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Direct-Current Treatment as a Safe Sterilization Method for Electrospun Biodegradable Polymer
HyeLeeKim,JeongHyunLee,MiHeeLee,HakHeeKim,JungsungKim,InhoHan,BongJooPark,JeongKooKim,DongWookHan,SooHyunKim,SeungJinLee,JongChulPark 한국조직공학·재생의학회 2011 조직공학과 재생의학 Vol.8 No.3
Sterilization is an essential process for biodegradable polymers to be used as biomaterials or tissue engi-neered-scaffolds. The characteristics of biodegradable scaffolds can change due to decomposition of constituentpolymers due to high temperature, pressure, or moisture during sterilization. This study investigated direct-current (DC) treatment as a safe method that can prevent structural change and deformation. Treatment of electrospun poly (lactic-co-glycolic acid) (PLGA) with DC showed a bactericidal effect within 40 sec at 4 V. When DC was appliedat 6 V to the electrospun PLGA, the bactericidal effect emerged within 30 sec. The morphology of fibers and molec-ular weight of PLGA polymer was maintained after DC treatment. In contrast, electrospun PLGA exposed to ethyl-ene oxide showed fiber degradation, and gamma or e-beam irradiation resulted in decreased molecular weight. Thedemonstrated improvement in chemical and physical stability of biodegradable polymers after DC sterilization mayhelp extend their application.
Least squares sieve estimation of mixture distributions with boundary effects
Mihee Lee,Ling Wang,Haipeng Shen,Peter Hall,Guang Guo,J.S. Marron 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.2
In this study, we propose two types of sieve estimators, based on least squares (LS), for probability distributions that are mixtures of a finite number of discrete atoms and a continuous distribution under the framework of measurement error models. This research is motivated by the maximum likelihood (ML) sieve estimator developed in Lee et al. (2013). We obtain two types of LS sieve estimators through minimizing the distance between the empirical distribution/characteristic functions and the model distribution/characteristic functions. The LS estimators outperform the ML sieve estimator in several aspects: (1) they need much less computational time; (2) they give smaller integrated mean squared error; (3) the characteristic function based LS estimator is more robust against mis-specification of the error distribution. We also use roughness penalization to improve the smoothness of the resulting estimators and reduce the estimation variance. As an application of our proposed LS estimators, we use the Framingham Heart Study data to investigate the distribution of genetic effects on body mass index. Finally asymptotic properties of the LS estimators are investigated.