bluuebunny/crossref_metadata_2025 huggingface dataset:
Single-Cell RNA-seq Analysis Reveals Dysregulated Cell-Cell Interactions in a Tumor Microenvironment Related to HCC Development
Authors: Liu Zhenhao , Zhang Siwen , Ouyang Jian , Wu Dan , Chen Lanming , Zhou Wen , Xie Lu
Published: May 2022

The heterogeneity of tumor microenvironment (TME) of hepatocellular carcinoma (HCC) may relate to cell-cell interaction event (CCE) dysregulation and would affect therapeutic responses and clinical outcomes. To reveal the differentiation of CCEs in the liver tissue from healthy donors (HD) to HCC, scRNA-seq data of ~62000 cells from HD, paracancerous nontumor tissue (NT), and HCC were analyzed. The microenvironmental CCE landscape was constructed. Dysregulated cell types and changed molecular functions were identified with CCE alterations in HCC. Dysregulated CCEs which function as pivotal roles in tumorigenesis and development of HCC included SPP1-CD44, MIF-TNFRSF14, and VEGFA-NRP1. A CCE-based immune regulatory network was extracted to illustrate the mechanism of TME dysregulation. A prognostic signature based on CCE genes was identified and validated in independent datasets. Our study provided insights into the characteristics of the cross-talk between tumor cells and microenvironment in HCC and established a workflow strategy for CCE analyses based on scRNA-seq data.
Combination of Oxalobacter Formigenes and Veillonella Parvula in Gastrointestinal Microbiota Related to Bile-Acid Metabolism as a Biomarker for Hypertensive Nephropathy
Authors: Li Xin , Wang Li , Ma Shaojun , Lin Shaohui , Wang Chunyan , Wang Haiya
Published: May 2022

The human microbiome is a mixed group of microorganisms, which individually consists of 10–100 trillion symbiotic microbial cells. The relationship between gastrointestinal microbiota and blood pressure has been verified and the intestinal microbiota of chronic kidney disease (CKD) patients in the distribution of bacterial species is different from the flora of people with no CKD. The purpose of this research is to study the different intestinal microbiota of hypertensive patients with and without nephropathy and to find possible biomarkers of hypertensive nephropathy (H-CKD). The subjects of this research were divided into three groups, healthy control group, hypertension group, and hypertensive nephropathy group. Sequencing, bioinformatics, and statistical analysis were performed on the 16S rRNA gene of the subjects’ stool samples. This research study showed the differences of intestinal flora as biomarkers in hypertension patients with and without nephropathy; it investigated the relationship of the differences in the intestinal microbiota with bile-acid metabolism; it also explored bile-acid metabolism mechanism of intestinal microbiota differences in hypertension with or without nephropathy. In summary, the difference in the combination of O. formigenes and V. parvula in the gastrointestinal microbiota is related to bile-acid metabolism in hypertensive patients and can be one of the factors causing CKD. It is the first time to report such a biomarker or pathogenic factor of H-CKD in the world.
PFP‐LHCINCA: Pyramidal Fixed‐Size Patch‐Based Feature Extraction and Chi‐Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images
Authors: Kaplan Ela , Ekinci Tekin , Kaplan Selcuk , Barua Prabal Datta , Dogan Sengul , Tuncer Turker , Tan Ru-San , Arunkumar N , Acharya U. Rajendra
Published: May 2022

Objectives. Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X‐linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US‐based fetal sex classification model that can facilitate efficient screening and reduce misclassification.Methods. We have developed a novel feature engineering model termed PFP‐LHCINCA that employs pyramidal fixed‐size patch generation with average pooling‐based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi‐square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter‐derived k‐nearest neighbor‐based misclassification rates. The model was trained and tested on a sizeable expert‐labeled dataset comprising 339 males’ and 332 females’ fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally.Results. Standard model performance metrics were compared using five shallow classifiers—k‐nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)—with the hyperparameters tuned using a Bayesian optimizer. The PFP‐LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers.Conclusions. US‐based fetal sex classification is feasible and accurate using the presented PFP‐LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex classification. The model architecture can be modified into deep learning models for training larger datasets.
[Retracted] LINC01426 Triggers Growth and Metastasis of Lung Adenocarcinoma as a Prognostic Indicator
Authors: Deng Youjun , Cai Songhua , Liu Wenyi , Yang Chenglin , Guo Xiaotong
Published: May 2022

