In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

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Biological properties of extracellular vesicles and their physiological functions

Within the past decade, extracellular vesicles have emerged as important mediators of intercellular communication, being involved in the transmission of biological signals between cells in both prokaryotes and higher eukaryotes to regulate a diverse range of biological processes. In addition, pathophysiological roles for extracellular vesicles are beginning to be recognized in diseases including cancer, infectious diseases and neurodegenerative disorders, highlighting potential novel targets for therapeutic intervention. Moreover, both unmodified and engineered extracellular vesicles are likely to have applications in macromolecular drug delivery. Here, we review recent progress in understanding extracellular vesicle biology and the role of extracellular vesicles in disease, discuss emerging therapeutic opportunities and consider the associated challenges.

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Extracellular vesicles: biology and emerging therapeutic opportunities

MicroRNAs in Stress Signaling and Human Disease

The shear-responsive transcription factor Kruppel-like factor 2 (KLF2) is a critical regulator of endothelial gene expression patterns induced by atheroprotective flow. As microRNAs (miRNAs) post-transcriptionally control gene expression in many pathogenic and physiological processes, we investigated the regulation of miRNAs by KLF2 in endothelial cells. KLF2 binds to the promoter and induces a significant upregulation of the miR-143/145 cluster. Interestingly, miR-143/145 has been shown to control smooth muscle cell (SMC) phenotypes; therefore, we investigated the possibility of transport of these miRNAs between endothelial cells and SMCs. Indeed, extracellular vesicles secreted by KLF2-transduced or shear-stress-stimulated HUVECs are enriched in miR-143/145 and control target gene expression in co-cultured SMCs. Extracellular vesicles derived from KLF2-expressing endothelial cells also reduced atherosclerotic lesion formation in the aorta of ApoE(-/-) mice. Combined, our results show that atheroprotective stimuli induce communication between endothelial cells and SMCs through an miRNA- and extracellular-vesicle-mediated mechanism and that this may comprise a promising strategy to combat atherosclerosis.

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Atheroprotective communication between endothelial cells and smooth muscle cells through miRNAs.

State-of-the-art on use of insects as animal feed.

Radiation-induced Single Event Transient effects during the reconfiguration process of SRAM-based FPGAs

Selective mitigation or selective hardening is an effective technique to obtain a good trade-off between the improvements in the overall reliability of a circuit and the hardware overhead induced by the hardening techniques. Selective mitigation relies on preferentially protecting circuit instances according to their susceptibility and criticality. However, ranking circuit parts in terms of vulnerability usually requires computationally intensive fault-injection simulation campaigns. This paper presents a new methodology which uses machine learning clustering techniques to group flip-flops with similar expected contributions to the overall functional failure rate, based on the analysis of a compact set of features combining attributes from static elements and dynamic elements. Fault simulation campaigns can then be executed on a per-group basis, significantly reducing the time and cost of the evaluation. The effectiveness of grouping similar sensitive flip-flops by machine learning clustering algorithms is evaluated on a practical example.Different clustering algorithms are applied and the results are compared to an ideal selective mitigation obtained by exhaustive fault-injection simulation.

Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

VLIW architectures are widely employed in several embedded signal applications mainly because they offer the opportunity to gain high computational performances while maintaining reduced clock rate and power consumption. Recently, VLIW processors became more and more suitable to be employed in various embedded processing systems including safety critical applications such as aerospace, automotive and rail transport. Therefore, techniques to effectively estimate and improve the reliability of VLIW processor are of great interest. Terrestrial safety-critical applications based on newer nano-scale technologies raise increasing concerns about transient errors induced by neutrons. In this paper, we analyze the cross-domain failures affecting redundant mitigation techniques implemented on a statistically scheduled data path VLIW processor and we describe a fault injection analysis of transient faults affecting the r-VEX VLIW processor implemented on an FPGA platform. For a large set of benchmark applications, figures of application performances and errors analysis are provided and commented.

Fault injection analysis of transient faults in clustered VLIW processors

Transient faults can affect the behavior of electronic systems, and represent a major issue in many safety-critical applications. This paper focuses on Control Flow Errors (CFEs) and extends a previously proposed method, based on the usage of the debug interface existing in several processors/controllers. The new method achieves a good detection capability with very limited impact on the system development flow and reduced hardware cost: moreover, the proposed technique does not involve any change either in the processor hardware or in the application software, and works even if the processor uses caches. Experimental results are reported, showing both the advantages and the costs of the method.

A new solution to on-line detection of Control Flow Errors

Logic soft errors caused by radiation are a major concern when working with circuits that need to operate in harsh environments, such as space or avionics applications, where soft errors are traditionally referred as single event effects. In this paper, system knowledge-based hardening techniques using recursive structures for the implementation of moving average filters that provide protection against single event upsets are evaluated through two fault injection systems based on simulation and emulation respectively. Fault injection campaigns show that system knowledge-based redundancy techniques can achieve the same level of dependability as standard redundancy techniques, such as triple modular redundancy, while having optimal cost.

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Luca Sterpone

Papers

Radiation-induced Single Event Transient effects during the reconfiguration process of SRAM-based FPGAs

Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

Fault injection analysis of transient faults in clustered VLIW processors

A new solution to on-line detection of Control Flow Errors

An Experimental Analysis of SEU Sensitiveness on System Knowledge-based Hardening Techniques