Search

Background: The role of microbiota in the pathogenesis of ulcerative colitis (UC) has been increasingly recognized. However, most of the reports are from Western populations. In Middle Eastern countries, including Saudi Arabia, little is known about the role of microbiota. Therefore, our aim was to describe the bacterial microbiota profile and signature in pediatric UC in Saudi Arabia. Methods: Twenty children with UC and 20 healthy controls enrolled in the study gave stool samples. Twenty rectal mucosal samples were taken from UC and 20 from non-UC controls. Inclusion criteria included newly diagnosed and untreated children and lack of antibiotic exposure for at least 6 months before stool collection was required for children with UC and controls. Bacterial deoxyribonucleic acid was extracted and sequenced using shotgun metagenomic analysis. Statistical analysis included Shannon alpha diversity metrics, Bray-Curtis dissimilarity, DESeq2, and biomarker discovery. Results: The demographic characteristics were similar in children with UC and controls. There was a significant reduction in alpha diversity (P = 0.037) and beta diversity in samples from children with UC (P = 0.001). Many taxa were identified with log2 abundance analysis, revealing 110 and 102 species significantly depleted and enriched in UC, respectively. Eleven bacterial species’ signatures were identified. Conclusions: In Saudi Arabian children with UC, we demonstrate a dysbiosis similar to reports from Western populations, possibly related to changes of lifestyle. Microbial signature discovery in this report is an important contribution to research, leading to the development of adjunctive non-invasive diagnostic options in unusual cases of UC.

Background: Although the role of fungi in gut inflammation in IBD has been suggested, data are still limited in ulcerative colitis (UC). Our aim was to describe the gut fungal profile in a pediatric UC in Saudi Arabia. Methods: Fecal samples from children with UC and control samples provided by healthy school children were collected. The fungal DNA was analyzed using Shotgun metagenomic procedures. Shannon alpha diversity, beta diversity, differential abundance, random forest classification algorithm, and area under the curve were analyzed. Results: There were 20 children with UC and 20 healthy school children. The median age and range were 13 (0.5–21) and 13 (7–16) years for children with UC and controls, respectively. Male subjects were 40% and 35% for UC and controls, respectively. At diagnosis, the UC extent was E4 (38%); E3 (25%); E2 (37%) and 35% had a PUCAI ≥65. The reduction of alpha diversity and the significant dissimilarity in children with UC were similar to those of most published studies. However, a significant difference was found at all taxa levels with a remarkable enhancement of Candida genus and Saccharomyces cerevisiae in children with UC. Three species were identified as fungal signatures and an area under the curve of 98.4% (95.1–100% CI), indicating an association with UC that has not been reported thus far. Conclusion: We report significant fungal dysbiosis in children with UC consistent with published literature. However, the report of potential fungal signature and a strong association with UC deserves further studies with a bigger sample size from other populations.

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

Abstract Radio jamming represents a serious threat in wireless networks that can cause denial of service (DoS) attacks for the network users. The jammer transmits jamming signals to disrupt the normal operation of the wireless networks. With proliferation of unmanned aerial vehicle (UAV)-based wireless networks and Internet of Things technologies, jamming attacks have become a major challenge to the practical adoption of these technologies. In this paper, we investigate the problem of optimizing the performance of the UAV data collection from ground IoT devices in presence of a malicious jammer. The IoT devices are clustered, and the UAV collects the data from the cluster heads (CHs) such that the harmful effect of the jammer is mitigated. The objective is to minimize the flight time of the UAV while collecting the data from the ground IoT devices in presence of the jamming attack. We modeled our problem mathematically as an integer linear program (ILP). Due to the difficulty of solving the optimization problem when the scale of the problem is large, we proposed Jamming-Aware UAV Routing and IoT Devices Clustering Algorithm (JA-RC). This algorithm is Genetic-based and can solve the original optimization problem in a more efficient way. Then, we investigate the performance of the proposed algorithm under different settings and scenarios.