bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of literature for ethnic minority-led GP practices: impact and experience of CQC regulation
Authors: Stigwood Annabelle , Ashmore Ayisha Adeeba , Maru Devina , Soni Lakshya
Published: July 2022

Background/aim

The Care Quality Commissions’ (CQC) recent report into the impact and experience of CQC regulation for ethnic minority-led general practitioner (GP) practices found that ethnic minority-led practices are disproportionately situated in areas of deprivation, working single-handedly and without adequate systems of support. These challenges are not always accounted for in CQC’s processes or methodology (CQC, 2022).

This study summarises a review of literature carried out as part of research by the CQC, which was published in January 2022.

Methods

Search terms included ‘GP’, ‘CQC’, ‘Black and Ethnic Minority GPs’ combined with Boolean operators. Grey literature was reviewed, and searches were undertaken of known authors in the field. Backwards and forwards reference harvesting was performed on identified literature. Limitations included the capacity and subjectivity of the reviewer, as well as the availability of studies with a focus on ethnic minority GPs as opposed to doctors whose place of primary medical qualification was outside of the UK.

Results

20 evidence sources were identified and included. The literature review found that many ethnic minority-led GP practices are in complex cycle of inequality, which starts with recruitment and thereafter followed by deprivation, isolation, poor funding and low morale. The symptom of these factors is often poor regulatory outcomes and ratings. When these poor ratings are received, GP providers often struggle to recruit, which serves to perpetuate the cycle of inequality.

Conclusion

When CQC rates an ethnic minority-led practice as requires improvement or inadequate, this can perpetuate a cycle of inequality.
Biological treatment usage in patients with HIV and rheumatic disease, 2003–2021: long-term safety and follow-up
Authors: Naovarat Benjamin Sornrung , Salazar Gloria , Ishimori Mariko , Williams Francis M , Reveille John D
Published: July 2022

Objective

This study examined the safety and efficacy of biological agents, especially tumour necrosis factor (TNF) inhibitors, for HIV-positive rheumatology patients refractory to standard therapy.

Methods

This study is a retrospective case series including patients derived from a community HIV clinic as well as from two academic centres. Initial visit data collected included: sociodemographic characteristics, CD4 counts, HIV viral load and medication use. Patients with persistent disease activity despite standard conservative therapy were begun on biological agents.The main outcomes were patient and physician global assessment of treatment response and medication side effects in patients with rheumatological disorders treated with biological medications over time.

Results

Seventeen patients were seen from 2003 to 2021, including eight from our previous cohort published in 2008 and nine seen since then, five of whom taking TNF blockers for more than 10 years. Three (17.7%) had rheumatoid arthritis, five (29.4%) psoriatic arthritis, four (23.5%) axial spondyloarthritis and the rest (29.4%) peripheral spondyloarthritis. Antiretroviral therapy had been used in 15. All but one had at least a partial response to biological therapy. There were no major infectious episodes necessitating the discontinuation of medications with only one patient discontinuing treatment due to rising HIV viral load. Patients not on antiretroviral therapy reported no adverse side effects from biological therapy. Four patients were switched to ustekinumab, secukinumab, tocilizumab or upadacitinib from anti-TNF therapy without complications.

Conclusions

These data suggest that biological therapy, especially anti-TNF agents are safe and well tolerated in HIV positive individuals even over several years.
Hierarchical response surface method for reliability analysis of a pile-slope system
Authors: Zhang Jie , Wu Chenguang , Tan Xiaohui , Huang Hongwei
Published: July 2022

Stabilizing piles have been widely used as an effective measure to reinforce slopes. In this paper, a hierarchical response surface method is presented to evaluate the reliability of a pile-slope system efficiently. The suggested method can be used to identify the minimum reliability indexes of different types of failure modes. It can also be used to identify the representative failure modes governing the failure probability of the pile-slope system. This study found that the most critical sliding surface of an unreinforced slope and a reinforced slope is different. It may be nonconservative to design a pile-slope system according to the representative sliding surface of the slope without reinforcements. Even if many failure modes may exist, the reliability index of the pile-slope system is often controlled by several representative failure modes. For the slope examined in this paper, the reliability index of the pile-slope system is controlled by the reliability index of first representative failure mode. The first representative failure mode may vary with the reinforcement ratio, pile length, pile spacing, and location of the piles. The approach presented in this study provides a practical means to quantify the effect of such factors on the design of a pile-slope system.
Multiple-AGV Path Planning Method for Two Industrial Links
Authors: Chen Song , Zhang Qian , He Guo-Chao , Wu Yue-Bo
Published: July 2022

