bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion
Authors: Wang Yan , Chen Weiwei , Wang Yulan
Published: August 2022

The necessity of predicting and estimating river velocity motivates the development of a prediction method based on GAN image enhancement and multifeature fusion. In this method, in order to improve the image quality of river velocity, GAN network is used to enhance the image, so as to improve the integrity of image data set. In order to improve the accuracy of prediction, the image is extracted and fused with multiple features, and the extracted multiple features are taken as the input of CNN, so as to improve the prediction accuracy of convolution neural network. The results show that when the velocity is 0.25 m/s, 0.50 m/s, and 0.75 m/s, the accuracy of improved method can reach 85%, 90%, and 92%, which are higher than SVM, VGG-16, and BPNET algorithms. The above results indicate that the improvement has certain positive value and practical application value.
Traditional Medicine Use among Type 2 Diabetes Patients in KZN
Authors: Chetty Lauren , Govender Nalini , Reddy Poovendhree
Published: August 2022

Background. Traditional medicine (TM) is widely used in both developing and developed countries to assist in the attempt to curtail the prevalence and increase in diabetes mellitus. Approximately 53% of South Africans use TM to prevent and treat their diseases. There is no conclusive evidence regarding the safety and effectiveness of TM versus prescribed medicine. The most common therapies used by diabetics in Africa include herbal treatments, nutritional products, spiritual healing, and relaxation techniques. Therefore, this study aimed to evaluate the use of TM in patients with T2DM who are on chronic therapy and living in KwaZulu-Natal. Method. This cross-sectional study was conducted at a district hospital, in which purposive sampling was used to recruit participants and data were collected using a structured questionnaire. Information collected included demographic data, information pertaining to home remedies/TM, and self-care practices employed by participants while using TM. Data were analyzed using Pearson’s chi-squared test, t-test, and multivariate logistic regressions to determine predictors of TM usage. Results. Only 92 (27%) of 340 participants reported using TM, with Indians being the most frequent users (58.24%). Approximately, 83.72% (n = 72) used TM in conjunction with prescribed medication. Most participants (56.32%) acquired TM knowledge from family. The most frequently used TM was lemon and honey, Aloe vera, bitter gourd, green tea, and cinnamon. Traditional medicine use among African participants was 0.56 times (OR = 0.56, 95% CI = 0.34, 0.93) lower than Indian participants. There were no significant predictors for TM usage among the variables tested. Conclusion. A low prevalence rate of TM usage in T2DM patients was found. A significant correlation was noted between ethnicity and TM use. Large-scale studies are required to determine the additive and synergistic effects of TM in health care. Consideration should also be given to integrating TM into mainstream health care.
ATF4/MYC Regulates MTHFD2 to Promote NSCLC Progression by Mediating Redox Homeostasis
Authors: Gao Yixing , Feng Lan , Zhang Luping , Geng Jianhui , Zhang Erlong
Published: August 2022

Purpose. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) has been reported to be overexpressed in non-small-cell lung cancer (NSCLC) and to correlate with malignant proliferation. However, the mechanism of high MTHFD2 expression in NSCLC has not been clarified. Methods. qPCR, western blot, and immunofluorescence experiments were used to measure the expression of related mRNAs and proteins. Cell apoptosis was measured by flow cytometry and TUNEL assays. The CCK-8 assay was used to determine cell viability. Flow cytometry was used to analyze the cell cycle. ROS, H2O2, MDA, SOD, and NADPH/NADP+ were evaluated by relevant assay kits. Transfection of siRNA or vectors was used to downregulate or upregulate gene expression. Dual-luciferase reporter gene assays were used to evaluate the regulated relationship between MTHFD2 and ATF4 or MYC. Results. MTHFD2 was highly expressed in NSCLC cells. Knockdown of MTHFD2 inhibited proliferation and increased apoptosis. Furthermore, oxidative factors significantly increased, while antioxidant factors significantly decreased in NSCLC cells with MTHFD2 knockdown, indicating that MTHFD2 was involved in NSCLC progression through the redox pathway. Although MTHFD2 was downregulated with ATF4 silencing, the dual-luciferase reporter assay suggested that ATF4 did not directly mediate MTHFD2 transcription. Further studies revealed that MYC had a transcriptional effect on MTHFD2 and was also regulated by ATF4. PCR, and western blotting experiments with ATF4 knockdown and MYC overexpression as well as ATF4 overexpression and MYC knockdown proved that ATF4 stimulated MTHFD2 through MYC mediation. Conclusions. ATF4 promoted high expression of MTHFD2 in NSCLC dependent on MYC.
Toll-Like Receptor 4: A Promising Therapeutic Target for Alzheimer’s Disease
Authors: Wu Linyu , Xian Xiaohui , Xu Guangyu , Tan Zixuan , Dong Fang , Zhang Min , Zhang Feng
Published: August 2022

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that primarily manifests as memory deficits and cognitive impairment and has created health challenges for patients and society. In AD, amyloid β-protein (Aβ) induces Toll-like receptor 4 (TLR4) activation in microglia. Activation of TLR4 induces downstream signaling pathways and promotes the generation of proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β), which also trigger the activation of astrocytes and influence amyloid-dependent neuronal death. Therefore, TLR4 may be an important molecular target for treating AD by regulating neuroinflammation. Moreover, TLR4 regulates apoptosis, autophagy, and gut microbiota and is closely related to AD. This article reviews the role of TLR4 in the pathogenesis of AD and a range of potential therapies targeting TLR4 for AD. Elucidating the regulatory mechanism of TLR4 in AD may provide valuable clues for developing new therapeutic strategies for AD.
Study on the Sustainable Development Strategy of School Soccer Based on the Background of Big Data Era
Authors: Zhang Jun , Liang Dong , Sun Zhang
Published: August 2022

Big Data is the most popular concept in this era, which is the massive amount of information and related technology generated by the information explosion in the era of “Internet+.” Big Data is the most popular concept of our time. With the most advanced technology to collect, analyze, organize, and store data, Big Data can effectively handle all kinds of complex information. Because of this, big data is widely favored by all walks of life. In China’s sports industry, the use of big data has become mature and has shown its unique advantages. With the development of campus soccer in China in the past decade, how to use big data to promote the sustainable development of campus soccer in China has become a key issue for sports workers to consider today. Based on the above background, this paper proposes a system combining data mining and personalized data recommendation to collect and analyze the information of campus soccer to promote the sustainable development of campus soccer. First, we propose a data mining method based on deep learning data mining network model combined with migration learning to address the data mining problem. The method uses the knowledge of historical model parameters and applies them to new tasks, thus solving the problem of network training when samples are lacking and improving data utilization and data mining effects. Then, for the data recommendation problem, a new deep learning method is proposed, which performs effective intelligent recommendation by pretraining. In the initial phase, the corresponding low-dimensional embedding vectors are learned, which capture information reflecting the relevance of students to soccer sports. During the prediction phase, a feed-forward neural network is used to model the interaction of student and soccer sport information, where the corresponding pretrained representative vectors are used as inputs to the neural network. Finally, it is experimentally verified that the data mining method proposed in this paper can effectively improve the data mining performance and efficiency, and the proposed data recommendation method possesses better accuracy than the traditional methods. The use of this system can effectively collect and analyze campus soccer information, which helps to develop campus soccer and promote the sustainable development of campus soccer.
Acrid and Bitter Chinese Herbs in Decoction Effectively Relieve Lung Inflammation and Regulation of TRPV1/TAS2R14 Channels in a Rat Asthmatic Model
Authors: Yuan Yamei , Fang Xiangming , Ye Weidong
Published: August 2022

Background. Shegan Mahuang decoction (SGMHD) was widely used as a classic prescription of traditional Chinese medicine to treat asthma. However, there is no research on the acrid and bitter Chinese herbs in the SGMHD to treat asthma. This study aimed to investigate the effects of SGMHD and its acrid-bitter Chinese herbs composition on airway inflammation and the expression of TRPV1 and TAS2R14 genes and proteins in asthmatic rats. Methods. SD (Sprague Dawley) rats of asthma were induced by ovalbumin and aluminum hydroxide, then randomly divided into the Normal group, Model group, SGMHD group, Dexamethasone (Dex) group, Guilongkechuangning (GLKC) group, The Acrid Chinese Herbs group (ACH), and The Bitter Chinese Herbs group (BCH). The rats were given intragastric gavage after 21 days for 4 weeks. The bronchoalveolar lavage fluid (BALF) was collected, and the levels of IL-4, IL-13, nerve factors SP, CGRP, PGE2, and serum of IgE were determined by ELISA. Pathological changes in the lungs were determined by hematoxylin-eosin (HE) staining. The expression of TRPV1 and TAS2R14 in the rat lung group was detected by immunofluorescence (IF). The expression levels of TRPV1 and TAS2R14 were measured using western blotting. The mRNA levels of TRPV1 and TAS2R14 were measured using RT-qPCR. Results. The levels of serum IgE in treated rats and the cytokines IL-4, IL-13, SP, CGRP, and PGE2 were all decreased. HE-staining showed that significantly reduced inflammatory cell infiltration in lung tissue. IF-staining showed the expression levels except those of the normal group were enhanced. Acrid Chinese herbs inhibited TRPV1, and bitter Chinese herbs activated the gene and protein expression of TAS2R in the lung. Conclusion. The acrid Chinese herbs regulate TRPV1, and bitter Chinese herbs regulate the gene and protein expression of TAS2R14, through nerve and immune-inflammatory factors, reduced airway inflammation, reduced airway reactivity, promoted airway remodeling, and the combination of acrid-bitter Chinese herbs can enhance the above effects. This will lay a foundation for further in vivo study of specific compounds of acrid-bitter Chinese herbs.
[Retracted] Design of a Fuzzy Algorithm‐Based Evaluation System for the Effectiveness of International Online Chinese Listening and Speaking Teaching
Authors: Ge Dong , Wang Tianyu , Qi Chunhong , He Shan
Published: August 2022

With the increasing popularity of online foreign language teaching and learning practice, learners and teachers have developed a high demand for evaluation of teaching effectiveness. When we focus discussion on international Chinese teaching, which has been developed for a relatively short time and not experienced enough, online teaching effectiveness evaluation has become an important obstacle to the development of teaching. This paper introduces a hybrid technique based on fuzzy evaluation method, for determining and suggesting possible types of errors in international Chinese online listening and speaking instruction and giving suggestions for improvement. The system can help learners to identify and determine the types of errors in Chinese listening and speaking learning in a timely manner and make a more objective and comprehensive evaluation of learning performance; at the same time, it helps teachers to trace the effectiveness of teaching design and implementation in a targeted manner and make corresponding scientific decisions. This hybrid technology combines existing language teaching evaluation models, takes advantage of data from online education, and creates corresponding criteria through machine learning fuzzy algorithms and large data sample training, combined with the theory of effective teaching evaluation, which is beneficial for all participants of online Chinese listening and speaking teaching to improve their learning effectiveness.
[Retracted] Political Will and the Impact Assessment of the New Crown Epidemic on Economic and Social Development and Countermeasures
Authors: Wang Hongyan , Lin Min
Published: August 2022

Purpose. This paper aimed to study how to analyze and study economic and social development under the new crown epidemic based on the neural network and described the BP neural network. Methodology. Economic forecasts are affected by multiple influencing factors, the relationships between these factors are complex, and it is a nonlinear system with a high degree of uncertainty. The use of traditional forecasting methods has many limitations, and neural network methods can overcome these limitations and achieve good nonlinear forecasting. Research Findings. Through the analysis and statistics of the impact of the SARS epidemic and the new crown epidemic on the economy, by 2021, the economic contribution of final consumption expenditure, total capital formation, and net exports will be 65.4%, 13.7%, and 20.9%, respectively, and the impact of the current new crown virus epidemic on the economy will be greater than that of the SARS epidemic in 2003. Research Implications. The model applied to economic forecasting based on the BP network can achieve good forecasting effect, and scientific and reasonable forecasting methods depend on the in‐depth understanding of economic activities and dominance of familiarity with economic theory. Practical Implications. Through the analysis of the economy in the context of political will and the new crown epidemic, it will give more reference to more and more complex emergencies in the future.
Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis
Authors: Zhang Xinjun , Lyu Yingli
Published: August 2022

In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analyze the faults of electrical equipment and their causes, and establish knowledge base according to relevant data and expert judgment. The fault knowledge base was introduced into the neural network operation structure, and the fault diagnosis results were classified step by step through multiple subnetworks. In data preprocessing, in order to avoid the redundancy of primary fault information features, the principal component heuristic attribute reduction algorithm was used to select the fault data samples optimally. The neural network learning algorithm is used to calculate the forward direction and error rate of the initial error data, and the reliability function is used to optimize the initial weight threshold of the neural network, propagating the error backwards and high. Experimental results show that adding attribute reduction improves error classification performance, avoids the problem of local minima through neural network operation, and has fewer iteration steps, lower average error, and higher accuracy of fault diagnosis, reaching 95.6%.
[Retracted] The Application of Mental Health Teaching Method and Special Teaching Method in College Chinese Teaching under the Network Environment
Authors: Zhang Wei
Published: August 2022

