chain Monte Carlo simulation theory. The required battery sizes for the approximated profiles are calculated using the IEEE recommended practice for sizing batteries. The approximated load profile, as well the calculated battery size are used for simulating the auxiliary power system. A supercapacitor is introduced into the circuit and the simulations are repeated. The introduction of the supercapacitor relieves the battery of the inrush and momentary loads of the load profile. The battery sizing calculations are repeated so as to test the influence of the supercapacitor on the required battery capacity. In order to investigate the full influence of adding a supercapacitor to the design, the impact on various factors are considered. In this study, these factors include the battery size, charger size, H2 extraction system, as well as maintenance requirements and the life of the battery. No major cost savings where evident from the results obtained. Primary reasons for this low cost saving are the fixed ranges in which battery sizes are available, as well as conservative battery data obtained from battery suppliers. It is believed that applications other than control and monitoring systems will show larger savings. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Control and monitoring networks often operate on AC/DC power systems. DC batteries and chargers are commonly used on industrial plants as auxiliary DC power systems for these control and monitoring networks. The energy demand and load profiles for these control networks differ from application to application. Proper design, sizing, and maintenance of the components that forms part of the DC control power system are therefore required. Throughout the load profile of a control and monitoring system there are various peak currents. The peak currents are classified as inrush and momentary loads. These inrush and momentary loads play a large role when calculating the required battery size for an application. This study investigates the feasibility of using supercapacitors in conjunction with batteries, in order to reduce the size of the required battery capacity. A reduction in the size of the required battery capacity not only influences the cost of the battery itself, but also influences the hydrogen emissions, the physical space requirements, and the required rectifiers and chargers. When calculating the required size batteries for an auxiliary power system, a defined load profile is required. Control and monitoring systems are used to control dynamic processes, which entails a continuous starting and stopping of equipment as the process demands. This starting and stopping of devices will cause fluctuations in the load profile. Ideally, data should be obtained from a live plant for the purpose of defining load profiles. Unfortunately, due to the economic risks involved, installing data logging equipment on a live industrial plant for the purpose of research, is not allowed. There are also no historical data available from which load profiles could be generated. In order to evaluate the influence of supercapacitors, complex load profiles are required. In this study, an alternative method of defining the load profile for a dynamic process is investigated. Load profiles for various applications are approximated using a probabilistic approach. The approximation methodology make use of plant operating philosophies as input to the Markov Chain Monte Carlo simulation theory. The required battery sizes for the approximated profiles are calculated using the IEEE recommended practice for sizing batteries. The approximated load profile, as well the calculated battery size are used for simulating the auxiliary power system. A supercapacitor is introduced into the circuit and the simulations are repeated. The introduction of the supercapacitor relieves the battery of the inrush and momentary loads of the load profile. The battery sizing calculations are repeated so as to test the influence of the supercapacitor on the required battery capacity. In order to investigate the full influence of adding a supercapacitor to the design, the impact on various factors are considered. In this study, these factors include the battery size, charger size, H2 extraction system, as well as maintenance requirements and the life of the battery. No major cost savings where evident from the results obtained. Primary reasons for this low cost saving are the fixed ranges in which battery sizes are available, as well as conservative battery data obtained from battery suppliers. It is believed that applications other than control and monitoring systems will show larger savings. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Recent genomic data indicate that RNA polymerase II (Pol II) function extends beyond conventional transcription of primarily protein-coding genes. Among the five snRNAs required for pre-mRNA splicing, only the U6 snRNA is synthesized by RNA polymerase III (Pol III). Here we address the question of how Pol II coordinates the expression of spliceosome components, including U6. We used chromatin immunoprecipitation (ChIP) and high-resolution mapping by PCR to localize both Pol II and Pol III to snRNA gene regions. We report the surprising finding that Pol II is highly concentrated ∼300 bp upstream of all five active human U6 genes in vivo. The U6 snRNA, an essential component of the spliceosome, is synthesized by Pol III, whereas all other spliceosomal snRNAs are Pol II transcripts. Accordingly, U6 transcripts were terminated in a Pol III-specific manner, and Pol III localized to the transcribed gene regions. However, synthesis of both U6 and U2 snRNAs was α-amanitin-sensitive, indicating a requirement for Pol II activity in the expression of both snRNAs. Moreover, both Pol II and histone tail acetylation marks were lost from U6 promoters upon α-amanitin treatment. The results indicate that Pol II is concentrated at specific genomic regions from which it can regulate Pol III activity by a general mechanism. Consequently, Pol II coordinates expression of all RNA and protein components of the spliceosome.