Nowadays, increasing automatic guided vehicles (AGVs) are being introduced into the production line. The path planning and scheduling of multiple AGVs are complex tasks that are closely related to application scenarios. In this study, a robot’s working area was divided to minimize the collision probability. Improved ant colony optimization (ACO) and improved probabilistic road map (PRM) algorithms were used for path planning to enhance the transportation efficiency. The priorities of AGVs were specified. The collision criteria were determined, and the responses for different collision types were provided.
Gravitational fields and quantum mechanics
Authors: MacKenzie R. B. , Massart Victor , Paranjape M. B. , Semenoff Gordon , Yajnik U. A.
Published: July 2022

Massive particles in spatially nonlocal, quantum mechanical wave functions should have interesting gravitational fields. Should their gravitational fields be classical? What aspects of their gravitational fields are observable? We study these questions using quantum field theory to gravitationally probe the wave functions of such quantum mechanical states. We find that the observable signals seem to indicate that it is very difficult to phenomenologically observe the quantum mechanical, spatially nonlocal superposition.
Evidence for NO violation of the second law in extended black hole thermodynamics
Authors: El Moumni H. , Khalloufi J. , Masmar K.
Published: July 2022

The purpose of this paper is to give some relevant clarification on the validity of the laws of thermodynamics and the stability of the horizon by scalar field scattering formalism. On the one side, the connection between the energy of the absorbed particle and the change of the enthalpy of the black hole appears to resolve the violation of the second law. On the other side, such connection stipulates a fixed rank of the gauge group [Formula: see text] in the boundary conformal field theory which is against the extended phase-space spirit, where the cosmological constant is allowed to vary inducing a holographical dually changing of [Formula: see text]. By recalling the Grand potential, we suggest more stringent conditions under which the second law holds taking into account the missing information about the variation of the cosmological constant, i.e. the pressure. Our result offers direct evidence of no violation of the second law in the extended phase-space.
INVESTIGATION OF WEAR AND MICROSTRUCTURAL PROPERTIES OF A356/ TiC COMPOSITES FABRICATED BY FSP
Authors: AKBARI MOSTAFA , ASADI PARVIZ , ASIABARAKI HOSSEIN RAHIMI
Published: July 2022

Al–Si alloys are commonly utilized in the production of automobile components, including pistons and cylinders. Although this alloy offers desirable features for usage in pistons, several microstructural characteristics, such as the presence of needle-like silicones or dendrites, impair the performance of the parts produced. In this study, TiC reinforcement particulates were employed to generate matrix composites via friction stir processing (FSP) to increase the wear resistance of Al–Si alloys. The microstructural characteristics of the nugget zone of FSPed specimens were studied. The results reveal that the FSP improves the base alloy microstructural characteristics. The particle distribution and quality of the bonding between the TiC particles and the aluminum matrix were investigated using SEM. The hardness and wear tests were performed to establish the mechanical and wear properties of the particle reinforced composites. The hardness test results showed that the produced composites had a hardness of 144[Formula: see text]HV, which shows a growth of almost 220% compared to the base metal. The pin-on-disc wear test was carried out with sliding speeds of 1 and 2[Formula: see text]m/s, and loads of 5, 10 and 20[Formula: see text]N. The wear test results showed that the wear resistance of the produced particle-reinforced composite compared to the base alloy was improved by about 540%.
HANDLING THE EFFECT OF ATTRIBUTE SELECTION ON SUPPORT VECTOR MACHINES FOR DETECTING CHRONIC KIDNEY DISEASE
Authors: AKYOL KEMAL , ŞEN BAHA
Published: July 2022

Chronic kidney disease is a gradual loss of kidney function. Determining the important attributes that describe this disease plays a key role in screening and examining the disease by field specialists. The main aim of this study is to comprehensively compare the attribute selection algorithms for predicting this disease. With this aim, several models were built and compared using well-known performance metrics such as accuracy, sensitivity, and specificity in the experiments. Two different attribute selection methods; the stability selection and the minimum redundancy maximum relevance were compared comprehensively on the unbalanced and balanced datasets. In this framework, the stability selection method gave the important attributes. The support vector machines with radial bases function kernel successfully performed the classification using these attributes for this problem.
A New Hybrid Multiclass Approach Based on KNN and SVM
Authors: Limam Hela , Zouhair Amal , Oueslati Wided
Published: July 2022

