dataesr/scientific-paragraphs-categorization huggingface dataset:
The HRGβ 2 bait did not have any intrinsic activity of transcriptional activation for the reporters (data not shown). The X-α-Gal (5-Bromo-4-chloro-3-indolyl-α-D-galactopyranoside) indicator was included to screen transformants. Approximately 1 x 10 6 independent transformants were pooled and re-plated on selection media (Ade-, His -, Leu -and Trp -) containing 2% galactose (Gal) to induce the expression of cDNAs. In total, 35 transformants showed Gal-dependence in the selction medium. Plasmids from these transformants were extracted by yeast mini-prep and the cDNAs were amplified in the PCR with primers derived from the pJG4-5 vector, followed by sequence determination. To eliminate false positive, we performed separation and isolation of yeast plasmids followed by propagation in bacteria.
Generation of MCF-7 cells overexpressing fulllength HRGβ2 or HRGβ2 deletion mutants The PCR products generated using the HRGβ 2 cDNA accession number 183996 (full length HRGβ 2 ) or the structural deletion mutants (HRGβ 2 -∆NLS and HRGβ 2 -∆EGF) were cloned into the retroviral expression vector pBABE-Puro using BamHI and EcoRI restriction sites. Retroviral constructs were transfected into a high efficiency transient amphotropic packaging system (TSA54 cell line) with FuGENE reagent. Retroviruscontaining medium collected after 48 h was used to infect MCF-7 cells for 24 h in the presence of Polybrene (Sigma-Chemicals). Infected MCF-7 cells were grown for an additional 24 hours in standard medium and stable cell lines (MCF-7/pBABE, MCF-7/HRGβ 2 , MCF-7/HRGβ 2 -∆NLS and MCF-7/HRGβ 2 -∆EGF) were selected and expanded in the presence of 2.
Cell Quest software (Becton Dickinson) was used for data acquisition and analysis. The telomere fluorescence signal was defined as the mean fluorescence signal in G 0 /G 1 cells after subtraction of the background fluorescence signal (FISH procedure without probe). The flow cytometer was calibrated every day using fluorescein isothiocyanatelabeled fluorescence Sphero microparticles (Pharmingen). The resulting calibration curve was used for correction of experimental fluorescence values in each experiment. Green fluorescence was measured in a linear scale, and results were expressed in molecular equivalents of soluble fluorochrome units (kMESF).
5% agarose gel in 1X Tris-Borate EDTA (TBE) buffer at 2 V cm -1 for 17 hours. The gel was dried at 60 o C for 2 hours, denatured for 30-60 minutes in 0. 5 mol/L NaOH and 1. 5 mol/L NaCl, and neutralized for 30-60 minutes in 1 mol/L Tris-HCl, pH 8. 0 and 1. 5 mol/L NaCl. The dried gel was then subjected to in-gel hybridization with a [γ-32 P]-ATP 5' end-labeled oligonucleotide telomeric probe [γ-32 P-(TTAGGG) 3 ]. The autoradiography signal was digitized using a Phosphoimager (Amersham Biosciences) and Image-QuaNT software (Molecular Dynamics). The mean size of the TRF was using the formula L = E(OD i •L i )/ E(OD i ), where OD i and L i were the signal intensity and TRF length, respectively, at position i on the gel image. This method takes into account the greater intensity of signals from larger fragments.
ACKNOWLEDGMENTS Work in the corresponding authors' labs was supported by  Department of Defense  award  W81XWH-06-1-0703 , the  National Institute of Health/ National Cancer Institute  award  R01-CA118975  and by the  Department of Energy  under Contract No. DE-AC03-76SF00098  to  Ruth Lupu  and by grants from the  Ministerio de Ciencia e Innovación  (Grant  SAF2012-38914 ),  Plan Nacional de I+D+I, Spain  and the  Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)  (Grant  2014 SGR229 ),  Departament d'Economia I Coneixement, Catalonia, Spain  to  Javier A. Menendez.
cbs. cnrs. fr/AT2B/SERVER/EDMon. html) (Delfosse et al. , 2012)  to model zfERs in complex with this ligand. The structural  basis of the hERα and hERβ selectivity toward certain ligands has been associated with two amino acid differences in their ligand-binding pockets. Indeed, L384 and M421 of hERα are replaced by M336 and I373 in hERβ, respectively (Figure  2  and  Manas et al. , 2004). Superimposition of the 16α-LE2-bound zfERα model on the crystal structure of hERα in complex with E2 (PDB code 3UUD) showed that the phenol ring of 16α-LE2 occupies the same position as that of E2 and is engaged in a network of hydrogen bonds with E353 from helix 3 (H3) and R394 from H5 (Figure  2A ). On the other side of the ligand-binding pocket (LBP), it appears that the hydrogen bond observed between the 17-hydroxyl group of E2 and H524 (H11) is conserved in 16α-LE2.
Petrophysical and Geomechanical Properties Coupled fluid flow-geomechanical simulation, which allows one to compute the pressure and stress fields and simulate the CO 2 plume migration, requires petrophysical and geomechanical characterization of the storage system to associate property data with the matrix rock and the faults. The only available porosity and permeability values of the reservoir formations, ranging between 2. 5 and 24% and between 2. 6 and 2. 7 mD, respectively, come from Grifone well, which is located about 60 km from the analyzed Grazia  structure (which lies below the homonymous well) (Fig. 1a ). Thus, petrophysical properties were derived from different sources: well log analysis, measurements performed in the laboratory on core samples taken from outcropping analogs or the scientific literature.
The different methods for calculating the petrophysical and mechanical properties show a degree of uncertainty: the estimated permeability of the caprock obtained from well logs is too high to ensure the sealing characteristics; the measurements derived from outcropping analogs remain challenging because the burial history and diagenesis of the outcrop and the subsurface sample will probably be different. However, considering these uncertainties, four matrix scenarios were used in the dynamic simulation, integrating the petrophysical datasets obtained by the different methods.
Matrix Scenarios The different petrophysical datasets considered in the dynamic modeling are: -LOGS dataset (Tab. 1): corresponds to data derived from well log data. Caprock porosity was computed by applying a velocity-porosity relation proposed by  Han (1986)  for clastic sediments. A sonic-derived porosity (SPHI) was computed for the reservoir rocks (Scaglia formation) by applying the classical Wyllie time-average relation  (Wyllie et al. , 1956). Permeability values for the carbonate reservoir were computed starting from the rock-fabric petrophysical classification proposed by  Lucia (1995) , considering the empirical relationship based on the correlation between permeability and fractional interparticle porosity. For the clastic sediment of the caprock, the permeability was estimated using the relationship proposed by  Xiangui et al.
(2009). Density values for the caprock and reservoir were derived using empirical formulas reported in the literature  (Gardner et al. , 1974; Castagna et al. , 1993; Brocher, 2005). Regarding the mechanical properties, the Poisson ratio was computed by the relation proposed by  Brocher (2005)  and the elastic moduli (Young Modulus, Bulk Modulus and Shear Modulus) required for the geomechanical modeling were then obtained from the p-wave velocity formula, knowing the density and the Poisson ratio r: V p ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Eð1 À rÞ qð1 þ rÞð1 À 2qÞ s EðYoung ModulusÞ ¼ V 2 p qð1 þ rÞð1 À 2rÞ ð1 À rÞ KðBulk ModulusÞ ¼ E 3ð1 À 2rÞ lðShear ModulusÞ ¼ E 2ð1 þ rÞ -BIBLIO dataset (Tab. 2): this matrix scenario is based on the LOGS dataset except for the petrophysical data of caprock facies, which are replaced by values derived from published shale porosity-depth trends collected by  Hermanrud (1993).
A mean porosity trend was estimated from well logs and compared with the empirical porosity curves. The porosity values for the facies located at a certain depth were deduced using the porosity curve that fits the mean porosity curve derived from well logs. Permeability K for the caprock facies was determined by the model proposed by Temis TM (IFPEN basin simulator). It is based on Kozeny-Carman's law  (Kozeny, 1927; Carman, 1937 Carman, , 1956)) : K ¼ 0:2U 3 S 2 0 ðÀUÞ 2 where U is the porosity and S 0 is the specific surface area defined as the quantity of pore surface area per unit volume of solid; -CORE dataset (Tab. 3): this scenario is based on the LOGS dataset, but the petrophysical data of the caprock and the Example of a "composite log chart". reservoir formations were updated using core measurements.
The core samples were taken from an onshore analog succession outcropping in the area of Vieste-Peschici (Gargano promontory) (Fig. 5 ). The samples, collected from three different successions, consisted of limestones (reservoir analog) of the Turonian-Senonian Scaglia Formation, and marls (caprock analog) of the Lower Aptian Fucoidi Marls Formation. The Fucoidi Marls have a lithology and depositional environment very similar to the Scaglia Cinerea Formation that represents the lower portion of the caprock succession in the study area (Fig. 5 ). The following properties were measured by laboratory analysis: porosity, irreducible water saturation, threshold pressure, permeability, Young modulus, Poisson ratio, Coulomb parameters (cohesion and internal friction angle) and tensile strength; -OAb dataset (Tab.
4): the dataset of this scenario is a synthesis of the three previous cases, where the shale facies properties of the caprock come from the BIBLIO scenario; the marl facies of the caprock come from the CORE scenario. The properties of the platform, basinal carbonates and dolomite facies of the reservoir come from the LOGS scenario. Regarding the vertical permeability, the commonly used ratio between vertical and horizontal permeability of 0. 1  (Fanchi, 2006)  is taken for all facies in the absence of relevant data. In addition, since the Plio-Pleistocene Formation has been refined in 4 layers, it was decided to define one shale facies per layer in order to take into account the porosity decrease with depth due to rock compaction.
Structural Model The structural model is a 3D representation on a regional scale of the four identified key seismic horizons: the Seabed, Top Miocene succession, Top and Bottom Reservoir/ Scaglia Formation and base of the Pre-Scaglia, which represents an artificial surface that defines the base of the model (Fig. 2 ) and the major tectonic lineaments (Jolly, Gondola, Rovesti, Grifone, Rosaria and Cigno tectonic lineaments) affecting the study area (Fig. 7a, b ). These lineaments are sealed by Plio-Pleistocene successions, except for Grifone and Gondola, which seem to deform the sea floor. Structural interpretation of available seismic and borehole data was performed in a Two-Way Time (TWT) domain and successively converted to depth. The depth conversion required the creation of a velocity model calculated using velocity functions (i.
Facies-Based Petrophysical Property Attribution The lack of available petrophysical data did not allow us to populate the model with petrophysical properties using a geostatistical approach. These properties were considered constant for all the identified facies. The uncertainties were handled considering several scenarios in the dynamic modeling, combining the petrophysical properties derived from well log analysis, core measurements and the scientific literature, as described in Section 1. 2 (Tab. 1-4). However, each scenario presents some critical issues: -LOGS dataset (Tab. 1): the critical aspect is the calculated caprock permeability, which is too high to seal the injected CO 2 ; -BIBLIO dataset (Tab. 2): in this scenario only parameters from the literature were considered, no direct measurements were included; -CORE dataset (Tab.
3): this scenario presents better seal characteristics of the caprock marls (with lower permeability than that of the LOG scenario) but also very low permeability of the reservoir formation; -OAb dataset (Tab. 4): in this scenario the reservoir is characterized by the highest permeability and the caprock with the best sealing capacity, thus representing an ideal situation. Despite the uncertainty induced by the use of analogue core data or limited log data, these scenarios are still needed to initially characterize the storage site in the absence of essential field data, as is the case with the characterization of the Brindisi site. Uncertainty of the simulation results could be reduced by the use of more field static data; even a geostatistic model used to interpolate these local field data would also induce uncertainty in the simulation results.
Geomechanical Modeling The geomechanical simulations were performed with the Abaqus/Standard software. It is a general-purpose Finite Element software with an implicit integration scheme, developed by Dassault Systemes (http://www. 3ds. com/ products-services/simulia/products/abaqus). The aim of the geomechanical modeling is to simulate the geomechanical response after CO 2 injection and investigate any potential damage of faults. Geomechanical modeling computes the stress changes that result from the pressure variations (or overpressure) calculated by the fluid flow simulation. The resulting stress, the sum of the stress changes and the initial stress, is then compared with a damage criterion, which is here the commonly used Mohr-Coulomb criterion. No feedback from the geomechanical modeling, e.
Where this is not the case (in closed faults or seals), undrained parameters are used. Fluid flow was not modeled in such layers and the geomechanical simulator is then loaded with a null overpressure. Identical geomechanical properties were chosen for all fluid flow scenarios according to data derived from the available well logs, lab tests and literature (Tab. 6). Two sets of parameters are given for each facies: drained parameters are used when the facies is in a layer where fluids are allowed to flow, undrained parameters are used when the facies is in a layer where fluids are not allowed to flow. Commonly used boundary conditions were applied at each side of the model: the top surface is a free surface, the bottom surface is assumed to have a null vertical displacement, and side surfaces (located far from the perturbation solicitation so as not to influence the results) are assumed to be subjected to constant regional stresses: in the modeling process, side surfaces are thus assumed to be free surfaces, as the initial effective regional stress is added in the post-processing as described below.
