In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a variant of association rule mining, was used to obtain the rules with some targeted items or class labels. These rules can be used to improve public awareness of different cancer symptoms and could also be useful to initiate prevention strategies. In the second part of the thesis, ML techniques have been applied in healthcare or medical data to build a predictive model. Three different classification techniques on a real-world breast cancer risk factor data set have been investigated. Due to the imbalance characteristics of the data set various resampling methods were used before applying the classifiers. It is shown that there was a significant improvement in performance when applying a resampling technique as compared to applying no resampling technique. Moreover, super learning technique that uses multiple base learners, have been investigated to boost the performance of classification models. Two different forms of super learner have been investigated - the first one uses two base learners while the second one uses three base learners. The models were then evaluated against well-known benchmark data sets related to the healthcare domain and the results showed that the SL model performs better than the individual classifier and the baseline ensemble. Finally, we assessed cancer-relevant genes of prostate cancer with the most significant correlations with the clinical outcome of the sample type and the overall survival. Rules from the RNA-sequencing of prostate cancer patients was discovered. Moreover, we built the regression model and from the model rules for predicting the survival time of patients were generated.

Une précision à l’échelle de la milliseconde dans le transfert d'informations entre les neurones est essentielle pour la synchronisation et la plasticité des circuits neuronaux dans le cerveau. Les axones sont des prolongements neuronaux qui assurent la communication via des impulsions électriques ou des potentiels d’action (PA). A cause du manque de myéline et de leur diamètre très fin, les axones de l'hippocampe propagent les PA lentement et ainsi générer des délais de conduction très long (jusqu’à 100 ms) qui sont traditionnellement considérés comme invariants. Cependant, plusieurs études ont montré que l'activité change la morphologie des axones et module le temps de latence de la transmission. Il convient donc de se demander si le diamètre des axones varie en fonction de l'activité pouvant influencer lapropagation des PA.Les diamètres des axones non-myélinisés de l’hippocampe (compris entre 100-350 nm) sont généralement trop petits pour être résolu par la microscopie photonique conventionnelle. Le développement récent de l’imagerie super résolution STED permet désormais l'observation de la dynamique de leur morphologie détaillée dans le tissu vivant. En combinant la microscopie STED, l’électrophysiologie avec enregistrements en champs et patch-clamp dans des tranches de cerveau de souris et des simulations informatiques, nous avons découvert que les axones du CA3 subissent un élargissement de leur diamètre après l'induction de la potentialisation à long terme (PLT). Nous démontrons que cet élargissementde diamètre augmente la vitesse de conduction des PA. Dans l'ensemble, nos résultats indiquent que les axones peuvent réguler leur diamètre de manière dynamique changeant le délai de conduction des PA, ce qui modifie le timing du transfert d’information dans les circuits neuronaux. Cette étude suggère l’existence d’un nouveau type de mécanisme structurel dans le compartiment axonal jouant un rôle pour la plasticité neuronale. / Millisecond timing precision in the transfer of information between neurons is essential for the synchrony and plasticity of neural circuits in the brain. Axons are neuronal extensions that ensure the communication via brief electrical impulses called action potentials (AP). Because they are unmyelinated and are extremely thin, hippocampal axons propagate APsslowly and thus generate long delays of conduction (up to 100 ms) that are traditionally considered invariant. However, recent studies have shown that activity changes the morphology of axons and modulate the latency of transmission, thus raising the question whether axons undergo activity-dependent structural changes that could influence the propagation of APs. The diameter of hippocampal axons (ranging between 100-350 nm) are usually too thin to be properly resolved by conventional light microscopy. However, the development of super resolution STED imaging now enables the observation of their detailed morphological dynamics in living tissue. Using a novel combination of STED microscopy, field recordings, patch-clamp electrophysiology in mouse brain slices and computer simulations we discovered that CA3 axons undergo long-lasting enlargement in their diameter after the induction of long term potentiation (LTP). We provide strong evidence that this diameter enlargement increases AP conduction velocity. Taken together, our findings indicate that axons can dynamically tune AP propagation delays by changing their diameters, thereby altering the timing of information transfer in neural circuits. This study suggests a novel and powerful structural mechanism for neural plasticity.

