IL-17D-induced inhibition of DDX5 expression in keratinocytes amplifies IL-36R-mediated skin inflammation.

Aberrant RNA splicing in keratinocytes drives inflammatory skin disorders. In the present study, we found that the RNA helicase DDX5 was downregulated in keratinocytes from the inflammatory skin lesions in patients with atopic dermatitis and psoriasis, and that mice with keratinocyte-specific deletion of Ddx5 (Ddx5∆KC) were more susceptible to cutaneous inflammation. Inhibition of DDX5 expression in keratinocytes was induced by the cytokine interleukin (IL)-17D through activation of the CD93-p38 MAPK-AKT-SMAD2/3 signaling pathway and led to pre-messenger RNA splicing events that favored the production of membrane-bound, intact IL-36 receptor (IL-36R) at the expense of soluble IL-36R (sIL-36R) and to the selective amplification of IL-36R-mediated inflammatory responses and cutaneous inflammation. Restoration of sIL-36R in Ddx5∆KC mice with experimental atopic dermatitis or psoriasis suppressed skin inflammation and alleviated the disease phenotypes. These findings indicate that IL-17D modulation of DDX5 expression controls inflammation in keratinocytes during inflammatory skin diseases.

A stepwise surface ionization method for ion mobility spectrometry.

RATIONALE: The detection of organic nitrogen compounds in exhaled breath is expected to provide an early warning of diseases such as kidney disease. Detecting these trace disease markers in exhaled breath with complex composition and high moisture content is a challenge. Surface ionization (SI) shows a highly selective ionization of organic nitrogen compounds, and it is a good candidate for breath analysis combined with ion mobility spectrometry (IMS). METHODS: A stepwise SI method of low-temperature adsorption/high-temperature ionization was proposed, and trimethylamine (TMA) was detected when combined with an ion mobility spectrometer. TMA at different concentrations and humidity levels and spiked in human breath was detected to evaluate the method's properties. RESULTS: TMA with concentrations from 2 to 200 ppb was detected. The peak intensity of the TMA characteristic ions was linearly related to the "e" exponent of the concentration with a curve fit of 0.996. A standard deviation of less than 0.306% was obtained with 10 replicate analyses of 10 ppb TMA. The signal intensity difference between dry and wet (relative humidity > 93%) TMA samples is only 2.7%, and the recovery rate of the sample was 106.819%. CONCLUSIONS: SI-IMS based on the stepwise SI method has the advantages of low ionization temperature, high detection sensitivity, strong resistance to humidity interference, and good repeatability. It is a promising method for detecting organic nitrogen compounds in exhaled breath.

Purification, characterization and inactivation kinetics of polyphenol oxidase extracted from Cistanche deserticola.

MAIN CONCLUSION: PPO was purified from Cistanche deserticola, and its enzymatic characteristics were clarified. It was found that microwave treatment was an efficient way to inactivate PPO. Polyphenol oxidase (PPO) from Cistanche deserticola was obtained and purified through an acetone precipitation and anion exchange column, the enzymatic characteristics and inactivation kinetics of PPO were studied. The specific activity of PPO was 73135.15 ± 6625.7 U/mg after purification, the purification multiple was 48.91 ± 4.43 times, and the recovery was 30.96 ± 0.27%. The molecular weight of the PPO component is about 66 kDa by SDS-PAGE analysis. The optimum substrate of PPO was catechol (Vmax = 0.048 U/mL, Km = 21.70 mM) and the optimum temperature and pH were 30 °C and 7, respectively. When the temperature is above 50 °C, pH < 3 or pH > 10, the enzyme activity can be significantly inhibited. The first-order kinetic fitting shows that microwave inactivation has lesser k values, larger D values and shorter t1/2. It was found that microwave treatment is considered as an efficient and feasible way to inactive PPO by comparing the Z values and Ea values of the two thermal treatments.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Highly stretchable and sensitive strain sensors based on modified PDMS and hybrid particles of AgNWs/graphene.

