bluuebunny/crossref_metadata_2025 huggingface dataset:
The Pivotal Immunomodulatory and Anti-Inflammatory Effect of Histone-Lysine N-Methyltransferase in the Glioma Microenvironment: Its Biomarker and Therapy Potentials
Authors: Richard Seidu A. , Eugene Kuugbee D.
Published: October 2021

Enhancer of zeste homolog 2 (EZH2) is a histone-lysine N-methyltransferase that encrypts a member of the Polycomb group (PcG) family. EZH2 forms a repressive chromatin structure which eventually participates in regulating the development as well as lineage propagation of stem cells and glioma progression. Posttranslational modifications are distinct approaches for the adjusted modification of EZH2 in the development of cancer. The amino acid succession of EZH2 protein makes it appropriate for covalent modifications, like phosphorylation, acetylation, O-GlcNAcylation, methylation, ubiquitination, and sumoylation. The glioma microenvironment is a dynamic component that comprises, besides glioma cells and glioma stem cells, a complex network that comprises diverse cell types like endothelial cells, astrocytes, and microglia as well as stromal components, soluble factors, and the extracellular membrane. EZH2 is well recognized as an essential modulator of cell invasion as well as metastasis in glioma. EZH2 oversecretion was implicated in the malfunction of several fundamental signaling pathways like Wnt/β-catenin signaling, Ras and NF-κB signaling, PI3K/AKT signaling, β-adrenergic receptor signaling, and bone morphogenetic protein as well as NOTCH signaling pathways. EZH2 was more secreted in glioblastoma multiforme than in low-grade gliomas as well as extremely secreted in U251 and U87 human glioma cells. Thus, the blockade of EZH2 expression in glioma could be of therapeutic value for patients with glioma. The suppression of EZH2 gene secretion was capable of reversing temozolomide resistance in patients with glioma. EZH2 is a promising therapeutic as well as prognostic biomarker for the treatment of glioma.
Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
Authors: Wegayehu Eyob Betru , Muluneh Fiseha Behulu
Published: October 2021

Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention due to their high‐performance simulation capacity. In this study, we compared multilayer perceptron (MLP), long short‐term memory (LSTM), and gated recurrent unit (GRU) with the proposed new hybrid models, including CNN‐LSTM and CNN‐GRU. Hence, we can simulate one‐step daily streamflow in different agroclimatic conditions, rolling time windows, and a range of variable input combinations. The analysis used daily multivariate and multisite time series data collected from Awash River Basin (Borkena watershed: Ethiopia) and Tiber River Basin (Upper Tiber River Basin: Italy) stations. The datasets were subjected to rigorous quality control processes. Consequently, it rolled to a different time lag to remove noise in the time series and further split into training and testing datasets using a ratio of 80 : 20, respectively. Finally, the results showed that integrating the GRU layer with the convolutional layer and using monthly rolled average daily input time series could substantially improve the simulation of streamflow time series.
Active Aging through Volunteerism: A Longitudinal Assessment of Perceived Neighborhood Safety as a Predictor among Older Adults, 2008‐2018
Authors: Grinshteyn Erin G. , Sugar Judith A.
Published: October 2021

Volunteering can play an important role in active aging. The resource theory of volunteering posits that volunteerism depends on human, social, and cultural capital. Benefits of volunteering have been documented at the micro‐, meso‐, and macrolevels, positively affecting individual older people as well as their local communities and society at large. Taking a process‐oriented theoretical approach, this study focused on the mesolevel factor of the environment with the purpose of determining the relationship between perceived neighborhood safety and volunteerism over the course of a decade and the extent to which this relationship differs by gender and race. Longitudinal data from the Health and Retirement Study in the United States of America between 2008 and 2018 were used (N = 72,319 adults 60 years and older). Generalized estimating equations (GEE) with robust standard errors were employed while controlling for a number of covariates. A third of the sample volunteered in the past year (33%). The probability of volunteering among older adults who rated their perceived neighborhood safety as excellent was greater compared with those who rated their perceived neighborhood safety as fair/poor after controlling for all other model covariates (ME: 0.03, 95% CI: 0.02, 0.05). Among males rating their perceived neighborhood safety as excellent, the probability of volunteering was higher (ME: 0.04, 95% CI: 0.02, 0.07). Among females, the probability of volunteering was higher among those who perceived their neighborhood safety to be excellent (ME: 0.03, 95% CI: 0.01, 0.05) or very good (ME: 0.02, 95% CI: 0.00, 0.04). White respondents who rated their neighborhood safety as excellent (ME: 0.05, 95% CI: 0.03, 0.07) or very good (ME: 0.04, 95% CI: 0.02, 0.06) had a higher probability of volunteerism. Results were not significant among Black respondents and those who described their race as “other.” This study’s process‐oriented theoretical approach indicates that initiatives aimed at improving neighborhood safety and older adults’ perceptions of neighborhood safety could increase social capital and lead older adults to engage in more volunteering, providing benefits at micro‐, meso‐, and macrolevels—to older individuals, their local communities, and society at large.
Novel Technique for Group Decision-Making under Fuzzy Parameterized
q
-Rung Orthopair Fuzzy Soft Expert Framework
Authors: Ali Ghous , Sarwar Musavarah
Published: October 2021