The vital regulation of abnormally expressed lncRNAs in human cancers has been identified. This study is aimed at illustrating the role of LINC01426 in influencing malignant behaviors of lung adenocarcinoma (LUAD) and the possible mechanism. Differential expressions of LINC01426 in a downloaded profile containing LUAD and normal tissues were analyzed using Gene Expression Profiling Interactive Analysis (GEPIA) database and were reconfirmed in clinical samples collected in our hospital. In addition, LINC01426 level in lung carcinoma cell lines was detected by quantitative real‐time polymerase chain reaction (qRT‐PCR) as well. The relationship between LINC01426 expression and the age, tumor node metastasis (TNM) staging, lymphatic metastasis, tumor differentiation, and overall survival of LUAD was analyzed. After intervening LINC01426 level in H1299 and PC9 cells, proliferative and metastatic changes were assessed by functional experiments. LINC01426 was upregulated in LUAD tissues and cell lines. Its level was closely linked to TNM staging, lymphatic metastasis, tumor differentiation, and overall survival of LUAD. Knockdown of LINC01426 suppressed proliferative and metastatic abilities in H1299 and PC9 cells. LINC01246 is upregulated in LUAD samples and predicts a poor prognosis. It drives malignant process of LUAD via stimulating proliferative and metastatic abilities.
Chlorogenic Acid Alleviates the Inflammatory Stress of LPS-Induced BV2 Cell via Interacting with TLR4-Mediated Downstream Pathway
Authors: Xu Wuping , Luo Tao , Chai Juan , Jing Ping , Xiong Lijun
Published: May 2022

Background. Neuroinflammation is related with the inflammatory stress of brain tissue induced by the activation of microglial in the central nervous system (CNS), which is still an intractable disease for modern clinical system. Chlorogenic acid has multiple biological activities such as antivirus and anti-inflammation, while few researches have revealed its therapeutic functions in neuroinflammation. Methods. BV2 cells were treated with lipopolysaccharide (LPS) to establish neuroinflammation cell models, and the effects and mechanism of chlorogenic acid in improving the inflammatory progression were investigated. In brief, the toxicity of chlorogenic acid on BV2 cells was detected with MTT assay. The levels of the inflammatory factors including TNF-α, IL-6, IL-1β, and IFN-α were measured with ELISA, and the abundances of TLR4, MyD88, TRIF, and NF-κB were observed by qRT-PCR and western blot. Results. Chlorogenic acid did not exhibit obvious toxic and side effects on BV2 cells. The levels of TNF-α, IL-6, IL-1β, and IFN-α were observably upregulated in BV2 cells after treating with LPS. Chlorogenic acid significantly reduced the levels of TNF-α, IL-6, IL-1β, and IFN-α. Moreover, the abundances of TLR4, MyD88, TRIF, and NF-κB were increased in LPS-induced BV2 cells, while chlorogenic acid could obviously reduce their expressions. Conclusion. This study suggests that chlorogenic acid can improve the inflammatory stress of LPS-induced BV2 cell via interacting with the TLR4-mediated downstream pathway, which is a potential drug for neuroinflammation treatment.
COVID-19 and Decision-Making for Pregnant Women: Taking or Relinquishing Control in Response to a Pandemic
Authors: Kaselitz Elizabeth , Finkbeiner Chelsea , Javaid Sarah , Barringer Sarah , Compton Sarah D. , Muzik Maria , Moyer Cheryl A.
Published: May 2022

COVID-19 has uniquely impacted pregnant women. From the initial unknowns about its virulence during pregnancy, to frequent and rapidly changing hospital guidelines for prenatal care and delivery, pregnant women have felt intense uncertainty and, based on recent research, increased anxiety. This study sought to determine the impact COVID-19 had on women’s birth plans. Open-ended qualitative responses from an anonymous, online survey of pregnant women in the United States, conducted on April 3-24, 2020, were analyzed using the Attride-Stirling qualitative framework. A conceptual framework for understanding the impact of COVID-19 on women’s birth plans was generated. 2,320 pregnant women (mean age 32.7 years, mean weeks pregnant 24.6 weeks) responded to the open-ended prompts, reflecting the following themes: the impact(s) of COVID-19 on pregnant women (including unanticipated changes and uncertainty), the effect of COVID-19 on decision-making (including emotional reactions and subsequent questioning of the healthcare system), and how both of those things led women to either exercise or relinquish their agency related to their birth plan. These findings indicate that the changes and uncertainty surrounding COVID-19 are causing significant challenges for pregnant women, and absent more clarity and more provider-driven support, women seeking to cope are considering changes to their birth plans. Health systems and providers should heed this warning and work to provide pregnant women and their families with more information, support, and collaborative planning to ensure a positive, healthy birth experience, even during a pandemic.
Stress Cone Effect Analysis and Optimum Design of Extra High-Voltage Cable Joint Using Neural Networks
Authors: Köksal Çağrı , Dokur Emrah , Ersoy Aysel
Published: May 2022

