bluuebunny/crossref_metadata_2025 huggingface dataset:
Values of Apparent Diffusion Coefficient and Lesion‐to‐Spinal Cord Signal Intensity in Diagnosing Solitary Pulmonary Lesions: Turbo Spin‐Echo versus Echo‐Planar Imaging Diffusion‐Weighted Imaging
Authors: Lei Qiang , Wan Qi , Liu Lishan , Hu Jianfeng , Zuo Wei , Li Jianneng , Jiang Guihua , Li Xinchun
Published: August 2021

Objective. This study is aimed at comparing the image quality and diagnostic performance of mean apparent diffusion coefficient (ADC) and lesion‐to‐spinal cord signal intensity ratio (LSR) derived from turbo spin‐echo diffusion‐weighted imaging (TSE‐DWI) and echo‐planar imaging‐ (EPI‐) DWI in patients with a solitary pulmonary lesion (SPL). Methods. 33 patients with SPL underwent chest imaging using EPI‐DWI and TSE‐DWI with b = 600 s/mm2 in free breathing. A comparison of the distortion ratio (DR), signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR) was drawn between the two techniques using a Wilcoxon signed‐rank test. The interprotocol reproducibility between quantitative parameters of EPI‐DWI and TSE‐DWI was evaluated using a Bland‐Altman plot. ADCs and LSRs derived from EPI‐DWI and TSE‐DWI were calculated and compared between malignant and benign groups using the Mann–Whitney test. Results. TSE‐DWI had similar SNR and CNR compared with EPI‐DWI. DR was significantly lower on TSE‐DWI than EPI‐DWI. ADC and LSR showed slightly higher values with TSE‐DWI, while the Bland‐Altman analysis showed unacceptable limits of agreement between the two sequences. ADC and LSR of both DWI techniques differed significantly between lung cancer and benign lesions (P < 0.05). The LSR(EPI-DWI) showed the highest area under the curve (AUC = 0.818), followed by ADC(EPI-DWI) (AUC = 0.789), ADC(TSE-DWI) (AUC = 0.781), and LSR(TSE-DWI) (AUC = 0.748), respectively. Among these parameters, the difference in diagnostic accuracy was not statistically significant. Conclusions. TSE‐DWI provides significantly improved image quality in patients with SPL as compared with EPI‐DWI. However, there was no difference in diagnostic efficacy between these two techniques, according to ADC and LSR.
[Retracted] Research of Schema Evolution and Implementation Scheme Optimization in AI‐Enabled Embedded Systems
Authors: Zhang Xiaojuan , Zhang Yongheng , Zhang Feng , Yang Xiuyun
Published: August 2021

The demand of embedded artificial intelligence system for powerful computing power and diversified application scenarios will inevitably bring some new problems. This paper builds the system dynamics model of embedded system based on artificial intelligence (AI). By analyzing the causal relationship between the elements of the system dynamics model, the state equation is established, and the parameters are estimated and tested. At the same time, the influence of the model simulation experiment on the relevant factors is evaluated. The simulation results show that the proposed model is effective and efficient.
[Retracted] Optimization of CNC End Milling Process Parameters of Low‐Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis
Authors: Suresh Kumar R. , Senthil Kumar S. , Murugan K. , Guruprasad B. , Manavalla Sreekanth , Madhu S. , Hariprabhu M. , Balamuralitharan S. , Venkatesa Prabhu S.
Published: August 2021

The manufacturing sectors are consistently striving to figure out ways to minimize the consumption of natural resources through rational utilization. This is achieved by a proper understanding of every minute influence of parameters on the entire process. Understanding the influencing parameters in determining the machining process efficacy is inevitable. Technological advancement has drastically improved the machining process through various means by providing better quality products with minimum machining cost and energy consumption. Specifically, the machining factors such as cutting speed, spindle speed, depth of cut, rate of feed, and coolant flow rate are found to be the governing factors in determining the economy of the machining process. This study is focused on improving the machining economy by enhancing the surface integrity and tool life with minimum resources. The study is carried out on low‐carbon mold steel (UNS T51620) using Box–Behnken design and grey regression analysis. The optimized multiobjective solution for surface roughness (Ra), material removal rate (MRR), and power consumed (Pc) and tool life is determined and validated through the confirmatory run. The optimized set of parameters in Box–Behnken design and grey regression analysis with that of confirmatory runs shows a 10% deviation that proves the reliability of the optimization techniques employed.
Optimized Design for Large Geotextile Mats over Soft Soil
Authors: Huang Zhihuai , Yang Shuaidong , Yang Ningxin , Zhou Mi , Li Ang
Published: August 2021

