Current Chinese Science - Volume 2, Issue 3, 2022

Background: The problems of putting a space robot into a geostationary orbit using a combined scheme and its approach to a geostationary satellite are considered. The authors’ previous results on this topic are briefly analyzed. Objective: The paper deals with three problems: further development of a rational strategy for the robot’s add-launching on geostationary orbit and its approaching target using a plasma electric propulsion unit only; synthesis of the guidance and control laws; a nonlinear dynamical analysis of the robot’s attitude and orbit control system during these modes. Methods: The developed methods and algorithms are based on the local optimizing the fuel consumption of the plasma electric propulsion unit during each inter-orbital flight of a space robot. Results: Simulation results on the guidance and control algorithms are presented that demonstrate their effectiveness. Conclusion: The breakthroughs are as follows: original strategy of add-launching and forecasting the influence of gravitational disturbances as well as solar pressure forces when synthesizing a robot’s guidance law for its inter-orbital flights.

Background: Intrinsically disordered proteins (IDPs) are proteins that lack a predetermined 3D structure and play key cellular functions. IDPs are often involved in diseases and have been shown to be attractive targets for drug development. The IDPs have been intensively investigated, revealing important results. Objective: This study aimed to evaluate the latest research bibliography since 2010, including the latest findings, major contributors, institutions, and journals. Methods: The bibliographic data were retrieved from PubMed from 2010 to 2020. The data collected were then analysed by VOSviewer software (version 1.6.11). Results: In this study, 4590 publications were retrieved for analysis. They have been published in 579 journals. Over 9683 organisations have contributed to IDP publications, with United States of America and Russia being in the first place. Human proteins are most studied for their IDP features and mainly in the context of diseases and drug design. Conclusion: This bibliometric study reveals that the trend of publications is increasing year by year. USA and Russia have contributed the most to the IDP research field. Moreover, it suggests that IDP research remains a challenging issue that is still open for original contributions and original applications.

Aromatic nitro compounds are widely used to synthesize medicine, dyes, spices, and other organic synthesis reagents for their wide sources and low cost. One of the most important uses is the synthesis of phenolic compounds. Moreover, the nitro group on the aromatic ring can be converted to hydroxyl after reduction, diazotization, and hydrolysis, which is a traditional method to get hydroxyl. This mini-review describes the methodologies and mechanisms of nitro- hydroxylation on aromatic rings and other unique transformation methods.

Objective: The aim was to reconstruct the climatic parameters and ice of the Laptev Sea over the last centuries and identify the causes of their variability. Background: The ice-free water area of the Arctic Ocean has increased considerably during the summer-autumn period in recent decades, which may be attributed to several climatic and oceanographic factors. Methods: The ice-free period duration and mean annual surface air temperature were reconstructed using the transfer function technique to compare hydrometeorological data and the chemical composition of bottom sediments accumulated during the observation period. This approach was based on sub-millimeter scanning of sediments using synchrotron radiation X-ray fluorescence. Results: The results revealed a specific feature of the variations in the mean annual air temperature over the last 400 years in the Laptev Sea region, whereby higher (up to positive) temperature anomalies were observed during the Little Ice Age. Some discrepancies in the ice coverage and climate fluctuations were observed during the period from the end of the Little Ice Age to the beginning of the period of accelerated ice-cover degradation in the Arctic (1850–1980). These discrepancies can be caused by the beginning of global warming, which have disturbed the natural cyclicality of atmospheric processes, whereas natural variations in ice coverage were more stable. Conclusion: A joint analysis of the reconstructed variations in air temperature and the duration of the ice-free period revealed the influence of atmospheric processes on the ice conditions of the Laptev Sea.

Programmable DNA nanostructures are excellent modules for preparing artificial cells with complex structures and functions. As a biocompatible building material, DNA nanostructures can mimic cell components on the nanoscale and serve as artificial cell structural units, such as membrane proteins, cytoskeleton, organelles, or even protocell models. The incorporation of DNA strand displacement reactions and functional nucleic acids endowed artificial cells with the capability of mimicking dynamic biological processes, such as molecular transportation, and signal transduction pathways. Herein, we focus on the recent progress in the construction of artificial cells utilizing DNA nanotechnology. DNA nanostructures used as cellular structural and functional units are summarized, and the applications of DNA-based artificial cells as biosensors or smart drug carriers are highlighted. We also discuss the perspective of DNA-based artificial cells, including the challenges in designing smart artificial cells with controllable behavior and constructing artificial cells with the cell-like function, as well as the opportunities of their potential applications.

Background: Recently, COVID-19 cases have been increasing globally at an alarming rate due to the COVID-19 second wave despite the mass vaccination programs. Search for the potential vaccine for SARS-CoV-2 is still in progress. The epitope-based vaccine is effective and is a cornerstone in vaccine development. The quick prediction of epitopes could be a proficient way of monitoring vaccine development during a global health crisis. Objective: This study focused on predicting the potential epitopes with computational tools for effective vaccine development. Methods: NetCTLpan v. 1.1 and NetMHCIIpan v. 3.2 servers were used for T-cell epitope analysis. IEDB servers were employed for HLA and DRB1 peptide calculations. The epitope’s immunogenicity, toxicity, physiochemical character, and other features are measured by immunogen evaluation. Furthermore, the top-ranked immunogenic epitopes were computationally validated by molecular docking analysis. The epitopes are docked to toll-like receptors (TLRs), which are helpful in generating an immune response against SARS-CoV-2. Results: Overall, six HTL and CTL epitopes were predicted (IDGYFKIYSKH, HPLSHFVNLDNL, RIGNNYKLNT and WTAGAAAYYVG, MACLVGLMWLS, FRLKGGAPIKGVT), which had good immunogenicity scores and stable interaction with toll-like receptor (TLR). Therefore, these epitopes can bind with HLA and DRB1 molecules, respectively. Conclusion: The computationally predicted antigenic regions might be considered for an epitopebased vaccine against SARS-CoV-2 after in vitro testing.

Background: Through the analysis of the relevant data of industrial equipment, faults diagnosis is helpful for system maintenance and reducing economic losses. Objective: This study aimed at reducing the influence of irrelevant features and efficiently training the FIR-XgBoost model. Methods: An Extreme Gradient Boosting (XgBoost) approach based on feature importance ranking (FIR) is proposed in this article for fault classification of high-dimensional complex industrial systems. Gini index is applied to rank the features according to the importance, and feature selection is implemented based on their position in the ranking. Results: The dataset from the PHM 2021 data challenge, which is related to the process of fuse thermal imaging, is used. The classification accuracy of FIR-XgBoost has been found to be 99.63%, outperforming other existing algorithms. A case study is presented to show that excellent fault classification can be achieved through ensemble learning and feature selection. Conclusion: Data-driven machine learning methods are proposed for solving high-dimensional fault classification problems on the dataset of the PHM2021 Data Challenge. An FIR-XgBoost method is proposed, the core of which is to retain important features and to reduce redundancy of sensor data. Consequently, feature selection based on FIR has better interpretability than other algorithms. Furthermore, the FIR- XgBoost algorithm retaining the 50 most important features has been observed to achieve the best fault classification performance among the compared algorithms and can be implemented in specific industrial processes.