Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 80-85).

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 76-77) Also available in print.

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No description available.

The objective of this project was to apply machine learning methods to determine protein secondary structure content and protein fold class from ROA and Raman vibrational spectral data. Raman and ROA are sensitive to biomolecular structure with the bands of each spectra corresponding to structural elements in proteins and when combined give a fingerprint of the protein. However, there are many bands of which little is known. There is a need, therefore, to find ways of extrapolating information from spectral bands and investigate which regions of the spectra contain the most useful structural information. Support Vector Machines (SVM) classification and Random Forests (RF) trees classification were used to mine protein fold class information and Partial Least Squares (PLS) regression was used to determine secondary structure content of proteins. The classification methods were used to group proteins into α-helix, β-sheet, α/β and disordered fold classes. The PLS regression was used to determine percentage protein structural content from Raman and ROA spectral data. The analyses were performed on spectral bin widths of 10cm-1 and on the spectral amide regions I, II and III. The full spectra and different combinations of the amide regions were also analysed. The SVM analyses, classification and regression, generally did not perform well. SVM classification models for example, had low Matthew Correlation Coefficient (MCC) values below 0.5 but this is better than a negative value which would indicate a random chance prediction. The SVM regression analyses also showed very poor performances with average R2 values below 0.5. R2 is the Pearson's correlations coefficient and shows how well predicted and observed structural content values correlate. An R2 value 1 indicates a good correlation and therefore a good prediction model. The Partial Least Squares regression analyses yielded much improved results with very high accuracies. Analyses of full spectrum and the spectral amide regions produced high R2 values of 0.8-0.9 for both ROA and Raman spectral data. This high accuracy was also seen in the analysis of the 850-1100 cm-1 backbone region for both ROA and Raman spectra which indicates that this region could have an important contribution to protein structure analysis. 2nd derivative Raman spectra PLS regression analysis showed very improved performance with high accuracy R2 values of 0.81-0.97. The Random Forest algorithm used here for classification showed good performance. The 2-dimensional plots used to visualise the classification clusters showed clear clusters in some analyses, for example tighter clustering was observed for amide I, amide I & III and amide I & II & III spectral regions than for amide II, amide III and amide II&III spectra analysis. The Random Forest algorithm also determines variable importance which showed spectral bins were crucial in the classification decisions. The ROA Random Forest analyses performed generally better than Raman Random Forest analyses. ROA Random Forest analyses showed 75% as the highest percentage of correctly classified proteins while Raman analyses reported 50% as the highest percentage. The analyses presented in this thesis have shown that Raman and ROA vibrational spectral contains information about protein secondary structure and these data can be extracted using mathematical methods such as the machine learning techniques presented here. The machine learning methods applied in this project were used to mine information about protein secondary structure and the work presented here demonstrated that these techniques are useful and could be powerful tools in the determination protein structure from spectral data.

Mikroplastik (Kunststoff-Partikel < 5 mm) wurde in den vergangenen Jahren vermehrt in verschiedenen marinen Ökosystemen nachgewiesen und erreicht regelmäßig wissenschaftliche und öffentliche Aufmerksamkeit. Es wird als potentielle Gefahr für die marine Umwelt angesehen. Aufgrund der geringen Größe kann Mikroplastik von marinen Organismen mit der Nahrung verwechselt werden und infolge dessen in den Magen-Darm-Trakt gelangen. Ob die so aufgenommenen Partikel zu einer Schädigung der Organismen führen und welche Wirkungsmechanismen dabei eine Rolle spielen, ist derzeit noch nicht umfassend geklärt. In diesem Zusammenhang wird Mikroplastik beispielsweise als Transportvehikel für enthaltene Kunststoffadditive, für adsorbierte persistente organische Schadstoffe sowie für potentiell pathogene Mikroorganismen diskutiert. Für eine Risikobewertung sind in erster Linie zuverlässige Daten über die Mikroplastik-Gehalte in verschiedenen Umweltkompartimenten nötig. Dazu werden geeignete und sichere analytische Verfahren zur Identifizierung und Quantifizierung von Mikroplastik in Umweltproben benötigt. Ziel dieser Arbeit war es bestehende Wissenslücken im Bereich der Mikroplastik-Analytik zu schließen und Möglichkeiten und Grenzen der FTIR- und Raman-Spektroskopie für die analytische Untersuchung von marinen Mikroplastik-Proben aufzuzeigen. Dazu wurde zunächst ein Filtersubstrat entwickelt, das für eine umfassende Untersuchung von filtrierten Mikroplastik-Proben sowohl mittels Transmission FTIR- als auch mittels Raman-Mikroskopie geeignet ist. Des Weiteren wurde Raman Imaging als neuartige Methode zur Identifizierung von Mikroplastik etabliert und hinsichtlich verschiedener Messparameter optimiert. Die Anwendbarkeit dieses neuen Analyseansatzes wurde an realen Umweltproben gezeigt. Beide spektroskopische Verfahren (IR und Raman) wurden anhand von Modellproben und realen Umweltproben miteinander verglichen und validiert. Zusätzlich dazu wurden die spektroskopischen Ergebnisse an ausgewählten Proben mit der thermoanalytischen py-GC/MS-Methode verglichen und beurteilt. Im dritten Teil der Arbeit wurden die Mikroplastik-Gehalte in Sedimentproben aus dem Mündungsbereich der Warnow, einem bedeutenden Zufluss zur Ostsee, bestimmt. Dabei wurden lokale Eintragspfade abgeschätzt sowie Senke von Mikroplastik identifiziert.:1 Motivation und Zielstellung 2 Wissenschaftlicher Hintergrund 3 Experimenteller Teil 4 Ergebnisse und Diskussion 5 Zusammenfassung und Ausblick Anhang Literaturverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Danksagung Publikationsliste Versicherung

This study focuses on various aspects of nanocrystals embedded in a dielectric matrix. In the first part of this work, a new approach with the use of Fourier Transform Infrared spectroscopy (FTIR) in the nanocrystal analysis was developed and presented. Si and Ge nanocrystals embedded in SiO2 matrix were mainly studied. This new approach is based on the analysis of structural variations of SiO2 matrix during the formation of semiconductor nanocrystlas. It is shown that the chemical and structural variations of the host matrix are directly related to the precipitation of nanocrystals in it. This correlation provides valuable information about the presences of nanocrystals in the matrix. In the second part of this work, fabrication of SiC films with and without Si nanocrystals inclusions was studied. With this aim, stoichiometric SiC and Si rich SiC thin films were fabricated by using magnetron co-sputtering and Plasma Enhanced Chemical Vapor Deposition (PECVD) techniques. For SiC films, the structural and optical analyses were performed. For Si rich SiC films, the formation conditions of Si nanocrystals were investigated. Post annealing studies were carried out to track the evolution of the SiC matrix and formation of Si nanocrystals at different temperatures. Chemical and structural properties of the SiC host matrix were investigated with FTIR spectroscopy. Optimum conditions for the fabrication of stoichiometric SiC layers were determined. The crystallography of the nanocrystals was investigated by X-Ray Diffraction (XRD). The variation of the atomic concentrations and bond formations were investigated with X-Ray Photoelectron Spectroscopy (XPS). Raman spectroscopy and Transmission Electron Microscopy (TEM) were used to verify the formation of Si nanocrystals. We have shown that both single and multilayer Si nanocrystals can be fabricated in the amorphous SiC matrix for applications such as light emitting diodes and solar cells.