The Integrin Tail: A Tale of Cell Motility and Division

Integrin transmembrane receptor functions are regulated by adaptor molecules binding to their alpha and beta subunit intracellular domains, or tails, thus affecting integrin traffic and adhesion during e.g. cell motility. Interestingly, many cellular proteins function in both cell motility and cell division, thus raising the possibility that integrins might be involved in regulating the cell cycle. A thorough understanding of cell division is essential in cell biology and in human malignancies. It is well established that failures to complete cell cycle can give rise to genetically unstable cells with tumorigenic properties. Transformed cells promote the disruption of intercellular adhesions such as tight junctions, and this correlates with the onset of cell motility, invasion and unfavorable prognosis in cancer. In this study, we analyzed integrin regulation, mediated by adaptor binding to the  subunit tail, during cell motility and cell division. We revealed a novel molecular mechanism by which Rab21, through association with the integrin alpha subunits, drives integrin endosomal traffic during mitotic phases. In addition, we found indications for this finding in vivo, as RAB21 gene deletions were mapped in ovarian and prostate cancer samples. Importantly, the multinucleated phenotype of cultured ovarian cancer cells could be reverted by Rab21 overexpression. In this thesis work, we also show how the tight junction protein ZO-1 unexpectedly interacts with the 5 integrin cytoplasmic domain in the lamellipodia to promote cell motility and at the cleavage furrow to support separation of the daughter cells. The alpha5-ZO-1 complex formation was dependent on PKC which regulates ZO-1 phosphorylation and its subcellular localization. In addition, by an in situ detection method, we showed that a subset of metastatic human lung cancers expressed the alpha5beta-ZO-1 complex. Taken together, we were able to identify new molecular pathways that regulate integrin functions in an alpha tail-mediated fashion. These findings firmly suggest that genetic alterations in integrin traffic may lead to progression of tumorigenesis as a result of failed cell division. Also, the interplay of integrins and ZO-1 in forming spatially regulated adhesive structures broadens our view of crosstalk between pathways and distinct adhesive structures that can be involved in cancer cell biology.

Regulation of cell fate by c-FLIP phosphorylation

Programmed cell death is an important physiological cellular process that maintains homeostasis and protects multicellular organisms from diseases. Apoptosis is the principal mode of cell death, which eliminates unwanted cells and an enormous effort has been made to understand the molecular mechanisms of the signaling pathway and its regulatory systems. Irregular apoptosis often has life-threatening consequences to humans, including cancer, autoimmune diseases and degenerative diseases. In cancer for example, cell death is an attractive target to eradicate uncontrollably proliferating cells that have disregard pro-apoptotic signaling. Targeted therapeutic approaches are not as effective as once expected, since now we know that the cell death pathways are not sole entities in cells, but are highly associated with various cellular processes. Proteins that regulate apoptosis can also control non-apoptotic signaling pathways. For example, c-FLIP is a protein that can either inhibit or promote caspase-8 activation, which is required to induce apoptosis. Not only has c-FLIP opposing effects on initiating apoptosis, but it also regulates various pro-survival signaling pathways in the cell. It is well known that protein expression level is a determinant of how c-FLIP can regulate different signaling pathways, but other regulatory mechanisms potentially affecting the role of c-FLIP are less well understood. This work addresses novel insights into the mechanisms of c-FLIP post-translational modifications and their functional consequences. We have identified that phosphorylation is an important inception for subcellular localization of c-FLIP, thereby dictating which apoptotic and non-apoptotic signaling pathways c-FLIP could regulate to promote cell survival. Furthermore, we have constructed mathematical models to unite independent studies to establish more systematic c-FLIP signaling pathways to understand the dynamics of extrinsically-induced apoptosis.

