Advanced Applications of 3D Dosimetry and 3D Printing in Radiation Therapy

As complex radiotherapy techniques become more readily-practiced, comprehensive 3D dosimetry is a growing necessity for advanced quality assurance. However, clinical implementation has been impeded by a wide variety of factors, including the expense of dedicated optical dosimeter readout tools, high operational costs, and the overall difficulty of use. To address these issues, a novel dry-tank optical CT scanner was designed for PRESAGE 3D dosimeter readout, relying on 3D printed components and omitting costly parts from preceding optical scanners. This work details the design, prototyping, and basic commissioning of the Duke Integrated-lens Optical Scanner (DIOS).

The convex scanning geometry was designed in ScanSim, an in-house Monte Carlo optical ray-tracing simulation. ScanSim parameters were used to build a 3D rendering of a convex ‘solid tank’ for optical-CT, which is capable of collimating a point light source into telecentric geometry without significant quantities of refractive-index matched fluid. The model was 3D printed, processed, and converted into a negative mold via rubber casting to produce a transparent polyurethane scanning tank. The DIOS was assembled with the solid tank, a 3W red LED light source, a computer-controlled rotation stage, and a 12-bit CCD camera. Initial optical phantom studies show negligible spatial inaccuracies in 2D projection images and 3D tomographic reconstructions. A PRESAGE 3D dose measurement for a 4-field box treatment plan from Eclipse shows 95% of voxels passing gamma analysis at 3%/3mm criteria. Gamma analysis between tomographic images of the same dosimeter in the DIOS and DLOS systems show 93.1% agreement at 5%/1mm criteria. From this initial study, the DIOS has demonstrated promise as an economically-viable optical-CT scanner. However, further improvements will be necessary to fully develop this system into an accurate and reliable tool for advanced QA.

Pre-clinical animal studies are used as a conventional means of translational research, as a midpoint between in-vitro cell studies and clinical implementation. However, modern small animal radiotherapy platforms are primitive in comparison with conventional linear accelerators. This work also investigates a series of 3D printed tools to expand the treatment capabilities of the X-RAD 225Cx orthovoltage irradiator, and applies them to a feasibility study of hippocampal avoidance in rodent whole-brain radiotherapy.

As an alternative material to lead, a novel 3D-printable tungsten-composite ABS plastic, GMASS, was tested to create precisely-shaped blocks. Film studies show virtually all primary radiation at 225 kVp can be attenuated by GMASS blocks of 0.5cm thickness. A state-of-the-art software, BlockGen, was used to create custom hippocampus-shaped blocks from medical image data, for any possible axial treatment field arrangement. A custom 3D printed bite block was developed to immobilize and position a supine rat for optimal hippocampal conformity. An immobilized rat CT with digitally-inserted blocks was imported into the SmART-Plan Monte-Carlo simulation software to determine the optimal beam arrangement. Protocols with 4 and 7 equally-spaced fields were considered as viable treatment options, featuring improved hippocampal conformity and whole-brain coverage when compared to prior lateral-opposed protocols. Custom rodent-morphic PRESAGE dosimeters were developed to accurately reflect these treatment scenarios, and a 3D dosimetry study was performed to confirm the SmART-Plan simulations. Measured doses indicate significant hippocampal sparing and moderate whole-brain coverage.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Scheduling Optimization with LDA and Greedy Algorithm

Scheduling optimization is concerned with the optimal allocation of events to time slots. In this paper, we look at one particular example of scheduling problems - the 2015 Joint Statistical Meetings. We want to assign each session among similar topics to time slots to reduce scheduling conflicts. Chapter 1 briefly talks about the motivation for this example as well as the constraints and the optimality criterion. Chapter 2 proposes use of Latent Dirichlet Allocation (LDA) to identify the topic proportions in each session and talks about the fitting of the model. Chapter 3 translates these ideas into a mathematical formulation and introduces a Greedy Algorithm to minimize conflicts. Chapter 4 demonstrates the improvement of the scheduling with this method.

An investigation of the functional role of GRAB in the context of Rab3, Rab8 and Rab11

Rab GTPases are the largest family of the Ras superfamily and are key regulators of membrane trafficking within the cell. There are over 60 members of the Rab family which localise to specific membrane compartments and interact with effector proteins to regulate membrane trafficking processes, such as vesicle formation, vesicle trafficking within the cell and fusion with an acceptor compartment. Multiple effector proteins have been identified for many Rabs, some of which can interact with more than one Rab to link their function at a specific membrane location or to link them together in a Rab activation cascade. Rabin8 is one such protein which is an effector for Rab11a and a Guanine nucleotide Exchange Factor (GEF) for Rab8a. Rabin8 participates in a conserved Rab activation cascade which is critical in the formation of primary cilia. Data presented in this thesis has shown that GRAB interacts with Rab3a, Rab8a, Rab11a and Rab11b in a nucleotide dependent manner. Furthermore, the minimal interacting regionbetween these proteins has been investigated. The functional outcome of GRAB knockdown has also been examined and data in this thesis highlights the phenotypic outcome.

