Barrier Lyapunov functions-based dynamic surface control with tracking error constraints for ammunition manipulator electro-hydraulic system

This paper focuses on the dynamic tracking control of ammunition manipulator system. A standard state space model for the ammunition manipulator electro-hydraulic system(AMEHS) with inherent nonlinearities and uncertainties considered was established. To simultaneously suppress the violation of tracking error constraints and the complexity of differential explosion, a barrier Lyapunov functionsbased dynamic surface control(BLF-DSC) method was proposed for the position tracking control of the ammunition manipulator. Theoretical analysis prove the stability of the closed-loop overall system and the tracking error converges to a prescribed neighborhood asymptotically. The effectiveness and dynamic tracking performance of the proposed control strategy is validated via simulation and experimental results.

COVID-19 Related Research by Data Mining in Single Cell Transcriptome Profiles

The outbreak of coronavirus disease 2019(COVID-2019)has drawn public attention all over the world.As a newly emerging area,single cell sequencing also exerts its power in the battle over the epidemic.In this review,the up-to-date knowledge of COVID-19 and its receptor is summarized,followed by a collection of the mining of single cell transcriptome profiling data for the information in aspects of the vulnerable cell types in humans and the potential mechanisms of the disease.

Call for Papers: Special Section on in Silico Research on Microbiology and Public Health

Journal of Electronic Science and Technology(JEST)is soliciting original manuscripts for Special Section on in Silico Research on Microbiology and Public Health(isMPH).This Special Issue/Section intends to bring together the state-of-art research results of microbiology and public health,especially the researches of latest COVID-19(2019-nCoV).Authors are invited to submit unpublished papers,which are not under review in any other conference or journal,in the following topic areas,but not limited to:Theoretical and experimental studies and reviews on COVID-19,spread of virus,bioinformatics analysis on microbial genomes,computational simulation and analysis on public health.

Melanin nanoparticles alleviate sepsis-induced myocardial injury by suppressing ferroptosis and inflammation

Myocardial injury as one of the severe complications leads to the increasing morbidity and mortality in patients with sepsis.Recent studies reported that reactive oxygen species(ROS)-mediated ferroptosis plays a critical role in the development of heart diseases.Therefore,we hypothesized that anti-ferroptosis agent might be a novel potential therapeutic strategy for sepsis-induced cardiac injury.Herein,we demonstrated that a small biocompatible and MRI-visible melanin nanoparticles(MMPP)improves myocardial function by inhibiting ROS-related ferroptosis signaling pathway.In LPS-induced murine sepsis model,after a single dose intravenously injection of MMPP treatment,MMPP markedly alleviated the myocardial injury including cardiac function and heart structure disorder through suppressing iron-accumulation induced ferroptosis.In vitro,MMPP inhibited cardiomyocyte death by attenuating oxidative stress,inflammation and maintaining mitochondrial homeostasis.Collectively,our findings demonstrated that MMPP protected heart against sepsis-induced myocardial injury via inhibiting ferroptosis and inflammation,which might be a novel therapeutic approach in future.

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level

Cancer treatments always face challenging problems,particularly drug resistance due to tumor cell heterogeneity.The existing datasets include the relationship between gene expression and drug sensitivities;however,the majority are based on tissue-level studies.Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy.Fortunately,machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression.Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments.In this paper,we review machine learning and deep learning approaches in drug research.By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis,we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level.We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

Biological Sequence Classification:A Review on Data and General Methods

With the rapid development of biotechnology,the number of biological sequences has grown exponentially.The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information.There are many branches of biological sequence classification research.In this review,we mainly focus on the function and modification classification of biological sequences based on machine learning.Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and peptides.However,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first glance.Here,we aim to establish a long-term support website(http://lab.malab.cn/~acy/BioseqData/home.html),which provides readers with detailed information on the classification method and download links to relevant datasets.We briefly introduce the steps to build an effective model framework for biological sequence data.In addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included.Finally,we discuss the current challenges and future perspectives of biological sequence classification research.

A study of miRNAs targets prediction and experimental validation

microRNAs(miRNAs)are 20–24 nucleotide(nt)RNAs that regulate eukaryotic gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs(mRNAs).To identify miRNA target genes will help a lot by understanding their biological functions.Sophisticated computational approaches for miRNA target prediction,and effective biological techniques for validating these targets now play a central role in elucidating their functions.Owing to the imperfect complementarity of animal miRNAs with their targets,it is difficult to judge the accuracy of the prediction.Complexity of regulation by miRNA-mediated targets at protein and mRNAs levels has made it more challenging to identify the targets.To date,only a few miRNAs targets are confirmed.In this article,we review the methods of miRNA target prediction and the experimental validation for their corresponding mRNA targets in animals.

Construction and detection of expression vectors of microRNA-9a in BmN cells

MicroRNAs (miRNAs) are small endogenous RNAs molecules,approximately 21–23 nucleotides in length,which regulate gene expression by base-pairing with 3′ untranslated regions (UTRs) of target mRNAs.However,the functions of only a few miRNAs in organisms are known.Recently,the expression vector of artificial miRNA has become a promising tool for gene function studies.Here,a method for easy and rapid construction of eukaryotic miRNA expression vector was described.The cytoplasmic actin 3 (A3) promoter and flanked sequences of miRNA-9a (miR-9a) precursor were amplified from genomic DNA of the silkworm (Bombyx mori) and was inserted into pCDNA3.0 vector to construct a recombinant plasmid.The enhanced green fluorescent protein (EGFP) gene was used as reporter gene.The Bombyx mori N (BmN) cells were transfected with recombinant miR-9a expression plasmid and were harvested 48 h post transfection.Total RNAs of BmN cells transfected with recombinant vectors were extracted and the expression of miR-9a was evaluated by reverse transcriptase polymerase chain reaction (RT-PCR) and Northern blot.Tests showed that the recombinant miR-9a vector was successfully constructed and the expression of miR-9a with EGFP was detected.

DeepM6ASeq-EL:prediction of human N6-methyladenosine(m^(6)A)sites with LSTM and ensemble learning

N6-methyladenosine(m^(6)A)is a prevalent methylation modification and plays a vital role in various biological processes,such as metabolism,mRNA processing,synthesis,and transport.Recent studies have suggested that m^(6)A modification is related to common diseases such as cancer,tumours,and obesity.Therefore,accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics.However,traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs,significant time requirements and inaccurate identification of sites.But through the use of traditional experimental methods,researchers have produced many large databases of m^(6)A sites.With the support of these basic databases and existing deep learning methods,we developed an m^(6)A site predictor named DeepM6ASeq-EL,which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting.Compared to the state-of-the-art prediction method WHISTLE(average AUC 0.948 and 0.880),the DeepM6ASeq-EL had a lower accuracy in m^(6)A site prediction(average AUC:0.861 for the full transcript models and 0.809 for the mature messenger RNA models)when tested on six independent datasets.

Identification and classification of promoters using the attention mechanism based on long short-term memory

A promoter is a short region of DNA that can bind RNA polymerase and initiate gene transcription.It is usually located directly upstream of the transcription initiation site.DNA promoters have been proven to be the main cause of many human diseases,especially diabetes,cancer or Huntington’s disease.Therefore,the classification of promoters has become an interesting problem and has attracted the attention of many researchers in the field of bioinformatics.Various studies have been conducted in order to solve this problem,but their performance still needs further improvement.In this research,we segmented the DNA sequence in a k-mers manner,then trained the word vector model,inputted it into long short-term memory(LSTM)and used the attention mechanism to predict.Our method can achieve 93.45%and 90.59%cross-validation accuracy in the two layers,respectively.Our results are better than others based on the same data set,and provided some ideas for accurately predicting promoters.In addition,this research suggested that natural language processing can play a significant role in biological sequence prediction.

String kernels construction and fusion:a survey with bioinformatics application

The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.

VCSRA:A fast and accurate multiple sequence alignment algorithm with a high degree of parallelism

Multiple sequence alignment (MSA)is the alignment among more than two molecular biological sequences,which is a fundamental method to analyze evolutionary events such as mutations,insertions,deletions,and re-arrangements.In theory,a dynamic programming algorithm can be employed to produce the