Semliki Forest viiruse replikatsiooni ja patogeensuse uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Alfaviirused on maailmas väga laialt levinud selgroogsete organismide patogeenid ja nad põhjustavad erinevate sümptomitega haigusi, sealhulgas ka artriiti (liigesepõletik) ja vahel surmaga lõppevat entsefaliiti (ajupõletik). Alfaviiruste nakkuse ennetamist võimaldavad efektiivsed vaktsiinid ja nakkuse ravimiseks vajalikud spetsiifilised ravimid hetkel puuduvad. Selleks, et viiruste poolt põhjustatud haiguseid oleks võimalik ennetada või ravida, on esmalt vaja viiruse nakkust põhjalikult uurida ning detailselt mõista. Üheks kõige põhjalikumalt uuritud alfaviiruseks on Semliki Forest viirus (SFV), millele keskendus ka minu uurimistöö. SFV põhjustab hiirtes entsefaliiti ning SFV nakkust kasutatakse ühe võimaliku mudelina viirusliku entsefaliidi uurimisel. Hõlbustamaks viiruse paljunemise jälgimist organismis, disainisin ma SFV vormid, mis tootsid kergesti visualiseeritavaid või kvantiteeritavaid valke, nn markervalke. Loodud markerviirused võimaldasid detailselt jälgida SFV levikut täskasvanud hiire kesknärvisüsteemis. Minu uurimustöö näitas, et SFV nakkuse dünaamika on erinevat tüüpi rakkudes erinev – neuronid surusid viiruse valkude tootmise kiiresti maha, aga oligodendrotsüütid ei saanud sellega hakkama ning viiruse valkude tootmine toimus ka hilises nakkuse staadiumis. Leidsin ka, et nakkuse kulg neuronites sõltub nende rakkude küpsusastmest. Noored diferentseerumata neuronid ei suutnud viiruse valkude sünteesi efektiivselt maha suruda ja seetõttu toimus SFV paljunemine noortes rakkudes palju kiiremini kui küpsetes diferentseerunud neuronites. Saadud tulemused aitavad paremini mõista nii SFV kui teiste alfaviiruste infektsiooni ja patogeneesi selgroogsete, kaasa arvatud inimese, organismis.Alphaviruses are widely spread vertebrate pathogens, which cause different diseases, including arthritis (inflammation of joints) and encephalitis (inflammation of brain). To date, there are no available efficient vaccines for alphaviruses nor specific drugs to cure the disease. To protect or cure an individual form infection, one must first study the virus infection and understand it in detail. One of the best studied alphaviruses, Semliki Forest virus (SFV), has been the focus of my research. In mice, SFV infection leads to the development of encephalitis and SFV-infected mice serve as a good model for studying the disease. I designed recombinant SFVs, which produce marker proteins that are easy to visualise and quantify. These viruses enabled me to monitor the SFV infection in the central nervous system of adult mice and led to the discovery that the dynamics of SFV infection depend on the cell type infected. In neurons, the synthesis of viral proteins was rapidly downregulated. Oligodendrocytes, on the other hand, were unable to efficiently control virus propagation and the synthesis of viral proteins continued also during later stages of infection. I also found that the course of SFV infection depends on the differentiation state of neurons. Immature neurons were unable to control the synthesis of viral proteins and the virus was propagating in these cells more rapidly than in mature neurons. These data help us better understand the infection and pathogenesis of SFV and other alphaviruses in vertebrates, including humans

Täiskasvanute vabahariduslik õpe eesti muusikakoolides

Maailmas on viimasel ajal väga populaarne nähtus ja ütlus „elukestev õpe“. Tegelikult on see väga lai mõiste, kuhu mahuvad nii formaalsed kui ka mitteformaalsed täiskasvanute hariduslikud võimalused. Kuigi täiskasvanute haridusele on Eestis juba palju tähelepanu pööratud, on siiski teatud valdkonnad, kus on vaja veel uurida ja välja tuua kitsad kohad. Näiteks on väga vähe tähelepanu saanud täiskasvanute muusikaalane haridus, kuid olen kindel, et ka selle uurimine on hetkel vajalik.http://tartu.ester.ee/record=b2610800~S13*es

Spordiklubide sponsorlust mõjutavad tegurid ja võimalused jalgpalliklubi sponsoreerimist suurendada Narva Transi näitel

https://www.ester.ee/record=b5381182*es

Keskkonnamaksud ja riigi majandusareng

http://tartu.ester.ee/record=b2611110~S1*es

Uussisserändajate kohanemine - välisspetsialistide abikaasade kogemused

https://www.ester.ee/record=b5251147*es

Erialase hariduseta pikaajaliste töötute tööotsimisstrateegiad ja ettepanekud tööle suundumise protsessi efektiivsemaks muutmiseks Võru maakonna näite

https://www.ester.ee/record=b5368388*es

A new low-complexity patch-based image super-resolution

In this study, a novel single image super-resolution (SR) method, which uses a generated dictionary from pairs of high-resolution (HR) images and their corresponding low-resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches Afterwards, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state-of-the-art methods

Reducible Dictionaries for Single Image Super-Resolution based on Patch Matching and Mean Shifting

A single-image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) images and their corresponding low resolution (LR) representations. First, HR images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods