bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Method to Predict the Remaining Useful Life of Scroll Wheel of Control Rod Drive Mechanism
Authors: Zhu Kang , Zhao Xinwen , Zhang Liming , Yu Hang
Published: August 2022

As one of the rotating components in the reluctance motor type control rod drive mechanism (CRDM), the life of the scroll wheel is closely related to the service life of the CRDM. In addition, the prediction of the remaining useful life of the scroll wheel helps to optimize the maintenance process of the CRDM. This paper proposes a hybrid method to predict its remaining useful life when the available degradation data are rare and the failure threshold cannot be accurately defined. First, the particle filtering algorithm, whose state transfer equation is established on the segmental damage physical model, is used to predict the degradation state of the scroll wheel. Second, the proportional hazard model for the relationship between the scroll wheel damage characteristics and reliability model is established to predict the remaining useful life of it. The proposed method focuses on the establishment of segmental damage physical model and the clustering analysis of damage characteristics extracted from vibration signals, which can be used to predict the remaining useful life of the scroll wheel. In addition, the results provide an opportunity for the condition-based preventive maintenance of the CRDM.
Research on the Vulnerability of Government Procurement of Elderly Care Services: A Complex Network Perspective
Authors: Zhang Yuting , Xu Lan , Lu Zhengnan
Published: August 2022

The policy of government procurement of elderly care services has the vulnerability characteristics that all complex systems have. To maintain the policy’s robustness, this paper studies the vulnerability of government procurement of elderly care services from the perspective of complex network. Case analysis and sample statistics are used to obtain the vulnerability influencing factors of the policy. Then, complex network diagram of vulnerability influencing factors is constructed through Pajek software. The compatibility coefficient is used to investigate the network’s overall vulnerability from a macro perspective. From a micro perspective, a vulnerability index of the complex network is applied to measure each node’s vulnerability value, and the vulnerability priority of the nodes is established. Results show that government purchasing power and supervisory bodies’ diversity are at the first priority. The remaining vulnerability influencing factors are at the second and the third priority.
Knowledge, Attitude, and Preventive Practice Related to Onchocerciasis and Associated Factors among Resource-Limited Selamogo Woreda Residents, South West Ethiopia, 2021
Authors: Gilano Girma , Mulugeta shegaze  , Dubale Amanuel
Published: August 2022

Introduction. Ethiopians work very hard to control and eradicate the vector and the parasite of Onchocerciasis. However, some hard-to-reach areas are not adequately covered by interventions that have previously taken place in various endemic sites in the country. This study aimed to assess knowledge, attitude, and preventive practice of Onchocerciasis among Selamogo residents to enhance the expansion of interventions. Methods. We used a survey questionnaire to capture the data on 572 Selamago residents. We checked, cleaned, entered the data into EPI Info v.7, and analyzed it in STATA v.15. We fitted a binary logistic regression model to examine the associated factors. Variables significant at

P
<
0.20

were included in the model. We declared association at

P
<
0.05

. Results. Of the 578 residents we approached, 572 (99%) responded to the interview. We found 48.30% of poor knowledge, 90.90% of poor attitude, and 85.70% preventive practice related to Onchocerciasis. Factors like Amhara ethnicity (AOR = 2.35, 95% CI: 1.05, 5.27), Orthodox Christianity (AOR = 1.87, 95% CI: 1.12, 3.10), Muslims (AOR = 2.28, 95% CI: 1.05, 4.94), secondary school (AOR = 2.31, 95% CI: 1.50, 3.55), diploma (AOR = 10.34, 95% CI: 4.62, 23.16), and preventability of Onchocerciasis (AOR = 3.02, 95% CI: 1.39, 6.55) were associated with KAP. Other factors like medical treatability, admission history to the health facilities, sex, and the number of households were also associated. Conclusion. The KAP related to Onchocerciasis is very poor compared to the indicators and evidence in the country. An intervention that considers local resources like taking the experience of groups with good knowledge, attitude, and preventive practice led by educated plus positive attitude people regarding the preventability and treatability of Onchocerciasis might be mandatory to improve the status.
Prediction and Evaluation Method of e-Commerce Service Satisfaction Based on Intelligent Computing Method
Authors: Tu Fang , Tu Bo
Published: August 2022

Among the many service industries, e-commerce, which is based on the Internet and relies mainly on platforms and third-party transaction models, has developed rapidly. All localities have actively deployed their regional e-commerce development strategies to improve the core competitiveness of the regional economy. The rapid development of e-commerce provides a favorable development environment and construction environment for the spatial agglomeration of e-commerce service industry. We use the intelligent computing method to calculate the e-commerce service degree prediction experimental results that show that according to the curves of the three algorithms, we can also see that the curve values of the intelligent computing and fuzzy statistical algorithm models are very stable and the experimental results are also very stable. It shows that the performance of the intelligent computing algorithm is the most superior; the second-level indicators are the after-sales service of the merchant, the popularity of the merchant, and the attitude of the merchant’s customer service; in the establishment of the logistic satisfaction evaluation index system, we found that the logistic satisfaction is the first-level indicator; the secondary indicators are the speed of logistics, the safety of logistics, the service attitude of logistics, and the price of logistics; after running on the test set, the model accuracy rate of the fuzzy statistical algorithm is 89.12%, and the accuracy rate can reach 89.56%. The accuracy rate of the intelligent algorithm can reach 92.46%, and the accuracy rate can reach 93.27%, which is the one with the highest index value among the three experimental models. Among the many service industries, e-commerce, which is based on the Internet and relies on platforms and third-party transaction models, is developing rapidly. All localities have actively deployed their regional e-commerce development strategies to improve the core competitiveness of the regional economy. The rapid development of e-commerce provides a favorable development environment and construction environment for the spatial agglomeration of e-commerce service industry.
Kinetic and Isotherm Studies of the Adsorption Phenacetin onto Two Copper Porous Coordination Compounds: Nonlinear Regression Analysis
Authors: Alvine Loris Djoumbissie , Donald Raoul Tchuifon Tchuifon , Cyrille Atemkeng Donlifack , Idris-Hermann Kuete Tiotsop , Giscard Doungmo , Alain Clovis Tayo Djampouo , Gabche Anagho Solomon , Jean Ngoune
Published: August 2022

Two coordination compounds, copper (II) fumarate (CuFum) and copper (II) tartrate (CuTart), synthesized from copper (II) with fumaric acid and tartaric acid as ligands and using the slow evaporation method have been applied to study the adsorption of phenacetin in aqueous solution. These compounds were characterized by elemental analysis, IR-FT spectroscopy, and X-ray powder diffraction. The melting points of the synthesized coordination compounds were found to be above 350°C. The influence of parameters such as the initial pH, the contact time, and the initial concentration on the adsorption of phenacetin in an aqueous solution has been studied. The studies showed that adsorption equilibrium was reached after 80 minutes for both coordination compounds; the adsorption capacity increased with increasing phenacetin concentration, and the maximum adsorption capacity was obtained in the acidic medium at pH 4. The adsorbed amount of phenacetin on copper (II) fumarate (CuFum) was 25.158 mg/g while that on copper (II) tartrate (CuTart) was 25.906 mg/g. Nonlinear regression analysis showed the best fit for the Freundlich model isotherm for CuTart with R2 of 0.963 and a Chi-square test (χ2) of 0.529 while for the CuFum material, it is the Redlich-Peterson model with R2 of 0.975 and Chi-square test (χ2) of 0.263. The kinetic study shows that the pseudo-second-order model better describes the adsorption of the two materials. The results show that physisorption and chemisorption participate in the adsorption of phenacetin and that these materials can be used for the elimination of phenacetin in solution.
Key Factors Identification and Path Selection of Enterprise Digital Transformation under Multicriteria Interaction
Authors: Liu Chunmei
Published: August 2022

Enterprise digital transformation is a must-go phase in today’s market, for both large and small businesses. This is determined by the development of the market economy and people’s consumption concept. However, enterprises will face many challenges and factors in the process of digital transformation. For enterprise managers, it is more difficult to deal with these complex data and factors. Moreover, the digital transformation of enterprises needs to consider more development goals. For small businesses, the digital transformation of a business is even more difficult. This requires a full understanding of the key factors and development paths of the enterprise in the process of enterprise digital transformation. If the key factors of the enterprise and the reasonable development path of the enterprise cannot be fully grasped in the process of digital transformation of the enterprise, it will easily lead to the failure of the transformation of the enterprise. Therefore, in the process of enterprise transformation, it is necessary to fully consider the key factors faced by the enterprise and the reasonable development path of the enterprise. This study uses the multicriteria optimization algorithm and ConvLSTM method to study the key factors and paths in the digital transformation of enterprises. The research results show that the multicriteria optimization algorithm can efficiently handle multiple development goals in the digital transformation of enterprises, and the ConvLSTM algorithm can also accurately predict the key factors in the digital transformation of enterprises.
Sports Product Marketing and Economic Growth Econometric Dynamic Analysis Model Based on Random Matrix Theory
Authors: Yang Zhifeng , Lin Ye
Published: August 2022

With the development of sports spirit, the proportion of total output value of sports industry in economic growth is increasing day by day, which has become an important factor for the long-term stable medium and high growth of economy in the new normal period. In view of the background of sports economic growth, this paper adopts the data of sports product marketing and selects sports goods sales and GDP as indicators. Through the combination of consumption mode and sports industry, we trace the consumption mode of sports consumers, put forward the segmentation and positioning of sports product consumption market, and establish the dynamic quantitative system of sports economy combined with the random matrix. The dynamic influence mechanism of sports product marketing on economic growth is studied. The results show that all of them are stationary sequences after first-order difference in econometric analysis, and the values of test results are all less than 1. The effect of the model is good, the contribution of sports product sales to economic growth reaches the maximum, and the contribution to economic growth tends to be basically stable.
DWT‐SVD Based Watermarking for High‐Resolution Medical Holographic Images
Authors: Horasan Fahrettin , Ali Pala Muhammed , Durdu Ali , Akgül Akif , Akmeşe Ömer Faruk , Yıldız Mustafa Zahid
Published: August 2022

Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos‐based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in‐line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in the laboratory environment, were obtained. The standard USAF 1951 target was used to evaluate the resolution of our imaging setup. Various QR codes were generated for medical sample identification, and the captured medical data were processed by watermarking it with chaos‐based random number generators. A new method using chaos‐based discrete wavelet transform (DWT) and singular value decomposition (SVD) has been developed and applied to high‐resolution data to eliminate the problem of encrypted data being directly targeted by third‐party attacks. The performance of the proposed new watermarking method has been demonstrated by various robustness and invisibility tests. Experimental results showed that the proposed scheme reached an average PSNR value of 564588 dB and SSIM value of 0.9972 against several geometric and destructive attacks, which means that the proposed method does not affect the image quality and also ensures the security of the watermarking information. The results of the proposed method have shown that it can be used efficiently in various fields.
ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks
Authors: Sharma Tripti , Balyan Archana , Nair Rajit , Jain Paras , Arora Shivam , Ahmadi Fardin
Published: August 2022

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.
Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data
Authors: Chamangard Mona , Ghodrati Amiri Gholamreza , Darvishan Ehsan , Rastin Zahra
Published: August 2022

Among various methods proposed for health monitoring of structures, deep learning-based techniques with their powerful performance have attracted considerable attention in recent years. However, a major problem with these methods is that they usually need large amounts of data in the training phase, while such data may not be available in real applications. In this study, compact one-dimensional (1D) convolutional neural networks (CNNs) are utilized that require less data for training. The study is comprised of two parts: the first stage aims to develop a compact CNN that can recognize damages in a structure with high accuracy, when data are provided to some extent. The problem of inadequate training data in health monitoring of experimental and real-life structures is then investigated in the second part. Transfer learning is used to deal with this problem. A compact CNN is utilized as the source domain network and the target domain network receives all of its knowledge from this source. Acceleration time histories from a numerical model, an experimental structure, and a full-scale bridge are utilized to validate the proposed methodology. According to the results, the compact CNN can reach 100% accuracy when data are available for training. Also, for the case of insufficient data, using a compact network as well as transfer learning causes considerable improvement (about 95%) in the accuracy of damage detection.
[Retracted] Secure Big Data Processing in Multihoming Networks with AI‐Enabled IoT
Authors: Venu Sivakumar , Kotti Jayasri , Pankajam A. , Dhabliya Dharmesh , Rao G. Nageswara , Bansal Rohit , Gupta Ankur , Sammy F.
Published: August 2022

The constant investigation, as well as dispensation of data among various processing, has been influenced by computerized strategies enabled by artificial neural network associated with Internet of Things, as well as cloud‐dependent organizations. Multihoming is also a category of an organization that brings together several categories of organizations in its atmosphere during the dealing with various information. These days, the massive amounts of information being handled also observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of ANN‐associated frameworks among multihoming massive information using IoT as well as AI‐associated frameworks could be beneficial in a variety of ways. The constant investigation and dispensation of information among various processing are influenced by computerized strategies enabled by ANN associated with IoT, as well as cloud‐associated organizations. Multihoming is also a kind of organization that brings together several kinds of organizations into a unique situation while dealing with a large volume of information. When multihoming enormous amounts of data with IoT and AI‐integrated systems, there are various advantages to employing AI‐based solutions. Despite the fact that multihoming security difficulties and their evaluation have been carefully explored by various scientists and researchers, big data security analysis in multihoming, particularly when using automated methodologies and systems, receives little attention. These days, the massive amounts of information being handled and observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of AI‐based frameworks in multihoming massive information with IoT associated with AI frameworks could be beneficial in a variety of ways. Even though multihoming security challenges and their analysis have been widely focused on by many researchers and specialists, little attention has been paid to them.
Correlation between Genes of the ceRNA Network and Tumor-Infiltrating Immune Cells and Their Biomarker Screening in Kidney Renal Clear Cell Carcinoma
Authors: Kong Aoran , Dong Hui , Zhang Guangwen , Qiu Shuang , Shen Mengyuan , Niu Xiaohan , Wang Lixin
Published: August 2022

