bluuebunny/crossref_metadata_2025 huggingface dataset:
Selection of Cross-Border E-Commerce Import Model Based on Intelligent Data Analytics AHP Algorithm
Authors: Zhou Pei
Published: September 2021

With the gradual popularity of e-commerce in our country, Cross-Border E-Commerce (CBEC) has developed rapidly. In order to comply with this development trend, more and more enterprises in our country have embarked on the development path of CBEC, and CBEC import models are becoming increasingly diversified. Among them, the choice of CBEC import models based on the AHP algorithm helps a lot. The purpose of this article is to study the selection of CBEC import models based on the AHP algorithm. This article first analyzes the current situation of China’s CBEC, compares the types of CBEC import models, and summarizes the advantages of various CBEC supply models. This paper analyzes and discusses the specific situation of our country’s CBEC model and its operation through specific analysis of the current situation and problems of H company’s CBEC and puts forward suggestions for the development of our country’s CBEC import model. Based on the analysis of the AHP algorithm, the total scores for the direct mail import model, bonded stocking model, and general trade import model of H company are 27 points, 19 points, and 23 points, respectively. Therefore, it is recommended that company H choose the import model of direct mail import when conducting CBEC business.
Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain
Authors: Shen Yue , Ren Yixin
Published: September 2021

With the deepening of globalization, cross-border industrial e-commerce has increasingly become a mainstream means of trade exchanges, however, with the continuous increase in business volume. How to accurately estimate the procurement volume of cross-border materials is a significant issue, which plays a vital role in quantifying economic efficiency. To this end, this study proposes a financial decision-making algorithm for cross-border industrial e-commerce material procurement based on the Markov chain. The algorithm first preprocesses the original logistics data, including data cleaning, missing data filling, and noisy data removal, represents the data as structured panel data, finally builds a Markov chain model based on the panel data, and then makes predictions on the new data. We verified the effectiveness of the proposed model on a simulated dataset and an actual dataset.
Effect of R Angle of the Outer Extension Tube against the in-Core Flux Thimble in Nuclear Power Plant on Its Wear Behavior
Authors: Chen Qiang , Che Yinhui , Guan Jianjun , Li Yang , Wang Qinhu
Published: September 2021

Wear failure of the in-core flux thimble is an important problem in the neutron flux measurement system, which threatens the safety of the nuclear power plant. To figure out the wear mechanism of the thimble, a wear tester was designed and manufactured to simulate the wear process of the in-core flux thimble. Outer guide tubes with different R angles were used to abrade the thimbles. The designed tester can well simulate the wear process in the real power plant. R angle of the outer guide tube played important role in the wear behavior of the in-core flux thimbles.
Stingless Bee Propolis: New Insights for Anticancer Drugs
Authors: Campos Jaqueline Ferreira , dos Santos Helder Freitas , Bonamigo Thaliny , de Campos Domingues Nelson Luís , de Picoli Souza Kely , dos Santos Edson Lucas
Published: September 2021

Natural products are important sources of biomolecules possessing antitumor activity and can be used as anticancer drug prototypes. The rich biodiversity of tropical and subtropical regions of the world provides considerable bioprospecting potential, including the potential of propolis produced by stingless bee species. Investigations of the potential of these products are extremely important, not only for providing a scientific basis for their use as adjuvants for existing drug therapies but also as a source of new and potent anticancer drugs. In this context, this article organizes the main studies describing the anticancer potential of propolis from different species of stingless bees with an emphasis on the chemical compounds, mechanisms of action, and cell death profiles. These mechanisms include apoptotic events; modulation of BAX, BAD, BCL2‐L1 (BCL‐2 like 1), and BCL‐2; depolarization of the mitochondrial membrane; increased caspase‐3 activity; poly (ADP‐ribose) polymerase (PARP) cleavage; and cell death induction by necroptosis via receptor interacting protein kinase 1 (RIPK1) activation. Additionally, the correlation between compounds with antioxidant and anti‐inflammatory potential is demonstrated that help in the prevention of cancer development. In summary, we highlight the important antitumor potential of propolis from stingless bees, but further preclinical and clinical trials are needed to explore the selectivity, efficacy, and safety of propolis.
A New Survival Model Based on ADAMTSs for Prognostic Prediction in Clear Cell Renal Cell Carcinoma
Authors: Wu Guangzhen , Li Jianyi , Xu Yingkun , Che Xiangyu , Chen Feng , Wang Qifei
Published: September 2021

The main purpose of this study was to explore the genetic variation, gene expression, and clinical significance of ADAMTSs (a disintegrin and metalloprotease domains with thrombospondin motifs) across cancer types. Analysis of data from the TCGA (The Cancer Genome Atlas) database showed that the ADAMTSs have extensive CNV (copy number variation) and SNV (single nucleotide variation) across cancer types. Compared with normal tissues, the methylation of ADAMTSs in cancer tissues is also significantly different, which affects the expression of ADAMTS gene and the prognosis of cancer patients. Through gene expression analysis, we found that ADAMTS family has significant changes in gene expression across cancer types and is closely related to the prognosis of carcinoma, especially in ccRCC (clear cell renal cell carcinoma). LASSO regression analysis was used to establish a prognostic model based on the ADAMTSs to judge the prognosis of patients with ccRCC. Multiple Cox regression analysis suggested that age, grade, stage, and risk score of the prognostic model of ccRCC were independent prognostic factors in patients with renal clear cell carcinoma. These findings indicate that the ADAMTSs-based survival model can accurately predict the prognosis of patients with ccRCC and suggest that ADAMTSs are a potential prognostic biomarker and therapeutic target in ccRCC.
Very Elevated IgE, Atopy, and Severe Infection: A Genomics-Based Diagnostic Approach to a Spectrum of Diseases
Authors: Chin A. , Balasubramanyam S. , Davis C. M.
Published: September 2021

Elevated IgE has been long recognized as an important clinical marker of atopy but can be seen in a myriad of conditions. The discovery of autosomal dominant STAT3 deficiency marked the first recognition of hyper-IgE syndrome (HIES) and the first primary immunodeficiency linked to elevated IgE. Since then, genomic testing has increased the number of defects with associated mutations causing hyper-IgE syndrome and atopic diseases with FLG, DOCK8, SPINK5, and CARD11, among others. A spectrum of recurrent infections and atopy are hallmarks of elevated IgE with significant phenotypic overlap between each underlying condition. As treatment is predicated on early diagnosis, genomic testing is becoming a more commonly used diagnostic tool. We present a 6-year-old male patient with markedly elevated IgE and severe atopic dermatitis presenting with staphylococcal bacteremia found to have a heterozygous variant in FLG (p.S3247X) and multiple variants of unknown significance in BCL11B, ZAP70, LYST, and PTPRC. We review the genetic defects underpinning elevated IgE and highlight the spectrum of atopy and immunodeficiency seen in patients with underlying mutations. Although no one mutation is completely causative of the constellation of symptoms in this patient, we suggest the synergism of these variants is an impetus of disease.
Stress Induces Release of Extracellular Vesicles by Trypanosoma cruzi Trypomastigotes
Authors: Vasconcelos Camilla Ioshida , Cronemberger-Andrade A , Souza-Melo Normanda , Maricato Juliana Terzi , Xander Patrícia , Batista Wagner Luiz , Soares Rodrigo Pedro , Schenkman Sergio , Torrecilhas Ana Claudia
Published: September 2021

All extracellular forms of Trypanosoma cruzi, the causative agent of Chagas disease, release extracellular vesicles (EVs) containing major surface molecules of the parasite. EV release depends on several mechanisms (internal and external). However, most of the environmental conditions affecting this phenomenon are still unknown. In this work, we evaluated EV release under different stress conditions and their ability to be internalized by the parasites. In addition, we investigated whether the release conditions would affect their immunomodulatory properties in preactivated bone marrow-derived macrophages (BMDM). Sodium azide and methyl-cyclo-β-dextrin (CDB) reduced EV release, indicating that this phenomenon relies on membrane organization. EV release was increased at low temperatures (4°C) and acidic conditions (pH 5.0). Under this pH, trypomastigotes differentiated into amastigotes. EVs are rapidly liberated and reabsorbed by the trypomastigotes in a concentration-dependent manner. Nitrosative stress caused by sodium nitrite in acid medium or S-nitrosoglutathione also stimulated the secretion of EVs. EVs released under all stress conditions also maintained their proinflammatory activity and increased the expression of iNOS, Arg 1, IL-12, and IL-23 genes in IFN-γ and LPS preactivated BMDM. In conclusion, our results suggest a budding mechanism of release, dependent on the membrane structure and parasite integrity. Stress conditions did not affect functional properties of EVs during interaction with host cells. EV release variations under stress conditions may be a physiological response against environmental changes.
G-CAS: Greedy Algorithm-Based Security Event Correlation System for Critical Infrastructure Network
Authors: Lu Peng , Hu Teng , Wang Hao , Zhang Ruobin , Wu Guo
Published: September 2021

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.
Network Pharmacology Reveals That Resveratrol Can Alleviate COVID-19-Related Hyperinflammation
Authors: Xiao Zijian , Ye Qing , Duan Xiaomei , Xiang Tao
Published: September 2021

Hyperinflammation is related to the development of COVID-19. Resveratrol is considered an anti-inflammatory and antiviral agent. Herein, we used a network pharmacological approach and bioinformatic gene analysis to explore the pharmacological mechanism of Resveratrol in COVID-19 therapy. Potential targets of Resveratrol were obtained from public databases. SARS-CoV-2 differentially expressed genes (DEGs) were screened out via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE147507, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; then, protein-protein interaction network was constructed. The common targets, GO terms, and KEGG pathways of Resveratrol targets and SARS-CoV-2 DEGs were confirmed. KEGG Mapper queried the location of common targets in the key pathways. A notable overlap of the GO terms and KEGG pathways between Resveratrol targets and SARS-CoV-2 DEGs was revealed. The shared targets between Resveratrol targets and SARS-CoV-2 mainly involved the IL-17 signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Our study uncovered that Resveratrol is a promising therapeutic candidate for COVID-19 and we also revealed the probable key targets and pathways involved. Ultimately, we bring forward new insights and encourage more studies on Resveratol to benefit COVID-19 patients.
Association of Serum Total Bilirubin Concentration with Telomere Length: The National Health and Nutrition Examination Survey
Authors: Hao Lu , Chen Qiuyan , Chen Xi , Zhou Qing
Published: September 2021

Introduction. Mildly increased bilirubin concentration has a protective effect on oxidative stress–related diseases. However, it remains unknown whether elevated circulating bilirubin is associated with longer telomere length. The aim of this cross‐sectional study was to examine the association between total bilirubin concentration and telomere length. Methods. We used the data from the National Health and Nutrition Examination Survey (NHANES) 1999‐2002. The multivariable linear regression model was used to examine the association between total bilirubin concentration and telomere length. The nonlinear relationship was analyzed using a generalized additive model with the smoothing plot. Results. A total of 7818 participants with a mean age of 49.20 ± 18.82 years were included. Compared with the lowest concentration of total bilirubin (Q1), the highest quartile of total bilirubin concentration was associated with longer telomere length in male (β = 0.04, 95 CI%: 0.00, 0.07, P = 0.024) and female (β = 0.04, 95 CI%: 0.02, 0.04, P = 0.002). Furthermore, an inverted U‐shaped relationship between total bilirubin and telomere length was found. On the left of turning points (total bilirubin < 0.5 mg/dL), total bilirubin concentration was positively associated with telomere length (β = 0.23, 95 CI%: 0.14, 0.32, P < 0.001). However, the association between total bilirubin concentration and telomere length was not significant (β = 0.01, 95% CI: ‐0.01, 0.04, P = 0.346) above the turning point. Conclusion. This is the first evidence based on a nationally representative survey demonstrating a positive and nonlinear association between total bilirubin concentration and telomere length. Future large‐scale prospective studies are warranted to confirm our findings.
Using the Quarterly Compound Fractional Grey Model to Predict the Air Quality in 22 Cities of China
Authors: Guo Jianlong , Chen Yan , Wu Lifeng
Published: September 2021

The rapid development of industrialization leads to more and more serious air pollution, which affects human health and sustainable development of society. Predicting air quality is an important link in preventing air pollution and improving the atmospheric environment. In this paper, 22 cities of China with poor air quality in recent years are selected as the research objects. A quarterly compound accumulation grey model is used to predict the concentrations of PM2.5, PM10, SO2, and NO2 in the 22 cities. Two parameters are introduced into the model to optimize the accumulation method of the grey model. Also, seasonal factors are introduced to better simulate air quality. The forecasting results show that air quality in these cities, although varies widely on a quarterly basis, tends to decline overall. The concentrations of PM2.5 and PM10 in most cities will still exceed the standard in the next few years, especially in the first and fourth quarters of each year. The prediction results can provide reference for relevant departments.
Leaf Recognition Based on Joint Learning Multiloss of Multimodel Convolutional Neural Networks: A Testing for Vietnamese Herb
Authors: Dat Trinh Tan , Le Thien Vu Pham Cung , Truong Nguyen Nhat , Anh Dang Le Tran , Thanh Sang Vu Ngoc , Bao Pham The
Published: September 2021

A new modification of multi‐CNN ensemble training is investigated by combining multiloss functions from state‐of‐the‐art deep CNN architectures for leaf image recognition. We first apply the U‐Net model to segment leaf images from the background to improve the performance of the recognition system. Then, we introduce a multimodel approach based on a combination of loss functions from the EfficientNet and MobileNet (called as multimodel CNN (MMCNN)) to generalize a multiloss function. The joint learning multiloss model designed for leaf recognition allows each network to perform its task and cooperate with the others simultaneously, where knowledge from various trained deep networks is shared. This cooperation‐proposed multimodel is forced to deal with more complicated problems rather than a simple classification. Therefore, the network can learn much rich information and improve its generalization capability. Furthermore, a multiloss trade‐off strategy between two deep learning models can reduce the effect of redundancy problems in ensemble classifiers. The performance of our approach is evaluated by our custom Vietnamese herbal leaf species dataset, and public datasets such as Flavia, Leafsnap, and Folio are used to build test cases. The results confirm that our approach enhances the leaf recognition performance and outperforms the current standard single networks while having less low computation cost.
Three-Dimensional Inversion of Borehole-Surface Resistivity Method Based on the Unstructured Finite Element
Authors: Wang Zhi , Fang Sinan
Published: September 2021

The electromagnetic wave signal from the electromagnetic field source generates induction signals after reaching the target geological body through the underground medium. The time and spatial distribution rules of the artificial or the natural electromagnetic fields are obtained for the exploration of mineral resources of the subsurface and determining the geological structure of the subsurface to solve the geological problems. The goal of electromagnetic data processing is to suppress the noise and improve the signal-to-noise ratio and the inversion of resistivity data. Inversion has always been the focus of research in the field of electromagnetic methods. In this paper, the three-dimensional borehole-surface resistivity method is explored based on the principle of geometric sounding, and the three-dimensional inversion algorithm of the borehole-surface resistivity method in arbitrary surface topography is proposed. The forward simulation and calculation start from the partial differential equation and the boundary conditions of the total potential of the three-dimensional point current source field are satisfied. Then the unstructured tetrahedral grids are used to discretely subdivide the calculation area that can well fit the complex structure of subsurface and undulating surface topography. The accuracy of the numerical solution is low due to the rapid attenuation of the electric field at the point current source and the nearby positions and sharply varying potential gradients. Therefore, the mesh density is defined at the local area, that is, the vicinity of the source electrode and the measuring electrode. The mesh refinement can effectively reduce the influence of the source point and its vicinity and improve the accuracy of the numerical solution. The stiffness matrix is stored with Compressed Row Storage (CSR) format, and the final large linear equations are solved using the Super Symmetric Over Relaxation Preconditioned Conjugate Gradient (SSOR-PCG) method. The quasi-Newton method with limited memory (L_BFGS) is used to optimize the objective function in the inversion calculation, and a double-loop recursive method is used to solve the normal equation obtained at each iteration in order to avoid computing and storing the sensitivity matrix explicitly and reduce the amount of calculation. The comprehensive application of the above methods makes the 3D inversion algorithm efficient, accurate, and stable. The three-dimensional inversion test is performed on the synthetic data of multiple theoretical geoelectric models with topography (a single anomaly model under valley and a single anomaly model under mountain) to verify the effectiveness of the proposed algorithm.
Efficacy of Otomicroscopy Combined with Otoendoscopy Double-Lens Technology-Assisted Tympanic Membrane Repair on Elderly Patients with Chronic Suppurative Otitis Media
Authors: Cheng Xin , Wu Shaohua , Wang Wei
Published: September 2021

Objective. To investigate the effect of otomicroscopy combined with otoendoscopy double-lens technology-assisted tympanic membrane repair on elderly patients chronic suppurative otitis media (CSOM). Methods. 120 elderly CSOM patients from January 2017 to July 2019 were selected and divided into the otomicroscopy group (n = 40), the otoendoscopy group (n = 40), and the double-lens group (n = 40) by the random number method. All patients were treated with tympanic membrane repair. The otomicroscopy group was assisted by otomicroscopy, the otoendoscopy group was assisted by otoendoscopy. and the double-lens group was assisted by otomicroscopy combined with otoendoscopy. The three groups of operations status, clinical efficacy, the incidence of adverse reactions, hearing improvement rate, and satisfaction rate with incision after 6 months were compared. Results. The operation time, intraoperative blood loss, hospitalization time, and VAS score of the otoendoscopy group and the double-lens group were all lower than those of the otoendoscopy group, and the operation time of the double-lens group was lower than that of the otoendoscopy group (

P
<
0.05

). The clinical efficacy of the double-lens group was better than that of the otomicroscopy group and otoendoscopy group (

P
<
0.05

). The adverse reaction rate of the otoendoscopy group and the double-lens group was lower than that of the otomicroscopy group, and the average postoperative air-bone conductance of the double-lens group was lower than that of the otomicroscopy group and the otoendoscopy group, and the hearing improvement rate was higher than that of the otomicroscopy and otoendoscopy groups (

P
<
0.05

). The satisfaction rate with postoperative incision in the otoendoscopy group and double-lens group was higher than that in the otomicroscopy group (

P
<
0.05

). Conclusion. The double-lens technology-assisted tympanic membrane repair has an obvious effect on elderly patients with CSOM. Compared with the single-use otomicroscopy, the operation time, intraoperative blood loss, hospitalization time, patient’s dry ear condition, degree of surgical pain, clinical efficacy, adverse reaction rate, hearing improvement rate, and patient’s incision satisfaction of the double-lens technology are better. Compared with the single-use otoendoscopy, the operation time, clinical efficacy, and hearing improvement rate of the double-lens technology are better.
An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing
Authors: Zhang Qingyang , Peng Tianji , Zhang Guangchun , Liu Jie , Guo Xiaowei , Gong Chunye , Yang Bo , Fan Xukai
Published: September 2021

This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.
Assessing the Knowledge, Attitude, and Practices of Cigarette Smokers and Use of Alternative Nicotine Delivery Systems in Pakistan: A Cross-Sectional Study
Authors: Hameed Abdul , Malik Daud
Published: September 2021

Background. This research has been conducted to assess smokers’ knowledge and behavior vis-à-vis combustible smoking cessation, prevalence, and risk, and the use of alternative nicotine delivery systems to quit smoking. Methods. A mixed-method approach utilizing cross section primary survey data and comprising descriptive and s-KAP index analysis has been adopted to ascertain the relationship between dependent and independent s-KAP variables; the principal component analysis methodology has been used to determine the use of alternative nicotine delivery systems. Results. Most of the smokers were aged between 15 and 35 years. A predominant 69.8% of the smokers came from middle-class background. Moreover, 71.3% were unaware of any alternative Tobacco Harm Reduction product. A majority of the respondents (68.2%) were keen to quit smoking. However, when asked why they had not succeeded, 52.9% reported addiction to nicotine as the main impediment. In Pakistan, lack of smoking cessation services is the weak link in the fight against the tobacco epidemic. Smokers are generally unaware of the Tobacco Harm Reduction products available in Pakistan; moreover, only 10.9% of the respondents were willing to spend more than Rs. 4000 per month on Tobacco Harm Reduction products. The average s-KAP score for young adults below the age of 20 was much lower than the national average but improved with the level of education. Interestingly, the score of smokers who had ever tried to quit smoking was slightly higher than that of those who had never tried to quit. Conclusion. There is intent to quit combustible smoking but the policy and infrastructure necessary for successful quitting are missing. Pakistan needs to concentrate on two fronts: a large scale awareness campaign against the use and harms of combustible smoking and simultaneously providing affordable and accessible smoking services across the country. Pakistan should look at the use and regulation of safer nicotine products in the UK. The country should carefully weigh the options of ensuring how to incorporate the use of safer nicotine delivery systems in its tobacco control efforts.
Obstructive Sleep Apnea Syndrome Treated Using a Positive Pressure Ventilator Based on Artificial Intelligence Processor
Authors: Chen Zhuxiang , Zhao Zhang , Zhang Zhimin
Published: September 2021

With the acceleration of people’s life rhythm, obstructive sleep apnea syndrome appears more and more frequently. This research mainly discusses the treatment of obstructive sleep apnea syndrome with a positive pressure ventilator based on artificial intelligence processor. The information storage function of the smart positive pressure ventilator is included in the local medical terminal, presented after logging in with the user authority. It is mainly composed of data collection, data processing, and medical interface design, which embeds data request, data transmission, data analysis, and detailed tasks such as data compression and storage, and functions such as data display, image drawing, and alarm notification are realized by the medical interface. When the CPAP ventilator transmits respiratory data to the local medical terminal, it sends real-time respiratory information data packets. The data packet is collected and sent in real time in a fixed period and then received and analyzed by the local medical terminal. In the CPAP ventilator telemedicine system, the function of alarm message processing is mainly used to detect the patient’s breathing status in real time, extract the alarm-related information, and generate an alarm. This function specifically includes several tasks such as alarm detection, alarm prompt, alarm storage, and remote transmission of alarm messages. The confirmed OSAS patients were pressure-titrated with a smart CPAP ventilator and then treated for 5 hours a day, followed by echocardiography after 5 months of continuous treatment. During the study, the average BMI was (28.9 ± 7.2) kg/m2 and the average AHI index was (53.1 ± 37.8) times/h. This study may help improve the quality of life of patients with obstructive sleep apnea syndrome.
Eurotium Cristatum Postfermentation of Fireweed and Apple Tree Leaf Herbal Teas
Authors: Efimenko Tatiana A. , Shanenko Elena F. , Mukhamedzhanova Tatiana G. , Efremenkova Olga V. , Nikolayev Yuriy A. , Bilanenko Elena N. , Gernet Marina V. , Grishin Artem G. , Serykh Ivan N. , Shevelev Sergey V. , Vasilyeva Byazilya F. , Filippova Svetlana N. , El-Registan Galina I.
Published: September 2021