In this study, the regular expressions forming abstract states in Brzozowski’s algorithm are not remapped to sequential state transition table addresses as would be the case in the classical approach, but are hashed to integers. Two regular expressions that are hashed to the same hash code are assigned the same integer address in the state transition table, reducing the number of states in the automaton. This reduction does not necessarily lead to the construction of a minimal automaton: no restrictions are placed on the hash function hashing two regular expressions to the same code. Depending on the quality of the hash function, a super-automaton, previously referred to as an approximate automaton, or an exact automaton can be constructed. When two regular expressions are hashed to the same state, and they do not represent the same regular language, a super-automaton is constructed. A super-automaton accepts the regular language of the input regular expression, in addition to some extra strings. If the hash function is bad, many regular expressions that do not represent the same regular language will be hashed together, resulting in a smaller automaton that accepts extra strings. In the ideal case, two regular expressions will only be hashed together when they represent the same regular language. In this case, an exact minimal automaton will be constructed. It is shown that, using the hashing approach, an exact or super-automaton is always constructed. Another outcome of the hashing approach is that a non-deterministic automaton may be constructed. A new version of the hashing version of Brzozowski’s algorithm is put forward which constructs a deterministic automaton. A method is also put forward for measuring the difference between an exact and a super-automaton: this takes the form of the k-equivalence measure: the k-equivalence measure measures the number of characters up to which the strings of two regular expressions are equal. The better the hash function, the higher the value of k, up to the point where the hash function results in regular expressions being hashed together if and only if they have the same regular language. Using the k-equivalence measure, eight generated hash functions and one hand coded hash function are evaluated for a large number of short regular expressions, which are generated using G¨odel numbers. The k-equivalence concept is extended to the average k-equivalence value in order to evaluate the hash functions for longer regular expressions. The hand coded hash function is found to produce good results. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Computer Science / unrestricted

The novel method herein proposed, aims to study Deoxyribonucleic acid (DNA) and Rad51 repair protein in its resting state after their interaction by using a combination of biological preparation and physical measures. Rad51 is a highly conserved protein; it is involved in eukaryotes genome stability and can interact with single strand (ss) and double strands (ds) DNA. In our work, a droplet of the solution containing the dsDNA/Rad51 complexes was deposited on micro-fabricated super-hydrophobic substrates (SHS) to obtain self-organized and suspended fibers. The silicon-based SHS were designed to incorporate a regular circular array of pillars and to maintain a high contact angle with the drop. The samples were let dehydrate at controlled temperature and humidity conditions. At the end of the buffer evaporation process, non-suspended material and salt excess are concentrated on the top of a few micro-pillars in a limited area (drop residual) of the device while ordered and self-assembled DNA/Rad51 fibers are suspended between micro-pillars. To find the ideal conditions to obtain and suspend the nucleic acid/protein complexes, several parameters were investigated: saline buffer, DNA and protein concentrations were widely titrated and showed a significant effect on the biomolecule suspension on SHS. The samples were then preliminarily checked by microscopy techniques and then described by the Raman spectra acquired. Several techniques were used: optical microscopy, Energy Dispersive X-Ray Spectroscopy (EDAX), Scanning Electron Microscopy (SEM) and Raman Spectroscopy. Protein expressions, DNA suspension, micro-fabrication and characterization were all performed in KAUST Core Labs and Structural Molecular Imaging Light Enhanced Spectroscopies (SMILEs) Lab. The novel approach presented in this work is highly multidisciplinary and comprises physical measurements (Raman spectroscopy and EM imaging), chemistry and biology. In future the method can be used further expanded supporting the data with HRTEM direct imaging to elucidate the nucleic acids/proteins behavior in the multiple phases of the genome repair processes. Also, it and can serve as a fingerprint of the biological molecules involved in biological interactions, their localization and structural characterization, providing a new tool for structural analysis, screening and diagnostics.

This thesis is focused on the impact of using soil conditioners in growing grapevine. In the experimental part the effect of Super Hume was evaluated at selected locations. It is impact on the quality and production was evaluated. The results were used to create the economic analysis when using Super Hume.

Automatic target recognition (ATR) is the ability for a computer to discriminate between different objects in a scene. ATR is often performed on point cloud data from a sensor known as a Ladar. Increasing the resolution of this point cloud in order to get a more clear view of the object in a scene would be of significant interest in an ATR application. A technique to increase the resolution of a scene is known as super resolution. This technique requires many low resolution images that can be combined together. In recent years, however, it has become possible to perform super resolution on a single image. This thesis sought to apply Gabor Wavelets and Compressive Sensing to single image super resolution of digital images of natural scenes. The technique applied to images was then extended to allow the super resolution of a point cloud.