In order to improve the quality of College Chinese teaching, innovate the teaching mode, enrich the teaching content, and fundamentally improve the application and practice ability of college students so as to promote the reform of College Chinese teaching, this article briefly describes the research background of this subject, introduces the special teaching mode under the network environment, and briefly describes the basic content and practice of the special teaching method. This article mainly explores the construction of a network environment based on blockchain technology, including the construction of a blockchain framework and the application of the blockchain system. This article briefly analyzes the functional requirements of the network environment, explores the application practice of the mental health teaching method and the special subject teaching method based on the network environment, and finally studies the creation and implementation of the network environment conditions of the special subject teaching method. By creating a high‐quality network environment, it provides a technical guarantee for the application and realization of special teaching method, effectively meets the needs of College Chinese teaching, and cultivates students’ interest. Under the joint action of the above algorithms, the BTP congestion control system is verified by the laboratory simulation environment: it has more than 40% random packet loss resistance, the bandwidth is still available as low as 300 kbps, and the network jitter is 1200 ms. It has been proved that the mental health teaching method, the special subject teaching method, and the network environment have remarkable application effects in College Chinese teaching and are worthy of wide application and promotion.
ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD
Authors: Esmaeili Mona , Goki Seyedamiryousef Hosseini , Masjidi Behnam Hajipour Khire , Sameh Mahdi , Gharagozlou Hamid , Mohammed Amin Salih
Published: August 2022

The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.
Mesenchymal Stem Cells (MSCs): A Novel Therapy for Type 2 Diabetes
Authors: Gao Shuang , Zhang Yuanyuan , Liang Kaini , Bi Ran , Du Yanan
Published: August 2022

Although plenty of drugs are currently available for type 2 diabetes mellitus (T2DM), a subset of patients still failed to restore normoglycemia. Recent studies proved that symptoms of T2DM patients who are unresponsive to conventional medications could be relieved with mesenchymal stem/stromal cell (MSC) therapy. However, the lack of systematic summary and analysis for animal and clinical studies of T2DM has limited the establishment of standard guidelines in anti-T2DM MSC therapy. Besides, the therapeutic mechanisms of MSCs to combat T2DM have not been thoroughly understood. In this review, we present an overview of the current status of MSC therapy in treating T2DM for both animal studies and clinical studies. Potential mechanisms of MSC-based intervention on multiple pathological processes of T2DM, such as β-cell exhaustion, hepatic dysfunction, insulin resistance, and systemic inflammation, are also delineated. Moreover, we highlight the importance of understanding the pharmacokinetics (PK) of transplanted cells and discuss the hurdles in MSC-based T2DM therapy toward future clinical applications.
Polyphenols Targeting Oxidative Stress in Spinal Cord Injury: Current Status and Future Vision
Authors: Islam Fahadul , Bepary Sristy , Nafady Mohamed H. , Islam Md. Rezaul , Emran Talha Bin , Sultana Sharifa , Huq Md. Amdadul , Mitra Saikat , Chopra Hitesh , Sharma Rohit , Sweilam Sherouk Hussein , Khandaker Mayeen Uddin , Idris Abubakr M.
Published: August 2022

A spinal cord injury (SCI) occurs when the spinal cord is deteriorated or traumatized, leading to motor and sensory functions lost even totally or partially. An imbalance within the generation of reactive oxygen species and antioxidant defense levels results in oxidative stress (OS) and neuroinflammation. After SCI, OS and occurring pathways of inflammations are significant strenuous drivers of cross-linked dysregulated pathways. It emphasizes the significance of multitarget therapy in combating SCI consequences. Polyphenols, which are secondary metabolites originating from plants, have the promise to be used as alternative therapeutic agents to treat SCI. Secondary metabolites have activity on neuroinflammatory, neuronal OS, and extrinsic axonal dysregulated pathways during the early stages of SCI. Experimental and clinical investigations have noted the possible importance of phenolic compounds as important phytochemicals in moderating upstream dysregulated OS/inflammatory signaling mediators and axonal regeneration’s extrinsic pathways after the SCI probable significance of phenolic compounds as important phytochemicals in mediating upstream dysregulated OS/inflammatory signaling mediators. Furthermore, combining polyphenols could be a way to lessen the effects of SCI.
Construction of Network Organization Structure of College Students’ Education Management Based on Distributed Network
Authors: Bo Pang
Published: August 2022

To construct the network organizational structure of college students’ educational management and realize the informatization development of college students’ educational management, the construction method of network organizational structure of college students’ educational management is proposed based on the distributed network. MySQL and PostgreSQL are used as the bottom data structure models of the network-based organizational structure of college students’ education management, and the multisource information scheduling method, which is used to construct the transaction identifier (TID) labeling tuple model of college students’ education management network. The initial topological structure model of the network-based organizational structure nodes of college students’ education management network is constructed by the 4-tuple model, and the linear structure decomposition of the construction nodes of the network-based organizational structure of college students’ education management is carried out. The correlation characteristics of the transmission channel of the network organization structure of college students’ education management are extracted; the three-layer architecture system of data layer, network layer, and application layer is adopted; the architecture model of the network organization structure of college students’ education management is established; the multinode distributed network transmission technology is used to build a tree structure model, so as to realize the design of the network organization structure model of college students’ education management and the scheduling of multi-source information parameters; and the fuzzy multi-attribute decision-making method to realize the adaptive scheduling of the network organization structure of college students’ education management is adopted, so as to improve the storage management, fault tolerance, and safe access ability of the network organization structure of college students’ education management. The test results show that the network organizational structure of college students’ educational management is designed by this method, which improves the ability of information transmission and scheduling and improves the informatization level of college students’ educational management.
Thermal Convective Instabilities and Chaos in a Rotating Hybrid Nanofluid Layer with Cattaneo–Christov Heat Flux Model
Authors: Dèdèwanou Sèmako Justin , Monwanou Adjimon Vincent , Koukpémèdji Aimé Audran , Hinvi Amoussou Laurent , Miwadinou Clément Hodévèwan , Chabi Orou Jean Bio
Published: August 2022

The linear and nonlinear dynamics of thermal convection of a rotating hybrid nanofluid layer heated from below with the Cattaneo–Christov heat flux model are studied in this paper. Starting from the flow equations of a hybrid nanofluid and exploiting the free boundary conditions, the analytical expressions of the stationary and oscillatory Rayleigh numbers of the base fluid are determined as a function of the dimensionless parameters of the heat transfer fluid and the thermophysical properties of the hybrid nanofluid. The effects of hybrid nanoparticles and Taylor number on the onset of stationary convection in the base fluid are investigated graphically. Then, a numerical study of the transition from natural convection to chaotic behaviour of the hybrid nanofluid is made using the truncated Galerkin approximation. This approximation allowed us to find a novel six‐dimensional nonlinear system depending on the parameters of the base fluid and the thermophysical properties of the hybrid nanofluid that can be reduced to five, four, or three dimensions when we tend some parameters to zero. The different results showed that the addition of hybrid nanoparticles (alumina‐copper) to a thermal fluid (water) subjected to the rotation force in the presence or absence of the thermal relaxation time allows control of the chaotic convection in the base fluid. On the other hand, the increase of the rescaled Taylor number and the Cattaneo number widens the domain of chaos in the hybrid nanofluid with the increase of the rescaled Rayleigh number of the base fluid.
The Path of Agricultural Policy Finance in Smart Service for Rural Revitalization under Big Data Technology
Authors: Wu Yingzhi
Published: August 2022

In order to explore the path of agricultural policy finance in the intelligent service of rural revitalization under the background of big data, this paper combines data mining technology for financial data processing and big data technology for rural revitalization data analysis. Moreover, this paper combines rural revitalization with agricultural policy-based financial smart services. In addition, with the support of data mining technology, this paper constructs a research system on the path of agricultural policy finance in smart services for rural revitalization based on big data technology. Finally, this paper analyzes the data from the National Statistical Yearbook. Through experimental research, it can be known that the system constructed in this paper has certain effects. Moreover, with the support of the data mining system, this paper conducts research on the path of agricultural policy finance in smart services for rural revitalization and puts forward some suggestions.
[Retracted] Spatial‐Temporal Variation Characteristics of Soil Moisture and Nutrients Based on Nanomaterials in the Root Zone of Haloxylon ammodendron Seedlings
Authors: Wang Ze , Gu Haibin , Yan An , Jiang Pingan , Ma Hao , Sheng Jiandong , Zhang Wentai
Published: August 2022

Haloxylon ammodendron is the main constituent species in the desert area of Junggar Basin, which plays a key role in biodiversity protection and regional ecological balance. Replenishment and settling capacity of seedlings determine population regeneration and community stability, and soil moisture and nutrients are the main limiting factors of natural regeneration. In view of the serious degradation and low seedling survival rate of Haloxylon ammodendron forest in Junggar, typical degraded Haloxylon ammodendron population was selected in Gurbantunggut Desert. The effects of soil moisture on soil nutrients were explored, and the temporal and geographical fluctuations of soil moisture and nutrients in the root zone of Haloxylon ammodendron seedlings were clarified. The results showed that soil moisture and nutrient content in the root zone of seedlings had obvious temporal and spatial variation characteristics. With the advance of the growth period, it decreased at first and then increased, reaching the maximum in April and the minimum in August. In the soil horizontal space (0~40 cm), with the extension of the horizontal distance, the soil moisture content has an obvious “wet island” effect, and the soil moisture content at the 10 cm level is 1.99 times that at the 40 cm level in August, when the growth period is the driest. The nutrients available in the soil were clearly “poor on the inside and high on the outside.” The degrees of soil natural matter, soluble hydrolyzable nitrogen, accessible phosphorus, and accessible potassium in August were 57.97%, 44.37%, 75.46%, 19, and 55% higher at the 40 cm level of the sapling spine, 10 cm level each. The soil moisture content increased first, then declined, and the soil nutrient content gradually fell in the vertical (0.50 cm) space of soil as soil depth increased. The soil moisture content in the 20~30 cm layer is the highest, reaching 11.04%, 7.90%, 2.92%, and 4.29%, respectively, in each period. Soil water content in 0~30 cm was significantly affected by habitat rainfall (P < 0.05), and soil water had a higher effect on soil organic matter, alkali‐hydrolyzable nitrogen, and available potassium (P < 0.05).
Evaluation of the Efficacy of Artificial Neural Network-Based Music Therapy for Depression
Authors: Ding Qian
Published: August 2022

In order to evaluate the therapeutic effect of music therapy on patients with depression, this paper proposes a CNN-based noise detection method with the combination of HHT and FastICA for noise removal, with good data support from the DBN model. DBN-based feature extraction and classification are completed. As the training process of DBN itself requires a large number of training samples, there are also disadvantages such as slow convergence speed and easy to fall into local minima, which lead to a large amount of effort and time, and the learning efficiency is relatively low. A DBN optimization algorithm based on artificial neural network was proposed to evaluate the efficacy of music therapy. First of all, through the comparison of music therapy experimental group and control group, to verify that music therapy is effective for the treatment of depressed patients. Secondly, we propose to optimize the selection of features based on the frequency band energy ratio and the sliding average sample entropy, respectively, and then to classify the EEG of depressed patients under different music perceptions by training the DBN model and continuously adjusting the parameters, combined with the surtax classifier, and the classification accuracy is high. In particular, it can detect the different effects of different music styles, which is of great significance for the selection of appropriate music for the treatment of depressed patients.
Expression of Thymic Stromal Lymphopoietin in Immune-Related Dermatoses
Authors: Li Si-Zhe , Jin Xin-Xing , Shan Yin , Jin Hong-Zhong , Zuo Ya-Gang
Published: August 2022

Thymic stromal lymphopoietin (TSLP), long known to be involved in Th2 response, is also implicated in multiple inflammatory dermatoses and cancers. The purpose of this study was to improve our understanding of the expression of TSLP in the skin of those dermatoses. Lesional specimens of representative immune-related dermatoses, including lichen planus (LP), discoid lupus erythematosus (DLE), eczema, bullous pemphigoid (BP), psoriasis vulgaris (PsV), sarcoidosis, and mycosis fungoides (MF), were retrospectively collected and analyzed by immunohistochemistry. Morphologically, TSLP was extensively expressed in the epidermis of each dermatosis, but the expression was weak in specimens of DLE. In a semiquantitative analysis, TSLP was significantly expressed in the epidermis in LP, BP, eczema, PsV, sarcoidosis, and MF. TSLP expression was higher in the stratum spinosum in LP, eczema, BP, PsV, and MF and higher in the stratum basale in sarcoidosis and PsV. Moreover, we found positive TSLP staining in the dermal infiltrating inflammatory cells of BP, PsV, and sarcoidosis. Our observation of TSLP in different inflammatory dermatoses might provide a novel understanding of TSLP in the mechanism of diseases with distinctly different immune response patterns and suggest a potential novel therapeutic target of those diseases.
Energy Auditing and Broken Path Identification for Routing in Large-Scale Mobile Networks Using Machine Learning
Authors: Venu Nookala , Revanesh M. , Supriya M. , Talawar Manjunath B. , Asha A. , Isaac Lydia D. , Ferede Alachew Wubie
Published: August 2022

In mobile computing, all nodes are movable nodes, which causes many problems for transmitting data packets in a sequence manner; since the mobile nodes are connected with each other, during movement, nodes make the connection fail or damaged. This kind of link damage is caused by nodes that travel out of range from the network limit and also affect the packet success rate. This reduces the network lifetime and detection efficiency and increases the communication overhead. Every mobile node in mobile computing is an unstable node, causing numerous problems for broadcasting data packets in a series method. When the mobile nodes are connected to each other, relay nodes cause the link to break or else sustain damage. This type of connection failure is brought on by nodes that leave the network’s permitted range, which also lowers the packet success rate. The link failure cannot be recovered by the multipath routing algorithm. As a result, the communication overhead is increased while the network lifetime and detection effectiveness are reduced. Then, the novel energy routing (NER) method that has been proposed is employed to support the energetic routing path across the middle nodes. It is challenging to locate the failed channels and carry on with the successful packet transfer. The master node selection algorithm is intended to identify the best relaying node, fault-free packet transmission process among the network structure’s relaying nodes. The master node is efficiently chosen in this manner. The master node, also known as the energy-based important node, is employed in the mobile network to carry out error-free packet transmission procedures. The other nodes are lower energy nodes that do not participate in packet forwarding, and this algorithm only detects the higher energy successful nodes. This lengthens the network’s lifespan, improves detection effectiveness, and lowers communication overhead. The energy-based heavy node is also known as the master node, which is used to perform a problem-free communication process in the mobile network. This algorithm only accepts the higher energy successful node; the remaining nodes are lower energy nodes which do not perform the communication process. This increases the network lifetime and detection efficiency and reduces the communication overhead. The performance metrics for the proposed system is evaluated by end to end delay, communication overhead, throughput, detection efficiency, network lifetime, and packet drop rate.
Usability Evaluation of Educational Games: An Analysis of Culture as a Factor Affecting Children’s Educational Attainment
Authors: Alshar’e Marwan , Albadi Ali , Jawarneh Malik , Tahir Noman , Al Amri Marya
Published: August 2022

Educational games have been employed among Omani schools but those used by local Omani schools were imported and were mostly designed based on western contexts. For Omani children, these games may be culturally inappropriate and difficult to comprehend and follow, impeding children’s learning. Three questionnaires and one observational checklist were used to gather data from 40 respondents (observers). SPSS was used in data analysis. Through experiments, the behavior of Omani students towards the use of imported educational games was examined. Five main factors, namely, efficiency, learnability, memorability, errors, and satisfaction, of educational games for a target user were measured using Hybrid User Evaluation Methodology for Remote Evaluation (HUEMRE), Training Framework for Untrained Observer (TFUO), and Framework on Educational Games Behavior Intention (EGsBI), which are specifically designed frameworks for this purpose. The results of this study explained that the Omani children are facing difficulties in using the imported educational games; furthermore, this study proves that culture, language, animation, and interaction are contributing heavily to benefiting from educational games, and, therefore, these factors shall be highly considered in the process of educational games design to facilitate and ensure children learning; furthermore, the findings of this study enrich the comprehension of how the specified factors positively affect behavioral intention of Omani students in the use of educational games and in improving the behavior intention level of these students.
Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma
Authors: Liu Zixu , Wang Ling , Du Minjun , Liang Yicheng , Liang Mei , Li Zhili , Gao Yushun
Published: August 2022

Background. The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases. Methods. Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified. Results. In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions. Conclusion. Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.
Spontaneous Pneumothorax in a Patient with Systemic Lupus Erythematosus and Recent Infection with Coronavirus
Authors: Graves Nicholas , Flint Julia , Sagdeo Amol , Askari Ayman , Ball Patrick , Morrissey Hana
Published: August 2022

A 50-year-old woman with a history of systemic lupus erythematosus and a recent infection with COVID-19 presented to the emergency department with acute shortness of breath twice in 10 days. She was diagnosed with myopericarditis attributed to COVID-19 infection (first admission), and chest X-ray revealed a small left-sided pneumothorax, pericardial effusion (second admission), with no mediastinal shift or other signs of tension. Computed tomography confirmed these results and revealed a few small cysts in the right lung. An echocardiogram demonstrated normal heart anatomy and filling dynamics. The patient was diagnosed with simple pneumothorax and ongoing myopericarditis managed with colchicine, ibuprofen, and low-dose prednisolone. The patient responded to treatment and was discharged. Pneumothorax association with COVID-19 is reported in a small number of publications, but the association is less clear with SLE. Our patient may have been predisposed to developing pneumothorax after COVID-19 infection due to her existing connective tissue disorder.
Pan-Cancer Analysis of PDIA3: Identifying It as a Potential Biomarker for Tumor Prognosis and Immunotherapy
Authors: Zhang Jing , Wang Kai , Hainisayimu Tuersun , Li Hui
Published: August 2022

Protein disulfide isomerase A3 (PDIA3) is a kind of thiol oxidoreductase with a wide range of functions, and its expression is elevated in a variety of tumors, which is closely related to the invasion and metastasis of tumor cells, and has a significant impact on the immunogenicity of tumor cells. Although more and more studies have shown that PDIA3 plays an important role in the occurrence and development of many tumors, there is no systematic pan-cancer study on PDIA3. Therefore, in this study, the differential expression of PDIA3 in 33 kinds of tumors was analyzed to explore its ability to regulate tumor immunity as a biomarker and evaluate its role in different cancer onset stages or clinical prognosis. In this paper, by analyzing the multilevel data including 33 kinds of cancers in the databases of Cancer Genome Atlas (TCGA), UCSC Xena, Cancer Cell Encyclopedia (CCLE), Genotypic Tissue Expression (GTEx), Human Protein Atlas (HPA), cBioPortal, and GDC; the differential expression level of PDIA3 in different types of malignant tumors and its relationship with prognosis and the potential correlation between PDIA3 expression and microsatellite instability (MSI), tumor mutation load (TMB), mismatch repair gene (MMR), DNA methylation level, and immune infiltration level were analyzed with bioinformatics. The results showed that PDIA3 was highly expressed in 19 types of cancers, but downregulated only in THCA. Next, PDIA3 in different tumors was positively or negatively correlated with patient outcome, Kaplan-Meier survival analysis showed that PDIA3 plays an important role in the prognosis of patients with KIRP, KICH, and CESC and may be used as a prognostic biomarker, and the methylation level of PDIA3 promoter region was closely related to patient outcome in eight tumors. The expression level of PDIA3 was correlated with TMB in 13 tumors and MSI in 9 tumors. Among them, the expression level of PDIA3 in THYM has the strongest correlation with TMB, and the expression level of PDIA3 in READ has the strongest correlation with MSI. In addition, the expression of PDIA3 in eight kinds of tumors, including BRCA, HNSC, THYM, LGG, LUAD, LUSC, PRAD, and THCA, had the highest correlation with the infiltration degree of immune cells, and the expression of PDIA3 had the highest correlation with the infiltration degree of 11 kinds of immune cells, including regulatory T cell and macrophages. And LGG is the tumor most likely to be affected by the tumor microenvironment to affect its development and prognosis. To sum up, this study suggests that PDIA3 plays an important role in the occurrence and development of KIRP, KICH, and CESC and in the immunotherapeutic response of THYM, READ, and LGG and can be used as a prognostic biomarker for these tumors.
Associations of Mitochondrial Function, Stress, and Neurodevelopmental Outcomes in Early Life: A Systematic Review
Authors: Zhao Tingting , Alder Nathan N. , Starkweather Angela R. , Chen Ming-Hui , Matson Adam P. , Xu Wanli , Balsbaugh Jeremy L. , Cong Xiaomei
Published: August 2022

Early life stress is commonly experienced by infants, especially preterm infants, and may impact their neurodevelopmental outcomes in their early and later lives. Mitochondrial function/dysfunction may play an important role underlying the linkage of prenatal and postnatal stress and neurodevelopmental outcomes in infants. This review aimed to provide insights on the relationship between early life stress and neurodevelopment and the mechanisms of mitochondrial function/dysfunction that contribute to the neuropathology of stress. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to develop this systematic review. PubMed, Scopus, PsycINFO, and Biosis databases were searched for primary research articles published between 2010 and 2021 that examined the relationships among mitochondrial function/dysfunction, infant stress, and neurodevelopment. Thirty studies were identified. There is evidence to support that mitochondrial function/dysfunction mediates the relationship between prenatal and postnatal stress and neurodevelopmental outcomes in infants. Maternal transgenerational transmission of mitochondrial bioenergetic patterns influenced prenatal stress induced neurodevelopmental outcomes and behavioral changes in infants. Multiple functionally relevant mitochondrial proteins, genes, and polymorphisms were associated with stress exposure. This is the first review of the role that mitochondrial function/dysfunction plays in the association between stress and neurodevelopmental outcomes in full-term and preterm infants. Although multiple limitations were found based on the lack of data on the influence of biological sex, and due to invasive sampling, and lack of longitudinal data, many genes and proteins associated with mitochondrial function/dysfunction were found to influence neurodevelopmental outcomes in the early life of infants.