The major problem in high-voltage cable accessories is the electric field stress caused by the high-voltage level at the cable junction. In this paper, a neural network and simulation-based optimal design for extra high-voltage (EHV) cable accessory is proposed to ensure the optimum electrical design. The different kinds of geometries of the EHV underground cable accessory are realized in the Comsol Multiphysics software program, and the electric field analyses are conducted based on the finite element method. The design of high-voltage underground cable accessories is examined both within and without the optimal stress cone geometry. A neural network-based model is developed according to the analysis results obtained in the simulation. Thanks to this model, the geometry of the stress cone is determined. In the simulation-based analysis, the electric field stress caused by the high-voltage level at critical points is controlled with an optimum joint design using neural networks. The proposed model will contribute to EHV cable joint design at different stress levels.
Autonomous Anticollision Decision and Control Method of UAV Based on the Optimization Theory
Authors: Zhou Huan , Zhao Xin , Khalil Ahmed Mostafa
Published: May 2022

Autonomous anticollision of unmanned aerial vehicle (UAV) is one of the key technologies to realize intelligent decision-making and autonomous control, and it is of great significance to improve the flight safety and survivability of UAV in complex environment. Firstly, the UAV autonomous anticollision system configuration is constructed in this paper, and the UAV autonomous anticollision problem and related models are described. Then, the potential collision conflict prediction rules are defined, and a practical three-dimensional collision conflict prediction method is proposed. Finally, the UAV autonomous avoidance decision-making method is designed by using the optimization theory, and the corresponding simple and feasible flight control law is put forward. Numerical simulation results show that the proposed method can ensure the flight safety of UAV by relying on autonomous decision-making and control strategy, so as to realize the autonomous anticollision between a single UAV and non-cooperative dynamic obstacles in three-dimensional airspace.
A Novel Mining Approach for Data Analysis and Processing Using Unmanned Aerial Vehicles
Authors: Alenezi Ali , Sawalmeh Ahmad , Shakhatreh Hazim , Almutiry Muhannad , Alreshidi Nasser Aedh
Published: May 2022

In the mining industry, smart surveying and exploration operations for the minerals are essential during mining missions. Usually, these missions are performed in remote areas that do not have a wireless communications infrastructure. This paper proposes to use the unmanned aerial vehicle (UAV) as a relay communication node between the exploration team and the ground control station (GCs). UAV can act as a relay node to provide mobile, flexible, and reliable communication links in remote environments and complex topologies. In this work, the pathloss models in millimeter‐wave technology are considered because they provide massive data rates for line of sight scenarios. The optimization problem of identifying a 3D location and trajectory of the UAV relay node is formulated to maximize the total team members’ data rate. Because the problem is non‐convex, the particle swarm optimization algorithm is used to solve it and determine an efficient location and trajectory of the UAV.
Design and Analysis of Optical Window Encapsulation Structure for Laser Doppler Deep-Sea Hydrothermal Velocity Measurement System
Authors: Wang Hao , Wu Junfei , Qu Zhiping , Song Yixuan
Published: May 2022

The optical window is an essential channel for optical signal transmission and return of a deep-sea hydrothermal velocimetry system. Due to its unique structural design, the stress concentration of the optical window will occur under the action of deep-sea high pressure, resulting in cracks at the corner of the high-pressure surface of the optical window, which leads to the inaccurate convergence of the optical signal. A pressure self-balancing optical window package structure is designed to solve the problem. The stress at the optical window is numerically analyzed, and the forces of self-balancing and nonpressure self-balancing optical windows under the same seawater pressure are compared. The results show that the maximum stress at the stress concentration can be reduced by 53% using the pressure self-balancing method, and the phenomenon of stress concentration can be effectively eliminated. In addition, to verify the feasibility of the design and the accuracy of the numerical calculation results, the optical window and its encapsulation structure were experimentally studied, and the deep-sea performance test experiment was carried out on the “Jiaolong” submersible, which further verified the accuracy of the results.
How Does Digital Finance Affect People’s Income: Evidence from China
Authors: Wang Tingting , Ji Cheng
Published: May 2022

Using data from the China Family Panel Studies (CFPS) from 2010 to 2018 and the Peking University Digital Financial Inclusion Index from 2011 to 2018, this research explores the effect of digital financial development on residents’ income and the channels of influence. The findings suggest that the growth of digital financial services has a significant and favourable influence on citizens’ income. Furthermore, digital financial development benefits rural populations, low-skilled workers, and individuals working in basic industries the most. The extension of inhabitants’ employment space, better access to credit, and ease of access to information online are the major ways that digital financial development influences their income. In particular, the expansion of employment space has a greater positive impact on the low-skilled labor force, rural residents, and those in primary and secondary industries, while access to information is more likely to explain the increase in income of urban residents, and credit has an advantage in promoting income growth for the low-skilled laborers and those in traditional industries.
A Logistic Trigonometric Generalized Class of Distribution Characteristics, Applications, and Simulations
Authors: Mahmood Zafar , Khogeer Hazar A. , Hussam Hafez Eslam , Hossain Md. Moyazzem
Published: May 2022

We propose a trigonometric generalizer/generator of distributions utilizing the quantile function of modified standard Cauchy distribution and construct a logistic‐based new G‐class disbursing cotangent function. Significant mathematical characteristics and special models are derived. New mathematical transformations and extended models are also proposed. A two‐parameter model logistic cotangent Weibull (LCW) is developed and discussed in detail. The beauty and importance of the proposed model are that its hazard rate exhibits all monotone and non‐monotone shapes while the density exhibits unimodal and bimodal (symmetrical, right‐skewed, and decreasing) shapes. For parametric estimation, the maximum likelihood approach is used, and simulation analysis is performed to ensure that the estimates are asymptotic. The importance of the proposed trigonometric generalizer, G class, and model is proved via two applications focused on survival and failure datasets whose results attested the distinct better fit, wider flexibility, and greater capability than existing and well‐known competing models. The authors thought that the suggested class and models would appeal to a broader audience of professionals working in reliability analysis, actuarial and financial sciences, and lifetime data and analysis.
IoT-Driven Model for Weather and Soil Conditions Based on Precision Irrigation Using Machine Learning
Authors: Singh Dushyant Kumar , Sobti Rajeev , Kumar Malik Praveen , Shrestha Sachin , Singh Pradeep Kumar , Ghafoor Kayhan Zrar
Published: May 2022

To feed a growing population, sustainable agriculture practices are needed particularly for irrigation. Irrigation makes use of about 85% of the world’s freshwater resources. Thus, for efficient utilization of water in irrigation, conventional irrigation practices need to either be modified or be replaced with advanced and intelligent systems deploying Internet of Things, wireless sensor networks, and machine learning. This article proposes intelligent system for precision irrigation for monitoring and scheduling using Internet of Things, long range, low-power (LoRa)-based wireless sensor network, and machine learning. The proposed system makes use of soil and weather conditions for predicting the crop’s water requirement. The use of machine learning algorithms provides the proposed system capability of the prediction of irrigation need. Dataset of soil and weather conditions captured using sensors is used with six different machine learning algorithms, and the best one giving highest efficiency in predicting the irrigation scheduling is selected. Linear discriminant analysis algorithm gives the best efficiency of 91.25% with prediction efficiency.
Optimal Arrangement of Structural Sensors in Landfill Based on Stress Wave Detection Technology
Authors: Xie Xiankun , Xia Xiong , Gao Zhaoguo
Published: May 2022

Sensor arrangement is the primary link of landfill structure health monitoring, and the number of measuring points and the quality of data directly affect the effect of modal identification. Therefore, how to ensure the service safety of landfill structure has become an important research content in the field of landfill structure health monitoring. In this paper, the stress wave propagation principle and stress wave detection theory are analyzed, and an optimal sensor arrangement method based on AGSA (adaptive gravity search algorithm) algorithm is proposed. Use CS (Cuckoo search) algorithm to optimize the objective function value. The corresponding response value is calculated by the finite element model, and the initial proxy model is constructed. By comparing and analyzing the results of model correction, it is found that the error of parameters before and after correction is less than 2.5%. It is further verified that AGSA algorithm can be used to solve the optimal sensor placement problem. In this paper, the use of structural health monitoring technology for health diagnosis and performance evaluation is an important means to ensure structural safety, prolong service life, and reduce maintenance cost. As the primary link of structural health monitoring, sensor system directly determines the accuracy of structural safety diagnosis.
An NSCT Image Denoising Method Based on Genetic Algorithm to Optimize the Threshold
Authors: Zhang Zeliang , Wang Haoyang , Bi Xinwen , Wu Jing , Cheng Yanming , Lee Ilkyoo , Chen Jiufei
Published: May 2022

In order to solve the defect that the threshold value of the NSCT transform method is too large or the real signal coefficients are directly lost during image denoising, an adaptive threshold method of genetic algorithm is used to optimize the NSCT image denoising method. The genetic algorithm is used to generate the initial population, and the genetic operator is determined by selection, crossover, and mutation operations to achieve NSCT threshold optimization. The obtained optimized NSCT threshold is used to process different directions. The coefficients of different scales are processed by using NSCT inverse transform to obtain the denoised image. The results of the case analysis show that the proposed method is used to denoise the image, the peak signal-to-noise ratio of the image after denoising is higher than 30 dB, the image contains rich edge information and detailed information, and the denoising performance is superior.
An Application of Variational Minimization: Quasi-Harmonic Coons Patches
Authors: Ahmad Daud , Naz Kiran , Bashir Sadia , Bariq Abdul
Published: May 2022

