bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of Sports Training Performance Prediction Model Based on a Generative Adversarial Deep Neural Network Algorithm
Authors: Li Gang
Published: May 2022

The generative adversarial neural network algorithm is used for in-depth research and analysis of sports training performance prediction, and the corresponding model is built and used for practical applications. To address the problems of gradient disappearance, training instability, lack of local consistency of repair results, and long training time in the image restoration algorithm based on generative adversarial networks, this paper proposes a multigenerative adversarial image restoration algorithm based on multigranularity reconstruction sampling. The algorithm changes the distribution initialization of the generative network and uses reconstruction sampling to ensure that the Lebesgue measure of the overlapping part of the generative sample space and the real sample space is not 0 to further stabilize the gradient, and it is demonstrated that reconstruction sampling can stabilize the training and gradient. In addition, segmentation invariance is used to shorten the training time while ensuring the quality of the restored images, and an algorithm adaptability metric is proposed to comprehensively evaluate the image restoration algorithm. Based on the results of the fusion model analysis, an attention-based mechanism for the student performance prediction model is proposed. First, deep student behavioral features are extracted using a generative adversarial deep neural network, and the salient features in the student behavioral features are selected using a maximum pooling method; then, the extracted features are used as the input of the generative adversarial deep neural network for student performance prediction. Finally, a temporal attention mechanism is introduced at the output of the generative adversarial deep neural network to assign attention weights to different weekly student behavioral features.
Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China
Authors: Li Guang , Li Ping , Liu Shuo , You Bin
Published: May 2022

Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the follow-up results of patients after mitral valve repairs (MVRep) using the da Vinci robot. The clinical data of patients who underwent minimally invasive MVRep using the da Vinci robot between January 2016 and June 2021 and completed follow-ups were prospectively collected. All operations were performed by the same surgeon and assistants. The data of a total of 120 patients were available for analysis, including 78 males (65%) and 42 females aged 49.9 ± 12.1 years (range, 19–73 years). Among them, there were 30 cases (25%) of mitral valve prolapse, 87 cases (72.5%) of mitral regurgitation, and 40 cases of combined tricuspid regurgitation. Edwards Physio II annuloplasty rings were implanted intraoperatively, followed by continuous sutures. The intraoperative cardiopulmonary bypass time was 152.32 ± 45.77 min, and the aortic occlusion time was 95.13 ± 5.64 min. After surgery, patients were followed up regularly with echocardiography with a follow-up period of 3–57 months postoperatively. One patient died in the early stage, and five patients required sternotomy due to postoperative bleeding. Follow-up transesophageal echocardiography showed that the end-systolic diameter, end-diastolic diameter, and ejection fraction of the left ventricular all improved after surgery. Among Chinese patients, MVRep using the da Vinci robot is a safe and effective surgical approach.
Spinal Stroke following Kidney Transplant
Authors: Subramanian Jayanthan B. , Siddiqui Farjad , Chotai Pranit N. , Al-Adwan Yazan , Rajab Amer , Washburn Kenneth , Schenk Austin D. , Limkemann Ashley J. , Luttrull Michael , Al-Ebrahim Musab , Bumgardner Ginny , Singh Navdeep
Published: May 2022

Complications are a part of surgery. Spinal infarctions are a dreaded complication of aortic surgery. We present a patient who developed a spinal infarct after a kidney transplant. We were unable to find a causative factor in our search for etiology. In our review of the literature, we were unable to find a similar report. We present this case report to highlight a rare complication of kidney transplantation and to reinforce that patients requiring kidney transplant are complex patients with multiple comorbidities that can cause a multitude of complications in the periop period.
Analysis and Research on the Characteristics of Modern English Classroom Learners’ Concentration Based on Deep Learning
Authors: Shen Yu
Published: May 2022

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students’ class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students’ learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students’ inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application.
Scaling Laws of Spatial Visitation Frequency on Motorized Travel
Authors: Wang Changhua , Zhou Yujia , Xu Cheng
Published: May 2022

