Protective mechanism of Paeoniae Radix Alba against chemical liver injury based on network pharmacology,molecular docking,and in vitro experiments

Objective:To explore and validate the potential targets of Paeoniae Radix Alba(P.Radix,Bai Shao)in protecting against chemical liver injury through network pharmacology,molecular docking technology,and in vitro cell experiments.Methods:Network pharmacology was used to identify the common potential targets of P.Radix and chemical liver injury.Molecular docking was used to fit the components,which were subsequently verified in vitro.A cell model of hepatic fibrosis was established by activating hepatic stellate cell(HSC)-LX2 cells with 10 ng/mL transforming growth factor-β1.The cells were exposed to different concentrations of total glucosides of paeony(TGP),the active substance of P.Radix,and then evaluated using the cell counting kit-8 assay,enzyme-linked immunosorbent assay,and western blot.Results:Analysis through network pharmacology revealed 13 key compounds of P.Radix,and the potential targets for preventing chemical liver injury were IL-6,AKT serine/threonine kinase 1,jun protooncogene,heat shock protein 90 alpha family class A member 1(HSP90AA1),peroxisome proliferator activated receptor gamma(PPARG),PTGS2,and CASP3.Gene Ontology(GO)enrichment analysis indicated the involvement of response to drugs,membrane rafts,and peptide binding.Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis revealed that the main pathways involved lipid and atherosclerosis and chemical carcinogenesis-receptor activation.Paeoniflorin and albiflorin exhibited strong affinity for HSP90AA1,PTGS2,PPARG,and CASP3.Different concentrations of TGP can inhibit the expression of COL-I,COL-III,IL-6,TNF-a,IL-1β,HSP-90a,and PTGS2 while increasing the expression of PPAR-γand CASP3 in activated HSC-LX2 cells.Conclusion:P.Radix primarily can regulate targets such as HSP90AA1,PTGS2,PPARG,CASP3.TGP,the main active compound of P.Radix,protects against chemical liver injury by reducing the inflammatory response,activating apoptotic proteins,and promoting the apoptosis of activated HSCs.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing.Due to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes.Despite much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode condition.To overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern information.The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition.The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method.Compared with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Ubiquitous Sensor Network for Chemical Sensors

Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the practical deployment of large-scale,low-power,inexpensive sensor networks.Such an approach offers an advantage over traditional sensing methods in many ways:large-scale,dense deployment not only extends spatial coverage and achieves higher resolution,but also increases the system's fault-tolerance and robustness.Moreover,the ad-hoc nature of wireless sensor networks makes them even more attractive for military and other risk-associated applications,such as environmental observation and habitat monitoring.

Chemical diversity of scleractinian corals revealed by untargeted metabolomics and molecular networking

The chemical diversity of scleractinian corals is closely related to their physiological,ecological,and evolutionary status,and can be influenced by both genetic background and environmental variables.To investigate intraspecific variation in the metabolites of these corals,the metabolomes of four species(Pocillopora meandrina,Seriatopora hystrix,Acropora formosa,and Fungia fungites)from the South China Sea were analyzed using untargeted mass spectrometry-based metabolomics.The results showed that a variety of metabolites,including amino acids,peptides,lipids,and other small molecules,were differentially distributed among the four species,leading to their significant separation in principal component analysis and hierarchical clustering plots.The higher content of storage lipids in branching corals(P.meandrina,S.hystrix,and A.formosa)compared to the solitary coral(F.fungites)may be due to the high densities of zooxanthellae in their tissues.The high content of aromatic amino acids in P.meandrina may help the coral protect against ultraviolet damage and promote growth in shallow seawater,while nitrogen-rich compounds may enable S.hystrix to survive in various challenging environments.The metabolites enriched in F.fungites,including amino acids,dipeptides,phospholipids,and other small molecules,may be related to the composition of the coral's mucus and its life-history,such as its ability to move freely and live solitarily.Studying the chemical diversity of scleractinian corals not only provides insight into their environmental adaptation,but also holds potential for the chemotaxonomy of corals and the discovery of novel bioactive natural products.

Comprehensive evaluation of chemical breakers for multistage network ultra-high strength gel

Polymer gels have been accepted as a useful tool to address many sealing operations such as drilling and completion,well stimulation,wellbore integrity,water and gas shutoff,etc.Previously,we developed an ultra-high strength gel(USGel)for medium to ultra-low temperature reservoirs.However,the removal of USGel is a difficult problem for most temporary plugging operations.This paper first provides new insights into the mechanism of USGel,where multistage network structure and physical entanglement are the main reasons for USGel possessing ultra-high strength.Then the effects of acid breakers,encapsulated breakers,and oxidation breakers(including H_(2)O_(2),Na_(2)S_(2)O_(8),Ca(ClO)_(2),H_(2)O_(2)+NaOH,Na_(2)S_(2)O_(8)+NaOH,and Ca(ClO)_(2)+NaOH)were evaluated.The effects of component concentration and temperature on the breaking solution were studied,and the corrosion performance,physical simulation and formation damage tests of the breaking solution were carried out.The final formulation of 2%-4%NaOH+4.5%-6%H_(2)O_(2) breaking solution was determined,which can make USGel completely turn into water at 35e105C.The combinations of“acid t breaking solution”,“acid+encapsulated breaker”and“encapsulated breaker+breaking solution”were evaluated for breaking effect.The acid gradually reduced the volume of USGel,which increased the contact area between breaking solution and USGel,and the effect of“4%acid+breaking solution”was 23 times higher than that of breaking solution alone at 35C.However,the acid significantly reduced the strength of USGel.This paper provides new insights into the breaking of high-strength gels with complex network structures.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Extraction and analysis of risk factors from Chinese chemical accident reports

Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

From Chemical Langevin Equations to Fokker-Planck Equation: Application of Hodge Decomposition and Klein-Kramers Equation

The stochastic systems without detailed balance are common in various chemical reaction systems, such as metabolic network systems. In studies of these systems, the concept of potential landscape is useful However, what are the su^cient and necessary conditions of the existence of the potential function is still an open problem. Use Hodge decomposition theorem in differential form theory, we focus on the general chemical Langevin equations, which reitect complex chemical reaction systems. We analysis the conditions for the existence of potential landscape of the systems. By mapping the stochastic differential equations to a Hamiltonian mechanical system, we obtain the Fokker-Planck equation of the chemical reaction systems. The obtained Fokker-Planck equation can be used in further studies of other steady properties of complex chemical reaction systems, such as their steady state entropies.

A Survey on Machine Learning in Chemical Spectral Analysis

Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms,in particular convolutional networks.Hence,this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images,and overviews the current application,development and breakthrough in different spectral characterization.Brief categorization of reviewed literatures is provided for studies per application apparatus:X-Ray spectra,UV-Vis-IR spectra,Micro-scope,Raman spectra,Photoluminescence spectrum.End with the overview of existing circumstances in this research area,we provide unique insight and promising directions for the chemical imaging field to fully couple machine learning subsequently.

Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process

In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.

Uncovering Chemical Interactions between Danshen and Danggui Using Liquid Chromatography–Mass Spectrometry and Network Pharmacology-Based Research on Stroke

Objective: The objective of this study was to decipher chemical interactions between Danshen and Danggui using liquid chromatography–mass spectrometry(LC-MS) and explore the mechanisms of Danshen–Danggui against stroke using network pharmacology and molecular docking. Materials and Methods: First, the chemical compounds of Danshen–Danggui were profiled using ultra-high-performance liquid chromatography(HPLC)-quadrupole time-of-flight MS. Accurately characterized compounds in various proportions of Danshen–Danggui were quantified using HPLC combined with triple quadrupole electrospray tandem MS. Network pharmacology was used to uncover the essential mechanisms of action of Danshen–Danggui against stroke. Discovery Studio Software was used for the molecular docking verification of key active chemicals and stroke-related targets. Results: A total of 53 compounds were characterized, and 22 accurately identified constituents(10 phenolic acids, 8 phthalides, and 4 tanshinones) were quantified in 15 proportions of Danshen–Danggui. The quantification results showed that Danggui significantly increased the dissolution of most phenolic acids(compounds from Danshen), whereas Danshen promoted the dissolution of most phthalides(compounds from Danggui). Overall, the combination of Danshen and Danggui at a 1:1 ratio resulted in the maximum total dissolution rate. Further network pharmacology and molecular docking results indicated that Danshen–Danggui exerted anti-stroke effects mainly by regulating inflammation-related(tumor necrosis factor, hypoxia-inducible factor, and toll-like receptor) signaling pathways, which ranked among the top three pathways based on Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis. Conclusion: The chemical compounds in Danshen–Danggui could interact with each other to increase the dissolution of the most active compounds, which could provide a solid basis for uncovering the compatibility mechanisms of Danshen–Danggui and Danshen–Danggui-based formulae.

Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion

Artificial Neural Networks(ANNs)have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics.Complex reaction mechanisms,however,present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species.This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form,and only involves training a single ANN for a complete reaction mechanism.The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion.Both modifications are used to improve the overall ANN performance and individual prediction accuracies,especially for minor species mass fractions.To validate its effectiveness,the approach is compared to standard ANNs in terms of performance and ANN complexity.Four distinct reaction mechanisms(H_(2),C_(7)H_(16),C_(12)H_(26),OME_(34))are used as a test cases,and results demonstrate that considerable improvements can be achieved by applying both modifications.

Polynomials of Degree-Based Indices for Three-Dimensional Mesh Network

In order to study the behavior and interconnection of network devices,graphs structures are used to formulate the properties in terms of mathematical models.Mesh network(meshnet)is a LAN topology in which devices are connected either directly or through some intermediate devices.These terminating and intermediate devices are considered as vertices of graph whereas wired or wireless connections among these devices are shown as edges of graph.Topological indices are used to reflect structural property of graphs in form of one real number.This structural invariant has revolutionized the field of chemistry to identify molecular descriptors of chemical compounds.These indices are extensively used for establishing relationships between the structure of nanotubes and their physico-chemical properties.In this paper a representation of sodium chloride(NaCl)is studied,because structure of NaCl is same as the Cartesian product of three paths of length exactly like a mesh network.In this way the general formula obtained in this paper can be used in chemistry as well as for any degree-based topological polynomials of three-dimensional mesh networks.

Integrated UHPLC-MS and network pharmacology to explore the active constituents and pharmacological mechanisms of Shenzao dripping pills against coronary heart disease

Background:Shenzao dripping pills(SZDP)is an empirical prescription of traditional Chinese medicine that is mainly used to treat coronary heart disease.However,the chemical composition and pharmacological mechanisms of SZDP are unknown.Methods:In this study,ultra-high performance liquid chromatography-quadruple-Exactive Orbitrap mass spectrometry was used to identify the chemical components in extracts and medicated plasma of SZDP.Subsequently,we performed network pharmacology methods,including target prediction by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine,protein-protein interaction network via STRING database;further,the key targets and compounds were screened using Cytoscape.Finally,the key targets and compounds were validated by molecular docking.Results:72 chemical constituents were identified from SZDP by high performance liquid chromatography and mass spectrometry technology.Among the components absorbed into plasma by SZDP,24 prototype components and 9 metabolized components were identified.The network pharmacology analysis of the prototype components showed that there are 13 key compounds(including ginsenoside Rc,Rb1,Rb2,ferulic acid,etc.),90 proteins(including proto-oncogene tyrosine-protein kinase Src,nuclear receptor subfamily 3 group C member 1,caspase-3,etc.),and 10 pathways(including estrogen,IL-17 and VEGF signaling pathway,etc.)that play an essential role in the treatment of coronary heart disease with SZDP.In addition,the results of molecular docking revealed that ginsenosides Rc,Rb2 and Rb1 have strong binding activities to the caspase-3,as well as ginsenoside Rb2 to the nuclear receptor subfamily 3 group C member 1.Conclusion:This study showed that SZDP might act through multiple chemical constituents and targets against coronary heart disease.

Study of Properties of Intermetallic Compounds of Rare Earth Metals by Artificial Neural Networks

The results of an expert system of lanthanide intermetallic compounds using artificial neural networks and chemical bond parameter method were reported. Two pattern recognition neural models, one for prediction of the occurrence of 1 : 1 lanthanide intermetallic compounds with CsClstructure and the other for prediction of congruent or incongruent melting types, were developed. Four regression neural models were also developed for prediction of melting point of these compounds. In order to get rid of overfitting, cross-vahdation method was used for the neural models. And satisfactory results were obtained in all of the neural models in this paper.

驾驶疲劳对危险化学品道路运输事故风险的影响规律

近年来,随着危险化学品使用量的急剧攀升,危险化学品道路运输事故率也呈现上升的趋势,且此类事故的发生往往会导致严重后果。为研究危险化学品道路运输事故动态风险变化规律,在修正贝叶斯网络模型基础上,利用2017—2021年历史数据进行机器学习,根据驾驶疲劳程度计算得到“驾驶人行为”动态节点的状态转移概率矩阵,建立基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)的危险化学品道路运输动态风险预测模型并进行推理分析。研究显示:在驾驶3 h内,驾驶人“疲劳驾驶”发生概率随时间推移而增加,但增幅有所下降;在最常见情境下,随驾驶人“疲劳驾驶”概率增加,“侧翻”和“碰撞”事故类型的发生概率明显增加,进而导致“泄漏”事故后果的发生概率有所增加;驾驶人“疲劳驾驶”概率增加会导致“有伤亡事故”发生概率增加,即加重事故的严重程度;在驾驶3 h内,“侧翻”“碰撞”“泄漏”和“有伤亡事故”发生概率的变化趋势与驾驶人“疲劳驾驶”发生概率的变化趋势一致。