For a minimal surface, the mean curvature of the surface vanishes for all possible parameterizations which results in a second-order nonlinear partial differential equation (

p
d
e

), whose solution in general is the desired surface as the unknown function of surface parameters. The solution of this partial differential equation is known only for very few cases. Instead of solving the corresponding partial differential equation, we exploit an ansatz method (Ahmad et al. (2013), Ahmad et al. (2014) Ahmad et al. (2015)), used for Coons patches spanned by finite number of boundary curves for a quasi-minimal surface as the extremal of

r
m
s

of mean curvature. The ansatz method targets a slightly perturbed surface (the rational blending interpolants-based Coons patch, Hermite cubic polynomials interpolants-based Coons patch, and Ferguson surface (vanishing twist vectors) in our case) that comprises initially a nonminimal surface plus the product of a real parameter with a variational function of surface parameters (vanishes at the boundary curves along the unit normal to the slightly perturbed surface). The variational function can be deliberately chosen as the product of linear functions and the mean curvature of the initial nonminimal surface such that it is zero at the boundary curves and then replace this mean curvature by the mean curvature of the resulting surface to find a surface of reduced area, and the process can be repeated for further improvement. In this article, we extend the ansatz method (1) for the rational blending functions interpolants-based Coons patch with a parameter in their form for its different values and (2) for the blending functions comprising Hermite cubic polynomial interpolants-based Coons patch (bicubically blended Coons patch (BBCP) and the Ferguson surface). The ansatz method can be extended for the variational extremal of the surfaces for fuzzy optimal control problems (Filev et al. (1992), Emamizadeh (2005), Farhadinia (2011), Mustafa et al. (2021)).
Application of Mirror Materials in Wood Furniture Manufacturing Process Based on Big Data Analysis
Authors: Yang Yan
Published: May 2022

With the development of the national economy, the interior decoration work has received extensive attention, and the traditional decoration methods have been unable to meet its actual work needs. The application of mirror materials in interior decoration can not only enrich the technical methods of interior decoration, but also improve the decoration quality and comfort, so as to meet people’s growing decoration needs, achieving the purpose of promoting the sound development of interior decoration. Mirror material is one of the decorative materials widely used in modern indoor environment. It not only has decoration, but also can expand space. In recent years, the advent of the era of big data has promoted the reform of the design industry. Using big data thinking to assist the innovative design of furniture products will also become an important trend in the development of the furniture industry. According to the characteristics of customer demand in the era of big data, this paper designs and develops a set of customer demand analysis system based on big data by using cloud computing and big data mining technology. By collecting the behavior data of users when using products, this paper combines the mirror material with the manufacturing process of wooden furniture. The mining algorithm is applied to find out the improvement goal and direction of wood furniture manufacturing process based on mirror material, so as to enhance the competitiveness of furniture enterprises. The method proposed in this paper can obtain the furniture design elements according to the functional zoning and theme information and input the furniture design elements and zoning elements into the design element matching evaluation model to obtain the design matching element information.
Design of Online Ideological and Political Teaching of Building Architecture from the Perspective of Machine Learning
Authors: Li Xuhui
Published: May 2022

Under the background of the rapid progress of machine learning (ML) and information technology, the traditional classroom model is gradually replaced by the self-media classroom. This thesis aims to solve the problems of understanding abstract content and limited teaching in practical teaching. Based on the ideological and political integration teaching of building architecture, the online classroom design principle reflects the classroom optimization and design principle of building architecture from the perspective of ML. The data advantage is utilized, and the classroom application model is established using the deep learning algorithm through information input. The scene of field survey and building construction is presented from the perspective of mechanization. The simulated results are displayed in the classroom through online teaching combined with ideological education and political teaching mode. The research shows that ML combined with online classroom design breaks through the book face-to-face teaching mode of the traditional classroom through the dynamic demonstration of actual construction, the virtual building structure, and the innovative design of building material entity. It stimulates students’ interest in architecture courses, improves the overall learning efficiency, and promotes the ideological and political integration of building architecture courses. Besides, it is conducive to cultivating comprehensive architectural talents in the new era.
Ta-Xi-San Suppresses Atopic Dermatitis Involved in Multitarget Mechanism Using Experimental and Network Pharmacology Analysis
Authors: Zhi Wenbing , Li Chun , Zhang Hong , Zhao Yiding , Zong Shiyu , Liu Qiqi , Zhou Jie , Wang Chunliu , Sun Tingting , Liu Yang , Li Ye
Published: May 2022

