Evolutionarily missing and conserved tRNA genes in human and avian

Viral infection heavily relies on host transfer RNA (tRNA) for viral RNA decoding. Counterintuitively, not all tRNA species based on anticodon are matched to all 64-triplet codons during evolution. Life solves this problem by cognate tRNA species via wobbling decoding. We found that 14 out of 64 tRNA genes in humans and the main avian species (chicken and duck) were parallelly missing, including 8 tRNA-A34NN and 6 tRNA-G34NN species. By analyzing the conservation of key motifs in tRNA genes, we found that box A and B served as intragenic tRNA promoters were evolutionally conserved among human, chicken, and duck. Thus, decoding viral RNA by similar wobbling strategies and tRNA transcripts may be

A Conformation-Sensitive Monoclonal Antibody against the A2 Domain of von Willebrand Factor Reduces Its Proteolysis by ADAMTS13

The size of von Willebrand factor (VWF), controlled by ADAMTS13-dependent proteolysis, is associated with its hemostatic activity. Many factors regulate ADAMTS13-dependent VWF proteolysis through their interaction with VWF. These include coagulation factor VIII, platelet glycoprotein 1bα, and heparin sulfate, which accelerate the cleavage of VWF. Conversely, thrombospondin-1 decreases the rate of VWF proteolysis by ADAMTS13 by competing with ADAMTS13 for the A3 domain of VWF. To investigate whether murine monoclonal antibodies (mAbs) against human VWF affect the susceptibility of VWF to proteolysis by ADAMTS13 in vitro, eight mAbs to different domains of human VWF were used to evaluate the effects on VWF cleavage by ADAMTS13 under fluid shear stress and static/denaturing conditions. Additionally, the epitope of anti-VWF mAb (SZ34) was mapped using recombinant proteins in combination with enzyme-linked immunosorbent assay and Western blot analysis. The results indicate that mAb SZ34 inhibited proteolytic cleavage of VWF by ADAMTS13 in a concentration-dependent manner under fluid shear stress, but not under static/denaturing conditions. The binding epitope of SZ34 mAb is located between A1555 and G1595 in the central A2 domain of VWF. These data show that an anti-VWF mAb against the VWF-A2 domain (A1555-G1595) reduces the proteolytic cleavage of VWF by ADAMTS13 under shear stress, suggesting the role of this region in interaction with ADAMTS13

Use of nanomaterials in the pretreatment of water samples for environmental analysis

The challenge of providing clean drinking water is of enormous relevance in today’s human civilization, being essential for human consumption, but also for agriculture, livestock and several industrial applications. In addition to remediation strategies, the accurate monitoring of pollutants in water sup-plies, which most of the times are present at low concentrations, is a critical challenge. The usual low concentration of target analytes, the presence of in-terferents and the incompatibility of the sample matrix with instrumental techniques and detectors are the main reasons that renders sample preparation a relevant part of environmental monitoring strategies. The discovery and ap-plication of new nanomaterials allowed improvements on the pretreatment of water samples, with benefits in terms of speed, reliability and sensitivity in analysis. In this chapter, the use of nanomaterials in solid-phase extraction (SPE) protocols for water samples pretreatment for environmental monitoring is addressed. The most used nanomaterials, including metallic nanoparticles, metal organic frameworks, molecularly imprinted polymers, carbon-based nanomaterials, silica-based nanoparticles and nanocomposites are described, and their applications and advantages overviewed. Main gaps are identified and new directions on the field are suggested.publishe

Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC emb

Service Function Chain Embedding for NFV-Enabled IoT Based on Deep Reinforcement Learning

It is challenging to efficiently manage different resources in the IoT. Recently, Network function virtualization has attracted attention because of its prospect to achieve efficient resource management for IoT. In NFV-enabled IoT infrastructure, a service function chain (SFC) is composed of an ordered set of virtual network functions (VNFs) that are connected based on the business logic of service providers. However, the inefficiency of the SFC embedding process is one major problem due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this article, we decompose the complex VNFs into smaller VNF components (VNFCs) to make more effective decisions since VNF nodes and physical network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL)-based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios. Simulation results present the efficient performance of the proposed DRL-based dynamic SFC embedding scheme

Resource Allocation for Blockchain-Enabled Distributed Network Function Virtualization (NFV) with Mobile Edge Cloud (MEC)

Mobile Edge Cloud (MEC) has emerged as a promising paradigm shift from the centralized mobile cloud due to the explosive growth of edge devices and traffic volumes. Network Function Virtualization (NFV) is a key technology for managing and orchestrating the virtualized instances in MEC. However, it is challenging to perform efficient resource allocation in distributed NFV with MEC due to the multiple existence of NFV Management and Orchestration (MANO) systems in distributed NFV. In this work, we propose a blockchain-enabled NFV framework to reach consensus among multiple MANO systems for complex MEC scenarios. Moreover, we take both the latency of services and operational cost into consideration to achieve better resource allocation. Then, we formulate the efficient resource allocation for services in blockchain-enabled NFV with MEC as a multi-objective optimization problem. Due to the fact that it is difficult to solve this multi-objective optimization problem by traditional methods, we propose a dueling deep reinforcement learning approach. Simulation results are presented to show the effectiveness of our proposed scheme