bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolic Differences between Ex-Smokers and Nonsmokers: A Metabolomic Analysis
Authors: Liang Lirong , Feng Lin , Zhou Long , Chu Shuilian , Zhang Di , Jin Hang , Li Jiachen , Zhao Liancheng , Tong Zhaohui
Published: April 2022

The aim of this study was to compare changes in the metabolite levels of ex-smokers and nonsmokers using a metabolomics approach, accounting for the weight gain in ex-smokers. Volunteer ex-smokers and nonsmokers were recruited from two cohorts Shijingshan (174) and Xishan (78), respectively, at a 1 : 1 ratio for age and sex. Nontargeted metabolomics was performed on the volunteers’ blood samples using liquid chromatography-mass spectrometry, and multivariate statistical analysis was performed using principal component analysis and orthogonal partial least squares discriminant analysis. Enrichment analysis was used to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with differential metabolites and weighted gene co-expression network analysis and maximal correlation coefficient (MCC) algorithms were used to identify key metabolites. The results revealed no significant differences between the distribution of blood metabolite levels in the ex-smokers and nonsmokers. The biosynthesis of valine, leucine, and isoleucine was determined to be associated with differential metabolites, and five key metabolites were identified. Further analysis revealed differences in weight gain and regained metabolite levels in ex-smokers, and 10 differential metabolites were identified that may be associated with weight gain in ex-smokers. These findings suggest that quitting smoking restores metabolites to almost normal levels and results in weight gain. The identified key metabolites and metabolic pathways may also provide a basis for clinical studies.
Construction of a miRNA Signature Using Support Vector Machine to Identify Microsatellite Instability Status and Prognosis in Gastric Cancer
Authors: Wang Ya-nan , Wei Ya-ning , Fan Xiang-yu , Xu Fang , Dong Zheng-yang , Cheng Shu-jie , Zhang Jin-ku
Published: April 2022

Background. The specific role and prognostic value of DNA repair and replication-associated miRNAs in gastric cancer (GC) have not been clearly elucidated. Therefore, comprehensive analysis of miRNAs in GC is crucial for proposing therapeutic strategies and survival prediction. Methods. Firstly, clinical information and transcriptome data of TCGA-GC were downloaded from the database. In the entire cohort, we performed differential analysis in all miRNAs and support vector machine (SVM) was used to eliminate redundant miRNAs. Subsequently, we combined survival data and cox regression analysis to construct a miRNA signature in the training cohort. In addition, we used PCA, Kaplan-Meier, and ROC analysis to explore the prognosis value of risk score in the training and testing cohort. It is worth noting that multiple algorithms were used to evaluate difference of immune microenvironment (TME), microsatellite instability (MSI), tumor mutational burden (TMB), and immunotherapy in different risk groups. Finally, we investigated the potential mechanism about miRNA signature. Results. We constructed miRNA signature based on the following 4 miRNAs: hsa-miR-139-5p, hsa-miR-139-3p, hsa-miR-146b-5p, and hsa-miR-181a-3p. Univariate and multivariate Cox regression analyses suggested that risk score is a risk factor and an independent prognostic factor in GC patients. The AUC value of ROC analysis showed a robust prediction accuracy in each cohort. Moreover, significant differences in immune functions, immune cell content, immune checkpoint, MSI status, and TMB score were excavated in different groups distinguished by risk score. Finally, based on the above four miRNA target genes, we revealed that the signature was enriched in DNA repair and replication. Conclusion. We have developed a robust risk-formula based on 4 miRNAs that provides accurate risk stratification and prognostic prediction for GC patients. In addition, different risk subgroups may potentially guide the choice of targeted therapy.
Classification of Performance Grades of High Modulus Asphalt and Its Mixture: Taking China as an Example
Authors: Zhou Lu , Duan Kaixi , Gao Feng , Fu Yi , Chen Qian
Published: April 2022

With the increase in traffic volume and axle load, the requirement of road quality is continuously improving, and high modulus asphalt has attracted the attention of researchers due to its excellent road service performance and economic benefits. In order to further improve the durability and use quality of Chinese roads, realize the scientific evaluation of the performance of high modulus asphalt and its mixture, and further promote the development of high modulus asphalt technology in China, the performance indexes of high modulus asphalt and its mixture in China were comprehensively investigated. The differences in high- and low-temperature performance of matrix asphalt, basic modified high modulus asphalt, and composite modified high modulus asphalt were compared and evaluated. Based on the results of mathematical statistics and the relevant specifications of high modulus asphalt in China, the performance grades of high modulus asphalt and its mixture were proposed. The results showed that compared with the basic asphalt, the softening points of the basic modified high modulus asphalt and the composite modified high modulus asphalt were increased by 18.76% and 82.89%, respectively, and the rutting factors were increased by 2 and 9 times, respectively, but the improvement effect of the low-temperature performance of the high modulus asphalt was not obvious. Based on the data statistics node and relevant Chinese regulations, high modulus asphalt and its mixtures were divided into four grades: excellent, good, fair, and poor, aiming to provide scientific suggestions for the wide application of high modulus asphalt.
Factors Associated with Late Local Radiation Toxicity after Post-Operative Breast Irradiation
Authors: Batenburg M.C.T. , Bartels M. , Maarse W. , Witkamp A. , Verkooijen H.M. , van den Bongard H.J.G.D.
Published: April 2022

Purpose. To assess determinants associated with late local radiation toxicity in patients treated for breast cancer. Methods. A systematic review was performed. All studies reporting ≥2 variables associated with late local radiation toxicity after treatment with postoperative whole breast irradiation were included. Cohort studies, randomized controlled trials, and cross-sectional studies were eligible designs. Study characteristics and definitions of determinants and outcome measures were extracted. If possible, the measure of association was extracted. Results. Twenty-one studies were included in this review. Six out of seven studies focused on the association between radiotherapy (boost) dose or irradiated breast volume and late radiation toxicity found significant results. Tumor bed boost was associated with late radiation toxicity, fibrosis, and/or edema in six out of twelve studies. Lower age was associated with late breast toxicity in one study, while in another study, higher age was significantly associated with breast fibrosis. Also, no association between age and late radiation toxicity was found in eight out of twelve studies. Similar inconsistent results were found in the association between late radiation toxicity and other patient-related factors (i.e., breast size, diabetes mellitus) and surgical and systemic treatment-related factors (i.e., complications after surgery, chemotherapy, and time between surgery and radiotherapy). Conclusion. In modern 3D radiotherapy, radiotherapy (boost) dose and volume are—like in 2D radiotherapy—associated with late local radiation toxicity, such as breast fibrosis and edema. Treatment de-escalation, for example, partial breast irradiation in selected patients might be important to decrease late local toxicity without compromising locoregional control and survival.
Analysis of Horizontal Dynamic Response of a Single Pile in Layered Foundations Based on the Timoshenko–Pasternak Model
Authors: Jiang Jie , Chai Wencheng
Published: April 2022

To compensate for the deficiencies of the conventional Euler–Bernoulli beam‐dynamic Winkler foundation model in analyzing the horizontal dynamic response of a single pile, in this paper, the Timoshenko–Pasternak model, which considers both the soil shear effect and pile shear deformation, is established to reveal the pile‐soil dynamic interaction accurately. Based on Timoshenko beam theory and Pasternak foundation theory, the pile horizontal vibration control equation considering pile shear deformation and the soil shear effect is derived based on D’Alembert’s principle. On this basis, the transfer matrix method is applied to consider the layering characteristics of the soil, and the dynamic complex impedance frequency domain analytical solution of the pile top is obtained by using the initial parameter method combined with the pile bottom boundary conditions. A comparison with the existing model solution is carried out to verify the correctness and reasonableness of the model in this paper; finally, the parameters of the pile characteristics and soil properties around the pile are analyzed. The results show that (1) the pile bottom boundary condition and pile length have almost no effect on the pile top impedance after the single pile reaches the critical value of the length‐to‐diameter ratio. (2) The trend of pile top impedance becomes increasingly evident with increasing dimensionless frequency. (3) The pile top impedance increases with the decrease in the pile‐soil elastic modulus ratio, and the changing trend becomes increasingly apparent; meanwhile, the influence of the elastic modulus of the surface soil on the pile top impedance is much more significant than the influence of the underlying soil on it.
Classroom Learning Status Assessment Based on Deep Learning
Authors: Zhou Jie , Ran Feng , Li Guang , Peng Jun , Li Kun , Wang Zheng
Published: April 2022

Student classroom behavior performance is an important part of classroom teaching evaluation, and conducting student classroom behavior recognition is important for classroom teaching evaluation. The article proposes a deep learning-based student classroom behavior recognition method, which extracts the key information of the human skeleton from student behavior images and combines a 10-layer deep convolutional neural network (CNN-10) to recognize students’ classroom behavior. To verify the effectiveness of this method, the paper conducts a comparison experiment on the student classroom behavior dataset using CNN-10 and the student classroom behavior recognition method. The experimental results show that the student classroom behavior recognition method can effectively exclude the interference of irrelevant information such as students’ physique, dress, and classroom background, highlight the key effective information, and have higher recognition accuracy and generalization ability. Using the human skeleton and a deep learning-based student classroom behavior detection approach to identify students’ typical classroom behaviors might improve intelligent classroom teaching by reflecting students’ learning status in a timely and effective manner.
Fungal Community Investigation from Propolis Natural Products: Diversity and Antibacterial Activities Evaluation
Authors: Sallemi Souhir , Lekired Abdelmalek , Korbi Nedra , Saadouli Ilhem , Cherif Ameur , Zidi Ines , Klibi Naouel , Ouzari Hadda-Imene , Mosbah Amor
Published: April 2022

