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Sphingosine 1- Phosphate- Upregulated COX-2/PGE2 System Contributes to Human Cardiac Fibroblast Apoptosis: Involvement of MMP-9-Dependent Transactivation of EGFR Cascade
No full text[[abstract]]Human cardiac fibroblasts (HCFs) play key roles in normal physiological functions and pathological processes in the heart. Our recent study has found that, in HCFs, sphingosine 1-phosphate (S1P) can upregulate the expression of cyclooxygenase-2 (COX-2) leading to prostaglandin E2 (PGE2) generation mediated by S1P receptors/PKCα/MAPKs cascade-dependent activation of NF-κB. Alternatively, G protein-coupled receptor- (GPCR-) mediated transactivation of receptor tyrosine kinases (RTKs) has been proved to induce inflammatory responses. However, whether GPCR-mediated transactivation of RTKs participated in the COX-2/PGE2 system induced by S1P is still unclear in HCFs. We hypothesize that GPCR-mediated transactivation of RTKs-dependent signaling cascade is involved in S1P-induced responses. This study is aimed at exploring the comprehensive mechanisms of S1P-promoted COX-2/PGE2 expression and apoptotic effects on HCFs. Here, we used pharmacological inhibitors and transfection with siRNA to evaluate whether matrix metalloprotease (MMP)2/9, heparin-binding- (HB-) epidermal growth factor (EGF), EGF receptor (EGFR), PI3K/Akt, MAPKs, and transcription factor AP-1 participated in the S1P-induced COX-2/PGE2 system determined by Western blotting, real-time polymerase chain reaction (RT-PCR), chromatin immunoprecipitation (ChIP), and promoter-reporter assays in HCFs. Our results showed that S1PR1/3 activated by S1P coupled to Gq- and Gi-mediated MMP9 activity to stimulate EGFR/PI3K/Akt/MAPKs/AP-1-dependent activity of transcription to upregulate COX-2 accompanied with PGE2 production, leading to stimulation of caspase-3 activity and apoptosis. Moreover, S1P-enhanced c-Jun bound to COX-2 promoters on its corresponding binding sites, which was attenuated by these inhibitors of protein kinases, determined by a ChIP assay. These results concluded that transactivation of MMP9/EGFR-mediated PI3K/Akt/MAPKs-dependent AP-1 activity was involved in the upregulation of the COX-2/PGE2 system induced by S1P, in turn leading to apoptosis in HCFs
Tumor Necrosis Factor-alpha-Induced C-C Motif Chemokine Ligand 20 Expression through TNF Receptor 1-Dependent Activation of EGFR/p38 MAPK and JNK1/2/FoxO1 or the NF-κappaB Pathway in Human Cardiac Fibroblasts
No full text[[abstract]]Tumor necrosis factor (TNF)-α is involved in the pathogenesis of cardiac injury, inflammation, and apoptosis. It is a crucial pro-inflammatory cytokine in many heart disorders, including chronic heart failure and ischemic heart disease, contributing to cardiac remodeling and dysfunction. The implication of TNF-α in inflammatory responses in the heart has been indicated to be mediated through the induction of C-C Motif Chemokine Ligand 20 (CCL20). However, the detailed mechanisms of TNF-α-induced CCL20 upregulation in human cardiac fibroblasts (HCFs) are not completely defined. We demonstrated that in HCFs, TNF-α induced CCL20 mRNA expression and promoter activity leading to an increase in the secretion of CCL20. TNF-α-mediated responses were attenuated by pretreatment with TNFR1 antibody, the inhibitor of epidermal growth factor receptor (EGFR) (AG1478), p38 mitogen-activated protein kinase (MAPK) (p38 inhibitor VIII, p38i VIII), c-Jun amino N-terminal kinase (JNK)1/2 (SP600125), nuclear factor kappaB (NF-κB) (helenalin), or forkhead box O (FoxO)1 (AS1841856) and transfection with siRNA of TNFR1, EGFR, p38α, JNK2, p65, or FoxO1. Moreover, TNF-α markedly induced EGFR, p38 MAPK, JNK1/2, FoxO1, and NF-κB p65 phosphorylation which was inhibited by their respective inhibitors in these cells. In addition, TNF-α-enhanced binding of FoxO1 or p65 to the CCL20 promoter was inhibited by p38i VIII, SP600125, and AS1841856, or helenalin, respectively. Accordingly, in HCFs, our findings are the first to clarify that TNF-α-induced CCL20 secretion is mediated through a TNFR1-dependent EGFR/p38 MAPK and JNK1/2/FoxO1 or NF-κB cascade. We demonstrated that TNFR1-derived EGFR transactivation is involved in the TNF-α-induced responses in these cells. Understanding the regulation of CCL20 expression by TNF-α on HCFs may provide a potential therapeutic strategy in cardiac inflammatory disorders
Automatic Classification of Uroflow Patterns via the Grading-based Approach
No full text[[abstract]]Automatic classification of uroflowmetry curves into different patterns can help urologists to make an accurate diagnosis of the lower urinary track function in real time and increase the agreement of interpretation among urologists. In this paper, we propose a grading-based approach to the automatic classification of uroflowmetry curves by considering 87 cases of medical data, which are confirmed by the consensus of two highly experienced urologists. The interpretation of uroflowmetry is usually subjective and empirical. In this study, the same results identified by both urologists were 87 cases out of 160. To avoid the disadvantage of visual interpretation, our approach integrates the urologist’s experiences and different weights for different conditions, including several new conditions, the raising angle and the number of significant drops. Moreover, the objective view of classification is useful for teaching urologists to watch for voiding dysfunctions. From our experimental study with statistical analysis comparing the results of our approach with two urologists’ observations, we have shown that the agreement of normal / abnormal types is very good
Identification of traffic flow using multi-convolutional neural networks
No full text[[abstract]]Many countries suffer from traffic jams on a freeway or
expressways. Moreover, governments significantly consider treating traffic jam-related problems in most modern cities. Therefore, developing a valid and reliable automatic analysis method for detecting expressway traffic
flow for the control of each interchange gateway is essential. This study uses two convolutional neural networks
(CNNs) to recognize the expressway’s traffic flow. Initially, a road-region CNN is employed to recognize the
lane regions of a captured image. At the same time, a
traffic-flow CNN is used to identify the traffic flow of the
image processing by the road-region CNN. The identified
results contain three categories: block, more cars, and
smooth. The experimental results reveal that the recognized precision rate of the traffic flow can reach 92.5%.
Accordingly, the recognition results can control the number of vehicles entering and expressway in the interchange
gateways, preventing traffic jams
An improved efficient authentication scheme for vehicular ad-hoc networks with batch verification using bilinear pairings
No full text[[abstract]]Research related to the authentication schemes in vehicular ad hoc networks (VANETs) still becomes a hot issue to discuss. In 2019, Cui and Tu proposed an efficient authentication scheme for vehicular ad hoc networks (VANETs) with batch verification using bilinear pairings. Their scheme was designed to improve the utilisation of the double-secret key in the identity-based batch signature (IBS) scheme published by Jianhong et al. and Bayat et al. Unfortunately, we found that Cui and Tu's identity-based conditional privacy-preserving authentication (IBCPPA) scheme is also insecure against non-repudiation attack. By those defects, a malicious user can broadcast some wrong messages to mislead the roadside unit (RSU) and deny its behaviour when a trusted authority (TA) traces it. In this article, we address the issue and give our improvement to withstand the above security threat
Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic
No full text[[abstract]]The world has changed considerably in the previous two decades. Today, people are facing extreme poverty, global warming, and unwanted climate changes. The economic gap between countries is continuously growing. Moreover, with the expanding influence of technology, governance is getting more difficult. To address these issues, the UN announced Sustainable Development Goals (SDGs), also called Global Goals, in 2015. These goals fill in as an overall source of inspiration to annihilate poverty, protect the environment, and guarantee that all individuals live in harmony and thrive by 2030. The 17 SDGs are interconnected in that they recognize that activities in a single region sway result in others and that improvement should adjust to social, monetary, and natural sustainability. The SDGs intend to kill poverty, hunger, AIDS, and gender discrimination against women and girls. The COVID-19 epidemic, on the other hand, has hampered attempts to accomplish the 2030 Agenda for Sustainable Development. As a result, the impact of these SDGs must be thoroughly studied and analyzed. As a result, the purpose of this research is to examine the SDG before and after Covid-19, as well as how they have influenced various national and international markets. The research also assesses the 17 SDGs in each of India's 29 states in depth. Since SDGs have a larger scope, this paper predicts the SDG-9 scores of few countries like UAE, New Zealand, Japan, India, Germany, China, Bhutan, and USA
Blockchain-based random auditor committee for integrity verification
No full text[[abstract]]With the popularity of cloud storage, integrity verification for outsourced data has attracted more and more attention. The majority of existing research works rely on a unique third-party auditor (TPA) or fixed TPAs to audit the integrity of outsourced data, and thus they are vulnerable to DoS and collusion attacks as the TPA is not completely trusted. To conquer this problem, a blockchain-based random auditor committee is proposed to replace the fixed TPAs for the integrity verification (call scheme for short). The proposed blockchain consensus algorithm utilizes a verifiable random function (VRF) to select the third-party auditor committee (TPAC) which performs contract verification. The leader of TPAs (LTPA) packs the verification proof into a blockchain called verification chain. The main idea is employing the output of a VRF for leader election to make it probabilistic, such that specific nodes cannot be targeted in advance and a certain fraction of the nodes being malicious can be tolerated. We present rigorous security analysis to demonstrate the security of the proposed scheme. Meanwhile, the comprehensive performance evaluation shows that the proposed scheme improves the performance of data integrity verification effectively
Pretrained Configuration of Power-Quality Grayscale-Image Dataset for Sensor Improvement in Smart-Grid Transmission
No full text[[abstract]]The primary source of the various power-quality-disruption (PQD) concerns in smart grids
is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart
meters, measurement units, and computers that are linked by a large network. Because real-time data
exchange via a network of various sensors demands a small file size without an adverse effect on
the information quality, one measure of the power-quality monitoring in a smart grid is restricted
by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly
data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality
monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing
pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0.
The suggested layers, added between the pretrained base model and the classifier, modify the
pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model.
This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size,
and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable
and bandwidth-friendly data-packet transmission in WSN
Pretrained Configuration of Power-Quality Grayscale-Image Dataset for Sensor Improvement in Smart-Grid Transmission
No full text[[abstract]]The primary source of the various power-quality-disruption (PQD) concerns in smart grids
is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart
meters, measurement units, and computers that are linked by a large network. Because real-time data
exchange via a network of various sensors demands a small file size without an adverse effect on
the information quality, one measure of the power-quality monitoring in a smart grid is restricted
by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly
data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality
monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing
pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0.
The suggested layers, added between the pretrained base model and the classifier, modify the
pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model.
This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size,
and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable
and bandwidth-friendly data-packet transmission in WSN