A topic of research that is frequently studied in Structural Biology is the problem of determining the degree of similarity between two protein structures. The most common solution is to perform a three dimensional structural alignment on the two structures. Rigid structural alignment algorithms have been developed in the past to accomplish this but treat the protein molecules as immutable structures. Since protein structures can bend and flex, rigid algorithms do not yield accurate results and as a result, flexible structural alignment algorithms have been developed. The problem with these algorithms is that the protein structures are represented using thousands of atomic coordinate variables. This results in a great computational burden due to the large number of degrees of freedom required to account for the flexibility. Past research in dimensionality reduction techniques has shown that a linear dimensionality reduction technique called Principal Component Analysis (PCA) is well suited for high dimensionality reduction. This thesis introduces a new flexible structural alignment algorithm called FlexSADRA, which uses PCA to perform flexible structural alignments. Test results show that FlexSADRA determines better alignments than rigid structural alignment algorithms. Unlike existing rigid and flexible algorithms, FlexSADRA addresses the problem in a significantly lower dimensionality problem space and assesses not only the structural fit but the structural feasibility of the final alignment.

Microbial fuel cells were designed and operated using waste activated sludge as a substrate and as a source of microorganisms for the anodic chamber. Waste activated sludge provided a bacterial consortium predisposed to the solubilization of particulate matter and utilization of substrates commonly found in wastewater. Dissolved oxygen and ferricyanide were used as the electron acceptors in the catholytes. Microbial fuel cell comparisons were made while operating under identical conditions but using the two different electron acceptors. Comparisons were based on the electricity production observed during MFC operation, wastewater quality of the waste activated sludge anolytes and the community level physiological profiling of the microbial communities in the anolytes. Electrons liberated during substrate utilization in the anodic chamber traveled to the cathodic chamber where they reduced the electron acceptors. The anode and cathode chambers were connected by a Nafion ® proton exchange membrane to allow for cation migration. Various soluble carbon sources were dosed to the microbial fuel cells at measured intervals during operation via direct injection to the anolyte. During bovine serum albumin dosing, average power production levels reached 0.062 mW and 0.122 mW for the dissolved oxygen microbial fuel cell and the ferricyanide microbial fuel cell, respectively. These were 100% and 25% greater than the power production levels observed throughout the rest of the study. Increases in current production were observed following the dosing of sodium acetate, glucose and bovine serum albumin. No increase in current was observed following glycerol dosing. Sodium acetate dosing triggered an immediate response, while glucose and bovine serum albumin responded in approximately 2 minutes. A chemical oxygen demand mass balance was calculated for both microbial fuel cells. The lack of balance closure was attributed to unmeasured methane production. An accumulation of particulate waste activated sludge components was observed for both microbial fuel cells. The anolyte pH during operation was typically less than waste activated sludge pH, which was attributed to volatile fatty acid accumulation in the anolytes during fermentation processes. Community level physiological profiling was accomplished through the analysis of ecological data obtained with BIOLOG ® ECOplates. Samples were plated and analyzed under anaerobic conditions, mimicking the environment in the anode chamber of the MFCs. ECOplate data were transformed by a logarithmic function prior to principle component analysis. The community level physiological profiling indicated that shifts in the microbial community profile, as measured through the carbon source utilization patterns, occurred throughout acclimation and following the dosing of various carbon source substrates. Shifts due to glycerol dosing differed from shifts due to the dosing of sodium acetate, glucose and bovine serum albumin.

We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyperellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.

Proton magnetic resonance spectroscopy has been widely applied to the diagnosis of brain diseases. In the meanwhile, the classification of brain abscesses plays an important role on the accurate prognosis in clinics. Recently, the interest in using proton MRS to classify pyogenic brain abscesses has been arising because of its non-invasive property and good accuracy in detecting metabolites. The brain abscess can be classified by means of the metabolites observed in the MR spectra, which may thus benefit the accuracy of the brain abscess diagnosis clinically. However, the interpretation of MR spectra by experienced radiologists can be also very subjective and therefore results in the variation of diagnosis. In this study, we investigate the potential possibility of using Principal Component Analysis (PCA) to classify the short TE MR spectra in more objective way.

A novel ambient ionization method which is so-called direct electrospray probe (DEP) has been developed in this study. This method provides rapid and high throughput analysis due to its advantages such as no sample pretreatment, low sample consumption, easily to change sample. In the study, we improved the interface of direct electrospray probe, and extend spray time to 1 minute for chemical analysis. Because of extending spray time, the method could be combined with principal component analysis (PCA), which is one kind of statistical method. Therefore, we applied the method for determination of alcoholic beverage which contains a lot of ions and alcohol. First, the conditions such as applying voltage, sample volume and shape of probe have been modified. The probes we used in the study were made by used metal pieces. These metal pieces with different thickness and tip angle to be test. Then, we found the optimize conditions as: voltage 7.0 kV, sample volume 3 £gL, thicknss of metal piece 0.1 mm, angle of metal piece 25 degree. Classification of 14 brands of wines which were purchased from supermarket was determined and analyzed by the method. Otherwise, we used the method to distinguish real wines from hedge wines we simulated. The hedge wines were prepared by adding flavor in alcohol solutions. Furthermore, we also used this method to observe fermentation process of wine production. Finally, deterioration of wine under exposure in air was also determined by the method. The results of the study demonstrated that the method we developed can be applied to distinguish hedge wines from real wines. And, it can used to monitor the fermentation process of wine production for quality control.