This study aimed to using bioinformatics tools, qPCR, and the immunohistochemical analysis to find out factors related to the early diagnosis and prognosis of kidney renal clear cell carcinoma (KIRC). The expression profiles of lncRNA, miRNA, and mRNA of KIRC were downloaded from The Cancer Genome Atlas database. A ceRNA regulatory network was constructed based on the interaction between these three differentially expressed genes. The CIBERSORT deconvolution algorithm was used to analyze the differential distribution of 22 types of immune cells. The Kaplan–Meier survival and Cox analyses were used to screen genes of the ceRNA network and also immune cell subtypes related to the clinical and prognostic prediction of KIRC. Co-expression regulatory relationships were found among LINC01426, LINC00894, CCNA2, L1 cell adhesion molecule (L1CAM), and T follicular helper cells, which served as potential biomarkers. The results of quantitative reverse transcriptase-polymerase chain reaction showed that LINC01426 was upregulated while L1CAM was downregulated in KIRC, but no difference was found in the expression levels of LINC00894 and CCNA2 in cancer and adjacent samples. The immunohistochemical analysis showed that T follicular helper cells were more concentrated in core tissues and metastases of KIRC. In a word, co-expression relationships were found among LINC01426, L1CAM, and T follicular helper cells, and they may serve as biomarkers for early diagnosis and prognostic evaluation of KIRC.
Rasagiline as Adjunct to Levodopa for Treatment of Parkinson’s Disease: A Systematic Review and Meta-Analysis
Authors: Kano Osamu , Tsuda Hiroshi , Hayashi Ayako , Arai Masaki
Published: August 2022

Background. Rasagiline is a selective, irreversible monoamine oxidase type B inhibitor used as monotherapy in early Parkinson’s disease and as an adjunct therapy to levodopa in Parkinson’s disease with motor fluctuations. Objectives. This meta-analysis aimed to provide updated evidence on the efficacy for motor and nonmotor symptoms and the safety of rasagiline/levodopa versus levodopa in patients with Parkinson’s disease experiencing motor fluctuations. Methods. A systematic literature search was conducted (January 18-19, 2021) using PubMed, Cochrane Library, EMBASE, Web of Science, and Google Scholar to identify randomized controlled trials comparing rasagiline/levodopa versus placebo/levodopa in patients with Parkinson’s disease experiencing motor fluctuations. Outcomes included change in wearing-off time, Unified Parkinson’s Disease Rating Scale (UPDRS)/Movement Disorder Society-UPDRS (MDS-UPDRS) II and III scores, treatment-emergent adverse events (TEAEs), and Parkinson’s Disease Questionnaire (PDQ-39) summary index score. A random effect model was used to estimate the treatment effects. Results. Six studies were included (1912 patients). Significant improvements in wearing-off time (standardized mean difference [SMD]: −0.50, 95% confidence interval [CI]: –0.92 to –0.09,

p
=
0.002

), levodopa dosage (SMD: −0.18, 95% CI: −0.35 to –0.01,

p
=
0.041

), UPDRS/MDS-UPDRS II (SMD: −0.39, 95% CI: −0.52 to –0.25,

p
<
0.0001

), UPDRS/MDS-UPDRS III (SMD: −0.30, 95% CI: −0.44 to –0.16,

p
<
0.0001

), and PDQ-39 summary index score (SMD: –0.21, 95% CI: –0.37 to –0.04,

p
=
0.013

) were observed with rasagiline/levodopa versus placebo/levodopa. The incidence of TEAEs did not differ between treatments (risk ratio: 1.13, 95% CI: 0.98–1.30,

p
=
0.093

). Conclusions. This meta-analysis further indicated the superiority of rasagiline/levodopa in improving motor and nonmotor symptoms of Parkinson’s disease, with a similar safety profile to that of levodopa in Parkinson’s disease with motor fluctuations.
Microbiome Associated with Slovak Raw Goat Milk, Trace Minerals, and Vitamin E Content
Authors: Lauková Andrea , Micenková Lenka , Grešáková Ľubomíra , Maďarová Michaela , Simonová Monika Pogány , Focková Valentína , Ščerbová Jana
Published: August 2022

In Slovakia, goat milk production for direct consumption and cheese processing has attracted growing interest. However, there is a lack of information regarding the microbial consortium in Slovak raw goat milk analyzed by next-generation sequencing and trace elements and vitamin E as well. A randomly selected samples (G24-G50) of raw goat milk from different animals at farms in Slovakia were analyzed. The phylum Actinobacteria dominated (62.8%), followed by the phyla Firmicutes (20.5%), Proteobacteria (7.4%), and Bacteroidetes (6.4%). The family Microbacteriaceae was detected in the highest percentage (60.2%) followed by Staphylococcaceae, Bacteroidaceae, Streptococcaceae, Lactobacillaceae, Enterobacteriaceae, and others. Regarding the genera, the most prevalent was genus Curtobacterium (47.4%) followed by the genera such as Staphylococcus (8.3%) and Bifidobacterium (4%). The genera Streptococcus, Lactococcus, Enterococcus, Lactobacillus, and Lacticaseibacillus were evaluated in abundance percentage in range 1%-3.2%. The genus Veillonella reached abundance 3.2%. The genera Enterobacter, Pseudomonas (1.3% and 0.5%), and Bacteroides (6.4%) were evaluated in small percentage abundance too. Zinc was detected with the highest mean value (

