dataesr/scientific-paragraphs-categorization huggingface dataset:
6, trypsin (16 U/mL), chymotrypsin (4 U/mL) and 3. 1 mmol/L bile in the infant model)  (Mackie et al. , 2019). The authors selected 10 different control proteins from plant and animal origin with distinctly different digestibility and allergenic potential that were subjected to the different digestion scenarios. A simple SDS-PAGE readout alone was considered insufficient for risk assessment of newly expressed proteins since it cannot provide a readout of the peptide fragments arising from digestion. Inclusion of liquid chromatography-mass spectrometry (LC-MS) data addressed this shortcoming showing several correlations between some regions of high peptide abundance and the location of known epitopes, although the correlation was overall limited. This study concluded that the approach used was considered relevant to inform the risk assessment process, but more targeted research is needed to link these results to immunological outcomes.
Data-Set and Phase-Space The data-driven reduced homogenization relies on a data-set generated by micro-scale reducedorder simulations. The choice of the macroscopic quantities to be stored in the data-set depends on the expressions of the constitutive equations (4)-  (6)  and the data extracted from the microscale simulations. Here, we choose to store all quantities and their rates (except for the flux rate). The local data-set D m = μ m , μ m , ḡ m , ġ m , η m , η m , j m , c m , ċ m n dp I=1 , (8) is available at each material point m of the macroscopic discrete model. In present work, the data-set D m is constant in time, examples of temporally evolving data-sets i. e. D n+1 m can be found in  [16]. In  (8)  the prime • denotes a quantity that belongs to the data-set D m. Without any prime the quantity belongs to the physical state.
I represents a data-point postprocessed at each time-step from the micro-scale simulations and n dp is the total number of data-points. It should be noted that after the data-generation stage the homogenization model is disregarded, and thereafter data-driven problems solely rely on the data at hand collected in  (8). The local data-sets D m , in general, can be different for each macroscopic material point m. Collectively, from all the material points, the total number of data-sets available in a discrete system represent a global data-set D = D 1 × D 2 ×. × D M. (9) The physical-state of the material at the homogenized macro-scale can be characterized by a point in the local phase-space Z n+1 m z n+1 m = μm , μm , ḡm , ġm , η m , η m , jm , cm , ċm n+1 ∈ Z n+1 m , (10) evolving in time, whereby the dimensions of the phase-space are dim( Z n+1 m ) = R 1 × R 1 × R sd × R sd × R Nq × R Nq × R sd × R 1 × R 1 , in which sd is the spatial dimension of the problem under consideration.
The global distance function  (18)  is minimized by finding the minima of the local distance function  (16) , using all the points in the local data-set D m , at each material point m, and then integrating it using numerical quadrature. The minimum for the local distance function (  16 ) is found through a simple lookup array search algorithm, which amounts to performing the projection * y m,k+1 n+1 = P D z n+1 m,k+1. Searching through the data-set D m is the computationally expensive part of data-driven algorithm. If the data-set is large enough, a smart search algorithm, for instance, based on a tree search algorithm  [29] , should be used to accelerate this step. Then, using the newly found values of * y m,k+1 n+1 , the flux columns F * μ k+1 and F * λ k+1 are assembled. Finally, the convergence is checked and the iterations are terminated if there is no change in the optimum data point.
Data-Generation The data-generation is performed by solving the reduced model (  4 )-(  6 ), for μn+1 m (t) and ḡn+1 m (t) computed in the previous section, with the time discretization performed using the approximation in equation  (7). The data is stored in a local data-set D m = {(μ m , μ m , ḡ m , ġ m , η m , η m , j m , c m , ċ m )} n dp I=1 (30) In current work, for the data-driven simulations the same local data-set D m is available to all the material points i. e. D 1 = D 2 =. = D M ; in general, a different data-set can be available for each material point. As stated before, the selection criteria for the dominant eigenvectors, as proposed in  [8, 12] , provide N q = 6 for the considered unit-cell. It turns out, however, that out of these six, only one eigenvalue has the largest contribution to the lagging behavior at the macro-scale.
Noisy Data-Set Uncertainties during the data-generation steps may result in a noisy data-set, which can affect the final result and the convergence of a data-driven solver. To analyze how the proposed data-driven solver behaves in the presence of the noise in the data, a white Gaussian noise, with a signal-to-noise ratio of 30, is added to each element of the original data-sets D m which results in a data-sets with noise D m = {( μm , μm , ḡm , ġm , η m , η m , jm , cm , ċm )} n dp I=1. For the pairs ( ḡm n+1 , jm n+1 ) and ( μm n+1 , ηm (1) ,n+1 , ċm n+1 ) in the data-set D R the noise in the data is shown in Figure  7. As can be seen in Figure  8 , the relative L 2 -error between the macroscopic chemical potential fields μ n+1 E and μ n+1 D increases with the addition of noise in the data-set.