In this paper, the failure mechanisms of large geotextile mats over soft soil are carried out through finite element analyses. A finite element model is generated and validated against centrifuge testing data and previously published data of numerical simulation. Parametric study is then carried out to investigate the geotextile tension distribution and the arrangement of crashed stone. Based on the parametric study, an optimized design considering the arrangement of rock berm and a special arrangement of large geotextiles was proposed to enhance the performance of the geotextile mats. The findings of this study can provide an engineering guidance for this new technique.
Destructive Effects of Pyroptosis on Homeostasis of Neuron Survival Associated with the Dysfunctional BBB-Glymphatic System and Amyloid-Beta Accumulation after Cerebral Ischemia/Reperfusion in Rats
Authors: Lyu Zhongkuan , Chan Yuanjin , Li Qiyue , Zhang Qiang , Liu Kaili , Xiang Jun , Li Xiangting , Cai Dingfang , Li Yaming , Wang Bing , Yu Zhonghai
Published: August 2021

Neuroinflammation-related amyloid-beta peptide (Aβ) accumulation after cerebral ischemia/reperfusion (I/R) accounts for cerebral I/R injuries and poststroke dementia. Recently, pyroptosis, a proinflammatory cell death, has been identified as a crucial pathological link of cerebral I/R injuries. However, whether pyroptosis acts as a trigger of Aβ accumulation after cerebral I/R has not yet been demonstrated. Blood-brain barrier (BBB) and glymphatic system mediated by aquaporin-4 (AQP-4) on astrocytic endfeet are important pathways for the clearance of Aβ in the brain, and pyroptosis especially occurring in astrocytes after cerebral I/R potentially damages BBB integrity and glymphatic function and thus influences Aβ clearance and brain homeostasis. In present study, the method of middle cerebral artery occlusion/reperfusion (MCAO/R) was used for building models of focal cerebral I/R injuries in rats. Then, we used lipopolysaccharide and glycine as the agonist and inhibitor of pyroptosis, respectively, Western blotting for detections of pyroptosis, AQP-4, and Aβ1-42 oligomers, laser confocal microscopy for observations of pyroptosis and Aβ locations, and immunohistochemical stainings of SMI 71 (a specific marker for BBB integrity)/AQP-4 and Nissl staining for evaluating, respectively, BBB-glymphatic system and neuronal damage. The results showed that pyroptosis obviously promoted the loss of BBB integrity and AQP-4 polarization, brain edema, Aβ accumulation, and the formation of Aβ1-42 oligomers and thus increased neuronal damage after cerebral I/R. However, glycine could inhibit cerebral I/R-induced pyroptosis by alleviating cytomembrane damage and downregulating expression levels of cleaved caspase-11/1, N-terminal gasdermin D, NLRP3 (nucleotide-binding domain, leucine-rich repeat containing protein 3), interleukin-6 (IL-6) and IL-1β and markedly abate above pathological changes. Our study revealed that pyroptosis is a considerable factor causing toxic Aβ accumulation, dysfunctional BBB-glymphatic system, and neurological deficits after cerebral I/R, suggesting that targeting pyroptosis is a potential strategy for the prevention of ischemic stroke sequelae including dementia.
Deep Grid Scheduler for 5G NB-IoT Uplink Transmission
Authors: Zhong Han , Sun Ruize , Mei Fengcheng , Chen Yong , Jin Fan , Ning Lei
Published: August 2021

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.
Metformin and tBHQ Treatment Combined with an Exercise Regime Prevents Osteosarcopenic Obesity in Middle‐Aged Wistar Female Rats
Authors: Toledo-Pérez Rafael , Lopéz-Cervantes Stefanie Paola , Hernández-Álvarez David , Mena-Montes Beatriz , Pedraza-Vázquez Gibran , Sánchez-Garibay Carlos , López-Diazguerrero Norma Edith , Königsberg Mina , Luna-López Armando
Published: August 2021

Osteosarcopenic obesity (OSO) is characterized by bone density, mass, and muscle strength loss, in conjunction with adipose tissue increase. OSO impairs physical activity and mobility, provoking autonomy loss; also, it is known that augmenting body fat in the elderly decreases life expectancy. The main factors influencing this health deterioration are the inflammatory environment induced by adipose tissue and its infiltration into muscle tissue, which leads to oxidative stress generation. Currently, there are several treatments to delay OSO, among which exercise training stands out because it improves muscle fiber quality and quantity and decreases adipose tissue. We have recently demonstrated that the combined treatment between moderate exercise and metformin slows sarcopenia’s onset by a mechanism that includes adipose reduction and REDOX regulation. On the other hand, tert‐butylhydroquinone (tBHQ) is a well‐known antioxidant that counteracts oxidative stress. Therefore, to slow down obesity’s harmful effects on muscle mass and bone mineral density, we performed different interventions, including combining a Fartlek‐type exercise routine with metformin and tBHQ administration, in a model of middle‐aged female Wistar rats with obesity induced with a hypercaloric diet. Our results showed that the combined exercise‐metformin‐tBHQ treatment increased muscle mass and strength, decreased body weight, body mass index, and fat percentage, and improved redox status, thus increasing animal survival.
Exploring the Pharmacological Mechanisms of Tripterygium wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking
Authors: Huang Bingwu , Huang Chengbin , Zhu Liuyan , Xie Lina , Wang Yi , Zhu Ning
Published: August 2021

Background. Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach. Materials and Methods. The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD‐related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein–protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug‐targets‐diseases‐pathways was constructed by the Cytoscape software. Results. The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD‐related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen‐activated protein kinase 1 (MAPK), phosphatidylinositol 3‐kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components, and molecular functions) and KEGG pathways were screened with a P value ≤0.01. Finally, we constructed the network of TwHF‐targets‐CVD‐GO‐KEGG. Conclusions. These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.
Information Security Field Event Detection Technology Based on SAtt-LSTM
Authors: Yu Wentao , Huang Xiaohui , Yuan Qingjun , Yi Mianzhu , An Sen , Li Xiang
Published: August 2021

Detecting information security events from multimodal data can help analyze the evolution of events in the security field. The Tree-LSTM network that introduces the self-attention mechanism was used to construct the sentence-vectorized representation model (SAtt-LSTM: Tree-LSTM with self-attention) and then classify the candidate event sentences through the representation results of the SAtt-LSTM model to obtain the event of the candidate event sentence types. Event detection using sentence classification methods can solve the problem of error cascade based on pipeline methods, and the problem of CNN or RNN cannot make full use of the syntactic information of candidate event sentences in methods based on joint learning. The paper treats the event detection task as a sentence classification task. In order to verify the effectiveness and superiority of the method in this paper, the DuEE data set was used for experimental verification. Experimental results show that this model has better performance than methods that use chain structure LSTM, CNN, or only Tree-LSTM.
A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability
Authors: Xu Yingjie , Hu Gengran , You Lin , Cao Chengtang
Published: August 2021

In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .
[Retracted] Reform and Challenges of Ideological and Political Education for College Students Based on Wireless Communication and Virtual Reality Technology
Authors: Hang Xing
Published: August 2021

