dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein concentration after dialysis was 1 mg/ml. Data acquisition and analysis. Data were collected at the SWING beamline of Synchrotron Soleil (Saint-Aubin, France). Serial dilutions of the protein were measured (initial concentration, dilution 2 and dilution 4). The dialysis buffer was measured before and after each dilution. All buffer measurements were found to be superimposable and were averaged and subtracted from the protein solution measurements. Subsequent analyses were performed using various tools from the ATSAS suite, version 2. 8. 3  [41] , as indicated. The three dilutions were analyzed in PRIMUS and found to be superimposable after scaling. We further analyzed the data from the undiluted sample. Experimental radius of gyration and Dmax were computed from the pair distribution function p(r) with GNOM from the AutoRg routine in PRIMUS, to be consistent with our previous analysis of the GIII.
2 dimer data  [9]. We also generated a real space envelope using an automated Python script that streamlines and standardizes the classical procedure of multiple envelope averaging. 30 envelopes were generated with DAMMIN and aligned with SUPCOMB. We finally took the average envelope and the lowest envelope corresponding to the envelope with all common points between all envelopes. Rigid body modeling. We used EOM  [42]  for modeling with several conformations and DADIMODO  [43]  for modeling with a single conformation. To allow for sufficient flexibility we chose as flexible slightly enlarged versions of the termini and connecting segments. Thus in both cases the three rigid bodies were defined as residues 232 to 520 of both chains A and B (dimer of P), residues 29 to 217 of chain A and residues 29 to 217 of chain B.
or when generating non-symmetrical models (Fig  8B ). These searches result in a selection of 3 symmetric models whose combination yields a χ 2 goodness of fit of 4 or of 2 asymmetric ones with a χ 2 near 5, respectively (Table  3 ). In contrast, we obtain single models that fit our data better (χ 2 in the range 2-2. 5) by using the genetic algorithm implemented in the DADIMODO software, where generation of populations of random conformational variants is cycled with selection on their fit to the data  [43]. The model with the best fit is shown on (Fig  8C ). In all cases, we obtain models with the S domains separated and coming on either side of the P dimer, either as major components of multi-model ensembles (Fig 8A  and 8B , Table  3 ) or as the single-model solution (Fig  8C ). When not constrained to  two-fold symmetry solution (Fig 8B  and 8C ) the solutions also display a pronounced asymmetry with one S close to the P dimer while the other extends away from it, a shape that matches well the molecular envelope while the crystallographic dimer does not (Fig  8D ).
With pronouncedly asymmetrical dimers, the first step is not compromised (indeed the extension of one S domain away from the rest of the dimer may facilitate formation of an initial S-based pentamer of dimers), but symmetrical capture of five Table  3. Individual χ 2 for EOM models, GI. 1 crystallographic dimer and Dadimodo model. EOM models are presented with their fraction used to fit the experimental data. χ 2 are calculated with Crysol  [46]. a, for this model only the program FOXS  [47]  is used for computing χ 2. The RMSD was calculated with the VMD visualizer tools using the Dadimodo model as reference. All proteins were aligned on the P domain and the RMSD was calculated based on the S/P domains position.
Acknowledgments This work was granted access to the high performance computing resources of TGCC under the allocation t2016077583 made by  GENCI. We thank  Anna Koromyslova  and  Grant Hansman  for their generous gift of the GI. 1 VLP sample. We acknowledge synchrotron SOLEIL for allocation of beamtime, Aurelien Thureau for invaluable assistance in SAXS data collection and the automated DAMMIN script,  Olga Roudenko  for access to SOLEIL computing infrastructure and DADIMODO computations, and  Marc Baaden  for invaluable advice.
METHODOLOGY ZONE HEATING AND INTENDED USE The energy needs of "Forte Begato", used as an example for this study, have been calculated with Energy Plus software. Different thermal zones were taken into account, according to two different parameters: the orientation of the building and the intended use of the different areas. Considering the high thickness of the walls of the fortress, the heating zones have been designed from the middle of the wall, so that the overall volume of the building does not increase, once assigned the thickness of the walls. Instead, for the calculation of the heat flow dispersed through the windows, the effective glass window area is considered. In this case, it coincides with the glass window surface measured on the walkable surface of the internal perimeter of the building.
, 2012). Additionally, CFMIP-2 introduced satellite simulators to CMIP via the CFMIP Observation Simulator Package (COSP,  Bodas-Salcedo et al. , 2011) : not only the ISCCP simulator, but also additional simulators to facilitate the quantitative evaluation clouds using a new generation of active radars and lidars in space. CFMIP-2 also introduced into CMIP5 process diagnostics such as temperature and humidity budget tendency terms and high-frequency "cfSites" outputs at 120 locations around the globe. In an effort less directly connected to CMIP, CFMIP organized a joint project with the GEWEX Global Atmospheric System Study (GASS) called CGILS (the CFMIP-GASS Intercomparison of LES and SCMs) to develop cloud feedback intercomparison cases to assess the physical credibility of cloud feedbacks in climate models by comparing single-column model (SCM) versions of general circulation models (GCMs) with high-resolution large eddy simulation (LES) models.
Atmos-only 1979 36 ice should remain the same as in the AMIP DECK experiment. The warming pattern should be scaled to ensure that the global mean SST increase averaged over the ice-free oceans is 4 K. Care should be taken to ensure that SSTs are increased in any inland bodies of water and near coastal edges, for example by linearly interpolating the provided warming pattern dataset to fill in missing data before re-gridding to the target resolution. amip-4xCO2 (formerly amip4xCO2): the same as the amip experiment within DECK, except that the CO 2 concentration seen by the radiation scheme is quadrupled. The CO 2 seen by the vegetation should be the same as in the AMIP DECK experiment. This experiment gives an indication of the adjusted radiative forcing due to CO 2 quadrupling, including strato-spheric, land surface, tropospheric and cloud adjustments.
More generally, a better understanding of how clouds couple to the circulation is expected to improve our ability to answer the four science questions raised by the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity  (Bony et al. , 2015). These questions provided the scientific motivation for the Clouds On/Off Klima Intercomparison Experiment (COOKIE) project proposed by European consortium EU-CLIPSE and CFMIP  (Stevens et al. , 2012). The COOKIE experiments, which have been run by four to eight climate models (depending on the experiment), switched off the cloudradiative effects (clouds seen by the radiation code -and the radiation code only -were artificially made transparent) in an atmospheric model forced by prescribed SSTs. By doing so, the atmospheric circulation could feel the lack of cloud-radiative heating within the atmosphere, but the land surface could also feel the lack of cloud shading, which led to changes in land surface temperatures and land-sea contrasts.
We request that the modelling groups provide information on which CFMIP diagnostic sets are submitted in which ensemble members so that this information can be made available to those who may be analysing the output. Our analysis plans for the CFMIP diagnostic outputs in the CMIP DECK, CMIP6 Historical and CFMIP-3/CMIP6 experiments, including details of the CFMIP Diagnostics Code Catalogue, are summarized in Appendix A.
2. 1-2. 6. Clustering approaches (e. g. Jakob and Tselioudis, 2003) are now commonly used for assessing the contributions of different cloud regimes (e. g. stratocumulus, trade cumulus, frontal clouds) to present-day biases in cloud simulations and to inter-model differences in cloud feedbacks (e. g. Williams and Webb, 2009; Tsushima et al. , 2013 Tsushima et al. , , 2016)). We have also added some additional daily 2-D fields to the standard package of CFMIP daily outputs to allow further investigation of feedbacks between clouds and aerosols associated with the changing hydrological cycle (aerosol loadings and cloud top effective radii/number concentrations) and a clearer diagnosis of the roles of convective and stratiform clouds (convective vs. stratiform ice and condensed water paths and cloud top effective radii/number concentrations).