The emission of volatile organic compounds (VOCs) and odors from petrochemical industrial complex, including China Petroleum company (CPC),Renwu and Dazher petrochemical industrial parks, causes poor air quality of northern Kaohsiung. The removal efficiencies of elevated stacks and flares might play important roles on ambient air quality in metro Kaohsiung. Consequently, this study applied a tethered balloon technology to measure the vertical profile of VOCs, and ascertained their three dimensional dispersion in the atmosphere. The vertical profile of VOCs in ambient atmosphere surrounding the petrochemical industrial complex was measured during the intensive sampling periods (September 17-18th and December 20-21st, 2009 and April 8-9th and July 7-8th, 2010). Moreover, this study was designed to sample and analyze VOCs emitted from elevated stacks and flares, and estimate their emission factors. Finally, the source identification and ozone formation were further determined by principal component analysis (PCA) and ozone formation potential (OFP). This study found that some regions had relatively poorer air quality than other regions surrounding the petrochemical industrial complex. Most sampling sites with poor air quality were located at the downwind region of the petrochemical industrial complex, particularly with the prevailing winds blown from the northwest. Moreover, stratification phenomena were frequently observed at most sampling sites, indicating that high-altitude VOCs pollution should be considered for ambient air quality. This study revealed that the indicators of VOCs in northern Kaohsiung were toluene, C2 (ethylene+acetylene+ethane), and acetone. Vertical sampling of VOCs showed that the species of VOCs at the ground and high altitude were different, suggesting that ambient air quality at high altitude might be affected by the emission of VOCs from elevated stacks and flares at the petrochemical industrial complex. Results obtained from PCA showed that the major sources of VOCs in the ambient atmosphere of the petrochemical industrial complex were similar to the characteristics of VOCs emitted from the petrochemical industrial complex. The characteristics of VOCs at high altitude had strong correlation with petrochemical industry, indicating that the ambient air quality of northern Kaohsiung was highly influenced by the emission of VOCs from high stacks and flares. In addition, major VOCs for O3 formation potential at northern Kaohsiung were aromatics and vinyls, with particular species of toluene and C2. Moreover, air pollution episodes resulting from high O3 concentration was usually observed in early winter. Flare sampling results indicated that major VOCs emitted from the ground flare of CPC were alkanes and vinyls. The average removal efficiency of TVOCs was 98.2%. The average emission factor of VOCs was 0.0186 kg NMHC/kg flare gas. In addition, stack sampling results indicated that the emission factors of crude oil distillation process (P105), mixing process (P060), and rubber manufacturing process (P408) were 0.105, 1.11, and 61.97 g/Kl, respectively. The emission factor of P105 was lower than AP-42, while that of P408 was higher than AP-42.

In order to understand the relative impact from natural and anthropogenic sources, Principal Component Analysis - Structural Equation Modeling (PCA-SEM) was used to analyze the phytoplankton dynamics in coastal waters of Taiwan. PCA was used to analyze the changes of the water quality, followed by constructing of conceptual model which incorporated with biological data, and finally verified by SEM. Three study sites were selected: Chang Hua coastal waters, Kaohsiung mud dumping waters and the adjacent waters of Kaohsiung Nansing project. These sites represent the ordinary coastal water ecosystem of western Taiwan, off-shore ocean with one defined pollution sources, and anthropogenic impacted water area, respectively. The results showed that in Chang Hua coastal waters, river input and seasonal change were the primary factors effecting phytoplankton change. Water temperature was the main reason of phytoplankton changes, followed by the influence of dissolved organic matter in Kaohsiung mud dumping site. Whereas waters near Kaohsiung Nansing project, cooling water from Daling power plant coupled with the change of nutrients and heavy metal concentrations, as well as oil pollution, were the major causes of phytoplankton variation. The goodness-of-fits were good for the three models in this study, revealing that PCA- SEM is suitable to analyze the environmental changes of the costal waters of Taiwan. Logistic methods used in this study are also good for the study of benthic or fish community, and are suitable to apply on environmental impact assessments.