Convergence Analysis The convergence of the proposed data-driven reduced homogenization method with respect to the increase in the number of data-points n dp = T /∆t + 1 is analyzed here. The data-sets D (RS) , generated by the loading condition  (33) , and D (RS) , with added noise to D (RS) , were used in this regard to solve the macroscopic problem with the ramp loading conditions  (29). As observed in Figure  10 , the increase in the number of data points n dp in the data-set decreases the time averaged relative L 2 -error for both the noisy and noiseless data-sets, where, the noisy data-set D (RS) reveals higher errors than the noiseless data-set D (RS). After a certain data-set coverage, in this case n dp = 10 3 , the error first reaches a plateau and then slightly increases. This behavior suggests, that, for the problem at hand, the data-set D (RS) has reached its saturation at n dp = 10 3 and that it is incomplete by construction, since it does not contain the data-points from all the possible loading conditions with different frequencies.
Numerical examples are conducted for a macroscopically isotropic response in a one-dimensional domain, showing an adequate performance and robustness of the proposed methodology. A two-dimensional micro-scale problem, under one-dimensional loading conditions, is considered to obtain the macroscopic quantities and to provide the input for generating the data-sets. The enriched-continuum problem is used as a reference solution, and to generate the data-sets by post-processing the primary field and its gradient, as well. The actual generation of the dataset is done using stand-alone micro-scale simulations with different loading conditions having different frequencies. The obtained point in the data-set can then be added to the already existing data-set. The large number of coefficients in the distance function make the current data-driven problem more prone to numerical errors and instabilities, so a methodology is presented to carefully select the numerical values of these coefficients, based on the information available from the micro-scale simulations.
By doing so, a substantial decrease in the number of iterations and numerical error was obtained. Data-driven reduced homogenization captures the homogenized enriched-continuum response very well and also the post-processed micro-scale fields are in close agreement with each other. The proposed data-driven approach performs adequately in the presence of noise in the data-set and also in the case when a different dataset is used. Finally, by increasing the number of points in the data-set the error is reduced substantially, however at the expense of an increased number of iterations and computational effort. Obviously, the present work can be extended to an-isotropic macroscopic and two/threedimensional behavior. (R+S) and μn+1 E is the chemical potential field obtained by the enriched-continuum formulation (reference) under boundary conditions  (29).
The default values and the units of the coefficients are given in Table  4. The relative L 2 -error computed with the proposed default value for the coefficients J C m is marked with the black lines. The performance of the data-driven reduced homogenization is checked with different values of coefficients J C m , appearing in the definition of the norm  (15)  for the distance function  (18) , with a noisy data-set D (R+S) , the result is shown in Figure  14  and 15. An increase in the relative L 2 -error is observed as compared to the data without noise D (R+S) , With the introduction of the noise, the error is comparatively less influenced by the numerical values of the coefficients and more by the noisiness of the data. (R+S) and μn+1 E is the chemical potential field obtained by the enriched-continuum formulation (reference) under boundary conditions  (29).
Observations of near-surface atmospheric temperature (SAT) have been subject to major improvements recently. Commonly used datasets of observed temperature were based on spatially incomplete observations  (16)  and sea surface temperature (SST) instead of SAT over oceans  (17). Together, these two effects have been shown to artificially reduce estimates of past warming by about 15%, with implications, e. g. , in terms of remaining carbon budget  (18). Accounting for these recent findings and improved observed data is critical to constrain future warming. The statistical method is another key source of improvement over previous work. Most previous studies rely on linear regression techniques  (2) (3) (4) (5) (6) (7) 10)  and do not consider model uncertainty in the spatial or temporal pattern  (19, 20).
In the meantime, CMIP ensembles remain the best available multimodel dataset to quantify uncertainties about future climate change. The introduction of the new statistical method proposed in this study also opens new possibilities. Unlike techniques based on energy balance models, which are only suitable at the global or hemispheric scales  (11) , our technique could be easily adapted to constrain past and future changes at the regional scale. Incorporating spatial information, variables other than surface temperature, or other types of constraints (e. g. , physically based or paleoclimate constraints), is another promising line of research. Given that accurate climate projections with well-quantified uncertainties are essential for adaptation planning and mitigation policies, we expect the results of this study and similar applications on smaller scales to be of great value to a range of stakeholders.