Support vector machine (SVM) is a machine learning method widely used in solving binary data classification problems due to its performance. Nevertheless, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original dataset. The paper considers a solution to the problem of SVM multiclass with the aim to increase the data classification quality based on a new way of hybridisation between SVM and [Formula: see text]-nearest neighbour (KNN) algorithms. The first phase of the approach is called the filtering phase. At this level, the feature space is split into two classes separated by a hyperplane. In the next step called review, we generate a second hyperplane, then we calculate the distance between each test pattern and the second hyperplane in the feature space using e.g. the KNN function. The result of the two phases is three classes instead of two produced by the conventional SVM. For evaluation purposes, dataset experiments are conducted on seven benchmark datasets that have high dimensionality and large size. Numerical experiments show that the 3SVM approach can improve not only the accuracy compared to other multiclass SVM approaches, but also the precision, recall, and [Formula: see text]-score.
Speech enhancement via adaptive Wiener filtering and optimized deep learning framework
Authors: Jadda Amarendra , Prabha Inty Santi
Published: July 2022

In today’s scientific epoch, speech is an important means of communication. Speech enhancement is necessary for increasing the quality of speech. However, the presence of noise signals can corrupt speech signals. Thereby, this work intends to propose a new speech enhancement framework that includes (a) training phase and (b) testing phase. The input signal is first given to STFT-based noise estimate and NMF-based spectra estimate during the training phase in order to compute the noise spectra and signal spectra, respectively. The obtained signal spectra and noise spectra are then Wiener-filtered, then empirical mean decomposition (EMD) is used. Because the tuning factor of Wiener filters is so important, it should be computed for each signal by coaching in a fuzzy wavelet neural network (FW-NN). Subsequently, a bark frequency is computed from the denoised signal, which is then subjected to FW-NN to identify the suitable tuning factor for all input signals in the Wiener filter. For optimal tuning of [Formula: see text], this work deploys the fitness-oriented elephant herding optimization (FO-EHO) algorithm. Additionally, an adaptive Wiener filter is used to supply EMD with the ideal tuning factor from FW-NN, producing an improved speech signal. At last, the presented approach’s supremacy is proved with varied metrics.
Return Measures as a Link Between Financial Statements
Authors: Collins Andrew M.
Published: July 2022

Synopsis

The research problem

I examine whether and how presenting a return measure (e.g., return on assets) on the face of the income statement influences non-professional investors’ judgments of a company as a potential investment.

Motivation

Accounting research suggests investors focus on bottom-line earnings to the exclusion of other financial information. To evaluate a firm’s economic performance, valuation models suggest investors should assess not only income, but also the resources employed by the firm to generate the income. Value is created when companies earn a sufficient return on resources employed in the business. Therefore, positive and/or growing accounting earnings do not necessarily indicate a firm is performing well. Return measures, such as return on assets, are ways to measure a company’s return on its economic resources.

The test hypotheses

H1: Reporting a return measure on the face of the income statement affects nonprofessional investors’ perceptions of investment attractiveness, and it does so by priming investors to evaluate the return on resources that the company earns.

H2: Reporting a return measure on the face of the income statement affects nonprofessional investors’ perceptions of balance sheet relevance and acquisition of balance sheet information.

Target population

Standard setters, financial statement preparers, and accounting researchers.

Adopted methodology

In an experiment, participants viewed an abbreviated annual report of a hypothetical company and provided judgments about the firm as a potential investment. I manipulated whether a return measure was reported on the income statement. I also manipulated the level of return; while net income and earnings per share were the same in all conditions, I manipulated balance sheet amounts such that return on assets was relatively high or low.

Analyses

Analyses included analysis of variance (ANOVA), analysis of covariance (ANCOVA), and conditional process analysis. Dependent measures included participants’ judgments about the company as a potential investment, perceived efficiency and effectiveness of the company’s management, perceived relevance of balance sheet information, and recall of financial statement information.

