Session Details
[21p-D901-1~19]12.6 Nanobiotechnology
Thu. Sep 21, 2023 1:30 PM - 7:00 PM JST
Thu. Sep 21, 2023 4:30 AM - 10:00 AM UTC
Thu. Sep 21, 2023 4:30 AM - 10:00 AM UTC
D901 (TKP)
Tomohiro Hayashi(Tokyo Tech), Atsushi Miura(Hokkaido Univ.), Yuhei Hayamizu(Tokyo Tech), Takeo Miyake(Waseda Univ.)
[21p-D901-1]Size Dependence of Viscosity in Single Aerosol Microdroplets
〇Atsushi Miura1 (1.Hokkaido Univ.)
[21p-D901-2]Sub-nm resolution AFM imaging of multiple facets at molecular crystal surface in liquid
〇Kodai Tanigawa1, Masayuki Morimoto1, Hitoshi Asakawa1 (1.Kanazawa Univ.)
[21p-D901-3]Quantitative analysis of Fluorescence and atomic force microscopy images of self-assembled peptides on h-BN surfaces
〇(M1)Hiroki Maeda1, Yuhei Hayamizu1 (1.Tokyo Tech.)
[21p-D901-4]Electrochemical activity of molecular Hybrids: self-assembly of (XH)4 peptides and hemin on graphite electrodes
〇Marie Sugiyama1, Wei Luo1, Hayamizu Yuhei1 (1.Tokyo Tech)
[21p-D901-5]Development of self-assembly of positive-charged Mini-Ferritin Dps protein by peptides
〇Mitsuhiro Okuda1,2,3, Gabriela Pretre3 (1.Meiji University, 2.CIC nanoGUNE, 3.Komie Corp.)
[21p-D901-6]Examination of the adhesion tendency of biofilm-forming bacteria on model organic surfaces using quartz crystal microbalance with energy dissipation monitoring
〇(DC)Glenn Villena Latag1, Tomohiro Hayashi1,2 (1.Tokyo TECH, 2.U. Tokyo)
[21p-D901-7]Evaluation of Anti-biofilm property for Polymeric Nanopillars
〇(M1C)Satoka Matsumoto1, Shigemitsu Tanaka2, Toshihiro Nagao2, Shoso Shingubara1, Tomohiro Shimizu1, Takeshi Ito1 (1.Kansai Univ., 2.ORIST)
[21p-D901-8]Membrane damage assessment due to surface characteristics of polymeric nanopillars
〇(M1C)Yuito Matsushita1, Tomohiro Shimizu1, Shoso Shingubara1, Takeshi Ito1 (1.Kansai Univ.)
[21p-D901-9]Sugar Monitoring in Growing Plants Using a Needle-type Biosensor
〇(M2)Wakutaka Nakagawa1, Shiqi Wu1, Daniella Gatus1, Yuta Nishina2, Takeo Miyake1 (1.Waseda Univ., 2.Okayama Univ.)
[21p-D901-10]Intracellular Nanobeads Delivery using Nanoinjector and their Assessment
〇(DC)Kazuhiro Oyama1, Bowen Zhang1, Bingfu Liu1, Takeo Miyake1 (1.IPS, Waseda Univ.)
[21p-D901-11]Development of a new interface-sensitive vibrational spectroscopic technique based on combination of infrared spectroscopy and informatics
〇(DC)Shoichi Maeda1, Shunta Chikami1, Subin Song1, Tomohiro Hayashi1,2 (1.Tokyo TECH, 2.U. Tokyo)
[21p-D901-12]Analysis of hydration water on the surface of polymeric materials using a combination of infrared spectroscopy and multivariate curve resolution
〇(DC)Shunta Chikami1, Shoichi Maeda1, Subin Song1, Tomohiro Hayashi1,2 (1.Tokyo TECH, 2.U. Tokyo)
[21p-D901-13]RNA Folding Using Simulated Bifurcation Machine
〇Hiroaki Hata1, Kengo Tsuda1, Masaru Suzuki2, Yuki Matsubara1 (1.MKI, 2.TDSL)
[21p-D901-14]Enhancing Performance of Convolutional Neural Network-based Epileptic ·Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance
〇(M2)Zhuozheng Shi1, Zhiqiang Liao1, Hitoshi Tabata1 (1.Univ. of Tokyo)
[21p-D901-15]Analyses based on MM-MD/FMO collaborative calculation on the complex
of influenza virus hemagglutinin and Fab antibody (PDB-ID: 1KEN) - Part2
〇Shun Kitahara1, Kazuki Akisawa1, Koji Okuwaki1, Hideo Doi1, Eiji Yamamoto2, Yoshinori Hirano2, Kenji Yasuoka2, Yoshiharu Mori3, Shigenori Tanaka3, Yuji Mochizuki1,4 (1.Rikkyo Univ., 2.Keio Univ., 3.Kobe Univ, 4.Univ. Tokyo)
[21p-D901-16]Deep Learning Analysis of Scattered Light Intensity for Shape Classification of Nanoparticles Measured by Nano Tracking Analysis
〇Keisuke Yamamoto1, Hiromi Kuramochi1,2, Hiroaki Fukuda1, Yasushi Shibuta1, Takanori Ichiki1,2 (1.Tokyo Univ.okyo, 2.iCONM)
[21p-D901-17]Evaluation of effective parameters for FMO-DPD simulation of proteins - part 2
〇Yusuke Tachino1, Hideo Doi1, Koji Okuwaki1, Yoshinori Hirano2, Yuji Mochizuki1,3 (1.Rikkyo Univ., 2.Keio Univ., 3.Univ. Tokyo)
[21p-D901-18]Improvement of parameter calculation for FMO-DPD for proteins by machine learning
〇Sota Matsuoka1, Hideo Doi1, Koji Okuwaki1, Ryo Hatada1, Sojiro Minami1, Ryosuke Suhara1, Yusuke Tachino1, Yuji Mochizuki1,2 (1.Rikkyo Univ., 2.Univ. Tokyo)
[21p-D901-19]Non-Empirical calculation of parameters for DPD simulation with the aid of machine learning
〇Hideo Doi1, Sota Matsuoka1, Koji Okuwaki1, Ryo Hatada1, Sojiro Minami1, Ryosuke Suhara1, Yuji Mochizuki1,2 (1.Rikkyo Univ., 2.Univ. Tokyo)