LRP1 (low-density lipoprotein receptor–related protein 1), un récepteur endocytaire multifonctionnel, a récemment été identifié comme pivot d’un réseau de biomarqueurs pour la prédiction pronostique de plusieurs types de cancers. Son rôle dans le cancer du côlon n'a pas été caractérisé. Notre travail porte sur l’étude de la relation entre expression de LRP1 et cancer du côlon.L'expression de l'ARNm LRP1 a été déterminée dans des échantillons d'adénocarcinome et de muqueuses coliques appariées, ainsi que dans les cellules stromales et tumorales obtenues après microdissection laser. Les associations clinicopathologiques et moléculaires ont été étudiées par immunohistochimie dans une série de cancer colique (n = 307). La présence de méthylation ou mutation du gène LRP1 et l'expression de miR-205 ont été évaluées et comparées aux niveaux d'expression de LRP1.L’ARNm de LRP1 est sous exprimé dans les cellules d'adénocarcinome colique par rapport à la muqueuse colique par rapport aux cellules stromales. La faible expression immunohistochimique de LRP1 dans les adénocarcinomes était associée à un âge plus élevé, à localisation droite, une perte d'expression de CDX2, une expression d'Annexine A10, un statut CIMP-H, MSI-H et BRAFV600E muté. Cette faible expression était associée à un mauvais pronostic, en particulier chez les patients de stade IV. Les mutations du gène LRP1 entrainaient une sous-expression de LRP1. L’expression était peu modifiée par miR-205. Le promoteur de LRP1 n'était jamais méthylé.La perte d'expression de LRP1 est associée à un profil clinico-pathologique et moléculaire particulier et à un un mauvais pronostic dans les cancers du côlon. / LRP1 (low-density lipoprotein receptor–related protein 1), a multifunctional endocytic receptor, has recently been identified as a hub in a biomarker network for multi-cancer clinical outcome prediction. Its role in côlon cancer has not been characterized. Here, we investigate the relationship between LRP1 and colon cancer.LRP1 mRNA expression was determined in colon adenocarcinoma and paired colon mucosa samples, and in stromal and tumoral cells obtained after laser capture microdissection. The clinical potential was further investigated by immunohistochemistry in a population-based colon cancer series (n = 307). LRP1 methylation, mutation and miR-205 expression were evaluated and compared to LRP1 expression levels.LRP1 mRNA levels are significantly decreased in colon adenocarcinoma cells compared to colon mucosa and stromal cells. Low LRP1 immunohistochemical expression in adenocarcinomas was associated with higher age, right-sided tumor, loss of CDX2 expression, Annexin A10 expression, CIMP-H, MSI-H and BRAFV600E mutation. Low LRP1 expression correlates with poor clinical outcome, especially in stage IV patients. LRP1 expression was downregulated by LRP1 mutation. LRP1 expression was slightly modified by miR-205 expression. LRP1 promoter was never methylated.Loss of LRP1 expression is associated with peculiar clinocopathological and molecular characteristics and with worse colon cancer outcomes.

Self-drive day trip tourism has an enormous economic importance for the region of Lower Austria which surrounds Austria´s capital Vienna. The residents of Lower Austria and Vienna form the vast majority of day-trip visitors to Lower Austria. Despite the importance of the segment of day trip tourism for regional tourism marketing organizations (TMOs) this group of tourists is difficult to grasp. Promoting blogs of residents about their leisure time activities in a region is a promising marketing instrument for regional TMOs to address this market segment. This doctoral thesis project proposes and validates a behavioral model based on the technology acceptance model (TAM) for modeling behavioral intentions of three main participatory patterns in online communities based on blogs. Based on the results of this project possible starting points for subsequent research are identified and recommendations for TMOs intending to implement such regional blog communities are provided. The findings of this research project support practitioners by providing a deeper understanding of the motives of prospective participants. As the elements of the proposed behavioral model are based on previous research and conceptualized independent of the topic of interest of the blog community, the findings are additionally indicatory for research on blog communities in other fields. (author's abstract)