Findings

Results supported my first prediction — reporting a return measure on the income statement affected non-professional investors’ perceptions of the company as a potential investment; further, these judgments were driven by perceptions of managerial efficiency and effectiveness in earning a return on the company’s resources. Results did not support my second prediction that reporting a return measure on the face of the income statement influences non-professional investors’ perception of balance sheet relevance or affects acquisition of balance sheet information.
A New Constitutive Model for Hexagonal Close-Packed Sheet Metals Under Uniaxial Cyclic Loading
Authors: Mehrabi Hamed , Yang Richard Chunhui
Published: July 2022

This study develops a new phenomenological constitutive model to capture the unique, evolving cyclic elastoplastic behaviours of hexagonal close-packed (HCP) sheet metals under uniaxial loading condition. This new constitutive model is developed by adopting the concepts of multiple-yield surface approaches and a combined isotropic-kinematic hardening rule. Three phenomenological modes, including tension ([Formula: see text]), compression ([Formula: see text]), and untwining (UT), are considered to represent the hardening evolution of the materials, including twining/untwining behaviours. Three different hardening laws are applied, and a Cazacu-Barlat 2004 (CB2004) type yield surface is assigned to each deformation mode. The UT hardening parameters are defined as functions of plastic pre-strains to mitigate interpolation errors caused by parameter determination processes of existing models. The new constitutive model is numerically implemented for finite element analysis via the UMAT subroutine available in a commonly-used commercial finite element analysis (FEA) package — Abaqus and validated by experimental data available from literature. The obtained results show that the new constitutive model can precisely capture mechanical behaviours of HCP sheet metals, e.g., stress–strain curves, compared to those experimental results.
The dawn of crowdfarms
Authors: Wang Yihong , Papangelis Konstantinos , Lykourentzou Ioanna , Khan Vassilis-Javed , Saker Michael , Yue Yong , Grudin Jonathan
Published: July 2022

Some small companies are making crowdwork part of their formal business via teams that can complete multifaceted, complex tasks requiring specialized expertise.
Power Converter Circuit Design Automation Using Parallel Monte Carlo Tree Search
Authors: Fan Shaoze , Zhang Shun , Liu Jianbo , Cao Ningyuan , Guo Xiaoxiao , Li Jing , Zhang Xin
Published: July 2022

The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient—the sequential version can generate topologies with the same quality while being up to 67% faster. The parallelization schemes can further achieve high speedups compared to the sequential version.
Study on the Path of Continuous Participation of Digital Art in Public Space in 6G IoT Communication
Authors: Jiang Xu , Wang Feng , Jiang Ziwen
Published: July 2022

With the intervention of digital art in recent years, the quality and vitality of urban public space have been greatly enhanced. However, with the wide application of digital art and the gradual maturity of communication technology, especially in the upcoming 6G era, the application scenario of digital art will be an extension of the “line” to “surface” and then to “body.” Digital art design is no longer a single surface or a single dimension, but a comprehensive design in terms of the breadth and depth of information; the network and the user are seen as a unified body, and the user’s needs will be deeply explored and realized. With the most cutting-edge means of communication, artificial intelligence, and big data, these technologies and art merge to create a new way to appreciate art. To this end, digital art will also be in this artificial intelligence and human intelligence in a harmonious symbiosis of the environment in the depth of the user’s needs and the formation of a new dimension of interaction. Based on the “five-in-one” theory, the study introduces media literacy, technology acceptance model, and user experience theory as influencing factors and explores the influence of emotional experience, interaction experience, sensory experience, perceived usefulness, perceived ease of use, and media literacy on users’ willingness to continuously participate through qualitative comparative analysis. The results of the study show that there are three paths that influence users’ willingness to continuously engage, namely, affective experience

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interactive experience

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sensory experience

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perceived usefulness

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perceived ease of use; affective experience

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interactive experience

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perceived usefulness

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perceived ease of use

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perceived entertainment

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medium literacy, and ~ affective experience

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interactive experience

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sensory experience

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~perceived ease of use

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perceived usefulness

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medium literacy. The paths suggest that users’ willingness to sustain participation is influenced by a variety of factors, among which interactive experience and perceived usefulness are essential factors affecting the users’ willingness to participate. User participation is an important manifestation of the vitality of urban public space, and the analysis of the factors influencing users’ willingness to sustain participation provides theoretical guidance for the design and application of digital art in the upcoming Internet of everything space.
Minimization of Latency Using Multitask Scheduling in Industrial Autonomous Systems
Authors: Singhal Amit , Varshney Sudeep , Mohanaprakash T. A. , Jayavadivel R. , Deepti K. , Reddy Pundru Chandra Shaker , Mulat Molla Bayih
Published: July 2022

Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.
Evaluation and Clinical Validation of Guanidine-Based Inactivation Transport Medium for Preservation of SARS-CoV-2
Authors: Wiraswati Hesti L. , Gaffar Shabarni , Ekawardhani Savira , Fauziah Nisa , Rinawan Fedri R. , Widyatmoko Leonardus , Laelalugina Amila , Arimdayu Annissa R. , Kusniati Tri , Andari Clarisa D. , Faridah Lia
Published: July 2022

WHO declared the outbreak of COVID-19, caused by SARS-CoV-2, a pandemic in March 2020. More than 223 million cases and approximately 4.6 million deaths have been confirmed. Early diagnosis and immediate treatment became a priority during this pandemic. However, COVID-19 diagnostic testing resources are limited, especially early in the pandemic. Apart from being limited, the COVID-19 diagnostic tests using reverse transcription polymerase chain reaction (RT-PCR) have encountered storage, transportation, and safety issues. These problems are mainly experienced by developing poor countries, countries in the equatorial region, and archipelagic countries. VITPAD® is a guanidine-based inactivation transport medium (ITM) formulated to maintain the RNA quality of SARS-CoV-2 during transportation without cold chains. This study, conducted from September 2020 to March 2021, performed clinical validation of VITPAD® by comparing its performance with a globally commercially available ITM from the NEST brand. Its stability at room temperature, safety, and resistance at high temperatures was also tested using RT-PCR analysis. VITPAD® can reduce the infectious nature of the specimen, preserve the SARS-CoV-2 for 18 days at an ambient temperature, and resist high temperatures (40°C for 3 hours). A guanidine-based transport medium, such as VITPAD®, is compatible and recommended for RT-PCR-based molecular diagnosis of COVID-19.
Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records
Authors: Reegu Faheem Ahmad , Abas Hafiza , Hakami Zaid , Tiwari Sanatan , Akmam Rudzidatul , Muda Iskandar , Almashqbeh Hashem Ali , Jain Rituraj
Published: July 2022

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.
A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field
Authors: Alohali Yousef A. , Fayed Mahmoud S. , Mesallam Tamer , Abdelsamad Yassin , Almuhawas Fida , Hagr Abdulrahman
Published: July 2022

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy‐tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers’ abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.
IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning
Authors: Rahman Atta-ur , Nasir Muhammad Umar , Gollapalli Mohammed , Alsaif Suleiman Ali , Almadhor Ahmad S. , Mehmood Shahid , Khan Muhammad Adnan , Mosavi Amir
Published: July 2022

A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness is caused by a number of factors, including a defective maternal or paternal gene, excessive abortions, a lack of blood cells, and low white blood cell count. For premature or teenage life development, early detection of genetic diseases is crucial. Although it is difficult to forecast genetic disorders ahead of time, this prediction is very critical since a person’s life progress depends on it. Machine learning algorithms are used to diagnose genetic disorders with high accuracy utilizing datasets collected and constructed from a large number of patient medical reports. A lot of studies have been conducted recently employing genome sequencing for illness detection, but fewer studies have been presented using patient medical history. The accuracy of existing studies that use a patient’s history is restricted. The internet of medical things (IoMT) based proposed model for genetic disease prediction in this article uses two separate machine learning algorithms: support vector machine (SVM) and K-Nearest Neighbor (KNN). Experimental results show that SVM has outperformed the KNN and existing prediction methods in terms of accuracy. SVM achieved an accuracy of 94.99% and 86.6% for training and testing, respectively.
CircFMN2 Boosts Sorafenib Resistance in Hepatocellular Carcinoma Cells via Upregulating CNBP by Restraining Ubiquitination
Authors: Fan Chen , Zhu Xiaoli , Zhou Qi , Wang Weidong
Published: July 2022

