dataesr/scientific-paragraphs-categorization huggingface dataset:
A clinical trial has been completed assessing the association between response to sorafenib and bevacizumab and a variety of tissue based biomarkers including MVA, as well as imaging based predictors (NCT00126503). The results have not been published. Recent studies by Zhao et al. suggests that high MVA predicts better response to bevacizumab in non small cell lung cancer  [30]. VHL mutations might be associated with benefit from VEGF/VEGFR targeting drugs, and we are currently assessing the association between VHL mutations and clinical benefit from sorafenib and other drugs in RCC tumors. In our cohort, high MVA in nephrectomy samples was associated with smaller primary tumors. A large recent study showed a distinct subpopulation of RCC patients with smaller primary tumors who developed distant metastasis  [31].
HCV infection HuH-7 and HuH-7-RFP-NLS-IPS cells encapsulated in beads or in 2D cultures used as controls were in contact with one of the two following HCV particle types: (i) JFH1-CS-A4 (i24) virus (JFH1 virus), an adapted version of the full-length JFH1 virus (genotype 2a; GenBank access number AB237837; kindly provided by T. Wakita) which contains mutations leading to amino acid changes F172C and P173S at the C-terminus of the core protein  [22]. Both mutations have been shown to increase the viral titers  [23] ; (ii) JFH1-CS-A4-RLuc-TM virus (JFH1 -RLuc virus) which contains a Renilla luciferase reporter gene as described previously  [22]. Encapsulated and 2D cell cultures were incubated with the viral particles for 4h on 3D moving plates, the medium was then discarded and replaced with fresh medium.
Variations in the world's ocean heat storage and its associated volume changes are a key factor to gauge global warming and to assess the earth's energy and sea level budget. Estimating global ocean heat content (GOHC) and global steric sea level (GSSL) with temperature/salinity data from the Argo network reveals a positive change of 0. 5 ± 0. 1 W m -2 (applied to the surface area of the ocean) and 0. 5 ± 0. 1 mm year -1 during the years 2005 to 2012, averaged between 60 • S and 60 • N and the 10-1500 m depth layer. In this study, we present an intercomparison of three global ocean observing systems: the Argo network, satellite gravimetry from GRACE and satellite altimetry. Their consistency is investigated from an Argo perspective at global and regional scales during the period 2005-2010.
era of the international Argo program (Roemmich and the A. S. Team, 2009)  have a high potential to deliver accurate data to be used for such analyses  (Hansen et al. , 2011; von Schuckmann et al. , 2009) , in particular for the estimation of global ocean heat content (GOHC) and global steric sea level (GSSL), referred to here as global ocean indicators  (GOIs, von Schuckmann and Le Traon, 2011). Nevertheless, uncertainties of the Argo ocean observing system, sampling issues, and systematic biases still causes significant spread among the more recent estimates of GOHC and GSSL  (Abraham et al. , 2013; von Schuckmann and Le Traon, 2011). In particular, the detection of systematic biases represents a significant challenge for the Argo community, as they are associated with a coherent signature over large areas and are difficult to identify with current regional quality control procedures.
This study improves our understanding of how to monitor climate-related changes and contributes to the understanding of the energy and sea level budget contributions from an Argo perspective. In Sect. 2, the data sets, methods and uncertainty estimations are described. Results for GOHC and GSSL are presented in Sect. 3, together with the sensitivity check of Argo GOIs to systematic biases and Argo sampling via the global sea level budget, as well as for regional ocean areas. Our findings are discussed in Sect. 4.
, 2009). Profiles and platforms known to have problems (mainly with pressure sensors) are excluded from the data set. Every profile "on alert" (i. e. , detected as dubious by the delayed mode procedure) has been checked visually, which allows excluding spurious data (e. g. , data drift). This procedure minimizes systematic biases in the global Argo data set as discussed by  Barker et al. (2011). Uncertainties represent one standard error, accounting for reduced degrees of freedom in the mapping and uncertainty in the reference climatology as described in von  Schuckmann and Le Traon (2011). We have also used monthly gridded fields of temperature and salinity properties of the upper 2000 m over the period 2005-2012 (D2CA1S2 re-analysis). These fields were obtained by optimal analysis of the large in situ data set provided by the Argo array and complementary measurements from drifting buoys, CTDs and moorings from the CORIOLIS data center (www.
coriolis. eu. org). These data have undergone the same thorough quality control procedure as described above. In total, Ocean Sci. , 10, 547-557, 2014 www. ocean-sci. net/10/547/2014/ the Argo measurements account for more than 95 % of data used in the optimal analysis since 2005. More detailed information on this data product (and data access) can be found at http://wwz. ifremer. fr/lpo/SO-Argo/Products/ Global-Ocean-T-S/  Monthly-fields-2004 -2010 , and in von Schuckmann et al. (2009).
The gravimeter GRACE Variations in the mass component of sea level are computed using observations from the Gravity Recovery and Climate Experiment (GRACE). We use the most recent Release_05 data processed by the University of Texas Center for Space Research  (Bettadpur, 2012) , modified to correct deficiencies in the geocenter and C 2. 0 coefficient as described by  Chambers and Schröter (2011). The average bottom pressure (in terms of equivalent sea level) over specific regions is computed for each month using the averaging kernel method  (Swenson and Wahr, 2002). The quality of Argo coverage and the quality of altimetry product fall off rapidly poleward of ± 60 • latitude. We therefore limit our study to ± 60 • latitude and consequently define GRACE data averaging kernel over this specific area of interest.
The satellite altimeters Sea level anomalies are computed from the delayed-mode AVISO gridded merged data product (SSALTO/DUACS, www. aviso. oceanobs. com), based on multiple satellite altimeters. GIA affects altimetry differently than it does GRACE, being related to the rate of change of the sea floor due to GIA, not the gravity response. It therefore has a much smaller size, and has been estimated to be 0. 3 mm year -1 over the 60 • S-60 • N area  (Peltier, 2004). The value does differ slightly for sub-regions, and has been calculated to be 0. 2 mm year -1 for the 30 • S-30 • N area, 0. 4 mm year -1 for the 30 • N-60 • N area, and 0. 3 mm year -1 for 30 • S-60 • S, all based on the ICE5G-VM2 model provided by  Peltier (2004). These trends are applied to each regional average (by adding), and are also assumed to have an uncertainty of 30 %.
The global sea level budget Global sea level change SL TOTAL is related to global steric height time series (SL STERIC ) and mass variability (SL MASS ) through SL TOTAL = SL MASS + SL STERIC + SL RES , (1) where SL represents sea level (e. g. ,  Willis et al. , 2008; Leuliette and Miller, 2009). The residual of the sea level budget (SL RES ) includes deep-ocean steric changes below 1500 m depth (i. e. , depth range deeper than what we consider in our analysis of Argo), plus any source of uncertainty in observations and/or data treatment. In this section, we present results averaged for the entire region extending from 60 • S to 60 • N, which we will, hereafter, refer to as the global ocean estimate. www. ocean-sci. net/10/547/2014/ Ocean Sci. , 10, 547-557, 2014 We seek to estimate the residual, SL RES , using three major global observing systems: SL TOTAL is computed from altimetry products (Sect.
2. 3), SL MASS from satellite gravimetry (Sect. 2. 2), and SL STERIC in the upper 1500 m from temperature and salinity observations from Argo (Sect. 2. 1). Beside a signature of deep-ocean change, other information can be drawn from the intercomparison of these three global ocean observing systems. For instance, SL RES can also be produced by systematic observation biases, data processing uncertainties, sampling array issues, etc. The error bar of SL RES is derived from the residual sum of squares of the errors of the three time series, assuming that they are independent. This is not exactly true, as the GIA corrections for altimetry and GRACE are derived from the same ice history (see Sects. 2. 2 and 2. 3), but the way the correction is applied is quite different. We account for this by increasing the trend error from the fit with the uncertainty in GIA as described in Sect.
Argo sampling issues Argo has significantly coarser resolution in both, time and space than the satellite systems (especially multi-satellite altimetry) and can therefore alias high-frequency regional signals and be more affected by mesoscale eddies. The inconsistency between Argo sampling and sampling from satellitederived products (altimetry and gravimetry) could induce a systematic drift in SL RES as observed in Fig. 2. To test the sensitivity of GOIs to this sampling issue, we subsample altimeter data on position and time of Argo profiles. Global mean SL TOTAL is then recomputed following the procedure of von Schuckmann and Le Traon (2011). The residual trend derived using the subsampled altimeter data is referred to as [SL RES ] sub hereinafter. For the global ocean, the residual trend of [SL RES ] sub changes sign, but the magnitude is still within the calculated standard error (-0.
However, we note that the area covered by the NO is less than 10 % of the total area of the global ocean, so this apparent regional systematic error or sampling problem does not significantly affect the global residual analysis, accounting for only 0. 3 mm year -1 of the apparent global residual drift. The largest sensitivity of SL RES to Argo sampling is observed in the TO area. When using consistent sampling for altimetry and Argo, the significant positive bias observed for SL RES (Fig. 2b ) is strongly reduced for [SL RES ] SUB (0. 2 ± 0. 7 mm year -1 , Fig. 3b ). We can hence close the regional sea level budget for the TO area when correcting for inconsistent data sampling. But from which region is this sensitivity coming? Which areas of the TO are poorly sampled, leading to biased decadal to interannual sea level changes? In the next section we investigate the causes of such a sensitivity of sampling in the Tropical Ocean sector.
The importance of the tropical Asian archipelago for the global sea level budget Argo floats are rarely placed in shelves and marginal seas, nor do they cover regions of seasonal and permanent ice cover. With our method for GSSL estimations  (von Schuckmann and Le Traon, 2011)  we exclude all data where the bathymetry is shallower than 1000 m depth, which in turn eliminates the impact of marginal seas in our analysis of GSSL. In addition to these marginal seas, other regions of the world ocean are particularly difficult to observe. In particular, difficulties can arise due to political (exclusive economic zones) and security (e. g. , piracy) reasons. In these regions, Argo floats are usually less deployed than in other places creating "holes" in the observing system. A clear example of such a hole created by political and security tensions is the Tropical Asian Archipelago (TAA), which represents the largest marginal sea of the TO (see green line in Fig.
Acknowledgements. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme   FP7/2007-2013  under grant agreement no. 283367  ( MyOcean2 ). In addition, this work was partly funded through the  French Lefe/GMMC  research programme and through  NASA  grant  NNX12AL28G  for DPC. JBS received support from  French ANR  grant " Exciting ",  ANR-12-PDOC-0001 , and through the  British Antarctic Survey  as a BAS fellow. In addition,  Ocean Sci. , 10,  547-557 , 2014 www. ocean-sci. net/10/547/2014/.
Advances in neuroscience are underpinned by large, multicenter studies and a mass of heterogeneous datasets. When investigating the relationships between brain anatomy and brain functions under normal and pathological conditions, measurements obtained from a broad range of brain imaging techniques are correlated with the information on each subject's neurologic states, cognitive assessments and behavioral scores derived from questionnaires and tests. The development of ontologies in neuroscience appears to be a valuable way of gathering and handling properly these heterogeneous dataparticularly through the use of federated architectures. We recently proposed a multilayer ontology for sharing brain images and regions of interest in neuroimaging. Here, we report on an extension of this ontology to the representation of instruments used to assess brain and cognitive functions and behavior in humans.
Its implementation (specification and use) as a component of a specific federated architecture for facilitating data sharing in distributed data centers has been described in  (Michel et al. 2010)  and  (Gibaud et al. 2011). In this paper we focus on the contents of the ontology. The remainder of this paper is organized as follows. In Section 2, we describe the generic modules of OntoNeuroLOG that were reused for our present work. In Section 3, we describe our core ontology of the domain of instruments; it covers instruments, actions corresponding to the administration of instruments and the scores obtained as a result. In Section 4, we present specializations of the core ontology (domain ontologies) modeling three specific instruments that are widely used in clinical practice 3  : the Mini-Mental State (MMS) or Mini-Mental Status Examination (MMSE), the Expanded Disability Status Scale (EDSS) and the Clinical Dementia Rating (CDR).