The scaling law is a functional relationship between two parameters. In the field of urban studies, the distribution of various phenomena follows the scaling law, e.g., travel time, travel distance, and visitation frequency. To capture the scaling law of spatial visitation frequency on a motorized vehicle, this study carried out a numerical study based on the license plate recognition (LPR) data of three cities, Hangzhou, Guiyang, and Xiaoshan in China. Firstly, according to the LPR data, vehicles are classified into different types, including foreign vehicles, on-demand ride-sourcing cars, and taxis. Then, the spatial visitation frequency of different vehicle types, different cities, and different time periods was calculated. The results show that the scaling law of the spatial visitation frequency always conforms to the exponential function, no matter the city types, time periods, or type of vehicles.
Optimization of WEDM Process Parameters in Al2024‐Li‐Si3N4 MMC
Authors: Raju K. , Balakrishnan M. , Prasad D. V. S. S. S. V. , Nagalakshmi V. , Patil Pravin P. , Kaliappan S. , Arulmurugan B. , Radhakrishnan K. , Velusamy B. , Paramasivam Prabhu , El-Denglawey A.
Published: May 2022

The present study focuses on optimization of operating parameters in wire electric discharge machining of AA2024 aluminium alloy reinforced with lithium and silicon nitride particles. Aluminium composite was produced through the two‐step stir casting route with the combination of 2% lithium and 10% silicon nitride reinforcements. Experiments were performed using the Taguchi design of experiments to optimize the selected input parameters such as pulse on time, pulse off time, current and wire feed for the response parameter, material removal rate, and surface roughness. An ANOVA‐based regression equation with genetic algorithm was used to optimize the input variables. The gray relational grade was also performed to optimize multiple performance characteristics. Taguchi‐based optimization analysis results in wire feed as the domination factor for material removal rate and surface roughness. Increased wire feed increases the material removal rate with good surface finish as confirmed from gray relational grade analysis. Regression equation generated results with minimum error (<2%) proving the accuracy of the investigation. A genetic algorithm‐based study also confirms the analysis of Taguchi and gray relational grade. The wire feed rate at 3 m/min and pulse on time of 120 microseconds were found to be similar for material removal rate and surface finish. Current at 50 A increases the material removal rate and current at 30 A results in good surface finish.
Knowledge Based on Rough Approximations and Ideals
Authors: Hosny Rodyna A. , Al-shami Tareq M. , Azzam A. A. , Nawar Ashraf S.
Published: May 2022

Topology is a beneficial structure to study the approximation operators in the rough set theory. In this work, we first introduce six new types of neighborhoods with respect to finite binary relations. We study their main properties and show under what conditions they are equivalent. Then we applied these types of neighborhoods to initiate some topological spaces that are utilized to define new types of rough set models. We compare these models and prove that the best accuracy measures are obtained in the cases of

i

and

i

. Also, we illustrate that our approaches are better than those defined under one arbitrary relation. To improve rough sets’ accuracy, we define some topological spaces using the idea of ideals. With the help of examples, we demonstrate that our methods are better than some methods studied in some published literature. Finally, we give a real-life application showing the merits of the approaches followed in this manuscript.
Effects of Smartphone Overdependence and the Quality of Friendship on Depression among High School Students
Authors: Sung-min Son , Byoung-jin Jeon
Published: May 2022

This study analyzes the effects of smartphone overdependence and the quality of friendship on depression among high school students in Korea. The study subjects were 121 high school students. Smartphone overdependence was assessed using the smartphone overdependence scale for adolescents, depression was assessed using the Korean version of the patient health questionnaire-9, and the quality of friendship was assessed using the friendship quality questionnaire. The results of the correlation analysis showed statistically significant differences among all the variables. The results of the multiple linear regression analysis showed that smartphone overdependence increases the depression level, whereas the quality of friendship lowers it. To decrease depression among high school students, smartphone overdependence and the quality of friendship should be managed appropriately. Moreover, the causal relationships and mediating effects between smartphone overdependence and the quality of friendship should be analyzed.
Clinical and Microbiological Efficacy of Adjunctive Systemic Quinolones to Mechanical Therapy in Periodontitis: A Systematic Review of the Literature
Authors: Ardila Carlos-M. , Bedoya-García Jader-Alexander
Published: May 2022

Objectives. To assess the clinical and microbiological efficacy of systemic quinolones adjunctive to mechanical therapy in periodontitis patients. Materials and Methods. A systematic review of the scientific literature was carried out. The search scheme comprised the Scopus, PubMed/MEDLINE, SCIELO (Scientific Electronic Library Online), and LILACS (Literatura Latinoamericana y del Caribe en Ciencias de la Salud) databases, together with the gray literature. MeSH terms and keywords were utilized to explore publications in all idioms. Only randomized clinical trials (RCTs) that met the selection criteria were included. Results. A total of 4 RCTs were selected. These RCTs found superior clinical and microbiological efficacy of adjunctive systemic moxifloxacin (MOX) and levofloxacin (LV) compared to subgingival debridement plus placebo. Improvements in PD and CAL were 2.4 ± 0.8 mm and 2.7 ± 0.9 mm for LV, and 1.5 ± 0.5 mm and 1.8 ± 0.5 mm for MOX, respectively. After six months of follow-up, adjunctive MOX reduced the presence of Aggregatibacter actinomycetemcomitans to imperceptible levels, while LV markedly reduced this microorganism. Some adverse events were reported in the LV group and none in the MOX group. Conclusions. Adjunctive MOX and LV improve probing depth and clinical attachment level compared with subgingival debridement alone in patients with periodontitis. The efficacy of these quinolones against A. actinomycetemcomitans was also superior.
Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance
Authors: Pan Ting , Yang Yang
Published: May 2022

In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. A series of medical image segmentation models based on convolutional neural networks is proposed. In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block. This structure has multiple parallel data paths, which increases the width of the network and therefore improves the feature extraction capability of the model. Then, this paper proposes a bidirectional feature pyramid for medical image segmentation task, which has top-down and bottom-up data paths, and, together with jump connections, can fully interact with feature maps at different scales. After that, a new activation function Mish is introduced, and its advantages over other activation functions are experimentally demonstrated. Finally, for the situation that medical image annotations are not easy to obtain, a semisupervised learning method is introduced in the model training process, and the effectiveness of this method is experimentally demonstrated. The joint network first denoises the input image, then super-resolution mapping is performed on the noise-removed feature map, and finally, the super-resolution 3D-MR image is obtained. We update the network by combining the denoising loss and super-resolution loss during the joint network training process. The experimental results show that the joint network with denoising first and then super-resolution outperforms the joint network with other task order and outperforms the method that performs the two tasks separately and the proposed method in this paper has the optimal performance.
The Improvement of Natural Thai Bentonite Modified with Cationic Surfactants on Hexavalent Chromium Adsorption from an Aqueous Solution
Authors: Srikacha Nikom , Sriuttha Manop , Neeratanaphan Lamyai , Saiyasombat Chatree , Tengjaroensakul Bundit
Published: May 2022

This work was performed to evaluate the adsorption properties of modified Thai bentonites (MTBs) on hexavalent chromium (Cr(VI)) by using a popularly capable surfactant (hexadecyltrimethylammonium bromide (HDTMA)) compared to an alternative surfactant (cetylpyridinium chloride (CPC)). The adsorption properties of the surfactant load, adsorbent weight, contact time, initial Cr(VI) concentration, and temperature of the MTBs were evaluated. The results revealed that a higher surfactant load significantly affected the Cr(VI) adsorption, and the equilibrium adsorption was achieved at 60 min. The adsorption capacity improved when the adsorbent weight, contact time, initial concentration, and temperature increased as the highest adsorption capacities of 1CPC and 1HDTMA were 45.55 and 46.03 mg g-1, respectively. The isotherm and kinetic adsorptions were described by the Freundlich model and pseudo-second-order model, respectively, while thermodynamics indicated endothermic adsorption. After adsorption, X-ray absorption near-edge structure and extended X-ray absorption fine structure data showed that Cr ions did not change the valency state between Cr(VI) and Cr(III). Additionally, the adsorption mechanism can be depicted as the ion exchange between the Cr(VI) ion and the surfactant molecule. Structural evaluations by XRD, FTIR, FESEM, EDS, and TEM found that both MTBs (1CPC and 1HDTMA) with the best adsorption performance for Cr(VI) had obvious changes at both the interlayer structure and the external surface. The interlayer spacing was expanded from 14.85 Å to 20.48 Å (1CPC) and 18.79 Å (1HDTMA), and the new functional groups (CH2 scissoring, C–H symmetric stretching, C–H asymmetric stretching, and N–CH3 scissoring) and elemental compositions (Br and Cl) were observed in both MTBs. They demonstrated that the complete intercalation of surfactant molecules on bentonite structures supported Cr(VI) adsorption. Overall, the data indicate that MTBs were perfectly adsorbed on Cr(VI), and CPC was demonstrated to be a cheap alternative agent due to its adsorption capacity compared to the popularly capable HDTMA.
Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
Authors: Chen Ying , Wang Dan , Li Qingyun , Zhang Yiyi , Peng Zheng , He Yu , Lin Bin , Xu Meifang , Chen Qiong , Chen Yang
Published: May 2022

The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2,

P
≤
0.02

; SNRPD3,

P
=
0.12

; EIF4A3,

P
=
0.11

; GSE65904, SNRPD2,

P
=
0.04

; SNRPD3,

P
=
0.10

; EIF4A3,

P
<
0.01

; GSE19234, SNRPD2,

P
<
0.01

; SNRPD3,

P
<
0.01

; EIF4A3,

P
<
0.01

). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2,

988.5
±
47.83

vs.