Purpose. Noncoding RNAs exert critical biological effects in hepatocellular carcinoma (HCC). The role of circFMN2, a newly discovered functional RNA in prostate cancer and colorectal cancer, was investigated for the first time in sorafenib-resistance HCC cells. Methods. The level of circFMN2 was assessed via quantitative real-time PCR (qRT-PCR). Cell proliferation was detected via CCK-8 and colony formation assay. Cell apoptosis was measured via the TUNEL assay and flow cytometry analysis. A Western blot assay was conducted to detect the CCHC-type zinc finger nucleic acid binding protein (CNBP) level and ubiquitination. RNA pull-down assay and RNA immunoprecipitation were carried out to explore the interaction between circFMN2 and CNBP. Results. CircFMN2 was highly expressed in multidrug-resistant (MDR) cells. CircFMN2 overexpression exerted pro-proliferation effects in sorafenib-treated HCC cells, while depletion of circFMN2 displayed negative effect on sorafenib-treated MDR cells. Moreover, CNBP was verified as the binding protein of circFMN2. CNBP was upregulated in MDR cells, which was achieved by inhibition of ubiquitination by circFMN2. Besides, CNBP overexpression was found to boost sorafenib resistance in HCC cells. Conclusions. CircFMN2 is aberrantly expressed in sorafenib-resistant HCC cells and contributes to sorafenib resistance in HCC cells via upregulation of CNBP by restraining ubiquitination.
Investigation and Interpersonal Relationship of College Students with Intelligent Big Data
Authors: Zheng Yuxin , Zheng Siyi , Bao Lanlan
Published: July 2022

An interpersonal relationship is a relationship that arises and develops in the process of people’s extensive material and spiritual exchanges. College students, as a special group, not only have the general characteristics of modern young people’s interpersonal communication but also have remarkable characteristics of their own communication. The purpose of this paper is to study how to use intelligent big data to conduct a brief survey and research on the interpersonal relationships of college students so that college students can discover their own problems and establish sound and good interpersonal relationships. This paper proposes data mining based on intelligent big data. Data mining plays an important role in information processing. Data mining includes many algorithms. This paper selects cluster analysis for the need to classify the influencing factors in interpersonal relationships. The cluster analysis algorithm can classify similar information well. The experimental results of this paper show that the most important skills given by the five companies that are considered to be necessary for talents are the interpersonal relationship, self-ability, and academic qualifications. Among them, five companies gave the highest score of 8.9 points to the interpersonal relationship and the lowest score of 8.0 points, with an average of about 8 points, while the scores of their own ability and education were below 8 points. It can be seen that entrepreneurs think that the necessary skills for talents are not academic qualifications and their own abilities but good interpersonal relationships. Therefore, it is very important to establish good interpersonal relationships.
RUNX3-Regulated GALNT6 Promotes the Migration and Invasion of Hepatocellular Carcinoma Cells by Mediating O-Glycosylation of MUC1
Authors: Gao Fei , Zheng Gang
Published: July 2022

Background. Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Dysregulation of messenger RNAs (mRNA) has been recognized to be associated with HCC carcinogenesis and development. Polypeptide GalNAc Transferase 6 (GALNT6), an O-type glycosyltransferase, has been confirmed as tumor promoter in different cancers. However, the function of GALNT6 in HCC remains to be studied. Methods. RT-qPCR and western blot experiments were, respectively, performed for evaluating RNA expressions and protein levels. Supported by bioinformatics analysis, mechanism assays were conducted for validating the potential relation between different genes. Functional assays were implemented to analyze HCC cell migration and invasion after different transfections. Results. GALNT6 was aberrantly upregulated in HCC cells. Knockdown of GALNT6 could repress HCC cell migration and invasion. RUNX3 was verified to bind to GALNT6 promoter and activate GALNT6 transcription. GALNT6 depletion led to inhibited O-glycosylation and aggravated degradation of MUC1. MUC1 overexpression could rescue the impeded HCC cell migration and invasion induced by GALNT6 knockdown. Conclusion. To sum up, GALNT6 transcriptionally activated by RUNX3 mediated the O-glycosylation of MUC1, thus exerting promoting influence on HCC cell migration and invasion.
Transient Receptor Potential Vanilloid-1 (TRPV1) Alleviates Hepatic Fibrosis via TGF-β Signaling
Authors: Qian Ke , Lei Xiaohua , Liu Guoxing , Fang Yu , Xie Chengzhi , Wu Xiaolong , Liu Qiang , Liu Gao , Cao Zhenyu , Zhang Ju , Kuang Tao , Yan Likun , Fu Jie , Du Huihui , Liu Zhiqiang , Chu Yuan , Xu Ge , Yamamoto Hirofumi , Mori Masaki , Liang Xin M. , Xu Xundi
Published: July 2022

