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I nuläget finns flertalet företag som tillverkar distanser som är kompatibla med originalimplantat. Det finns dock ingen evidens för att dessa distanser är likvärdiga med originaldistanser när de används på originalimplantat. God passform hos implantatkomponenter är en viktig faktor för att undvika biologiska och tekniska komplikationer.Syftet med den här studien var att jämföra passformen mellan originaldistans och originlimplantat med kompatibel kopiadistans och originalimplantat.Material och metod: Fem originaldistanser och fem kompatibla distanser skruvades till originalimplantat. Provkroppar bäddades in och delade på mitten för att sedan analyseras i ljusmikroskop och SEM. Där mättes kontaktytans längd och mellanrummet mellan implantat och distans och distans och distansskruv. All data analyserades i SPSS.Resultatet: Implantatkoplex med kopiadistans hade fem gånger kortare kontaktyta mellan komponenterna och mellanrummet var större, vilket ger en försämrad stabilitet under andvändning. Analysen av distansskruvens yta visade morfologiska skillnader mot den inre ytan av distansen vilket kan leda till deformation och/eller fraktur av skruven.Konklusion: Användning av lågpris kopiadistanser till Astra originalimplantat kan leda till signifikant avvikande passform hos distansen och en lägre tolerans för stress hos distansskruven. / Nowadays there exist different abutments from different companies that are compatible to a certain manufacturer implant. But no scientific evidence exists on whether the fit of the compatible abutments are comparable to that of the genuine abutments when connected to the genuine implants. God fitting of implant components is an essential factor to avoid microgap formation in implant-abutment interface due to microgaps lead to biological and biomechanical complications. The aim of this study was to compare interface in original abutment and copy compatible abutment connected to original implant.Material and Methods: Five original abutments and five compatible abutments were connected to the original implants. The complex were sectioned in the middle and thereafter analyzed in a light microscope and SEM by measuring contact length and interface thickness between the implant and abutment and also between the abutment and abutment screw. All data was analyzed with SPSS-program.Result: Compatible implant complex had about five times shorter contact surface between components and interface was thicker which gives less stability under clinical function. The analyze of screw-abutment interface showed morphologic differences with the inner surface of the abutment which could lead to screw deformation and even fracture.Conclusion: Using low-cost compatible abutments connected to original Astra implants could lead to significant fit discrepancy of the copy abutments and lower stress tolerance in the abutment screw.

Escherichia coli and several other bacteria express a stationary phase sigma factor, RpoS, for RNA polymerase that is responsible for inducing the expression of stress response genes. rpoS expression is induced during early exponential phase and the concentration of RpoS dramatically increases during the transition from log phase to stationary phase. The goal of this study was to test whether rpoS expression could be modulated using an inducible rpoS antisense and rpoS sense. In the first part of this study, a rpoS antisense under the control of an IPTG-inducible promoter was tested for its efficiency for modulating the expression of the RpoS regulon. RpoS-dependent and RpoS-independent lacZ fusions were utilized to quantify the effect of rpoS antisense expression on rpoS translation. Unlike an earlier study, the results of this study suggest that the rpoS antisense was not induced and/or that it was not inhibiting the expression of RpoS-dependent genes. In the second part of this study, an IPTG-inducible rpoS sense was used to test whether expression of certain members of the RpoS regulon were solely dependent on RpoS and not additional factors present in stationary phase. Thus, their expression could be induced in exponential phase. The rpoS sense was integrated into the E. coli chromosome at the ebg locus by homologous recombination utilizing a one-step PCR method. Some putative rpoS sense recombinants showed an increase in catalase expression, which is known to be RpoS-dependent. However, upon further verification, the 5kb PCR fragment encoding the inducible rpoS sense did not appear to have integrated to the intended site. / Thesis / Master of Science (MS)

Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically dynamic changes in biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput biological data, and to demonstrate their applications in systems biological studies. The first part (Chapters 2-4) of the dissertation focuses on the dynamic changes taking placing at the biological network level. Biological networks are context-specific and dynamic in nature. Under different conditions, different regulatory components and mechanisms are activated and the topology of the underlying gene regulatory network changes. We report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. Further, we formalize and extend the DDN approach to an effective learning strategy to extract structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the observation that a group of genes involved in certain biological functions or processes coordinate to response to outside stimuli, producing distinct time course patterns. We apply the echo stat network, a new architecture of recurrent neural networks, to model temporal gene expression patterns and analyze the theoretical properties of echo state networks with random matrix theory. The second part (Chapter 5) of the dissertation focuses on the changes at the DNA copy number level, especially in cancer cells. Somatic DNA copy number alterations (CNAs) are key genetic events in the development and progression of human cancers, and frequently contribute to tumorigenesis. We propose a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately detect genomic deletion type, estimate normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. / Ph. D.

DNA microarray has become a powerful tool in genetics, molecular biology, and biomedical research. DNA microarray can be used for measuring the genotypes, structural changes, and gene expressions of human genomes. Detection and characterization of multilevel, high-throughput microarray genomic data pose new challenges to statistical pattern recognition and machine learning research. In this dissertation, we propose novel computational methods for analyzing DNA copy number changes and learning the trees of phenotypes using DNA microarray data. DNA copy number change is an important form of structural variations in human genomes. The copy number signals measured by high-density DNA microarrays usually have low signal-to-noise ratios and complex patterns due to inhomogeneous composition of tissue samples. We propose a robust detection method for extracting copy number changes in a single signal profile and consensus copy number changes in the signal profiles of a population. We adapt a solution-path algorithm to efficiently solve the optimization problems associated with the proposed method. We tested the proposed method on both simulation and real CGH and SNP microarray datasets, and observed competitively improved performance as compared to several widely-adopted copy number change detection methods. We also propose a chromosome instability measure to summarize the extracted copy number changes for assessing chromosomal instabilities of tumor genomes. The proposed measure demonstrates distinct patterns between different subtypes of ovarian serous carcinomas and normal samples. Among active research on complex human diseases using genomic data, little effort and progress have been made in discovering the relational structural information embedded in the molecular data. We propose two stability analysis based methods to learn stable and highly resolved trees of phenotypes using microarray gene expression data of heterogeneous diseases. In the first method, we use a hierarchical, divisive visualization approach to explore the tree of phenotypes and a leave-one-out cross validation to select stable tree structures. In the second method, we propose a node bandwidth constraint to construct stable trees that can balance the descriptive power and reproducibility of tree structures. Using a top-down merging procedure, we modify the binary tree structures learned by hierarchical group clustering methods to achieve a given node bandwidth. We use a bootstrap based stability analysis to select stable tree structures under different node bandwidth constraints. The experimental results on two microarray gene expression datasets of human diseases show that the proposed methods can discover stable trees of phenotypes that reveal the relationships between multiple diseases with biological plausibility. / Ph. D.

We propose a syntactic approach to apparent blocking effects in the realization of definiteness marking in the Scandinavian languages. The claim is that the differences in definiteness marking can be attributed to a requirement that a definiteness feature ([def], a property of N) must be located at the left edge of the DP phase in order to be PIC-accessible for probes outside of the DP. As a result, [def] can be spelled out on N if N is the only element within DP and [def] is therefore part of DP’s edge domain (giving rise to suffixal marking). In contrast, the presence of an (overt) adjectival modifier (at the left edge of DP) requires feature movement of [def] to D, which is then realized as a prenominal article (with additional spell-out of the lower copy of [def] in Swedish). The paper also addresses the (slightly different) behavior of definiteness marking in the context of relative clauses and certain issues pertaining to the interpretation of the different strategies.

DNA copy number change is an important form of structural variation in human genome. Somatic copy number alterations (CNAs) can cause over expression of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale, with high resolution. Quantitative analysis of somatic CNAs on genes has found broad applications in cancer research. Most tumors exhibit genomic instability at chromosome scale as a result of dynamically accumulated genomic mutations during the course of tumor progression. Such higher level cancer genomic characteristics cannot be effectively captured by the analysis of individual genes. We introduced two definitions of chromosome instability (CIN) index to mathematically and quantitatively characterize genome-wide genomic instability. The proposed CIN indices are derived from detected CNAs using circular binary segmentation and wavelet transform, which calculates a score based on both the amplitude and frequency of the copy number changes. We generated CIN indices on ovarian cancer subtypes' copy number data and used them as features to train a SVM classifier. The experimental results show promising and high classification accuracy estimated through cross-validations. Additional survival analysis is constructed on the extracted CIN scores from TCGA ovarian cancer dataset and showed considerable correlation between CIN scores and various events and severity in ovarian cancer development. Currently our methods have been integrated into G-DOC. We expect these newly defined CINs to be predictors in tumors subtype diagnosis and to be a useful tool in cancer research. / Master of Science