2.561
±
0.6823

 mg/L) in raw goat milk, followed by iron (

1.383
±
0.5087

 mg/L). The mean value of copper and manganese was

0.1746
±
0.0463

 mg/L and

0.051
±
0.0238

 mg/L. The vitamin E reached the mean value

0.3783
±
0.1976

 mg/L. This study is an original contribution showing microbial consortium in raw goat milk from Slovak farms. It also contributes to trace elements and vitamin E status in raw goat milk showing it as a nutritionally healthy food.
Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning
Authors: Wu Zhipan , Du Huaying
Published: August 2022

With the improvement of computer computing power and storage capacity, the emergence of massive data makes the methods based on human action feature detection and recognition unable to meet people’s needs due to poor generalization ability. Based on the detection and recognition of human action features based on deep learning algorithms, a suitable neural network can be constructed to identify locked human action features from surveillance video and analyze whether it is a specific behavior. In this paper, a deep learning algorithm is proposed to optimize the detection of human action features, and a multiview reobservation fusion action recognition model of 3D pose is designed. Several factors affecting the recognition of human action features are analyzed, and a detailed summary is made from the detection environment. Experiments show that adding one or two layers of feature attention enhancement to the multiview observation fusion network can improve the accuracy by 1% to 3%. In this way, the model can integrate action features from multiple observation angles to judge actions and learn to find observation angles suitable for action recognition, thereby improving the performance of action recognition.
Study on Allopathic and Alternative Treatments of Asthma and Factors Influencing Treatment Choices
Authors: Farooq Javeria , Khaliq Sheikh Abdul , Ghuman Faiza , Shaikh Javeria , Azhar Iqbal
Published: August 2022

Background and Objective. Several treatments are used to attain good control of symptoms and to reduce asthmatic exacerbations. The survey was designed to compare different systems of treatments for asthma and to explore the most preferred treatment in terms of efficacy, side effects, long-term use, and satisfaction. Materials and Methods. The cross-sectional survey was conducted; primary data were collected from asthmatic patients in different hospitals and clinics of allopathic, homeopathic, and herbal practitioners in Karachi, Pakistan. The study duration was from January 2020 to December 2020. Asthmatic patients aged over 13 years were selected for the study. A written informed consent was taken from the patients before the interview. Collected data were analyzed by the Statistical Package of Social Sciences (SPSS) 22. Result. Among 255 asthmatic patients; 51.4% (n = 131) were male and 48.6% (n = 124) were female. For control of acute attacks of asthma 88.2% (

p
=
0.0001

) of patients significantly preferred allopathic treatment while 6.3% (

p
=
0.008

) used homeopathic treatment and 5.5% chose herbal treatment. For maintenance of asthma, 78.8% (

p
=
0.0001

) patients used allopathic treatment while 12.4% (

p
=
0.0001

) homeopathic and 8.8% (

p
=
0.0001

) patients used herbal treatment. About 63.4% (

p
=
0.0001

) of the asthmatic patients used short-acting β-2 agonists for managing acute asthmatic episodes while long-acting β-2 agonists (

p
=
0.0001

) and inhaled corticosteroids (

p
=
0.0001

) were found to be the preferred medicines for maintenance therapy. Effectiveness of treatment (

p
=
0.004

) and cost effectiveness (

p
=
0.0001

) significantly act as contributing factors for the selection of the treatment. The majority of the patients were satisfied with their chosen treatments for control of asthmatic symptoms. Conclusion. Most asthmatic patients preferred allopathic treatment for the management of acute episodes and control of asthmatic symptoms. It was found that the major factors for selecting a specific treatment include effectiveness, cost, and minimal side effects.
Dysregulation of Pseudogenes/lncRNA-Hsa-miR-1-3p-PAICS Pathway Promotes the Development of NSCLC
Authors: Song Yichen , Wang Zhiying , He Lewei , Sun Feidi , Zhang Beilei , Wang Fu
Published: August 2022

