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Kim, Myunghun,Hong, Chang Kook,Choe, Soona,Shim, Sang Eun Interscience 2007 Journal of polymer science. Part A, Polymer chemis Vol.45 No.19
<P>Multiwalled carbon nanotubes (MWNTs) were effectively functionalized with KMnO<SUB>4</SUB> in the presence of a phase-transfer catalyst at room temperature. The hydroxyl functionalized MWNTs were reacted with a vinyl-group carrying silane-coupling agent and the terminal vinyl groups were used to fabricate polystyrene (PS) brushes by solution polymerization. Finally, PS-encapsulated MWNTs were obtained. The synthesis results were verified from FT-Raman, thermal gravimetric analysis, energy dispersive X-ray analysis, and transmission electron microscope. PS-encapsulated MWNTs had much improved dispersion stability in hydrophobic medium, toluene since grafted hydrophobic PS interacts with media and has improved compatibility. This functionalization technique would provide a facile route to prepare various polymer brushes on the surface of MWNTs to improve the dispersion of MWNTs for potential applications. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 4413–4420, 2007</P> <B>Graphic Abstract</B> <P>MWNTs were effectively functionalized with KMnO<SUB>4</SUB> with the aid of a phase transfer catalyst at room temperature. The hydroxyl functionalized MWNTs were reacted with a vinyl-group carrying silane coupling agent, and the terminal vinyl groups on MWNTs were used to fabricate polystyrene brushes. Finally, polystyrene-encapsulated MWNTs were obtained by simple in situ solution polymerization.</P><P> <img src='wiley_img/0887624X-2007-45-19-POLA22190-gra001.gif' alt='wiley_img/0887624X-2007-45-19-POLA22190-gra001'> </P>
AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples
Jeon Hyeonseong,Ahn Junhak,Na Byunggook,Hong Soona,Sael Lee,Kim Sun,Yoon Sungroh,Baek Daehyun 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth.