bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure
Authors: Jiang Chenyang , Jiang Weidong
Published: June 2022

Objective. This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease. Methods. The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolution tool CIBERSORT estimated the composition pattern of immune cell subtypes in heart failure and carried out a correlation analysis combined with gene signals. Results. Under the set threshold, we obtained 47 DE genes with the most significant differences. Enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, inflammatory immune pathway, cardiovascular disease, and inflammatory disease. Three machine learning methods assisted in determining the potential characteristic signals Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1). Validation of external datasets confirms that FREM1 is a diagnostic gene signal for heart failure. Immune cell subtypes of tissue specimens found T cell CD8, mast cell resting, T cell CD4 memory resting, T cell regulation (Tregs), monocytes, macrophages M2, T cell CD4 naive, macrophages M0, and neutrophils are associated with HF. Conclusion. The gene signal FREM1 may be a potential molecular target in the development of HF and is related to the difference in immune infiltration of HF tissue.
Long Noncoding RNA BCYRN1 Recruits BATF to Promote TM4SF1 Upregulation and Enhance HCC Cell Proliferation and Invasion
Authors: Liu Ya , Liu Meng-jie , Jiao Min , Jiang Li-li , Fu Xiao , Wang Wen-juan
Published: June 2022

Hepatocellular carcinoma (HCC) is a common form of cancer for which a subset of reliable clinical biomarkers has been defined. However, other factors including long noncoding RNAs (lncRNAs) can also regulate HCC development. This study was thus designed to understand how the lncRNA Brain cytoplasmic RNA 1 (BCYRN1) modulates HCC progression. Bioinformatics approaches were used to identify genes, lncRNAs, and transcription factors that were differentially expressed in the context of HCC, after which the relative expression of BCYRN1 in HCC and control tissues was assessed via qPCR. The ability of BCYRN1 to bind the transcription factor BATF was further evaluated in an RNA immunoprecipitation (RIP) assay, while chromatin immunoprecipitation (ChIP) was used to gauge the binding of the TM4SF1 promoter by BATF. Luciferase reporter assays were also used to assess the association between BCYRN1 and the TM4SF1 promoter. Subsequent loss- and gain-of-function assays were then conducted to explore the effects of altering BCYRN1 expression levels on the proliferative, invasive, and migratory activity of HCC cells. BCYRN1 upregulation was associated with poorer clinical outcomes in HCC patients, and knocking down this lncRNA impaired HCC cell migration and invasion. From a mechanistic perspective, BATF was recruited to the TM4SF1 promoter by BCYRN1, and reducing the expression of this lncRNA was sufficient to constrain xenograft tumor growth in mice. These results highlight BCYRN1 as a putative therapeutic target in HCC tumors.
Application of Modern Urban Landscape Design Based on Machine Learning Model to Generate Plant Landscaping
Authors: Liu Dan
Published: June 2022

With the continuous improvement of the living standards of people, the requirements of people for the environment in which they are located are gradually increasing, which makes urban landscape design work more and more important. Plant landscaping is the core component of urban landscape design. By analyzing and discussing the application of plant landscaping in urban landscape design, the quality level of urban landscape design can be further improved, thereby promoting the sustainable development of urban construction. Machine learning makes it possible to realize the intelligent processing of data and make full use of the knowledge and value contained in the data. This paper explores the way of intelligent analysis and application based on machine learning in the field of landscape architecture. First, the k-means machine learning clustering method is used to determine the types of plant landscaping required by modern urban landscape design. Experiments show that our model can be well applied to design applications of modern urban landscapes.
Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example
Authors: Zhang Yi , Yang Shuwang , Zhang Hang
Published: June 2022

This paper establishes a prediction model of traffic flow, where three cycle dependent components are used to model three characteristics of traffic data, respectively. CNN is used to extract spatial features, and the combination of LSTM and attention mechanism is used to dynamically capture the influence of historical period on target period. Finally, the results are obtained by weighted integration of each component. Its prediction result is more accurate, which can provide reference for governance of urban transportation industry under the background of big data.
Visual Design of Brand Image by Marine Bionic System in the Environment of Internet of Things and Machine Learning
Authors: Niu Minghui , Cho Joung Hyung
Published: June 2022

Based on the environment of economic globalization, many clothing companies have increased the requirements for clothing design styles to diversify them, thereby increasing the interest of consumers. The main purpose and motivation are to explore the pattern design of children’s clothing with the marine bionic environment as the design source. Firstly, the collaborative business concept of IoT machine learning is introduced into the field of clothing design. Design styles and elements related to marine bionic environments are introduced. A set of questionnaires about the visual design preferences of children in different families on marine style clothing patterns are designed to examine differences in the cognitive level of color patterns among children of different age groups. Through the sorting, statistics, and analysis of the questionnaire results, different families have a stronger interest in clothing with warm colors as the color style and lines and ordinary paintings as the pattern drawing style. This provides a certain degree of design ideas for related clothing design work and provides unique insights into the visual design of brand image based on the marine bionic system in the Internet of Things (IoT) and machine learning environment.
Sustainable Tourism Supply Chain Assessment Using Hybrid Decision-Making Methods under Fuzzy Uncertainty
Authors: Zhijun Chen , Hsieh Tsung-Shun , Huang Chao-Hsi , Ghaffari Mahdi
Published: June 2022

In recent years, sustainable environmental issues have attracted attention from all circles due to environmental variation and the depletion of natural resources. Modern thinking about the environment contains the human environment, historical tradition, and social life of the whole ecosystem. In this research, the design, development, and construction of the environmental system are assessed. In this regard, sustainable development, environmental issues, venue administrators and staff, tourists’ education, and service quality evaluation are investigated. After correction to build four broad categories of 26 education venue service quality evaluation indexes, the ecological environment issues venue service quality of the Hakka Romantic Avenue project was conducted by questionnaire survey in Taiwan. Moreover, the VIKOR method is used for service quality evaluation of different subjects, sorting visitors to the evaluation results of the reference, finding the government supervisors, and environmental planning venue administrators. The results showed that the Service Preference (SERVPERF) model combined with Fuzzy-AHP and VIKOR method was effective in the ranking of service quality evaluation of environmental planning venues, and it was significant to eliminate the complacent tendency of the administrators of environmental planning venues to improve the service quality of education venues.
Use Social Media Knowledge for Exploring the Portuguese Wine Industry: Following Talks and Perceptions?
Authors: Pérez-Rodríguez Gael , Baptista João Pedro , Igrejas Gilberto , Fdez-Riverola Florentino , Lourenço Anália
Published: June 2022

This work presents an exploratory study that retrieves, processes, and analyses Twitter data to gain insights about the relevance and perceptions of the wine industry in the Douro Portuguese region (including Porto and Douro wines), as well as other regions in the country. The main techniques and algorithms used in our work belong to the families of natural language processing and machine learning, and the practical relevance of the proposed methodology has been proven in the analysis of 1.2 million unique messages from more than 764,000 distinct users retrieved from the Twitter platform. Derived results from this study are valuable to provide insights that can be further used in the context of Business Informatics to promote better and more efficient marketing campaigns, for example, centering the topic on the most interested people or communicating with the most appropriate words.
Development and Characterization of a Nanobody against Human T-Cell Immunoglobulin and Mucin-3
Authors: Xia Mingyuan , Hu Xiangnan , Zhao Qiuxiang , Ru Yi , Wang He , Zheng Fang
Published: June 2022

Monoclonal antibodies and antibody-derived biologics are essential tools for cancer research and therapy. The development of monoclonal antibody treatments for successful tumor-targeted therapies took several decades. A nanobody constructed by molecular engineering of heavy-chain-only antibody, which is unique in camel or alpaca, is a burgeoning tools of diagnostic and therapeutic in clinic. In this study, we immunized a 4-year-old female alpaca with TIM-3 antigen. Then, a VHH phage was synthesized from the transcriptome of its B cells by nested PCR as an intermediate library; the library selection for Tim-3 antigen is carried out in three rounds of translation. The most reactive colonies were selected by periplasmic extract monoclonal ELISA. The nanobody was immobilized by metal affinity chromatography (IMAC) purification with the use of a Ni-NTA column, SDS-PAGE, and Western blotting. Finally, the affinity of TIM3-specific nanobody was determined by ELISA. As results, specific 15 kD bands representing nanomaterials were observed on the gel and confirmed by Western blotting. The nanobody showed obvious specific immune response to Tim-3 and had high binding affinity. We have successfully prepared a functional anti-human Tim-3 nanobody with high affinity in vitro.
Information Extraction and Knowledge Graph Construction for Enterprises in China’s Free Trade Zone
Authors: Meng LingYan
Published: June 2022