The COSP data request for CMIP DECK and CMIP6 has been designed to span model evaluation across different space scales and timescales. Monthly mean diagnostics allow for the evaluation and intercomparison of large-scale distributions of cloud properties and their interaction with radiation. High-frequency model outputs (daily, 3-hourly) are aimed at a process-oriented evaluation (e. g. Bodas-Salcedo et al. , 2012)  and offer the opportunity to exploit the synergy between multiple instruments (e. g. Konsta et al. , 2015). Recent observational developments have improved our ability to retrieve cloud-radiative properties. In particular, new methodologies for cloud-phase identification are available for CALIPSO and MODIS, and COSP has been enhanced to provide diagnostics that are compatible with these new observational datasets  (Cesana and Chepfer, 2013).
4. 1. Modelling centres are encouraged to update to COSP 1. 4. 1 to provide these new diagnostics, but may provide results from COSP 1. 4. Developed over the last few years, COSP 2 substantially revises the infrastructure for integrating satellite simulators in climate models. COSP 2 makes many fewer inherent assumptions about the model representation of clouds than do previous versions but contains an optional interface allowing it to be used as a drop-in replacement for COSP 1. 4 or COSP 1. 4. 1. At the time of this writing COSP 2 is undergoing final testing in two climate models. Availability of the final version will be announced on the CFMIP website and modelling groups are free to adopt it for use in CFMIP at that time. The CFMIP community has developed a set of observational datasets available via the CFMIP-OBS website (http: //climserv.
ipsl. polytechnique. fr/cfmip-obs/) that are defined consistently with the COSP diagnostics and the CFMIP-3/CMIP6 data request in terms of vertical grids and time averaging periods. These are mostly reported as monthly means, although some are reported at a higher temporal resolution for process oriented model evaluations (e. g. Konsta et al. , 2012). Table  3  summarizes the datasets relevant to the COSP CMIP6 data request. Some of the CFMIP-OBS datasets listed in Table  3  (CALIPSO, CloudSat, ISCCP, PARASOL) are also available from the ESGF as part of the obs4MIPs project  (Teixeira et al. , 2014). These datasets are periodically updated to include more recent data from the relevant satellites, many of which are still operational. Please refer to the CFMIP-OBS website for updates.
5×CO 2 experiments are proposed to address the question (  5 ) To what extent is regional-scale climate change per CO 2 doubling state-dependent (non-linear), and why? An amip experiment with pre-industrial forcing addresses this question: (6) are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? Time slice experiments forced with SSTs from pre-industrial and abrupt-4xCO2 simulations address this question: (7) how do regional climate responses (of e. g. precipitation) in a coupled model arise from the combination of responses to different aspects of CO 2 forcing and warming (uniform SST warming, pattern SST warming, direct CO 2 effect, plant physiological effect, sea-ice change)? The CFMIP-3/CMIP6 experiments will continue to include outputs from the CFMIP Observational Simulator Package (COSP) to support robust scale-aware and definition-aware evaluation of modelled clouds with observations and to relate cloud feedbacks to observed quantities.
Code and data availability COSP is published under an open-source license via GitHub (please see the CFMIP website for details). The model output from the DECK, CMIP6 Historical and CFMIP-3/CMIP6 simulations described in this paper will be distributed through the Earth System Grid Federation (ESGF) with digital object identifiers (DOIs) assigned. As in CMIP5, the model output will be freely accessible through data portals after registration. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6, the participating modelling groups, and the ESGF centres (see details on the CMIP Panel website at http://www. wcrp-climate. org/index. php/wgcm-cmip/about-cmip). Further information about the infrastructure supporting CMIP6, the metadata describing the model output, and the terms governing its use are provided by the WGCM Infrastructure Panel (WIP) in their invited contribution to this Special Issue.
Along with the data themselves, the provenance of the data will be recorded, and DOIs will be assigned to collections of output so that they can be appropriately cited. This information will be made readily available so that published research results can be verified and credit can be given to the modelling groups providing the data. The WIP is coordinating and encouraging the development of the infrastructure needed to archive and deliver this information. In order to run the experiments, datasets for natural and anthropogenic forcings are required. These forcing datasets are described in separate invited contributions to this Special Issue. The forcing datasets will be made available through the ESGF with version control and DOIs assigned. CFMIP-2 analysis activities are ongoing and the CFMIP community is ready to analyse CFMIP-3/CMIP6 data at any time.
, 2014). These studies will be used as starting points for multi-model analyses. The COSP Project Management Committee co-chairs will coordinate and encourage the exploitation of these resources. Lead coordinators: Alejandro Bodas-Salcedo and Steve Klein. Analysis of output from the CFMIP-3/CMIP6 and CMIP DECK experiments will also be facilitated by sharing of diagnostic codes via the CFMIP Diagnostics Code Catalogue (accessible via the CFMIP website: http://www. earthsystemcog. org/projects/cfmip/). This is a catalogue of programs written by various members of the CFMIP community, implementing a number of diagnostic approaches from published studies. These include daily cloud clustering evaluation metrics based on ISCCP and ISCCP simulator outputs  (Williams and Webb, 2009; Tsushima et al.
This ozone distribution is provided as a netCDF file which is archived on the Earth System Grid and available via the DOI http://dx. doi. org/10. 5065/ D61834Q6. Ozone values are provided up to 0. 28 hPa (about 60 km altitude in mid-latitudes). For models with tops above this level, a high top ozone dataset is also provided, which is available via the DOI http://doi. org/10. 5065/D64X5653. The ozone climatologies provided use pressure as a vertical coordinate. Most models use a sigma or hybrid vertical coordinate in pressure or altitude, which will mean that the pressure on a given model level varies in time, near the surface at the very least. Although the ozone climatology can be interpolated to the pressure of each model level as it varies in time within the model, for simplicity we recommend interpolating the ozone dataset onto the model vertical grid before the experiment is performed, and then specifying ozone values which are constant in time on each model level.
The patterned SST forcing dataset is available in a netcdf file called cfmip2_4k_patterned_sst_forcing. vn1. 0. nc which is available in the Supplement for this paper, and via the CFMIP website. This is a normalized multi-model ensemble mean of the ocean surface temperature response pattern (the change in ocean surface temperature (TOS) between years 0-20 and 140-160, the time of CO2 quadrupling in the 1 % runs) from 13 CMIP3 AOGCMs  (cccma, cnrm, gfdlcm20, gfdlcm21, gisser, inmcm3, ipsl, miroc-medres, miub, mpi, mri, ncar-ccsm3, and ncar-pcm1). Before computing the multi-model ensemble mean, each model's TOS response was divided by its global mean and multiplied by 4. This guarantees that the pattern information from all models is weighted equally and that the global mean SST forcing is the same as in the uniform +4 K experiment.
Acknowledgements. We are grateful to  Florent Brient ,  Hideo Shiogama ,  Aiko Voigt ,  Mark Ringer  and two anonymous referees for helpful comments on the manuscript. We thank the modelling groups and the wider CFMIP community for reviewing and supporting the CFMIP contribution to CMIP6, the  CMIP Panel  for their coordination of  CMIP6 , the  WGCM Infrastructure Panel (WIP)  overseeing the CMIP6 infrastructure, and  Martin Juckes  for taking the lead in preparing the CMIP6 data request. We are also grateful to  Robert Pincus  and  Yuying Zhang  for their contributions to COSP and to CFMIP-OBS, to  Dustin Swales  for his development work for COSP-2, and to  Gregory Cesana  and  Mathieu Reverdy  for their contributions to CFMIP-OBS. We are grateful to  Brian Soden  for producing the CMIP3 composite pattern dataset used for the CMIP5 amipFuture and CMIP6 amip-future4K experiments, and to PMIP representatives  Pascale Braconnot ,  Masa Kageyama , and  Masakazu Yoshimori  for discussions relating to the amip-m4K experiment.
