<p>Gene expression analysis has evolved as an extensive toolfor elucidation of various biological and molecular eventsoccurring in different organisms. A variety of techniques andsoftware tools have been developed to enable easier and morerapid means of exploring the genetic information. A moreeffective approach than exploring the whole content of genesexpressed under certain conditions is to study fingerprintassays or to use subtracted cDNA libraries to identify onlydifferentially expressed genes.</p><p>The objective for the work in this thesis has been toexplore differentially expressed genes in atherosclerosis. Thiswas done by applying and modifying a protocol for thesubtractive approach RDA (Representational Difference Analysis)in different model systems.</p><p>Initially, the molecular effects of an anti-atheroscleroticdrug candidate were elucidated. In addition, two alternativeapproaches to identify differentially expressed genes obtainedafter iterative rounds of RDA subtraction cycles wereevaluated. This revealed that in most cases, the shotgunapproach in which the obtained gene fragments are clonedwithout any prior selection has clear advantages compared tothe more commonly used selection strategy, whereby distinctbands are excised after gel electrophoresis.</p><p>A key process in the atherosclerotic plaque initiation isthe phenotypic change of macrophages into foam cells, which canbe triggered in a model system by using macrophages exposed tooxidised LDL. To investigate the genes expressed in thisprocess, the RDA technique was combined with microarrayanalysis, which allows for selectivity and sensitivity throughRDA, as well as rapid high-throughput analysis usingmicroarrays. The combination of these techniques enablessignificant differences in gene expression to be detected, evenfor weakly expressed genes and the results to be reliablyvalidated in a high throughput manner.</p><p>Finally, investigation of the focal nature ofatherosclerotic lesions and gene expression profiling werestudied using in vivo aortic tissues from ApoE-/- and LDLR -/-mice. The study was based on a comparison between localisationsthat are likely, and others that are unlikely, to developatherosclerotic plaques, and the RDA technique was employed toexplore differential gene expression.</p><p><b>Keywords:</b>Representational Difference Analysis,atherosclerosis, gene expression profiling</p>

The web is evolving: what was once primarily used for sharing static content has now evolved into a platform for rich client-side applications. These applications do not run exclusively on the client; while the client is responsible for presentation and some processing, there is a significant amount of processing and persistence that happens server-side. This has advantages and disadvantages. The biggest advantage is that the user’s data is accessible from anywhere. It doesn’t matter which device you sign into a web application from, everything you’ve been working on is instantly accessible. The largest disadvantage is that large numbers of servers are required to support a growing user base; unlike traditional client applications, an organization making a web application needs to provision compute and storage resources for each expected user. This infrastructure is designed in tiers that are responsible for different aspects of the application, and these tiers may not even be run by the same organization. As these systems grow in complexity, it becomes progressively more challenging to identify and solve performance problems. While there are many measures of software system performance, web application users only care about response latency. This “fingertip-to-eyeball performance” is the only metric that users directly perceive: when a button is clicked in a web application, how long does it take for the desired action to complete? MT-WAVE is a system for solving fingertip-to-eyeball performance problems in web applications. The system is designed for doing multi-tier tracing: each piece of the application is instrumented, execution traces are collected, and the system merges these traces into a single coherent snapshot of system latency at every tier. To ensure that user-perceived latency is accurately captured, the tracing begins in the web browser. The application developer then uses the MT-WAVE Visualization System to explore the execution traces to first identify which system is causing the largest amount of latency, and then zooms in on the specific function calls in that tier to find optimization candidates. After fixing an identified problem, the system is used to verify that the changes had the intended effect. This optimization methodology and toolset is explained through a series of case studies that identify and solve performance problems in open-source and commercial applications. These case studies demonstrate both the utility of the MT-WAVE system and the unintuitive nature of system optimization.

Low Emission Vehicles (LEVs) represent a car classification which utilizes advancements in automotive technology to address policy objectives associated with energy security and greenhouse gas emissions. The effectiveness of LEVs at addressing these objectives will not only depend on their technical performance but also on their levels of adoption by consumers. With LEVs encompassing cars which are significantly different compared to conventional market options, the understanding of consumer response to these vehicles remains limited. This thesis addresses this limitation in existing knowledge by providing a detailed examination of consumer demand for LEVs in the UK. Through the application of psychometric methods, this thesis assesses the influence of socio-psychological constructs over LEV preference. A bespoke conceptual framework has been developed to provide insights regarding the influence of attitudes, emotions and values. This framework was applied to the design of a self completion household questionnaire distributed over the cities of Newcastle upon Tyne and Dundee. The meanings placed on car ownership are measured alongside the concept of innovativeness to determine if these traits are likely to hinder or advance LEV adoption. Additionally, past research has tended to consider consumer demand for LEVs at a market level perspective with little attention given to social stratification. This thesis advances knowledge in this area by producing a structural analysis of the emerging market for LEVs. Noteworthy consumer segments are identified, described and compared according to their socio-psychological profiles, socio-economic characteristics and LEV preferences. Principle results of the thesis are that socio-psychological constructs account for a greater degree of variance compared to socio-economic characteristics when explaining evaluations of LEVs. The propensity to consider cars through symbolic, emotive and functional meanings tends to decrease assessments made concerning the functional capabilities of LEVs. The concept of innovativeness displays a positive influence over preferences towards LEVs indicating that these vehicles are being considered as innovations. Moreover, heterogeneous consumer segments with unique socio-psychological and demographic profiles are emerging in the LEV market. These segments display a range of LEV preference structures from those which hold high preferences and are likely to characterize early adopters to those which exhibit low preferences and are likely to represent non-adopters.

Raw material accounts for a large proportion (approximately 75 percent) of a sawmill’s operating costs. However, about 15 percent of raw material ends up as low valued sawdust and planer shavings due to inaccurate cutting. Sizable financial benefits can be realized through maximizing conversion of raw material into valuable solid wood. Advanced process control in a sawmill can help achieve straighter cuts closer to final product dimensions and reduce loss of valuable raw material. A novel and practical method for enhanced process control in a sawmill is presented. A laser arrangement consisting of industrial point and line scanners is used to obtain a surface profile of the entire (two-dimensional) top and bottom surfaces of a lumber board. Each surface profile is independent of the other and free of data contamination caused by relative motions between the measured surface and sensors. Point scanners and line scanners simultaneously record 1-D and 2-D height data, respectively, along the length of the board. One-dimensional height data are used to identify relative motions through a mathematical technique based on linear inverse theory. Subtracting relative motion information from raw line scanner data provides an accurate 2-D surface profile. A second line scanner placed below the board can be used to obtain a separate 2-D profile of the bottom lumber surface. Separate-sided profiling is advantageous because typically a different saw or machine mills each side of a board. Thus, knowing the surface profile of each side of a board is crucial not only in diagnosing a deficiency in the milling process but also in determining the location of this deficiency. Results demonstrate that two-dimensional surface profiling can identify common surface defects such as step, washboard and knot tear-out with an accuracy of 0.3mm. Reproduction of each surface is rapid (approximately 0.2 seconds) and stable.

RNA molecules isolated from FFPE samples are highly fragmented and modified, and generally deemed unsuitable for downstream gene expression profiling. With the development of molecular biology, there has been growing interest in profiling archival FFPE samples. Successful profiling of transcripts from FFPE samples would greatly expand tissue sources for large scale gene expression studies; also it would pave the way for future applications on the type of tissue readily available in the clinical setting. So far, there is a lack of systemic studies evaluating the quality of RNA isolated from routinely processed FFPE samples, and it has remained difficult to assess how well FFPE-derived RNA mirrors the status of RNA isolated before fixation. In this project, the similarity of miRNA and mRNA profiles between matched frozen and FFPE lymphoid hyperplasia tissues (N=7 for miRNA comparison, N=4 for mRNA comparison) were evaluated. We found consistently good correlation (mean of Pearson coefficient=0.939, mean of Spearman coefficient=0.905, mean of Kendall tau=0.744) between matched frozen and FFPE-derived miRNA profiles, suggesting FFPE samples may retain miRNA expression information quite well. This has major positive implications for research using FFPE samples, as miRNA profiling becomes more prominent in bioprofiling studies. On the contrary, mRNA isolated from FFPE samples showed less correlation (Spearman coefficient less than 0.75) with its frozen counterpart on the Agilent microarray platform. With a post extraction heat treatment aimed at reversing base modifications and cross linking structures, obvious global mRNA quality improvement was observed in cases where samples appeared to be heavily cross linked, but was less effective and even detrimental in cases where cross linking was less prominent. This research suggests that the extent of cross linking may be critical in terms of determining whether a particular FFPE tissue will become a useful source of mRNA for global profiling studies / Thesis (Master, Pathology & Molecular Medicine) -- Queen's University, 2008-09-26 10:49:50.044

No description available.

