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pLog: User Generated Media for Personal LBS
Kaji, Hideki,Arikawa, Masatoshi Korea Spatial Information System Society 2009 한국공간정보시스템학회 논문지 Vol.11 No.2
This paper proposes a framework for personal location based services with personal life content, for example diaries, schedules and to-do lists. A lot of Internet users are recording their personal experiences and knowledge as text and other digital media on the network. Our proposed tool provides users with an environment to store personal records with related place attributes, and to retrieve these personal records at the right place. There are two applications on this tool, a place-enhanced blog and a LBS client on a mobile phone. The place enhanced blog provides users with blog interfaces for inputting place information. The place rem inder is a browser for spatial data on the place enhanced blog. Users can generate place information by writing personal records on their blog. Furthermore, using the LBS client, other users can retrieve personal records at the appropriate spots.
Identification of glyco-biomarker candidates for lung cancer using novel glyco-technologies
Yoshitoshi Hirao,Hideki Matsuzaki,Jun Iwaki,Minako Abe,Akira Togayachi,Atsushi Kuno,Takashi Ohkura,Hiroyuki Kaji,Masaharu Nomura,Masayuki Noguchi,Yuzuru Ikehara,Hisashi Narimatsu 한국당과학회 2012 한국당과학회 학술대회 Vol.2012 No.1
Lung cancer is the leading cause of cancer death worldwide. Currently, lung cancer is classified into two major types, small-cell lung cancer carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), based on the histological appearance. The histological classification has important implications in the clinical practice guideline and the prediction of the patient prognosis. However, conventional serum markers used in clinical tests are insufficient for clinical demands due to the low sensitivity and the low specificity to distinguish them. We have identified a number of glyco-biomarker candidate molecules from lung cancer cell lines using our developed glycoproteomics technologies such as lectin microarray and LC/MS-based protein analysis. On the validation studies, we found out that the selected molecules showed characteristic lectin biding profiles depending on either SCLC or NSCLC. Therefore, combination of these glyco-biomarkers could be expected to improve the diagnostic accuracy for histological classification in lung cancer compared to protein expression alone.