HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

Background Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples. Results HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify response order preserving DEGs that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools. Conclusions HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.This work, including publication costs, was supported by National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (No.NRF-2017M3C4A7065887). This work was also supported by the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI15C3224). This work was supported for W.J. by the Agenda program (No.PJ012465032019), Rural Development of dministration of Republic of Korea

Rational design of coaxial structured carbon nanotube-manganese oxide (CNT-MnO2) for energy storage application

Recently, there has been great research interest in the development of composites (core-shell structures) of carbon nanotubes (CNTs) with metal oxides for improved electrochemical energy storage, photonics, electronics, catalysis, etc. Currently, the synthetic strategies for metal oxides/hydroxides are well established, but the development of core-shell structures by robust, cost-effective chemical methods is still a challenge. The main drawbacks for obtaining such electrodes are the very complex synthesis methods which ultimately result in high production costs. Alternatively, the solution based method offers the advantages of simple and cost effective synthesis, as well as being easy to scale up. Here, we report on the development of multi-walled carbon nanotube-manganese oxide (CNT-MnO2) core-shell structures. These samples were directly utilized for asymmetric supercapacitor (ASC) applications, where the CNT-MnO2 composite was used as the positive electrode and ZIF-8 (zeolitic imidazolate framework, ZIF) derived nanoporous carbon was used as the negative electrode. This unconventional ASC shows a high energy density of 20.44 W h kg(-1) and high power density of 16 kW kg(-1). The results demonstrate that these are efficient electrodes for supercapacitor application

Rational design of coaxial structured carbon nanotube-manganese oxide (CNT-MnO2) for energy storage application

Recently, there has been great research interest in the development of composites (core-shell structures) of carbon nanotubes (CNTs) with metal oxides for improved electrochemical energy storage, photonics, electronics, catalysis, etc. Currently, the synthetic strategies for metal oxides/hydroxides are well established, but the development of core-shell structures by robust, cost-effective chemical methods is still a challenge. The main drawbacks for obtaining such electrodes are the very complex synthesis methods which ultimately result in high production costs. Alternatively, the solution based method offers the advantages of simple and cost effective synthesis, as well as being easy to scale up. Here, we report on the development of multi-walled carbon nanotube-manganese oxide (CNT-MnO2) core-shell structures. These samples were directly utilized for asymmetric supercapacitor (ASC) applications, where the CNT-MnO2 composite was used as the positive electrode and ZIF-8 (zeolitic imidazolate framework, ZIF) derived nanoporous carbon was used as the negative electrode. This unconventional ASC shows a high energy density of 20.44 W h kg-1 and high power density of 16 kW kg-1. The results demonstrate that these are efficient electrodes for supercapacitor application

Presenting highest supercapacitance for TiO2/MWNTs nanocomposites: Novel method

A facile two step, binder-free method is successfully developed for the synthesis of TiO2 nanodots on the walls of multi-walled carbon nanotubes (MWNTs). TiO2/MWNTs nanocomposite exhibited excellent specific capacitance and stability as supercapacitor electrode materials due to the synergistic effect of both components as well as the nanodots-like structure of TiO2, which increases the specific surface area of the nanocomposite. The TiO2/MWNTs prepared by this binder-free approach yields the largest specific and interfacial capacitances of 329Fg-1 and 52mFcm-2 at a scan rate of 0.005Vs-1, which is the utmost value of capacitance obtained till date. Importantly, TiO2/MWNTs showed remarkable rate capability with 6mFcm-2 capacitance at higher scan rate (0.4Vs-1) with good long-term cycling stability. The Ragone plot of TiO2/MWNTs nanocomposite discovers better power and energy density values. Lastly, the method used here is promising for producing high performance supercapacitors which can be scalable for large area application for industrial route

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

© 2021 IEEE.Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.N