Fungi Eurotium spp. are the main biological agents that ferment the leaves of the Camellia sinensis tea bush to form a popular food product, postfermented tea. The fungus E. cristatum, stored in the collection of the Gause Institute of New Antibiotics under the number INA 01267, was isolated and identified from a briquette of Fujian Chinese tea. The species identification was carried out based on morphocultural characteristics and DNA sequencing. This study is aimed at determining the feasibility of making postfermented herbal teas using E. cristatum and to evaluate their quality. Autofermented herbal teas from Chamaenerion angustifolium (fireweed) and Malus domestica (apple tree) served as the starting material for this study. The change in the concentration of phenolic compounds, organic acids, sugars, and free amino acids was observed for herbal teas subjected to postfermentation with E. cristatum INA 01267. It was found that the E. cristatum INA 01267 strain does not have antimicrobial activity and does not form mycotoxins, which is an indicator of food safety.
PAK1 Silencing Attenuated Proinflammatory Macrophage Activation and Foam Cell Formation by Increasing PPARγ Expression
Authors: Cheng Wen-Lin , Zhang Quan , Li Bo , Cao Jian-Lei , Jiao Lin , Chao Sheng-Ping , Lu Zhibing , Zhao Fang
Published: September 2021

Macrophage polarization in response to environmental cues has emerged as an important event in the development of atherosclerosis. Compelling evidences suggest that P21‐activated kinases 1 (PAK1) is involved in a wide variety of diseases. However, the potential role and mechanism of PAK1 in regulation of macrophage polarization remains to be elucidated. Here, we observed that PAK1 showed a dramatically increased expression in M1 macrophages but decreased expression in M2 macrophages by using a well‐established in vitro model to study heterogeneity of macrophage polarization. Adenovirus‐mediated loss-of-function approach demonstrated that PAK1 silencing induced an M2 macrophage phenotype‐associated gene profiles but repressed the phenotypic markers related to M1 macrophage polarization. Additionally, dramatically decreased foam cell formation was found in PAK1 silencing‐induced M2 macrophage activation which was accompanied with alternation of marker account for cholesterol efflux or influx from macrophage foam cells. Moderate results in lipid metabolism and foam cell formation were found in M1 macrophage activation mediated by AdshPAK1. Importantly, we presented mechanistic evidence that PAK1 knockdown promoted the expression of PPARγ, and the effect of macrophage activation regulated by PAK1 silencing was largely reversed when a PPARγ antagonist was utilized. Collectively, these findings reveal that PAK1 is an independent effector of macrophage polarization at least partially attributed to regulation of PPARγ expression, which suggested PAK1‐PPARγ axis as a novel therapeutic strategy in atherosclerosis management.
Industry 4.0‐Oriented Chipless RFID Backscatter Signal Variable Polarization Amplitude Deep Learning Coding
Authors: Shi Guolong , He Yigang , Gu Lichuan , Jiao Jun
Published: September 2021

Due to the weak network security protection capabilities of control system network protocols under Industry 4.0, the research on industrial control network intrusion detection is still in its infancy. This article discussed and researched the intrusion prevention technology of industrial control networks based on deep learning. According to the electromagnetic scattering theory, the backscatter signal model of the chipless tag was established as a chipless tag structure. Polarized deep learning coding was used for the label; that was, deep learning coding was performed on the copolarization component and the cross‐polarization component at the same time, and a 16‐bit deep learning coding bit number was obtained. The wave crest deep learning coding was used for the split ellipse ring patch label, and the 6‐bit deep learning coding bit number was obtained. Then, the poles of the scattered signal of the tag were extracted to identify the tag. The variable polarization effect was achieved by adopting the dipole resonant unit with the two ends bent. Aiming at the problem of low detection rate caused by the shallow selection of feature classification of intrusion prevention systems, an industrial control network intrusion prevention model based on self‐deep learning encoders and extreme learning machines was proposed to extract features from industrial control network data through deep learning. For accurate classification, the theoretical judgment was also verified through simulation experiments, and it was proved that the detection rate of the model has also improved. It forms a set of industrial control network intrusion prevention system with complete functions and superior performance with data acquisition module, system log module, defense response module, central control module, etc. The matrix beam algorithm was used to extract the poles and residues for the late response, and the extracted poles and residues were used to reconstruct the signal. The reconstructed signal was compared with the scattered signal to verify the correctness of the pole extraction. Finally, the tags were processed and tested in the actual environment, and the measured results were consistent with the theoretical analysis and simulation results.
Adsorption of As(III), Pb(II), and Zn(II) from Wastewater by Sodium Alginate Modified Materials
Authors: Li Hongchuan , Jin Rui , Hu Hongxiang , Kianpoor Kalkhajeh Yusef , Zhao Yingying , Gao Yue , Zhang Borui
Published: September 2021

Sodium alginate (SA), polyvinyl oxide (PEO), and ceramic nanomaterials were used to prepare alginate composite gel. The present study examined the removal rate and adsorption capacity of alginate composite gel for removal of wastewater As(III), Pb(II), and Zn(II). Batch experiments were conducted to study the influence of experimental parameters such as pH and temperature, as well as the mechanism of As(III), Pb(II), and Zn(II) adsorption with the new adsorbent. The results showed the high efficiency of sodium alginate composite gel for removal of wastewater As(III), Pb(II), and Zn(II). Under the condition of the best liquid-solid ratio and the contact time, the removal rates of As(III), Pb(II), and Zn(II) were 67.42%, 95.31%, and 93.96%, respectively. The pseudo-second-order kinetic equation was superior to fit the adsorption kinetics process. The isothermal adsorption models of As(III) and Pb(II) fitted well with the Freundlich model, and Zn(II) fitted well with the Langmuir model. The results of SEM, EDS, XPS, and FTIR analyses revealed that the adsorption process occurred mainly via chemisorption. The results of the present study suggest that new adsorbents can be effectively utilized for As(III), Pb(II), and Zn(II) removal from water.
New Exact Solutions, Dynamical and Chaotic Behaviors for the Fourth-Order Nonlinear Generalized Boussinesq Water Wave Equation
Authors: Chen Cheng , Wang Zuolei
Published: September 2021

Based on the extended homogeneous balance method, the auto-B

a
¨

cklund transformation transformation is constructed and some new explicit and exact solutions are given for the fourth-order nonlinear generalized Boussinesq water wave equation. Then, the fourth-order nonlinear generalized Boussinesq water wave equation is transformed into the planer dynamical system under traveling wave transformation. We also investigate the dynamical behaviors and chaotic behaviors of the considered equation. Finally, the numerical simulations show that the change of the physical parameters will affect the dynamic behaviors of the system.
Prospective Clinical Study of Postoperative Individualized Adjuvant Chemotherapy for Patients with Non-Small-Cell Lung Cancer Based on mRNA Expression of the Molecular Markers RRM1, TUBB3, and ERCC1
Authors: Li Jingyao , Qiu Yang , Yi Junxiu , Liu Xi , Zhang Shixin , Tan Deli , Jing Tao , Liao Yi , Tang Meng , Liu Jie , Wang Haidong
Published: September 2021

Objective. To investigate the clinical significance of the mRNA expression of RRM1, TUBB3, and ERCC1 in non-small-cell lung cancer (NSCLC) tissues for the selection of adjuvant/postoperative chemotherapy regimens. Methods. Patients diagnosed with stage Ib-IIIa NSCLC were enrolled and randomly divided into a control group (undetected group) and an experimental group (detected group) after radical operation. The control group randomly received chemotherapy with gemcitabine plus cisplatin or paclitaxel plus cisplatin. The mRNA expression of RRM1, TUBB3, and ERCC1 was detected in the experimental group before chemotherapy, and based on the detected expression, the chemotherapy regimen of cisplatin plus gemcitabine or cisplatin plus paclitaxel was chosen. The disease-free survival (DFS) of the control group and experimental group was compared. Results. Pathological type, stage, gene expression detection, and treatment method were not significantly correlated with DFS (

P
>
0.05

). In the subgroups treated with gemcitabine, the median DFS was 17 months in the detected group and 10.5 months in the undetected group (hazard ratio = 0.2147, 95% confidence interval: 0.07909–0.5827,

P
=
0.0025

). Multivariate regression analysis was performed to analyse whether gene expression detection was independently correlated with DFS in the subgroups treated with gemcitabine (

P
=
0.025

). In the detected group, the prognosis of patients with low expression of RRM1 was better than that of patients with high expression of RRM1 after paclitaxel treatment (

P
=
0.0039

). Conclusions. The selection of chemotherapy regimen based on mRNA expression of the RRM1, TUBB3, and ERCC1 genes may improve selection of candidate patients to receive clinical chemotherapy. However, large-scale prospective clinical studies are needed for in-depth investigation.
PDZ Binding Kinase/T-LAK Cell-Derived Protein Kinase Plays an Oncogenic Role and Promotes Immune Escape in Human Tumors
Authors: Feng Tingting , Zhang Yan , Ling Sunbin , Xu Chenyang , Lyu Yingqi , Lu Tingting , Liu Xinyuan , Ying Lisha , Wan Yafeng , Zhong Haijun , Su Dan
Published: September 2021

Background. PDZ binding kinase (PBK)/T-LAK cell-derived protein kinase (TOPK) is an important mitotic kinase that promotes tumor progression in some cancers. However, the pan-cancer analysis of PBK/TOPK and its role in tumor immunity are limited. Methods. The oncogenic and immune roles of PBK in various cancers were explored using multiple databases, including Oncomine, Human Protein Atlas, ULCAN, Tumor Immune Estimation Resource 2.0, STRING, and Gene Expression Profiling Interactive Analysis 2, and data collected from The Cancer Genome Atlas and Genotype-Tissue Expression Project. Several bioinformatics tools and methods were used for quantitative analyses and panoramic descriptions, such as the DESeq2 and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Results. PBK was expressed at higher levels in most solid tumors than in normal tissues in multiple databases. PBK was associated with an advanced tumor stage and grade and a poor prognosis in most cases. PBK was associated with tumor immune cell infiltration in most cases and was especially positively correlated with TAMs, Tregs, MDSCs, and T cell exhaustion in KIRC, LGG, and LIHC. PBK was closely related to TMB, MSI, and immune checkpoint genes in various cancers, and patients with higher expression of PBK in KIRC, LGG, and LIHC had higher TIDE scores and lower immune responses in the predicted results. PBK was closely related to cell cycle regulation and immune-related processes in LIHC and LGG according to GO and KEGG enrichment analyses. Conclusions. PBK may play an oncogenic role in most solid tumors and promotes immune escape, especially in KIRC, LGG, and LIHC. This study suggests the potential value of PBK inhibitors combined with immunotherapy.
Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid
Authors: He Bingjie , Xiao Jinxiu , Dai Qiaorong
Published: September 2021

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.
Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention
Authors: Prabhakar Sunil Kumar , Won Dong-Ok
Published: September 2021

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short‐term memory (QC‐LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC‐LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.
Facile One-Step Flame Synthesis of La1−xSrxMnO3 Nanoparticles for CO Catalytic Oxidation
Authors: Zhou Hao , Wu Qiwei , Qi Boyang
Published: September 2021

A large amount of CO as hazardous emission in the iron ore sintering process has caused severe harm to the environment and human health. To control the emission of CO more effectively, the preparation of highly efficient catalysts has attracted much attention. In this study, the La1-xSrxMnO3 (0 ≤ x < 1) perovskite catalysts with different Sr2+ contents were prepared by the one-step flame synthesis method to treat CO pollutants in the iron and steel industry. The influence of Sr2+ doping on the structure and activity of catalytic were characterized and analyzed. La1-xSrxMnO3 perovskite catalysts exhibit good perovskite phases and loose spherical structures. The specific surface areas are between 4.1 and 12.0 m2 g−1. Combined with the results of H2-TPR and O2-TPD, the improvement of catalytic activity of La1-xSrxMnO3 perovskite can be attributed to the high concentration of active centers and oxygen vacancies. Significantly, the La0.4Sr0.6MnO3 catalyst presented the best reducibility and high content of absorbed active oxygen species, leading to a superior CO oxidation catalytic activity and reaches 50% CO conversion at 134.9°C and 90% at 163.2°C, respectively. The effects of water vapor and CO2 on the oxidation activity of La1-xSrxMnO3 perovskite was investigated. The flame-produced catalysts exhibit favorable catalytic stability and antisintering ability, achieving 100% CO conversion after fifth consecutive oxidation cycles.