With the continuous changes in ideological and political education methods in universities, the previous ideological and political teaching mode of blackboard plus PPT dictation is gradually facing the risk of being eliminated. Wireless communication and VR technology are the key points of the reform. The sense of situation, immersion, and participation that they bring is exactly what traditional ideological and political teaching lacks. This article will take college students’ ideological and political teaching as the key object, analyze the possible application of VR technology and wireless communication technology in the process of college students’ ideological and political teaching, use VR technology and wireless communication technology to simplify the process of college students’ ideological and political teaching and the current wireless communication and the advantages and difficulties of VR technology in the application of college students’ ideological and political teaching, explore how to scientifically and efficiently use wireless communication and VR technology to solve the difficulties encountered in the traditional teaching mode in the process of ideological and political teaching of college students, and provide direction for college students’ ideological and political education reform.
A Descent Four-Term Conjugate Gradient Method with Global Convergence Properties for Large-Scale Unconstrained Optimisation Problems
Authors: Alhawarat Ahmad , Alhamzi Ghaliah , Masmali Ibitsam , Salleh Zabidin
Published: August 2021

The conjugate gradient method is a useful method to solve large-scale unconstrained optimisation problems and to be used in some applications in several fields such as engineering, medical science, image restorations, neural network, and many others. The main benefit of the conjugate gradient method is not using the second derivative or its approximation, such as Newton’s method or its approximation. Moreover, the algorithm of the conjugate gradient method is simple and easy to apply. This study proposes a new modified conjugate gradient method that contains four terms depending on popular two- and three-term conjugate gradient methods. The new algorithm satisfies the descent condition. In addition, the new CG algorithm possesses the convergence property. In the numerical results part, we compare the new algorithm with famous methods such as CG-Descent. We conclude from numerical results that the new algorithm is more efficient than other popular CG methods such as CG-Descent 6.8 in terms of number of function evaluations, number of gradient evaluations, number of iterations, and CPU time.
The Role of Respiratory Flora in the Pathogenesis of Chronic Respiratory Diseases
Authors: Guo Mei-Ying , Chen Hao-Kun , Ying Hua-Zhong , Qiu Fen-Sheng , Wu Jun-Qi
Published: August 2021

Large quantities of bacteria, including Firmicutes, Actinobacteria, and Bacteroidetes, colonize the surface of the respiratory mucosa of healthy people. They interact and coexist with the local mucosal immune system of the human airway, maintaining the immune stability and balance of the respiratory system. While suffering from chronic respiratory diseases, the microbial population in the airway changes and the proportion of Proteobacteria is increased in patients with asthma. The abundance of the microbial population in patients with chronic obstructive pulmonary disease (COPD) is decreased, and conversely, the proportion of Firmicutes and Proteobacteria increased. The diversity of airway microorganisms in cystic fibrosis (CF) patients is decreased, while pathogenic bacteria and conditional pathogenic bacteria are proliferated in large numbers. The proportion of Firmicutes and Proteobacteria is increased in patients with upper airway cough syndrome (UACS), which replaces the dominance of Streptococcus and Neisseria in the pharynx of a normal population. Therefore, a clear understanding of the immune process of the airway flora and the immune dysfunction of the flora on the pathogenesis of chronic respiratory diseases can provide new ideas for the prevention and treatment of human respiratory diseases.
Enhanced Activity of Exportin‐1/CRM1 in Neurons Contributes to Autophagy Dysfunction and Senescent Features in Old Mouse Brain
Authors: Gorostieta-Salas Elisa , Moreno-Blas Daniel , Gerónimo-Olvera Cristian , Cisneros Bulmaro , Court Felipe A. , Castro-Obregón Susana
Published: August 2021

Brain aging is characterized by dysfunctional autophagy and cellular senescence, among other features. While autophagy can either promote or suppress cellular senescence in proliferating cells, in postmitotic cells, such as neurons, autophagy impairment promotes cellular senescence. CRM1 (exportin‐1/XPO1) exports hundreds of nuclear proteins into the cytoplasm, including the transcription factors TFEB (the main inducer of autophagy and lysosomal biogenesis genes) and STAT3, another autophagy modulator. It appears that CRM1 is a modulator of aging‐associated senescence and autophagy, because pharmacological inhibition of CRM1 improved autophagic degradation in flies, by increasing nuclear TFEB levels, and because enhanced CRM1 activity is mechanistically linked to senescence in fibroblasts from Hutchinson–Gilford progeria syndrome patients and old healthy individuals; furthermore, the exogenous overexpression of CRM1 induced senescence in normal fibroblasts. In this work, we tested the hypothesis that impaired autophagic flux during brain aging occurs due to CRM1 accumulation in the brain. We found that CRM1 levels and activity increased in the hippocampus and cortex during physiological aging, which resulted in a decrease of nuclear TFEB and STAT3. Consistent with an autophagic flux impairment, we observed accumulation of the autophagic receptor p62/SQSTM1 in neurons of old mice, which correlated with increased neuronal senescence. Using an in vitro model of neuronal senescence, we demonstrate that CRM1 inhibition improved autophagy flux and reduced SA‐β‐gal activity by restoring TFEB nuclear localization. Collectively, our data suggest that enhanced CRM1‐mediated export of proteins during brain aging perturbs neuronal homeostasis, contributing to autophagy impairment, and neuronal senescence.
Modeling the Amount of Waste Generated by Households in the Greater Accra Region Using Artificial Neural Networks
Authors: Chapman-Wardy Charlotte , Asiedu Louis , Doku-Amponsah Kwabena , Mettle Felix O.
Published: August 2021

Waste can be defined as solids or liquids unwanted by members of the society and meant to be disposed. In developing countries such as Ghana, the management of waste is the responsibility of the metropolitan authorities. These authorities do not seem to have effective management of the waste situation, and therefore, it is not unusual to see waste clog the drains and litter the streets of the capital city, Accra. The impact of waste on the environment, along with its associated health-related problems, cannot be overemphasized. The Joint Monitoring Programme report in 2015 ranked Ghana as the seventh dirtiest country in the world. The lack of effective waste management planning is evident in the large amount of waste dumped in open areas and gutters that remains uncollected. In planning for solid waste management, reliable data concerning waste generation, influencing factors on waste generation, and a reliable forecast of waste quantities are required. This study used two algorithms, namely, Levenberg–Marquardt and the Bayesian regularization, to estimate the parameters of an artificial neural network model fitted to predict the average monthly waste generated and critically assess the factors that influence solid waste generation in some selected districts of the Greater Accra region. The study found Bayesian regularization algorithm to be suitable with the minimum mean square error of 104.78559 on training data and 217.12465 on test data and higher correlation coefficients (0.99801 on training data, 0.99570 on test data, and 0.99767 on the overall data) between the target variables (average monthly waste generated) and the predicted outputs. House size, districts, employment category, dominant religion, and house type with respective importance of 0.56, 0.172, 0.061, 0.027, and 0.026 were found to be the top five important input variables required for forecasting household waste. It is recommended that efforts of the government and its stakeholders to reduce the amount of waste generated by households be directed at providing bins, increasing the frequency of waste collection (especially in highly populated areas), and managing the economic activities in the top five selected districts (Ledzekuku Krowor, Tema West, Asheidu Keteke, Ashaiman, and Ayawaso West), amongst others.
Sentiment Classification Algorithm Based on the Cascade of BERT Model and Adaptive Sentiment Dictionary
Authors: Duan Ruixue , Huang Zhuofan , Zhang Yangsen , Liu Xiulei , Dang Yue
Published: August 2021

The mobile social network contains a large amount of information in a form of commentary. Effective analysis of the sentiment in the comments would help improve the recommendations in the mobile network. With the development of well‐performing pretrained language models, the performance of sentiment classification task based on deep learning has seen new breakthroughs in the past decade. However, deep learning models suffer from poor interpretability, making it difficult to integrate sentiment knowledge into the model. This paper proposes a sentiment classification model based on the cascade of the BERT model and the adaptive sentiment dictionary. First, the pretrained BERT model is used to fine‐tune with the training corpus, and the probability of sentiment classification in different categories is obtained through the softmax layer. Next, to allow a more effective comparison between the probabilities for the two classes, a nonlinearity is introduced in a form of positive‐negative probability ratio, using the rule method based on sentiment dictionary to deal with the probability ratio below the threshold. This method of cascading the pretrained model and the semantic rules of the sentiment dictionary allows to utilize the advantages of both models. Different sized Chnsenticorp data sets are used to train the proposed model. Experimental results show that the Dict‐BERT model is better than the BERT‐only model, especially when the training set is relatively small. The improvement is obvious with the accuracy increase of 0.8%.
Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis
Authors: Zhou Mi , Guo Ruru , Wang Yong-Fei , Yang Wanling , Li Rongxiu , Lu Liangjing
Published: August 2021

Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome‐wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r = 0.8, p = 3e−29), whereas the green‐yellow module was found to be closely related to the non‐sJIA module (r = 0.62, p = 1e−14). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green‐yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green‐yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA‐related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.
Dietary Fats High in Linoleic Acids Impair Antitumor T-cell Responses by Inducing E-FABP–Mediated Mitochondrial Dysfunction
Authors: Jin Rong , Hao Jiaqing , Yi Yanmei , Yin Di , Hua Yuan , Li Xiaohong , Bao Hanmei , Han Xianlin , Egilmez Nejat K. , Sauter Edward R. , Li Bing
Published: August 2021

The most recent American Dietary Guidelines (2020–2025) recommend shifting dietary fats from solid saturated fats to unsaturated oils. Dietary oils contain different compositions of unsaturated fatty acids (UFA). Oleic acid (OA) and linoleic acid (LA) are the most common UFA in dietary oils. How individual UFA in oils regulate immune cell function and cancer risk remains unclear. Here we demonstrated that high-fat diets (HFD) rich either in OA or LA induced a similar degree of murine obesity, but the LA-rich HFD specifically promoted mammary tumor growth. LA impaired antitumor T-cell responses by promoting naïve T-cell apoptosis and inhibiting TNFα production. While exogenous OA and LA were taken up by T cells with similar efficacy, only LA induced significant mitochondrial reactive oxygen species production and lipid peroxidation. Importantly, naïve T cells predominantly expressed epidermal fatty acid binding protein (E-FABP), which is central in facilitating LA mitochondrial transport and cardiolipin incorporation. Genetic depletion of E-FABP rescued LA-impaired T-cell responses and suppressed LA-rich HFD-associated mammary tumor growth. Collectively, these data suggest that dietary oils high in LA promote mammary tumors by inducing E-FABP–mediated T-cell dysfunction.

Significance:

These findings suggest that modulation of dietary oil composition and inhibition of E-FABP activity may represent novel strategies to enhance T-cell function in the prevention and treatment of obesity-associated cancers.
Inhibiting Fatty Acid Synthase with Omeprazole to Improve Efficacy of Neoadjuvant Chemotherapy in Patients with Operable TNBC
Authors: Sardesai Sagar D. , Thomas Alexandra , Gallagher Christopher , Lynce Filipa , Ottaviano Yvonne Lynn , Ballinger Tarah Jean , Schneider Bryan P. , Storniolo Anna Maria , Bauchle Amber , Althouse Sandra K. , Perkins Susan M. , Masters Andrea R. , Stratford Robert E. , Dong Zizheng , Liu Jing-Yuan , Zhang Jian-Ting , Miller Kathy D.
Published: August 2021

Purpose:

Fatty acid synthase (FASN) is overexpressed in 70% of operable triple-negative breast cancer (TNBC) and is associated with poor prognosis. Proton pump inhibitors selectively inhibit FASN activity and induce apoptosis in TNBC cell lines.

Patients and Methods:

Patients with operable TNBC were enrolled in this single-arm phase II study. Patients began omeprazole 80 mg orally twice daily for 4–7 days prior to neoadjuvant anthracycline–taxane-based chemotherapy (AC-T) and continued until surgery. The primary endpoint was pathologic complete response (pCR) in patients with baseline FASN overexpression (FASN+). Secondary endpoints included pCR in all surgery patients, change in FASN expression, enzyme activity, and downstream protein expression after omeprazole monotherapy, safety, and limited omeprazole pharmacokinetics.

Results:

Forty-two patients were recruited with a median age of 51 years (28–72). Most patients had ≥cT2 (33, 79%) and ≥N1 (22, 52%) disease. FASN overexpression prior to AC-T was identified in 29 of 34 (85%) evaluable samples. The pCR rate was 72.4% [95% confidence interval (CI), 52.8–87.3] in FASN+ patients and 74.4% (95% CI, 57.9–87.0) in all surgery patients. Peak omeprazole concentration was significantly higher than the IC50 for FASN inhibition observed in preclinical testing; FASN expression significantly decreased with omeprazole monotherapy [mean change 0.12 (SD, 0.25); P = 0.02]. Omeprazole was well tolerated with no grade ≥ 3 toxicities.

Conclusions:

FASN is commonly expressed in early TNBC. Omeprazole can be safely administered in doses that inhibit FASN. The addition of omeprazole to neoadjuvant AC-T yields a promising pCR rate that needs further confirmation in randomized studies.
Lactic Acidosis as an Unusual Finding in Undifferentiated Carcinoma
Authors: Viteri-Noel Adrian , Cobo-Reinoso Maria Eugenia , de Felipe-Noguerales Blanca , Manzano Luis
Published: August 2021

Lactic acidosis associated with solid neoplasms is a rare complication; its occurrence is poorly described, and it is associated with a poor prognosis. We present the case of an 84-year-old woman who was admitted to the internal medicine department with a diagnosis of urinary tract infection accompanied by a blood gas analysis showing lactic acidosis. During her admission, an abdominal mass was evidenced and finally diagnosed as undifferentiated carcinoma. We wanted to emphasize the importance of correlating clinical and laboratory data at the time of making a diagnostic approach and also highlight other possible explanations of lactic acidosis that should be considered in addition to hypoperfusion due to sepsis, especially in the elderly who are at increased risk of malignancy.
Primary Pleural Hemangioendothelioma: A Case Report and Literature Review
Authors: Askari Elham , Yaghmaei Shekoofeh , Haseli Sara , Pouradollah Totkaboni Mihan
Published: August 2021

Epithelioid hemangioendothelioma (EHE) is a rare tumor of the vascular endothelial cells. It can originate from any tissue; however, it occurs most frequently in the liver and lung. Pleural epithelioid hemangioendothelioma (PEH), EHE developing from pleura, is even more infrequent and less reported in the literature. In the following report, we discuss a 40-year-old man who initially presented with right-sided chest pain. Computed tomography revealed pleural thickening and effusion in his right thoracic cavity. PEH diagnosis was confirmed with immunohistochemistry. In order to provide the readers with an inclusive understanding of the disease, we collected the PEH cases reported in the literature. Despite the scarcity of the reported PEH cases (to our best knowledge), the compiled literature review of the disease enables the readers to grasp a better comprehension of the disease.
Basic school teachers’ personality type as determinant of classroom management in Lagos State, Nigeria
Authors: Badamas Omoshalewa Lasbat
Published: August 2021

Teachers encounter many problems in the classroom and amongst is classroom management. Classroom management pose a problem to teachers especially the novice teachers who are not privileged to undergo the training of teaching profession. Teachers’ personality type play an important role in whether to increase positive behaviours or decrease negative behaviours among the students in classroom environment. This study therefore examined teachers’ personality type as determinant of classroom management in Lagos State. Descriptive method of research was used for this study. The respondent comprised of 180 teachers selected from 15 schools in Lagos State. Two instruments were used in gathering data for the study. Mean rating and Pearson Product Moment Coefficient were used to answer and test research questions and hypotheses respectively. Findings revealed that teachers personality determines their classroom management.
An approach to assess the quality of honey using partial least square method
Authors: Rajalakshmi G. , Gopal A. , Pandian R.
Published: August 2021

The objective of the present study is to obtain the quantity of honey components such as moisture, glucose, fructose and sucrose in order to access the quality of honey. The tested honey samples are authenticated if the characteristics of a pure honey. The average ratio of 56% fructose to 44% glucose, but the ratios in the individual honeys ranged from a high of 64% fructose and 36% glucose to a low of 50% fructose and 50% glucose. The contents such as fructose and sucrose in honey is due to the presence of invertase enzymes. The organic acids present in the honey is responsible for the flavor and stability against the contamination of honey due to microorganisms. The natural food items are adulterated intentionally to increase the quantity and there by the quality gets affected. The main adulterants added in honey are sucrose, corn syrup, sugar syrup and jaggery syrup. The quantification deals in finding out the amount of basic constituents present in pure honey and adulterated honey using Fourier transform infrared (FTIR) spectrometer with the multivariate analysis and validating the same using chemical analysis method. The partial least square model is used in predicting the constituents of the samples.
A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing
Authors: Al-Momin Mohammed , Almomin Ammar
Published: August 2021

The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy.
A small vessel detection using a co-located multi-frequency FMCW MIMO radar
Authors: Zainuddin Suraya , Abd Rashid Nur Emileen , Pasya Idnin , Raja Abdullah Raja Syamsul Azmir , Cengiz Korhan
Published: August 2021

Small vessels detection is a known issue due to its low radar cross section (RCS). An existing shore-based vessel tracking radar is for long-distance commercial vessels detection. Meanwhile, a vessel-mounted radar system known for its reliability has a limitation due to its single radar coverage. The paper presented a co-located frequency modulated continuous waveform (FMCW) maritime radar for small vessel detection utilising a multiple-input multiple-output (MIMO) configuration. The radar behaviour is numerically simulated for detecting a Swerling 1 target which resembles small maritime’s vessels. The simulated MIMO configuration comprised two transmitting and receiving nodes. The proposal is to utilize a multi-frequency FMCW MIMO configuration in a maritime environment by applying the spectrum averaging (SA) to fuse MIMO received signals for range and velocity estimation. The analysis was summarised and displayed in terms of estimation error performance, probability of error and average error. The simulation outcomes an improvement of 2.2 dB for a static target, and 0.1 dB for a moving target, in resulting the 20% probability of range error with the MIMO setup. A moving vessel's effect was observed to degrade the range error estimation performance between 0.6 to 2.7 dB. Meanwhile, the proposed method was proven to improve the 20% probability of velocity error by 1.75 dB. The impact of multi-frequency MIMO was also observed to produce better average error performance.
Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq
Authors: K. AL-Taie Rana Riad , Saleh Basma Jumaa , Falih Saedi Ahmed Yousif , Salman Lamees Abdalhasan
Published: August 2021

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.
Distributed reflection denial of service attack: A critical review
Authors: Nuiaa Riyadh Rahef , Manickam Selvakumar , Alsaeedi Ali Hakem
Published: August 2021

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks.
Developed security and privacy algorithms for cyber physical system
Authors: Khudhur Dhuha Dheyaa , Croock Muayad Sadik
Published: August 2021

Cyber-physical system (CPS) is a modern technology in the cyber world, and it integrates with wireless sensor network (WSN). This system is widely used in many applications such as a smart city, greenhouse, healthcare, and power grid. Therefore, the data security and integrity are necessary to ensure the highest level of protection and performance for such systems. In this paper, two sides security system for cyber-physical level is proposed to obtain security, privacy, and integrity. The first side is applied the secure sockets layer (SSL)/transport layer security (TLS) encryption protocol with the internet of things (IoT) based message queuing telemetry transport (MQTT) protocol to secure the connection and encrypt the data exchange between the system's parties. The second side proposes an algorithm to detect and prevent a denial of service (DoS) attack (hypertext transfer protocol (HTTP) post request) on a Web server. The experiment results show the superior performance of the proposed method to secure the CPS by detecting and preventing the cyber-attacks, which infect the Web servers. They also prove the implementation of security, privacy and integrity aspects on the CPS.
Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization
Authors: Yotsawat Wirot , Wattuya Pakaket , Srivihok Anongnart
Published: August 2021

Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants.
Internet of things-based photovoltaics parameter monitoring system using NodeMCU ESP8266
Authors: Sutikno Tole , Satrian Purnama Hendril , Pamungkas Anggit , Fadlil Abdul , Mohd Alsofyani Ibrahim , Jopri Mohd Hatta
Published: August 2021

The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an open-source software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.