ACKNOWLEDGMENTS The research leading to these results received funding from the  European Union Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement No. 256705  ( SiteChar project ) as well as from  Enel, Gassnova, PGNiG, Statoil, Vattenfall, Veolia Environnement  and the  Scottish Government. ( FP7/2007 ( FP7/ -2013 ) ) in the framework of the  SiteChar  project -(grant agreement No. 256705 ). We thank  HIS Global Inc. for providing an academic educational license for KingdomSuite.
We also identified enrichment of polycomb repressor complex 2 (PRC2) target genes (Figure  4D ), particularly CpG island/shore/shelf regions for G26 and NS-1 (p < 0. 001) (Figure  3A ). A set of GBM MVPs that are associated with primary GBM tumors, but not low-grade tumors or normal neural cell types, was compiled from published datasets (see Experimental Procedures). Approximately 12% of these sites, including those associated with SFRP2, a WNT-signaling pathway antagonist, were altered in response to BMP in G26 (Figure  3D ). These data are consistent with G26 cell responses to BMP involving progression to a differentiated and less malignant state. Despite the above observations, we noted that DNA methylation changes were delayed in G26 compared to normal NS cells (Figure  2C ). Also, for G19 we identified only limited numbers of MVPs (<500 compared to >5,000 for NS and G26), even after 48 days of BMP treatment (Figure  2C ).
ACKNOWLEDGMENTS We thank  Tomi Ba ¨hr-Ivacevic  ( EMBL Genomics Core Facility, Heidelberg, Germany ) for microarray processing. This work was supported by grants from  The Brain Tumour Charity  (grant  8/105 ) and  Cancer Research UK  ( A9160 ). H. C. was supported by the  Swedish Research Council  and the  Wenner-Gren foundation. P. B. was supported by  EMBL and Biotechnology and Biological Sciences Research Council (BBSRC). Research in the S. B. lab was supported by  Wellcome Trust  ( WT093855 ),  Royal Society   Wolfson Research Merit Award  ( WM100023 ), and  EU-FP7  projects EPIGENESYS (257082) and BLUEPRINT (282510). S. M. P. was supported by a  Wellcome Beit Memorial research fellowship , an  Alex Bolt Research fellowship , and the  Cancer Research UK   Senior Research Fellowship  ( C25858/A19778 ).
Imaging and image analysis Images of immunofluorescent stainings were captured with a charge-coupled device camera (Zeiss, Thornwood, NY) coupled to a Zeiss Axioplan microscope using MetaSystems ® software that allows automatic image scanning. MetaCyte was used to photograph interphasic nuclei and quantify the fluorescence of ␥H2AX. Background staining intensity was used to normalize the obtained data so that the ␥H2AX signal at 0 Gy was equal to 0 for each experiment.
Flow cytometry staining Cells were fixed with 70% ethanol for at least 10 min and stored at -20 • C until use. Prior to staining, cells were washed with PBS-2% FCS and incubated for 1 h at RT in PBS-2% FCS with anti-␥H2AX antibody (1/800). After washing in PBS-2% FCS, cells were incubated with an anti-mouse FITC secondary antibody (1/100) at RT for 1 h. Cells were then washed and resuspended in PBS containing propidium iodide (20 g/ml) and RNAse (10 g/ml Sigma-Aldrich). At least 10 4 cells were analyzed using an LSR II flow cytometer (Becton Dickinson) and FSC Express 4 software (Denovosoftware ® ). Only cells in the G1 cell cycle phase were analyzed for ␥H2AX signal. Background staining intensity was used to normalize the obtained data so that the ␥H2AX signal at 0 Gy was equal to 0 for each experiment.
As expected, the correlation coefficient and the curve equation for the radioresistant Group I was very similar to that depicted in Fig. 2A  (radioresistant donors). These results demonstrate that individual radiosensitivity must be taken into account to guarantee a good estimation of the dose and the possible long-term health effects following accidental irradiation. Human fibroblasts were irradiated (0. 5 to 5 Gy) with X-rays (200 kV) or with ␥-rays from a Cesium-137 source and placed at 37 • C for repair. Cells were harvested at 4 h after irradiation, divided into two parts, and then stained for flow cytometry or immunofluorescent imaging. MetaCyte ® software (MetaSystems ® ) was used to quantify global ␥H2AX fluorescence, and foci were also scored manually. An LSRII flow cytometer (BD Biosciences ® ) was used to acquire cells, and data were analyzed using FCS Express 4 (Denovosoftware ® ).
