Offshore culture becomes the most attractive industry in last decades because of overfishing and excessively pumping groundwater causing land subsidence social problems. Oyster culture is one of offshore culture with several advantages such as easy set-up and without feeding cost. In this study, we focus on a longline-style oyster aquaculture system because it is recyclable, high growing efficiency and advanced technique against traditional tidal flat culture. This study establishes a numerical model through a lumped mass method, then employs fourth order Runge-Kutta method to solve the system of motion equations and evaluates the tension on the anchor rope. The results show the discrepancy between the numerical model and experimental data is lower than 4% in current-only situation, and similarly in wave-only situation the discrepancy is lower than 6%. The comparison results indicate that the numerical model is capable to predict the environmental loadings on longline-style oyster culture system. An in-situ case study of a longline-style oyster aquaculture system via regular and random waves, located in the Penghu Bay, is investigated based on the sea-state, Chebi Typhoon occurred in June, 2001, causing a catastrophic damage to the cage aquaculture. The conclusions of dynamic responses of the case study may be utilized as guidance for the local oyster farmers to build and protect their oyster culture system in the Bay.

Background: MicrO is an ontology of microbiological terms, including prokaryotic qualities and processes, material entities (such as cell components), chemical entities (such as microbiological culture media and medium ingredients), and assays. The ontology was built to support the ongoing development of a natural language processing algorithm, MicroPIE (or, Microbial Phenomics Information Extractor). During the MicroPIE design process, we realized there was a need for a prokaryotic ontology which would capture the evolutionary diversity of phenotypes and metabolic processes across the tree of life, capture the diversity of synonyms and information contained in the taxonomic literature, and relate microbiological entities and processes to terms in a large number of other ontologies, most particularly the Gene Ontology (GO), the Phenotypic Quality Ontology (PATO), and the Chemical Entities of Biological Interest (ChEBI). We thus constructed MicrO to be rich in logical axioms and synonyms gathered from the taxonomic literature. Results: MicrO currently has similar to 14550 classes (similar to 2550 of which are new, the remainder being microbiologically-relevant classes imported from other ontologies), connected by similar to 24,130 logical axioms (5,446 of which are new), and is available at (http://purl.obolibrary.org/obo/MicrO.owl) and on the project website at https://github.com/carrineblank/MicrO. MicrO has been integrated into the OBO Foundry Library (http://www.obofoundry.org/ontology/micro.html), so that other ontologies can borrow and re-use classes. Term requests and user feedback can be made using MicrO's Issue Tracker in GitHub. We designed MicrO such that it can support the ongoing and future development of algorithms that can leverage the controlled vocabulary and logical inference power provided by the ontology. Conclusions: By connecting microbial classes with large numbers of chemical entities, material entities, biological processes, molecular functions, and qualities using a dense array of logical axioms, we intend MicrO to be a powerful new tool to increase the computing power of bioinformatics tools such as the automated text mining of prokaryotic taxonomic descriptions using natural language processing. We also intend MicrO to support the development of new bioinformatics tools that aim to develop new connections between microbial phenotypes and genotypes (i.e., the gene content in genomes). Future ontology development will include incorporation of pathogenic phenotypes and prokaryotic habitats.

Metabolomics is a rising field combining bioinformatics and cheminformatics together. A major component of research is having a reliable data source, which usually comes in the form of metabolomic databases. This paper documents arising issues revolving categorizing metabolome compounds within databases, and a possible solution in the form of an R package that is capable of matching up various metabolome identifiers that originate from various metabolome databases. Then, by using this package we reflect on the average coverage of external reference between metabolome databases to highlight the lack of a universal compound primary identifier. / <p>The thesis presentation was held over Zoom due to the recent COVID19 pandemic.</p>