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Summary: Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA.

Drones have been developed for more than two decades. They have become central to the functions of various civil aviation and military services. Commercial usage of drones continues to grow steadily. As the drones have been used widely in different areas, this raises a safety concern, i.e., all the multi-rotors have an increased risk of motor or sensor faults. This paper considers a fault-tolerant control (FTC) problem against the inertial motion unit (IMU) sensor fault. First, a neural network estimator is built for the purpose of fault diagnosis. Second, a fault detection scheme is designed by comparing the IMU reading with the estimator, where it uses a logic rule to monitor the IMU state. Third, if the IMU sensor is in faulty state, the Euler angle estimator with neural network built is used to recover the IMU information which is fed into the controller designed. Finally, simulation studies are given to illustrate the effectiveness of the proposed FTC.

Multiple unmanned multirotor (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search and rescue, monitoring operations in hazardous environments and providing communication coverage services. Currently, an important issue in MUM is coverage control. In this paper, an existing coverage control algorithm has been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). Two new constraints, namely view angle and collision avoidance, have also been included. Mobile network coverage among the MUMs is studied. Finally, the proposed scheme is tested in computer simulations.

Strobilanthes biocullata is a plietesial species endemic to China. The complete chloroplast genome (cp genome) of S. biocullata was sequenced for the first time. The cp genome of S. biocullata is 144,012 bp in length. It consists of a large single copy (LSC) region (91,628 bp) and a small single copy (SSC) region (17,666 bp), which are separated by two inverted repeats (IRs, 34,718 bp). It contains 114 unique genes, including 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. The overall GC content is 38.2%. Phylogenetic analysis of 13 species has been conducted. This newly sequenced cp genome will be useful to further genetic diversity, phylogeny, and genomic studies of the genus Strobilanthes.

The genus Dicliptera (Justicieae, Acanthaceae) consists of approximately 150 species distributed throughout the tropical and subtropical regions of the world. Newly obtained chloroplast genomes (cp genomes) are reported for five species of Dilciptera (D. acuminata, D. peruviana, D. montana, D. ruiziana and D. mucronata) in this study. These cp genomes have circular structures of 150,689–150,811 bp and exhibit quadripartite organizations made up of a large single copy region (LSC, 82,796–82,919 bp), a small single copy region (SSC, 17,084–17,092 bp), and a pair of inverted repeat regions (IRs, 25,401–25,408 bp). Guanine-Cytosine (GC) content makes up 37.9%–38.0% of the total content. The complete cp genomes contain 114 unique genes, including 80 protein-coding genes, 30 transfer RNA (tRNA) genes, and four ribosomal RNA (rRNA) genes. Comparative analyses of nucleotide variability (Pi) reveal the five most variable regions (trnY-GUA-trnE-UUC, trnG-GCC, psbZ-trnG-GCC, petN-psbM, and rps4-trnL-UUA), which may be used as molecular markers in future taxonomic identification and phylogenetic analyses of Dicliptera. A total of 55-58 simple sequence repeats (SSRs) and 229 long repeats were identified in the cp genomes of the five Dicliptera species. Phylogenetic analysis identified a close relationship between D. ruiziana and D. montana, followed by D. acuminata, D. peruviana, and D. mucronata. Evolutionary analysis of orthologous protein-coding genes within the family Acanthaceae revealed only one gene, ycf15, to be under positive selection, which may contribute to future studies of its adaptive evolution. The completed genomes are useful for future research on species identification, phylogenetic relationships, and the adaptive evolution of the Dicliptera species.

Rungia pectinata is an important traditional Chinese herbal medicine from the family Acanthaceae. The complete chloroplast genome (cp genome) of the genus Rungia was sequenced for the first time. The cp genome of R. pectinata was 149,627 bp in length. It was consisted of a large single copy (LSC) region (81,976 bp) and a small single copy (SSC) region (16,626 bp), which were separated by two inverted repeats (IRs, 25,511 bp). This plastome contained 114 unique genes, including 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. The overall GC content was 38.0%. Phylogenetic analysis of nine species in Acanthaceae was also conducted. This newly sequenced cp genome will be useful to further evolutionary studies, phylogenetic studies, and pharmacognostic identification in the genus Rungia.

Justicia leptostachya is an endemic species in China. The complete chloroplast genome sequence of J. leptostachya was generated by de novo assembly using whole genome next generation sequencing. The complete chloroplast genome was 150,898 bp in length, contained four parts: a large single copy (LSC) region of 82,585 bp, a small single copy (SSC) region of 17,477 bp and two inverted repeat (IRs) regions of 25,418 bp each. The genome annotation contained a total of 114 genes, including 80 protein-coding genes, 30 tRNA genes, and 4 rRNA genes. The overall GC content was 38.5%. Phylogenetic analysis with the reported chloroplast genomes was also conducted.

Aphelandra knappiae is an important plant from the family Acanthaceae. The complete chloroplast genome of the genus Aphelandra was sequenced for the first time. The plastome of A. knappiae was 152,457 bp in length. It consisted of a large single-copy (LSC) region (83861 bp) and a small single-copy (SSC) region (17,888 bp), which were separated by two inverted repeats (IRs, 25,354 bp). This plastome contained 114 unique genes, including 80 protein-coding genes, 30 tRNAgenes, and 4 rRNA genes. The overall GC content was 38.5%. Furthermore, phylogenetic analysis of five species in the Acanthaceae was also conducted. The whole chloroplast genome of this species will be useful for the future plant evolutionary, phylogeny and genomic studies in the family Acanthaceae.

It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.