Notably, of the three methods studied, only the scoring of individual ␥H2AX foci allowed for the discrimination of different radiosensitivity groups several hours after exposure; this inter-individual variability was not detectable using the global fluorescence techniques. Therefore, for triage purposes using global quantification of ␥H2AX fluorescence via flow cytometry, a calibration curve applicable for the Fig. 3. Global fluorescence measurements using flow cytometry in fibroblasts for radiological triage. Fibroblasts representing three radiosensitivity groups (Groups I < II < III) were irradiated with X-rays (200 kV) and placed at 37 • C for repair. Cells were harvested 4 h after irradiation and stained for flow cytometry. An LSRII flow cytometer (BD Biosciences ® ) was used to acquire cells, and data were analyzed using FCS Express 4 (Denovosoftware ® ).
Cells were harvested at 1, 4, or 6 h post-irradiation as indicated and stained for flow cytometry. An LSRII flow cytometer (BD Biosciences ® ) was used to acquire cells, and data were analyzed using FCS Express 4 (Denovosoftware ® ). Each data point represents the normalized mean ± standard error of all donors. A. ␥H2AX signals observed for the indicated doses (0. 5 to 6 Gy) after the indicated repair times. B. Calibration curve obtained for lymphocytes at 4 h after the indicated doses (0. 5 to 6 Gy). C. Proposed triage categories based on the calibration curve in B: asymptomatic or unaffected population requiring minimal care (green section), population with moderate symptoms who will require no immediate medical attention (yellow section), and the population who should be hospitalized (red section).
A software tool for prediction of clinical effects after irradiation using the database SEARCH (System for Evaluation and Archiving of Radiation Accident based on Case Histories) has been employed recently as part of a blind test. Blood cell counts measured at different times allowed the prediction of the severity of ARS (Acute Radiation Syndrome) with 80% accuracy  [9]. We could speculate that at least a part of the 20% failure of the software could be due to the moderately radiosensitive Group II. This software could potentially be adapted to take into account this group and help first responders to make better-informed decisions. Recent work has shown that radiation biodosimetry could be greatly enhanced by the automation of several steps of the process. For example, a robotically based biodosimetry workstation (RABIT) that can quantify total ␥H2AX fluorescence in nuclei has been developed  [19].
Acknowledgments This work was funded in part by the European 7th Security framework grant BOOSTER: grant agreement no. 242361  ( FP7-SEC-2009-1 , topic SEC-2009-4. 3. 2. ). This work was also supported in part by a  CEA grant  from the  NRBC-C2. We are grateful to  Géraldine Leduc  and  Alberto Bravin  for providing laboratory and irradiation facilities at the  ESRF. We thank  Nicolas Foray  and  Radhia M'Kacher  for stimulating discussions and valuable advice.
The assimilation test with normalized FAPAR data (scenario A4) is designed to illustrate that FAPAR constrains mainly the phenology and the seasonality of the growing season. The scenario using normalized FAPAR (see section 2. 1. 2) is tested only for the deciduous site (Fontainebleau) that shows a strong seasonality. Whenever the observed FAPAR are normalized, the model simulations are processed using the same procedure. (scenario A1). For Fontainebleau, the prior ORCHIDEE simulation shows a relatively good agreement with the observations: the seasonal patterns are well captured for both fluxes, as well as the synoptic (10 to 20 day) variations. The prior model, however, overestimates the ecosystem respiration in winter by about 1 g C/m 2 / d for both sites. This feature follows from an incorrect initialization (overestimation) of the carbon pools after the spin-up runs, where the soil carbon pool may represent the maximum soil carbon content for such forest, without considering past land use and disturbances.
1). In contrast, the assimilation of the in situ flux data have a small impact on the simulated FAPAR time series; the optimized simulations remain very close to the prior values, with nearly no change in RMSE with respect to the in situ FAPAR data (see values in Figure  1 ). A small change in the simulated phenology can, however, be observed for Fontainebleau, with a later start of the growing season (~10 days), as explained by the increase of K pheno_crit (see section 3. 4. 1). However, this results in an even stronger bias in the timing of the start of the growing season depicted by FAPAR measurements compared to the prior model. the in situ measurements, in particular for Puechabon where the magnitude of the MERIS FAPAR is up to half that of the in situ data. This discrepancy might be due to errors resulting from the satellite FAPAR processing chain (radiative transfer modeling, including incorrect representation of the optical properties of Mediterranean oak species; atmospheric correction) and also partly from differences in spatial footprint (given the heterogeneity of the landscape around the tower).