Objective. Non-small cell lung cancer (NSCLC) explains about 80 percent of whole lung cancers, and its 5-year survival rate is impoverished, as when people are first diagnosed, 68% of whom are identified at a dangerous stage. The molecular mechanisms of NSCLC are still being explored. Methods. GSE18842 and GSE19804 were exerted to scan for diversely expressed genes (DEGs) in NSCLC, and then we used GEPIA for the validation of DEGs expression. The prognostic values were determined through Kaplan–Meier analysis. Three target prediction databases indicated potential microRNAs (miRNAs), while miRNet predicted hsa-miR-1-3p′s upstream long non-coding RNAs (lncRNAs) and pseudogenes. UALCAN was utilized to identify the co-expressed genes of PAICS, while enrichment analysis on them was managed with Enrichr. Results. We initially found that the gene expression level of cyclin B1 (CCNB1), cyclin-dependent kinases1 (CDK1), and phosphoribosylaminoimidazole succinocarboxamide synthetase (PAICS) had a notable increase in NSCLC. We predicted 6, 10, and 7 microRNAs to target CCNB1, CDK1, and PAICS, respectively. Among miRNA-mRNA (microRNA-messenger RNA) pairs, we deduced that the hsa-miR-1-PAICS axis was the most potential one to inhibit the occurrence of NSCLC. We also noted that the hsa-miR-1-3p-PAICS axis participated in regulating the process of mitosis with mechanical functions. Moreover, we identified 5 pseudogenes and 33 long non-coding RNAs (lncRNAs) that might inhibit the hsa-miR-1-3p-PAICS axis in NSCLC. Conclusions. The pseudogene/lncRNA-hsa-miR-1-3p-PAICS is very important in NSCLC on the basis of this study, thus providing us with effective treatments and promising biomarkers for the diagnosis of NSCLC.
The Role of Cell and Gene Therapies in the Treatment of Infertility in Patients with Thyroid Autoimmunity
Authors: Medenica Sanja , Abazovic Dzihan , Ljubić Aleksandar , Vukovic Jovana , Begovic Aleksa , Cucinella Gaspare , Zaami Simona , Gullo Giuseppe
Published: August 2022

There is a rising incidence of infertility worldwide, and many couples experience difficulties conceiving nowadays. Thyroid autoimmunity (TAI) is recognized as one of the major female infertility causes related to a diminished ovarian reserve and potentially impaired oocyte maturation and embryo development, causing adverse pregnancy outcomes. Growing evidence has highlighted its impact on spontaneously achieved pregnancy and pregnancy achieved by in vitro fertilization. Despite the influence of thyroid hormones on the male reproductive system, there is insufficient data on the association between TAI and male infertility. In past years, significant progress has been achieved in cell and gene therapies as emerging treatment options for infertility. Cell therapies utilize living cells to restore healthy tissue microenvironment and homeostasis and usually involve platelet-rich plasma and various stem cells. Using stem cells as therapeutic agents has many advantages, including simple sampling, abundant sources, poor immunogenicity, and elimination of ethical concerns. Mesenchymal Stem Cells (MSCs) represent a heterogeneous fraction of self-renewal, multipotent non-hematopoietic stem cells that display profound immunomodulatory and immunosuppressive features and promising therapeutic effects. Infertility has a genetic component in about half of all cases, although most of its genetic causes are still unknown. Hence, it is essential to identify genes involved in meiosis, DNA repair, ovarian development, steroidogenesis, and folliculogenesis, as well as those involved in spermatogenesis in order to develop potential gene therapies for infertility. Despite advances in therapy approaches such as biological agents, autoimmune disorders remain impossible to cure. Recent research demonstrates the remarkable therapeutic effectiveness of MSCs in a wide array of autoimmune diseases. TAI is one of many autoimmune disorders that can benefit from the use of MSCs, which can be derived from bone marrow and adipose tissue. Cell and gene therapies hold great potential for treating autoimmune conditions, although further research is still needed.
Clinical Profile and Risk Factors of Group B Streptococcal Colonization in Mothers from the Eastern District of China
Authors: Jiao Jin , Wu Weiwei , Shen Feng , Liu Zhongyuan , Zhou Huiru , Fan Gang , Zhou Yuxia
Published: August 2022

Objective. The main aim of this study was to determine the prevalence, capsular genotyping, antimicrobial susceptibility, and associated factors of colonizing Group B Streptococcus (GBS) in pregnant women admitted to a hospital in Jinan, East China. Methods. Demographic data, clinical characteristics, and vaginal and rectal swabs were obtained from a group of expecting mothers subjected to GBS screening at the late stage of pregnancy who went into labor over the period from November 2019 to October 2020. Identification of GBS and determination of antimicrobial resistance patterns were performed using a BD Phoenix-100 system. Capsular genotypes were analyzed using polymerase chain reaction and the associated factors were evaluated via logistic regression. Result. A total of 2761 pregnant women were recruited for this study. The GBS colonization rate was 6.70% (185/2761). Among the 172 GBS strains examined, all were susceptible to vancomycin and linezolid. Resistance was the highest for erythromycin (80.2%), followed by clindamycin (75.0%), levofloxacin (65.1%), and tetracycline (57.6%). The most common serotype identified was Ia (61.0%), followed by III (29.7%), VI (4.6%), II (3.5%), VII (0.6%), and a nontypeable strain. Risk factors for maternal GBS colonization included maternal age (older than 30 years) (OR = 1.913 (1.662, 2.478)), gestational age at birth (average gestational age) (OR = 1.992 (1.445, 2.746)), and prelabor rupture of membrane (OR = 3.838 (1.619, 9.099)). Conclusion. The prevalence of GBS was relatively low. The maternal age was a factor associated with GBS colonization. Subjects showing GBS positivity during late pregnancy were prone to prolonged rupture of the membrane (PROM) and birth at lower a gestation age than the GBS-negative group. Penicillin could still be used as the first agent of choice for intrapartum antibiotic prophylaxis (IAP).
An Enhanced Triadic Color Scheme for Content-Based Image Retrieval
Authors: Sangeetha S. K. B. , Mathivanan Sandeep Kumar , Pandi Thanapal , Arivu selvan K. , Jayagopal Prabhu , Teshite Dalu Gemmachis
Published: August 2022

