110023 research outputs found
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Transcriptome profiling of eutopic and ectopic endometrial stromal cells in women with endometriosis based on high throughput sequencing
No full text[[abstract]]Endometriosis is a common gynecological disease that affects approximately 5-10% of reproductive-aged women. However, the etiology and pathophysiology of endometriosis are currently unclear. The objective of this study was to identify a potential pathogenic gene of endometriosis using RNA sequencing (RNA-seq) analysis. Human endometrial stromal cells were isolated from four patients receiving surgical treatment for endometriosis during laparoscopic surgery, and RNA-seq was used to examine differentially expressed genes (DEGs) in eutopic and ectopic endometrial stromal cells. The functional significance of the differentially expressed genes was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A total of 1309 upregulated and 663 downregulated genes were identified through the analysis of the transcriptomes of eutopic and ectopic endometrial stromal cells. Furthermore, KEGG analysis indicated that these DEGs were mainly enriched in the PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, and MAPK signaling pathway. Our study identified differential gene expression in eutopic as compared to ectopic endometrial tissue stromal cells. We strongly believe that our findings can bring new insights into the underlying mechanisms of endometriosis. However, future research is necessary to clarify the roles of the identified genes
Participation of lipopolysaccharide in hyperplasic adipose expansion: Involvement of NADPH oxidase/ROS/p42/p44 MAPK-dependent Cyclooxygenase-2
No full text[[abstract]]Obesity is a world-wide problem, especially the child obesity, with the complication of various metabolic diseases. Child obesity can be developed as early as the age between 2 and 6. The expansion of fat mass in child age includes both hyperplasia and hypertrophy of adipose tissue, suggesting the importance of proliferation and adipogenesis of preadipocytes. The changed composition of gut microbiota is associated with obesity, revealing the roles of lipopolysaccharide (LPS) on manipulating adipose tissue development. Studies suggest that LPS enters the circulation and acts as a pro-inflammatory regulator to facilitate pathologies. Nevertheless, the underlying mechanisms behind LPS-modulated obesity are yet clearly elucidated. This study showed that LPS enhanced the expression of cyclooxygenase-2 (COX-2), an inflammatory regulator of obesity, in preadipocytes. Pretreating preadipocytes with the scavenger of reactive oxygen species (ROS) or the inhibitors of NADPH oxidase or p42/p44 MAPK markedly decreased LPS-stimulated gene expression of COX-2 together with the phosphorylation of p47phox and p42/p44 MAPK, separately. LPS activated p42/p44 MAPK via NADPH oxidase-dependent ROS accumulation in preadipocytes. Reduction of intracellular ROS or attenuation of p42/p44 MAPK activation both reduced LPS-mediated COX-2 expression and preadipocyte proliferation. Moreover, LPS-induced preadipocyte proliferation and adipogenesis were abolished by the inhibition of COX-2 or PEG2 receptors. Taken together, our results suggested that LPS enhanced the proliferation and adipogenesis of preadipocytes via NADPH oxidase/ROS/p42/p44 MAPK-dependent COX-2 expression
A Location-based Privacy-preserving Oblivious Sharing Scheme for Indoor Navigation
No full text[[abstract]]In recent years, with the rapid development of wireless communication technology and mobile devices, etc., indoor localization and navigation using WiFi sensing are increasingly being widely employed in life. On this basis, various applications of location-based services have been rapidly developed. However, when users enjoy the location query service of LBS providers, there is a risk of exposing personal information and location privacy simultaneously. In particular, the sensitive information about users based on their location privacy can be inferred by malicious users. Therefore, how to implement location hiding, query privacy protection and query result feedback at the same time is one of the crucial issues of LBS privacy protection. A novel oblivious data sharing scheme employing the designed 1-out-of-n oblivious transfer protocol is proposed to achieve an efficient location-based service for users while effectively hiding location coordinates and protecting the privacy of users and servers. In addition, once a user accesses the same keyword several times, the server is bound to be able to master the accessed frequency of keywords. For this reason, the string length-based transformation algorithm based on private information retrieval is designed and added as a part of OT protocol, which effectively reduces the communication overhead and ensures the privacy of required keywords. The security and performance analysis indicate that the proposed protocol can be instantiated in indoor navigation with high security and efficiency
AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf
No full text[[abstract]]With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise
An Anonymous Pre-Authentication and Post-Authentication Scheme Assisted by Cloud for Medical IoT Environments
No full text[[abstract]]The Internet of Things (IoT) environment assisted by a cloud server is an emerging conceptual model which incorporates the benefits of both IoT and cloud. In such an integrated model, IoT gathers information from the sensors, and the cloud server significantly increases the importance of this information by exchanging and processing them. Session key agreement among the user and sensors is practiced nowadays to accelerate the authentication process in Medical IoT (MIoT). However, the use of GateWay Node (GWN) or Trusted Third Party (TTP) for the control of participant registration, session key agreement and data storage was not efficient in IoT because they suffer from increased communication and computation costs. Hence, in this paper, we have proposed a new cloud based session key agreement and data storage scheme which consists of an improved authentication mechanism for MIoT. The proposed scheme achieves anonymous pre-authentication and post-authentication. In this proposed system, with the support of the user data, the cloud server generates the pseudo-identity and this identity is used by a cloud server to anonymously authenticate the legitimacy of a requesting user. After successful login, the cloud server and the user share the session key and data communication occurs. The security analysis of our proposed scheme demonstrates that it is resistant to a variety of security attacks. Moreover, the simulated performance analysis proves that the communication and computation costs associated with our work are acceptable than the existing works
