Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design

When herpes simplex virus encephalitis meets antiviral innate immunity

Herpes simplex virus (HSV) is the most common pathogen of infectious encephalitis, accounting for nearly half of the confirmed cases of encephalitis. Its clinical symptoms are often atypical. HSV PCR in cerebrospinal fluid is helpful for diagnosis, and the prognosis is usually satisfactory after regular antiviral treatment. Interestingly, some patients with recurrent encephalitis have little antiviral effect. HSV PCR in cerebrospinal fluid is negative, but glucocorticoid has a significant effect after treatment. Specific antibodies, such as the NMDA receptor antibody, the GABA receptor antibody, and even some unknown antibodies, can be isolated from cerebrospinal fluid, proving that the immune system contributes to recurrent encephalitis, but the specific mechanism is still unclear. Based on recent studies, we attempt to summarize the relationship between herpes simplex encephalitis and innate immunity, providing more clues for researchers to explore this field further

Inhibition of p38 MAPK Signaling Regulates the Expression of EAAT2 in the Brains of Epileptic Rats

Seizures induce the release of excitatory amino acids (EAAs) from the intracellular fluid to the extracellular fluid, and the released EAAs primarily comprise glutamic acid (Glu) and asparaginic acid (Asp). Glu neurotransmission functions via EAA transporters (EAATs) to maintain low concentrations of Glu in the extracellular space and avoid excitotoxicity. EAAT2, the most abundant Glu transporter subtype in the central nervous system (CNS), plays a key role in the regulation of glutamate transmission. Previous studies have shown that SB203580 promotes EAAT2 expression by inhibiting the p38 mitogen-activated protein kinase (MAPK) signaling pathway, but whether SB203580 upregulates EAAT2 expression in epileptic rats is unknown. This study demonstrated that EAAT2 expression was increased in the brain tissue of epileptic rats. Intraperitoneal injection of a specific inhibitor of p38 MAPK, SB203580, reduced the time to the first epileptic seizure and attenuated the seizure severity. In addition, SB203580 treatment increased the EAAT2 expression levels in the brain tissue of epileptic rats. These results suggest that SB203580 could regulate epileptic seizures via EAAT2

The Rice HGW Gene Encodes a Ubiquitin-Associated (UBA) Domain Protein That Regulates Heading Date and Grain Weight

Heading date and grain weight are two determining agronomic traits of crop yield. To date, molecular factors controlling both heading date and grain weight have not been identified. Here we report the isolation of a hemizygous mutation, heading and grain weight (hgw), which delays heading and reduces grain weight in rice. Analysis of hgw mutant phenotypes indicate that the hemizygous hgw mutation decreases latitudinal cell number in the lemma and palea, both composing the spikelet hull that is known to determine the size and shape of brown grain. Molecular cloning and characterization of the HGW gene showed that it encodes a novel plant-specific ubiquitin-associated (UBA) domain protein localized in the cytoplasm and nucleus, and functions as a key upstream regulator to promote expressions of heading date- and grain weight-related genes. Moreover, co-expression analysis in rice and Arabidopsis indicated that HGW and its Arabidopsis homolog are co-expressed with genes encoding various components of ubiquitination machinery, implying a fundamental role for the ubiquitination pathway in heading date and grain weight control

N6-Methyladenosine Regulator-Mediated RNA Methylation Is Involved in Primary Sjögren’s Syndrome Immunoinfiltration

The crucial role of epigenetic regulation, especially the modifications of RNA N6-methyladenosine (m6A), in immunity is a current research hotspot. However, the m6A modifications in primary Sjögren’s syndrome (pSS) and the immune infiltration pattern they govern remain unknown. Thus, the patterns of 23 m6A regulator-mediated RNA modifications in parotid or blood samples from pSS patients were evaluated by bioinformatics analysis in the current study. Comparing m6A regulators between control and pSS patients showed that m6A regulators are associated with pSS, and regulators also had differential correlations. Further clustering analysis and comparison of gene expression and immune cell infiltration between m6A modification patterns revealed that each modification pattern had its own unique genetic and immune profile. Multiple immune cell infiltrations were differentially expressed between the patterns. The enrichment of gene ontology between the two patterns in parotid was concentrated on RNA metabolism and processing. The KEGG pathway enrichment and weighted correlation network analysis further showed that the autophagy pathway might be involved in the m6A modification patterns in pSS. Together, these findings suggest that m6A regulators play a certain role in the immune cell infiltration of parotid tissue in pSS

Robust Time-Varying Output Formation Control for Swarm Systems with Nonlinear Uncertainties

Time-varying output formation control problems for high-order time-invariant swarm systems are studied with nonlinear uncertainties and directed network topology in this paper. A robust controller which consists of a nominal controller and a robust compensator is applied to achieve formation control. The nominal controller based on the output feedback is designed to achieve desired time-varying formation properties for the nominal system. And the robust compensator based on the robust signal compensator technology is constructed to restrain nonlinear uncertainties. The time-varying formation problem is transformed into the stability problem. And the formation errors can be arbitrarily small with expected convergence rate. Numerical examples are provided to illustrate the effectiveness of the proposed strategy

Team-Triggered Practical Fixed-Time Consensus of Double-Integrator Agents with Uncertain Disturbance

This article addresses the team-triggered fixed-time consensus problems for a class of double-integrator agents subject to uncertain disturbance. Compared with the finite-time results, the convergence time of the fixed-time results is independent of the initial conditions. Furthermore, a novel team-triggered control (TTC) strategy is presented. This control strategy incorporates the event-triggered control (ETC) and self-triggered control (STC). The ETC and STC are proposed to achieve the fixed-time consensus of second-order multiagent systems (MASs), and no Zeno behavior occurs. The TTC scheme, derived by combining the ETC scheme and the STC scheme, is able to relax the requirement of continuous communication and thus lowering the energy consumption of communication while ensuring the performance of the system. The effectiveness of the proposed algorithms is validated by numerical simulations

Gastric polyp detection module based on improved attentional feature fusion

Abstract Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected

An Adaptive Strategy via Reinforcement Learning for the Prisoner\u27s Dilemma Game

The iterated prisoner\u27s dilemma (IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod\u27s tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy