Enzymatic Polymerization on DNA Modified Gold Nanowire for Label-Free Detection of Pathogen DNA

This paper presents a label-free biosensor for the detection of single-stranded pathogen DNA through the target-enhanced gelation between gold nanowires (AuNW) and the primer DNAs branched on AuNW. The target DNA enables circularization of the linear DNA template, and the primer DNA is elongated continuously via rolling circle amplification. As a result, in the presence of the target DNA, a macroscopic hydrogel was fabricated by the entanglement of the elongated DNA with AuNWs as a scaffold fiber for effective gelation. In contrast, very small separate particles were generated in the absence of the target DNA. This label-free biosensor might be a promising tool for the detection of pathogen DNAs without any devices for further analysis. Moreover, the biosensor based on the weaving of AuNW and DNAs suggests a novel direction for the applications of AuNWs in biological engineering

Optimal Power Flow for Microgrids with Faulty Generators

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Size-Controllable Enzymatic Synthesis of Short Hairpin RNA Nanoparticles by Controlling the Rate of RNA Polymerization

Thanks to a wide range of biological functions of RNA, and advancements in nanotechnology, RNA nanotechnology has developed in multiple ways for RNA-based therapeutics. In particular, among RNA engineering techniques, enzymatic self-assembly of RNA structures has gained great attention for its high packing density of RNA, with a low cost and one-pot synthetic process. However, manipulation of the overall size of particles, especially a reduction in size, has not been studied in depth. Here, we reported the enzymatic self-assembly of short hairpin RNA particles for the downregulation of target genes, and a rational approach to the manipulation of the resultant particle size. This is the first report of the size-controllable enzymatic self-assembly of short hairpin RNA nanoparticles. While keeping all the benefits of an enzymatic approach, the overall size of the RNA particles was controlled on a scale of 2 μm to 100 nm, falling within the therapeutically applicable size range

Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background Polysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patients breathing sounds. Methods Breathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation. Results The overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5. Conclusions The results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds

Enzyme-Driven Hasselback-Like DNA-Based Inorganic Superstructures

DNA structures have gained much attention due to its ease of self-assembly and precise controllability. Although DNA technology has been successfully applied to generate a variety of DNA structures, there are only few attempts to apply DNA technology to generate inorganic materials due to lack of controllability of interactions between DNA and inorganic materials. In addition, the synthesis of a predictable structure of hybrid materials still remains a significant challenge. To address the challenge, here a novel strategy for the synthesis of DNA-based inorganic superstructures using DNA polymerase is reported. In particular, strategic feeding of metal ions for generating DNA-inorganic hybrid superstructures during DNA polymerization is established. This approach can produce a variety of structures with varying metal ions and can easily add functionality to the product. The structural features are also easily studied by first-principles calculations. With these advantages, DNA-Mn particles show the potential as a cell tracking agent, a contrast agent for MRI, and an electrode material for supercapacitors. The enzyme-driven synthesis in this study will provide a novel route for the generation of a range of organic-inorganic hybrid superstructures for biomedical and energy applications.DNA structures have gained much attention due to its ease of self-assembly and precise controllability. Although DNA technology has been successfully applied to generate a variety of DNA structures, there are only few attempts to apply DNA technology to generate inorganic materials due to lack of controllability of interactions between DNA and inorganic materials. In addition, the synthesis of a predictable structure of hybrid materials still remains a significant challenge. To address the challenge, here a novel strategy for the synthesis of DNA-based inorganic superstructures using DNA polymerase is reported. In particular, strategic feeding of metal ions for generating DNA-inorganic hybrid superstructures during DNA polymerization is established. This approach can produce a variety of structures with varying metal ions and can easily add functionality to the product. The structural features are also easily studied by first-principles calculations. With these advantages, DNA-Mn particles show the potential as a cell tracking agent, a contrast agent for MRI, and an electrode material for supercapacitors. The enzyme-driven synthesis in this study will provide a novel route for the generation of a range of organic-inorganic hybrid superstructures for biomedical and energy applications.1

Correction: Kamruzzaman, M., et al. Evaluating the Impact of Climate Change on Paddy Water Balance Using APEX-Paddy Model. Water 2020, 12, 852

The authors wish to make the following corrections to this paper [...