An Edge-AI based Forecasting Approach for Improving Smart Microgrid Efficiency
No full text[[abstract]]The smart ocean has aroused the interest of government, business, and academia because of the wealth of marine resources. It has been suggested to use underwater Internet of Things (IoT) frameworks to collect a variety of data from smart seas that can aid in the underwater green transport system, ecological sustainability, military intelligence gathering, and a variety of other operations. Because of the limited resources accessible to IoT devices regarding communication overhead, processing expenses, and battery capacity, security and privacy concerns in underwater green transport systems have lately been a critical source of worry. In this context, We presented a unique identity-based authentication mechanism for underwater green transport systems. Our suggested solution uses lightweight authentication mechanisms that prove secure communication between different elements of the green transport system
Effects of Mg doping on structural and optoelectronic properties of p-type semiconductor CuCrO2 thin films
No full text[[abstract]]In this study, CuCrO2 thin films are doped with Mg. In the experiment, the Mg content of CuCr1?xMgxO2 (x = 0, 0.015, 0.035, 0.055, 0.075, and 0.095) is increased gradually from 0.2 at% (x = 0.015) to 2.17 at% (x = 0.095) for increased carrier concentration and electrical conductivity. The amounts of Cu, Cr, and O in the CuCrO2 thin films diverge from the atomic stoichiometric ratio (Cu/Cr/O ratio = 25:25:50). In particular, the thin films contain less Cu and Cr and more O than this ratio, which is suitable for p-type conduction. The results show that Mg is present in the lattice sites of delafossite CuCrO2 and that Mg doping influences the structural and photoelectric properties. By increasing the amount of Mg, the root-mean-square roughness of the thin films decreases from 23.4 to 16.6 nm. In addition, an appropriate increase of Mg doping in the CuCrO2 thin films yields enhanced transmittance of visible light and enhanced absorption of ultraviolet light (300–400 nm). With the increasing amount of Mg, the band gap of CuCrO2 increases from 3.09 eV (undoped, x = 0) to 3.12 eV (x = 0.095). The experiment shows that replacing Cr3+ with Mg2+ effectively increases the concentration of carriers, leading to a 64-fold increase in electrical conductivity from 2.43 × 10?2 to 1.56 (Ω-cm)?1. A mechanism based on point defects for electrical conduction of hole carriers is proposed. Mg-doped CuCrO2 is thus a candidate for an optoelectronic semiconductor with desirable properties
Fog-Enabled Secure and Efficient Fine-Grained Searchable Data Sharing and Management Scheme for IoT-Based Healthcare Systems
No full text[[abstract]]In recent times, fog computing has emerged as a helpful extension of cloud computing. It can efficiently handle the prevalent issue of managing silos of data generated by today's digital healthcare services. Moreover, the application of the Internet of Things (IoT) in the development of smart healthcare systems further adds tons of data tirelessly to these silos, thus making the cloud congested. To manage such continuously growing data, the concept of adding a fog layer between the cloud and the end-users (EUs) proved to be beneficial. These intermediary fog nodes (FNs) can handle and store data, and thus facilitate the cloud and alleviate the burden from the EUs. Most of the existing search schemes for encrypted data have been developed for the cloud platform and ignored this helpful extension, which can improve the scheme's efficiency by delegating most of the heavy computations to the intermediary FNs. In this article, a fine-grained searchable data sharing scheme has been proposed using the fog computing platform. The resulting scheme is efficient and lightweight because the FN facilitates EUs by performing computationally intensive tasks on their behalf. A significant reduction in storage and computational cost has been achieved by the proposed scheme at the data owner's end, representing the resource-constrained IoT devices. The storage cost has been reduced to two source group elements, and the computational cost has been reduced to three exponent operations in the source group and one hash operation. Furthermore, the proposed scheme is secure against the selectively chosen keyword attack in the generic bilinear group model
Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks
No full text[[abstract]]Internet-of-Things (IoT) has become an enthralling attacking surface for attackers to explode multitude of cyber-attacks. Distributed Denial of Service (DDoS) attack has transpired as the most menacing attack in the IoT networks. In this article, we propose an attack detection system to identify anomalous activities in the fog-enabled IoT network. Initially, authors have investigated exhaustively on the performance of filter-based feature selection algorithms comprising ReliefF, Correlation Feature Selection (CFS), Information Gain (IG), and Minimum-Redundancy-Maximum-Relevancy (mRMR) and distinct categories classification algorithms upon the prepared dataset consisting of IoT network specific features. Performance of the tested classification algorithm is assessed using prominent evaluation measures. Moreover, response time of classifiers is calculated for centralized and fog-enabled IoT network infrastructure. The experimental outcomes unveil that, in terms of both accuracy and latency, J48 classifier outperforms all other tested classifier with mRMR feature selection algorithm
Intelligent deep fusion network for urban traffic flow anomaly identification
No full text[[abstract]]This paper presents a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with an efficient decomposition strategy is explored to find the anomalous behavior of urban traffic flow data. The urban traffic flow data set is decomposed into similar clusters, each containing homogeneous data. The convolutional neural network is used for each data cluster. In this way, different models are trained, each learned from highly correlated data. A merging strategy is finally used to fuse the results of the obtained models. To validate the performance of the proposed framework, intensive experiments were conducted on urban traffic flow data. The results show that our system outperforms the competition on several accuracy criteria