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.

/pdf/biological-properties-of-extracellular-vesicles-and-their-bef7axtqvb.pdf

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.

/pdf/extracellular-vesicles-biology-and-emerging-therapeutic-4wc2updtyj.pdf

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.

/pdf/atheroprotective-communication-between-endothelial-cells-and-2ijakoa7r0.pdf

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

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

SRAM-based FPGA devices manufactured in FinFET technologies provide performances and characteristics suitable for avionics andaerospace applications. The estimation of error rate sensitivity to harsh environments is a major concern for enabling their usage on such application fields. In this paper, we propose a new estimation approach able to consider the radiation effects on the configuration memory and logic layer of FPGAs, providing a comprehensive Application Error Rate probability estimation. Experimental results provide a comparison between radiation test campaigns, which demonstrates the feasibility of the proposed solution.

A Novel Error Rate Estimation Approach forUltraScale+ SRAM-based FPGAs

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life.

Test, Reliability and Functional Safety Trends for Automotive System-on-Chip

This paper proposes an innovative data structure to be used as a backbone in designing microarray phenotype sample classifiers. The data structure is based on graphs and it is built from a differential analysis of the expression levels of healthy and diseased tissue samples in a microarray dataset. The proposed data structure is built in such a way that, by construction, it shows a number of properties that are perfectly suited to address several problems like feature extraction, clustering, and classification.

/pdf/differential-gene-expression-graphs-a-data-structure-for-36zl9mihxy.pdf

Differential gene expression graphs: A data structure for classification in DNA microarrays

De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements. Determining the Functional De-Rating of sequential logic cells typically requires computationally intensive fault-injection simulation campaigns. In this paper a new approach is proposed which uses Machine Learning to estimate the Functional De-Rating of individual flip-flops and thus, optimising and enhancing fault injection efforts. Therefore, first, a set of per-instance features is described and extracted through an analysis approach combining static elements (cell properties, circuit structure, synthesis attributes) and dynamic elements (signal activity). Second, reference data is obtained through first-principles fault simulation approaches. Finally, one part of the reference dataset is used to train the Machine Learning algorithm and the remaining is used to validate and benchmark the accuracy of the trained tool. The intended goal is to obtain a trained model able to provide accurate per-instance Functional De-Rating data for the full list of circuit instances, an objective that is difficult to reach using classical methods. The presented methodology is accompanied by a practical example to determine the performance of various Machine Learning models for different training sizes.

On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques

We describe a methodology for analyzing the robustness of circuits implemented by SRAM-based FPGAs against the accumulation of soft errors within the configuration memory. A detailed analysis of the fault injection data is presented.

Luca Sterpone

Papers

A Novel Error Rate Estimation Approach forUltraScale+ SRAM-based FPGAs

Test, Reliability and Functional Safety Trends for Automotive System-on-Chip

Differential gene expression graphs: A data structure for classification in DNA microarrays

On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques

Robustness analysis of soft error accumulation in SRAM-FPGAs using FLIPPER and STAR/RoRA