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user’s query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, and

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based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort.
Optimal Look-Angle Guidance with Field-of-View and Impact Angle Constraints for Strapdown Munition
Authors: Wang Peng , Fan Jun-fang , Chen Shi-wei , Wang Hai-sen , Wang Wei
Published: August 2022

A strapdown seeker with limited field-of-view (FOV) can simplify and decrease cost of the guidance system for a munition. However, the inherent defect of the strapdown seeker is that it could not provide the necessary line-of-sight (LOS) angular information for typical guidance application. This paper proposed an optimal look-angle guidance law (OLAGL) which directly utilized seeker angle information and satisfies the constraints of FOV and terminal impact angle. The coefficient determination, effects of switching threshold, and gains for the guidance law are analyzed and detailed. The simulation results compared with typical trajectory shaping guidance for a ground target show that the presented guidance law can satisfy the impact angle constraint under the limitation of FOV and provide a well engineering reference.
Innovation of Trade Union Work in Colleges and Universities Based on Mathematical Modeling and Multivariable Optimal Design
Authors: Dou Min
Published: August 2022

The constant expansion of higher education has led to the continuous increase of the teaching staff and the complexity of the work in the modern era of education, forcing the traditional colleges and universities (it is abbreviated as CU in this article) to participate in trade union activities to open up the road of innovation. What kind of method can effectively and quickly optimize trade union work (TUW) and improve satisfaction has become one of the research topics that has attracted much attention. Aiming at this problem, it has great research significance for the field of CU to participate in trade union activities. With the in-depth research on the work of trade unions in CU, the research on multivariate optimal design in the work of trade unions in CU is gradually carried out, and its advantages are of great significance for solving problems in the work of trade unions in CU. The purpose of this paper is to study the innovation of TUW mode in CU based on multivariate optimization. Through the analysis and research of the multivariate optimization idea, it can be applied to the innovation of the construction of the labor union work model in CU, and the multiobjective linear weighting method is used to solve the objective function, so as to solve the problem of improving the evaluation of labor union work satisfaction in CU. This paper analyzed the TUW in CU (it is abbreviated as TUW in this article) and the multivariate optimization design, conducted an experimental analysis on the improved TUW mode in CU, and used relevant theoretical formulas to explain. The results have shown that the overall evaluation score of the college TUW method proposed in this paper is higher than the traditional college TUW method, and the difference between the two is 18.7%. It can be seen that the university TUW based on mathematical modeling and multivariable optimal design can meet the innovation needs of university TUW, and the satisfaction and work efficiency are greatly improved.
SLC26A4 Mutation Promotes Cell Apoptosis by Inducing Pendrin Transfer, Reducing Cl- Transport, and Inhibiting PI3K/Akt/mTOR Pathway
Authors: Dai Xiang , Li Jun , Hu XiJiang , Ye Jian , Cai WenQian
Published: August 2022