Turkish speakers differentiate the blue region of color spectrum into mavi (blue) and lacivert (dark blue) / whereas non-Turkish speakers in this study had only one color term in the blue region. The present study aimed to explore the predictions of the Linguistic Relativity Hypothesis. Operationally, Categorical Perception (CP) effects were used. In Experiment 1, Turkish speakers performed a naming task to determine an average category boundary between mavi and lacivert. In Experiment 2, both Turkish and non-Turkish speakers&rsquo / color-difference detection thresholds were estimated on the average boundary as well as within the mavi and lacivert categories. The thresholds were also estimated in the green region, in which both groups had only one color term. 2-TAFC method, which eliminates the effects of memory or labeling and isolates the perceptual processes, was used to estimate the thresholds. Turkish speakers, and not non-Turkish speakers, were predicted to show CP effects only in the blue region: thresholds should be lower on the boundary than within-category. The result revealed that Turkish speakers&rsquo / color-difference detection thresholds were lower than those of non-Turkish speakers both in the blue and the green regions. The difference in the green region does not rule out the LRH. It is possible that this difference resulted from the limitations of the study. Finally, in Experiment 3, Turkish speakers&rsquo / thresholds were also estimated on their individual boundaries. The patterns of the thresholds revealed by Experiment 3 were similar to the pattern of the thresholds in Experiment 2.

The perceptual reality of the music theoretical relation between the Neapolitan chord and the dominant / and the secondary dominant chord and its diatonic associate was investigated within the chord priming paradigm. In Experiment 1, expectation towards the dominant chord after the Neapolitan chord was observed in Turkish musicians and non-musicians with piano timbre. In Experiment 2, expectation towards the dominant chord after the Neapolitan chord was observed in European musicians but not in European non-musicians. In Experiment 3, Turkish non-musicians were tested with Shepard tones / but it was not possible to observe any priming effects. To understand effects of cultural background on the difference between the results of Experiments 1 and 2 further studies are necessary. In Experiments 4-5, the perceptual reality of the relation between the secondary dominant chord and its diatonic associate was investigated in Turkish non-musicians. In Experiment 4, chord sequences that included secondary dominant chords were played with Shepard tones / and they were scrambled with 2by2 scrambling algorithm. Experiment 5 was identical with Experiment 4, except chord sequences were played with the piano timbre. Experiment 6 was identical with Experiment 5, except chord sequences were scrambled with 4by4. However, in Experiments 4-6 detrimental effects of scrambling sequences that include secondary dominant chords on the priming of chords were not observed. Turkish non-musicians did perceive the relation between the secondary dominant chord and its diatonic associate. In neural network simulations of this thesis it was shown that statistical learning from the musical environment with self-organization could be achieved without committing the questionable assumptions of previous studies.

The aim of this study was to find if manipulating fluency, that is, the ease of processing, could affect confidence ratings about whether an event occurred in the respondents&rsquo / past. To test the familiarity misattribution hypothesis, which states that familiarity caused by fluent processing can be misattributed to past experience if the source of fluency cannot be identified, two methods were used: a revelation task, which was anagram solving and repetition priming. In the revelation task the familiarity misattribution hypothesis and the activation based hypothesis were tested by presenting one of the words in each one of the Life Event Inventory (LEI) items as an anagram or an unrelated anagram before the LEI, respectively. Higher confidence ratings for LEIs with an anagram compared to LEIs without anagrams would indicate that a revelation effect. A revelation effect was not observed for either condition. Therefore, the previous findings of revelation effect for autobiographical memories (Bernstein et al., 2002) could not be replicated when Turkish counterparts of LEI and anagrams were used. In the repetition priming experiments, the participants&rsquo / awareness of the source of fluency was manipulated by presenting either a subliminal or a supraliminal prime before they responded to a LEI item. The prime was either the same as the verb of the LEI sentence, or a different verb. Participants gave higher confidence ratings if subliminal primes were identical to, rather than different from, the verb of the sentence. If the participants were aware of seeing the primes, this difference disappeared. These results were consistent with the familiarity misattribution hypothesis.

