MicroRNAs as modulators of the platelet proteome

MicroRNAs are short 21- to 24-nucleotide (nt) RNA species that act as key regulators of gene expression. Known primarily to modulate mRNA translation through recognition of specific binding sites located in the 3untranslated region (UTR) of messenger RNA (mRNA) targets, microRNAs may regulate between 30% to 92% of the genes in human, thereby controlling a plethora of biological processes. Although devoid of a nucleus and lacking genomic DNA, platelets may be no exception, as recent experimental evidences indicate that they contain all the protein and RNA components and features required for microRNA-regulated mRNA translation: (i) the platelet transcriptome is astonishingly diverse, representing between 15 and 32% of all human genes, (ii) platelet mRNAs can be translated into proteins, (iii) platelets contain an abundant and diverse array of microRNAs, and (iv) the host Dicer and Argonaute 2 (Ago2) complexes. The latter ones are functional in microRNA biogenesis and function, respectively. In this review article, we will summarize and discuss the experimental evidences as well as the most recent advances supporting a role for microRNAs as modulators of the platelet proteome. Expected to play a central role in health and disease, a dysfunctional microRNA-based regulation of gene expression in platelets may represent an important etiologic factor underlying platelet-related and cardiovascular diseases

Interférence à l'ARN chez les pêchers infectés par le viroïde de la mosaïque latente du pêcher (PLMVd)

Les viroïdes sont de petits ARN circulaires simple-brin qui infectent les plantes supérieures, causant ainsi d'importantes pertes économiques dans le domaine agro-alimentaire. Même si plus de 30 ans se sont écoulés depuis leur découverte, leur mode de pathogénicité demeure un mystère. Par contre, depuis un peu plus d'une dizaine d'année, un nouveau mécanisme de défense contre des ARN exogènes a fait son apparition. On nomme ce phénomène interférence à l'ARN (RNAi) ou PTGS (post-transcriptional gene silencing) chez les plantes. Il est défini comme un procédé de dégradation d'un ARN de manière séquence-spécifique déclenché par un ARN double-brin. D'abord retrouvé chez les plantes, il s'est avéré présent chez plusieurs autres organismes, dont les mammifères. Des petits fragments d'ARN de 20-25 nucléotides (siRNA) caractéristiques à ce phénomène ont été identifiés chez des plantes infectées par divers virus ainsi que par des viroïdes. Le but de ce travail était de démontrer et de mieux comprendre l'implication du viroïde de la mosaïque latente du pêcher (PLMVd) dans le phénomène d'interférence à l'ARN.--Résumé abrégé par UMI

A User Friendly Phase Detection Methodology for HPC Systems' Analysis

International audienceA wide array of today's high performance computing (HPC) applications exhibits recurring behaviours or execution phases throughout their run-time. Accurate detection of program phases allows reconfiguring the system for a better power/performance trade off; and can reduce the simulation time of programs by identifying regions of code whose performance is critical to the entire program. Program phases are also reflected in different behaviours the system goes through or system phases, which can be used as an alternative means of program phase detection for users lacking expertise. In this paper, we present an execution vector based (EV-based) phase detection, which is an on-line methodology for detecting phases in the behaviour of a HPC system and determining execution points that correspond to these phases. We also present a methodology for defining a small set of EVs representative of the system's behaviour over a fixed period of time and show that EV-based phase detection identifies recurring phases. Our methodology is illustrated with benchmarks and a real life application

The repertoire and features of human platelet microRNAs

Playing a central role in the maintenance of hemostasis as well as in thrombotic disorders, platelets contain a relatively diverse messenger RNA (mRNA) transcriptome as well as functional mRNA-regulatory microRNAs, suggesting that platelet mRNAs may be regulated by microRNAs. Here, we elucidated the complete repertoire and features of human platelet microRNAs by high-throughput sequencing. More than 492 different mature microRNAs were detected in human platelets, whereas the list of known human microRNAs was expanded further by the discovery of 40 novel microRNA sequences. As in nucleated cells, platelet microRNAs bear signs of post-transcriptional modifications, mainly terminal adenylation and uridylation. In vitro enzymatic assays demonstrated the ability of human platelets to uridylate microRNAs, which correlated with the presence of the uridyltransferase enzyme TUT4. We also detected numerous microRNA isoforms (isomiRs) resulting from imprecise Drosha and/or Dicer processing, in some cases more frequently than the reference microRNA sequence, including 5′ shifted isomiRs with redirected mRNA targeting abilities. This study unveils the existence of a relatively diverse and complex microRNA repertoire in human platelets, and represents a mandatory step towards elucidating the intraplatelet and extraplatelet role, function and importance of platelet microRNAs

