PubMed Health⌕ Search

Biomedical subjects

Seza Orcun

Publications and source records attributed to Seza Orcun.

2 recordsLinked to original sources

Purdue ionomics information management system. An integrated functional genomics platform.

The advent of high-throughput phenotyping technologies has created a deluge of information that is difficult to deal with without the appropriate data management tools. These data management tools should integrate defined workflow controls for genomic-scale data acquisition and validation, data storage and retrieval, and data analysis, indexed around the genomic information of the organism of interest. To maximize the impact of these large datasets, it is critical that they are rapidly disseminated to the broader research community, allowing open access for data mining and discovery. We describe here a system that incorporates such functionalities developed around the Purdue University high-throughput ionomics phenotyping platform. The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high-throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories. PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including ethyl methanesulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations. PiiMS is accessible at www.purdue.edu/dp/ionomics.

Arabidopsis↗

Target volume uncertainty and a method to visualize its effect on the target dose prescription.

PURPOSE: To consider the uncertainty in the construction of target boundaries for optimization, and to demonstrate how the principles of mathematical programming can be applied to determine and display the effect on the tumor dose of making small changes to the target boundary. METHODS: The effect on the achievable target dose of making successive small shifts to the target boundary within its range of uncertainty was found by constructing a mixed-integer linear program that automated the placement of the beam angles using the initial target volume. RESULTS: The method was demonstrated using contours taken from a nasopharynx case, with dose limits placed on surrounding structures. In the illustrated case, enlarging the target anteriorly to provide greater assurance of disease coverage did not force a sacrifice in the minimum or mean tumor doses. However, enlarging the margin posteriorly, near a critical structure, dramatically changed the minimum, mean, and maximum tumor doses. CONCLUSION: Tradeoffs between the position of the target boundary and the minimum target dose can be developed using mixed-integer programming, and the results projected as a guide to contouring and plan selection.

Humans↗