With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results.
Ehrlichiosis in a Recent Liver Transplant Recipient Leading to Multiorgan Failure
Authors: Almajali Fawwaz , Oleary Catherine , Hallcox Taylor , Lok Justin , Hermelin Daniela , Guenette Alexis , Nazzal Mustafa
Published: June 2022

Ehrlichia infection has a broad spectrum of diseases ranging from asymptomatic to fatal. While Ehrlichia often presents as a mild form of the disease in immunocompetent patients, immunosuppressed patients are at increased risk for a more virulent and potentially fatal infection. Our liver transplant patient presented with fever, persistent headaches, and negative Ehrlichia antibodies. Empiric antibiotic therapy was started and along with knowledge of prior tick infection, doxycycline was added. Subsequent positive PCR and observation of Ehrlichia chaffeensis in peripheral blood smear confirmed the diagnosis. The patient did recover from infection but not before it manifested in hepatic, renal, and pulmonary involvement. Therefore, a high level of suspicion is necessary for early detection and treatment initiation to prevent a devastating progression of the disease in immunosuppressed patients.
An Empirical Analysis of Jiangxi Province’s Financial Development, Economic Growth, and Environmental Pollution
Authors: Lu Yonghong , Liu Rixing
Published: June 2022

Since the reform and opening up in 1978, the economic development in China has experienced a rapid growth stage. Statistics show that annual GDP has grown at an average rate of nearly 10%, and the annual per capita GDP growth rate has also approached 9%. With the rapid economic growth, the problem of environmental pollution across the country has become increasingly serious, and the environmental quality has been deteriorating, which has attracted the attention of many parties in the whole society. The relationship between economic growth and environmental quality has become a hot issue in various fields. First of all, this article introduces basic concepts such as economic development, environmental quality, environmental Kuznets curve, and the relationship between economic development and environmental quality. Afterwards, it analyzes the current situation of Jiangxi Province’s economy and environment from the aspects of Jiangxi Province’s economic aggregate, industrial structure, development mode, overall law of environmental quality, and industrial waste release. Secondly, this study constructs the evaluation index system of the economic and environmental relationship in Jiangxi Province and uses the principal component analysis method to screen the primary economic and environmental indicators, providing evaluation criteria for the establishment of the economic development and environmental quality relationship model of Jiangxi Province. Third, the relationship between the main economic environment indicators after the principal component analysis is simulated, and the EKC model of Jiangxi Province is established. The research results show that the environmental Kuznets curve in Jiangxi Province is not all the typical inverted U shape in developed countries, that is, environmental pollution does not necessarily show a trend of first rising and then falling with the increase of per capita income, but it appears as N-shaped, half of the inverted U shape, and even repeated fluctuations.
The Relationship between the Degree of Urban Intelligence and the Performance of Enterprises in the City Based on Artificial Intelligence and Soft Computing
Authors: Feng Xiaobing , Wang Yongke , Han Fei
Published: June 2022

With the rapid development of Internet technology, artificial intelligence and soft computing are gradually applied in various fields. Through active transformation, many companies use artificial intelligence, software computing, and other technologies to develop sales channels, improve corporate performance, and obtain greater benefits. However, some traditional enterprises have been gradually eliminated by the market because they have not rapidly transformed and improved their intelligence. Urban intelligence is the trend of urban economic development, and the degree of intelligence in a city is closely related to the number and scale of intelligent enterprises. In the context of artificial intelligence and soft computing, this paper expounds the relationship between enterprise performance and the degree of urban intelligence and analyzes the key factors affecting enterprise performance and the impact of enterprise performance on the degree of urban intelligence. The results show that Internet technologies such as artificial intelligence and soft computing can improve the performance of enterprises, thereby improving the level of intelligence in cities.
Design and Implementation of Internet of Things Technology in College Students’ Innovation and Entrepreneurship Integration System
Authors: Li Huilong , Sun Xiping
Published: June 2022