A Runtime Framework for Energy Efficient HPC Systems Without a Priori Knowledge of Applications

International audienceThe rising computing demands of scientific endeavours often require the creation and management of High Performance Computing (HPC) systems for running experiments and processing vast amounts of data. These HPC systems generally operate at peak performance, consuming a large quantity of electricity, even though their workload varies over time. Understanding the behavioural patterns i.e., phases) of HPC systems during their use is key to adjust performance to resource demand and hence improve the energy efficiency. In this paper, we describe (i) a method to detect phases of an HPC system based on its workload, and (ii) a partial phase recognition technique that works cooperatively with on-the-fly dynamic management. We implement a prototype that guides the use of energy saving capabilities to demonstrate the benefits of our approach. Experimental results reveal the effectiveness of the phase detection method under real-life workload and benchmarks. A comparison with baseline unmanaged execution shows that the partial phase recognition technique saves up to 15% of energy with less than 1% performance degradation

DNA-inspired Scheme for Building the Energy Profile of HPC Systems

International audienceEnergy usage is becoming a challenge for the design of next generation large scale distributed systems. This paper explores an inno- vative approach of profiling such systems. It proposes a DNA-like solution without making any assumptions on the running applications and used hardware. This profiling based on internal counters usage and energy monitoring allows to isolate specific phases during the execution and enables some energy consumption control and energy usage prediction. First experimental validations of the system modeling are presented and analyzed

Exploiting Performance Counters to Predict and Improve Energy Performance of HPC Systems

International audienceHardware monitoring through performance counters is available on almost all modern processors. Although these counters are originally designed for performance tuning, they have also been used for evaluating power consumption. We propose two approaches for modelling and understanding the behaviour of high performance computing (HPC) systems relying on hardware monitoring counters. We evaluate the effectiveness of our system modelling approach considering both optimising the energy usage of HPC systems and predicting HPC applications' energy consumption as target objectives. Although hardware monitoring counters are used for modelling the system, other methods -- including partial phase recognition and cross platform energy prediction -- are used for energy optimisation and prediction. Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimisation indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24\% of the overall HPC system's energy consumption under benchmarks and real-life workloads

Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

International audienceModern high performance computing subsystems (HPC) - including processor, network, memory, and IO - are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems consid- ering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads

Examining the Relationship between Community Orientation and Hospital Financial Performance

A community orientation strategy may be a socially responsible way for hospitals to simultaneously improve financial performance and community health, in accordance with the Affordable Care Act. Using data from the AHA Annual Survey, AHRF, and CMS Cost Reports, this study examined the association between hospital community orientation and three measures of financial performance, and whether that relationship differs for some types of hospitals. The analysis revealed that hospital community orientation was positively associated with total margin and that not-for-profit hospitals engaging in higher levels of community orientation experienced lower operating margins, on average, relative to for-profit hospital

Energy efficiency in HPC with and without knowledge of applications and services

International audienceThe constant demand of raw performance in high performance computing often leads to high performance systems' over-provisioning which in turn can result in a colossal energy waste due to workload/application variation over time. Proposing energy efficient solutions in the context of large scale HPC is a real unavoidable challenge. This paper explores two alternative approaches (with or without knowledge of applications and services) dealing with the same goal: reducing the energy usage of large scale infrastructures which support HPC applications. This article describes the first approach "with knowledge of applications and services'' which enables users to choose the less consuming implementation of services. Based on the energy consumption estimation of the different implementations (protocols) for each service, this approach is validated on the case of fault tolerance service in HPC. The approach "without knowledge'' allows some intelligent framework to observe the life of HPC systems and proposes some energy reduction schemes. This framework automatically estimates the energy consumption of the HPC system in order to apply power saving schemes. Both approaches are experimentally evaluated and analyzed in terms of energy efficiency