Hepatic fibrosis is a major global health problem and considered a leading cause of liver-related morbidity and mortality worldwide. Although previous studies have suggested that transient receptor potential vanilloid-1 (TRPV1) is protective against cardiac and renal fibrosis, its functional role in hepatic fibrosis has remained elusive. Herein, we characterize the effects of TRPV1 on carbon tetrachloride- (CCl4-) induced mice, in vitro transforming growth factor-β- (TGF-β-) treated hepatic stellate cells (HSCs), and human fibrosis specimens. Finally, our results demonstrated the significant TRPV1 downregulation in human liver fibrosis tissues. Knocking out TRPV1 significantly increased the expression of various hepatic fibrosis markers, while the expression of these biomarkers declined markedly in capsaicin-activated mice. Moreover, our study revealed that knocking down TRPV1 would enhance the promotive effect of TGF-β on HSC proliferation, cell cycle, cell apoptosis, and ECM expression. Also, such promotive effect can be partially reversible by capsaicin, an exogenous activator of TRPV1. Collectively, the obtained data suggest that TRPV1 may alleviate CCl4-induced hepatic fibrosis and attenuate the effect of TGF-β on HSC activation, proliferation, and apoptosis, which overall implies that targeting TRPV1 channel activity may be an effective therapeutic strategy for treating hepatic fibrosis.
Renal Mucormycosis: Post-COVID-19 Infection Presenting as Unilateral Hydronephrosis in a Young Immunocompetent Male
Authors: Nepali Rabin , Shrivastav Shreya , Singh Shah Dibya
Published: July 2022

Mucormycosis is a rare invasive fungal infection with a high mortality rate caused by members of the family Mucoraceae. It mainly affects immunocompromised hosts such as poorly controlled diabetes mellitus, previous solid organ transplant, high-dose steroids, and hematologic malignancy. The most common sites of the disease are rhinocerebral, the skin, the lungs, and the gastrointestinal tract. In this era of COVID-19 infection, there has been a significant rise in invasive mucormycosis predominantly reported from southeast. We present a case of isolated renal mucormycosis in an apparently healthy individual with post-COVID-19 infection presenting as unilateral hydronephrosis. Timely identifying at-risk populations and having a high degree of suspicion with involvement of multidisciplinary teams are of utmost importance to diagnose and treat a rare and fatal infection. Even if there is a long history, antifungal drugs and removal of the source can result in a good outcome.
Modeling and Analysis of Influencing Factors of Competitive Performance of Wushu Athletes
Authors: Li Wenya
Published: July 2022

In view of the problems that there are many influencing factors in wushu athletes’ competitive performance, which lead to low accuracy of index evaluation and large error of weight calculation, this paper puts forward the modeling and analysis of influencing factors of wushu athletes’ competitive performance. Preprocess and segment wushu routine characteristic signals, extract wushu routine characteristics by using fast Fourier transform coefficients, construct the index system of the influence of competitive performance ability, and determine the ideal solution and negative ideal solution of the index weight after standardizing the indexes of influencing factors. The index weight of the influencing factors of wushu athletes’ competitive performance is determined by calculating the pasting progress, and the evaluation model of the influencing factors is constructed with the help of the grey correlation degree method. The experimental results show that the proposed model can effectively determine the key degree of the influencing factors of wushu athletes’ competitive performance and improve the competitive performance of wushu athletes.
Long-Read Nanopore Sequencing Identifies Mismatch Repair-Deficient Related Genes with Alternative Splicing in Colorectal Cancer
Authors: Qu Hao , Wang Zhenjun , Zhang Yudong , Zhao Baocheng , Jing Shuai , Zhang Jianwei , Ye Chunxiang , Xue Yaohan , Yang Lei
Published: July 2022

Background. Alternative splicing (AS) plays a crucial role in regulating the progression of colorectal cancer (CRC), but its distribution remains to be explored. Here, we aim to investigate the genes edited by AS which show differential expression in patients with mismatch repair deficiency (dMMR)/microsatellite instability (MSI). Materials and Methods. We applied long-read nanopore sequencing to determine the mRNA profiles and screen AS genes using Oxford Nanopore Technologies (ONT) method in ten paired CRC tissues. CRC tissue and plasma samples were used to validate the differential genes with AS using real-time fluorescent quantitative PCR, immunohistochemistry, and enzyme-linked immunosorbent assay. Results. ONT sequencing identified 404 genes were downregulated, and 348 genes were upregulated in MSI cancer tissues compared with microsatellite stability (MSS) cancer tissues. In total, 6,200 AS events were identified in 2,728 mRNA transcripts. WGCNA revealed dMMR/MSI-correlated gene modules, including INHBA and RPL22L1, which were upregulated; conversely, HMGCS2 was downregulated in MSI cancer. Overexpression of RPL22L1, INHBA, and CAPZA1 was further confirmed in CRC tissues. INHBA was found to be associated with tumor lymphatic metastasis. Importantly, the levels of INHBA in CRC plasma were significantly increased compared with those in noncancer plasma. INHBA showed a higher level in dMMR/MSI CRC than in MSS CRC, indicating that INHBA is a useful biomarker. Conclusion. Our results showed that ONT-identified genes provide a pool to explore AS-associated markers for dMMR/MSI CRC. We demonstrated INHBA as a promising signature for clinical application in predicting tumor lymphatic metastasis and screening dMMR/MSI candidates.
Internet of Things Background: An Empirical Study on the Payment Intention of Central Bank Digital Currency Design
Authors: Ma Chao , Jin Zhuang , Mei Ziwen , Zhou Fei , She Xinyu , Huang Jia , Liu Da
Published: July 2022

This article proposes and verifies the consumer acceptance intention model under the background of central bank digital currency payment. It modifies the theory of technology acceptance (TAM), rational action theory (TRA), and extended valence framework (EVF) through the construction of perceived privacy, perceived security, perceived system quality, and perceived benefits. The data collected from 325 questionnaires on consumers’ attitudes toward the central bank’s digital currency were used to apply the structural equation model to evaluate the prediction model. The empirical results indicate that perceived privacy, perceived security, and perceived system quality are important predictors of perceived trust, and perceived trust is an important predictor of perceived interest and intention. Perceived privacy, perceived security, and perceived system quality are key factors influencing consumers’ intentions.
Influence of Rainfall‐Induced Erosion on the Stability of Sandy Slopes Treated by MICP
Authors: Liu Shihui , Du Kang , Wen Kejun , Armwood-Gordon Catherine , Li Lin
Published: July 2022

As an environmentally friendly technology, microbially induced calcite precipitation (MICP) is widely used to improve the engineering properties of soil. The goal of this study was to investigate the effect of rainfall‐induced erosion on the stability of sandy slopes which were treated by MICP technology. The observation of the erosion pattern of low concentration (0.25 M Ca) and high concentration (0.5 M Ca) of MICP‐treated slopes, the mechanical behaviors of MICP‐treated and cement‐treated samples, and the effects of rainfall‐induced erosion on the roughness of 0.5 M Ca MICP‐treated and 10% cement‐treated slope were studied through visual observation, unconfined compressive tests, and roughness tests. For the 0.25 M Ca MICP‐treated sample, surface erosion was found to occur soon after the start of the rainfall erosion test, while for the 0.5 M Ca MICP‐treated sample, the slope surface remained intact after exposing to the rainfall for 24 hours. Through unconfined compressive tests, it can be concluded that the 0.5 M Ca MICP treatment achieved a high strength, which was similar to 10% cement‐treated sand. The roughness test results showed that the surface of 0.5 M Ca MICP‐treated slope looked smoother than the uneroded surface after 24‐h rainfall‐induced erosion. On the contrary, the surface of the 10% cement‐treated slope became rougher after 24‐h rainfall‐induced erosion. These results indicated that the MICP‐treated sandy slope had lower resistance against rainfall‐induced erosion compared to the cement‐treated sandy slope.
Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach
Authors: Huang Ya
Published: July 2022

Dance is a unique art with the human body movement as the main means, but dance is not limited to the human body movement itself. Like any art, dance is the product of human social behavior and a romantic behavior of human thoughts and emotions in the virtual world. Dances with different characteristics will also reflect different aesthetics, different cultural psychology, different living styles, and emotional trajectories of different times and different nationalities. People rely on the image of dance artists to develop and inherit the profound ideological connotation and philosophy of life. Viewers may form their own diversified and unique aesthetic characteristics. In the new era, in order to better promote the development, communication, and dissemination of dance art, it is very necessary to analyze and explore the connotation and aesthetic characteristics of dance art. Only through specific movements can the value and ideological connotation of works be expressed. Therefore, this paper comparatively analyzes dance movement aesthetic emotion based on deep learning. Experimentations are performed to systematically analyze the models from various perspectives. Findings of the evaluation show that CAP and CNN are effective models that can successfully extract high-level emotional features. The method proposes and effectively selects the best models among the five standard models based on key features and is, therefore, suitable in predicting the dancer’s emotion and for the analysis of the dance movement in the future.
Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm
Authors: Chu Peng , Zhang He , Chen Yarong , Zhu Rui , Wang Feng
Published: July 2022

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior.