bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier
Authors: Singh Ashutosh Kumar , Sreenivasu SVN , Mahalaxmi U.S.B. K. , Sharma Himanshu , Patil Dinesh D. , Asenso Evans
Published: February 2022

Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.
Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain
Authors: Yadav Divakar , Lalit Naman , Kaushik Riya , Singh Yogendra , Mohit  , Dinesh  , Yadav Arun Kr. , Bhadane Kishor V. , Kumar Adarsh , Khan Baseem
Published: February 2022

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.
Overexpression of Stathmin 1 Predicts Poor Prognosis and Promotes Cancer Cell Proliferation and Migration in Ovarian Cancer
Authors: Nie Lekai , Zhang Chen , Song Haiyun , Zhao Qianqian , Cheng Lei , Zhang Peihai , Yang Xingsheng
Published: February 2022

Purpose. The aim of this study was to investigate the expression of stathmin 1 (STMN1) in ovarian cancer and its effect on prognosis. The effect and mechanism of STMN1 on the proliferation and migration of ovarian cancer cells were also investigated. Methods. Expression of STMN1 was measured by immunohistochemical staining in ovarian cancer tissues. The effects of STMN1 on the proliferation and migration capacity of ovarian cancer were evaluated using Cell Counting Kit-8 (CCK-8) assays, colony formation assays, immunofluorescence staining, wound healing assays, and Transwell assays. Transcription factors were predicted by bioinformatic analysis of TCGA database. Results. STMN1 was upregulated in ovarian cancer tissues as compared to paracancerous tissues and associated with shorter overall survival. STMN1 expression significantly correlated with FIGO staging and tumor differentiation (

P
<
0.05

). Furthermore, STMN1 promoted proliferation and migration in ovarian cancer cell lines. Bioinformatic analysis revealed that STMN1 was potentially regulated by E2F transcription factors. Then, we found that E2F1 regulated the expression of STMN1 and affected proliferation. Conclusion. STMN1 is overexpressed in ovarian cancer, and its high expression suggests a poor prognosis. STMN1 promotes the proliferation and migration of ovarian cancer and is regulated by E2F1. Thus, STMN1 may serve as a negative prognostic factor and possible target for the treatment of ovarian cancer patients.
Smart Home Control and Management Based on Big Data Analysis
Authors: Chi Hao , Chi Yuyan
Published: February 2022

In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value.
Macroabrasion and/or Partial Veneers: Techniques for the Removal of Localized White Spots
Authors: Gaião Ubiracy , Pasmadjian Ana Carolina Portes , Allig Gabriela Resende , Rezende Liliana Vicente Melo de Lucas , da Silva Vitória Beatriz Souza , da Cunha Leonardo Fernandes
Published: February 2022

Macroabrasion is a technique for the removal of localized white spots using a high-speed, intermittent high speed turbine finishing diamond tip. It is fast, safe, efficient, and an alternative to enamel microabrasion. However, when the stain is deeper, these localized intrinsic stains or defects can be treated with partial direct veneers. A conservative preparation should be done and that allows stratification of the resin to mask the hypoplasia and provide naturalness to the tooth. Thus, the objective of this work is to demonstrate, through a clinical case, macroabrasion and a partial veneer from the preparation, restoration, to the finishing and polishing to remove hypoplastic stains.
Public Security Video Image Detection System Construction Platform in Cloud Computing Environment
Authors: Yin Hexiao
Published: February 2022

The public security image detection system is an important way to assist the police in the investigation. In today’s cloud computing environment, the processing power of cloud computing is gradually improving. In order to explore its application in the investigation system, this paper constructs a public security video image investigation system based on cloud computing environment. This paper uses cloud computing technology to improve the processing capacity of the system. It then combines the storage capabilities of the backend server implementation technology and the working principle of Hadoop to construct a basic model. Then combined with the storage capability of the backend server implementation technology and the working principle of Hadoop and CP-ABE encryption, decryption, and reconstruction, a basic model is constructed. This paper also designs public security video surveillance system vehicle detection and test experiments, cloud storage encryption algorithm experiments, and computational storage requirements analysis experiments. It optimizes the system based on the results of the experiment and finally compares it with the traditional investigation system. The experimental results show that the public security video image detection system based on cloud computing can improve the accuracy by 5%–25% compared with the traditional detection system. And the public security video image detection system based on cloud computing can increase the efficiency by 2%–17% compared with the traditional detection system.
Optimal Design of Clearances of Cylindrical Roller Bearing Components Based on Dynamic Analysis
Authors: Chen Lihai , Ma Fang , Qiu Ming , Dong Yanfang , Pang Xiaoxu , Li Junxing , Yang Chuanmeng
Published: February 2022

With the continuous advancement in bearing speed, bearing assembly clearance (pocket, radial, and guide clearances) has a particularly significant impact on dynamic characteristics of bearing. In order to improve its performance, it is necessary to generate an optimized design for assembly clearance based on dynamic analysis. In this paper, a dynamic model of cylindrical roller bearing has been put forth based on the variable step fourth-order Runge–Kutta method, while the simulation results have been verified by a high-speed bearing cage motion testing machine. Considering pocket, radial, and guide clearances as independent variables and maximum impact force, whirl deviation ratio, and minimum power loss as the objectives, a multiobjective optimized model of cylindrical roller bearing has been developed using central composite experimental design (CCD) and response surface method. After optimization, the maximum impact force was reduced by 9.26%, the whirl deviation ratio was reduced by 2.87%, and power loss was reduced by 1.45%.
Performance of Shear Connector in Composite Slab and Steel Beam with Reentrant and Open Trough Profiled Steel Sheeting
Authors: Feyissa Alemu , Kenea Goshu
Published: February 2022

The composite construction strategies realize an increasing number of sizeable applications in regions like bridges, ramps, homes, and different exceptional places. The component of composite structure considered in the study contains composite slab, profiled steel sheeting, steel beam, shear connector, and reinforcement cages. The investigation mainly focused on the structural performance of demountable shear connectors in composite slabs and steel beams with profiled steel sheeting. An extensive nonlinear finite element model was used to carry out in FE software Abaqus 6.14. Nonlinear behavior of concrete, stud connector, profiled steel sheeting, reinforcement cage, and steel beam are included in the finite element model. In this study, 24 specimens were taken into consideration to investigate the capacity of the shear connection, the load‐slip curve of stud, failures modes of demountable shear connectors, and concrete crushing in the composite floor with profiled steel sheeting. The analysis confirmed that as the diameter of the shear connector increased from 19 to 22 mm, the capacity of the shear connector also increased by 54.313% and 28.903% in both open through and reentrant profiled steel sheeting, respectively. In addition, as the height of the profiled steel sheeting increased from 50 to 64 mm, the capacity of the shear connector is increased by 11.601% and 32.564% in open through and reentrant profiled steel sheeting, respectively. Similarly, when the concrete grade increased from C‐40 to C‐60, the capacity of the shear connector also increased by 70.128% and 48.301% with open and reentrant profiled steel sheeting, respectively. Furthermore, sensitivity analysis is done to see how much each input parameters affect the output parameter.
Irisin Activates M1 Macrophage and Suppresses Th2-Type Immune Response in Rats with Pelvic Inflammatory Disease
Authors: Zhang Zhenge , Zhang Chongyuan , Zhang Shuirong
Published: February 2022

Objective. To investigate the mechanism of irisin to treat rats with acute pelvic inflammatory disease (APID). Methods. Female rats were established as APID. Firstly, the content of IL-6, IL-8, TNF-α, and NF-kB was tested in rats’ serum by enzyme linked immunosorbent assay (ELISA). Interferon-γ (IFN-γ) and IL-4 in the supernatant of pelvic homogenates were also detected. The mRNA expression of the inducible nitric oxide synthase (iNOS), TNF-α, chemokine ligand 1 (CXCL1), arginase-1(Arg1), and chitinase-3-like-3 (Chi313) genes in the pelvic cavity was detected by quantitative reverse transcription polymerase chain reaction (RT-qPCR). IFN-γ and IL-4 secreted by spleen CD4+T cells and CD8+T cells were counted by flow cytometry, and the ratio of IFN-γ/IL-4 in CD4+T cells and CD8+T cells in the spleen was also detected by flow cytometry. Results. Irisin reduced the levels of IL-6, IL-8, TNF-α, and NF-kB in serum. Compared with the APID group, the expression level of IL-4 in the APID + Irisin group was reduced in the homogenate. At the same time, Irisin promotes the activation of M1 macrophages in the uterus, ovaries, and uterine tubes of rats with APID. Irisin also inhibited Th2-type immune response. Conclusions. Irisin activates M1 macrophage and suppresses Th2-type immune response in APID rats.
Prognosis of Tumor Microenvironment in Luminal B-Type Breast Cancer
Authors: Lv Ji , Ren Jia , Zheng Jie , Zhang Fuliang , Han Meng
Published: February 2022

Objective. Tumor microenvironment as an important element of malignancy could help predict cancer prognosis and therapeutic response; thus, a prognostic landscape map of the tumor microenvironment in luminal B breast cancers should be developed. Methods. The GEO and TCGA databases were employed to retrieve clinical follow-up data and expression profiles of luminal B breast cancer. CIBERSORT was applied to assess the infiltration of the tumor microenvironment of 209 patients and to construct tumor microenvironment-based subtypes of luminal B breast cancer. We also conducted Cox multivariate regression analysis to select features that could be used to develop a microenvironment signature for cancer. Samples were categorized as having low and high TME scores according to the median TME score. The correlations of prognosis and TME score, expression levels of immune factors and genomic variation, and clinical features were further investigated. Results. We found that high TME scores were correlated with poor prognosis. The current findings showed that the expressions of multiple immune-related genes, including CXCL9, CXCL10, GZMB, and PDCD1LG2, were upregulated in cancer with high TME scores. The high-risk group showed lower TP53 gene mutation frequency as opposed to that of the low-risk group. For the purpose of developing a TME scoring system, the TME infiltration levels of 209 patients with luminal B breast cancer from TCGA were comprehensively analyzed. Conclusions. Our analysis revealed that the TME score was an indicator of patients’ response to immune checkpoint modulators and an effective prognostic biomarker. TME scoring improves current immunotherapy on luminal B breast cancer.
Distributed Sliding Mode Fault‐Tolerant LFC for Multiarea Interconnected Power Systems under Sensor Fault
Authors: Zhou Xichao , Dou Zhenlan , Zhang Weiming , Zhang Yiwei , Wang Deyi , Zhang Chunyan , Xu Dezhi
Published: February 2022

This paper focuses on the problem of load frequency control and sensor fault‐tolerant control in the multiarea power grid. To solve these problems, a sliding mode control strategy based on an interval observer is designed. First, an interval observer is designed to obtain the boundary estimation information of the system state online, which is used to reconstruct the load disturbance online. Based on the reconstructed values, an integral sliding mode controller is designed to keep the system frequency stable when the load is disturbed. Then, sensor fault is diagnosed by interval residual. An augmented observer is designed to estimate sensor fault online by equivalent transformation, and the sensor fault is compensated by the fault estimation to reduce the influence of fault on system performance and ensure the reliable and stable operation of the power system. The simulation results show the superiority of the proposed control strategy.
Selection Method of Cross‐Border e‐Commerce Export Logistics Mode Based on Collaborative Filtering Algorithm
Authors: Yang Ying
Published: February 2022

The combination of the Internet and foreign trade has promoted the rapid development of cross‐border e‐commerce, and cross‐border e‐commerce logistics has risen due to the development of cross‐border e‐commerce. However, the costs incurred by enterprises in the operation of intermediate logistics links have remained high for many years. Considering the cross‐border electricity export logistics mode selection, the author of this paper tackles the main existing problems, using literature study, field survey method, case analysis and the collaborative filtering method of major consumer goods export logistics mode, and the advantages and disadvantages in the enterprise’ internal and external environmental analysis and evaluation, and puts forward a scientific logistics coordination mechanism and new export logistics mode. Firstly, the development background of cross‐border e‐commerce logistics is sorted out, and the research content, research method, and research framework are proposed based on the theoretical research of relevant scholars. Secondly, several logistics modes of cross‐border e‐commerce export are summarized, including international postal parcel, international express, international special line, etc., and the indicator system of logistics mode selection is determined through comparative analysis of application scope and advantages and disadvantages. Secondly, a logistics selection model based on collaborative filtering algorithm is constructed, and the model is verified by a practical case of a specific export cross‐border e‐commerce company. Finally, the research conclusions and application strategies of this paper are proposed, so as to provide the most appropriate logistics mode and guarantee suggestions for cross‐border e‐commerce enterprises and provide ideas and methods for cross‐border e‐commerce enterprises to choose international logistics mode for different types of import and export goods. The significance of this study was aimed at the current cross‐border electricity development mainly by restricting factors, planning the most suitable for the development of China’s cross‐border electronic business logistics mode, to some extent, and it has enriched the cross‐border electricity business related research in the field of logistics, to inspire transformation of foreign trade enterprises, so as to better promote the cross‐border electricity business healthy and orderly development and export logistics mode optimization selection.
Sports Action Recognition Based on Particle Swarm Optimization Neural Networks
Authors: Zhang Rui
Published: February 2022

Video acquisition has become more convenient as science and technology have progressed, and the development of mobile Internet has resulted in a large amount of video data being generated every day. The question of how to analyze these videos automatically has become urgent. Among them, the study of sports movement recognition in video has important theoretical implications in sports research as well as practical application value. This paper proposes a PSO-NN-based sports action recognition model. Kernel principal component analysis is used to extract and analyze the characteristics of sports movements. The improved neural network is used to identify common human postures in sports, and the classification and block background estimation method is used to detect human targets. The feature extraction of targets is completed according to the edge features, and the feature extraction of targets is completed according to the edge features. Finally, the feature vectors are trained using a backpropagation neural network (BPNN), and the parameters of the BPNN are chosen using the PSO algorithm to create a classifier for sports action recognition. The results show that this model improves the accuracy of sports video recognition and is an effective method of sports action recognition when compared to the comparison model.
Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network
Authors: Arif Muhammad , Philip Felix M. , Ajesh F. , Izdrui Diana , Craciun Maria Daniela , Geman Oana
Published: February 2022

One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.
Effect of the Initial Support of the Tunnel on the Characteristics of Rockburst: Case Study and Mechanism Analysis
Authors: Ma Chunchi , Fan Junqi , Ji Xiang , Shi Xiaoyan , Zeng Jun
Published: February 2022

Rockburst is still a stubborn disease in the field of engineering geology. The present research pays more attention to the influence of geological conditions on rockburst and less to the influence of type and stiffness of engineering support on rockburst. We explore the influence of support stiffness from weak to strong on rockburst and reveal the characteristics of fracture and microseismicity during rockburst through microseismic monitoring and numerical simulation. The main results and conclusions can be drawn: (1) Strong stiffness support makes the surrounding rock accumulates higher energy before rockburst. The evolution of microseismicity and its indexes can be used as precursors of potentially strong rockburst. (2) Strong stiffness support is easy to concentrate high stress under the action of surrounding rock pressure, and it is easy to fail under the disturbance of external load. This will produce a “sudden unloading effect” on the surrounding rock mass and may lead to a more serious rockburst. Numerical simulation verifies the existence of that effect and are consistent with the actual signs of failure. Our research is helpful to clarify the rockburst problem in the field of engineering geology, specifically to reveal the mechanism of rockburst and the early warning criteria of rockburst hazard under the action of supporting structure, which can provide practical data and theoretical support for scientific and reasonable prevention and control of rockburst risk in tunnel and underground engineering.
Using the Digital Flow to Increase Efficiency in Complex Partial Rehabilitation with Dental Implants
Authors: Melenikiotis Andrew Sotirios , Vianna Camila Pereira , Caldas Waleska , Trojan Larissa Carvalho , de Freitas Rubens Moreno
Published: February 2022

Background. This report presents a clinical case in which the CAD-CAM technology was applied to optimize a complex partial rehabilitation with implant-supported prostheses of a patient with several functional and aesthetic issues. Case presentation. A 40-year-old patient with several complaints due to the absence of multiple teeth and great dissatisfaction regarding his oral health was referred to a Dental College (Curitiba, Brazil). Guided surgery of 11 implants was planned. Digital flow and immediate loading protocol were applied. The patient was followed up for 2 years presenting good clinical and radiographic outcomes. Conclusions. The digital flow brought agility and precision to implant placement, immediate provisionalization added to satisfaction in the provisional phase, and CAD/CAM technology provided predictability and comfort to deliver the definite restorations.
System Analysis of Adaptor-Related Protein Complex 1 Subunit Mu 2 (AP1M2) on Malignant Tumors: A Pan-Cancer Analysis
Authors: Yi Yuanxue , Zhang Qiufang , Shen Youfeng , Gao Yueyi , Fan Xiaohong , Chen Sini , Ye Xin , Xu Jian
Published: February 2022

Objective. To identify new tumor marker genes available for early tumor screening, differentially expressed gene profiles of multiple tumors were compared using Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA) databases. As AP1M2 was highly and differentially expressed in invasive breast carcinoma, the purpose of this study was to explore the association of AP1M2 gene with the survival, immune invasion, and tumor neoantigens of patients on a pan-cancer basis. Methods. The expression and distribution of AP1M2 gene in tumor tissues and the corresponding normal control tissues were analyzed using the pan-cancer databases GTEx, CCLE, and TCGA. Kaplan-Meyer survival plots and proportional hazards model (COX) were employed to evaluate actions of AP1M2 on the clinical prognosis of tumor patients. Subsequently, the association of AP1M2 expression with immune invasion in different tumor types was explored. Simultaneously, the investigation of the interrelationship of AP1M2 and tumor neoantigens of the immune system, unstable microsatellite, DNA repair genes, and DNA methyltransferases were explored, and the mutation frequency of AP1M2 gene in diverse tumors was studied. Several tumor types were analyzed using gene-set enrichment analysis (GSEA). Results. AP1M2 was abundantly expressed in a wide range of cancers, and its expression level was positively correlated with the outcome of tumor victims. Through a study on AP1M2 action on clinical prognosis and immune infiltration in tumor patients, AP1M2 expression in breast-infiltrating carcinoma was found to be highly associated with patients’ overall survival and infiltration levels of macrophages, dendritic cells, T cells (CD4+ and CD8+), and B cells. Also, AP1M2 expression was positively correlated with tumor immune neoantigens and microsatellite instability in breast invasive carcinoma. The effect of AP1M2 on tumors was analyzed by GSEA, and findings demonstrated that AP1M2 expression levels in most tumors influenced the activation of tumor-associated pathways and immune-associated pathways. Conclusions. These findings suggest that AP1M2 expression levels are significantly correlated to patients’ outcomes and levels of immune infiltration in most cancer types, including T cells (CD8+ and CD4+), macrophages, neutrophils, and dendritic cells (DCs), particularly in breast cancer. The results indicate that AP1M2 may influence the tumor environment of invasive breast cancer patients and it may be a target contributing to early screening and treatment for breast cancer, helping improve the efficiency of early screening and overall survival rate in invasive breast cancer patients.
LncRNA ABHD11-AS1 Participates in the Progression of Cervical Carcinoma by Targeting miR-1254 and Is the Key to the Diagnosis and Treatment of Cervical Carcinoma in the Future
Authors: Zhu Duanrong , Hao Qun , Qian Min , Hu Yuli , Wu Feifei
Published: February 2022

Cervical carcinoma is the most common gynecologic tumor in the clinic. The incidence of cervical carcinoma has been increasing in recent years, and the age of the affected population is showing a younger trend. Long-chain noncoding RNA (LncRNA) acts in the cell cycle. In cervical carcinoma, many studies have also confirmed the important role of LncRNA. LncRNA ABHD11-AS1 is one of the genes abnormally expressed in cervical carcinoma, but the specific situation has not been fully explained. This study intended to confirm whether LncRNA ABHD11-AS1 can be applied for the treatment of cervical carcinoma in the future. From January 2015 to January 2017, 72 cases of cervical carcinoma patients and 78 cases of healthy people during the same period in our hospital were selected for prospective analysis. ABHD11-AS1 and miR-1254 in serum and carcinoma tissues of cervical carcinoma patients were detected. In addition, human cervical carcinoma cells HeLa and CaSki were obtained to analyze the effects of interference with ABHD11-AS1 and miR-1254 on the biological behavior of cervical carcinoma cells. Finally, the correlation of ABHD11-AS1 with miR-1254 was verified by double fluorescein reporter enzyme and immunocoprecipitation. ABHD11-AS1 was upregulated, and miR-1254 was reduced in serum and carcinoma tissues of cervical carcinoma patients (

P
<
0.05

). The expression levels of the two were negatively correlated (

P
<
0.001

). ABHD11-AS1 decreased and miR-1254 increased in serum of cervical carcinoma patients after treatment (

P
<
0.05

). High ABHD11-AS1 and low miR-1254 had a close correlation with the poor prognosis of cervical carcinoma patients (

P
<
0.05

). Silencing LncRNA ABHD11-AS1 could inhibit the activity of cervical carcinoma cells (

P
<
0.05

), while inhibiting miR-1254 could promote the activity of cervical carcinoma cells (

P
<
0.05

). ENCORI online website found that LncRNA ABHD11-AS1 and miR-1254 had binding sites. Bifluorescein reporter enzyme experiment found that ABHD11-AS1-WT fluorescence activity was inhibited by transfected miR-1254-mimics (

P
<
0.05

). LncRNA ABHD11-AS1 accelerates proliferation, invasion, and migration of cervical carcinoma cells through targeted regulation of miR-1254, which may become the key to the treatment of cervical carcinoma.
Comparative Studies of Dynamic Mechanical Properties of Desert Sand Concrete and Ordinary Concrete
Authors: Chen Qiaoli , Liu Haifeng , Han Li , Wang Yiying
Published: February 2022

Ningxia is located in the western part of China, which abounds with desert resources. The application of desert sand in concrete has not been fully studied. In order to study the failure mechanism of desert sand concrete under impact loading, dynamic impact experiments on desert sand concrete and ordinary concrete were carried out by using the 74 mm diameter split Hopkinson pressure bar (SHPB) experimental apparatus. The dynamic mechanical properties of desert sand concrete under different impact velocities were analyzed. The random distribution program of round coarse aggregate in desert sand concrete was designed to generate the random distribution of coarse aggregate by ANSYS/APDL language. The dynamic failure process of single-graded concrete and two-graded concrete was numerically simulated and compared. The results showed that desert sand concrete and ordinary concrete have obvious rate-dependent effect. Detailedly, the ordinary concrete is totally destroyed when impact velocity is 10 m/s, but the desert sand concrete still maintains the cone shape at impact velocity of 18 m/s, which indicates that the impact resistance of desert sand concrete is better than that of ordinary concrete. When impact velocity is 5 m/s, the single-graded concrete and two-graded concrete are both destroyed, but the desert sand concrete is not damaged. When impact velocity is 10 m/s, the dynamic failure mode of single-graded concrete, two-graded concrete, and desert sand concrete is basically the same. When impact velocity is 15 m/s, the single-grade concrete shows different dynamic failure mode from that of desert sand concrete and two-graded concrete.
Efficacy and Safety of Lianhua Qingke Tablets in the Treatment of Mild and Common-Type COVID-19: A Randomized, Controlled, Multicenter Clinical Study
Authors: Zhang Ling , Wu Lei , Xu Xiaolong , Yuan Yadong , Jiang Rongmeng , Yan Xixin , Zhang Xin , Gao Yong , Shang Huanxia , Lian Bo , Hu Jing , Mei Jianqiang , Wu Shucai , Liu Qingquan
Published: February 2022

Background. Lianhua Qingke (LH) tablets is an effective traditional Chinese medicine against various viral infections, especially in relieving coughing. However, its effects on COVID-19 are unknown. Methods. To examine the therapeutic effectiveness of LH tablets in COVID-19 patients with mild and common types, a randomized, multicenter, controlled study was carried out. COVID-19 cases were randomized to undergo routine treatment with or without LH tablets (4 tablets, three times a day) for 14 days. The primary endpoints were the rate of achieving clinical symptom resolution and the corresponding time. Results. There were 144 participants in the full analysis set (72 each in the LH and control groups). The LH group participants had elevated symptom alleviation rate at 14 days compared with control cases (FAS: 98.61% vs. 84.72%,p = 0.0026). In comparison with control group participants, the LH group participants had reduced median time to clinical symptom alleviation (median: 4 vs. 7 days,p < 0.0001). Higher resolution rates of coughing (98.44% vs. 84.51%,p = 0.0045) and expectoration (100% vs. 82.35%,p = 0.0268) were observed in the LH group. Times to recovery of fever (median: 2 vs. 3 days,p = 0.0007), coughing (median: 4 vs. 7 days,p < 0.0001), and expectoration (median: 3 vs. 6 days,p < 0.0001) were also notably shorter in the LH group. Moreover, the LH group had elevated improvement rates in chest computed tomography signs (FAS: 86.11% vs. 72.22%,p = 0.0402) and clinical cure at day 28 (FAS: 83.33% vs. 68.06%,p = 0.0326). However, no differences were found in the laboratory test and viral assay. Serious adverse events were not detected. Conclusion. These preliminary findings indicate LH tablets may be effective in symptomatic COVID-19, especially in relieving coughing. This trial was registered in Chinese Clinical Trial Registry (ChiCTR2100042069).
Basilar Artery Vasospasm as a Cause of Post-Operative Cerebellar Mutism Syndrome
Authors: Deghedy Marwa , Pizer Barry , Kumar Ram , Mallucci Conor , Avula Shivaram
Published: February 2022

Post-operative cerebellar mutism syndrome (CMS), also known as posterior fossa syndrome (PFS), is a well-recognized and frequent complication of surgery for posterior fossa tumours in children and young people. Its incidence varies between 8 and 31%, and the pathophysiological mechanisms of delayed onset and resolution of cerebellar mutism are not clear, but axonal damage, oedema, and perfusion defects may be involved. Magnetic resonance imaging has failed to reveal a universal anatomical substrate or a single definite mechanism of injury. We present a case of 16-year-old boy who developed CMS three days after resection of a medulloblastoma, a primary fourth ventricular tumour. Early post-operative imaging showed bleeding in the posterior fossa which required evacuation. CT angiography seven days after surgery demonstrated basilar artery vasospasm. Magnetic resonance brain angiography confirmed persistent narrowing of a segment of the basilar artery closely related to a left cerebellopontine (CP) angle peri-operative haematoma. The patient was treated with nimodipine and hypervolemia. The patient started vocalisation without speech five days later with reversal of radiological lesions. Further recovery of post-operative neurological deficits occurred over a protracted period of several months. This case represents a rare cause of post-operative CMS, with rapid initial recovery that occurred after specific treatment directed at the cause. To our knowledge, this is the first reported case showing mutism associated with basilar artery vasospasm with imaging evidence. This case may suggest the need to undertake urgent vascular imaging in selected cases of post-operative CMS.
Antibacterial, Cytotoxic, and Cellular Mechanisms of Green Synthesized Silver Nanoparticles against Some Cariogenic Bacteria (Streptococcus mutans and Actinomyces viscosus)
Authors: Ghabban Hanaa , Alnomasy Sultan F. , Almohammed Hamdan , Al Idriss Ohoud M. , Rabea Sameh , Eltahir Yasir
Published: February 2022

Background. The present study focused on the green synthesis of silver nanoparticles (AgNPs) using the Astragalus spinosus Forssk. aqueous extract. In addition, we evaluated the antibacterial activity of AgNPs as well as some cellular mechanisms against Actinomyces viscosus and Streptococcus mutans as the most causative agents of tooth decay. Methods. In this study, AgNPs were green synthesized by the precipitation method based on the reduction of silver ions (AgNO3) by A. spinosus extract. Antibacterial effects of the green synthesized AgNPs were performed by measuring the minimum inhibitory concentration (MIC) and minimum bactericidal concentrations (MBC) through micro broth dilution method. In addition, we evaluated the reactive oxygen species (ROS) production, nucleic acid leakage, and protein leakage as the main antibacterial mechanisms of the green synthesized AgNPs against A. viscosus and S. mutans. The cytotoxicity effects of AgNPs against on human normal (NOF18 cells) and oral cancer (SCC4 cells) cell lines were also evaluated using MTT assay. Results. The green synthesized AgNPs have a spherical shape and are relatively uniform in size in the range of 30‐40 nm. The MIC values for S. mutans and A. viscosus of the green synthesized AgNPs were 10.6 and 13.3 μg/ml, respectively, whereas the MBC values for S. mutans and A. viscosus of the green synthesized AgNPs were 21.3 and 26.6 μg/ml. The findings exhibited that ROS production, nucleic acid leakage, and protein leakage were increased after treatment of A. viscosus and S. mutans by the green synthesized AgNPs. The results demonstrated that the 50% inhibitory concertation (IC50) values of AgNPs on NOF18 and SCC4 cells were 93.3 μg/ml and 41.2 μg/ml, respectively. Conclusion. Overall, the results of this study showed that A. spinosus extract has a good ability to produce silver nanoparticles. The AgNPs produced have significant antibacterial effects against some tooth decay bacteria. Our results also revealed that the green synthesized AgNPs are more cytotoxic against cancerous cell line than normal cell line. Further in vivo studies are required to investigate the side effects and to evaluate the effectiveness of these bacteria.
Automatic Arrangement of Sports Dance Movement Based on Deep Learning
Authors: Feng Hua , Zhao Xiang , Zhang Xiaomin
Published: February 2022

Sports dance is a new form of sports that integrates sports, dance, music, and other elements. The core content of “dance” is an important carrier for athletes to display their body art. This article aims to study the automatic arrangement of sports dance based on deep learning. This article first introduces the development process of deep learning. As the latest research direction developed from artificial neural network technology in machine learning, deep learning has attracted widespread attention from the society. And then proposing a shallow regression model based on deep learning, a convolutional neural network based on deep learning, and an offline sorting regression model, given the general process of deep learning, then, based on the clustering algorithm, the deep learning was researched, and the sport dance movement arrangement was analyzed based on the deep learning. The experimental results of this article show that deep learning can effectively enhance the artistic ability of automatic choreography in sports dance and increase the accuracy of dance movements by 80%. At the same time, on the basis of deep learning, the practical ability is strengthened on the basis of consolidating theory, to further improve one’s own business ability and educational technology level, actively absorb advanced teaching methods, and earnestly delve into reasonable teaching methods. It is also used in curriculum training practice to actively gain insight into new development trends in educational methods and skills, to enhance the artistic creativity of students’ arrangements.
Corrigendum to “Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm”
Authors: Zhang Jinlei , Qiu Xue , Li Xiang , Huang Zhijie , Wu Mingqiu , Dong Yumin
Published: February 2022

As we all know, the weather forecast is very complex, which is related to the location of the environment, climate, season, and other factors. The accuracy of weather prediction is of great significance for the application of hydrological and agroclimatological research. In this research, the improved quantum genetic algorithm (IQGA) and support vector machine (SVM) are combined to model the rainfall of medium-sized cities in Australia. The daily time scale measured weather information, such as min temperature (MT), evaporation, sunshine, humidity, cloud, and so on, which was used to build the proposed predictive models. Next, the effects of IQGA-SVM, GA-SVM, and other classical machine learning algorithms on the dataset are compared. Experiments show that the IQGA-SVM model has the optimal prediction ability and less running time. Statistical evaluation metrics such as accuracy, area under curve (AUC), and running time were used to validate the model efficiency. The IQGA-SVM model uses IQGA to optimize the parameters of SVM. Compared with the traditional random walk and grid search method, it improves the calculation efficiency and the accuracy of parameter optimization. This parameter optimization method has good expansibility and can be applied to other algorithms that need parameter adjustment to achieve the optimal model prediction effect. The results of this study proved that IQGA-SVM is a reliable modeling technique for forecasting rainfall.
Application of Multichannel Active Vibration Control in a Multistage Gear Transmission System
Authors: Zhang Feng , Sun Weihao , Liu Chao , Xiao Hongchao , Zhang Yong
Published: February 2022

Gears are the most important parts of rotating machinery and power transmission devices. When gears are engaged in meshing transmission, vibration will occur due to factors such as gear machining errors, meshing rigidity, and meshing impact. The traditional FxLMS algorithm, as a common active vibration algorithm, has been widely studied and applied in gear transmission system active vibration control in recent years. However, it is difficult to achieve good performance in convergence speed and convergence precision at the same time. This paper proposes a variable-step-size multichannel FxLMS algorithm based on the sampling function, which accelerates the convergence speed in the initial stage of iteration, improves the convergence accuracy in the steady-state adaptive stage, and makes the modified algorithm more robust. Simulations verify the effectiveness of the algorithm. An experimental platform for active vibration control of the secondary gear transmission system is built. A piezoelectric actuator is installed on an additional gear shaft to form an active structure and equipped with a signal acquisition system and a control system; the proposed variable-step-size multichannel FxLMS algorithm is experimentally verified. The experimental results show that the proposed multichannel variable-step-size FxLMS algorithm has more accurate convergence accuracy than the traditional FxLMS algorithm, and the convergence accuracy can be increased up to 123%.
Breast Cancer Index Is a Predictive Biomarker of Treatment Benefit and Outcome from Extended Tamoxifen Therapy: Final Analysis of the Trans-aTTom Study
Authors: Bartlett John M.S. , Sgroi Dennis C. , Treuner Kai , Zhang Yi , Piper Tammy , Salunga Ranelle C. , Ahmed Ikhlaaq , Doos Lucy , Thornber Sarah , Taylor Karen J. , Brachtel Elena F. , Pirrie Sarah J. , Schnabel Catherine A. , Rea Daniel W.
Published: February 2022

Purpose:

The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor–positive (HR+) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance.

Experimental Design:

BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test.

Results:

Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69–1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14–0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (−1.2% absolute benefit; HR, 1.11; 95% CI, 0.76–1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2− subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15–0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849).

Conclusions:

Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2− disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.