High-performance strain sensors have received extensive attention due to their wide range of applications in pulsebeat detection, speech recognition, motion detection, and blood pressure monitoring. However, it is difficult to simultaneously attain high sensitivity and excellent stretchability. In this work, a strain sensor based on modified polydimethylsiloxane (PDMS) and conductive hybrid particles of silver nanowires (AgNWs)/graphene was successfully fabricated. A facile solvothermal polymerization process was used to change the structure of cross-linking networks and to obtain the PDMS elastomer with excellent stretchability. The application of the modified PDMS endows the strain sensor with a large strain range (∼20%), which is 100% higher than that of the strain sensor with unmodified PDMS. The AgNWs/graphene hybrid particles were prepared by a simple coprecipitation, reduction, and drying method. AgNWs serve as bridges between graphene sheets, endowing the strain sensor with a large gauge factor (GF = 400). The stability of the strain sensor was also verified. Besides, the strain sensor was successfully used in fields such as finger bending and speech recognition. Considering its high sensitivity, excellent stretchability, and high working stability, the sensor has great potential in health monitoring and motion detection.

Improved Local Search with Momentum for Bayesian Networks Structure Learning.

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

An Improved Approach towards Multi-Agent Pursuit-Evasion Game Decision-Making Using Deep Reinforcement Learning.

A pursuit-evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit-evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

A Novel BN Learning Algorithm Based on Block Learning Strategy.

Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated blocks. Then, a pruned dynamic programming algorithm is performed sequentially for all possible graph structures to get possible BNs and find the best BN by scoring function. Experiments show that for high-dimensional and sparse data, the BLMKM algorithm can achieve the same accuracy in a reasonable time compared with non-blocking classical learning algorithms. Compared to the existing block learning algorithms, the BLMKM algorithm has a time advantage on the basis of ensuring accuracy. The analysis of the real radar effect mechanism dataset proves that BLMKM algorithm can quickly establish a global and accurate causality model to find the cause of interference, predict the detecting result, and guide the parameters optimization. BLMKM algorithm is efficient for BN learning and has practical application value.

Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks.

In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV.

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV's Autonomous Motion Planning in Complex Unknown Environments.

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP-DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP-DDPG can stably complete a variety of tasks in complex, unknown environments.

Two-photon absorption and non-resonant electronic nonlinearities of layered semiconductor TlGaS2.

The ultrafast nonlinear optical properties of bulk TlGaS2 crystal, a semiconductor with a layered structure, are studied by combining intensity dependent transmission, time-resolved transient absorption, and optical Kerr effect coupled to optical heterodyne detection. TlGaS2 demonstrates obvious two-photon absorption and electronic nonlinearities at 800 nm. The two-photon absorption coefficient and the nonlinear refractive index are determined to be of the order of 10-10 cm/W and 10-14 cm2/W, respectively. Furthermore, both the real and imaginary parts of the complex third-order susceptibility tensor elements are extracted. The large ultrafast optical nonlinearities make TlGaS2 a promising material for application in photonic techniques.

Yangonin protects against cholestasis and hepatotoxity via activation of farnesoid X receptor in vivo and in vitro.

Cholestasis is a clinical syndrome with systemic and intrahepatic accumulation of excessive toxic bile acids that ultimately cause hepatobiliary injury. Recently obeticholic acid (OCA) which is a farnesoid X receptor (FXR) agonist was approved by FDA to treat cholestatic liver diseases, which provided us a newly therapeutic strategy against cholestasis. The purpose of the current study is to screen novel FXR agonists and verify the anti-cholestasis effect of yangonin in vivo and in vitro. The computational strategy of two-dimensional virtual screening was used to search for new FXR agonists, and dual-luciferase reporter gene assay was used to further demonstrate FXR activation by yangonin. Then, the hepatoprotective effect of yangonin via FXR activation against cholestasis and hepatotoxity was evaluated in mice and was investigated using FXR silence in cells. Yangonin was found to activate FXR to exert hepatoprotective effect against cholestatic liver injury. Dynamic change analysis of bile acids and gene analysis revealed that yangonin promoted bile acid efflux into bile and reduced hepatic uptake via the regulation of FXR-target genes Bsep, Mrp2 and Ntcp expression. Furthermore, yangonin modulated enzymes involved in bile acid synthesis and metabolism including Cyp7a1 Cyp8b1 and Sult2a1. In addition, yangonin promoted liver repair and suppressed liver inflammation. However, the changes in these genes and protein, as well as ameliorative liver histology induced by yangonin were abrogated by FXR antagonist guggulsterone in vivo and FXR siRNA in vitro. Yangonin produces protective effect against cholestasis via FXR activation. Yangonin may be an effective approach for the prevention and treatment for cholestatic liver diseases.