Equation-based object-oriented (EOO) modeling languages such as Modelica provide a convenient, declarative method for describing models of cyber-physical systems. Because of the ease of use of EOO languages, large and complex models can be built with limited effort. However, current state-of-the-art tools do not provide the user with enough information when errors appear or simulation results are wrong. It is of paramount importance that such tools should give the user enough information to correct errors or understand where the problems that lead to wrong simulation results are located. However, understanding the model translation process of an EOO compiler is a daunting task that not only requires knowledge of the numerical algorithms that the tool executes during simulation, but also the complex symbolic transformations being performed. As part of this work, methods have been developed and explored where the EOO tool, an enhanced Modelica compiler, records the transformations during the translation process in order to provide better diagnostics, explanations, and analysis. This information is used to generate better error-messages during translation. It is also used to provide better debugging for a simulation that produces unexpected results or where numerical methods fail. Meeting deadlines is particularly important for real-time applications. It is usually essential to identify possible bottlenecks and either simplify the model or give hints to the compiler that enable it to generate faster code. When profiling and measuring execution times of parts of the model the recorded information can also be used to find out why a particular system model executes slowly. Combined with debugging information, it is possible to find out why this system of equations is slow to solve, which helps understanding what can be done to simplify the model. A tool with a graphical user interface has been developed to make debugging and performance profiling easier. Both debugging and profiling have been combined into a single view so that performance metrics are mapped to equations, which are mapped to debugging information. The algorithmic part of Modelica was extended with meta-modeling constructs (MetaModelica) for language modeling. In this context a quite general approach to debugging and compilation from (extended) Modelica to C code was developed. That makes it possible to use the same executable format for simulation executables as for compiler bootstrapping when the compiler written in MetaModelica compiles itself. Finally, a method and tool prototype suitable for speeding up simulations has been developed. It works by partitioning the model at appropriate places and compiling a simulation executable for a suitable parallel platform.

No description available.

The growing interest in the industrialization of construction process; promotes opportunities for automation. Automation brings improvement in quality and productivity, while reducing worker’s exposure to hazardous work environments. The integration of robotics in interior finishing works, such as sanding and painting of drywalls is a relatively new concept. Progressing to a stage where fully autonomous robots are used for interior finishing works requires intermediate steps; namely surface profiling. This thesis describes a theoretical concept of shadow profilometery to profile the surface of an installed drywall. A shadow was cast over the area under consideration, and the shadow profile was captured as a 2D image by a camera. Digital image processing techniques were utilized for identifying regions that deviate from a flat surface. The methodology discussed in this research, was tested on a virtual system, and the results were found to be encouraging. / Construction Engineering and Management

Raw material accounts for a large proportion (approximately 75 percent) of a sawmill’s operating costs. However, about 15 percent of raw material ends up as low valued sawdust and planer shavings due to inaccurate cutting. Sizable financial benefits can be realized through maximizing conversion of raw material into valuable solid wood. Advanced process control in a sawmill can help achieve straighter cuts closer to final product dimensions and reduce loss of valuable raw material. A novel and practical method for enhanced process control in a sawmill is presented. A laser arrangement consisting of industrial point and line scanners is used to obtain a surface profile of the entire (two-dimensional) top and bottom surfaces of a lumber board. Each surface profile is independent of the other and free of data contamination caused by relative motions between the measured surface and sensors. Point scanners and line scanners simultaneously record 1-D and 2-D height data, respectively, along the length of the board. One-dimensional height data are used to identify relative motions through a mathematical technique based on linear inverse theory. Subtracting relative motion information from raw line scanner data provides an accurate 2-D surface profile. A second line scanner placed below the board can be used to obtain a separate 2-D profile of the bottom lumber surface. Separate-sided profiling is advantageous because typically a different saw or machine mills each side of a board. Thus, knowing the surface profile of each side of a board is crucial not only in diagnosing a deficiency in the milling process but also in determining the location of this deficiency. Results demonstrate that two-dimensional surface profiling can identify common surface defects such as step, washboard and knot tear-out with an accuracy of 0.3mm. Reproduction of each surface is rapid (approximately 0.2 seconds) and stable.