Objective. Pendrin is encoded by SLC26A4, which is expressed in the apical membrane of inner ear epithelial cells and drives chloride reabsorption in the apical septum. In the inner ear, pendrin dysfunction and hypofunctional mutations lead to vestibular aqueduct (EVA) enlargement and sensory neural hearing loss. Mutations in SLC26A4 are a common reason of deafness. However, the underlying mechanisms of SLC26A4 mutants in hearing loss remain unknown. Methods. In the present study, pEGFP‐N1 carrying wild‐type and mutant SLC26A4 (c.85G>A, c.2006A>T, and c.853G>A) were transfected into HEK‐293T cells. GFP fluorescence and GFP levels were determined. SLC26A4 mRNA levels were examined by quantitative real‐time polymerase chain reaction (qRT‐PCR). Then, the expression of chloride intracellular channel 1 (CLIC1) and CLIC2 was measured by Immunofluorescence assay. Intracellular chloride concentration and apoptotic rate were analyzed by flow cytometry. The levels of membrane/cytoplasmic pendrin, apoptosis‐associated proteins, and the PI3K/Akt/mTOR pathway members were determined by Western blot. Results. Constructed SLC26A4 mutant 1 (c.85G>A), SLC26A4 mutant 2 (c.2006A>T), and SLC26A4 mutant 3 (c.853G>A). The wild‐type and 3 mutations were stably expressed in HEK‐293T. SLC26A4 mRNA expression was significantly increased after transfection with wild‐type SLC26A4 and mutant SLC26A4 compared with the untransfected vector group (P < 0.01). Compared with the vector group, the expression levels of membrane pendrin, cytoplasmic pendrin, CLIC1, CLIC2, Bcl‐2, p‐PI3K, p‐Akt, and p‐mTOR were upregulated. Compared with the vector group, the chloride concentration, cell apoptotic rate, and the expression levels of caspase‐3, caspase‐9, and Bax were downregulated. Compared with the vector group, the above effects of SLC26A4 were reversed after the SLC26A4 mutant. Conclusion. After SLC26A4 mutation, pendrin was transferred from the membrane, the chloride intracellular channel function was reduced, and the excessive accumulation of chloride in the cytoplasm induced cell apoptosis by inhibited PI3K/Akt/mTOR pathway phosphorylation.
Study on the Lightweighting Strategy of Target Detection Model with Deep Learning
Authors: Hu Junli
Published: August 2022

Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. Firstly, the real-time and efficient target detection backbone network VoVNet is used to replace the feature extraction network VGG16. The residual structure is integrated to solve the problem of VoVNet network degradation to improve network performance. Secondly, self-attention mechanism is introduced to capture multiscale local and global information to obtain richer image semantic features. According to the characteristics of the target sample size, the anchor frame is designed by using a priori information. In the network training, the anchor frame by selection enhancement based on IoU optimization is used to fully train the target information and strengthen the reading ability of the network to small-scale targets. Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm.
Clinical Prediction Nomograms to Assess Overall Survival and Disease‐Specific Survival of Patients with Salivary Gland Adenoid Cystic Carcinoma
Authors: Cai Hong-shi , Huang Shuo-jin , Liang Jian-feng , Zhu Yue , Hou Jin-song
Published: August 2022

Aim. Salivary gland adenoid cystic carcinoma (SACC) is the second highest incidence of malignant salivary gland tumor. The purpose of this study was to establish nomograms combined with SACC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods. Patients with SACC were included in the SEER∗Stat Database from 2004 to 2016. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to filter potential prognostic clinical variables. Multivariate analysis from the Cox proportional hazards model was performed to determine the independent prognostic factors on overall survival (OS) and disease‐specific survival (DSS), applied to develop nomograms. The Schönfeld residual test verified the proportional hazard assumption. The discrimination and consistency of nomograms was assessed and validated according to concordance index (C‐index), receiver operating characteristic (ROC) curves, and calibration curves using an internal 1,000 times bootstrap resampling. The nomogram’s net clinical benefit was assessed through decision curve analysis (DCA). Results. A total of 658 patients with SACC were included. Age, T stage, N stage, M stage, histologic grade, and surgery were independent prognostic factors for OS and DSS. Based on these independent prognostic factors, nomograms were developed to predict 3‐, 5‐, and 10‐year OS and DSS. In the validation of 1,000 times bootstrap resampling, the C‐index and ROC curves had good discriminatory ability. The calibration curves indicated excellent consistency between the predicted and actual survival results in the nomograms. The DCA curves demonstrated that the nomograms had good clinical benefit and were superior to the TNM stage and other variables. Conclusions. Two nomograms developed in this study precisely predicted the 3‐, 5‐, and 10‐year OS and DSS rates of patients with SACC in accordance with independent prognostic factors, and their clinical value is better than TNM staging, providing a prognostic reference for other SACC patients.
A Fast Scheduling Method for Massive Oblique Photography 3D Models
Authors: Chen Ruibo , Qu Ruichao , Chen Jiaxing , Lei Jiangtao , Luo Yitan , Liu Rundong , Ma Zhanyu
Published: August 2022

In view of the large amount of oblique photography 3D model data and the fact that high-performance hardware devices cannot load and render massive oblique photography 3D models at one time, this paper provides a fast scheduling method for massive oblique photography 3D models: get the tile radius, side length, scheduling range, and other parameters from the original data, normalize the original tile to a hundred grid, use a regular grid + quadtree + hexadecimal tree to build a tile spatial index, resample the tile data at each level to generate the elevation model DEM and the real radiographic image TDOM, and use the DEM+TDOM method to reconstruct the layered and block top-level 3D model to achieve rapid scheduling and rendering of massive oblique photography 3D models. Experiments show that the method is efficient and feasible and can meet the visualization and real-time interaction requirements of massive urban 3D models.