Geostatistical prediction of clay percentage based on soil survey data

Selostus: Viljelymaiden savespitoisuuden alueellistaminen geostatistiikan ja pistemäisen tiedon avulla

The use of quantile trees in the prediction of the diameter distribution of a stand

Abstract

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract

Coarse Global Visual Localization of a Mobile Robot

Localization, which is the ability of a mobile robot to estimate its position within its environment, is a key capability for autonomous operation of any mobile robot. This thesis presents a system for indoor coarse and global localization of a mobile robot based on visual information. The system is based on image matching and uses SIFT features as natural landmarks. Features extracted from training images arestored in a database for use in localization later. During localization an image of the scene is captured using the on-board camera of the robot, features are extracted from the image and the best match is searched from the database. Feature matching is done using the k-d tree algorithm. Experimental results showed that localization accuracy increases with the number of training features used in the training database, while, on the other hand, increasing number of features tended to have a negative impact on the computational time. For some parts of the environment the error rate was relatively high due to a strong correlation of features taken from those places across the environment.

Mobile robot localization using sonar ranging and wlan intensity maps

 Main goal of this thesis was to implement a localization system which uses sonars and WLAN intensity maps to localize an indoor mobile robot. A probabilistic localization method, Monte Carlo Localization is used in localization. Also the theory behind probabilistic localization is explained. Two main problems in mobile robotics, path tracking and global localization, are solved in this thesis. Implemented system can achieve acceptable performance in path tracking. Global localization using WLAN received signal strength information is shown to provide good results, which can be used to localize the robot accurately, but also some bad results, which are no use when trying to localize the robot to the correct place. Main goal of solving ambiguity in office like environment is achieved in many test cases.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Prediction of heat transfer on kraft recovery boiler

Työssä tutkittiin Andritz-Ahlstrom toimittamien soodakattiloiden lämmönsiirtoa ANITA 2.20- suunnitteluohjelmalla feedback- laskentaa apuna käyttäen. Data laskentaan saatiin kattiloiden takuukokeissa mitatuista arvoista. Mittaukset on suoritettiin Andritz-Ahlstromin henkilökunnan toimesta tehdashenkilökunnan avustuksella. Feedback -laskenta tapahtui mittaustulosten perusteella, joten tiettyä virhettä luonnollisesti esiintyi. Aluksi laskettiin taseet molempien ekojen yli erikseen sekä molemmat yhdessä Excel-taulukkolaskentaohjelmalla. Täältä saatiin oletettu savukaasuvirtaus kattilassa. Tämän jälkeen lämpöpintoja muokattiin todellisuutta vastaaviksi yleislikaisuuskerrointa muuttamalla (overall fouling factor). Kertoimet ovat liikkuivat noin 0.4 ja 1.6 välillä riipuen kattilan tyypistä ja ANITAn oletuksesta lämpöpintojen likaisuudelle. Havaittin että yhtä varsinaista syytä lämpöpintojen eroavaisuuteen oletetusta ei saatu. Syitä toiminnan poikkeamiseen oli monia. Mm. etukammion koolla havaittiin olevan suurtakin vaikutusta tulistimien, etenkin savukaasuvirrassa ensimmäisen tulistimen toimintaan. Yleisesti todettiin muiden tulistimien vastaavan oletettua toimintaa. Keittopinnan ja ekonomiserien toimintaa tutkittiin hivenen suppeammin ja havaittiin niiden toimivan huomattavasti stabiilimmin kuin tulistimien. Likaisuus kertoimet oletetusta vaihtelivat noin ±20 %.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

Prediction in transformed images

The study is related to lossless compression of greyscale images. The goal of the study was to combine two techniques of lossless image compression, i.e. Integer Wavelet Transform and Differential Pulse Code Modulation to attain better compression ratio. This is an experimental study, where we implemented Integer Wavelet Transform, Differential Pulse Code Modulation and an optimized predictor model using Genetic Algorithm. This study gives encouraging results for greyscale images. We achieved a better compression ration in term of entropy for experiments involving quadrant of transformed image and using optimized predictor coefficients from Genetic Algorithm. In an other set of experiments involving whole image, results are encouraging and opens up many areas for further research work like implementing Integer Wavelet Transform on multiple levels and finding optimized predictor at local levels.