Ginsenoside Rg1 protects against acetaminophen-induced liver injury via activating Nrf2 signaling pathway in vivo and in vitro.

Acetaminophen (APAP) is a worldwide used drug for treating fever and pain. However, APAP overdose is the leading cause of drug-induced liver injury. The purpose of the current study is to evaluate the hepatoprotective effect of ginsenoside Rg1 (Rg1), the main pharmacologically active compounds of Panax ginseng, against APAP-induced acute liver injury, and further to elucidate the involvement of Nrf2 signaling pathway by in vivo and in vitro experiments. Male C57BL/6 mice were treated with Rg1 for 3 days before injection of APAP. Serum and liver tissue samples were collected 6 h later. The results indicated that Rg1 significantly attenuated APAP-induced hepatotoxicity and oxidative stress in a dose-dependent manner. Rg1 effectively enhanced antioxidant and detoxification capacity, which is largely dependent on up-regulating Nrf2 nuclear translocation, reducing Keap1 protein expression and up-regulating Nrf2 target genes including GCLC, GCLM, HO-1, NQO1, Ugt1a1, Ugt1a6, Ugt2b1, Sult2a1, Mrp2, Mrp3 and Mrp4. Furthermore, Rg1 repressed the activities of Cyp2e1, Cyp3a11, Cyp1a2, which are important enzymes in the formation of APAP toxic metabolite N-acetyl-p-benzoquinone imine. However, the changes in transporters and enzymes, as well as ameliorative liver histology induced by Rg1 were abrogated by Nrf2 antagonist all-transretinoic acid in vivo and Nrf2 siRNA in vitro. In conclusion, Rg1 produced hepatoprotective effects against APAP-induced acute liver injury via Nrf2 signaling pathway. Rg1 might be an effective approach for the prevention against acute liver injury.

Hepatoprotective effect of ginsenoside Rg1 from Panax ginseng on carbon tetrachloride-induced acute liver injury by activating Nrf2 signaling pathway in mice.

Oxidative stress and inflammatory response are well known to be involved in the pathogenesis of acute liver injury. This study was performed to examine the hepatoprotective effect of ginsenoside Rg1 (Rg1) against CCl4 -induced acute liver injury, and further to elucidate the involvement of Nrf2 signaling pathway in vivo and in vitro. Mice were orally administered Rg1 (15, 30, and 60 mg/kg) or sulforaphane (SFN) once daily for 1 week prior to 750 µL/kg CCl4 injection. The results showed that Rg1 markedly altered relative liver weights, promoted liver repair, increased the serum level of TP and decreased the serum levels of ALT, AST and ALP. Hepatic oxidative stress was inhibited by Rg1, as evidenced by the decrease in MDA, and increases in GSH, SOD, and CAT in the liver. Further research demonstrated that Rg1 suppressed liver inflammation response through repressing the expression levels of inflammation-related genes including TNF-α, IL-1ß, IL-6, COX-2, and iNOS. In addition, Rg1 enhanced antioxidative stress and liver detoxification abilities by up-regulating Nrf2 and its target-genes such as GCLC, GCLM, HO-1, NQO1, Besp, Mrp2, Mrp3, Mrp4, and down-regulating Cyp2e1. However, the changes in Nrf2 target-genes, as well as ameliorative liver histology induced by Rg1 were abrogated by Nrf2 antagonist all-transretinoic acid in vivo and Nrf2 siRNA in vitro. Overall, the findings indicated that Rg1 might be an effective approach for the prevention against acute liver injury by activating Nrf2 signaling pathway.

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles.

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty.

This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.

Microshell Arrays Enhanced Sensitivity in Detection of Specific Antibody for Reduced Graphene Oxide Optical Sensor.