738.4
±
35.35

,

P
<
0.01

; SNRPD3,

502.7
±
25.7

vs.

416.4
±
23.88

,

P
=
0.02

; EIF4A3,

567.6
±
19.56

vs.

495.2
±
21.1

,

P
=
0.01

). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients.
Detecting Anomaly Data for IoT Sensor Networks
Authors: Wei Zhe , Wang Fang
Published: May 2022

The Internet of Things, or IoT, has been widely recognized as a new perception paradigm for interacting between the digital world and the physical one. Acting as the interface and integral part of the Internet of Things, sensors embedded within the network are the principal components that collect the unprocessed data, and these sensors are usually deployed in unattended, hostile, or harsh areas, which inevitably makes the sensor readings prone to faults and even anomalies. Therefore, the quality of sensor readings will ultimately affect the quality of various data-oriented IoT services, and the sensor data are of vital importance affecting the performance of the system. However, the data anomaly detection is a nontrivial task for IoT because sensors are usually resource-constrained devices with limited computing, communication, and capacity. Therefore, an efficient and lightweight detecting method is needed to meet the requirements. In this study, we deal with the anomaly data by detecting the source sensor nodes through combination methods of the local outlier factor and time series. Simulations show that the proposed method can effectively detect the anomaly data and presents a better normal data rate.
Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System
Authors: Nanehkaran Y. A. , Licai Zhu , Chen Junde , Zhongpan Qiu , Xiaofeng Yuan , Navaei Yahya Dorostkar , Einy Sajad
Published: May 2022

Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if not diagnosed in time, can cause irreparable damage to patients. The use of patients’ medical record data for early diagnosis of chronic diseases has recently attracted the attention of many researchers. On the other hand, the application of machine learning methods in the form of recommender systems has taken an important step in improving medical services and health care. In this paper, a medical recommender system was presented to identify and treat chronic diseases using an IoT device. In the present method, the electronic patient health record dataset that is loaded in the PhysioNet data repository has been used. In the present dataset, patients’ health records have been recorded according to the identified diseases and the physician’s diagnosis. In the proposed method, the

K

-nearest neighbor classification method is used to identify the type of disease, and the collaborative filtering method is used to find the appropriate treatment for patients. The results of the implementation of the proposed method show that this approach, based on the use of symptom similarity among patients, has good accuracy in diagnosing and predicting chronic diseases and has provided higher results than previous methods.
Chinese Character Culture Communication and Comparative Analysis Based on Big Data Visualization
Authors: He Pingting
Published: May 2022

With the promotion of China’s international status, the study of Chinese characters has become the trend of scholars all over the world. Nowadays, the international promotion of Chinese has become an important part of the national diplomatic strategy. With the rapid development of big data visualization technology, Chinese culture can be spread quickly, and it is easy to achieve the above requirements to achieve visual accessibility. In view of the problems existing in the current dissemination of Chinese culture, such as too scattered dissemination content, unsystematic dissemination channels, and poor dissemination effect, this paper puts forward a method of Chinese character culture dissemination based on big data visualization. Firstly, the visual features of Chinese characters are recognized, and the rules of Chinese characters are detected; secondly, the large data visualization analysis of Chinese character imaging is carried out, and the performance comparison under different iterations is compared. Chinese visual technology has obvious advantages in the effect and performance of Chinese character transmission. Finally, the development trend of Chinese communication is analyzed, and the acceptance, speed, coverage, understanding, learning degree, and practical degree of Chinese communication abroad are compared. The big data visualization model has certain advantages in functional comparison.
Additive Manufacturing for Aerospace from Inception to Certification
Authors: Balaji Devarajan , Ranga Jarabala , Bhuvaneswari V. , Arulmurugan B. , Rajeshkumar L. , Manimohan Mohan Prasad , Devi G. Ramya , Ramya G. , Masi Chandran
Published: May 2022

Metal additive manufacturing (MAM) does not require any preface for its potential applications in various engineering and technological sectors. This article comprehensively discusses about the application of additive manufacturing technique specifically aerospace components. The structure of this article begins with an introduction to the current state‐of‐the‐art MAM technologies with the aid of patent landscape analysis. Any manufacturing starts with understating of the manufacturing cycle, so herein, the aerospace manufacturing cycle has been discussed commencing from the design phase and followed by the process parameters selection. The immediate effect after printing is the selection of evaluation parameters, wherein the surface texture analysis of AM printed components is discussed. This paves to discuss about the specific alloys such as titanium alloy and Inconel alloys which are widely used in the aerospace industry. This analysis paves a path for the utilization of these materials to manufacture specific aerospace components which are also discussed. Thereby, the impact of MAM over the aerospace sector and the guidelines to decision making on the suitable variant of MAM has been discussed clearly with the help of earlier literatures. Finally, the qualification and certification procedure are discussed therein, leading to the conclusion about the future scope of MAM in the aerospace sector.
Combined Efficacy of CXCL5, STC2, and CHI3L1 in the Diagnosis of Colorectal Cancer
Authors: Li Hui , Zhou Xiaoping , Zhang Haining , Jiang Jifeng , Fu Hu , Wang Fang
Published: May 2022

Objective. To improve the diagnostic capacity of serum biomarkers for colorectal cancer (CRC), we introduced three novel indicators, namely, the C-X-C motif chemokine ligand 5 (CXCL5), stanniocalcin 2 (STC2), and chitinase 3 like 1 (CHI3L1) and assessed their performances in the detection of CRC. Methods. A total of 887 serum samples (153 health, 342 polyps, and 392 CRCs) were collected. Concentrations of CXCL5, STC2, and CHI3L1 were measured by the ELISA. CEA and CA199 were determined by electrochemiluminescence. Binary logistic regression was used to build the combination model. ROC analysis was used to evaluate the performance of biomarkers alone or in combination. Results. Model_2 that based on CXCL5, STC2, and CHI3L1 was the best approach in discriminating CRC from non-CRC controls (AUC, 0.943 (0.922–0.960); sensitivity, 0.848; specificity, 0.917; and accuracy, 0.887 in the training cohort and 0.959 (95% CI 0.927–0.980), 0.878, 0.917, and 0.900 in the testing cohort, respectively). In the detection of early CRC, Model_2 revealed AUC, sensitivity, specificity, and accuracy of 0.925 (0.897–0.947), 0.793, 0.917, and 0.886 in the training cohort and those of 0.926 (0.979–0.959), 0.786, 0.931, and 0.898 in the testing cohort. Furthermore, Model_2 exhibited an excellent diagnostic performance in CEA-negative cases (0.938 (0.913–0.957), 0.826, 0.917, and 0.888 in the training cohort and 0.961 (0.925–0.983), 0.887, 0.931, and 0.918 in the testing cohort). As used alone, STC2 achieved the capacities that is second only to that of Model_2 (0.866 (0.837–0.892), 0.859, 0.842, and 0.853 in the training cohort and 0.887 (0.842–0.923), 0.922, 0.799, and 0.853 in the testing cohort). STC2 alone also yielded acceptable results for early CRC detection (0.815 (0.776–0.849), 0.767, 0.849, and 0.829 in the training cohort and 0.870 (0.812–0.914), 0.952, 0.799, and 0.833 in the testing cohort). Moreover, STC2 maintained diagnostic accuracy for CRC patients with negative CEA (0.874 (0.842–0.901), 0.862, 0.849, and 0.853 in the training cohort and 0.898 (0.848–0.936), 0.930, 0.801, and 0.842 in the testing cohort). In comparison, the performances of the CEA and CA199 based Model_1 were far from satisfactory, especially in early cases (0.767 (0.726–0.805), 0.491, 0.863, and 0.771 in the training cohort and 0.817 (0.754–0.870), 0.476, 0.889, and 0.796 in the testing cohort). Conclusions. STC2 was a promising serum biomarker for CRC diagnosis either used alone or in combination with CXCL5 and CHI3L1.
Design and Analysis of Immersive Interaction Scenarios for Social Products
Authors: Qin Cheng , Peng He
Published: May 2022

In the Internet era, with the continuous iterative updating of communication technology and the development of artificial intelligence, Internet of things, 5g, and other technologies, all kinds of social products have become one of the main ways of communication. For example, Facebook, WeChat, and ins are essential products for people in modern society. From personal communication to world interconnection, they are inseparable from the support of social products. At present, various social products in the market have different advantages or specific users and often cannot deal with all user groups. Because the needs of different customers are different and cannot fully meet the needs of different users, coupled with the development of technology, the communication media is becoming more and more innovative, the boundary between virtual and reality is broken, and the immersive scene breaks through the boundary of traditional communication, which is the way people want to communicate. In view of these shortcomings and needs, this paper introduces the methods of immersive scene diversification. First, sort out the user needs, then build different user models according to the specific characteristics of different scenarios, and realize the interaction of different scenarios according to different scenarios. The end-user model interacts with the corresponding design. After continuous testing, feedback, and improvement, the efficiency, quality, and specific methods of socialized products are improved. The experimental analysis results of this paper show the effectiveness and rationality of the immersive scene diversification method. The design can break the barriers and enhance the interaction efficiency and quality of both sides of information flow. The main body of information dissemination also presents a multilateral form and improves the inequality of information dissemination.
Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images
Authors: Rani Shilpa , Ghai Deepika , Kumar Sandeep , Kantipudi MVV Prasad , Alharbi Amal H. , Ullah Mohammad Aman
Published: May 2022

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.
Acetabular Reconstruction Using Multiple Porous Tantalum Augments: Three-Quarter Football Augment
Authors: Ansorge C. H. , Ohlmeier M. , Ballhause T. M. , Gehrke T. , Citak M. , Lee M.
Published: May 2022

Reconstruction of a large acetabular bone defect is a complex problem in revision hip arthroplasty. The authors report a novel method of reconstructing an uncontained acetabular defect (Paprosky type IIIb) using multiple tantalum augments. A 73-year-old female patient presented to our institution with a chronically dislocated primary left total hip arthroplasty with radiographs demonstrating migration of acetabular component and formation of pseudoarthrosis within the left ilium. Extensive arthrolysis and anatomic reconstruction of the acetabular bone defect were performed using the novel method of multiple tantalum augments. Postoperatively, recovery was initially complicated by multiple dislocations requiring an exchange to an elevated liner, however subsequently achieved good function.
M2-Macrophage-Derived Exosomes Promote Meningioma Progression through TGF-β Signaling Pathway
Authors: Fu Xiao-Hong , Li Jian-Ping , Li Xue-Ying , Tan Yan , Zhao Min , Zhang Shao-Fu , Wu Xue-Dong , Xu Jian-Guo
Published: May 2022

Tumor-associated macrophages (TAMs) have been shown to be an essential component of the tumor microenvironment and facilitate the proliferation and invasion of a variety of malignancies. However, the contribution of TAMs to meningioma progression has not been characterized in detail. In this study, we aimed to discover a novel regulatory pathway by which exosome-mediated M2-polarized macrophages participate in meningioma tumorigenesis and progression. Methods. First, the distribution and functional phenotype of macrophages in meningioma tissues were assessed by immunohistochemistry. Macrophage-derived exosomes (MDEs) were characterized, and further cell coculture experiments were performed to explore the effects of M2-MDEs on the proliferation, migration, and invasion of meningioma cells. RNA sequencing was used to analyze the transcriptomic signatures in meningioma cells treated with M2-MDEs. Three-dimensional tumorspheres and xenograft tumor models were used to evaluate the effects of M2-MDEs on meningioma tumorigenesis and development. Results. We found that M2 macrophages were enriched in meningioma tissue. Coculture with meningioma cells induced the M2 polarization of macrophages. We also found that M2-MDEs were able to significantly promote cell proliferation, cell migration, cell invasion, and tumorigenesis in meningiomas. Bioinformatic analysis suggested that the TGF-β pathway was activated in meningioma cells treated with M2-MDEs. Functional experiments demonstrated that blocking the TGF-β signaling pathway could effectively reverse the tumor-promotive effects mediated by M2-MDEs. Conclusions. Overall, our study showed that M2-MDEs promoted meningioma development and invasion by activating the TGF-β signaling pathway. Targeting exosome-mediated intercellular communication in the tumor microenvironment may be a novel therapeutic strategy for meningioma patients.
Underwater Imaging Formation Model-Embedded Multiscale Deep Neural Network for Underwater Image Enhancement
Authors: Li Fucui , Lu Difei , Lu ChengLang , Jiang Qiuping
Published: May 2022

Underwater image enhancement (UIE) plays an essential role in improving the quality of raw images captured in an underwater environment. Existing UIE methods can be categorized into two types: handcraft-designed and deep learning-based methods. Generally, the handcraft-designed methods are more explainable due to the leverage of knowledge-based image priors, while the deep learning-based methods are usually criticized for their weak interpretability. In this study, we address this issue by integrating the merits of both handcraft-designed and deep learning-based methods. Specifically, a physical underwater imaging model-inspired deep CNN for UIE is designed. Instead of estimating a global background light magnitude and a transmission matrix separately in traditional image restoration-based UIE methods, we directly generate a single variable as the joint estimation of these two parameters within a deep CNN and directly recover the enhanced image as an output according to a reformulated physical underwater imaging model. The whole network is trained in an end-to-end manner and more importantly has good interpretability. The proposed method has been validated for the UIE task on a real-world underwater image dataset and the experimental results well demonstrate the superiority of our method over the existing ones for UIE.
Identification of Three Prognosis-Related Differentially Expressed lncRNAs Driven by Copy Number Variation in Thyroid Cancer
Authors: Tian Jinyi , Luo Bin
Published: May 2022

Thyroid cancer as the malignant tumor with the highest incidence in the endocrine system also shows a fast growth and development. In this work, we developed a new method to identify copy number variation– (CNV–) driven differentially expressed lncRNAs in thyroid cancer for predicting cancer prognosis. The data of RNA sequencing, CNV, methylation, mutation, and clinical details of thyroid cancer were obtained from the Cancer Genome Atlas database (TCGA). Molecular subtypes were clustered by iClusterPlus. Weighted gene co-expression network analysis (WGCNA) was employed to show co-expression modules. DEseq2 was conducted to identify protein coding genes (PCGs) and differentially expressed lncRNAs. CNV was detected using GISTIC 2.0. Three molecular subtypes were identified, and 68 differentially expressed lncRNAs (DElncRNAs) related to cancer were found among different molecular subtypes. CNV of FOXD2-AS1, FAM181A-AS1, and RNF157-AS1 was associated with overall survival and was involved in cancer-related pathways. These three DElncRNAs discovered based on CNV could serve as prognostic biomarkers to predict prognosis for thyroid cancer and new targets to explore molecular drugs.
Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy
Authors: Chen Shengnan , Chen Lei , Jiang Hongli
Published: May 2022

Background. Diabetic nephropathy (DN) is a common microvascular complication of diabetes and a major cause of end-stage renal disease, resulting in a substantial socioeconomic burden around the world. Some unknown biomarkers, mechanisms, and potential novel agents regarding DN are yet to be identified. Methods. GSE30528 and GSE1009 were downloaded as training datasets to identify differentially expressed genes (DEGs) of DN. Common DEGs were selected for further analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed to explore molecular mechanisms and pathways. Protein-protein interaction (PPI) network of DEGs was used to identify the top 10 hub genes of DN. Expression profiles of the hub genes were validated in GSE96804 and GSE47183 datasets. The clinical correlation analyses were conducted to confirm the association between key genes and clinical characteristics in the Nephroseq v5 database. The Drug Gene Interaction Database was used to predict potential targeted drugs. Results. 345 and 1228 DEGs were identified in GSE30528 and GSE1009, respectively; and 120 common DEGs were found. The biological process of DEGs was significantly enriched in kidney development. PI3K-Akt signaling pathway, focal adhesion, complement and coagulation cascades were significantly enriched KEGG pathways. The identified top10 hub genes were VEGFA, NPHS1, WT1, TJP1, CTGF, FYN, SYNPO, PODXL, TNNT2, and BMP2. VEGFA, NPHS1, WT1, CTGF, SYNPO, PODXL, and TNNT2 were significantly downregulated in DN. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL were positively correlated with glomerular filtration rate. The targeted drugs or molecular compounds were enalapril, sildenafil, and fenofibrate target for VEGFA; losartan target for NPHS1; halofuginone, deferoxamine, curcumin, and sirolimus target for WT1; and purpurogallin target for TNNT2. Conclusions. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL are promising biomarkers for diagnosing and evaluating the progression of DN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of DN.
Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions
Authors: Sharma Vahiny , Gupta Ankur , Hasan Najam Ul , Shabaz Mohammad , Ofori Isaac
Published: May 2022

Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented.
Panax notoginseng Alleviates Sepsis-Induced Acute Kidney Injury by Reducing Inflammation in Rats
Authors: Shou Di-Wen , Yu Zi-Lin , Meng Jian-Biao , Lai Zhi-Zhen , Pang Li-Sha , Dai Mu-Hua , Yang Xue , Tu Yue-Xing
Published: May 2022

Background. Sepsis is defined as a host inflammatory response to infection that can result in end-organ dysfunction. One of the most common consequences of sepsis is acute kidney injury (AKI). Panax notoginseng powder (PNP) has been previously reported to protect against overactive inflammation process. However, the potential effect of PNP on septic AKI is poorly described. The current study was conducted to investigate the protective effects of PNP in septic AKI rats. Methods. A model of septic AKI was established on male SD rats by using the cecal ligation and puncture procedure. PNP was administrated by gavage after the cecal ligation and puncture (CLP) procedure, and the mice were sacrificed at 6, 12, and 72 h after induction of sepsis. The serum and kidney samples were collected and assayed for biochemical tests, histopathological staining, inflammation, and apoptosis-related gene/protein expression. In addition, 15 rats in each group were used to calculate the 7-day survival rate. Results. CLP-induced kidney injury was observed by the histopathological score, which markedly was attenuated by PNP treatment. Consistently, PNP intervention significantly alleviated the elevated levels of serum creatinine and blood urea nitrogen in CLP-induced sepsis rats. The CLP procedure also triggered proinflammatory cytokine production and increased the expression of various inflammation-related proteins in the kidneys. However, PNP inhibited the renal expression of IL-18, IL-1β, TNF-α, and IL-6 to substantially improve inflammatory response. Mechanistically, CLP induced the increase of the NF-κB p65 level in the injured kidneys, while PNP notably inhibited the corresponding protein expression. Conclusion. PNP attenuated kidney inflammation to protect against CLP-induced septic AKI in rats via inhibiting the NF-κB signaling pathway.
Dynamic Characteristics of Soil Microorganism in Rhizosphere Soils of Tobacco Over Different Growth Stages
Authors: Ruan Yanan , Tang Zuoxin , Lin Li , Fan Zhiwei , Xu Shengguang , Chen Zebin
Published: May 2022

To explore the composition and changing patterns of microbes in the rhizosphere soils of tobacco over different growth stages, we extracted samples from rhizosphere soils at the resettling stage, vigorous growing stage, budding stage, mature stage and the decaying stage in the tobacco
lifecycle. The V4–V5 fragments of the 16S rDNA and the ITS1 fragments of the 18S rRNA have been sequenced by Illumina MiSeq, a high-flux sequencer. A total of 8031 bacterial OTUs and a total of 1983 fungal OTUs have been tested. Both the Chao1 index and Shannon index of bacteria in the
rhizosphere soil samples change in a V-shaped pattern over the five growth stages, which peak at the budding stage and increase at the decaying stage after a period of falling. In the growing cycle of tobacco, the dominant bacteria include γ-Metamorphomycetes, α-Metamorphomycetes,
Actinomycetes and Acidobacteria, and the dominant fungal phyla include Crustacea, Ascomycetes, Agaricomycetes, IS-s-Retroconis sp XAE_090. The sufficiency and diversity of microbial communities in the rhizosphere soil samples range greatly over the five growth stages. This
study lays a theoretical foundation supporting further explorations into the changing rules of microbial communities in the rhizosphere soil of tobacco growth and the relationship between microbial communities and tobacco soil-borne diseases.
Effect of Spent Green Tea Leaf Extracts, Butylated Hydroxytoluene and Repeated Freezing–Thawing on Physicochemical and Oxidative Properties of Chevon
Authors: Majumder Sumana , Das Pabitra Chandra , Sami Rokayya , Ismail Khadiga Ahmed , Al-Mushhin Amina A. M. , Iqbal Abdullah , Ranganathan Thottiam Vasudevan , Mazumder Md. Anisur Rahman
Published: May 2022

Microbial growth and lipid oxidation are the leading causes for degradation of quality attributes of meat. For the preservation and prolonged the shelf life of meat generally, artificial additives are commonly used to delay lipid oxidation and prevent microbial growth. However, their
usage is getting concerned due to adverse health effect. Similarly, repetitions of freezing–thawing cycles also deteriorate meat quality. This aim of the current research was to explore the effect of spent green tea leaves extract (SGTLE), butylated hydroxytoluene (BHT) and repeated
freezing–thawing on physicochemical and biochemical properties of Chevon. Total five samples were prepared by soaking equal amount of Chevon in SGTLE and/or BHT solution. The samples were stored at −18 °C and thawed with distilled water (1:10 ratio). Freezing–thawing
was continued for 5-cycles (30 day/cycles). Dried SGTLE resulted in total phenol content of 73.023±1.45 mg GAE/g, though its antioxidant activity was lower than BHT. Quality changing rate of Chevon was increased with freeze– thawing cycles, but decreased by SGTLE and/or BHT treatments.
Changing in quality parameters followed 1st order kinetics model (R21st > R2zero), though color and protein degraded following zero order. 1% SGTLE+0.01% BHT treatment showed the least degradation rate, whilst 2% SGTLE exhibited almost similar effect as like 0.01%
BHT. Conclusively this study evinced that SGTLE has potentiality to be used as a natural antioxidant.