Discovering new species and interesting bioactive metabolites from customary sources is becoming progressively laborious. Propolis constitutes the largest diversified reserve of microbial constituents in the beehive. However, fungal communities associated with these environments remain insufficiently established. We present the first detailed investigation of the cultivable fungal community associated with Tunisian propolis, and we evaluate its antibacterial properties against pathogenic bacteria. A total of 80 fungal strains were isolated from propolis samples derived from seven different Tunisian locations. The majority of the isolated fungi were classified as Ascomycota (97.5%), and only 2.5% belonged to Basidiomycota. Our collection was clustered into 15 genera, among which Coniochaeta (36.25%), Aspergillus (15%), Penicillium (13.75%), Cladosporium (10%), Fusarium (7.5%), Didymella (5%), and Alternaria (3.75%) were the most common. Evaluation of the antibacterial activity revealed that 25.6% of the total community showed a broad range of antibacterial activity. Particularly, the Penicillium griseofulvum CC8 strain has manifested the strongest inhibitory effects against all the tested bacteria.
Current Implementation of Digital Dentistry for Removable Prosthodontics in US Dental Schools
Authors: Ishida Yoshiki , Kuwajima Yukinori , Kobayashi Takuya , Yonezawa Yu , Asack Derek , Nagai Manavi , Kondo Hisatomo , Ishikawa-Nagai Shigemi , Da Silva John , Lee Sang J.
Published: April 2022

Objectives. Although digital technology has been widely integrated into dental education, there is limited literature investigating the extent of the integration of computer-aided design and computer-aided manufacturing (CAD-CAM) for removable systems in the dental curriculum. The purpose of this study was to assess the current implementation of CAD-CAM complete and partial dentures in predoctoral (PP) and advanced graduate prosthodontic (AGP) education in US dental schools. The study also aimed to identify potential barriers to its implementation in the dental curriculum. Methods. An online survey with 15 questions was created using online survey software. The survey was distributed to the directors of predoctoral prosthodontics in 56 schools and advanced graduate programs of prosthodontics in 52 schools listed in the 2018–19 American Dental Education Association (ADEA) Directory. Results. The percentage of programs (PP and AGP) implementing CAD-CAM complete dentures (CAD-CAM CDs) and CAD-CAM removable partial dentures (CAD-CAM RPDs) in their didactic, preclinical, and clinical curricula was recorded. CAD-CAM CDs are taught in didactic courses in 54.2% of PP and 65.2% of AGP. However, CAD-CAM RPDs are only taught in 37.5% of PP and 47.8% of AGP. Programs are largely limited by a lack of funds, resources, time, and faculty members. Conclusion. While digital technologies have indeed become more prevalent in dental education, many institutions face barriers to implementation. More research must be conducted in order to support the continued incorporation of digital technologies into dental education.
Research on the English Classroom Teaching Effect Evaluation with Interval-Valued Intuitionistic Fuzzy Grey Relational Analysis Method
Authors: Zhang Zhen , Su Pengfei
Published: April 2022

The English classroom teaching effect evaluation is looked as the multiattribute group decision-making (MAGDM). Thus, a useful MAGDM algorithm is needed to cope with it. Depending on the classical GRA process and interval-valued IFSs (IVIFSs), this study builds the IVIF-GRA process to assess the English classroom teaching effect. First of all, the concepts of IVIFSs are reviewed. In addition, the weights of criteria are derived through the CRITIC method. Afterwards, the GRA model is extended to IVIFSs to get the final result of the alternative. Therefore, all alternatives could be ranked and the optimal one with English classroom teaching effect can be identified. At last, a given numerical example and some given comparative studies are obtained. The analysis results show that the defined algorithms are effective for solving the English classroom teaching effect evaluation.
Stabilization Design of Three-Phase LCL-Filtered Grid-Connected Inverter Using IDA-PBC Controller
Authors: Huang Min , Zhang Zhicheng , Chen Fan , Wu Weimin , Yao Zhilei , Blaabjerg Frede
Published: April 2022

With the aim of improving the stability of renewable energy system with high permeability in the weak grid, a modified passivity-based control based on interconnection and damping assignment (IDA) is presented for LCL-filtered grid-connected inverters to eliminate the interactive resonances. The object is modeled in the form of port-controller Hamiltonian, including the partial differential of stored energy function. On the premise of ensuring Lyapunov stability, a more flexible interconnection matrix design method is applied to simplify the design process. The closed-loop stability is ensured with the selected Hamiltonian energy function at the desired balanced point. Moreover, a step-by-step design process for damping gains is provided to guarantee stable operation and fast dynamic response under variation of complex grid impedance. With the designed injected damping parameters, considering the effect of delay, it is possible for the real part of the inverter output admittance to be positive within the switching frequency. The performance and robustness of the proposed method for LCL-filtered inverter system are validated via simulated and experimental results under both unbalanced and balanced grid conditions.
Screening Potential Drugs for the Development of NAFLD Based on Drug Perturbation Gene Set
Authors: Gao Zhengzheng , Dai Lina , Zhang Haifeng
Published: April 2022

Nonalcoholic fatty liver disease (NAFLD) has become one of the problems affecting the health of the population worldwide. The progressive disease includes nonalcoholic steatohepatitis (NASH) and fibrosis, which with no approved therapy, system identification of effective drugs remains challenging. In this work, we applicated drug perturbation gene set enrichment analysis to screen drugs for the development of NAFLD. A total 15490 small-molecule compounds were analyzed in our study; based on the

p

value of enrichment score, 7 small-molecule compounds were found to have a potential role in NASH and fibrosis. After pathway analyses, we found indoximod had effects on nonalcoholic fatty liver disease through regulated TNFa, AP-1, AKT, PI3K, etc. Furthermore, we established the NAFLD cell model with LO2 cells induced using PA; ELISA showed that the levels of TG, ALT, and AST were significantly improved by indoximod. In summary, our study offers optimal therapeutic drugs, which may provide novel insight into the precise treatment of NAFLD and promote researches.
Measurement Method of Plant Phenotypic Parameters Based on Image Deep Learning
Authors: Dong Mo , Yu Haiye , Zhang Lei , Wu Mingzhi , Sun Zhipeng , Zeng Dequan , Zhao Ruohan
Published: April 2022

This article applies deep learning and electromechanical technology to plant phenotype measurement. First, an electromechanical device is designed to collect plant phenotype images, which solves the difficulty of collecting deep learning training data. The data set required for deep learning model training for plant phenotype detection is made by an automated method. This paper takes the Lactuca sativa plant image as an example and uses the ASM-based data enhancement method to solve the problem of insufficient image data of Lactuca sativa leaf pests and effectively avoid the phenomenon of overfitting. The plant image recognition method based on deep learning proposed breaks through the limitations of plant local feature recognition, gets rid of the limitation of highly specialized data collection, lowers the threshold of plant image recognition, and has advantages in recognition speed and accuracy. This method requires a large amount of training data. In the future, we can explore the collection of massive plant pictures from the Internet as a training set to achieve rapid iteration and optimization of the model.
Roadway Protection by Roof-Cutting in the Support Removing Channel of the Long-Wall Mining Face
Authors: Ren Xingyun , Hao Bingyuan , Wang Hongwei , Zhao Junjie , Zhang Ji , Zhang Chen
Published: April 2022

Aiming at strong mining pressure and large deformation of surrounding rock in the main roadway, a mechanical model of the advanced abutment pressure distribution in the long-wall mining face was established. The main reason for the large deformation of the surrounding rock in the main roadway was found to be unreasonable position of the stop line, which brings the main roadway under the influence of mining operations. Therefore, on the premise of an unaltered stop line, the technique of cutting the roof and use of pressure relief to protect the main roadway were proposed. The technique will form a weak surface along the stop line, blocking the rock layer from transmitting the abutment pressure. Engineering practice shows that by using the advanced roof cutting and pressure-relief technique, the abutment pressure on the long-wall mining face is reduced and the main roadway is protected.
Assessment of a Biosafety Device to Control Contamination by Airborne Transmission during Orthodontic/Dental Procedures
Authors: Santa Rita de Assis Josilene , Garcez Aguinaldo Silva , Suzuki Hideo , Montalli Victor Angelo Martins , Fujii Denise Nami , Prouvot Michelle Bertini , Suzuki Selly Sayuri
Published: April 2022

During the COVID-19 pandemic, dental professionals have faced high risk of airborne contamination between dentists, staff, and patients. The objective of this study was to evaluate the effect of an individual biosafety capsule in dentistry (IBCD) on reducing the dispersion of droplets and aerosols during orthodontic treatment and evaluate the clinician and patient’s perception of using the IBCD. For the in-vitro part of the study, aerosol quantification was performed with and without the IBCD, using a nonpathogenic bacterial strain and viral strain in the reservoir and high-speed dental handpiece. Petri dishes with MRS agar were positioned from the head of the equipment at distances of 0.5, 1, and 1.5 m. After 15 minutes of passive aerosol sampling, the dishes were closed and incubated using standard aerobic conditions at 37°C for 48 hours to count colony forming units (CFUs). For the clinical part of the study, a questionnaire was sent to clinicians and patients to understand their perception of orthodontically treat and receive treatment using the barrier. The use of IBCD showed an effective means to reduce the dispersion of bacterial and viral contamination around 99% and 96%, respectively, around the main source of aerosol (

p
<
0.05

). Clinical results showed a 97% bacterial reduction during patient’s consultations (

p
<
0.05

). The vast majority of clinicians and patients understand the importance of controlling the airborne dispersion to avoid contamination.
New Robust Reward‐Risk Ratio Models with CVaR and Standard Deviation
Authors: Xu Lijun , Zhou Yijia
Published: April 2022

In this paper, we present two robust reward‐risk ratio optimization models. Two new models contain the worst case of not only conditional value‐at‐risk (CVaR), but also standard deviation (SD). Using properties of reward measure, CVaR measure, and standard deviation measure, new models can be proved to equivalent to min‐max problems. When the uncertainty set is an ellipsoid, new models can be further rewritten as second‐order cone problems step by step. Finally, we implement new models to portfolio problems. It shows that new models are robust and comparable with mean‐CVaR ratio model. Since considering standard deviation, allocation decision obtained by new models can give reasonable rewards according to personal preferences.
Blow‐Up Phenomena in Reaction‐Diffusion Problems with Nonlocal and Gradient Terms
Authors: Shen Xuhui , Wu Dandan
Published: April 2022

This paper considers the blow‐up phenomena for the following reaction‐diffusion problem with nonlocal and gradient terms: Here m > 1, and Ω ⊂ ℝN(N ≥ 2) is a bounded and convex domain with smooth boundary. Applying a Sobolev inequality and a differential inequality technique, lower bounds for blow‐up time when blow‐up occurs are given. Moreover, two examples are given as applications to illustrate the abstract results obtained in this paper.
A Combined Prediction Model Composed of the GM (1,1) Model and the BP Neural Network for Major Road Traffic Accidents in China
Authors: Guo Qingwen , Guo Baohua , Wang Yugang , Tian Shixuan , Chen Yan
Published: April 2022

This paper compared the expected accuracy of the gray GM (1, 1) model and the combined GMBP model using a data set for major road traffic accidents. A combined GMBP prediction model composed of the very first parameter gray model GM (1, 1) is able to make exact predictions for forecasting dreary type of processes, and BP (back-propagation) neural network for a major traffic accident is proposed to overcome the limitations of a single prediction model for a major traffic accident. The method first obtains predicted data using the gray GM (1, 1) model, then trains the BP neural network using the GM (1, 1) model’s predicted data and the original data as input and output data, respectively, and finally the trained BP neural network can be considered a combined GMBP prediction model. The predicted data of the digit of major traffic accidents, the digit of fatalities, and the digit of damages from 2008 to 2020 were obtained using the combined GMBP prediction model, and it is compared with the expected data of the single gray GM (1, 1) model. The grades showed that the exactitude of the combined GMBP prediction model was significantly higher than that of the single gray GM (1, 1) model. Finally, from 2021 to 2033, the combined GMBP prediction model is used to forecast the number of significant road traffic incidents, mortalities, and damages. The prediction results show that the number of accidents, faculties, and injuries is on the decline in the future years.
Relationship between Physical Training and Tactical Training in Sports Training Relying on Boosting and Bagging Algorithms
Authors: Liu Kaining , Li Xinwen , Rochester Christine A.
Published: April 2022

With the development of sports, competition is becoming more and more confrontational and modern sports are developing in the direction of fast speed, fast rhythm, good skills, and high-altitude combat. And, this development requires good conditions and technology as a guarantee. Sports denote a competitive physical activity, which follows four aspects of competition: technology is the foundation, tactics are the means, the body is the cornerstone, and the psychology is the decisive point. Among these four factors, if the athlete does not have a good body, all techniques and know-how are empty talks. In this paper, two algorithms are introduced to track the athletes’ physical training and tactical training videos and the relevant data in the game are counted to obtain the physical indicators required by the athletes in each position. In a certain game, the sprint distance in the first round was 1979 m, the high-speed running distance was 2426 m, the high-intensity running distance was 4398 m, and the jogging distance was 1267 m.
Pyrogallol-Phloroglucinol-6, 6-Bieckol Restored Primary Cilia Length, Which Was Decreased by High-Fat Diet in Visceral Adipose Tissue, and Decreased Adipogenesis
Authors: Oh Seyeon , Son Myeongjoo , Jang Ji Tae , Park Chul Hyun , Son Kuk Hui , Byun Kyunghee
Published: April 2022

Length of primary cilia, which involves cell cycle reentry and disassembly of cilia, promotes cell mitosis. It is known that the cilia length in adipose tissue of the high-fat diet (HFD) animals was shortened and accompanied by increased adipogenesis. Male C57BL/6N mice were randomly divided into groups. The mice group was given the normal fat diet (NFD/saline), HFD mice group for 4 weeks, and then HFD was also treated for the next 4 weeks with saline (HFD/saline), Ecklonia cava extract (HFD/ECE), or pyrogallol-phloroglucinol-6, 6-bieckol, a segment of ECE (HFD/PPB). We evaluated the effect of ECE and PPB on modulating cilia length of visceral adipose tissue and decreasing adipogenesis by decreasing cell cycle reentry using an HFD-fed mouse model. ECE and PPB decreased physiological changes, which increased by HFD, but ECE and PPB decreased the upregulation of the IL-6/STAT3/AURKA signaling pathway, which is involved in cilia disassembly. In addition, ECE or PPB elongated the cilia and decreased cyclin A2 and Cdk2 expression, which promote cell cycle reentry, and decreased the adipogenesis genes. PPB and ECE restored cilia length and decreased adipogenesis through modulating the IL-6/STAT3/AURKA pathway and decreasing cell cycle reentry in the visceral adipose tissue of HFD/saline mice group.
Association of Sirtuin 1 Gene Polymorphisms with the Risk of Coronary Heart Disease in Chinese Han Patients with Type 2 Diabetes Mellitus
Authors: Wang Yuxin , Tong Linchao , Gu Nan , Ma Xiaowei , Lu Difei , Yu Dahong , Yu Na , Zhang Junqing , Li Jianping , Guo Xiaohui
Published: April 2022

Aims. To explore the associations between polymorphisms in SIRT1 and coronary heart disease (CHD) risk in Chinese Han patients with type 2 diabetes (T2D). Methods. This case-controlled study enrolled 492 patients with T2D: 297 with CHD and 195 without CHD. Five SIRT1 haplotype-tagging single-nucleotide polymorphisms (rs3818291, rs12242965, rs3818292, rs4746720, and rs16924934) were selected from Chinese Han data in the GRCh37.p13 phase 3 database and genotyped by polymerase chain reaction-restriction fraction length polymorphism or sequencing. Results. The rs16924934 G allele was associated with a higher risk of CHD than the A allele (odds ratio

OR

=
1.429

; 95% confidence interval

CI

=
1.003
–
2.037

;

P
=
0.048

). Using an additive inheritance model, the rs3818291 G/A genotype was associated with a higher CHD risk than the G/G genotype (

OR

’

=
1.683

;

95
%
CI
=
1.033
–
2.743

;

P

’

=
0.037

after adjustment for CHD risk factors). Smokers carrying G/A or A/A rs3818291 genotypes had a 3-fold higher CHD risk than those carrying GG (adjusted

OR

’

=
3.035

;

P

’

=
0.011

) and a 2.6-fold higher CHD risk than nonsmokers carrying GG (adjusted

OR

’

=
2.604

;

P

’

=
0.033

). Conclusions. Genetic polymorphisms of SIRT1 are associated with the risk of CHD in a Chinese Han population with T2D.
Financial Data Anomaly Detection Method Based on Decision Tree and Random Forest Algorithm
Authors: Zhang Qingyang
Published: April 2022

The fast‐developing computer network not only brings convenience to people but also brings security problems to people due to the appearance of various abnormal flows. However, various current detection systems for abnormal network flows have more or less flaws, such as the most common intrusion detection system (IDS). Due to the lack of self‐learning capabilities of market‐oriented IDS, developers and maintenance personnel have to update the virus database of the system in real time to make the system work normally. With the emergence of machine learning and data mining in recent years, new ideas and methods have emerged in the detection of abnormal network flows. In this paper, the random forest algorithm is introduced into the detection of abnormal samples, and the concept of abnormal point scale is proposed to measure the abnormal degree of the sample based on the similarity of the samples, and the abnormal samples are screened out according to this scale. Simulation experiments show that compared with the other two distance‐based abnormal sample detection techniques, the random forest‐based abnormal sample detection has greater advantages than the other two methods in terms of improving the accuracy of the model and reducing the computing time.
Empirical Matching-Based Computation Offloading Optimization for 5G and Edge Computing-Integrated EIoT
Authors: Zhang Hui , Ding Huixia , Wang Yang , Meng Sachula , Zhu Sicheng , Teng Ling , Dong Fangyun
Published: April 2022

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.
Predictive Value of Soluble Growth Stimulator Gene 2 Protein for Coronary Slow Flow/No-Reflow in ST-Elevation Myocardial Infarction Patients Receiving Percutaneous Coronary Intervention
Authors: Chang Shu-min , Yu Yan-tan , Luan Bo , Hou Ai-jie , Wang Yong
Published: April 2022

Background. Soluble growth stimulator gene 2 protein (sST2) is associated with heart failure and myocardial infarction; however, the predictive value of plasma sST2 level for coronary slow flow/no-reflow (CSF/NRF) is unclear. This study aimed to explore the predictive value of plasma sST2 levels for CSF/NRF in patients with ST-elevation myocardial infarction (STEMI) who underwent emergency percutaneous coronary intervention (PCI). Methods. A total of 242 STEMI patients who underwent emergency PCI at our hospital between November 2020 and July 2021 were enrolled in this study. According to the postprocedural procedure, these patients were divided into the CSF/NRF and control groups. Clinical data were collected from both groups and were used to explore the predictive value of serum sST2 levels for CSF/NRF. Results. Of the total 242 patients, CSF/NRF was observed in 50 patients (20.7%). Statistically significant differences (

P
<
0.05

) were observed in age, diabetes mellitus, sST2 level, neutrophil-to-lymphocyte ratio (NLR), fasting blood sugar, preprocedural blood pressure, intraprocedural hypotension, N-terminal pro-B-type natriuretic peptide, MB isoenzyme of creatine kinase (CK-MB), and cardiac troponin I (cTNI). Multivariate analysis showed that the sST2 level, NLR, and intraoperative hypotension were independent risk factors for CSF/NRF. ROC curve analysis showed that the sensitivity and specificity of the sST2 level for predicting CSF/NRF were 68.0% and 75.5%, respectively, when the sST2 level was more than 64.6 ng/mL (AUC = 0.780, 95% CI: 1.003–1.020,

P
=
0.009

). Conclusion. For STEMI patients, preprocedural sST2 levels significantly correlated with CSF/NRF occurring in PCI. sST2 level is a potential predictor for CSF/NRF occurrence.
Application of Multisource Data Fusion Analysis in College Vocal Music Teaching
Authors: Li Beibei , Zhou Zhi
Published: April 2022

This paper constructs a two-level data fusion model with the classroom environment monitoring background of colleges and universities. By judging the validity of the data received by each sensor, the model eliminates the influence of neglected monitoring values on fusion accuracy. It uses the adaptive weighted average method to fuse the data of the same type of sensors in each area and then uses the BP neural network to fuse the heterogeneous sensor data in the area. After each region sends the fusion result to the gateway node, the second-level fusion is performed. In the second-level fusion, the error between the actual output result and the expected output of the BP neural network is calculated, and it is used as the basic probability assignment in DS; then the D-S synthesis rule is used for decision-level fusion so as to realize the integration of the college classroom environment. Aiming at the shortcomings of the D-S evidence theory, we improved the algorithm with respect to the distance of evidence and the conflict factor. Through the exploration of the multisource data fusion analysis method, it is found that it plays an important role in early vocal music teaching. The quality of early vocal music-teaching teachers has a particularly important impact on the music level of college students. Schools with preschool education majors are important bases for teacher training in early vocal music teaching. In order to expand the application scope of the concept of multisource data fusion in the field of vocal music teaching in China, it is necessary to change the traditional vocal music teaching mode of existing college teachers and pay attention to the integration of multisource data fusion analysis methods and the reforms of colleges and universities. In this paper, the effectiveness of the two-level fusion model is verified by using two evaluation indicators: the mean absolute percentage error and the correlation coefficient. Then, comparing the calculation results of the improved algorithm and the classical algorithm in this paper, it is proved that the probability accumulation of this algorithm is more obvious and consistent with the expected results, which shows the optimization effect of the improved method.
Ginseng-plus-Bai-Hu-Tang Combined with Western Medicine for the Treatment of Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Authors: Zhou Min , Yu Rong , Liu Xiu , Lv Xialin , Xiang Qin
Published: April 2022

Objective. Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by chronic hyperglycemia, which is also accompanied by changes in blood lipids and protein. According to research reports, Ginseng-plus-Bai-Hu-Tang (GBHT) has significant antihyperglycemic activity. Nevertheless, the evidence of effectiveness is not enough. In order to verify the effectiveness and safety of GBHT combined with conventional Western medicine (CWM) in the treatment of T2DM, we carried out this meta-analysis. Method. We collected 7 electronic databases from the inception to September 1, 2021; then, 12 studies were selected. The data analysis and methodological evaluation were conducted by the software RevMan 5.3.3 and Stata 12.0. Results. The meta-analysis revealed that when GBHT was adopted in combination with CWM, the effective rate (OR = 2.98, 95% CI = [2.01, 4.43],

P

 < 0.00001), the FBG (MD = −0.86, 95% CI = [−1.06, −0.65],

P

 < 0.00001), 2hBG (MD = −0.80, 95% CI = [−1.05, −0.55],

P

 < 0.00001), and HbA1c (MD = −0.64, 95% CI = [−0.98, −0.30],

P

 = 0.0002) of T2DM patients improved significantly compared with the control group. After GBHT combined with CWM treatment, HOME-RI (MD = −0.75, 95% CI = [−1.38, −0.12]

P

 = 0.02) of T2DM patients was superior to CWM alone. In comparison, the benefit from FINS (MD = −1.42, 95% CI = [−4.46, −1.62],

P

 = 0.36) was not apparent. In addition, none of the adverse events mentioned occurred, indicating that it is safe enough. Conclusion. GBHT combined with CWM is an effective and safe as adjunctive treatment for patients with T2DM. Nevertheless, due to the limitation of the quality of the included studies, additional high-quality researches are required to further confirm these results.
Urban Complex Public Space Design Method Based on Support Vector Machine
Authors: Liu Long
Published: April 2022

Urban commercial complex is the product of social and economic development, as well as the inevitable trend of commercial development. The public space design of urban complex project is an important link in its development process, which is the link between the contact and the building, the city and the building, and the external image display of the complex. Therefore, choosing a suitable and excellent public space design scheme is of great significance to show the superiority of the complex project and improve the project satisfaction and the sustainable operation in the later stage. This paper first defines the concept of urban complex public space and focuses on the analysis of the urban complex public space from four aspects of constituent elements, functional classification, design theory, and principle. Then, the Delphi method is used to determine 26 evaluation indicators to evaluate the urban complex public space design scheme, and the relevant index data of 20 urban complex public space design schemes are collected. The grey correlation analysis method is used to analyze the correlation degree between each indicator and the scheme winning bid. The final evaluation system of urban complex public space design scheme is obtained after removing the 8 indicators with low correlation degree. Support vector mechanism was used to build the optimization model of urban complex public space design scheme, and its accuracy was verified, which provides a relatively simple and quantitative method for urban public space design in the future, helps to improve the city’s business structure, improves the business environment of the city, makes the commercial complex more effectively play its own characteristics and advantages, effectively stimulates the vitality of urban space, and creates more perfect architectural space and urban space for people.
Analysis of Driver Gaze and Attention to Traffic Signs
Authors: Shabani Shabnam , Beauchemin Steven , Bauer Michael
Published: April 2022

A driver’s actions and intent can be factors in enabling advance driver assistance systems (ADASs) to assist drivers and avoid accidents. A driver’s gaze can provide insight into the driver’s intent or awareness of situations. Knowing that a driver gazed at a traffic sign or missed a traffic could provide indications of whether the driver is alert to impending changes in the driving environment, such as curves and stop signs. For ADASs to determine the importance of a driver seeing or missing a sign, it is important to understand the driving environment and situation. A first step is to understand what signs drivers do see or miss while driving. This contribution presents the results of analyzing driving sequences to assess traffic signs that drivers may or may not have gazed upon. The results suggest that drivers may miss 20% of traffic signs though the percentage varies depending on the type of sign. The analysis uses image sequences of the driving environment and gazes data captured during driving. The methods used in our analysis included determining whether a driver’s gaze has fallen on the image of a traffic sign or not and subsequently determining signs missed during driving. The methods presented can be useful in other scenarios involving the analysis of driver gaze and have implications for the design of future ADASs and for understanding of driver gaze and awareness.
Outcomes of Symptomatic Anterior Large Vessel Occlusion by Initial Imaging Assessment Using Diffusion‐Weighted Imaging Versus Noncontrast Computed Tomography
Authors: Kamogawa Naruhiko , Tanaka Kanta , Yamagami Hiroshi , Yoshimoto Takeshi , Uchida Kazutaka , Morimoto Takeshi , Imamura Hirotoshi , Sakai Nobuyuki , Ohara Nobuyuki , Matsumoto Yasushi , Takeuchi Masataka , Shigeta Keigo , Toyoda Kazunori , Yoshimura Shinichi
Published: April 2022

Background

We aimed to compare outcomes after stroke due to anterior circulation large vessel occlusion with initial imaging assessments using diffusion‐weighted imaging (DWI) or noncontrast computed tomography (NCCT).

Methods

Among 2399 patients with large vessel occlusion stroke in a prospective, multicenter registry, patients with (1) prestroke modified Rankin Scale scores 0 to 1, (2) occlusion of the internal carotid artery or M1 segment of the middle cerebral artery, and (3) onset‐to‐hospital‐arrival time <6 hours were included. The primary effectiveness outcome was good functional outcome, defined as modified Rankin Scale scores 0 to 2 at 3 months. Safety outcomes included symptomatic intracranial hemorrhage. Intergroup biases were accounted for by mixed‐effects multivariable modeling and inverse probability of treatment weighting.

Results

A total of 343 patients (130 women [37.9%]; median age, 74 years [interquartile range, IQR, 66–82 years]) were analyzed. DWI‐based assessment was performed in 217 patients, and NCCT‐based assessment was performed in 126 patients. The DWI group showed a lower baseline National Institutes of Health Stroke Scale score (
P
<0.01) and lower Alberta Stroke Program Early CT Score (
P
<0.01) than the NCCT group. Frequency of endovascular therapy was lower in the DWI group (71.9%) than in the NCCT group (84.1%;
P
<0.01). Median hospital‐arrival‐to‐arterial‐puncture time was 55 minutes (IQR, 40–77.5 minutes) in the DWI group and 55 minutes (IQR, 35–80 minutes) in the NCCT group (
P
=0.89), with similar rates of successful recanalization (77.6% versus 76.4%, respectively;
P
=0.88). Frequency of good functional outcome was 47.0% in the DWI group and 41.3% in the NCCT group (adjusted odds ratio, 1.32; 95% CI, 0.49–3.55). Symptomatic intracranial hemorrhage was encountered in 2.8% in the DWI group and 1.6% in the NCCT group (
P
=0.71).

Conclusions

Potential difference in accuracy of ischemic damage assessment between the 2 modalities did not seem to affect outcomes of anterior circulation large vessel occlusion stroke.

Registration

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT02419794.
Frailty as a Predictor of Surgical Outcomes Following Femoral Hernia Repair
Authors: Shehadeh Ayman A. , McLaren Graham W. , Collins John T. , Munene Gitonga , Sawyer Robert G. , Shebrain Saad A.
Published: April 2022

Background

Femoral hernias are associated with significant morbidity and mortality due to risk of strangulation. Frailty has shown to be strongly associated with adverse outcomes. A modified five-factor frailty index (mFI-5) is a simple validated predictor of postoperative complications and mortality within the ACS-NSQIP® database. This study aims to evaluate the impact of frailty and age on 30-day outcomes after femoral hernia repair.

Methods

Patients who underwent femoral hernia repair were queried using the ACS-NSQIP database (2017) and divided into two groups based on frailty score (FS): Frail (FS = 1-5) and Non-frail (FS = 0). We evaluated the association between postoperative outcomes and frailty, age, sex, presentation, ASA class, timing of surgery, and surgical approaches. Univariate analysis followed by a multivariable logistic regression model was performed to evaluate postoperative morbidity.

Results

Of a total of 1,295 patients, 540 (42.7%) were in the Frail group. No differences in sex and race proportions were observed between groups. The Frail group had a higher rate of serious morbidity (4.4% vs 1.9%, P < .001), overall morbidity (7.8% vs 3.4%, P < .010), readmission rate (5.4% vs 2.3%, P = .003), and median (IQR) hospital length of stay (1 [0, 4] vs 0 [0, 1] days, P < .001). In multivariable analysis, male sex, presentation with complication, emergency surgery, and FS were associated with increased odds of overall morbidity. All deaths were in the Frail group.

Conclusion(s)

Frailty, male sex, presentation with obstruction/strangulation, and emergency surgery are independent predictors of increased 30-day morbidity. Thirty-day mortality was noted in the Frail group.