The seasonal and diurnal concentrations of atmospheric carbonyls were measured by the LpDNPH-Cartridge and the microcomputer air sampling device at Nan-Chie and Hsiung-Kong sites in Kaohsiung area. Then, factor analysis and absolute principal component analysis were also used to determine the source apportionment in Kaohsiung area. Total concentrations of carbonyls were higher in Summer than in winter at Nan-Chie and Hsiung-Kong sites. Measurements showed that the highest carbonyls were formaldehyde and acetaldehyde, due to the fact that photochemical activities are stronger in summer than in winter. The concentrations of total carbonyls, formaldehyde, acetaldehyde were showed similar diurnal variations, that highest concentrations were found in the morning and noon, then drop down at afternoon and increased at night. Due to the fact that photochemical activities and vehicle exhausts. C1-C3 ratio indicated the local participation of anthropogenic hydrocarbons was important in the production of carbonyls in the Kaohsiung area. C1/C2 was highest in the summer than in the winter, that photochemical activities cause highest concentrations of formaldehyde, especially in the summer noon. The results of factor analysis and absolute principal component analysis showed that the primary pollution sources at Nan-Chie were traffic exhausts (diesel and gasoline vehicle) and stationary sources (petrochemical and food industry) and restaurant emissions, and the primary pollution sources at Hsiung-Kong were traffic exhausts (diesel and gasoline vehicle), stationary emissions (metal assembly and petrochemical industry) and restaurant emissions.

Steel industry is a highly polluted industry and one of the most important stationary sources in Kaohsiung City. The steel manufacturing process could emit a huge amount of particles, such as the sintering process, the blast furnace operation, and the raw material handling process. Suspended particles emitted from steel industry could deteriorate ambient air quality and cause adverse effects on human health. In order to understand the impact of steel industry on ambient air quality in Siaogang District and to identify potential pollution sources, this study selected a integrated steel manufacturing plant located at Siaogang District to conduct a sampling protocol of suspended particulate matter (PM) at ambient sites (A1~A5) and sensitive sites (S1~S5) from July 2011 to March 2012. The size distribution of suspended particles in four seasons was measured with PM10 high-volume samplers, dichotomous samplers, and MOUDI for 3 days (24 hours for single sampling), and dustfall samplers for one month, to investigate the spatial distribution and temporal variation of PM concentration. After sampling, the physicochemical properties of PM, including mass concentration, particle size distribution, dustfall concentration, water-soluble ionic species, metallic elements, and carbonaceous contents, were further analyzed. Field measurement of ambient PM showed that the averaged ambient PM10 concentration (53.54 - 203.56 £gg/m3) were higher than sensitive sites (55.06 - 140.07 £gg/m3) and the averaged ambient PM2.5 concentration of ambient (23.10 - 120.21£gg/m3) were higher than sensitive sites (12.52 - 65.62 £gg/m3). No matter ambient or sensitive sites, it showed a tendency of lower concentration in summer, indicating that concentration variation of PM10 and PM2.5 were highly affected by meteorological factors (such as wind direction, wind speed, and relative humidity) in Siaogang District. Furthermore, a t-test result showed that ambient and sensitive sites have similar pollution sources since the p-values were in significantly different. Chemical analysis of PM results showed that the most abundant water-soluble ionic species of PM at the ambient and sensitive sites were secondary inorganic aerosols (SO42-, NO3-, and NH4+) and [NO3-]/[SO42-] showed that ionic species were mainly emitted from stationary sources. Fe, Al, K and Ca were the major metallic elements of this study, and the major pollution sources contain industries, traffics, and road dusts. Additionally, the raw material handling process was the major pollution source of PM. Correlation analysis of OC and EC showed that PM at ambient and sensitive sites were originated from primary sources, such as vehicles, industries, road dusts, and human activities. Results obtained from PCA and CMB receptor modeling showed that both PM2.5 and PM10 highly correlated with wind direction in different season and the major pollution sources were industry pollution (including petroleum refineries, power plants, waste incinerators, consistent operating steel mills and electric arc furnace steel mills, etc.), followed by local traffics and derivative. Furthermore, marine aerosols were one of the important pollution sources at sensitive sites (S1, S4, and S5) where close to the ocean.

Magnetic Resonance Imaging (MRI) can obtain the tissues of in vivo non-invasively. Proton MR Spectroscopy uses the resonance principle to collect the signals of proton and transforms them to spectrums. It provides information of metabolites in patient¡¦s brain for doctors to observe the change of pathology. Observing the metabolites of brain abscess patients is most important process in clinical diagnosis and treatment. Then, doctors use different spectrums of echo time (TE) to enhance the accuracy in the diagnosis. In our study, we use independent component analysis (ICA) to analyze MR spectroscopy. After analyzing, the independent components represent the elements which compose the input data. Then, we use the projection which is mentioned by Ssu-Ying Lu¡¦s Thesis to help us observe the relationship between independent components and spectrums of patients. We also discuss the result of spectrums with using ICA and PCA and discover some questions (whether it need to do scale normalization before inputting data or not, the result of scale normalization doesn¡¦t expect, and the peak in some independent components confuse us by locating in indistinct place) to discuss and to find possible reason after experiments.