DNA detection technology has developed rapidly due to its extensive application in clinical diagnostics, bioengineering, environmental monitoring, and food science areas. Currently developed methods such as surface Plasmon resonance (SPR) methods, fluorescent dye labeled methods and electrochemical methods, usually have the problems of bulky size, high equipment cost and time-consuming algorithms, so limiting their application for in vivo detection. In this work, an intrinsic Fabry-Perot interferometric (IFPI) based DNA sensor is presented with the intrinsic advantages of small size, low cost and corrosion-tolerance. This sensor has experimentally demonstrated its high sensitivity and selectivity. In theory, DNA detection is realized by interrogating the sensor's optical cavity length variation resulting from hybridization event. First, a microgap structure based IFPI sensor is fabricated with simple etching and splicing technology. Subsequently, considering the sugar phosphate backbone of DNA, layer-by-layer electrostatic self-assembly technique is adopted to attach the single strand capture DNA to the sensor endface. When the target DNA strand binds to the single-stranded DNA successfully, the optical cavity length of sensor will be increased. Finally, by demodulating the sensor spectrum, DNA hybridization event can be judged qualitatively. This sensor can realize DNA detection without attached label, which save the experiment expense and time. Also the hybridization detection is finished within a few minutes. This quick response feature makes it more attractive in diagnose application. Since the sensitivity and specificity are the most widely used statistics to describe a diagnostic test, so these characteristics are used to evaluate this biosensor. Experimental results demonstrate that this sensor has a sensitivity of 6nmol/ml and can identify a 2 bp mismatch. Since this sensor is optical fiber based, it has robust structure and small size ( 125μm ). If extra etching process is applied to the sensor, the size can be further reduced. This promises the sensor potential application of in-cell detection. Further investigation can be focused on the nanofabrication of this DNA sensor, and this is very meaningful topic not only for diagnostic test but also in many other applications such as food industry, environment monitoring. / Master of Science

Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. Examples of such data include medical records, internet surfing records, transaction records, industrial process or system control records, and activity diary data. This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal trends, relationships and similarities across the data, allow for comparisons to be made within and between the records, and can also help predict forthcoming events.The presented work has researched methods for identifying and exploring such event-sequences which are based on modern visualization, interaction and data mining techniques. An interactive visualization environment that facilitates analysis and exploration of event-based data has been designed and developed, which permits a user to freely explore different aspects of this data and visually identify interesting features and trends. Visual data mining methods have been developed within this environment, that facilitate the automatic identification and exploration of interesting sequences as patterns. The first method makes use of a sequence mining algorithm that identifies sequences of events as patterns, in an iterative fashion, according to certain user-defined constraints. The resulting patterns can then be displayed and interactively explored by the user.The second method has been inspired by web-mining algorithms and the use of graph similarity. A tree-inspired visual exploration environment has been developed that allows a user to systematically and interactively explore interesting event-sequences.Having identified interesting sequences as patterns it becomes interesting to further explore how these are incorporated across the data and classify the records based on the similarities in the way these sequences are manifested within them. In the final method developed in this work, a set of similarity metrics has been identified for characterizing event-sequences, which are then used within a clustering algorithm in order to find similarly behavinggroups. The resulting clusters, as well as attributes of the clusteringparameters and data records, are displayed in a set of linked views allowing the user to interactively explore relationships within these. The research has been focused on the exploration of activity diary data for the study of individuals' time-use and has resulted in a powerful research tool facilitating understanding and thorough analysis of the complexity of everyday life.