Background Fanconi anaemia (FA) is a group of disorders characterised by progressive bone marrow failure and a characteristic but variable craniofacial and skeletal involvement. Recessive mutations in any of 15 genes linked to FA lead to the pathognomonic increased susceptibility to double-strand DNA breaks. Methods Autozygome and exome analysis of a patient with classic FA phenotype Results The authors identified a novel truncating mutation in XRCC2. Consistent with the proposed causal link to FA, this gene is an essential non-redundant component of the RAD51 family of homologous repair proteins and its deficiency in a murine model has been shown to lead to a highly similar phenotype to that of this patient both at the cellular and organismal level. Conclusion This study implicates XRCC2 in the pathogenesis of FA and calls for further investigation of the potential contribution of XRCC2 mutations to the overall mutational load of FA.
Accessing distant sources in MANETs leads to poor performance and sometimes impossible due to regular disconnection of mobile hosts. Several approaches have been proposed to improve data accessibility and reduce delay in serving requests. These approaches adopted the collaborative caching techniques, enabling various mobile hosts to cache and share data items in their local caches. However, processing requests based on their classification have not been tackled in previous works to reduce the average delay. In this paper, we propose a collaborative caching priority approach, which serves requests based on their classifications either priority or normal. This is to ensure that priority requests are served with minimum cache discovery overhead and with less delay in fetching data items that are cached in MANETs. The experimental results show that the proposed approach improved the performance of collaborative caching and outperformed the cooperative and adaptive system (COACS), with a decrement of 30.42% in average delay and an increment of 21.26% in hit ratio.
Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.
Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.