Effects of hypercapnia on aspects of feeding, nutrition, and growth in the edible sea urchin Lytechinus variegatus held in culture

Land-based aquaculture facilities experience occasional hypercapnic conditions due to the accumulation of the metabolic waste product carbon dioxide. Pre-gonadal Lytechinus variegatus (horizontal diameter=20 mm) were exposed to control (608 µatm pCO2, pH 8.1) or hypercapnic conditions (1738 µatm pCO2, pH 7.7) in synthetic seawater for 14 weeks. Sea urchins exposed to hypercapnic conditions exhibited significantly slower growth (reduced dry matter production), primarily due to reduced test production. Higher fecal production rates and lower ash absorption efficiency (%) in individuals exposed to hypercapnic conditions suggest the ability to process or retain dietary carbonates may have been affected. Significant increases in neutral lipid storage in the gut and increased soluble protein storage in the gonads of individuals exposed to hypercapnic conditions suggest alterations in nutrient metabolism and storage. Furthermore, organic production and energy allocation increased in the lantern of those individuals exposed to hypercapnic conditions. These results suggest chronic exposure to hypercapnic conditions alters nutrient allocation to organ systems and functions, leading to changes in somatic and reproductive production.

No detectable effect of CO2 on elemental stoichiometry of Emiliania huxleyi in nutrient-limited, acclimated continuous cultures

Effects of CO2 concentration on elemental composition of the coccolithophore Emiliania huxleyi were studied in phosphorus-limited, continuous cultures that were acclimated to experimental conditions for 30 d prior to the first sampling. We determined phytoplankton and bacterial cell numbers, nutrients, particulate components like organic carbon (POC), inorganic carbon (PIC), nitrogen (PN), organic phosphorus (POP), transparent exopolymer particles (TEP), as well as dissolved organic carbon (DOC) and nitrogen (DON), in addition to carbonate system parameters at CO2 levels of 180, 380 and 750 µatm. No significant difference between treatments was observed for any of the measured variables during repeated sampling over a 14 d period. We considered several factors that might lead to these results, i.e. light, nutrients, carbon overconsumption and transient versus steady-state growth. We suggest that the absence of a clear CO2 effect during this study does not necessarily imply the absence of an effect in nature. Instead, the sensitivity of the cell towards environmental stressors such as CO2 may vary depending on whether growth conditions are transient or sufficiently stable to allow for optimal allocation of energy and resources. We tested this idea on previously published data sets where PIC and POC divided by the corresponding cell abundance of E. huxleyi at various pCO2 levels and growth rates were available.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Optimal location of solid waste containers in Alcalá de Henares

The waste’s rise is a problem that affects the environment as a whole and we cannot forget about it. A good waste’s management is the key to improve the future prospect, and the waste collection is key within the management activities. To find out the better way to collect wastes leads to a reduction of the social, economic and environmental cost. With the use of the Geographic Information Systems it has been intended to elaborate a methodology which allowed us to identify the most suitable places for the location of the collection containers of the different sorts of the solid urban wastes. Taking into account that different types of wastes exist, not all of them should be managed in the same way. Therefore we have to differentiate between models where we apply efficiency and models where we apply equity for the collection of wastes, bearing in mind the necessities of each waste.

Performance of downlink massive MIMO in Ricean fading channels with ZF precoder

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multiple-input multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

Financial Inclusion of The Small and Marginal Farmers by the Banking Sector in Kerala

Financial inclusion for inclusive growth is central to the developmental philosophy of most of the nations over the past decade. It has been a priority for policy makers and regulators in financial sector development for improving access and usage of financial services to achieve comprehensive financial inclusion. The initiatives taken towards financial inclusion can promote a more effective and efficient process to achieve significant improvements in financial inclusion are to establish and achieve shared and sustainable development and growth. Realising this, an increasing number of countries are committing to promote financial inclusion, encouraged by the growing body of country level experiences (World Bank, 2012). Financial inclusion basically means, broad based growth through participation as well as sharing the benefits from the growth process along with the under privileged and marginal segments of the economy. Evidence suggests that it has substantial benefits for equitable and sustainable growth. Inclusive growth ensures that while economy grows rapidly, all segments of society are involved in this growth process, ensuring equal opportunities, devoid of any regional or sectoral disparitiesIt is widely acknowledged that the objective ofinclusive growth is accomplished through the process of financial inclusion. Financial inclusion envisages bringing everyone, irrespective of financial status, into the banking fold for the individual progress and development and thereby achieving comprehensive growth with equity

Improved Implicit Optimal Modeling of the Labor Shift Scheduling Problem

This paper presents an integer programming model for developing optimal shift schedules while allowing extensive flexibility in terms of alternate shift starting times, shift lengths, and break placement. The model combines the work of Moondra (1976) and Bechtold and Jacobs (1990) by implicitly matching meal breaks to implicitly represented shifts. Moreover, the new model extends the work of these authors to enable the scheduling of overtime and the scheduling of rest breaks. We compare the new model to Bechtold and Jacobs' model over a diverse set of 588 test problems. The new model generates optimal solutions more rapidly, solves problems with more shift alternatives, and does not generate schedules violating the operative restrictions on break timing.

Measurement of Training Intensity and Work During Submaximal Isokinetic Progressive Resistance Training Protocols

Physical exercise programmes are routinely prescribed in clinical practice to treat impairments, improve activity and participation in daily life because of their known physiological, health and psychological benefits (RCP, 2009). Progressive resistance exercise is a type of exercise prescribed specifically to improve skeletal muscle strength (Latham et al., 2004). The effectiveness of progressive resistance exercise varies considerably between studies and populations. This thesis focuses on how training parameters influence the delivery of progressive resistance exercise. In order to appropriately evaluate the influence of training parameters, this thesis argues the need to record training performance and the total work completed by participants as prescribed by training protocols. In the first study, participants were taken through a series of protocols differentiated by the intensity and volume of training. Training intensity was defined as a proportion of the mean peak torque achieved during maximal voluntary contractions and was set at 80% and 40% respectively of the MVC mean peak torque. Training volume was defined as the total external work achieved over the training period. Measures of training performance were developed to accurately report the intensity, repetitions and work completed during the training period. A second study evaluated training performance of the training protocols over repeated sessions. These protocols were then applied to 3 stroke survivors. Study 1 found sedentary participants could achieve a differentiated training intensity. Participants completing the high and low intensity protocols trained at 80% and 40% respectively of the MVC mean peak torque. The total work achieved in the high intensity low repetition protocol was lower than the total work achieved in the low intensity high repetition protocol. With repeated practice, study 2 found participants were able to improve in their ability to perform manoeuvres as shown by a reduction in the variation of the mean training intensity achieving total work as specified by the protocol to a lower margin of error. When these protocols were applied to 3 stroke survivors, they were able to achieve the specified training intensity but they were not able to achieve the total work as expected for the protocol. This is likely to be due to an inability in achieving a consistent force throughout the contraction. These results demonstrate evaluation of training characteristics and support the need to record and report training performance characteristics during progressive resistance exercise, including the total work achieved, in order to elucidate the influence of training parameters on progressive resistance exercise. The lack of accurate training performance may partly explain the inconsistencies between studies on optimal training parameters for progressive resistance exercise.

Regulation of osteoclast activation and autophagy through altered protein kinase pathways in Paget's disease of bone

Résumé : La maladie osseuse de Paget (MP) est un désordre squelettique caractérisé par une augmentation focale et désorganisée du remodelage osseux. Les ostéoclastes (OCs) de MP sont plus larges, actifs et nombreux, en plus d’être résistants à l’apoptose. Même si la cause précise de la MP demeure inconnue, des mutations du gène SQSTM1, codant pour la protéine p62, ont été décrites dans une proportion importante de patients avec MP. Parmi ces mutations, la substitution P392L est la plus fréquente, et la surexpression de p62P392L dans les OCs génère un phénotype pagétique partiel. La protéine p62 est impliquée dans de multiples processus, allant du contrôle de la signalisation NF-κB à l’autophagie. Dans les OCs humains, un complexe multiprotéique composé de p62 et des kinases PKCζ et PDK1 est formé en réponse à une stimulation par Receptor Activator of Nuclear factor Kappa-B Ligand (RANKL), principale cytokine impliquée dans la formation et l'activation des OCs. Nous avons démontré que PKCζ est impliquée dans l’activation de NF-κB induite par RANKL dans les OCs, et dans son activation constitutive en présence de p62P392L. Nous avons également observé une augmentation de phosphorylation de Ser536 de p65 par PKCζ, qui est indépendante d’IκB et qui pourrait représenter une voie alternative d'activation de NF-κB en présence de la mutation de p62. Nous avons démontré que les niveaux de phosphorylation des régulateurs de survie ERK et Akt sont augmentés dans les OCs MP, et réduits suite à l'inhibition de PDK1. La phosphorylation des substrats de mTOR, 4EBP1 et la protéine régulatrice Raptor, a été évaluée, et une augmentation des deux a été observée dans les OCs pagétiques, et est régulée par l'inhibition de PDK1. Également, l'augmentation des niveaux de base de LC3II (associée aux structures autophagiques) observée dans les OCs pagétiques a été associée à un défaut de dégradation des autophagosomes, indépendante de la mutation p62P392L. Il existe aussi une réduction de sensibilité à l’induction de l'autophagie dépendante de PDK1. De plus, l’inhibition de PDK1 induit l’apoptose autant dans les OCs contrôles que pagétiques, et mène à une réduction significative de la résorption osseuse. La signalisation PDK1/Akt pourrait donc représenter un point de contrôle important dans l’activation des OCs pagétiques. Ces résultats démontrent l’importance de plusieurs kinases associées à p62 dans la sur-activation des OCs pagétiques, dont la signalisation converge vers une augmentation de leur survie et de leur fonction de résorption, et affecte également le processus autophagique.