In order to improve the operation efficiency of the innovation and entrepreneurship integration system of college students, this document proposes to formulate and implement the innovation and entrepreneurship integration system of college students based on “Internet technology.” The web front end is implemented by Django technology, the back end is developed by Apache server pycharm, the database is written in Python, and the database is MySQL. The system meets the expected objectives of implementing, monitoring, and managing innovation and entrepreneurship projects. How the system realizes the functions of repeated comparison projects, easy tracking projects, and fast search, and how managers can make progress at any time. By creating different account function permissions, the system realizes the login, declaration, management, and other functions required by the users of the innovation and entrepreneurship project management system. It is very suitable for college students to improve the project application, information review, and application process. At the same time, it is helpful to supervise and select teachers and greatly improve management efficiency. The test results show that the minimum values of 30, 80, and 150 simulated users in the system performance test are 4, 5, and 7, respectively. The maximum users of the system performance test model are 30, 80, 150, 64, 284, and 398. The system performance tests are taken under the conditions of 30, 80, and 150 user bandwidth, with the results of 113/min, 104/min, and 90/min. The system basically meets the daily work management standards and fully achieves the expected system design goals. The development and use of this system are related to the development of college students’ innovation and entrepreneurship project management and have a clear reference for the development of innovation and entrepreneurship project management systems similar to manage workflow in the future.
People Deaths and Injuries Caused by Lightning in Himalayan Region, Nepal
Authors: Adhikari Pitri Bhakta
Published: June 2022

Several natural disasters such as lightning, flood, fire, epidemics, and landslides frequently occur in the Himalayan region, of Nepal. The altitude of this region ranges from 59 m to “8848.86 m”—the height of Mount Everest—along with a distance of just 160 km. As a result, there is a considerable (i.e., nearly 95°C) variation in temperature in the region. Moreover, Nepal has a heterogeneous topography. All these features influence various natural disasters including lightning activity. This paper is aimed at analyzing the variations of lightning within and across the years from January 2011 to December 2021—a total of 11 years. For this, the data were taken from the Disaster Risk Reduction (DRR) portal of the Ministry of Home Affairs (MOHA). The analysis showed that there were no lightning events in November, and the lightning density was higher in the premonsoon period of study in this region. It also showed that the number of injured people is about thrice the death of the people due to the lightning. The null occurrence of lightning in November in the Himalayan region is a contribution novel to this field.
AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization
Authors: Wen Zheng , Yu Keping , Qi Xin , Sato Toshio , Myint San Hlaing , Tamesue Kazuhiko , Katsuyama Yutaka , Dobashi Hironori , Murakami Yasushi , Koyama Ikuo , Tokuda Kiyohito , Kameyama Wataru , Sato Takuro
Published: June 2022

In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.
Hibrid Δ‐Statistical Convergence for Neutrosophic Normed Space
Authors: Gonul Bilgin Nazmiye
Published: June 2022

This study is about neutrosophic structures, which is one of the popular topics of recent days. In this study, different types of convergence concepts were applied to difference sequences. With the help of the properties of double type sequences, the concept of difference sequences is combined with structures that are advantageous to work like Lacunary sequences.
The Bioactivities of Phycocyanobilin from Spirulina
Authors: Li Yi
Published: June 2022

Phycocyanobilin (PCB) is a linear open-chain tetrapyrrole chromophore that captures and senses light and a variety of biological activities, such as anti-oxidation, anti-cancer, and anti-inflammatory. In this paper, the biological activities of PCB are reviewed, and the related mechanism of PCB and its latest application in disease treatment are introduced. PCB can resist oxidation by scavenging free radicals, inhibiting the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, and delaying the activity of antioxidant enzymes. In addition, PCB can also be used as an excellent anti-inflammatory agent to reduce the proinflammatory factors IL-6 and IFN-γ and to up-regulate the production of anti-inflammatory cytokine IL-10 by inhibiting the inflammatory signal pathways NF-κB and mitogen-activated protein kinase (MAPK). Due to the above biological activities of phycocyanobilin PCB, it is expected to become a new effective drug for treating various diseases, such as COVID-19 complications, atherosclerosis, multiple sclerosis (MS), and ischaemic stroke (IS).
An Empirical Analysis of the Role of Piano Performance in Alleviating Psychological Trauma in People with Psychological Isolation Disorder
Authors: Cao Xia
Published: June 2022

This paper presents an in-depth analysis and study of the role of piano performance in alleviating psychological trauma in people with psychological isolation disorder. In this paper, we designed a music modulation system based on EEG signals of people with isolation disorder and designed an interface with real-time emotion recognition. MATLAB is responsible for data processing and classification, while Python is responsible for communication and real-time transmission between modules, EEG signal collection, and processing. For the EEG signals in the DEAP emotion database, a Butterworth bandpass filter is used to denoise the signals, and then, a wavelet packet decomposition reconstruction is used to remove the artifacts and complete the preprocessing of the signals. Finally, the support vector machine with optimized parameters of the genetic algorithm was used to classify the positive, neutral, and negative samples with 89.23% accuracy. In this study, all subjects were divided into experimental and control groups by experimentally measuring the changes in heart rate, skin electrical conduction, skin temperature, and EEG signals before and after the experimental group, and statistical analysis was also performed on the data tabulation of the experimental and control groups. The experimental results proved that piano playing has a significant effect on relieving the psychological trauma of people with psychological isolation disorder when the training frequency of piano playing therapy reaches a certain intensity. This study provides a certain theoretical basis for clinical, educational, and health services.
The Spatio-Temporal Variation of Isothermal Water Mass in Jiaozhou Bay
Authors: Yang Dongfang
Published: June 2022

According to the survey materials of the waters of Jiaozhou in April and August 1981, the author determined the variation range of temperature, location, and the variation process of isothermal water mass in the waters of Jiaozhou Bay. The results showed that in April, high-temperature water masses were formed in the coastal waters of Jiaozhou Bay from the northeast to the northwest, and the water temperature reached 12.82–13.70°C, where the length of the interval of seawater temperature change was 0.88°C. In August, the high-temperature water masses were formed in the coastal waters of Jiaozhou Bay from the northwest to the north with the high temperature within 27.32–27.37°C, where the length of the interval was 0.05°C. In April and August, the high-temperature water masses were formed in the northern coastal waters where the depth was 1.00–5.00 m. The heat of high-temperature water masses was sourced from solar radiation energy. In August and in the coastal waters near the estuary of Haibo River, high-temperature water masses were formed, with a high water temperature of 28.00–30.90°C. The high temperature of the Haibo River provided heat to the waters of Jiaozhou Bay. In April, a circular low-temperature water mass was formed in the central waters of the bay, and the water temperature reached a low temperature of 7.52–8.51°C, which indicated that the central waters of the bay still maintained the uniform low temperature in winter and formed a low-temperature water mass. Therefore, the innovation of this paper is to put forward the spatio-temporal variation of the isothermal water mass in Jiaozhou Bay, established the block diagram of temperature changes in the waters of Jiaozhou Bay in April and August, and further elaborated the characteristics of isothermal water masses. (1) At the same time change, both the high-temperature water mass and the low-temperature water mass have stability. (2) In different time changes, whether it is a high-temperature water mass with a relatively higher water temperature or a high-temperature water mass with a very high water temperature, the water mass is stable.
PEGylated Protamine Letrozole Nanoparticles: A Promising Strategy to Combat Human Breast Cancer via MCF‐7 Cell Lines
Authors: Khan Muhammad Tafhim , Uddin Zia , Javed Muhammad Arslan , Shah Nabi , Bashir Hamid , Shaikh Ahson Jabbar , Rajoka Muhammad Shahid Riaz , Amirzada Muhammad Imran , Asad Muhammad Hassham Hassan Bin
Published: June 2022

The objective of the study was to develop PEGylated protamine letrozole nanoparticles to combat human breast cancer by modifying the release pattern of letrozole. Breast cancer is amongst the most prevalent diseases in women due to overactivity of human epidermal growth factor receptor 2 (HER2). PEG‐protamine letrozole nanoparticle formulation was designed and optimized to alter the release pattern of the drug. The size, morphology, and structure of PEG‐protamine letrozole NP were characterized by FTIR, XRD, Zetasizer, and SEM analysis. The result showed the PEG‐protamine letrozole nanoparticles were irregular in shape and have size ranging from 258 nm to 388 nm, polydispersity index 0.114 to 0.45, zeta potential of 11.2 mV, and entrapment efficiency 89.93%. XRD studies have confirmed that the crystal structure of letrozole has become amorphous. The drug release study maintained the prolonged release for 72 hours. Moreover, the PEG‐protamine letrozole NPs displayed a strong anticancer action compared to MCF‐7 cells with an IC50 70 μM for letrozole and 50 μM for PEG‐protamine letrozole NPs. Overall, our results indicate that letrozole PEG‐protamine NPs alter the release profile of letrozole, which could be an excellent approach for overcoming letrozole resistance in human breast cancer.
Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm
Authors: Zhou Shutian , Xie Yanhong
Published: June 2022

Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good.
Crocus sativus L. Tepal Extract Induces Apoptosis in Human U87 Glioblastoma Cells
Authors: Baba Shoib Ahmad , Vahedi Maryam , Ahmad Irfan , Rajab Bodour S. , Babalghith Ahmad O. , Irfan Safia , Hossain Md. Jamal
Published: June 2022