As intangible, propositional content, an Instrument is expressed in language and is physically inscribed on a medium of some kind. Indeed, Instruments are usually physically materialized by several documents. 9 In order to conceptualize Instruments, we chose to focus on their conceptual structure and function. 7 Because these constraints depend on local usages and/or national legislations, the core ontology does not provide with a set of predefined c o n s t r a i n t s. C o n s t r a i n t s c a n b e s p e c i f i e d u s i n g t h e 'isEmpoweredToPerform' relationship in the module Action-OS. 8 CDISC (Clinical Data Interchange Standards Consortium), CDISC Clinical Research Glossary Version 7. 0, Applied Clinical Trials (2008), 1 2 -5 8. ( s e e h t t p : / / w w w. c d i s c. o r g / g l o s s a r y / CDISC2008GlossaryVersion7.
Thus, for many Instruments based on population screening, normative data has been published as function of age, gender and/or educational level. This information is included in the definition of the Instrument. For example, WAIS-III included normative data for the IQ Variable separated into 13 age-dependent groups. Variables are then categorized as Gender-Dependent Variables, Age-Dependent Variables and Cultural-Skill-Dependent Variables. Normative data are often used to quote individual Scores obtained by Subjects. In such a case, Raw Scores are converted to Corrected Scores and Standard Scores by using charts and tables provided with the Instrument.
e. the types of stimuli, sequences, instructions and expected responses) used in fMRI and PET experiments. The CogPO is based on the Cognitive Atlas, a growing knowledge base that lists and classifies the concepts used in cognitive science (http://www. cognitiveatlas. org/). One can also note the recent work on standardization undertaken by the US National Institute of Neurological Disorders and Stroke; the Common Data Elements (http://www. commondataelements. ninds. nih. gov/) initiative is aimed at.
Conclusion Many neuroscience research centers and networks are now collecting neurologic, neuropsychological, behavioral and imaging data in large databases. In this context, we consider that our ontology of instruments is relevant for two main reasons. Firstly, it represents an ontological repository based on indepth clinical expertise acquired at Pitié-Salpêtrière Hospitalan institution known for its outstanding experience in neurology and neuropsychology. The exhaustive list of entities (instruments, variables, explored domains and measured qualities) reflects a broad, well-understood, clinical state of the art. Moreover, the output of this acquired expertise (in the form of documents describing instruments and variables) matched the clinical procedures applied in three other French university medical centers (in Rennes, Grenoble and Nice) that participated in the NeuroLOG project.
The fact that about 70 % of the tests incorporated into OntoNeuroLOG were used in all four institutions justifies our on-going efforts to standardize neuropsychological and behavioral assessments. Secondly, our ontology may be extended to the representation of imaging data and ancillary, behavioral, neuropsychological and cognitive data. It provides a coherent semantic space and a knowledge repository for structuring and designing a new generation of databases and associated services in neurosciences. OntoNeuroLOG may facilitate automatic reasoning and knowledge extraction from appropriately designed and structured databases. An example of a query that can be expressed using our structured ontological framework is the selection of instruments that measure variables related to memory dysfunction, the retrieval of subjects with imaging markers such as cortical thickness and neuropsychological markers such as memory impairment or severe dementia, and the retrieval of the associated scores from a battery of tests.
Information Sharing Statement The ontology (ONL-MSA, Mental State Assessment (RRID:nlx_157474)) is available through the NCBO BioPortal ontologies (http://bioportal. bioontology. org/ontologies/ONL-MSA). OWL and Ontospec (a semi-informal ontology language) files can be downloaded from http://neurolog. unice. fr/ ontoneurolog/v3. 0/. Information about NeuroLOG project (ANR-06-TLOG-024) can be found on the wiki site at neurolog. i3s. unice. fr/.
See the ESI † for a more detailed discussion. The fact that the vesicle structure remains basically unchanged is shown by SANS measurements in which the NPs are hardly visible, due to their rather low contrast in D 2 O and hardly any change between the spectra with and without added NPs can be seen (Fig. 3 ). The SANS curves have been tted using eqn (1), (  3 ) and (  4 ), with the average vesicle size and polydispersity as the only free parameters. Both curves can be described with the same dataset, only adding a second shell with the thickness of the NPs and the contrast calculated as explained in 2. 2. 1. Fig. S2  † shows the ratio of the scattered intensity of the vesicles with and without added NPs. The peak at about 0. 06 nm À1 is due to the shi of the form factor minimum (as with the NP decoration the vesicle shell effectively becomes somewhat bigger), whereas the shallow peak at 0.