Firn models use empirical tuning to account for an imperfect description of physics. If an intercomparison experiment were designed to force the set of firn models for reconstructed boundary conditions for the last 2000 years, the modeled depth-density profile at present could be compared with observed depth-density profiles for a particular site. Models empirically tuned to particular sites or regions would likely outperform other models (e. g. firn models tuned for Summit, Greenland are not necessarily expected to fit East-Antarctica data well), and the experiment might demonstrate which models have been empirically tuned for the site rather than which has the best representation of firn-densification physics. An experiment using site-specific reconstructed accumulation rates and temperatures must also consider the uncertainties in the reconstructed boundary conditions.
Steady-state firn-model variability The models' similar approach to tuning (to measured depth-density profiles) may explain some of the agreement in their steady-state responses. The ART model's lower sensitivity to accumulation rate may reflect differences in the number of adjustable parameters, the target used for tuning (warmer, higher accumulation sites), the representation of the underlying physical mechanisms, or in a combination of the three. Until the sensitivity of density-depth profiles to accumulation rate and temperature can be reproduced by a physically-based model without any adjustable parameters, there will be some uncertainty as to the physical explanation for this sensitivity. Further, this will translate into uncertainty in the accuracy of the models when they are applied to climatic conditions different from the tuning datasets, or to modeling the response to temporal changes in climate.
Parameter dependence and nonuniqueness In Section 1 we discussed the difficulty of separating the influences of site accumulation rate and temperature when  empirically tuning models to steady-state firn-profile data. Inferring model parameters from a dataset is an inverse problem (e. g. Aster and others, 2005). When models are calibrated with incomplete data over a limited range, nonuniqueness is difficult to avoid, in that multiple combinations some model parameters can all allow output from a model to match a dataset at an acceptable tolerance level. When transients are considered, additional confounding interactions can arise because model parameters are not independent. Additional trade-offs are possible. For example different values of thermal conductivity can produce different transient temperature profiles, but could still match transient data from depth-density profiles with a different value of the activation energy, which can stiffen or soften the firn to compensate.
This indicates that ensembles that bracket a range of different models, a range of different parameter choices and a range of forcing data might prove just as useful in altimetric applications as they have in studies of Δage reconstruction. The strategies identified here for moving beyond the shortcomings of the current generation of firn-densification models (i. e. developing a next-generation physicallyinformed model, and increased use of model ensembles and data constraints) will have to be pursued in parallel. Developing the next-generation model requires timedependent strain-rate observations and in the meantime important scientific questions in ice-sheet altimetry and ice core science must be addressed. We believe that the FirnMICE project both provides a motivation to increase efforts in firn-model development, as well as informs the glaciological community about the existing model shortcomings in steady-state and transient behavior.
Strain-rate data offer the potential to constrain transient responses in densification, and concurrent measurements of firn microstructure and chemistry offer an opportunity to better understand the constitutive relationship. These data are currently sparse, but progress is being made (e. g. Arthern and others, 2010; Freitag and others, 2013; Morris and Wingham, 2014; Proksch and others, 2015)  to expand these datasets and to better understand firn evolution. here since multiple future models build on these physics.
A. 3. ESS -Essery and others (2013) The  Essery and others (2013)  model determines the seasonal snow-model surface boundary condition for atmospheric modeling and is therefore expected to be an outlier among the participating models, due to model tuning for mountain snow packs outside of polar regions. The densification equation, Dρ Dt ¼ ρ σ n 0 exp k T T m À k T T À ρ ρ 0 ; ðA16Þ has no limitation of compaction beyond firn densities and differs in the exponential temperature dependence. The terms include viscosity ν 0 (kg m -1 s -1 ), reference density ρ 0 , temperature scale k T (K); and melting point T m (K). The model is tuned to datasets, as described in  Kojima (1967). A. 4. GOU -Goujon and others (2003) The densification scheme of  Goujon and others (2003)  builds upon metallurgy material science  (Arnaud and others, 2000).
Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman's Rho ranged between 0.