Gestante con mutación heterocigótica del gen de la protrombina y antecedente de trombosis venosa cerebral

La mutación G20210A deriva en una sustitución de guanina por adenina en el gen que codifica la protrombina. La prevalencia de este polimorfismo es mayor en el sur de Europa (2%-6%) que en el norte de Europa (1%-2%), siendo muy rara en Asia. Los pacientes afectos de esta mutación presentan concentraciones plasmáticas de protrombina elevadas respecto a la normalidad que oscilan entre un 30% superiores en portadores heterocigotos a un 70% en portadores homocigotos, alterando de este modo la vía común de la coagulación. Esta alteración constituye un riesgo de trombosis venosa profunda (y posiblemente arterial), que se suma a otros factores de riesgo como embarazo, puerperio, cirugía y otros por todos conocidos. Además, varios estudios han intentado relacionar esta mutación como factor independiente para el desarrollo de una trombosis venosa cerebral (1,2). El manejo de los pacientes con esta mutación protrombótica no difiere del que llevamos a cabo en pacientes con otras trombofilias hereditarias, pero es necesario conocer las complicaciones a las que nos enfrentamos, evitar en lo posible factores de riesgo añadidos y plantear un buen plan anestésico – quirúrgico

Optimizing monitorability of multi-cloud applications

When adopting a multi-cloud strategy, the selection of cloud providers where to deploy VMs is a crucial task for ensuring a good behaviour for the developed application. This selection is usually focused on the general information about performances and capabilities offered by the cloud providers. Less attention has been paid to the monitoring services although, for the application developer, is fundamental to understand how the application behaves while it is running. In this paper we propose an approach based on a multi-objective mixed integer linear optimization problem for supporting the selection of the cloud providers able to satisfy constraints on monitoring dimensions associated to VMs. The balance between the quality of data monitored and the cost for obtaining these data is considered, as well as the possibility for the cloud provider to enrich the set of monitored metrics through data analysis

Joint Computing and Electric Systems Optimization for Green Datacenters

This chapter presents an optimization framework to manage green datacenters using multilevel energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS). At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server’s energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs, while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill, considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings

Les impacts informationnels occasionnes par l'emission de bons de souscription d'actions sur la valeur du titre sous-jacent

Available at INIST (FR), Document Supply Service, under shelf-number : DO 4748 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

A reliable power management scheme for consistent hashing based distributed key value storage systems

Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%

Rumen eukaryotes are the main phenotypic risk factors for larger methane emissions in dairy cattle

Mitigation of methane emissions from dairy cattle is a relevant strategy to reduce environmental impact from livestock as well as to increase farm profitability through improvement of energy usage. The objective of this study was to compare how microbiome composition determines methane concentration (MET) and methane intensity (MI, ppm CH4/kg Milk) with other traditional proxies (e.g. milk yield and conformation traits). A total of 1359 Holstein cows from 17 herds in 4 northern regions of Spain were included in this study. Microbiome data came from a subset of 437 cows from 14 herds. Cows were classified in quartiles for MET and MI, according to individual records of methane measurements during the cow's visit to the automatic milking system unit. A probit approach under a Markov chain Monte Carlo (McMC) Bayesian framework was used to determine risk factors for high MET and high MI. Reducing MET and MI genetic merit by unit of standard deviation (SD) reduced the probability of being classified in the upper quartile by 35.2% (33.9% to 36.4%) and 28.8% (27.6% to 29.6%), respectively. Increasing the relative abundance of most bacteria reduced the probability of a cow to be classified as high emitter (e.g., Firmicutes 9.9% (8.3 to 11.3) for MET and 7.1% (6.2 to 8.2) for MI, per unit of SD). An opposite effect was observed for the relative abundance of Eukaryotes. Larger abundance of most eukaryote caused larger risk for a cow to be classified as a high emitter animal (e.g., Oomycetes 14.2% (11.7% to 16.4%) for MET and 11.8% (9.4% to 14.0%) for MI, per unit of SD). One more unit of milk yield SD increased the probability of being classified in the upper quartile for MET by 3.7% (2.3% to 4.2%) and reduced the probability for MI by 12.6% (12.2% to 13.3%). Structure and capacity traits were not main drivers of being classified in the higher quartile of methane emission and intensity, with risk odds lower than 2% per unit of SD. Cow genetic merit for methane concentration and her microbiome composition (86 phylum and 1240 genus) were the main drivers for a cow to be classified as high MET or MI. This study suggests that mitigation of MET and MI could be addressed through animal breeding programs including genetic merits and strategies that modulate the microbiome.This research was financed by RTA2015-00022-C03 (METALGEN) project from the national plan of research, development, and innovation 2013-2020. The first author of this paper was granted a scholarship from Universidad de Costa Rica for course doctorate studies which partially conducted to the progress of this study.Peer reviewe

Toward data governance in the internet of things

The diffusion of Internet of Things (IoT) technologies not only enables the provision of advanced and valuable services, but also raises several challenges. First of all, the increasing number of heterogeneous interconnected devices creates scalability and interoperability issues, and thus, a flexible middleware platform is needed to manage all the sources together with all the tasks related to data collection and integration. In fact, the large amount of data has to be properly managed. In particular, on the one hand, data have to be protected from security threats; on the other hand, it is necessary to consider that data are useful only if their quality is suitable for the processes in which they have to be used. For these reasons, it is important that applications/users that aim to exploit the collected data are aware of data quality and security levels in order to understand if data can be trusted and thus used. In this chapter, we present a distributed architecture for managing IoT data extraction and processing that also includes algorithms for the assessment of data quality and security levels of considered sources. A prototype of such an architecture has been realized; through a user interface, it is possible to access data services able to filter data from IoT devices on the basis of security and data quality requirements. The chapter describes the prototype and shows some experiments performed by using several real-time open data feeds characterized by different levels of reliability, quality and security

Asymmetric Response toward Molecular Fluorination in Binary Copper–Phthalocyanine/Pentacene Assemblies

We report a didactic and simple example of the subtleness in the balance of intermolecular and molecule-substrate interactions and its effect on molecular self-assembly. The study is performed on two closely related molecular blends of copper phthalocyanines and pentacene, in each of which one of the two molecules is fluorinated. Reversing the fluorination brings about changes in the intermolecular hydrogen bonds, as well as in the interactions with the substrate. As a result, on Au(100) substrates one blend assembles into a crystalline structure, whereas the other, displaying weaker intermolecular interactions and a larger corrugation in the molecule-substrate interaction potential, results in a disordered layer. However, the difference between the two blend's structures vanishes when substrates with less corrugated interaction potentials are used. © 2014 American Chemical Society.This work was supported by the Spanish Grant Nos. MAT2010-21156-C03- and PIB2010US-00652 and the Basque Government Grant No. IT-621-13. J.M.G.-L. acknowledges support from the Spanish Ministry of Economy and Competitiveness under projects FIS2012-30996 and FIS2010-21282-C02-01 and from the ReLiable project funded by the Danish Council for Strategic Research-Programme Commission on Sustainable Energy and Environment (project no. 11-116792).Peer Reviewe