Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments.

In complex and dynamic environments, traditional pursuit-evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit-evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models.

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

Distributed Robust Learning-Based Backstepping Control Aided With Neurodynamics for Consensus Formation Tracking of Underwater Vessels.

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

Whole-brain mapping of long-range inputs to the VIP-expressing inhibitory neurons in the primary motor cortex.

The primary motor cortex (MOp) is an important site for motor skill learning. Interestingly, neurons in MOp possess reward-related activity, presumably to facilitate reward-based motor learning. While pyramidal neurons (PNs) and different subtypes of GABAergic inhibitory interneurons (INs) in MOp all undergo cell-type specific plastic changes during motor learning, the vasoactive intestinal peptide-expressing inhibitory interneurons (VIP-INs) in MOp have been shown to preferentially respond to reward and play a critical role in the early phases of motor learning by triggering local circuit plasticity. To understand how VIP-INs might integrate various streams of information, such as sensory, pre-motor, and reward-related inputs, to regulate local plasticity in MOp, we performed monosynaptic rabies tracing experiments and employed an automated cell counting pipeline to generate a comprehensive map of brain-wide inputs to VIP-INs in MOp. We then compared this input profile to the brain-wide inputs to somatostatin-expressing inhibitory interneurons (SST-INs) and parvalbumin-expressing inhibitory interneurons (PV-INs) in MOp. We found that while all cell types received major inputs from sensory, motor, and prefrontal cortical regions, as well as from various thalamic nuclei, VIP-INs received more inputs from the orbital frontal cortex (ORB) - a region associated with reinforcement learning and value predictions. Our findings provide insight on how the brain leverages microcircuit motifs by both integrating and partitioning different streams of long-range input to modulate local circuit activity and plasticity.

Infrared and Visible Image Fusion Technology and Application: A Review.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

Enhanced Reduced-Order Extended State Observer for Motion Control of Differential Driven Mobile Robot.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Functionally distinct NPAS4-expressing somatostatin interneuron ensembles critical for motor skill learning.

During motor learning, dendritic spines on pyramidal neurons (PNs) in the primary motor cortex (M1) undergo reorganization. Intriguingly, the inhibition from local somatostatin-expressing inhibitory neurons (SST-INs) plays an important role in regulating the PN plasticity and thus new motor skill acquisition. However, the molecular mechanisms underlying this process remain unclear. Here, we identified that the early-response transcription factor, NPAS4, is selectively expressed in SST-INs during motor learning. By utilizing in vivo two-photon imaging in mice, we found that cell-type-specific deletion of Npas4 in M1 disrupted learning-induced spine reorganization among PNs and impaired motor learning. In addition, NPAS4-expressing SST-INs exhibited lower neuronal activity during task-related movements, and chemogenetically increasing the activity of NPAS4-expressing ensembles was sufficient to mimic the effects of Npas4 deletion. Together, our results reveal an instructive role of NPAS4-expressing SST-INs in modulating the inhibition to downstream task-related PNs to allow proper spine reorganization that is critical for motor learning.

A Variable Radius Side Window Direct SLAM Method Based on Semantic Information.

Simultaneous Localization and Mapping (SLAM) is a challenging and key issue in the mobile robotic fields. In terms of the visual SLAM problem, the direct methods are more suitable for more expansive scenes with many repetitive features or less texture in contrast with the feature-based methods. However, the robustness of the direct methods is weaker than that of the feature-based methods. To deal with this problem, an improved direct sparse odometry with loop closure (LDSO) is proposed, where the performance of the SLAM system under the influence of different imaging disturbances of the camera is focused on. In the proposed method, a method based on the side window strategy is proposed for preprocessing the input images with a multilayer stacked pixel blender. Then, a variable radius side window strategy based on semantic information is proposed to reduce the weight of selected points on semistatic objects, which can reduce the computation and improve the accuracy of the SLAM system based on the direct method. Various experiments are conducted on the KITTI dataset and TUM RGB-D dataset to test the performance of the proposed method under different camera imaging disturbances. The quantitative and qualitative evaluations show that the proposed method has better robustness than the state-of-the-art direct methods in the literature. Finally, a real-world experiment is conducted, and the results prove the effectiveness of the proposed method.

