Model-View-Controller architectural pattern and its evolution in graphical user interface frameworks

Model-View-Controller (MVC) is an architectural pattern used in software development for graphical user interfaces. It was one of the first proposed solutions in the late 1970s to the Smart UI anti-pattern, which refers to the act of writing all domain logic into a user interface. The original MVC pattern has since evolved in multiple directions, with various names and may confuse many. The goal of this thesis is to present the origin of the MVC pattern and how it has changed over time. Software architecture in general and the MVC’s evolution within web applications are not the primary focus. Fundamen- tal designs are abstracted, and then used to examine the more recent versions. Prob- lems with the subject and its terminology are also presented.

Theoretical and numerical study of thermomagnetic convection in magnetic fluids

In this thesis, the magnetic field control of convection instabilities and heat and mass transfer processesin magnetic fluids have been investigated by numerical simulations and theoretical considerations. Simulation models based on finite element and finite volume methods have been developed. In addition to standard conservation equations, themagnetic field inside the simulation domain is calculated from Maxwell equations and the necessary terms to take into account for the magnetic body force and magnetic dissipation have been added to the equations governing the fluid motion.Numerical simulations of magnetic fluid convection near the threshold supportedexperimental observations qualitatively. Near the onset of convection the competitive action of thermal and concentration density gradients leads to mostly spatiotemporally chaotic convection with oscillatory and travelling wave regimes, previously observed in binary mixtures and nematic liquid crystals. In many applications of magnetic fluids, the heat and mass transfer processes including the effects of external magnetic fields are of great importance. In addition to magnetic fluids, the concepts and the simulation models used in this study may be applied also to the studies of convective instabilities in ordinary fluids as well as in other binary mixtures and complex fluids.

Tyypin 1 diabeteksen matemaattisista ennustamismenetelmistä

Tutkielmassa käsitellään matemaattisia ennustamismenetelmiä, jotka soveltuvat tyypin 1 diabeteksen ennustamiseen. Aluksi esitellään menetelmiä, jotka soveltuvat puuttuvia havaintoja sisältävien aineistojen paikkaamiseen. Paikattua aineistoa on mahdollista analysoida useilla tavallisilla tilastollisilla menetelmillä, jotka sopivat täydellisiin aineistoihin. Seuraavaksi pyritään mallintamaan aineistoa semiparametrisilla komponenttimalleilla (eng. mixture model), jolloin mallin muotoa ei ole tiukasti etukäteen rajoitettu. Sen jälkeen sovelletaan kolmea luokittelevaa ennustajaa: logistista regressiomallia, eteenpäinsyöttävää yhden piilotason neuroverkkoa ja SVM-menetelmää (eng. support vector machine). Esiteltäviä menetelmiä on sovellettu todelliseen aineistoon, joka on kerätty Turun yliopistossa käynnissä olevassa tutkimusprojektissa. Projektin tavoitteena on oppia ennustamaan ja ehkäisemään tyypin 1 diabetesta (Type 1 diabetes prediction and prevention project, lyh. DIPP-projekti). Erityisesti projektissa on pyritty löytämään uusia tuntemattomia taudinaiheuttajia. Tässä tutkielmassa paneudutaan sen sijaan kerätyn havaintoaineiston matemaattisiin analysointimenetelmiin. Parhaat ennusteet saatiin perinteisellä logistisella regressiomallilla. Tutkielmassa kuitenkin todetaan, että tulevaisuudessa on mahdollista löytää parempia ennustajia parantamalla muita edellä mainittuja menetelmiä. Erityisesti SVM-menetelmä ansaitsisi lisähuomiota, sillä tässä tutkielmassa sitä sovellettiin vain kaikkein yksinkertaisimmassa muodossa.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Differentiation of Metabolically Distinct Areas within Head and Neck Region using Dynamic 18F-FDG Positron Emission Tomography Imaging

Positron Emission Tomography (PET) using ¹⁸F-FDG is playing a vital role in the diagnosis and treatment planning of cancer. However, the most widely used radiotracer, ¹⁸F-FDG, is not specific for tumours and can also accumulate in inflammatory lesions as well as normal physiologically active tissues making diagnosis and treatment planning complicated for the physicians. Malignant, inflammatory and normal tissues are known to have different pathways for glucose metabolism which could possibly be evident from different characteristics of the time activity curves from a dynamic PET acquisition protocol. Therefore, we aimed to develop new image analysis methods, for PET scans of the head and neck region, which could differentiate between inflammation, tumour and normal tissues using this functional information within these radiotracer uptake areas. We developed different dynamic features from the time activity curves of voxels in these areas and compared them with the widely used static parameter, SUV, using Gaussian Mixture Model algorithm as well as K-means algorithm in order to assess their effectiveness in discriminating metabolically different areas. Moreover, we also correlated dynamic features with other clinical metrics obtained independently of PET imaging. The results show that some of the developed features can prove to be useful in differentiating tumour tissues from inflammatory regions and some dynamic features also provide positive correlations with clinical metrics. If these proposed methods are further explored then they can prove to be useful in reducing false positive tumour detections and developing real world applications for tumour diagnosis and contouring.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Transgenic mice overexpressing neuropeptide Y: An experimental model of metabolic and cardiovascular diseases

Neuropeptide Y (NPY) is an abundant neurotransmitter in the brain and sympathetic nervous system (SNS). Hypothalamic NPY is known to be a key player in food intake and energy expenditure. NPY’s role in cardiovascular regulation has also been shown. In humans, a Leucine 7 to Proline 7 single nucleotide polymorphism (p.L7P) in the signal peptide of the NPY gene has been associated with traits of metabolic syndrome. The p.L7P subjects also show increased stress-related release of NPY, which suggests that more NPY is produced and released from SNS. The main objective of this study was to create a novel mouse model with noradrenergic cell-targeted overexpression of NPY, and to characterize the metabolic and vascular phenotype of this model. The mouse model was named OE-NPY^DBH mouse. Overexpression of NPY in SNS and brain noradrenergic neurons led to increased adiposity without significant weight gain or increased food intake. The mice showed lipid accumulation in the liver at young age, which together with adiposity led to impaired glucose tolerance and hyperinsulinemia with age. The mice displayed stress-related increased mean arterial blood pressure, increased plasma levels of catecholamines and enhanced SNS activity measured by GDP binding activity to brown adipose tissue mitochondria. Sexual dimorphism in NPY secretion pattern in response to stress was also seen. In an experimental model of vascular injury, the OE-NPY^DBH mice developed more pronounced neointima formation compared with wildtype controls. These results together with the clinical data indicate that NPY in noradrenergic cells plays an important role in the pathogenesis of metabolic syndrome and related diseases. Furthermore, new insights on the role of the extrahypothalamic NPY in the process have been obtained. The OE-NPY^DBH model provides an important tool for further stress and metabolic syndrome-related studies.

Grass-endophyte coevolution and ploidy levels in fescues

ABSTRACT Fescues consist of wild and cultivated grasses that have adapted to a wide range of environmental conditions. They are an excellent model species for evolutionary ecology studies that investigate symbiosis and polyploidization and their effects on plant performance. First, they are frequently infected with symbiotic endophytic fungi known to affect a plant’s ability to cope with biotic and abiotic environmental factors. Second, fescue species have been reported to have substantial intraspecific variation in their ploidy level and morphology. In my thesis, I examined large-scale generalizations for frequency of polyploidy and endophyte infections and their effects on plant morphology. As a model species, I selected red (Festuca rubra) and viviparous sheep’s (F. vivipara) fescues. They are closely related, but they differ in terms of distribution and endophyte infection frequency. I investigated the biogeographic pattern and population biology of 29 red and 12 viviparous sheep’s fescue populations across ≈300 latitudes in Europe (400-690 N). To examine plant ploidy levels, I implemented time- and cost-efficient plate-based high throughput flow cytometric analysis. This efficient procedure enabled me to analyze over 1000 red fescue individuals. I found three ploidy levels among them: overall 84 %, 9 % and 7 % of the red fescue plants were hexaploid, tetraploid and octoploid, respectively. However, all viviparous sheep’s fescue plants were tetraploid. Ploidy level of red fescue appeared to some extent follow gradients in latitude and primary production as suggested by previous studies, but these results could be explained better by taking the sampling design and local adaptation into account. Three Spanish populations were mostly tetraploids and one high elevation population in northernmost Finland (Halti) was octoploid, while most other populations (25 sites) were dominated by hexaploids. Endophyte infection frequencies of wild fescue populations varied from 0 to 81 % in red fescue populations and from 0 to 30 % in viviparous sheep’s fescue populations. No gradients with latitude or primary production of the sites were detected. As taxonomy of red fescues is somewhat unclear, I also studied morphology, ploidy variation and endophyte status of proposed subspecies of European red fescues. Contrary to previous literature, different ploidy levels occurred in the same subspecies. In addition to wild fescues, I also used two agronomically important cultivars of meadow and tall fescue (Schedonorus phoenix and S. pratensis). As grass-legume mixtures have an agronomic advantage over monocultures in meadows, I carried out a mixture/competition experiment with fescues and red clover to find that species composition, nutrient availability and endophyte status together determined the total biomass yield that was higher in mixtures compared to monocultures. The results of this thesis demonstrate the importance of local biotic and abiotic factors such as grazing gradients and habitat types, rather than suggested general global geographical or environmental factors on grass polyploidization or its association with symbiotic endophytic fungi. I conclude that variation in endophyte infection frequencies and ploidy levels of wild fescues support the geographic mosaic theory of coevolution. Historical incidents, e.g., glaciation and present local factors, rather than ploidy or endophyte status, determine fescue morphology.