VictorJuiz/thesis huggingface dataset:
Membrane Surface Charge and Lipid Signaling in Phagocytosis
Keywords: cell biology

Phagocytosis is an important component of innate and adaptive immunity. The formation of phagosomes and the subsequent maturation that capacitates them for pathogen elimination and antigen presentation are complex processes that involve signal transduction, cytoskeletal reorganization and membrane remodeling. Lipids are increasingly appreciated to play a crucial role in these events. Sphingolipids, cholesterol and glycerophospholipids, notably the phosphoinositides, are required for the segregation of signaling microdomains and for the generation of second messengers. They are also instrumental in the remodeling of the actin cytoskeleton and in directing membrane traffic. They accomplish these feats by congregating into liquid-ordered domains, by generating active metabolites that activate receptors and by recruiting and anchoring specific protein ligands to the membrane, often altering their conformation and catalytic activity. A less appreciated role of acidic phospholipids is their contribution to the negative surface charge of the inner leaflet of the plasmalemma. The unique negativity of the inner aspect of the plasma membrane serves to attract and anchor key signaling and effector molecules that are required to initiate phagosome formation. Conversely, the loss of charge that accompanies phospholipid metabolism as phagosomes seal facilitates the dissociation of proteins and the termination of signaling and cytoskeleton assembly. In this manner, lipids provide a binary electrostatic switch to control phagocytosis. Phosphatidylserine, an abundant but relatively little studied anionic phospholipid, is expected to contribute significantly to the surface charge of membranes. The development of a PS-specific biosensor revealed that this lipid is distributed to the cytosolic leaflet of the plasma membrane and of the membranes of the endocytic pathway. Consequently, the charge conferred by PS directs the targeting of proteins with cationic domains or polybasic clusters to these membrane compartments. PS is also present on the phagosome membrane and similarly helps to recruit cationic proteins to this organelle. The electrostatic switch theory as proposed by McLaughlin and Aderem provides a novel biophysical mechanism in which proteins can be regulated in cells, in parallel with the traditional biochemical mechanisms that regulate the activity and conformational status of proteins.
Solving Quantified Boolean Formulas
Keywords: Artificial Intelligence
Satisfiability
Search
Quantified Boolean Formulas

Abstract Solving Quantified Boolean Formulas Horst Samulowitz Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Many real-world problems do not have a simple algorithmic solution and casting these problems as search problems is often not only the simplest way of casting them, but also the most efficient way of solving them. In this thesis we will present several techniques to advance search-based algorithms in the context of solving quantified boolean formulas (QBF). QBF enables complex realworld problems including planning, two-player games and verification to be captured in a compact and quite natural fashion. We will discuss techniques ranging from straight forward pre-processing methods utilizing strong rules of inference to more sophisticated online approaches such as dynamic partitioning. Furthermore, we will show that all of the presented techniques achieve an essential improvement of the search process when solving QBF. At the same time the displayed empirical results also reveal the orthogonality of the different techniques with respect to performance. Generally speaking each approach performs well on a particular subset of benchmarks, but performs poorly on others. Consequently, an adaptive employment of the available techniques that maximizes the performance would result in further improvements. We will demonstrate that such an adaptive approach can pay off in practice, by presenting the results of using machine learning methods to dynamically select the best variable ordering heuristics during search.
Characterization of the DNA Damage Resistance Gene RTT107
Keywords: DNA damage response

