Analysis of significant protein abundance from multiple reaction-monitoring data

Background Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM). Results Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent. Conclusion As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165). Publication of this article was sponsored by HI16C2037 grant

광패턴 기능성 실록산 폴리머 기반의 패시베이션층을 활용한 초박막 유연 전자 소자의 응용

학위논문(박사) - 한국과학기술원 : 신소재공학과, 2023.2,[x, 130 p. :]Unlike the materials and designs used in conventional rigid, planar forms of electronic devices, flexible electronic devices must have mechanical properties applicable to human skin or curved bodies for application for bio-signal monitoring. Therefore, flexible passivation layer is essential to protect the electronic device from degradation or external contamination of the device as well as mechanical durability of the device in the flexible electronic device. Such a flexible passivation layer requires a material innovation capable of a solution-process and photo patterning that can be made large-area and easy to manufacture complex multi-layer at the same time. In this study, introduced a functional siloxane material used as a passivation layer material of a flexible electronic device and manufactured an ultra-thin flexible electronic device with a passivation layer composed of a siloxane material with water repellency and upconversion nanoparticle with photo-stability in water condition. Sol-gel processed photopatternable siloxane materials were applied to ultra-thin flexible electronic devices, we confirmed reliability, mechanical durability and biocompatibility of materials. We expect our functional siloxane materials to be promising materials in the passivation layer applications of large-area bio-integrated or wearable devices.한국과학기술원 :신소재공학과

A BUSINESS PROCESS SIMULATION FRAMEWORK INCORPORATING THE EFFECTS OF ORGANIZATIONAL STRUCTURE

Organizations constantly change their business processes and/or organizational structure to innovate and adapt to the rapidly changing environment. Business process simulation is one of the most popular methodologies for more effectively predicting the effects of process and organizational redesign. Most existing approaches, however, consider only business processes and not organizational structures that can significantly affect business process performance. This study presents a framework for incorporating the effects of organizational structure into business process simulation. Further, it demonstrates how to use and analyze the proposed model. Finally, a case study of the Korean prosecutor's office is presented to illustrate the importance and feasibility of the proposed approach, which will enable a more precise prediction of the changes caused by process and organizational redesignopen