An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR)

The Amazon basin is likely to be increasingly affected by environmental changes: higher temperatures, changes in precipitation, CO2 fertilization and habitat fragmentation. To examine the important ecological and biogeochemical consequences of these changes, we are developing an international network, RAINFOR, which aims to monitor forest biomass and dynamics across Amazonia in a co-ordinated fashion in order to understand their relationship to soil and climate. The network will focus on sample plots established by independent researchers, some providing data extending back several decades. We will also conduct rapid transect studies of poorly monitored regions. Field expeditions analysed local soil and plant properties in the first phase (2001–2002). Initial results suggest that the network has the potential to reveal much information on the continental-scale relations between forest and environment. The network will also serve as a forum for discussion between researchers, with the aim of standardising sampling techniques and methodologies that will enable Amazonian forests to be monitored in a coherent manner in the coming decades

VHMPID: a new detector for the ALICE experiment at LHC

This article presents the basic idea of VHMPID, an upgrade detector for the ALICE experiment at LHC, CERN. The main goal of this detector is to extend the particle identification capabilities of ALICE to give more insight into the evolution of the hot and dense matter created in Pb-Pb collisions. Starting from the physics motivations and working principles the challenges and current status of development is detailed.Comment: 4 pages, 6 figures. To be published in EPJ Web of Conference

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions

Late Cenozoic tephrostratigraphy offshore the southern Central American Volcanic Arc: 1. Tephra ages and provenance

We studied the tephra inventory of 18 deep sea drill sites from six DSDP/ODP legs (Legs 84, 138, 170, 202, 205, 206) and two IODP legs (Legs 334 and 344) offshore the southern Central American Volcanic Arc (CAVA). Eight drill sites are located on the incoming Cocos plate and ten drill sites on the continental slope of the Caribbean plate. In total we examined ∼840 ash-bearing horizons and identified ∼650 of these as primary ash beds of which 430 originated from the CAVA. Correlations of ash beds were established between marine cores and with terrestrial tephra deposits, using major and trace element glass compositions with respect to relative stratigraphic order. As a prerequisite for marine-terrestrial correlations we present a new geochemical data set for significant Neogene and Quaternary Costa Rican tephras. Moreover, new Ar/Ar ages for marine tephras have been determined and marine ash beds are also dated using the pelagic sedimentation rates. The resulting correlations and provenance analyses build a tephrochronostratigraphic framework for Costa Rica and Nicaragua that covers the last >8 Myr. We define 39 correlations of marine ash beds to specific tephra formations in Costa Rica and Nicaragua; from the 4.15 Ma Lower Sandillal Ignimbrite to the 3.5 ka Rincón de la Vieja Tephra from Costa Rica, as well as another 32 widely distributed tephra layers for which their specific region of origin along Costa Rica and Nicaragua can be constrained

X chromosome inactivation does not necessarily determine the severity of the phenotype in Rett syndrome patients

Rett syndrome (RTT) is a severe neurological disorder usually caused by mutations in the MECP2 gene. Since the MECP2 gene is located on the X chromosome, X chromosome inactivation (XCI) could play a role in the wide range of phenotypic variation of RTT patients; however, classical methylation-based protocols to evaluate XCI could not determine whether the preferentially inactivated X chromosome carried the mutant or the wild-type allele. Therefore, we developed an allele-specific methylation-based assay to evaluate methylation at the loci of several recurrent MECP2 mutations. We analyzed the XCI patterns in the blood of 174 RTT patients, but we did not find a clear correlation between XCI and the clinical presentation. We also compared XCI in blood and brain cortex samples of two patients and found differences between XCI patterns in these tissues. However, RTT mainly being a neurological disease complicates the establishment of a correlation between the XCI in blood and the clinical presentation of the patients. Furthermore, we analyzed MECP2 transcript levels and found differences from the expected levels according to XCI. Many factors other than XCI could affect the RTT phenotype, which in combination could influence the clinical presentation of RTT patients to a greater extent than slight variations in the XCI pattern