In Saccharomyces cerevisiae, RTT107 (ESC4, YHR154W) encodes a BRCT-domain protein that is important for recovery from DNA damage during S phase. I have found that Rtt107 forms a complex with the Slx1/Slx4 structure-specific nuclease. Deletion of SLX4 confers many of the same phenotypes observed in rtt107∆, including DNA damage sensitivity, prolonged DNA damage checkpoint activation, and increased spontaneous DNA damage, suggesting that Slx4 and Rtt107 function in concert. These defects are not shared by Slx1 indicating that the function of Slx4 and Slx1 in the DNA damage response is not entirely overlapping. Furthermore, I found that Slx4 regulates the phosphorylation of Rtt107 by the checkpoint kinase Mec1. The phenotypes conferred by deletion of RTT107 and the spectrum of its synthetic genetic interactions indicates that Rtt107 may function at stalled replication forks. I have shown that Rtt107 is recruited to chromatin in the presence of DNA damaging agents that cause DNA replication forks to stall. Recruitment of Rtt107 to chromatin requires Rtt109, an acetyltransferase, and the cullin Rtt101, but is not dependent on Slx4 or the checkpoint kinases. Rtt109 acetylates histone H3 on lysine 56 (H3-K56), yet recruitment of Rtt107 to chromatin does not require acetylation of H3-K56, indicating that Rtt109 may have additional targets. Chromatin immunoprecipitation indicates that the sites of Rtt107 binding correspond to regions at or near stalled replication forks throughout the genome. I propose that Rtt107 acts in the recovery from DNA damage by localizing to stalled replication forks and acting as a scaffold for assembly of DNA damage response proteins, ultimately promoting replication fork restart.
Biological and Physical Strategies to Improve the Therapeutic Index of Photodynamic Therapy
Keywords: photodynamic
inverse problem
diffuser
erythropoietin
prostate
glioma
therapeutic index
optimization
dosimetry
tissue optics

Photodynamic therapy (PDT) derives its tumour selectivity from preferential photosensitizer accumulation and short light penetration in tissue. However, additional strategies are needed to improve the therapeutic index of PDT in oncological applications where light is delivered interstitially to large volumes (e.g. prostate), or when adjacent normal tissue is extremely sensitive (e.g. brain). Much research to improve PDT's selectivity is directed towards developing targeted photosensitizers. Here, I present two alternative strategies to improve PDT's selectivity, without compromising its efficacy. For interstitial delivery, I investigated whether customizable cylindrical diffusers can be used to deliver light doses that conform better to target geometries, specifically the prostate. Additionally, I examined whether the neuroprotectant erythropoietin, used as an adjuvant to PDT for brain tumours, can reduce the sensitivity of normal tissue, thereby improving treatment selectivity. To determine if tailored diffusers constitute an improvement over conventional ones, I introduce a novel optimization algorithm for treatment planning. I also analyze the sensitivity of the resulting plans to changes in the optical properties and diffuser placement. These results are contextualized by a mathematical formalism to characterize the light dose distributions arising from tailored diffusers. In parallel, I investigate the neuroprotective effects of erythropoietin in PDT of primary cortical neurons in culture and normal rat brain in vivo. I show that the most important parameter determining prostate coverage is the number of diffusers employed. Moreover, while tailored diffusers do offer an improvement over conventional ones, the improvement is likely masked by perturbations introduced by the uncertainties of light delivery. Although these results largely discard the use of tailored diffusers in prostate PDT, significant insight has been gained into PDT treatment planning, and tailored diffusers may still be advantageous in more complicated geometries. Additionally, I show that erythropoietin does not improve survival of PDT-treated neurons PDT, nor reduces the volume of necrosis in vivo, for the ranges of conditions and doses studied. To our knowledge, this is the first time this strategy has been tested in brain PDT and deserves to be investigated further, by using later time-points, functional outcomes, and other neuroprotectants.
Pressure-sensitive Pen Interactions
Keywords: HCI
Pressure
Pen
Interfaces

Pen-based computers bring the promise of tapping into people’s expressiveness with pen and paper and producing a platform that feels familiar while providing new functionalities only possible within an electronic medium. To this day, pen computers’ success is marginal because their interfaces mainly replicate keyboard and mouse ones. Maximizing the potential of pen computers requires redesigning their interfaces so that they are sensitive to the pen’s input modalities and expressiveness. In particular, pressure is an important and expressive, yet underutilized, pen input modality. This dissertation advances our knowledge about pressure-aware, pen-based interactions and how people use these techniques. We systematically explore their design by first investigating how pressure can affect pen interactions. We propose novel techniques that take advantage of the pressure modality of a pen to control, link, and annotate digital video. We then study people’s performance using pressure to navigate through a set of elements and find that they can discriminate a minimum of six different pressure regions. We introduce the concept of Pressure Widgets and suggest visual and interaction properties for their design. We later explore pressure’s use to enhance the adjustment of continuous parameters and propose Zliding, a technique in which users vary pressure to adjust the scale of the parameter space, while sliding their pen to perform parameter manipulations. We study Zliding and find it a viable technique, which is capable of enabling arbitrarily precise parameter adjustments. We finally present a novel interaction technique defined by the concurrent variation in pressure applied while dragging a pen. We study these pressure marks and find that they are a compact, orientation-independent, full interaction phrase that can be 30% faster than a stateof-the-art selection-action interaction phrase. This dissertation also makes a number of key contributions throughout the design and study of novel interaction techniques: -It identifies important design issues for the development of pressure-sensitive, pen operated widgets and interactions, -It provides design guidelines for interaction techniques and interface elements utilizing pressure-enabled input devices, -It presents empirical data on people’s ability to control pressure, and -It charts a visual design space of pressure-sensitive, pen-based interactions.
High-speed Baud-rate Clock Recovery
Keywords: Clock recovery
Baud-rate

Baud-rate clock recovery (CR) is gradually gaining popularity in modern serial data transmission systems since these CR techniques do not require edge-samples for extracting timing information. However, previous baud-rate techniques for high-speed serial links either rely on specific 4-bit patterns or uncorrelated random data. This work describes the modeling and design of analog filter front-end aided baud-rate CR schemes. Unlike other baud-rate schemes, this technique is not constrained by the properties of the input random data. Firstly, the thesis develops a hardware-efficient baud-rate algorithm that requires only the slope information of the incoming random data. Called modified sign-sign minimum mean squared error (SSMMSE), this algorithm adjusts the clock sampling phase until the slope is zero through a bang-bang control loop. Secondly, the performance of a modified SSMMSE phase detector is investigated and compared with a conventional edge-sampled phase detector. It is shown that, at severe noise levels, the proposed modified SSMMSE method has better performance compared to the edge-sampled method for equal loop bandwidths.Thirdly, the thesis investigates different hardware-efficient slope detection techniques. Both passive and active filter based slope detection techniques are demonstrated in this work. In addition to slope generation, the active filter performs linear equalization as well. However, the passive filter generates the slope information at higher speeds than the active filter and also consumes less power. The two filters are used to recover a 2-GHz clock by using an external bang-bang loop. In short, the thesis demonstrates that area and power savings can be achieved by utilizing slope information from front-end filters without compromising the performance of the CR unit.
Mechanisms of Genetic Resistance To Dioxin-induced Lethality
Keywords: Dioxin
aryl hydrocarbon receptor
expression array

Dioxins are environmental contaminants that raise concern because they are potent and persistent. The most potent dioxin congener, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), causes a wide variety of biochemical and toxic effects in laboratory animals and in humans. Major toxicities of TCDD are initiated by their binding to the AH receptor (AHR), a ligand-activated transcription factor that regulates expression of numerous genes. However, the specific genes whose dysregulation leads to major toxicities such as wasting, hepatotoxicity, and lethality are unknown. The objective of this thesis research was to identify the molecular mechanisms by which dioxins cause lethality. To this end, a powerful genetic rat model was utilized – the Han/Wistar (Kuopio) rat which is highly resistant to dioxin toxicity due to a major deletion in the AHR’s transactivation domain (TAD) leading to 3 potential AHR variant transcripts. We found that insertion-variant transcripts (IVs) are the dominant forms of AHR expressed in H/W rats, constitutively and after TCDD treatment. Gene expression array analysis revealed that the total number of TCDD-responsive genes in liver was significantly lower in H/W rats (that carry the TAD deletion) than in dioxin-sensitive rats (that carry wildtype AHR). Genes that are well-known to be AHR-regulated and dioxin-inducible  such as CYP1 transcripts  remained responsive to TCDD in H/W rats; thus the TAD deletion selectively interferes with expression of a subset of hepatic genes rather than abolishing global AHR-mediated responses. Genes that differed in response to TCDD between dioxin-sensitive rats and dioxin-resistant rats are integral parts of pathways known to be disrupted by dioxin treatment such as protein synthesis/degradation, fatty acid transport/metabolism, and apoptosis. These genes are worthy candidates for further mechanistic studies to test their role in major dioxin toxicities. Numerous differentially-regulated genes were downregulated; however, microRNAs, which downregulate mRNA levels in other systems, likely play no role in downregulation of mRNAs by dioxins in adult liver and are unlikely to be involved in hepatotoxicity. Findings in this research support the hypothesis that H/W rats are resistant to TCDD lethality because the TAD deletion prevents the AHR from dysregulating specific mRNA transcripts but not hepatic miRNAs.
Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data
Keywords: Machine Learning

Real world data is not random: The variability in the data-sets that arise in computer vision, signal processing and other areas is often highly constrained and governed by a number of degrees of freedom that is much smaller than the superficial dimensionality of the data. Unsupervised learning methods can be used to automatically discover the “true”, underlying structure in such data-sets and are therefore a central component in many systems that deal with high-dimensional data. In this thesis we develop several new approaches to modeling the low-dimensional structure in data. We introduce a new non-parametric framework for latent variable modelling, that in contrast to previous methods generalizes learned embeddings beyond the training data and its latent representatives. We show that the computational complexity for learning and applying the model is much smaller than that of existing methods, and we illustrate its applicability on several problems. We also show how we can introduce supervision signals into latent variable models using conditioning. Supervision signals make it possible to attach “meaning” to the axes of a latent representation and to untangle the factors that contribute to the variability in the data. We develop a model that uses conditional latent variables to extract rich distributed representations of image transformations, and we describe a new model for learning transformation features in structured supervised learning problems.
Stochastic Mortality Modelling
Keywords: Mortality modelling
Time-changed Markov process

For life insurance and annuity products whose payoffs depend on the future mortality rates, there is a risk that realized mortality rates will be different from the anticipated rates accounted for in their pricing and reserving calculations. This is termed as mortality risk. Since mortality risk is difficult to diversify and has significant financial impacts on insurance policies and pension plans, it is now a well-accepted fact that stochastic approaches shall be adopted to model the mortality risk and to evaluate the mortality-linked securities. The objective of this thesis is to propose the use of a time-changed Markov process to describe stochastic mortality dynamics for pricing and risk management purposes. Analytical and empirical properties of this dynamics have been investigated using a matrix-analytic methodology. Applications of the proposed model in the evaluation of fair values for mortality linked securities have also been explored. To be more specific, we consider a finite-state Markov process with one absorbing state. This Markov process is related to an underlying aging mechanism and the survival time is viewed as the time until absorption. The resulting distribution for the survival time is a so-called phase-type distribution. This approach is different from the traditional curve fitting mortality models in the sense that the survival probabilities are now linked with an underlying Markov aging process. Markov mathematical and phase-type distribution theories therefore provide us a flexible and tractable framework to model the mortality dynamics. And the time-changed Markov process allows us to incorporate the uncertainties embedded in the future mortality evolution. The proposed model has been applied to price the EIB/BNP Longevity Bonds and other mortality derivatives under the independent assumption of interest rate and mortality rate. A calibrating method for the model is suggested so that it can utilize both the market price information involving the relevant mortality risk and the latest mortality projection. The proposed model has also been fitted to various type of population mortality data for empirical study. The fitting results show that our model can interpret the stylized mortality patterns very well.
Analysis of Risk Measures and Multi-dimensional Risk Dependence
Keywords: Risk measures
Nonparametric estimation