The

q

-rung orthopair fuzzy sets and their hybrid models are capable of dealing with uncertain situations very effectively than the theories of intuitionistic and Pythagorean fuzzy sets and thus have numerous decision-making applications in daily life, while the fuzzy parameterized soft set theory has its impact on different decision-making scenarios. Motivated by these facts, in this research article, these theories are combined to form a new structure named fuzzy parameterized

q

-rung orthopair fuzzy soft expert sets (

FP

q

ROFSESs) for dealing with more generalized information. The developed model is an efficient extension of fuzzy parameterized intuitionistic fuzzy soft expert sets. Some of its basic notions, including subset, complement, OR operation, AND operation, intersection, and union are studied and illustrated via examples. Moreover, to show the applicability and efficiency of the developed model, two real-life applications are solved under the

FP

q

ROFSES approach, which is supported by an algorithm, the first application is about selecting an appropriate site for a cafe outlet, and the second application is about selecting the Best News Channel for an award. At last, a comparison of the initiated model with some existing approaches is presented to verify its advantages over them.
Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction
Authors: Lu Ting , Zhang Beibei , Hu Yunpeng , Chen Jianyong
Published: October 2021

This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm (

P
<
0.05

). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher (

P
<
0.05

). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C (

P
<
0.05

). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.
Overexpression of PAX8-AS1 Inhibits Malignant Phenotypes of Papillary Thyroid Carcinoma Cells via miR-96-5p/PKN2 Axis
Authors: Zhou Ping , Xu Tongdao , Hu Hao , Hua Fei
Published: October 2021

Background. Thyroid carcinoma (THCA) is the most frequent endocrine malignancy. Papillary thyroid carcinoma (PTC) is the major subtype of THCA, accounting for over 80% of all THCA cases. LncRNA PAX8-AS1, a tumor suppressor associated with various human cancers, has been reported to be relevant to the regulation of all sorts of cellular processes. The purpose of this study was to verify the role of PAX8-AS1 in PTC. Methods. Three human PTC cell lines (K1, TPC-1, and IHH4) and one normal human thyroid cell line, Nthy-ori3-1, were used in our study. The expression of genes was detected by qRT-PCR. The bioinformatic analysis and luciferase reporter assay were used to confirm the binding relationship of PAX8-AS1 to miR-96-5p, and the targeting relationship of miR-96-5p to PKN2 was also predicted. Cell proliferation and apoptosis capacities were assessed by MTT and flow cytometry, respectively. EdU assay was used to detect cell proliferation. Western blot assay was employed to examine protein expression. Results. The expression of PAX8-AS1 was decreased in PTC tissues and cells. PAX8-AS1 overexpression inhibited the proliferation of PTC cells and promoted cell apoptosis. In addition, PAX8-AS1 bonds with miR-96-5p, whose downregulation elevated the expression of PKN2 in PTC cells. Importantly, according to the rescue experiments, PKN2 silencing partially reversed the inhibitory effects of PAX8-AS1 expression on PTC cell proliferation and apoptosis. Conclusions. We found that the PAX8-AS1/miR-96-5p/PKN2 axis was closely related to the progression of PTC, which could be a potential target for treating PTC patients.
Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
Authors: Fan Kai , Gu Xiaoye
Published: October 2021

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real‐time analysis of sports video images is formed, so as to achieve the real‐time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality.
Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
Authors: Zhu Lingxia , Xu Zhiping , Fang Ting
Published: October 2021

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.
Iterative Learning‐Based PID Precision Control for Sports Performance Analysis
Authors: Li Xin , Xu Xunxun , Shen Zhijuan , Sun Mengjun
Published: October 2021

Traditional function algorithms are contradictory to accuracy and performance. Therefore, taking into account the balance of accuracy and performance, the research of accuracy control‐oriented mathematical function algorithms is of great significance to the design of high‐precision and high‐performance mathematical function algorithms. This paper is aimed at the design of mathematical function algorithm for precision control and has conducted indepth research on traditional PID control algorithm and fuzzy logic control theory. By analyzing the advantages and disadvantages of the two in practical applications, a parameter fuzzy cascade PID control is designed. The algorithm and its performance simulation and comparative analysis provide a theoretical basis for the follow‐up accuracy control algorithm research and realization process. The accuracy control algorithm is then used to calculate and statistically analyze the sports performance including speed, strength (comprehensiveness and explosiveness), endurance, sensitivity, and coordination. The results show that the optimized function random point (nonextreme) test calculation accuracy is 99.5%, and the control accuracy improvement rate of the parameter fuzzy cascade PID control algorithm is about 18.24%. It has better control effect, stronger stability, and higher control accuracy. In the test of extreme points, the optimized test results are obviously better than those before optimization, which can effectively calculate sports results with high accuracy.
The Protective Effect of Ethyl Acetate and n-Butanol Fractions of Wine-Steamed Ligustri Lucidi Fructus on Diabetic Nephropathy in Rats
Authors: Luan Ruqiao , Sun Linlin , Zhang Xuelan , Zhao Pan , Zhou Qiao , Zhang Zhihui
Published: October 2021

Ligustri Lucidi Fructus (LLF), the dry and ripe fruit of Ligustrum lucidum W. T. Aiton (Oleaceae), is a traditional Chinese medicine for nourishing the liver and kidney in clinics for thousands of years. Wine-steamed Ligustri Lucidi Fructus (WLL) can alleviate coolness and smoothness of LLF and enhance the function of nourishing the liver and kidney, so ancient and modern medicine usually used it in clinics. First of all, we prepared the extracts of different polar fractions of WLL to explore the effective fractions and potential mechanisms of WLL in the treatment of diabetic nephropathy (DN). Then, HPLC method was used to determine the contents of 12 active components in WLL and its different polar components. Finally, the potential relationship between 12 active components and physicochemical parameters of DN rats was explored. The pharmacological experiments showed that WLL, ethyl acetate (EtOAc), and n-butanol (n-BuOH) extracts not only significantly alleviated the clinical symptoms and kidney damage of DN rats but also had obvious anti-inﬂammatory and antioxidant eﬀects. In addition, the results of HPLC analysis showed that the 12 active components of WLL mainly existed in the extracts of EtOAc and n-BuOH. The Pearson correlation analysis showed 12 active components and physicochemical parameters had different degrees of correlation. In conclusion, we proved that the extracts of EtOAc and n-BuOH were the effective fractions of WLL in treating DN in rats, and they could regulate the levels of inflammatory cytokines and decrease oxidation stress, which provides a basis for further research on the mechanism of WLL in treating DN and provides a pharmacological and chemical foundation for the development of new anti-DN drugs.
Coupling Effect of Eccentricity and Slenderness Ratios on RCFST Column Instability Modes
Authors: Guo Chao , Lu Zhengran
Published: October 2021

The instability damage modes of rectangle concrete-filled steel tube (RCFST) columns that are subjected to eccentric compression can be divided into two types based on the modified Jezek analytical procedure, namely, the eccentricity ratio (γ) and the slenderness ratio (λ) coupling effect. The RCFST columns have unilateral compression yield failure mode when γ is small. However, it has compressive and tensile mode on both sides when the value of γ is large. In this work, parametric analyses were performed to test 32 RCFST long columns by varying γ with different λ combinations. From the analysis, it was found that the results of the theoretical analysis of the load-tension strain (P-ε) responses and the instability modes of the RCFST long columns are similar to the experimental results. Further, the proposed analytical method aids in better understanding the effects of γ and λ coupling on the stability behavior of the RCFST columns.
LCHI: Low‐Order Correlation and High‐Order Interaction Integrated Model Oriented to Network Intrusion Detection
Authors: Lei Shengwei , Xia Chunhe , Wang Tianbo
Published: October 2021

Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low‐ or high‐order feature interaction. Moreover, they always extract all possible low‐order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low‐order correlation and high‐order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low‐order correlation between the same‐type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high‐order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low‐ and high‐order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower‐order feature correlation and the nonlinearity of DNN in modeling higher‐order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL‐KDD, UNSW‐NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.
Comprehensive Analysis of Ferroptosis‐Related Markers for the Clinical and Biological Value in Gastric Cancer
Authors: Shao Yanfei , Jia Hongtao , Li Shuchun , Huang Ling , Aikemu Batuer , Yang Guang , Zhang Sen , Sun Jing , Zheng Minhua
Published: October 2021

Gastric cancer is a highly malignant tumor with poor survival rate. Ferroptosis, a newly defined regulated cell death, is closely related to several tumors. Introduction of ferroptosis is promising for cancer treatments. However, the predictive role of ferroptosis in GC remains elusive. In this study, we screened the ferroptosis‐related genes which were differentially expressed between normal and GC tissues. Then, based on these differentially expressed genes (DEGs), the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions were applied to construct the 10‐gene prognostic signature (SP1, MYB, ALDH3A2, KEAP1, AIFM2, ITGB4, TGFBR1, MAP1LC3B, NOX4, and ZFP36) in TCGA training dataset. Based on the median risk score, all GC patients in TCGA training dataset and GSE84437 testing dataset were classified into a high‐ or low‐risk group. GC patients in the low‐risk group showed significantly higher survival possibilities than those in the high‐risk group (P < 0.001). Combined with the clinical characteristics, the risk score was proven as an independent factor for predicting the OS of GC patients. Besides, the GC patients in the high‐ or low‐risk group showed significantly different GO and KEGG functional enrichments, somatic mutation, fractions of immune cells, and immunotherapy response. Then, the expression levels of these genes in signature were further verified in the GC cell lines and our own GC samples (30‐paired tumor/normal tissues). Furthermore, the effects of ferroptosis inducer Erastin on these 10 ferroptosis‐related genes in GC cell lines were also explored in our study. In conclusion, our study constructed a prognostic signature of 10 ferroptosis‐related genes, which could well predict the prognosis and immunotherapy for GC patients.
Optimization of Ice and Snow Sports Industry Chain Structure Based on Sensor Network Communication and Artificial Intelligence
Authors: Li Xingxing , Song Lulu , Wu Hao , Wang Yongan
Published: October 2021

As a new tourism industry model, the ice and snow sports tourism industry has played an increasingly important role in the development of China’s entire tourism industry. This paper analyzes the structural optimization of the ice and snow sports industry chain based on sensor network communication and artificial intelligence and aims to fully apply sensor network communication and artificial intelligence technology to the development of the ice and snow sports industry. Let us look for an optimized method to promote the development of the ice and snow sports industry chain structure. This paper conducts a research on the optimization of the ice and snow sports industry chain structure based on sensor network communication and artificial intelligence. In the method part, this article explains the architecture of sensor nodes and networks, uses the long-term monitoring of wireless sensor networks and the flexibility of the data acquisition structure, combined with the superiority of artificial intelligence in information processing and analysis, to obtain and analyze the experimental data of this research, and introduces the specific content of artificial intelligence and the ice and snow sports industry. In the algorithm, the grey relational analysis method is introduced. In the part of the experimental results, experiments were carried out on the ice and snow city resources, competitive advantages, the quarterly average value of the ice and snow sports enterprise prosperity index, ice and snow sports venues, GDP, and the speed comparison and structure of the development of the sports industry, and simulation experiments were carried out from the perspective of sensors. On the whole, the development curve of China’s ice and snow sports industry basically conforms to the economic development curve. With the slowdown of GDP growth rate, the growth rate of the sports industry slowed down simultaneously. However, despite the continued decline in economic growth, the sports industry has ushered in a peak of development, with a growth rate of 89.45%, a year-on-year increase of 69.3 percentage points. This phenomenon shows that the sports industry has sufficient endogenous development momentum and huge development potential. It is an important driving force and emerging growth point of the economic society and modern services. It has a comprehensive driving role in stimulating urban consumption, promoting economic transformation and upgrading, and driving the development of the service industry.
A Joint Channel Allocation and Power Control Scheme for D2D Communication in UAV‐Based Networks
Authors: Sun Enchang , Qu Hanxing , Yuan Yongyi , Li Meng , Wang Zhuwei , Chen Dawei
Published: October 2021

With the increasing application of unmanned aerial vehicles (UAVs), UAV‐based base stations (BSs) have been widely used. In some situations when there is no ground BSs, such as mountainous areas and isolated islands, or BSs being out of service, like disaster areas, UAV‐based networks may be rapidly deployed. In this paper, we propose a framework for UAV deployment, power control, and channel allocation for device‐to‐device (D2D) users, which is used for the underlying D2D communication in UAV‐based networks. Firstly, the number and location of UAVs are iteratively optimized by the particle swarm optimization‐ (PSO‐) Kmeans algorithm. After UAV deployment, this study maximizes the energy efficiency (EE) of D2D pairs while ensuring the quality of service (QoS). To solve this optimization problem, the adaptive mutation salp swarm algorithm (AMSSA) is proposed, which adopts the population variation strategy, the dynamic leader‐follower numbers, and position update, as well as Q‐learning strategy. Finally, simulation results show that the PSO‐Kmeans algorithm can achieve better communication quality of cellular users (CUEs) with fewer UAVs compared with the PSO algorithm. The AMSSA has excellent global searching ability and local mining ability, which is not only superior to other benchmark schemes but also closer to the optimal performance of D2D pairs in terms of EE.
Expression and Significance of AQP3 in Cutaneous Lesions
Authors: Niu Dongfeng , Bai Yanhua , Yao Qian , Hou Wei , Zhou Lixin , Huang Xiaozheng , Zhao Chen
Published: October 2021

Aquaporin 3 (AQP3) is the membrane channel of water and involved in fluid homeostasis. The aim of this study was to reveal the expression and significance of AQP3 in cutaneous lesions. We analyzed AQP3 mRNA levels using RT-PCR in 311 cutaneous lesions and confirmed AQP3 expression in these lesions by immunohistochemistry. AQP3 mRNA was detected in normal epidermis, seborrheic keratosis, solar keratosis, Bowen’s disease, squamous cell carcinoma, eccrine poroma, apocrine carcinoma, and sebaceoma; however, AQP3 mRNA was absent in basal cell carcinoma, nevocellular nevus, or malignant melanoma. By immunohistochemistry, diffuse AQP3 expression was seen in all keratotic lesions including seborrheic keratosis, verruca vulgaris, molluscum contagiosum, solar keratosis, Bowen’s disease, and squamous cell carcinoma. Diffuse AQP3 expression was also present in all extramammary Paget’s disease. No AQP3 staining was obtained in basal cell carcinoma. Positive AQP3 staining was seen in sweat gland tumors including hidradenoma, eccrine poroma, and apocrine carcinoma. Among sebaceous tumors, AQP3 expressed diffusely in all sebaceous hyperplasia and sebaceous adenoma, but not in sebaceous carcinomas. Only focal AQP3 staining was seen in nevocellular nevus and no AQP3 staining in melanoma. Our findings indicate the function of AQP3 maintained in most skin tumors. AQP3 may be used for differential diagnosis in skin tumors.
A Corporate Reputation Propagation Dynamic Model
Authors: Wang Yufeng , Yang Xu , Chai Yanyou , Yang Xuguang , Tian Qi
Published: October 2021

This study analyzes the process of corporate reputation (CR) propagation. We consider that different positions play different roles via social word of mouth (sWOM). In essence, our interest lies in the following question: how does sWOM affect the process of CR propagation? As a benchmark, we develop a dynamic model to reflect the factors and laws influencing corporate reputation propagation. We find that the stability of the system is not what corporations want to see in the propagation of positive CR after analyzing two basic reproduction numbers and conducting a numerical simulation. Another important finding is that stability is what corporations expect in the propagation of negative CR. This study makes the logical analysis of the relationship between CR and consumer loyalty more complete and helps us to clearly understand the overall process of CR propagation. The study provides a new way to analyze the law of CR propagation and can provide a reference for decision-making for corporations.
Global Information Management Model and Identification Method Based on Deep Reinforcement Learning
Authors: Lan RuiLe , He XiJia
Published: October 2021

Many countries in the world are actively conducting research on information technology and management. And deep learning is a new type of efficient and open technology system and tool. It develops and utilizes information and provides new knowledge based on the information processing capabilities of the human brain. Its essence lies in understanding the inner connection of things and thinking about problems by obtaining relevant data, so as to achieve the learning goal. Therefore, the research on global information management models and identification methods in this article is based on the consideration of global information security and is to create a good global environment. This article mainly uses the data analysis method, system construction method, and investigation method to sort out and study the content of this article. The survey results show that 270 people put the focus of global information on the ecological environment, and about 30% of them attach great importance to the security of global information.
Research on Spoken Language Understanding Based on Deep Learning
Authors: Yanli Hui
Published: October 2021

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.
Effect of Electroacupuncture on Noise-Induced Hearing Loss in Rats
Authors: Chang Chia-Hao , Lin Chia-Der , Hsieh Ching-Liang
Published: October 2021

Acupuncture has long been used to relieve some inner ear diseases such as deafness and tinnitus. The present study examined the effect of electroacupuncture (EA) on noise-induced hearing loss (NIHL) in animals. A NIHL rat model was established. Electroacupuncture pretreatment at 2 Hz or posttreatment at the right Zhongzhu (TE3) acupoint was applied for 1 hour. Auditory thresholds were measured using auditory brainstem responses (ABRs), and histopathology of the cochlea was examined. The results indicated that the baseline auditory threshold of ABR was not significantly different between the control (no noise), EA-only (only EA without noise), noise (noise exposure only), pre-EA (pretreating EA then noise), and post-EA (noise exposure then posttreating with EA) groups. Significant auditory threshold shifts were found in the noise, pre-EA, and post-EA groups in the immediate period after noise exposure, whereas auditory recovery was better in the pre-EA and post-EA groups than that in the noise group at the three days, one week (W1), two weeks (W2), three weeks (W3), and four weeks(W4) after noise stimulation. Histopathological examination revealed greater loss of the density of spiral ganglion neurons in the noise group than in the control group at W1 and W2. Although significant loss of spiral ganglion loss happened in pre-EA and post-EA groups, such loss was less than the loss of the noise group, especially W1. These results indicate that either pretreatment or posttreatment with EA may facilitate auditory recovery after NIHL. The detailed mechanism through which EA alleviates NIHL requires further study.
A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing
Authors: Shafiei Allahkaram , Jamshidi Mohammad (Behdad) , Khani Farzad , Talla Jakub , Peroutka Zdenêk , Gantassi Rahma , Baz Mohammed , Cheikhrouhou Omar , Hamam Habib
Published: October 2021

Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with

s

-norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.
A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements
Authors: Zheng Yuhuang
Published: October 2021

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.
m6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment
Authors: Liu Fei , Yu Xiaopeng , He Guijin
Published: October 2021

Background. We analyzed the n6-methyladenosine (m6A) modification patterns of immune cells infiltrating the tumor microenvironment of breast cancer (BC) to provide a new perspective for the early diagnosis and treatment of BC. Methods. Based on 23 m6A regulatory factors, we identified m6A-related gene characteristics and m6A modification patterns in BC through unsupervised cluster analysis. To examine the differences in biological processes among various m6A modification modes, we performed genomic variation analysis. We then quantified the relative infiltration levels of different immune cell subpopulations in the tumor microenvironment of BC using the CIBERSORT algorithm and single-sample gene set enrichment analysis. Univariate Cox analysis was used to screen for m6A characteristic genes related to prognosis. Finally, we evaluated the m6A modification pattern of patients with a single BC by constructing the m6Ascore based on principal component analysis. Results. We identified three different m6A modification patterns in 2128 BC samples. A higher abundance of the immune infiltration of the m6Acluster C was indicated by the results of CIBERSORT and the single-sample gene set enrichment analysis. Based on the m6A characteristic genes obtained through screening, the m6Ascore was determined. The BC patients were segregated into m6Ascore groups of low and high categories, which revealed significant survival benefits among patients with low m6Ascores. Additionally, the high-m6Ascore group had a higher mutation frequency and was associated with low PD-L1 expression, and the m6Ascore and tumor mutation burden showed a positive correlation. In addition, treatment effects were better in patients in the high-m6Ascore group. Conclusions. In case of a single patient with BC, the immune cell infiltration characteristics of the tumor microenvironment and the m6A methylation modification pattern could be evaluated using the m6Ascore. Our results provide a foundation for improving personalized immunotherapy of BC.
Temporal Trends and Prognosis of Physical Examination Findings in Patients With Acute Decompensated Heart Failure: The ARIC Study Community Surveillance
Authors: Kolupoti Abhigna , Fudim Marat , Pandey Ambarish , Kucharska-Newton Anna , Hall Michael E. , Vaduganathan Muthiah , Mentz Robert J. , Caughey Melissa C.
Published: October 2021

Background:

Bedside evaluation of congestion is a mainstay of heart failure (HF) management. Whether detected physical examination signs have changed over time as obesity prevalence has increased in HF populations, or if the associated prognosis differs for HF with reduced or preserved ejection fraction (HFrEF or HFpEF) is uncertain.

Methods:

From 2005 to 2014, the ARIC study (Atherosclerosis Risk in Communities) conducted adjudicated hospital surveillance of acute decompensated HF. We analyzed trends in physical examination findings, imaging signs, and symptoms related to congestion, both over time and by obesity class, and associated 28-day mortality risks.

Results:

Of 24 937 weighted hospitalizations for acute decompensated HF (mean age 75 years, 53% women, 32% Black), 47% had HFpEF. The prevalence of obesity increased from 2005 to 2014 for both HF types. With increasing obesity category, detected edema increased, while jugular venous distension decreased, and rales remained stable. Detected edema also increased over time, for both HF types. Associations between 28-day mortality and individual signs and symptoms of congestion were similar for HFpEF and HFrEF; however, the adjusted mortality risk with all 3 (edema, rales, and jugular venous distension) versus <3 physical examination findings was higher for patients with HFpEF (odds ratio, 2.41 [95% CI, 1.53–3.79]) than HFrEF (odds ratio, 1.30 [95% CI, 0.87–1.93]);
P
for interaction by HF type =0.02.

Conclusions:

In patients hospitalized with acute decompensated HF, detected physical examination findings differ both temporally and by obesity. Combined findings from the physical examination are more prognostic of 28-day mortality for patients with HFpEF than HFrEF.
Associations of Early Systolic Blood Pressure Control and Outcome After Thrombolysis-Eligible Acute Ischemic Stroke: Results From the ENCHANTED Study
Authors: Wang Xia , Minhas Jatinder S. , Moullaali Tom J. , Luca Di Tanna Gian , Lindley Richard I. , Chen Xiaoying , Arima Hisatomi , Chen Guofang , Delcourt Candice , Bath Philip M. , Broderick Joseph P. , Demchuk Andrew M. , Donnan Geoffrey A. , Durham Alice C. , Lavados Pablo M. , Lee Tsong-Hai , Levi Christopher , Martins Sheila O. , Olavarria Veronica V. , Pandian Jeyaraj D. , Parsons Mark W. , Pontes-Neto Octavio M. , Ricci Stefano , Sato Shoichiro , Sharma Vijay K. , Silva Federico , Thang Nguyen H. , Wang Ji-Guang , Woodward Mark , Chalmers John , Song Lili , Anderson Craig S. , Robinson Thompson G. ,   on behalf of the ENCHANTED Investigators
Published: October 2021

Background and Purpose:

In thrombolysis-eligible patients with acute ischemic stroke, there is uncertainty over the most appropriate systolic blood pressure (SBP) lowering profile that provides an optimal balance of potential benefit (functional recovery) and harm (intracranial hemorrhage). We aimed to determine relationships of SBP parameters and outcomes in thrombolyzed acute ischemic stroke patients.

Methods:

Post hoc analyzes of the ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study), a partial-factorial trial of thrombolysis-eligible and treated acute ischemic stroke patients with high SBP (150–180 mm Hg) assigned to low-dose (0.6 mg/kg) or standard-dose (0.9 mg/kg) alteplase and intensive (target SBP, 130–140 mm Hg) or guideline-recommended (target SBP <180 mm Hg) treatment. All patients were followed up for functional status and serious adverse events to 90 days. Logistic regression models were used to analyze 3 SBP summary measures postrandomization: attained (mean), variability (SD) in 1–24 hours, and magnitude of reduction in 1 hour. The primary outcome was a favorable shift on the modified Rankin Scale. The key safety outcome was any intracranial hemorrhage.

Results:

Among 4511 included participants (mean age 67 years, 38% female, 65% Asian) lower attained SBP and smaller SBP variability were associated with favorable shift on the modified Rankin Scale (per 10 mm Hg increase: odds ratio, 0.76 [95% CI, 0.71–0.82];
P
<0.001 and 0.86 [95% CI, 0.76–0.98];
P
=0.025) respectively, but not for magnitude of SBP reduction (0.98, [0.93–1.04];
P
=0.564). Odds of intracranial hemorrhage was associated with higher attained SBP and greater SBP variability (1.18 [1.06–1.31];
P
=0.002 and 1.34 [1.11–1.62];
P
=0.002) but not with magnitude of SBP reduction (1.05 [0.98–1.14];
P
=0.184).

Conclusions:

Attaining early and consistent low levels in SBP <140 mm Hg, even as low as 110 to 120 mm Hg, over 24 hours is associated with better outcomes in thrombolyzed acute ischemic stroke patients.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT01422616.
Antithrombotic Therapy for Stroke Prevention in Patients With Ischemic Stroke With Aspirin Treatment Failure
Authors: Lusk Jay B. , Xu Haolin , Peterson Eric D. , Bhatt Deepak L. , Fonarow Gregg C. , Smith Eric E. , Matsouaka Roland , Schwamm Lee H. , Xian Ying
Published: October 2021

Background and Purpose:

Many older patients presenting with acute ischemic stroke were already taking aspirin before admission. However, the management strategy for patients with aspirin treatment failure has not been fully established.

Methods:

We used data from the American Heart Association Get With The Guidelines Stroke Registry to describe discharge antithrombotic treatment patterns among Medicare beneficiaries with ischemic stroke who were taking aspirin before their stroke and were discharged alive from 1734 hospitals in the United States between October 2012 and December 2017.

Results:

Of 261 634 ischemic stroke survivors, 100 016 (38.2%) were taking aspirin monotherapy before stroke. Among them, 44.4% of patients remained on aspirin monotherapy at discharge (20.9% 81 mg, 18.2% 325 mg, 5.3% other or unknown dose). The next most common therapy choice was dual antiplatelet therapy (24.6%), followed by clopidogrel monotherapy (17.8%). The remaining 13.2% of patients were discharged on either aspirin/dipyridamole, warfarin, or nonvitamin K antagonist oral anticoagulants with or without antiplatelet, or no antithrombotic therapy at all.

Conclusions:

Nearly half of patients with ischemic stroke while on preventive therapy with aspirin are discharged on aspirin monotherapy without changing antithrombotic class, while the other half are discharged on clopidogrel monotherapy, dual antiplatelet therapy, or other less common agents. These findings emphasize the need for future research to identify best management strategies for this very common and complex clinical scenario.
International Ozone Negotiations and the Green Spiral
Authors: Kelsey Nina
Published: October 2021

Scholarship examining the highly successful ozone negotiations is rare today, as lessons derived from them do not seem to have produced comparable success in climate negotiations. This article argues that there is a “missing piece” critical to understanding ozone negotiation success. I draw on path dependency and feedback literature as well as detailed historical research into the ozone negotiation process to propose a coherent feedback mechanism I refer to as the “green spiral.” In a green spiral, an iterative interaction between negotiation outcomes and changes to the sticky, internal material interests of industry works to make more stringent regulation feasible in subsequent negotiating rounds. Such dynamics offer a consistent explanation for the overall success of the ozone negotiations as well as the timing and nature of individual countries’ shifts in negotiating position and regulatory behavior over time. Understanding environmental negotiation through this lens offers insight into how outcomes of climate and other environmental negotiations might be improved.