A Novel Rapid-Flooding Approach With Real-Time Delay Compensation for Wireless-Sensor Network Time Synchronization.

One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network.

In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control voltage, feedback current, and rotational speed of the deep-sea HOV thruster; and the output is the corresponding fault degree parameter ( s ), which indicates the pattern and degree of the thruster fault. In order to illustrate the effectiveness of the proposed fault diagnosis method, a pool experiment under different simulated fault cases is conducted in this study. The experimental results have proved that the DBN information fusion fault diagnosis method can not only diagnose the continuously changing, uncertain, and unknown thruster fault but also has higher identification accuracy than the information fusion fault diagnosis methods based on traditional artificial neural networks.

Identification of novel genes involved in apoptosis of HIV-infected macrophages using unbiased genome-wide screening.

BACKGROUND: Macrophages, besides resting latently infected CD4+ T cells, constitute the predominant stable, major non-T cell HIV reservoirs. Therefore, it is essential to eliminate both latently infected CD4+ T cells and tissue macrophages to completely eradicate HIV in patients. Until now, most of the research focus is directed towards eliminating latently infected CD4+ T cells. However, few approaches have been directed at killing of HIV-infected macrophages either in vitro or in vivo. HIV infection dysregulates the expression of many host genes essential for the survival of infected cells. We postulated that exploiting this alteration may yield novel targets for the selective killing of infected macrophages. METHODS: We applied a pooled shRNA-based genome-wide approach by employing a lentivirus-based library of shRNAs to screen novel gene targets whose inhibition should selectively induce apoptosis in HIV-infected macrophages. Primary human MDMs were infected with HIV-eGFP and HIV-HSA viruses. Infected MDMs were transfected with siRNAs specific for the promising genes followed by analysis of apoptosis by flow cytometry using labelled Annexin-V in HIV-infected, HIV-exposed but uninfected bystander MDMs and uninfected MDMs. The results were analyzed using student's t-test from at least four independent experiments. RESULTS: We validated 28 top hits in two independent HIV infection models. This culminated in the identification of four target genes, Cox7a2, Znf484, Cstf2t, and Cdk2, whose loss-of-function induced apoptosis preferentially in HIV-infected macrophages. Silencing these single genes killed significantly higher number of HIV-HSA-infected MDMs compared to the HIV-HSA-exposed, uninfected bystander macrophages, indicating the specificity in the killing of HIV-infected macrophages. The mechanism governing Cox7a2-mediated apoptosis of HIV-infected macrophages revealed that targeting respiratory chain complex II and IV genes also selectively induced apoptosis of HIV-infected macrophages possibly through enhanced ROS production. CONCLUSIONS: We have identified above-mentioned novel genes and specifically the respiratory chain complex II and IV genes whose silencing may cause selective elimination of HIV-infected macrophages and eventually the HIV-macrophage reservoirs. The results highlight the potential of the identified genes as targets for eliminating HIV-infected macrophages in physiological environment as part of an HIV cure strategy.

Delayed motor learning in a 16p11.2 deletion mouse model of autism is rescued by locus coeruleus activation.

Children with autism spectrum disorder often exhibit delays in achieving motor developmental milestones such as crawling, walking and speech articulation. However, little is known about the neural mechanisms underlying motor-related deficits. Here, we reveal that mice with a syntenic deletion of the chromosome 16p11.2, a common copy number variation associated with autism spectrum disorder, also exhibit delayed motor learning without showing gross motor deficits. Using in vivo two-photon imaging in awake mice, we find that layer 2/3 excitatory neurons in the motor cortex of adult male 16p11.2-deletion mice show abnormally high activity during the initial phase of learning, and the process of learning-induced spine reorganization is prolonged. Pharmacogenetic activation of locus coeruleus noradrenergic neurons was sufficient to rescue the circuit deficits and the delayed motor learning in these mice. Our results unveil an unanticipated role of noradrenergic neuromodulation in improving the delayed motor learning in 16p11.2-deletion male mice.