Crocus sativus (C. sativus) is considered as the costliest spice and an important medicinal plant. Herein, we investigated the effects of tepal extract (TE) of C. sativus on the viability of the human glioblastoma cells. Results revealed that TE significantly (P < 0.05) inhibited the proliferation of U87 glioblastoma cells in a dose‐dependent manner with comparatively lower toxic effects against normal astrocytes. The IC50 of TE against U87 glioblastoma cells was found to be 130 μg/mL as compared to 600 μg/mL against normal astrocytes. TE also inhibited the colony formation of U87 cells significantly (P < 0.05). The AO/EB and Annexin V/PI staining assays indicated that TE stimulated apoptosis in U87 cells dose dependently. The early and late apoptotic U87 cells increased from 0.66% and 2.3% at control to 14.2% and 21.4% at 260 μg/mL of TE. Moreover, TE caused upregulation of Bax and suppression of Bcl‐2. Wound healing assay showed that migration of the U87 cells was suppressed significantly (P < 0.05) at 80 μg/mL of TE. Taken together, these results suggest that TE exhibits antiproliferative effects against U87 glioma cells and may prove to be an important source of natural anticancer agents.
Effect of Painless Rehabilitation Nursing for Hip Replacement Patients
Authors: Zhao Xiaona , Bai Ru , Yang Jing
Published: June 2022

Objective. To analyze the effect of painless rehabilitation nursing for hip replacement patients. Method. 124 elderly patients who underwent total knee arthroplasty in our hospital from June 2019 to June 2020 were selected as study subjects. They were randomly divided into observation group and control group. The control group was given routine nursing care, and the observation group was given painless rehabilitation care on the basis of the control group. Knee circumference, knee pain, knee function, agitation and sleep duration were recorded and compared between the two groups. Results. The changes of knee circumference diametral in both groups were significantly decreased at 1, 3 and 7 days after operation; The changes of knee circumference diametral in the observation group were significantly smaller than those in the control group at 3,7 days after operation (P <0.05). VAS (Visual Analogue Scale) scores at 1, 3 and 7 d after operation were significantly decreased in both groups; The score of the observation group was significantly lower than that of the control group, and the difference was statistically significant (P <0.05). HSS (Hospital for special surgery) scores increased significantly in both groups at 1 week, 1 month, 3 months and 6 months after operation. All the comparisons were statistically significant (P <0.05); HSS scores of observation group were significantly higher than control group at 1 week, 1 month, 3 months and 6 months after surgery. The difference was statistically significant (P <0.05). The agitation rate of the observation group was lower than that of the control group (P <0.05). Compared with the control group, the sleep time of observation group increased significantly in each period, with statistically significant difference (P <0.05). Conclusion. Perioperative painless rehabilitation nursing interventions for patients with hip replacement could significantly relieve swelling and pain, it was helpful for the patients to recover the function of knee joint after operation and worthy of clinical application.
Application of 5G Network Technology in Basketball Teaching Innovation and Reform
Authors: Xie Zhe , He Xiaohua
Published: June 2022

The advent of 5G technology has brought ease to many sectors of society and lifestyle. 5G technology has also made significant contributions to education, with a significant positive influence on physical education, especially for online learning. This study examined the online basketball learning system based on the 5G mobile communication technology as well as traditional face-to-face teaching and online learning and analyzed the students’ performance using different evaluation techniques. The network latency is minimized by detecting and correcting the main energy-consuming activities of the 5G network to deliver effective online training to students utilizing 5G basketball teaching (5G_BBT). The features of 5G network nodes and channel characteristics are used to build a comprehensive model of basketball training. The information that could be sent to the user protocol is obtained from the teaching database, and the cluster formation is processed using crossover swarm optimization based on passive clustering. The maximum-likelihood naïve Bayes function is employed to compute the transmission of basketball data transmission, and the border gateway algorithm is used to calculate the optimum cluster path for transmitting the training material, based on distance and residual energy constraints. The model is evaluated in terms of latency energy efficiency and speed to prove the effectiveness of the proposed 5G networking scheme. The proposed algorithm achieved a high network speed of 98%, data transfer efficiency of 90%, and time complexity of 32%, respectively. The 5G-driven basketball training approach will provide students with objective, fair, and diversified learning and adaptive learning services to promote the overall development of students.
Effect of Manual Wheelchair Type on Mobility Performance, Cardiorespiratory Responses, and Perceived Exertion
Authors: da Silva Bertolaccini Guilherme , Sandnes Frode Eika , Medola Fausto Orsi , Gjøvaag Terje
Published: June 2022

This study is aimed at comparing the design and configuration of the most commonly used manual wheelchair models through cardiorespiratory responses, perceived exertion, and mobility performance using two different manual wheelchairs, during mobility tasks. A within-group

2
×
3
×
2

controlled experiment was designed with three independent and four dependent variables. The independent variables included wheelchairs, with the levels active wheelchair with a rigid frame and passive wheelchair with foldable frame; conditions with the levels straight line, slalom, and agility; and speed with levels comfortable and fast. Dependent variables included oxygen uptake (VO2), distance travelled, speed, and perceived exertion. Results show that the active wheelchair yielded more beneficial characteristics although only the effect of wheelchair type on VO2 efficiency (oxygen uptake per meter travelled) was statistically significant with a large effect size (

F

1
,
14

=
118.298
,
p
<
0.001

,

η

2

=
0.541

). The better VO2 efficiency was achieved with the active wheelchair under all tested conditions. The effect of wheelchair type on Borg scores was also statistically significant, although with a small effect size (

F

1
,
14

=
10.340
,
p
=
0.006

,

η

2

=
0.119

); thus, active wheelchair use had lower Borg scores under all trials and was considered less exhausting than the passive wheelchair. In summary, use of the active wheelchair resulted in the users expending less energy per meter travelled and at the same time experiencing less fatigue. This may benefit overall wheelchair mobility and possibly reduce health complications.
Performance Evaluation Method of Rural Forestry Economic Cooperation Organization Based on Intelligent Fuzzy Algorithm
Authors: Chen Zhuo , Huang Heliang , Hong Yanzhen
Published: June 2022

Aiming at the problems of low accuracy and long evaluation time of performance evaluation of rural forestry economic cooperation organizations, a performance evaluation method of rural forestry economic cooperation organizations based on intelligent fuzzy algorithm is proposed. Under the principles of scientificity, feasibility, comprehensiveness, and importance, build the performance evaluation system of rural forestry economic cooperation organization, and obtain 17 evaluation indexes, such as the supply of means of production, the internal management of the cooperation organization, and the unified product brand. Deal with the missing data and standardize the index data, construct the evaluation index judgment matrix according to the judgment matrix scale, and test the consistency of the judgment matrix through CI. The square root method is used to calculate the performance evaluation index weight of rural forestry economic cooperation organization. Intelligent fuzzy algorithm is used to evaluate the performance of rural forestry economic cooperation organization. Simulation results show that this method has high accuracy and short evaluation time.
Clinical Characteristics and Predictors of Mortality in Elderly Patients Hospitalized with COVID-19 in Bangladesh: A Multicenter, Retrospective Study
Authors: Asaduzzaman Md. , Nazmul Alam Z. H. M. , Zabed Jillul Bari Mohammad , Jahangir Alam M. M. , Ranjan Chakraborty Shishir , Ferdousi Tasnim
Published: June 2022

Purpose. Elderly patients are at high risk of fatality from COVID-19. The present work aims to describe the clinical characteristics of elderly inpatients with COVID-19 and identify the predictors of in-hospital mortality at admission. Materials and Methods. In this retrospective, multicenter cohort study, we included elderly COVID-19 inpatients (n = 245) from four hospitals in Sylhet, Bangladesh, who had been discharged between October 2020 and February 2021. Demographic, clinical, and laboratory data were extracted from hospital records and compared between survivors and nonsurvivors. We used univariable and multivariable logistic regression analysis to explore the risk factors associated with in-hospital death. Principal Results. Of the included patients, 202 (82.44%) were discharged and 43 (17.55%) died in hospital. Except hypertension, other comorbidities like diabetes, chronic kidney disease, ischemic heart disease, and chronic obstructive pulmonary disease were more prevalent in nonsurvivors. Nonsurvivors had a higher prevalence of leukocytosis (51.2 versus 30.7;

p
=
0.01

), lymphopenia (72.1 versus 55;

p
=
0.05

), and thrombocytopenia (20.9 versus 9.9;

p
=
0.07

). Multivariable regression analysis showed an increasing odds ratio of in-hospital death associated with older age (odds ratio 1.05, 95% CI 1.01–1.10, per year increase;

p
=
0.009

), thrombocytopenia (OR = 3.56; 95% CI 1.22–10.33,

p
=
0.019

), and admission SpO2 (OR 0.91, 95% CI 0.88–0.95;

p
=
0.001

). Conclusions. Higher age, thrombocytopenia, and lower initial level of SpO2 at admission are predictors of in-hospital mortality in elderly patients with COVID-19.
Glymphatic System: Emerging Therapeutic Target for Neurological Diseases
Authors: Xuan Xianjun , Zhou Guoyi , Chen Caihong , Shao Anwen , Zhou Yunxiang , Li Xiaobo , Zhou Jiaqi
Published: June 2022

The newly discovered glymphatic system acts as pseudolymphatic vessels subserving brain waste clearance and is functionally dependent on astrocytic aquaporin-4 channels. The glymphatic system primarily functions during sleep as an interchange between cerebrospinal fluid and interstitial fluid, with cerebrospinal fluid flowing into the parenchyma via the perivascular spaces and then exchanging with interstitial fluid. The discovery of meningeal lymphatics helps refine the conceptual framework of glymphatic pathway, as certain waste products collected alongside perivascular spaces ultimately drain into the cervical lymph nodes via meningeal lymphatics, whose function regulates the functioning of the glymphatic system. The glymphatic and meningeal lymphatic systems are critical for the homeostasis of central nervous system, and their malfunctions complicate cerebral dysfunction and diseases. The present review will shed light on the structure, regulation, functions, and interrelationships of the glymphatic system and meningeal lymphatics. We will also expound on their impairments and corresponding targeted intervention in neurodegenerative diseases, traumatic brain injury, stroke, and infectious/autoimmune diseases, offering valuable references for future research.
Management of Transplant Renal Artery Pseudoaneurysm and Literature Review
Authors: Anders Luke , Stephens Rachel , Laub Melissa , Amarath-Madav Rushay , Mirza Ahmad , Saeed Muhammad Irfan
Published: June 2022

Renal transplantation is the ultimate treatment for end-stage renal disease patients. However, vascular complications can impact renal allograft outcomes. Extrarenal pseudoaneurysms (EPSA) are a rare complication occurring in 1% of transplant recipients. We report a case series of extrarenal pseudoaneurysm after kidney transplant with different clinical presentations and management strategies. Given the rarity of EPSA, literature describing this complication is limited to single case reports or small retrospective case series. We also provide an up-to-date review of 76 articles on mycotic, bacterial, and idiopathic EPSAs. Allograft removal is considered standard treatment, but new endovascular alternatives may allow allograft salvage. EPSA should be managed with a multidisciplinary approach. Surveillance with renal ultrasound is recommended in patients considered high risk.
Optimization of Substation Alarm Information Processing Based on BP Neural Network
Authors: Han Tuanjun
Published: June 2022

When a fault occurs in the power grid, a large number of alarm messages will come out. In order to reduce the damage caused by power outages, the staff should find out the location and type of faults as soon as possible based on the alarm messages uploaded from the control center. However, we are currently facing problems in handling these alarm messages such as reliability of protection actions and circuit breaker tripping, correctness of received alarm messages, and the possibility of the existence of unreceived alarm messages. The fault tolerance of the BP neural network is studied in this paper, and the fault tolerance of the network corresponds to the size of the fuzzy zone formed by the test samples. The fault tolerance of the network is improved by eliminating the fuzzy zone, and a hierarchical causal rule reasoning network with a structure of four layers is established for each element in the station. An accessible path for the candidate cause of the fault is divided into two stages as follows: from the candidate cause to the protection operation and from the protection operation to the circuit breaker trip, which are assigned different credibility contribution rates. At the same time, the artificial bee colony algorithm is combined to identify the operation mode of the substation using its reasoning and judgment capacity and make the necessary corrections to get some output results of the neural network. The results of the examples suggest that the proposed evaluation method has a small workload of calculation and can identify the faulty components with high reliability and their causes, quickly and accurately.
Construction and Application of Medical Economic Information System under Big Data
Authors: Yu Xiaopeng , Yang Chuanjia , Yang Jing
Published: June 2022

The development of medical detection, storage, and other technologies makes China’s medical and health field accumulate massive data. Big data analysis technology can mine and analyze these data and establish medical information systems to enhance hospital treatment and service levels. Therefore, this work studies the construction and application of medical economic information systems. Specifically, neural networks, as a means to handle a large number of medical economic data, and has received great attention. This work uses BP neural network to extract the medical economic information feature, which is a big data technology and can extract deep features of data, after that, we compare and analyze the characteristic of different medical data. Finally, we establish the medical economic information system for the follow-up medical diagnostic services.