Browsing by Author "Lo, Yu-Lun"

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Azide-alkyne cycloaddition for universal post-synthetic modifications of nucleic acids and effective synthesis of bioactive nucleic acid conjugates
(2014)
Su, Yu-Chih
;
Lo, Yu-Lun
;
Hwang, Chi-Ching
;
Wang, Li-Fang
;
Wu, Min Hui
The regioselective post-synthetic modifications of nucleic acids are essential to studies of these molecules for science and applications. Here we report a facile universal approach by harnessing versatile phosphoramidation reactions to regioselectively incorporate alkynyl/azido groups into post-synthetic nucleic acids primed with phosphate at the 5\' termini. With and without the presence of copper, the modified nucleic acids were subjected to azide-alkyne cycloaddition to afford various nucleic acid conjugates including a peptide-oligonucleotide conjugate (POC) with high yield. The POC was inoculated with human A549 cells and demonstrated excellent cell-penetrating ability despite cell deformation caused by a small amount of residual copper chelated to the POC. The combination of phosphoramidation and azide-alkyne cycloaddition reactions thus provides a universal regioselective strategy to post-synthetically modify nucleic acids. This study also explicated the toxicity of residual copper in synthesized bioconjugates destined for biological systems.
Diffuse to fuse EEG spectra - Intrinsic geometry of sleep dynamics for classification
(2020-01-01)
Liu, Gi-Ren
;
Lo, Yu-Lun
;
Malik, John
;
Sheu, Yuan-Chung
;
Wu, Hau-Tieng
We propose a novel algorithm for sleep dynamics visualization and automatic annotation by applying diffusion geometry based sensor fusion algorithm to fuse spectral information from two electroencephalograms (EEG). The diffusion geometry approach helps organize the nonlinear dynamical structure hidden in the EEG signal. The visualization is achieved by the nonlinear dimension reduction capability of the chosen diffusion geometry algorithms. For the automatic annotation purpose, the support vector machine is trained to predict the sleep stage. The prediction performance is validated on a publicly available benchmark database, Physionet Sleep-EDF [extended] SC* (SC = Sleep Cassette) and ST* (ST = Sleep Telemetry), with the leave-one-subject-out cross validation. When we have a single EEG channel (Fpz-Cz), the overall accuracy, macro F1 and Cohen's kappa achieve 82.72%, 75.91% and 76.1% respectively in Sleep-EDF SC* and 78.63%, 73.58% and 69.48% in Sleep-EDF ST*. This performance is compatible with the state-of-the-art results. When we have two EEG channels (Fpz-Cz and Pz-Oz), the overall accuracy, macro F1 and Cohen's kappa achieve 84.44%, 78.25% and 78.36% respectively in Sleep-EDF SC* and 79.05%, 74.73% and 70.31% in Sleep-EDF ST*. The results suggest the potential of the proposed algorithm in practical applications. (C) 2019 Elsevier Ltd. All rights reserved.
Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages
(2020-04-01)
Liu, Gi-Ren
;
Lustenberger, Caroline
;
Lo, Yu-Lun
;
Liu, Wen-Te
;
Sheu, Yuan-Chung
Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.