Selective killing of human M1 macrophages by Smac mimetics alone and M2 macrophages by Smac mimetics and caspase inhibition.

The inflammatory and anti-inflammatory Mϕs have been implicated in many diseases including rheumatoid arthritis, multiple sclerosis, and leprosy. Recent studies suggest targeting Mϕ function and activation may represent a potential target to treat these diseases. Herein, we investigated the effect of second mitochondria-derived activator of caspases (SMAC) mimetics (SMs), the inhibitors of apoptosis (IAPs) proteins, on the killing of human pro- and anti-inflammatory Mϕ subsets. We have shown previously that human monocytes are highly susceptible whereas differentiated Mϕs (M0) are highly resistant to the cytocidal abilities of SMs. To determine whether human Mϕ subsets are resistant to the cytotoxic effects of SMs, we show that M1 Mϕs are highly susceptible to SM-induced cell death whereas M2a, M2b, and M2c differentiated subsets are resistant, with M2c being the most resistant. SM-induced cell death in M1 Mϕs was mediated by apoptosis as well as necroptosis, activated both extrinsic and intrinsic pathways of apoptosis, and was attributed to the IFN-γ-mediated differentiation. In contrast, M2c and M0 Mϕs experienced cell death through necroptosis following simultaneous blockage of the IAPs and the caspase pathways. Overall, the results suggest that survival of human Mϕs is critically linked to the activation of the IAPs pathways. Moreover, agents blocking the cellular IAP1/2 and/or caspases can be exploited therapeutically to address inflammation-related diseases.

Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer.

OBJECTIVE: Some excellent prognostic models based on survival analysis methods for breast cancer have been proposed and extensively validated, which provide an essential means for clinical diagnosis and treatment to improve patient survival. To analyze clinical and follow-up data of 12119 breast cancer patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, we developed a gradient boosting algorithm, called EXSA, by optimizing survival analysis of XGBoost framework for ties to predict the disease progression of breast cancer. METHODS: EXSA is based on the XGBoost framework in machine learning and the Cox proportional hazards model in survival analysis. By taking Efron approximation of partial likelihood function as a learning objective for ties, EXSA derives gradient formulas of a more precise approximation. It optimizes and enhances the ability of XGBoost for survival data with ties. After retaining 4575 patients (3202 cases for training, 1373 cases for test), we exploit the developed EXSA method to build an excellent prognostic model to estimate disease progress. Risk score of disease progress is evaluated by the model, and the risk grouping and continuous functions between risk scores and disease progress rate at 5- and 10-year are also demonstrated. RESULTS: Experimental results on test set show that the EXSA method achieves competitive performance with concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively. CONCLUSION: The proposed EXSA method can be utilized as an effective method for survival analysis. SIGNIFICANCE: The proposed method in this paper can provide an important means for follow-up data of breast cancer or other disease research.

Consensus Affinity Graph Learning for Multiple Kernel Clustering.

Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in recent years, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many existing MKGC methods design a fat model that poses challenges for computational cost and clustering performance, as they learn both an affinity graph and an extra consensus kernel cumbersomely. To tackle this challenging problem, this article proposes a new MKGC method to learn a consensus affinity graph directly. By using the self-expressiveness graph learning and an adaptive local structure learning term, the local manifold structure of the data in kernel space is preserved for learning multiple candidate affinity graphs from a kernel pool first. After that, these candidate affinity graphs are synthesized to learn a consensus affinity graph via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- k neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes. The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods.

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone.

Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask's micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I 'normal use' and the recall of type III 'not recommended' reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.

Pyridine alkaloids with activity in the central nervous system.

This review discusses all pyridine alkaloids with CNS activity, their therapeutic potential, and the interesting array of sources whence they originate.

EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography.

We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.