Synthetic X-ray or digitally reconstructed radiographs (DRRs) are simulated X-ray images projected from computed tomography (CT) data that are commonly used for CT and real X-Ray image registration. High-quality synthetic X-ray images can facilitate various applications such as guiding images for virtual reality (VR) simulation and training data for deep learning methods such as creating CT data from X-Ray images. It is challenging to generate high-quality synthetic X-ray images from CT slices, especially in various view angles, due to gaps between CT slices, high computational cost, and the complexity of algorithms. Most synthetic X-ray generation methods use fast ray-tracing in a situation where the image quality demand is low. We aim to improve image quality while maintaining good accuracy and use two steps; 1) to generate synthetic X-ray images from CT data and 2) to increase the resolution of the synthetic X-ray images. Our synthetic X-ray image generation method adopts a matrix-based projection method and dynamic multi-segment lookup tables, which shows better image quality and efficiency compared to conventional synthetic X-ray image generation methods. Our method is tested in a real-time VR training system for image-guided intervention procedures. Then we proposed two novel approaches to raise the quality of synthetic X-ray images through deep learning methods. We use a reference-based super-resolution (RefSR) method as a base model to upsampling low-resolution images into higher resolution. Even though RefSR can produce fine details by utilizing the reference image, it inevitably generates some artifacts and noise. We propose texture transformer super-resolution with frequency domain (TTSR-FD) which introduces frequency domain loss as a constraint to improve the quality of the RefSR results with fine details and without apparent artifacts. To the best of our knowledge, this is the first work that utilizes frequency domain as a part of loss functions in the field of super-resolution (SR). We observe improved performance in evaluating TTSR-FD when tested on our synthetic X-ray and real X-ray image datasets. A typical SR network is trained with paired high-resolution (HR) and low-resolution (LR) images, where LR images are created by downsampling HR images using a specific kernel. The same downsampling kernel is also used to create test LR images from HR images. As a result, most SR methods only perform well when the testing image is acquired using the same downsampling kernel used during the training process. We also propose TTSR-DMK, which uses multiple downsampling kernels during training to generalize the model and adopt a dual model that trains together with the main model. The dual model can form a closed-loop with the main model to learn the inverse mapping, which further improves the model’s performance. Our method works well for testing images produced by multiple kernels used during training. It can also help improve the model performance when testing images are acquired with kernels not used during training. To the best of our knowledge, we are the first to use the closed-loop method in RefSR. We have achieved: (i) synthetic X-ray image generation from CT data, which is based on a matrix-based projection and lookup tables ; (ii) TTSR-FD: synthetic X-ray image super-resolution using a novel frequency domain loss ; (iii) TTSR-DMK: an adaptation network to overcome the performance drop for testing data which do not match to downsampling kernels used in training. Our TTSR-FD results show improvements (PSNR from 37.953 to 39. 009) compared to the state-of-the-art methods TTSR. Our experiment with real X-Ray images using TTSR-FD can remove visible artifacts in the qualitative study even though PSNR is similar. Our proposed adaptation network, TTSR-DMK, improved model performance for multiple kernels even with unknown kernel situations.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19501号 / 理博第4161号 / 新制||理||1598(附属図書館) / 32537 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 中家 剛, 教授 谷森 達, 准教授 市川 温子 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

“Violent men and superficial women” (“Våldsamma män och utseendefixerade kvinnor”) by authors Magda Wendel Bommarco and Lina Larsson aims to evaluate how male and female stereotypes are constructed in Super Bowls commercials 2023. The subject illustrates the issues behind stereotypes as a part of wider social ideologies and power relations, which makes the topic highly relevant to examine. The study examines how male and female stereotypes are constructed in the commercials, how they differ as well as how they can be understood. The research questions are examined through a semiotic content analysis of 10 commercials from Super Bowl 2023. The essay's theoretical framework consists of hegemonies, stereotypes and the gender system. The main results reveal that women are stereotyped as dumb, crazy, fixated on appearance as well as motherly in the commercials. This differs from the male stereotypes, which are related to violence and criminality, beer-drinking, sports and being a father. The results reveal that both genders are stereotyped as clichés, but that the female stereotypes are portrayed as more problematic than the male stereotypes are. The results can complement previous research in the field as well as display the stereotypes' function in society as a whole. The results are therefore relevant for the academic field as well as society.

We study positive solutions to classes of steady state reaction diffusion equations that arise naturally in applications. In particular, we study models arising from population dynamics and combustion theory. The main focus of this dissertation is the mathematical analysis of a challenging new class of problems when a certain nonlinear boundary condition is satisfied. In particular, we establish existence and multiplicity results by making use of the Quadrature method, the method of sub-super solutions, and degree theory. The results in this dissertation provide a significant contribution towards the analysis of elliptic boundary value problems with nonlinear boundary conditions.