Protein-protein interactions play an important role in the investigation of biomolecules. In this paper, we reported on the use of a reduced graphene oxide microshell (RGOM)-based optical biosensor for the determination of goat anti-rabbit IgG. The biosensor was prepared through a self-assembly of monolayers of monodisperse polystyrene microspheres, combined with a high-temperature reduction, in order to decorate the RGOM with rabbit IgG. The periodic microshells allowed a simpler functionalization and modification of RGOM with bioreceptor units, than reduced graphene oxide (RGO). With additional antibody-antigen binding, the RGOM-based biosensor achieved better real-time and label-free detection. The RGOM-based biosensor presented a more satisfactory response to goat anti-rabbit IgG than the RGO-based biosensor. This method is promising for immobilizing biomolecules on graphene surfaces and for the fabrication of biosensors with enhanced sensitivity.

Emodin-Loaded PLGA-TPGS Nanoparticles Combined with Heparin Sodium-Loaded PLGA-TPGS Nanoparticles to Enhance Chemotherapeutic Efficacy Against Liver Cancer.

PURPOSE: Heparin sodium (HS)-loaded polylactic-co-glycolic acid-D-α-tocopheryl polyethylene glycol 1000 succinate (PLGA-TPGS) nanoparticles (HPTNs) were prepared as a sustained and targeting delivery carrier and combined with emodin (EMO)-loaded PLGA-TPGS nanoparticles (EPTNs), which were investigated previously to form a combination therapy system for the treatment of liver cancer. METHODS: To assess cellular uptake and evaluate the liver-targeting capacity by analyzing the drug concentrations and frozen slices, HS/eosin-loaded PLGA-TPGS nanoparticles, HS/fluorescein- loaded PLGA-TPGS nanoparticles and EMO/C6-loaded PLGA-TPGS nanoparticles, which contained eosin, fluorescein and C6 as fluorescent probes, respectively, were also prepared. All of these nanoparticles were characterized in terms of their size, size distribution, surface charge, drug loading, encapsulation efficiency, in vitro release profile and cellular uptake. The apoptosis of HepG2 cells induced by EPTNs in combination with HPTNs was determined by Annexin V-FITC staining and PI labelling. RESULTS: Transmission electron microscopy indicated that these nanoparticles were stably dispersed spheres with sizes ranging from 100 to 200 nm. The results demonstrated that fluorescent nanoparticles were internalized into HepG2 and HCa-F cells efficiently and had improved liver-targeting properties. The combination of EPTNs and HPTNs effectively inhibited cell growth in vitro and had a remarkable synergistic anticancer effect in vivo. EPTNs combined with HPTNs induced HepG2 cell apoptosis with synergistic effects. The liver H&E slice images of a hepatocarcinogenic mouse model indicated that EPTNs in combination with HPTNs significantly suppressed tumour growth in vivo. CONCLUSIONS: The research suggests that the combination therapy system of EPTNs and HPTNs could be a new direction for liver cancer therapy.

Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models.

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks.

Bubbles-Induced Porous Structure-Based Flexible Piezoresistive Sensors for Speech Recognition.

Flexible piezoresistive sensors with a porous structure that are used in the field of speech recognition are seldom characterized by both high sensitivity and ease of preparation. In this study, a piezoresistive sensor with a porous structure that is both highly sensitive and can be prepared by using a simple method is proposed for speech recognition. The preparation process utilizes the interaction of bubbles generated by ethanol evaporation and active agents with polydimethylsiloxane to produce a porous flexible substrate. This preparation process requires neither templates nor harsh experimental conditions such as a low temperature and a low pressure. Furthermore, the prepared piezoresistive sensor has excellent properties, such as a high sensitivity (27.6 kPa-1), a satisfactory response time (800 µs), and a good stability (10,000 cycles). When used for speech recognition, more than 1500 vocalizations and silent speech signals obtained from subjects saying numbers from "0" to "9" were collected by the sensor for training a convolutional neural network model. The average accuracy of the recognition reached 94.8%. The simple preparation process and the excellent performance of the prepared flexible piezoresistive sensor endow it with a wide application prospect in the field of speech recognition.