In this thesis, we try to provide a broad econometric analysis of a class of risk measures, distortion risk measures (DRM). With carefully selected functional form, the Value-at-Risk (VaR) and Tail-VaR (TVaR) are special cases of DRMs. Besides, the DRM also admits interpretation in the sense of non-expected utility type of preferences. We first provide a unified statistical framework for the nonparametric estimators of the DRMs in a univariate case. The asymptotic properties of both the DRMs and their sensitivities with respect to the parameters representing risk aversion and/or pessimism are derived. Moreover, the relationships between the VaR and TVaR are also investigated in detail, which, we hope, can shed new lights on the way passing one risk measure to another. Then, the analysis of DRMs are extended to a multi-dimensional framework, where the DRM is computed for a portfolio consisting of many primitive assets. Analogous to the mean-variance frontier analysis, we study the efficient portfolio frontier when both objective and constraint are replaced by the DRMs. We call this the DRM-DRM framework. Under a nonparametric setting, we propose three asymptotic test statistics for evaluating the efficiency of a given portfolio. Finally, we discuss the criteria used for evaluating models used to forecast the VaRs. More precisely, we propose a criterion which takes into account the loss levels beyond the VaRs.
Controlled Trans-lymphatic Delivery of Chemotherapy for the Treatment of Lymphatic Metastasis in Lung Cancer
Keywords: trans-lymphatic
chemotherapy
targeted delivery
lung cancer
metastasis
lymph node
polymeric microparticulate
biodegradable gelatin sponge
paclitaxel
PLGA

Lymph node metastasis is a critical prognostic factor for lung cancer. Effective therapy to control lymphatic metastasis may improve survival. The work described in this thesis focuses on the development of a microparticulate lymphatic targeting system, which can be applied as an adjuvant therapy in the control of lymphatic metastasis in lung cancer. The study shows that intrapleural administered colloidal particulates are predominantly taken up by regional lymphatic tissue in rat models including healthy rats, rats bearing orthotopic lung tumours and rats following pneumonectomy. The effect of particle size on lymphatic particle distribution was examined by intrapleural administration of 111In-aminopolystyrene beads. Approximately 2 µm is a suitable size for intrapleural lymphatic targeting. Biodegradable polylactide-co-glycolide (PLGA) microparticles containing the anticancer agent paclitaxel (PTX) were subsequently formulated in the desired size by spray drying. PLGA-PTX microspheres were incorporated into a biodegradable and biocompatible gelatin sponge matrix to form an implantable lymphatic targeted drug delivery system. The system was characterized in vitro and its lymphatic targeting ability was examined in vivo. Fluorescence labeled microspheres embedded within the sponge were selectively taken up by regional lymphatics as the sponge matrix disintegrated following intrapleural implantation. A pharmacokinetic study showed that the total PTX exposure in lymphatic tissue was dramatically higher than that achieved through intravenous administration. The peak plasma drug concentration, which governs systemic toxicity, was significantly reduced. The low but persistent detection of plasma PTX indicates that PTX was control released from the system after intrapleural implantation. In a therapeutic efficacy study performed in the H460 orthotopic lung cancer model, gelatin sponges containing PLGA-PTX microspheres were placed in the pleural cavity as an adjuvant treatment after surgical resection of the primary lung tumour. Trans-lymphatic chemotherapy resulted in a significantly lower incidence of lymphatic tumour recurrence (20%) compared to no treatment and placebo control animals (100%). PLGA-PTX microspheres were seen in regional lymphatic tissue over 4 weeks after the sponge placement. It is concluded that the trans-lymphatic targeting drug delivery system described in this thesis may improve the control of lymphatic metastasis in lung cancer.