dataesr/scientific-paragraphs-categorization huggingface dataset:
To date, clinical trials run through the Neurofibromatosis Clinical Trials Consortium have included studies of pharmacodynamic biomarkers and predictive biomarkers. As drugs with activity against NF become available, these studies will provide valuable information about which patients are likely to require treatment in the future and which are likely to respond to therapy. Existing data on biomarkers in NF1, NF2, and SWN. During development, biomarkers pass through a series of stages, including early research and development (exploratory biomarker), demonstration and validation (probable biomarker), characterization and qualification (known biomarker), and surrogacy (biomarker can substitute for clinical endpoint). For the neurofibromatoses, all Neurology 87 (Suppl 1) August 16, 2016 S41 ª 2016 American Academy of Neurology.
The group anticipates that modifications of this dataset may be required in the future to optimize the utility of biomarker research. 4. Incorporate the decentralized biorepository into existing biorepositories that are used for diagnostic purposes. The biomaterial could thus be used for both diagnosis and research and is not necessarily restricted to an upfront definition of the amount of surplus tissue. Patient care takes preference when allocating the amount of samples used for biomarker investigations. 5. Provide open access to deposited biomarkers to facilitate research. The aim would be to have samples and data open to all qualified researchers with approval of an institutional review board. A biorepository council would govern database requests. 6. An operational and an executive committee will govern requests and audit implementation of SOPs and quality control measures.
The aim would be to collect all tissues using an identical protocol that meets standards set forth by the American Association for Cancer Research-Food and Drug Administration-National Cancer Institute Can- cer Biomarker Collaborative 4 and would be linked via a shared, anonymized registry (on a Web site). Par- ticipating sites would share common consent, SOPs, quality control (see supplemental data on the Neurology ® Web site at Neurology. org), minimal clin- ical dataset, and database. 3. Annotate samples with an agreed minimal clinical dataset. The goal is to link anonymously the pheno- typic data to deidentified samples in the bioreposi- tory. A proposed minimal clinical dataset developed at a consensus meeting in October 2014 is shown in.
In summary, we have shown that the MnAs mesoscopic structures exhibit a rich variety of magnetic domain structures that can be tuned by adjusting their size and orientation. These characteristics are extremely promising for its future application in DW-based devices, such as memories and logic circuits. FIG. 7. a-phase fraction, n, as a function of temperature, determined from XPEEM images of S 5-PA -50 ribbon. Both series of data were obtained while heating the sample from low temperature in different magnetic states: (᭹) saturated and () remnant states, respectively. The two curves are shifted due to differences in the thermal contact between the sample and the support, a Mo ring onto which a WRe type S thermocouple is spot-welded.
ACKNOWLEDGMENTS L. B. S. , M. T. , F. F. B. , V. H. E. , M. E. , and M. S. are members of the  Laboratorio Internacional Franco-Argentino en Nanociencias (LIFAN). We acknowledge the partial financial support from the  PICT  Contract No. 03/1187 ,  CONICET  Grant No. PIP 112-201101-00482 , and  Elettra. L. B. S. and F. F. B. acknowledge the support from the  ICTP-Elettra Users Programme  by Grant No. 20120129. We thank  J. P. Sinnecker  for his valuable contribution on micromagnetic simulations and  M. Marangolo  for fruitful discussions.
Epithelial GFP + cells express genes suggestive of prominent Wnt signaling We performed transcriptional profiling of LSC GFP + vs LSC GFP -cells to distinguish a stem cell from a basal cell profile using Agilent microarrays technology based on 55,681 probes. After probes filtration, transcriptional levels of 37,023 probes were analyzed among which 2,819 differentially expressed transcripts between the two cell populations (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE73758). They corresponded to 2,090 Entrez Gene IDs that were mapped to 1,878 unique Entrez Gene IDs being differentially expressed (p-value < 0. 05) between LSC GFP + and LSC GFP -cells. Among these, 1,121 probes corresponding to 999 transcripts whose expression was statistically up-or down-regulated ≥ 1. 5 fold in the LSC GFP + cells vs the LSC GFP -cells.
Fluorescence-activated cell sorting and analysis Dissociated prostate cells were suspended in DME-10% FBS, preincubated with purified rat anti-mouse CD16/CD32 (mouse BD Fc Block) antibody (1:50, BD Pharmingen, San Jose, CA) at 4°C for 10 min, and stained with specific antibodies at 4°C for 30 min. Flow cytometry analysis was performed using a BD FACS Canto II and analysed by NovoExpress TM software (ACEA Biosciences, San Diego, CA). Cell sorting was conducted on a BD FACS Vantage or a BD FACS Aria. Primary antibodies used were: PE-Cy5 rat anti-human/mouse CD49f (1:100, BD Pharmingen), APC rat anti-human/mouse CD49f (1:100, BD Pharmingen), APC rat anti-mouse CD24 (1:100, BD Pharmingen), PE rat anti-mouse Sca-1 (1:100, eBioscience, San Diego, CA). Isotype controls used were from BD Pharmingen : PE rat IgG (1:100), PE-Cy5 Rat IgG (1:100), APC Rat IgG (1 :100).
Signal intensity was quantified from the scanned image by using Feature Extraction software version 11. 0. 1. 1 (Agilent Technologies). These experiments were performed by Imaxio (Clermont-Ferrand, France). The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [79]  and are accessible through GEO Series accession number GSE73758 (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE73758).
ACKNOWLEDGMENTS We thank  Susana Hernandez  ( FHCRC ),  Philippe Persoons  ( IPL ),  Elisabeth Werkmeister  ( CNRS ),  Jérôme Vicogne  ( CNRS ),  Frédéric Leprêtre , and  Martin Figeac  ( University of Lille ) for their valuable help. With deep sadness we dedicate this work to the memory of  Dr. Larry Rohrschneider.
Our approach is twofold: (i) first we will use the large sample of cluster statistics afforded by the Millennium -XXL simulation  (Angulo et al. 2012 ) -exploiting its large size (3 Gpc/h box side), that allows to follows the formation of many massive haloes; (ii) second we will complement the statistics with a cosmological study based on clusters modelled using the MOKA code  (Giocoli et al. 2012a). We want to spend few words about the fact that the Einstein radius of lenses is not a direct observable quantity. The Einstein radius, defined by the location of the tangential critical lines (more will be discussed about this in the first section) is a byproduct of the mass reconstruction pipeline by mean of parametric algorithms that typically assume that mass traces the light  (Jullo et al.
Strong lensing models of Clusters using the MOKA code Running and analysing large numerical simulations are usually non trivial tasks; in addition performing self consistent lensing analysis of different cosmological models including also baryon physics requires large computational resources and various post-processing pipelines. However, the results from different numerical simulations -N -body only or including hydrodynamical processes -can be implemented using a pseudo-analytic approach to construct convergence maps of different galaxy clusters in various physical cases as, done with the MOKA code  (Giocoli et al. 2012a). MOKA pseudo-analytically reconstructs highresolution convergence maps of galaxy clusters free from particles and numerical resolution limitations, implementing results obtained from the most recent numerical runs.
ACKNOWLEDGMENTS CG thanks CNES for financial support. ML acknowledges the Centre National de la Recherche Scientifique (CNRS) for its support. This work was performed using facilities offered by CeSAM (Centre de donneS Astrophysique de Marseille-(http://lam. oamp. fr/cesam/). This work was granted access to the HPC resources of Aix-Marseille Universite financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the program "Investissements d'Avenir" supervised by the Agence Nationale pour la Recherche (ANR). This work was carried out with support of the OCEVU Labex This paper has been typeset from a T E X/ L A T E X file prepared by the author.
(ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the "Investissements d'Avenir" French government program managed by the ANR. We acknowledge support from the Programme National de Cosmologie et Galaxie (PNCG). MM acknowledges support from Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion, from INAF via PRIN-INAF 2014 1. 05. 01. 94. 02, and from ASI via contract ASI/INAF/I/023/12/0. LM acknowledges the grants ASI n. I/023/12/0 "Attività relative alla fase B2/C per la missione Euclid", MIUR PRIN 2010-2011 "The dark Universe and the cosmic evolution of baryons: from current surveys to Euclid" and PRIN INAF 2012 "The Universe in the box: multiscale simulations of cosmic structure". REA acknowledges support from AYA2015-66211-C2-2.
Operational Approaches" have been more and more widely developed and used for providing marine data and information service for different socio-economic sectors of the Blue Growth and to advance knowledge about the marine environment. The objective of operational oceanographic research is to develop and improve the efficiency, timeliness, robustness and product quality of this approach. This white paper aims to address key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5-10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European Ocean Observations, Modelling and Forecasting Technology, Coastal Operational Oceanography and Operational Ecology.
Such Operational Approaches share common features in their value-chain, i. e. , user-and science-driven, knowledge-and technologybased, operation-practiced and service-oriented  (She, 2015). The areas of interests for future research are determined by both user needs and current state of the scientific frontier. New knowledge and technologies, generated from the research, will then be incorporated into operational oceanography systems that provide the users with data and information products. Operational oceanography in Europe was mainly initiated and sustained at national level before the 1990s. Aiming at integrating the operational oceanography development in regional and European scales, EuroGOOS from its very start established Regional Operational Oceanography Systems (ROOSes), such as for the Arctic Ocean, Baltic Sea, Northwest Shelf Sea, Ireland-Biscay-Iberia Seas and the Mediterranean Sea, EuroGOOS and its ROOSes have played an active role in data exchange, sharing the best practice and knowledge, harmonising monitoring networks and forecasting systems and stimulating joint research activities.
Since Framework Program IV, the European Commission (EC) has continuously supported research on integration and development of European operational oceanography monitoring and forecasting systems, especially through Operational Forecasting Cluster projects  (Cieslikiewicz et al. , 2004) , MERSEA (Marine Environment and Security for the European Area,  Johannessen et al. , 2006)  and GMES (Global Monitoring for Environment and Security, currently referred to as Copernicus) Marine Service program  (Bahurel et al. , 2010)  for global, Arctic and European regional seas has been established, which is now transformed into the Copernicus Marine Environmental Monitoring Service (CMEMS, http://marine. copernicus. eu/) program in the period 2015-2020. Thanks to these national-and EU-funded programs we have seen major scientific achievements in the development of Earth Observation (EO) data management, short-term forecasting systems (including data assimilation) and reconstruction of longterm historical database through reanalysis and reprocessing.
Long-term prediction, ecosystem prediction, coastal services and optimisation of European marine monitoring systems, have also been improved but with relatively lower levels of maturity and integration than the physical part of the CMEMS system. In recent years, user requirements for operational marine data and information have largely increased due to the growing blue economy (e. g. marine energy, maritime transport, coastal and offshore engineering and marine bio-resources), implementation of European polices in marine-related Directives and regional marine environmental conventions (e. g. ecosystem-based management), adaptation to and mitigation of climate change as well as public services (e. g. disaster warning and protections). Although European operational oceanography has made significant advancements in the last two-decades, great challenges still exist in view to serve fast growing user needs.
European Ocean Observations Since the establishment of EuroGOOS, it has been a central focal issue of EuroGOOS research to sustain, enhance and optimise the European ocean observing systems  (Prandle et al. , 2003; Nittis et al. , 2014). With dual roles in ocean monitoring, i. e. , both as observation providers and users, EuroGOOS members have different concerns. As a data provider, one needs to maximise the value of end-to-end data delivery and improve the cost-efficiency for making observations; as a user, one requires easy, fast and open access to a maximum of available qualified observations for operational oceanography applications. Maximising the value delivery: as monitoring agencies, EuroGOOS members are responsible for delivering observations with maximised benefits to users for supporting European Blue Growth and public affairs: -Values from data to product: improving observational data use for core marine products through (i) the timely delivery of available observations for operational use, (ii) the maximum use of observations in analysis, forecast, reanalysis and reprocessing, (iii) improved understanding of product skill through improved use of observations in validation and verification activities.
-Values from data to knowledge: new knowledge generation by using observations together with models to understand physical and ecosystem processes and improve model parameterisations/forecasts. -Values from data to socio-economic benefit: exploiting societal value of marine observations through innovative fit-for-purpose socio-economic applications in a variety of social benefit areas by using observations together with models and sectorial data. Improving the cost-efficiency: EuroGOOS members need to undertake cost-efficient monitoring activities. This requires research and development on the assessment and design of cost-effective ocean observing networks through optimisation of sampling strategy, integration and coordination of observational infrastructure and efficient data management.
Data access and harvesting for operational oceanography applications: EuroGOOS needs to quantify the needs of ocean observations for operational oceanography applications, including parameters, data quality, sampling density and delivery time window. This analysis is instrumental to produce a coherent vision on future development of the observational component and its research and innovation priorities. In addition, timely access to the observations, both in online and offline modes, must be ensured. This requires EuroGOOS to work closely with other European ocean monitoring and data providers and management centres. Among the former are the environmental monitor- be further coordinated. EuroGOOS members are directly involved in EMODnet and the CMEMS in situ TAC and this ensures that these two major initiatives contribute to the overarching goal of facilitating the access to ocean data for operational oceanography.
EuroGOOS also has a vision on observing systems for establishing a close dialogue with major users (e. g. COPERNICUS Marine Services) in order to align efforts to their requirements (and take advantages of feedbacks) and at same time to influence/harmonize the development of the national components. Operational monitoring and data handling in emerging areas: our knowledge on marine ecosystems are evolving in the process of serving the growing blue economy and ecosystem-based management, and new challenges are also identified for data and information needs in emerging areas. Such emerging areas include, but not limited to, bottom sedimentation and resuspension, ocean acidification, marine pollution in related to noise and marine litter especially plastic/paraffin etc. These areas are normally beyond the existing scope of operational oceanography hence new monitoring and modelling technology should be developed.
The satellite constellation should be improved and new missions with a potentially large impact for operational oceanography (such as the Sentinel missions) should be demonstrated. International collaboration will be crucial to optimize and make best use of the satellite observations (e. g. sensor synergy, calibration, validation) from the growing number of space agencies. Moreover, more efforts will also be required to ensure homogenized and inter-calibrated data sets from multiple missions for all essential ocean variables. The on-going and forthcoming EC Horizon 2020 supported projects such as At-lantOS for the Atlantic Ocean, JERICO-NEXT for coastal observatories, and the calls Figures on the Integrated Arctic Observing System and the Mediterranean Observing System with submission in February 2016 will strengthen the integration of European ocean observing systems.
-Exploring the operational potential of present and innovative initiatives in the field of citizen science (sea state observation, marine litter, ocean colour, jellyfish, etc. ). -Efficient big data management: it has been a challenge to quickly access and extract increasing amounts of Earth Observation (EO) data which can be of order of Peta-to Exabyte scale. The Earth System Grid Framework has been developed to facilitate data extraction from multiple data centres. However bottlenecks exist inside each data centre for online access to medium amounts of data (10 2 -10 3 Tb). Figures.
Back Close Full An efficient data management framework should be developed for online access, download, view and analysis to data from a distributed multi-server local network. Novel technologies will be foreseen to move toward an open source array-oriented database management system. Further development of data mining and image processing techniques is needed to facilitate the automatic extraction and analysis of patterns from big data sets. -Interoperability: identifying a strategy to move from the NetCDF, file transfer based, data exchange technology to the GEOSS philosophy (compliant where necessary to ISO standards) based on interoperable web services.
Assessment and optimal design of ocean observing networks The goal of the marine monitoring network assessment and optimal design research is to identify the gaps in existing observing systems and to optimise their costeffectiveness. The EC has continuously supported this research area since early 2000. The assessment and design studies can be divided into ad-hoc studies and quantitative studies. The ad-hoc studies have been carried out in many EC funded observing system projects such as EDIOS, SeaDataNet and recent fit-for-purpose assessment by DG-MARE (Sea Basin Checkpoint projects for European Seas). The ad hoc assessment work has led to the establishment of the meta database and identification of data availability and accessibility etc. On the other hand, a variety of quantitative assessment and optimal design research have also been carried out in EC projects ODON, ECOOP, JERICO and OPEC, and are now continuing in JERICO-NEXT and AtlantOS.
Progress in the above areas will directly improve the model quality. Development of coupled systems: research in the development of the coupled system and predictability study will evolve in Horizon 2020 program and the Copernicus Service especially CMEMS systems. While coupled atmosphere-ocean-ice-wave models have been developed in global level for climate research and seasonal forecasting, regional coupled systems for synoptic scale prediction remain to be developed. Proper implementation of the air-sea-ice interaction and data assimilation for the coupled system are essential for correctly resolving corresponding diurnal variability. Predictability is expected to be prolonged in a coupled forecasting system, which should be explored. The future development will also contribute and draw momentum from on-going GODAE-OceanView  (Brassington et al.
Data assimilation The reduction of the product uncertainties is a central challenge for operational modelling and services, which requires continuous innovations in data assimilation. Present day assimilation approaches encompass a hierarchy of methods of increasing complexity, ranging from optimal interpolation to non-linear stochastic methods  (CMEMS STAC, 2015). For open oceans, satellite measurements such as sea surface temperature, sea ice concentration and sea surface height and in situ observations of SST and T/S profiles have been assimilated in global and regional forecasting systems for the North Atlantic, Arctic and Mediterranean Sea, such as in CMEMS Marine Forecasting Centres. For coastal and shelf sea assimilation, there have been a number of successful stories, e. parameters.
Research priorities in coastal waters The long-term goal is to develop an operational oceanography framework which can resolve major marine data and information service issues especially in the CSW. This requires upgrading existing operational coastal ocean forecasting system with new components (e. g. , sediment transport, inundation model, marine optics model) and new dynamic processes which are currently missing. Establishment of operational oceanography addressing CSW is a significant initiative and big step to lift the role of operational oceanography in Blue Growth. This needs support at European scale. EuroGOOS has revised the agreement for membership which now allows a private company to be a formal member. This will largely facilitate the cooperation between the operational community and private sectors.
There is an expectation that the integrated Atlantic Ocean Observing System (developed through AtlantOS) will increase the number and quality of in situ observations on chemistry, biology and ecology over the next decade. A coevolution of the data use in assessment and predictive models holds great potential for new products and users. It is expected that results from these projects as well as similar advances in the field will be transferred to operational services such as CMEMS. The relevant short-to longterm research objectives in this area have been identified in the MSFD Session in the CMEMS Service Evolution and User Uptaking Workshop  (Brussels, 2015). They are further evolved in following sections. -Quality assurance: developing a standardized validation method/system for ecosystem model products/variables (particularly related to non-assimilated observations/variables), to identify major weaknesses of existing operational ecological models regarding to needs of ecosystem-based management in national and regional levels (e.