Changes in the bilayer rigidity NSE measurements have been performed to investigate the inuence of the NPs on the bending modulus of the vesicles. Good t results of the NSE decay curves are obtained with eqn (12) (see Fig. 6  and S7 †) simultaneously tting all curves with a single set of parameters for all curves but the changes in S(Q, t) upon addition of NPs are in general quite small, yet systematic. Here it should be noted that determining the absolute scale of the bending moduli from NSE data is not a simple task as for that a number of other, not easily accessible parameters has to be known precisely. In contrast, a relative change of the bending modulus can be discerned with rather high precision. It has been found previously that using a value for d (see eqn (10)) which is close (2d/d bilayer ¼ 1.
Acknowledgements Financial support by the  DFG (Deutsche For-schungsgemeinscha)  in the framework of the IGRTG 1524 (SSNI) and the  BMBF (Bundesministerium für Bildung und Forschung)  through project  05K10KT1  as much as the allocation of beam time by the  ILL  and the JCNS is gratefully acknowledged. T. Weikl is thanked for fruitful discussions. Dr F. Polzer  likes to thank the  CRC 951 of the Deutsche For-schungsgemeinscha  and the  Joint Lab for Structural Research within IRIS Adlershof  for funding.
In the current context of climate change and landscape fragmentation, efficient conservation strategies require the explicit consideration of life history traits. This is particularly true for amphibians, which are highly threatened worldwide, composed by more than 7400 species, which is constitute one of the most species-rich vertebrate groups. The collection of information on life history traits is difficult due to the ecology of species and remoteness of their habitats. It is therefore not surprising that our knowledge is limited, and missing information on certain life history traits are common for in this species group. We compiled data on amphibian life history traits from literature in an extensive database with morphological and behavioral traits, habitat preferences and movement abilities for 86 European amphibian species (50 Anuran and 36 Urodela species).
It is undoubted that rising temperatures, changes in precipitation and UV-radiation are considered stressful and might be associated with disease outbreaks in amphibian populations  (Blaustein and Kiesecker 2002 , Kiesecker and Blaustein 1995 , Walker et al. 2010). UV-B may, however, also enhance tadpole growth in some species  (Pahkala et al. 2003), but  with not yet anticipated effects on survival of metamorphs and population dynamics. Our database on life history traits of 86 European Amphibian species is an important prerequisite for understanding change in amphibian life history, community composition and migration behavior. Such data is important to inform the Essential Biodiversity Variables  (Pereira et al. 2013) , develop new indicators and ultimately inform the decisionmaking process to improve amphibian conservation.
General description Purpose: Our database summarizes life history traits, including morphology, reproductive strategies, movement abilities, habitat preferences, distribution and IUCN status for all European amphibian species (N=86), all in all 44 traits subdivided into 253 modalities. Our database comprises information from 304 scientific publications assembled by searching Web of Science®, Amphibiaweb (AmphibiaWeb 2012) and herpetological books. The IUCN status, from 1 (least concern) to 6 (extinct in the wild), and their population trends (-1: decreasing, 0: stable or +1: increasing) were extracted from the IUCN red list website  (IUCN 2011). In total, we were able to compile data for 50 Anurans and 36 Urodela (Fig. 1 ). When several values were available for a continuous trait, we averaged them across studies (i.
e. between populations). When they are available, data for males, females, juveniles (larvae) and tadpoles were reported. Summary of mean data, range and missing values are given in Table  2. Because habitat fragmentation is currently the most threat affecting amphibian populations, movement specific data could help for conservation plans. By this way, we first selected traits related to movement abilities. Then, the costs associated with movements, and particularly with dispersal, might constrain the allocation of resources among all components of an individual's life, and could lead to relationships between movement abilities and several other traits. Indeed, and compared to relationships found between movement and life history traits in other groups (in mammals and birds:  Bowman 2003 , Bowman et al.