Atopic dermatitis (AD) is a relapsing and chronic skin inflammation with a common incidence worldwide. Ta-Xi-San (TXS) is a Chinese herbal formula usually used for atopic dermatitis in clinic; however, its active compounds and mechanisms of action are still unclear. Our study was designed to reveal the pharmacological activities, the active compounds, and the pharmacological mechanisms of TXS for atopic dermatitis. Mice were induced by 2,4-dinitrocluorobenzene (DNCB) to build atopic dermatitis model. The pathological evaluation, enzyme-linked immunosorbent assay (ELISA), and hematoxylin and eosin (H&E) assay were performed. The UPLC-Q-Exactive-MSE and network pharmacology analysis were performed to explore active ingredients and therapeutic mechanisms of TXS. TXS treatment decreased levels of immunoglobulin E (IgE), interleukin-4 (IL-4), and tumor necrosis factor-α (TNF-α) in serum induced by DNCB. TXS reduced scratching behavior and alleviated inflammatory pathology of skin and ear. Meanwhile, TXS decreased the spleen index and increased spleen index. The UPLC-Q-Exactive-MSE results showed that 65 compounds of TXS were detected and 337 targets were fished. We collected 1371 AD disease targets, and the compound-target gene network reveled that the top 3 active ingredients were (−)-epigallocatechin gallate, apigenin, and esculetin, and the core target genes were PTGS2, PTGS1, and HSP90AA1. The KEGG pathway and GO analysis showed that TXS remedied atopic dermatitis via PI3K-Akt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and Toll-like receptor (TLR) signaling pathway with the regulation of inflammatory response and transcription. Further, we found that the targets of PTGS2 and HSP90AA1 were both elevated in ears and skin of AD model mouse; however, TXS decreased the elevated expressions of PTGS2 and HSP90AA1. Our study revealed that TXS ameliorated AD based on (−)-epigallocatechin gallate, apigenin, and esculetin via targeting PTGS2 and HSP90AA1.
Design and Implementation of Chinese Common Braille Translation System Integrating Braille Word Segmentation and Concatenation Rules
Authors: Zhang Ju-Xiao , Chen Hai-Feng , Chen Bing , Chen Bei-Qin , Zhong Jing-Hua , Zeng Xiao-Qin
Published: May 2022

An important sign of the accessibility of Braille information is the realization of the mutual translation between Chinese and the Braille. Due to the irregularity and uncertainty of the Prevailing Mandarin Braille, coupled with the lack of a large-scale Braille corpus, the quality of Chinese-Braille translation seems to be poor. In July 2018, the National Language Commission released the “Chinese Common Braille Scheme” and advocated replacing the “Prevailing Mandarin Braille.” Aimed at improving translation accuracy, this research, which is based on the self-built Chinese Common Braille corpus and combined with the HanLP (Han Language Processing) dictionary and the Chinese-Braille word corpus (a Braille word segmentation and concatenation dictionary for generating a unigram language model), uses the n-gram language model to design and implement a Chinese-Braille intertranslation system that integrates Chinese and Braille Word Segmentation and Concatenation Rules. More importantly, this research proposes an experimental plan for improving the Braille Word Segmentation and Concatenation Rules using a Chinese-Braille word corpus. Experiments show that in the field of educational literature, the accuracy rate of translation from Chinese to Chinese Common Braille has reached 95.01%, and the accuracy of Chinese Common Braille to Chinese translation has reached 90.15%.
Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients
Authors: Yan Zhaoyue , Gao Yushuai , Yu Jinliang , Shen Zhiyuan , Bu Xingyao
Published: May 2022

Background. Glioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas. Materials and Methods. We investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms. Results. In total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients. Conclusion. We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.
Introducing Business Visual Analytics into Business Education by Information Technology and Computing Methods
Authors: Zhang Yuyu , Chan Kan Kan , Liu Jun
Published: May 2022

Business students feel an urgency to be competent involving business intelligence in their studies and career. This study examines the current literature on business visual analytic technologies (VATs), both in the business community and in business learning, and points out consideration as a research gap in the existing literature. We conduct a systematic review with database searches from 2006 to 2020. The objectives of the review are to identify (a). the existing terminologies related to VATs in the literature; (b). the purpose of VAT application and the benefits of VATs in the business community; and to examine (c). how the VATs are related to the educational context, particularly, in business learning. The findings indicate that VATs provide positive enhancement in business analytics; however, the effect of VAT application in business learning is rather limited. We provide insights for further studies involving VAT-enhanced business learning.
Study on the Optimization of Macroeconomics Teaching Model Based on Cluster Analysis in the Context of Data
Authors: Wu Jinshun
Published: May 2022

Macroeconomics is a basic course among majors related to economic management. It plays a connecting role and can help students grasp economic phenomena and solve economic problems from an overall perspective. Many teachers still teach macroeconomics using the old teaching method, which ignores students' accepting abilities and preferences. It can not properly encourage pupils to take initiative in their learning, resulting in disconnect between curricular instruction and actual requirements. Teachers should correctly face the teaching reform situation, actively rely on advanced big data technology, upgrade macroeconomics teaching mode, and enhance students' ability to actively learn and solve practical problems and use teaching reform to obtain ideal teaching results. We present an optimization approach for macroeconomics teaching mode based on cluster analysis in the context of data based on this. The model's viability is confirmed through the creation of instructional situations and practical teaching. The experimental results show that students can improve their core knowledge, communication, and learning ability. The method has certain feasibility and effectiveness, which lays a foundation for the optimization of macroeconomics teaching mode.
Crankshaft Optimization Based on Experimental Design and Response Surface Method
Authors: Zheng Bin , Zhang Jingdong , Lei Jilin
Published: May 2022