Channel order estimation is an important problem in many fields including signal processing, communications, acoustics, and more. In this thesis, blind channel order estimation problem is considered for single-input, multi-output (SIMO) FIR systems. The problem is to estimate the effective channel order for the SIMO system given only the output samples corrupted by noise. Two new methods for channel order estimation are presented. These methods have several useful features compared to the currently known techniques. They are guaranteed to find the true channel order for noise free case and they perform significantly better for noisy observations. These algorithms show a consistent performance when the number of observations, channels and channel order are changed. The proposed algorithms are integrated with the least squares smoothing (LSS) algorithm for blind identification of the channel coefficients. LSS algorithm is selected since it is a deterministic algorithm and has some additional features suitable for order estimation. The proposed algorithms are compared with a variety of dierent algorithms including linear prediction (LP) based methods. LP approaches are known to be robust to channel order overestimation. In this thesis, it is shown that significant gain can be obtained compared to LP based approaches when the proposed techniques are used. The proposed algorithms are also compared with the oversampled single-input, single-output (SISO) system with a generic decision feedback equalizer, and better mean-square error performance is observed for the blind setting. Channel order estimation problem is also investigated for semi-blind systems where a pilot signal is used which is known at the receiver. In this case, two new methods are proposed which exploit the pilot signal in dierent ways. When both unknown and pilot symbols are used, a better estimation performance can be achieved compared to the proposed blind methods. The semi-blind approach is especially effective in terms of bit error rate (BER) evaluation thanks to the use of pilot symbols in better estimation of channel coecients. This approach is also more robust to ill-conditioned channels. The constraints for these approaches, such as synchronization, and the decrease in throughput still make the blind approaches a good alternative for channel order estimation. True and effective channel order estimation topics are discussed in detail and several simulations are done in order to show the significant performance gain achieved by the proposed methods.

Geostatistics and more specifically stochastic modeling of reservoir heterogeneities are being increasingly considered by reservoir analysts and engineers for their potential in generating more accurate reservoir models together with usable measures of spatial uncertainty. Geostatistics provides a probabilistic framework and a toolbox for data analysis with early integration of information. The uncertainty about the spatial distribution of critical reservoir parameters is modeled and transferred all the way to a risk conscious reservoir management. The stochastic imaging (modeling) algorithms allow the generation of multiple, equiprobable, unsmoothed reservoir models yet all honoring the data available. This thesis presents stochastic reserve estimation methods as related to the various stages of development of an oil field. Advances in technology are leading to better deterministic estimates as well as stochastic estimates with narrower ranges. Practices in the industry vary from complete dedication to deterministic or stochastic to a choice of the method depending on the stage of the development. In this study, reserves are calculated from the data available in Southeastern Turkey by using stochastic methods. Probability density functions, number of iterations are important statistical concepts. Increasing number of iterations leads to a normal distribution of histogram.

A stiffened DKT shell element is formulated for the linear static analysis of stiffened plates and shells. Three-noded triangular shell elements and two-noded beam elements with 18 and 12 degrees of freedom are used respectively in the formulation. The stiffeners follow the nodal lines of the shell element. Eccentricity of the stiffener is taken into account. The dynamic and stability characteristic of the element is also investigated. With the developed computer program, the results obtained by the proposed element agrees fairly well with the existing literature.

In August, 2014, the Financial Accounting Standards Board issued an update concerning the disclosure of uncertainties about an entity’s ability to continue as a going concern. The standard requires an entities management to evaluate whether there is substantial doubt about the entity’s ability to continue as a going concern and to provide related footnote disclosures in certain circumstances. One consequence of this regulation is the need for guidance for audit testing of management’s assessments in each phase of the audit. This research evaluates the usefulness of bankruptcy prediction models as analytical tools in the planning stage of an audit for going concern assertions and questions the use of precision as the only measure of a model’s effectiveness. I use simulation to manipulate the fundamental accounting data within five bankruptcy prediction models, explore failure rates in an environment with materiality concerns, and consider the total change in market value due to simulated errors. Given the inherent limitations of the information environment and/or current prediction models, my results indicate auditors’ current failure rates are not an indication of audit failure. The results suggest that bright-line testing using bankruptcy prediction models are sensitive to materiality and that the cost trade-off between Type I and Type II errors is an important indicator of model choice.

Denoising signals is an important task of digital signal processing. Many linear and non-linear methods for signal denoising have been developed. Wavelet based denoising is the most famous nonlinear denoising method lately. In the linear case, Kalman filter is famous for its easy implementation and real-time nature. Wavelet- Kalman filter developed lately is an important improvement over Kalman filter, in which the Kalman filter operates in the wavelet domain, filtering the wavelet coeffi- cients, and resulting in the filtered wavelet transform of the signal in real-time. The real-time filtering and multiresolution representation is a powerful feature for many real world applications. This study explains in detail the derivation and implementation of Real-Time Wavelet-Kalman Filter method to remove noise from signals in real-time. The filter is enhanced to use different wavelet types than the Haar wavelet, and also it is improved to operate on higer block sizes than two. Wavelet shrinkage is integrated to the filter and it is shown that by utilizing this integration more noise suppression is obtainable. A user friendly application is developed to import, filter and export signals in Java programming language. And finally, the applicability of the proposed method to suppress noise from seismic waves coming from eartquakes and to enhance spontaneous potentials measured from groundwater wells is also shown.