Mudanpioside C Discovered from Paeonia suffruticosa Andr. Acts as a Protein Disulfide Isomerase Inhibitor with Antithrombotic Activities.

Paeonia suffruticosa Andr. is a well-known landscape plant worldwide and also holds significant importance in China due to its medicinal and dietary properties. Previous studies have found that Cortex Moutan (CM), the dried root bark of P. suffruticosa, showed antiplatelet and cardioprotective effects, although the underlying mechanism and active compounds remain to be revealed. In this study, protein disulfide isomerase (PDI) inhibitors in CM were identified using a ligand-fishing method combined with the UHPLC-Q-TOF-MS assay. Further, their binding sites and inhibitory activities toward PDI were validated. The antiplatelet aggregation and antithrombotic activity were investigated. The results showed that two structurally similar compounds in CM were identified as the inhibitor for PDI with IC50 at 3.22 µM and 16.73 µM; among them Mudanpioside C (MC) is the most effective PDI inhibitor. Molecular docking, site-directed mutagenesis, and MST assay unequivocally demonstrated the specific binding of MC to the b'-x domain of PDI (Kd = 3.9 µM), acting as a potent PDI inhibitor by interacting with key amino acids K263, D292, and N298 within the b'-x domain. Meanwhile, MC could dose-dependently suppress collagen-induced platelet aggregation and interfere with platelet activation, adhesion, and spreading. Administration of MC can significantly inhibit thrombosis formation without disturbing hemostasis in mice. These findings present a promising perspective on the antithrombotic properties of CM and highlight the potential application of MC as lead compounds for targeting PDI in thrombosis therapy.

Rutin hydrate relieves neuroinflammation in zebrafish models: Involvement of NF-κB pathway as a central network.

Neuroinflammation is prevalent in multiple brain diseases and may also lead to dementia, cognitive impairment, and impaired spatial memory function associated with neurodegenerative diseases. A neuroprotective and antioxidant flavonoid, rutin hydrate (RH), was evaluated for the anti-neuroinflammatory activity mediated by copper sulfate (CuSO4) solution and lipopolysaccharide (LPS) in zebrafish. The results showed that 100 mg/L RH significantly reduced the ratio of neutrophil mobility in caudal hematopoietic tissue (CHT) region caused by CuSO4 and the number of neutrophils co-localized with facial peripheral nerves. In the LPS model, RH co-injection significantly diminished neutrophil and macrophage migration. Therefore, RH exhibited a significant rescue effect on both models. In addition, RH treatment remarkably reduced the effects of neuroinflammation on the locomotor ability, expression levels of genes associated with behavioral disorders, and acetylcholinesterase (AChE) activity. Furthermore, network pharmacology techniques were employed to investigate the potential mechanisms, and the associated genes and enzyme activities were validated in order to elucidate the underlying mechanisms. Network pharmacological analysis and zebrafish model indicated that RH regulated the expressions of NF-κB pathway-related targets (Toll-like receptor 9 (tlr9), nuclear factor kappa B subunit 1 (nfkb1), RELA proto-oncogene (RelA), nitric oxide synthase 2a, inducible (nos2a), tumour necrosis factor alpha-like (tnfα), interleukin 6 (il6), interleukin 1ß (il1ß), chemokine 8 (cxcl8), and macrophage migration inhibitory factor (mif)) as well as six key factors (arachidonic acid 4 alpha-lipoxygenase (alox4a), arachidonate 5-lipoxygenase a (alox5), prion protein a (prnpa), integrin, beta 2 (itgb2), catalase (CAT), and alkaline phosphatase (ALP) enzymes). Through this study, a thorough understanding of the mechanism underlying the therapeutic effects of RH in neuroinflammation has been achieved, thereby establishing a solid foundation for further research on the potential therapeutic applications of RH in neuroinflammatory disorders.

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

Prediction of MoRFs based on sequence properties and convolutional neural networks.

BACKGROUND: Intrinsically disordered proteins possess flexible 3-D structures, which makes them play an important role in a variety of biological functions. Molecular recognition features (MoRFs) act as an important type of functional regions, which are located within longer intrinsically disordered regions and undergo disorder-to-order transitions upon binding their interaction partners. RESULTS: We develop a method, MoRFCNN, to predict MoRFs based on sequence properties and convolutional neural networks (CNNs). The sequence properties contain structural and physicochemical properties which are used to describe the differences between MoRFs and non-MoRFs. Especially, to highlight the correlation between the target residue and adjacent residues, three windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict MoRFs through the constructed CNN. Comparing with other existing methods, MoRFCNN obtains better performance. CONCLUSIONS: MoRFCNN is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRFCNN is effective and competitive.

Active components in Ephedra sinica stapf disrupt the interaction between ACE2 and SARS-CoV-2 RBD: Potent COVID-19 therapeutic agents.

ETHNOPHARMACOLOGICAL RELEVANCE: Ephedra sinica Stapf is a widely used folk medicine in Asia to treat lung diseases, such as cold, cough and asthma. Many efforts have revealed that some traditional Chinese medicine (TCM) prescriptions containing Ephedra sinica could effectively alleviate the symptoms and prevent the fatal deterioration of COVID-19. AIM OF THE STUDY: The present study aims to discover active compounds in Ephedra sinica disrupting the interaction between angiotensin-converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein receptor-binding domain (SARS-CoV-2 RBD) to inhibit SARS-CoV-2 virus infection. MATERIALS AND METHODS: The ethanol extracts of Ephedra sinica were prepared. Activity guided isolation of constituents was carried out by measuring the inhibitory activity on ACE2-RBD interaction. The structures of active compounds were identified by HPLC-Q-TOF-MS/MS and NMR. To testify the contribution of main components for the inhibitory activity, different samples were prepared by components knock-out strategy. The mechanism of compounds inhibiting protein-protein interaction (PPI) was explored by competition inhibition assays, surface plasmon resonance (SPR) assays and molecular docking. SARS-CoV-2 S protein-pseudoviruses were used to observe the viropexis effect in cells. RESULTS: Ephedra sinica extracts (ESE) could effectively inhibit the interaction between ACE2 and SARS-CoV-2 RBD (IC50 = 95.01 µg/mL). Three active compounds, 4,6-dihydroxyquinoline-2-carboxylic acid, 4-hydroxyquinoline-2-carboxylic acid and 4-hydroxy-6-methoxyquinoline-2-carboxylic acid were identified to inhibit ACE2-RBD interaction (IC50 = 0.58 µM, 0.07 µM and 0.15 µM respectively). And knock-out the three components could eliminate the inhibitory activity of ESE. Molecular docking calculations indicated that the hydrogen bond was the major intermolecular force. Finally, our results also showed that these compounds could inhibit the infectivity of SARS-CoV-2 S protein-pseudoviruses to 293T-ACE2 (IC50 = 0.44-1.09 µM) and Calu-3 cells. CONCLUSION: These findings suggested that quinoline-2-carboxylic acids in Ephedra sinica could be considered as potential therapeutic agents for COVID-19. Further, this study provided some justification for the ethnomedicinal use of Ephedra sinica for COVID-19.

A Subspace Approach to Sparse Sampling based Data Gathering in Wireless Sensor Networks.

Data gathering is an essential concern in Wireless Sensor Networks (WSNs). This paper proposes an efficient data gathering method in clustered WSNs based on sparse sampling to reduce energy consumption and prolong the network lifetime. For data gathering scheme, we propose a method that can collect sparse sampled data in each time slot with a fixed percent of nodes remaining in sleep mode. For data reconstruction, a subspace approach is proposed to enforce an explicit low-rank constraint for data reconstruction from sparse sampled data. Subspace representing spatial distributions of the WSNs data can be estimated from previous reconstructed data. Incorporating total variation constraint, the proposed reconstruction method reconstructs current time slot data efficiently. The results of experiments indicate that the proposed method can reduce the energy consumption and prolong the network lifetime with satisfying recovery accuracy.

[Simulation Study on Radiated Immunity Test of Active Implantable Medical Device].

In the electromagnetic compatibility standards of active implantable medical devices such as ISO 14117,radiated immunity test above 450 MHz frequency is recommended to be carried out in the electromagnetic shielding room.However,different test locations and the shape/size of the shielding room may lead to very different electromagnetic field distribution in the radiation exposure area of the sample,thus affecting the consistency of the test.With the model built by COMSOL software,this paper analyzes the impact of different parameters,such as size of the room and position of torso simulator on the distribution of field intensity,and reaches results about the distribution of field intensity on the torso simulator area under tow sizes of shielding rooms and two typical test positions.The results show that the experimental consistency of the electric field intensity on the surface directly below the center of the antenna is not good enough,which may affect the repeatability of the test.

High-performance symmetric supercapacitors based on carbon nanotube/graphite nanofiber nanocomposites.

This work reports the nanocomposites of graphitic nanofibers (GNFs) and carbon nanotubes (CNTs) as the electrode material for supercapacitors. The hybrid CNTs/GNFs was prepared via a synthesis route that involved catalytic chemical vapor deposition (CVD) method. The structure and morphology of CNTs/GNFs can be precisely controlled by adjusting the flow rates of reactant gases. The nest shape entanglement of CNTs and GNFs which could not only have high conductivity to facilitate ion transmission, but could also increase surface area for more electrolyte ions access. When assembled in a symmetric two-electrode system, the CNTs/GNFs-based supercapacitor showed a very good cycling stability of 96% after 10 000 charge/discharge cycles. Moreover, CNTs/GNFs-based symmetric device can deliver a maximum specific energy of 72.2 Wh kg-1 at a power density of 686.0 W kg-1. The high performance of the hybrid performance can be attributed to the wheat like GNFs which provide sufficient accessible sites for charge storage, and the CNTs skeleton which provide channels for charge transport.

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture.

The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models.

Analysis of the distance between two classes for tuning SVM hyperparameters.

An important step in the construction of a support vector machine (SVM) is to select optimal hyperparameters. This paper proposes a novel method for tuning the hyperparameters by maximizing the distance between two classes (DBTC) in the feature space. With a normalized kernel function, we find that DBTC can be used as a class separability criterion since the between-class separation and the within-class data distribution are implicitly taken into account. Employing DBTC as an objective function, we develop a gradient-based algorithm to search the optimal kernel parameter. On the basis of the geometric analysis and simulation results, we find that the optimal algorithm and the initialization problem become very simple. Experimental results on the synthetic and real-world data show that the proposed method consistently outperforms other existing hyperparameter tuning methods.