To improve the applicable performance of the engine and accomplish the determination of optimization, the crankshaft, as one of its essential components, is optimized based on experimental design and response surface method (RSM). First, the static analysis of the crankshaft is carried out. Over the solution, the maximum equivalent stress and deformation distribution of the crankshaft and the area with the most concentrated stress are obtained to provide the direction for subsequent optimization. Then the modal analysis is carried out to define the portions with large amplitude to prevent resonance. At the same time, based on the modal analysis results, the harmonic response of the crankshaft is examined to ensure that the crankshaft can avoid resonance and fatigue caused by forced vibration at different engine speeds. It also provides a theoretical basis for the further optimization design of the crankshaft. Then, through the experimental design and response surface optimization analysis, the optimization purpose of reducing the maximum equivalent stress, maximum deformation, and lightweight of the crankshaft is accomplished. Through the design expert software, the response surface model and fitting equation of the crankshaft are solved by the RSM, which provides a reference basis for optimizing the crankshaft. Finally, according to the optimization scheme, the crankshaft is reconstructed, and the optimized crankshaft model is compared with the original model. After optimization, the maximum equivalent stress is reduced by 9.43%, the maximum deformation is reduced by 3.68%, and the mass is reduced by 1.30%. At the same time, the first three natural frequencies are reduced by 10.28, 10.28, and 10.34%, which achieves the purpose of optimizing the crankshaft design. The research results provide effective theory and evidence for the structural design of the crankshaft.
Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis
Authors: Ren Xuejiao , Ding Xiaozhou
Published: May 2022

To improve the motivation analysis effect of the technological startups business model, this study combines intelligent data mining technology to analyze related factors and proposes a method of adjusting parameters of machine-learning model based on Bayesian optimization algorithm. Moreover, this study integrates the prior information and sample information of the machine learning algorithm performance function and builds an intelligent analysis model based on the elements of the technological startups business model. Finally, this article designs experiments to evaluate the effect of intelligent data mining of the system model in this study. The research shows that the motivation analysis model of the technological startups’ business model based on intelligent data mining analysis proposed in this study meets the basic requirements of the design system of this study.
Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks
Authors: Rahmaty Maryam , Daneshvar Amir , Salahi Fariba , Ebrahimi Maryam , Chobar Adel Pourghader
Published: May 2022

The customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to become the customer of another company. In recent years, many methods have been developed including data mining for predicting the customer churn and manners that customers are likely to behave in the future and therefore, taking action early to prevent their leaving. This study proposes a hybrid system based on fuzzy entropy criterion selection algorithm with similar classifiers, grey wolf optimization algorithm, and artificial neural network to predict the customer churn of those companies that suffer losses from losing customers over time. The research results are evaluated by other methods in the criteria of accuracy, recall, precision, and F_measure, and it is declared that the proposed method is superior over other methods.
Overcoming Taxane Resistance: Preclinical and Phase 1 Studies of Relacorilant, a Selective Glucocorticoid Receptor Modulator, with Nab-Paclitaxel in Solid Tumors
Authors: Munster Pamela N. , Greenstein Andrew E. , Fleming Gini F. , Borazanci Erkut , Sharma Manish R. , Custodio Joseph M. , Tudor Iulia Cristina , Pashova Hristina I. , Shepherd Stacie Peacock , Grauer Andreas , Sachdev Jasgit C.
Published: May 2022

Purpose:

Chemotherapy resistance remains a major problem in many solid tumors, including breast, ovarian, and pancreatic cancer. Glucocorticoids are one potential driver of chemotherapy resistance as they can mediate tumor progression via induction of cell-survival pathways. We investigated whether combining the selective glucocorticoid receptor (GR) modulator relacorilant with taxanes can enhance antitumor activity.

Patients and Methods:

The effect of relacorilant on paclitaxel efficacy was assessed in OVCAR5 cells in vitro and in the MIA PaCa-2 xenograft. A phase 1 study of patients with advanced solid tumors was conducted to determine the recommended phase 2 dose of relacorilant + nab-paclitaxel.

Results:

In OVCAR5 cells, relacorilant reversed the deleterious effects of glucocorticoids on paclitaxel efficacy (P < 0.001). Compared with paclitaxel alone, relacorilant + paclitaxel reduced tumor growth and slowed time to progression in xenograft models (both P < 0.0001). In the heavily pretreated phase 1 population [median (range) of prior regimens: 3 (1–8), prior taxane in 75.3% (55/73)], 33% (19/57) of response-evaluable patients achieved durable disease control (≥16 weeks) with relacorilant + nab-paclitaxel and 28.6% (12/42) experienced longer duration of benefit than on prior taxane (up to 6.4×). The most common dose-limiting toxicity of the combination was neutropenia, which was manageable with prophylactic G-CSF. Clinical benefit with relacorilant + nab-paclitaxel was also associated with GR-regulated transcript-level changes in a panel of GR-controlled genes.

Conclusions:

The observed preclinical, clinical, and GR-specific pharmacodynamic responses demonstrate that selective GR modulation with relacorilant combined with nab-paclitaxel may promote chemotherapy response and is tolerable. Further evaluation of this combination in tumor types responsive to taxanes is ongoing.
Phenotypic Composition of Commercial Anti-CD19 CAR T Cells Affects In Vivo Expansion and Disease Response in Patients with Large B-cell Lymphoma
Authors: Monfrini Chiara , Stella Federico , Aragona Vanessa , Magni Martina , Ljevar Silva , Vella Cristina , Fardella Eugenio , Chiappella Annalisa , Nanetti Francesca , Pennisi Martina , Dodero Anna , Guidetti Anna , Corradini Paolo , Carniti Cristiana
Published: May 2022

Purpose:

In clinical trials, the expansion and persistence of chimeric antigen receptor (CAR) T cells correlate with therapeutic efficacy. However, properties of CAR T cells that enable their in vivo proliferation have still to be consistently defined and the role of CAR T bag content has never been investigated in a real-life setting.

Experimental Design:

Residual cells obtained after washing 61 anti-CD19 CAR T product bags were analyzed to identify tisagenlecleucel/Tisa-cel and axicabtagene ciloleucel/Axi-cel phenotypic features associated with postinfusion CAR T-cell in vivo expansion and with response and survival.

Results:

While Tisa-cel was characterized by a significant enrichment in CAR+CD4+ T cells with central memory (P < 0.005) and effector (P < 0.005) phenotypes and lower rates of CAR+CD8+ with effector memory (P < 0.005) and naïve-like (P < 0.05) phenotypes as compared with Axi-cel, the two products displayed similar expansion kinetics. In vivo CAR T-cell expansion was influenced by the presence of CAR T with a CD8+ T central memory signature (P < 0.005) in both Tisa-cel and Axi-cel infusion products and was positively associated with response and progression-free survival (P < 0.05).

Conclusions:

Our data indicate that despite the great heterogeneity of Tisa-cel and Axi-cel products, the differentiation status of the infused cells mediates CAR T-cell in vivo proliferation that is necessary for antitumor response.
The Effect of Delivery Systems on the Induction of T Helper 1 Cell Response to an ESAT6-Like Protein Rv3619c and Identification of Its Immunodominant Peptides
Authors: Safar Hussain A. , Mustafa Abu Salim , Amoudy Hanady A. , El-Hashim Ahmed
Published: May 2022

Objective: This study determined the effects of chemical adjuvants, incomplete Freund’s adjuvant (IFA) and aluminum hydroxide (Alum), mycobacteria, and a DNA plasmid as delivery systems on the induction of protective Th1 (interferon-gamma (IFN-γ)) and nonprotective Th2 (IL-5) and Treg (IL-10) cytokine responses to Rv3619c and its peptides. Rv3619c is an immunodominant Mycobacterium tuberculosis-specific antigen and belongs to the early-secreted antigenic target of 6 kDa-family of proteins. Delivery systems are needed to deliver such antigens in animal models and induce protective immune responses. Methods: The rv3619c gene was amplified from the genomic DNA of M. tuberculosis and cloned into appropriate vectors for expression in Escherichia coli, Mycobacterium smegmatis, and eukaryotic cells. Spleen cells from mice immunized with rv3619c using different delivery systems were stimulated in vitro with synthetic peptides (P1 to P6) of Rv3619c, and secreted cytokines were estimated by ELISA. Results: The recombinant M. smegmatis and DNA plasmid induced the secretion of the protective cytokine IFN-γ in response to peptide-pool of Rv3619c and all the individual peptides, whereas rv3619c/IFA induced the secretion of IFN-γ in response to the peptide pool, and the peptides P5 and P6. However, the secretions of the nonprotective cytokines IL-5 and IL-10 were induced to none of the peptides with the delivery systems used. Conclusion: Rv3619c is a major antigen of M. tuberculosis with multiple immunogenic epitopes; however, immune responses to individual epitopes can vary based on delivery systems used.
Multiple educational data mining approaches to discover patterns in university admissions for program prediction
Authors: Mamaril Julius Cesar O. , Ballera Melvin A.
Published: May 2022

This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.