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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.

In the past decade, there has been much interest in operating quadrotors in uncertain and cluttered environments due to their simplicity. Executing precise agile quadrotor flight maneuvers is an important open research problem for navigating in such environment. Collisions can occur if actuator constraints are not considered in the trajectory tracker design. This paper presents a cascade nonlinear constrained control approach (CNCCA) for trajectory tracking with explicit consideration of actuator constraints, where the output signal of the position controller is converted into the desired command of the attitude controller. The proposed position control loop is shown to be stable and bounded, satisfying the total thrust limits. The proposed attitude tracking control loop is designed as a saturated control law based on a modified geometric controller in SO(3) space. A proof of its stability is provided. The simulation results also show good tracking performance of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we analyze Xu and Yuille's robust principal component analysis (RPCA) learning algorithms by means of the distance measurement in space. Based on the analysis, a family of fuzzy RPCA learning algorithms is proposed, which is robust against outliers. These algorithms can explicitly be understood from the viewpoint of fuzzy set theory, though Xu and Yuille's algorithms were proposed based on a statistical physics approach. In the proposed algorithms, an adaptive learning procedure overcomes the difficulty of selection of learning parameters in Xu and Yuille's algorithms. Furthermore, the robustness of proposed algorithms is investigated by using the theory of influence functions. Simulations are carried out to illustrate the robustness of these algorithms. [ABSTRACT FROM AUTHOR]

This article investigates the limit cycles within type-1 linear systems under PID-type of relay feedback. The problem is generalised from the identification of friction models of servo mechanical systems via limit-cycle experiments under dual-channel relay feedback. Locations of limit cycles are given so that the exact durations between two consecutive switchings of relays can be determined via numerical computation. After this, local stability of limit cycles can be checked via the Jacobian of Poincare map. Examples are analysed using proposed theorems. [ABSTRACT FROM AUTHOR]

In this paper, we consider the adaptive control problem for piezoelectric actuators with nonlinear uncertainties. The controller consists of two parts: sliding mode and adaptive component. The sliding-mode part rejects dominant uncertainties, while the adaptive component is continuously refined based on just prevailing input and output signals. The stability of the designed control system can be guaranteed by a rigorous proof. Simulation and real-time experimental results are provided to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The convergence of a class of Hyvarinen-Oja's independent component analysis (ICA) learning algorithms with constant learning rates is investigated by analyzing the original stochastic discrete time (SDT) algorithms and the corresponding deterministic discrete time (DDT) algorithms. Most existing learning rates for ICA learning algorithms are required to approach zero as the learning step increases. However, this is not a reasonable requirement to impose in many practical applications. Constant learning rates overcome the shortcoming. On the other hand, the original algorithms, described by the SDT algorithms, are studied directly. Invariant sets of these algorithms are obtained so that the nondivergence of the algorithms is guaranteed in stochastic environment. In the invariant sets, the local convergence of the original algorithms is analyzed by indirectly studying the convergence of the corresponding DDT algorithms. It is rigorously proven that the trajectories of the DDT algorithms starting from the invariant sets will converge to an independent component direction with a positive kurtosis or a negative kurtosis. The convergence results can shed some light on the dynamical behaviors of the original SDT algorithms. Furthermore, the corresponding DDT algorithms are extended to the block versions of the original SDT algorithms. The block algorithms not only establish a relationship between the SDT algorithms and the corresponding DDT algorithms, but also can get a good convergence speed and accuracy in practice. Simulation examples are carried out to illustrate the theory derived. [ABSTRACT FROM AUTHOR]

Recent developments in nuclear reprogramming and intracytoplasmic sperm injection reflect an increasing need for more advanced and automatic micromanipulation technologies. In this paper, we present an automatic cell injection system, which is capable of visually monitoring the injecting process and controlling the microactuators. Traditionally, cell injection was manually operated, and it was laborious, time consuming, of low accuracy, and prone to contamination due to the handling requirements. An automatic and efficient strategy is required to eliminate these drawbacks. In this paper, a system is developed where the injection process is monitored and controlled automatically via integration of a vision system to an injector manipulation system. The cell is located, and the pipette is positioned and driven by the algorithm to achieve effective penetration. The precision achieved is physically proven to be within a good tolerance range. [ABSTRACT FROM AUTHOR]

Abstract  Independent component analysis (ICA) neural networks can estimate independent components from the mixed signal. The dynamical behavior of the learning algorithms for ICA neural networks is crucial to effectively apply these networks to practical applications. The paper presents the stability and chaotic dynamical behavior of a class of ICA learning algorithms with constant learning rates. Some invariant sets are obtained so that the non-divergence of these algorithms can be guaranteed. In these invariant sets, the stability and chaotic behaviors are analyzed. The conditions for stability and chaos are derived. Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior. [ABSTRACT FROM AUTHOR]

This paper presents the application of self-sensing actuation (SSA) to facilitate the implementation of piezoelectric actuator in an intelligent mechatronic system. SSA is a technique to employ smart materials, such as piezoelectric materials, simultaneously as a sensor and an actuator; thereby increasing the level of integration of the system. The piezoelectric actuator is equipped with an exclusive adaptive controller amidst its nonlinearities and system's disturbance. The application area to be discussed is a microdispensing system, which is an example of a micromanufacturing process, combining a fluidic system and a positioning system. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the decentralized adaptive control design problem for uncertain mechanical systems, where uncertainty may arise due to isolated subsystem and/or interconnections among subsystems. Radial basis function neural networks are used to approximate the nonlinear functions to include both dynamic and interconnection uncertainties in each subsystem. The stability of the thus designed control system can be guaranteed by a rigid proof. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The modeling and control of two piezoelectric actuators (PAs), with an objective application towards intracytoplasmic sperm injection (ICSI), is addressed in this paper. The PAs are used to realizing the fast and precise movements of an injector so as to penetrate an oocyte (egg cell) with precision and without causing a large deformation. Two specific PA designs are considered for this application: linear reciprocating and partially rotating. General models are presented for the purpose of control system design for both designs. To address the nonlinearities and uncertainties of the actuators, as represented in the model, an adaptive controller is proposed to yield robust control performance. Application results are presented and discussed in this paper. Successful penetration of the zona pellucida (cell membrane) of the oocyte to the ICSI problem has been achieved with both designs and the merits and demerits of both configuration are duly discussed. [ABSTRACT FROM AUTHOR]

There is a growing trend in developing the wireless machine-to-machine (M2M) communication for factory automation. Currently, most wireless protocols for infrastructureless mobile ad-hoc networks focus on the mobility issue and are mainly suitable for narrowband radio devices. The mechanisms for route update and discovery that work well for wireless mobile ad-hoc networks prove to be redundant and instead add to processing overheads in a static wireless factory setting. This paper presents the development and implementation of a new wireless routing protocol, Bluewave, that caters specifically to wireless communication between machines in a factory setting. Bluewave utilizes features of Bluetooth technology when performing the route setup. The merits of the proposed protocol, in terms of shorter initialization time and route setup time, are duly discussed. A case study illustrating the application of Bluewave to an online condition-based maintenance scenario is presented. Experimental results obtained are furnished to show the viability of Bluewave in industrial automation [ABSTRACT FROM PUBLISHER]

In this paper, we present a new statistical approach towards soft geometrical error compensation of machines with significant errors. The approach, based on an analysis of the probability of the random error recurring, can reduce the adverse influence of random errors on the compensation of systematic errors. The proposed methodology is made up of three steps. First, error classes are defined from the error bands obtained from calibration. Second, the probability of the magnitude of random error belonging to each of these classes is computed based on the density of the data set within the class. Based on these probabilities, the most probable systematic part of the error measurement can be statistically deduced. Finally, the geometrical error compensation is carried out based on this value. Experimental results are provided for the linear error compensation of a single-axis piezo-ceramic motion system. [ABSTRACT FROM AUTHOR]

In this paper, a learning control scheme is proposed which is suitable for high-precision and repetitive motion control applications. It comprises of a self-tuning radial basis function (RBF) network operating in parallel with an iterative learning control (ILC) component. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. The RBF network is employed as a nonlinear function estimator to model the tracking error over a cycle, and this error model is subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. The ILC component further enhances the tracking performance, particularly over the sections of the trajectory where the RBF network is less adequate in its modeling function. Simulation examples and real-time experimental results are fully furnished to elaborate the various highlights of the proposed method. [ABSTRACT FROM AUTHOR]

Presents a technology which allows manually controlled and automatically controlled vehicles to co-exist, thereby reducing much of the cost and infrastructure requirements in highway automation. Presentation of an Autonomous Vehicle Driving System (AVDS) that uses information from vehicle sensors; Gradual market penetration of AVDS.