De novo motif discovery in biological sequences is an important and computationally challenging problem. A myriad of algorithms have been developed to solve this problem with varying success, but it can be difficult for even a small number of these tools to reach a consensus. Because individual tools can be better suited for specific scenarios, an ensemble tool that combines the results of many algorithms can yield a more confident and complete result. We present a novel and fast tool MCAT (Motif Combining and Association Tool) for de novo motif discovery by combining six state-of-the-art motif discovery tools (MEME, BioProspector, DECOD, XXmotif, Weeder, and CMF). We apply MCAT to data sets with DNA sequences that come from various species and compare our results with two well-established ensemble motif finding tools, EMD and DynaMIT. The experimental results show that MCAT is able to identify exact match motifs in DNA sequences efficiently, and it has a better performance in practice. / Master of Science / Finding hidden motifs in DNA or protein sequences is an important and computationally challenging problem. A motif is a short patterned DNA/protein sequence that has biological functions. Motifs regulate the process of gene expression, which is the fundamental biological process in which DNA is transcribed into RNA which is then translated to protein. In the past 20 years, a myriad of algorithms have been developed to solve the motif finding problem with varying success, but it can be difficult for even a small number of these tools to reach a consensus. Because individual tools can be better suited for specific scenarios, an ensemble tool that combines the results of many algorithms can yield a more confident and complete result. I present a novel and fast tool MCAT (Motif Combining and Association Tool) for motif discovery by combining six state-of-the-art motif discovery tools (MEME, BioProspector, DECOD, XXmotif, Weeder, and CMF). I apply MCAT to data sets with DNA sequences that come from various species and compare our results with two well-established ensemble motif finding tools, EMD and DynaMIT. The experimental results show that MCAT is able to identify exact match motifs in DNA sequences efficiently, and it has an improved performance in practice.

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In my whole working, I have always wanted to identify myself through the visualization of images from the ideal thought. The skillful manipulation of physical materials enables my work to reflect the innocent willingness of humanity's struggle to create and achieve. Thus I choose to create work that demands intensive labor and technological excellence, combined with the tradition of Oriental craftsmanship, in its fabrication. In the 1990s, when I had my first solo exhibition, I was deeply involved with life modeling in a realistic style, exploring and questioning "human" themes like anxiety, pain, self-marginalization and the other. Since 2000, I have been working on manipulation and transformation of objects and materiality. Solid metal is finally reborn into totally new images after being cut into pieces, welded with electricity, severed with gas, and finally polished. I found that the aggressive involvement of the artists' idea could transform the existing materiality and defy the reputation of metal as rock-hard and cold. The newly appearing or sometimes transformed forms hold another perception different from the old one. I have developed my artistic identity by establishing my own viewpoint in relation to the labor-intensive fabrication process, while recognizing my position as an activist. In these days, my interest is wood-work through accumulating pieces with used woods. I still find various thought and image from another work in my working.

Objective: to assess the prevalence of incidental findings in pre-orthodontic orthopantomograms, the need for referral as a result of these findings and to evaluate the quality of the radiographs. Methods: 300 radiographs of males and females (10-60 years old) were analyzed. The PABAK test was used to calculate intra-rater reliability. The findings were recorded in a customized data entry form. Results: positioning errors were frequent, with the most common being the tongue dropping from the palate, presence of anatomical ghost images and chin positioning errors. 62.6% of the patients showed at least one incidental finding, with hypodontia, impacted third molars and other impacted teeth being the most common. 20% of the findings merited a referral. Conclusions: technical errors are common. The prevalence of incidental findings in this study was higher than previously reported. 20% of the findings needed a referral, with impacted teeth representing the most common cause for referral.

Background: Undiagnosed chronic obstructive pulmonary disease (COPD) and asthma remain prevalent health issues. The current global and Canadian prevalence reported for obstructive lung disease do not reflect the true prevalence since undiagnosed cases remain missed and uncounted. Spirometry testing is viewed as the current gold standard for diagnosing obstructive lung disease. However, barriers associated with inaccessibility and underuse have contributed to undiagnosed lung disease. While guidelines advise against spirometry for asymptomatic persons, active case-finding for persons at-risk and those presenting with symptoms has been recommended. Given early treatment and management has the potential to improve health-related quality of life and reduce the progression of lung decline, identifying undiagnosed lung disease is critical to preventing adverse health outcomes. To date, this marks the first study to incorporate both obstructive lung diseases into a single-case finding instrument. Objective: To develop and validate a case-finding questionnaire to identify undiagnosed COPD and asthma in community-dwelling adults, and to prospectively evaluate reliability and predictive performance. Methods: This study uses data obtained from the Undiagnosed Chronic Obstructive Pulmonary Disease and Asthma Population (UCAP) study from June 2017 to March 2020. Eligible participants were >18 years, had a history of chronic respiratory symptoms, and had no previous physician diagnosis of obstructive lung disease. Presence of obstructive lung disease was confirmed with spirometry. Multinomial logistic regression and recursive partitioning were used to develop a case-finding questionnaire. Predictors available from six questionnaires completed during spirometry visit. Diagnostic accuracy of the models was used to evaluate performance. Risk score externally validated in a cohort of participants recruited between October 2020 and January 2021 at study sites open during the COVID-19 pandemic. Results: Derivation cohort included 1615 participants, with 136 ultimately diagnosed with asthma and 195 diagnosed with COPD. A 13-item questionnaire was developed using logistic regression: age, pack-years of cigarette smoking, wheeze, cough, sleep, chest tightness, level of tiredness, physical activity limitation, occupational exposure, primary or second-hand smoke exposure, frequency of chest attacks, and salbutamol medication. Internal validation showed an area under the curve (AUC) of 0.79 (0.70-0.90) for COPD and 0.64 (0.45-0.80) for asthma. At a predicted probability of greater than or equal to 6%, specificity was 17% for no OLD, sensitivity was 91% for asthma, and sensitivity was 96% for COPD. External cohort included 74 subjects, with 8 diagnosed with COPD and 6 diagnosed with asthma. The AUC for COPD was 0.89 (95% CI: 0.62-0.90) and AUC was 0.65 (95% CI: 0.63-0.72) for asthma. Sensitivity was 100% for both asthma and COPD, specificity was 13%, and positive predictive value was 23%. Conclusion: The 13-item case-finding questionnaire was shown to be reliable and with modest predictive ability in identifying COPD and asthma. Prospective evaluation with the UCAP study is still ongoing to recruit a larger sample to re-evaluate predictive performance.

Current radio direction finding techniques are limited in flexibility and focus on specific applications. Commercial off the shelf systems exist for a wide range of applications from navigation to search and rescue and wildlife tracking. However these systems rely on commercially available VHF receivers and are limited in transmission modulation techniques and frequency ranges. The majority of these systems are expensive which places them outside the reach of most individuals while the current open source designs require specialized skills and knowledge to build. The goal of this work was to design a low cost system capable of determining the approximate location of a non-cooperative VHF transmitter that could easily be implemented on a variety of unmanned systems. One unmanned aerial system was designed, built, and evaluated. Existing open source hardware and software systems were utilized for the development of the pseudo Doppler direction finding system, and work was conducted utilizing recursive Bayesian techniques to estimate the VHF transmitter's location. Results and explanations of system behaviors are presented along with limitations and possible modifications to improve performance and reliability. / Master of Science / Radio direction finding uses specialized radio equipment to determine the direction that a radio signal is coming from. Commercial systems are often expense, and existing hobbyist designs require specialized skills, and both are not flexible in application or frequency. The same is true for commercially available drones, which tend to be expensive or face other limitations. In this work a low cost radio direction finding system that uses easily found open source hardware and software was built and evaluated, along with a low cost unmanned aerial system. Then using the data collected, a computer algorithm was tested that could estimate the transmitting radio’s location. After testing it was determined that all systems did work, but still had room for improvement. Future steps and system modifications are presented that could improve the system’s performance.

In-service performance deterioration of gas turbine engines can be identified, at the engine module level, in terms of reductions in the component mass flow and the efficiency. Continued operation of a deteriorated gas turbine is (i) uneconomical and (ii) unsafe. Timely identification of the faults and ensuing maintenance could prevent both. Gas Path Analysis is a technique to establish the current performance level of the gas turbines and identify the faulty modules. Computer models can predict the off_design performance of gas turbines by aero-thermo-dynamically matching the engine components. This thesis describes the development of DETEM (DEeteriorated Turbine Engine Model), a generalised computer program, developed to model degraded gas turbine engines and analyse faults. The program has an integrated graphics module and creates windows on the VDU terminal,for displaying the program output and accepting the user input. This enables the user to compare the results of two different types of runs at the same time. The program incorporates sensor models that modify the output, with noise and in bias, based on the sensor characteristics, thus simulating a real engine. It is possible to simulate the engine performance at design point, off-design and under transient conditions. The runs could be for a "clean" and a deteriorated engine. Three techniques, iterative, fault coefficient matrix, and a statistical best-estimation technique, have been used to analyse the engine performance and identify the fault. Analysis of two and three shaft turbo-shaft engines and two spool turbo-fan and turbo-jet engines have been worked out in the thesis. Effects of reducing the number of measurements and measuring different engine parameters, on the accuracy of the fault identification, have been studied. The program is considered to have a potential for the generation of fault trees for rule-based expert system applied to gas turbine diagnostics. Because of the controlled output to the screen, a direct comparison of two different runs side by side, on the same screen, makes the program a good teaching aid for gas turbine diagnostics.

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