Observed data We use observations from Cowtan and Way  (16) , hereafter denoted HadCRUT4-CW. This dataset provides an extension of the HadCRUT4 dataset  (36)  with global coverage, thus alleviating issues related to missing data. From this dataset, we compute the global mean nearsurface temperature (GMST), without applying any observational mask. Furthermore, this dataset uses a blending of SSTs over ocean and near-surface air temperature over land. Several studies have shown that this metric of global mean temperature warms significantly less than the GSAT  (17, 18). To take this discrepancy into account, we multiply the Cowtan and Way GMST by a factor of 1. 06, corresponding to a 6% increase  (16). This factor was estimated in historical runs specifically, while previous calculations made over the 21st century range between 1.
05 and 1. 08  (17, 18). In addition to affecting the long-term trend, this correction inflates the year-to-year variability in the observed time series. The HadCRUT4-CW dataset is provided with an estimate of measurement uncertainties, through a set of equally plausible realizations  (36). This is a remarkable feature, as HadCRUT4-CW is the only dataset with global coverage providing an estimate of observational uncertainty-which is why we chose to use this dataset. In the following, we take the median of this dataset as a best estimate. All realizations are used to estimate the measurement uncertainty. Note that we do not consider other datasets for GMST because they usually fall within the uncertainty of HadCRUT4-CW (1); considering the entire HadCRUT4-CW ensemble allows a more comprehensive treatment of uncertainty.
CMIP data We make use of a large set of models from CMIP6 and CMIP5  (13, 37). We took all models providing at least 200 years of preindustrial control simulations, one historical simulation, one 2. 6 W m -2 of scenario (either RCP2. 6 or SSP1-2. 6), one 4. 5 W m -2 of scenario, one 8. 5 W m -2 of scenario, and either a histGHG simulation [i. e. , a historical simulation in which GHGs follow their historical concentrations, while other forcings are kept constant  (38) ], or a 1% CO 2 simulation (in which the CO 2 concentration increases by 1% each year). Only the tas variable, corresponding to near-surface atmospheric temperature, is used here. We consider 22 models from the CMIP6 ensemble: ACCESS-CM2, AWI-CM-1-1-MR, BCC-CSM2-MR, CAMS-CSM1-0, CanESM5, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, GFDL-ESM4, GISS-E2-1-G, HadGEM3-GC31-LL, INM-CM4-8, IPSL-CM6A-LR, MIROC-ES2L, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, NESM3, NorESM2-LM, and UKESM1-0-LL.
Discussion of the method and estimation of input parameters Applying the observational constraint this way offers many advantages. The model prior captures various facets of model uncertainty in a consistent way. This includes uncertainty in both the magnitude and the temporal pattern of the forced response, unlike usual D&A methods  (6, 7, 10, 19, 20, 27). Using the entire observed dataset avoids the arbitrary choice of a reference period (as performed, e. g. , for historical trends), facilitates annual updates, and helps distinguish the responses to GHGs from OA factors (thanks to the different emission time series for these two forcings). In addition, the entire time series of constrained projections is derived in a consistent way, which is particularly attractive for near-term projections.
Acknowledgments: We acknowledge insightful comments and constructive criticisms from three reviewers, which highly improved the quality of this manuscript. We are very grateful to  M. Stolpe  and  K. Tokarska  for sharing estimates of climate sensitivity in CMIP6 models and discussions related to their recent work. We thank  O. Geoffroy  and  D. Saint-Martin  for insightful discussions on climate sensitivity and emerging constraints. We thank the climate modeling groups involved in CMIP5 and  CMIP6  exercises for producing and making available their simulations. Funding: A. R. and S. Q. acknowledge support by  Météo-France ,  CNRS , the  European Union 's  Horizon 2020 Research and Innovation Program  under the  EUCP (grant agreement   776613 ) and the  CONSTRAIN  (grant.
In the published interim analysis of 755 patients included in EURO-SKI, the TFR rate at 36 months was 49%. Thus, the follow-up data presented here cover about 30% of all the eligible TFR patients in EURO-SKI. The detailed design of the EURO-SKI trial has been published  [2]. In short, patients ≥ 18 years with BCR-ABL1-positive CP-CML with a typical BCR-ABL1-transcript (major: e13a2 and/or e14a2), receiving first-, or second-line TKI treatment due to toxicity to first-line TKI were included. Minimum TKI treatment duration was 3 years and minimum duration of DMR (MR4 or better) was 1 year. Patients with prior stem-cell transplantation, TKI failure, or having other malignancy were excluded. Nineteen European centers from the Czech Republic, Finland, Germany, Greece, Norway, and Sweden participated in the AFTER-SKI follow-up of EURO-SKI.
In agreement with each country's public health code, the respective ethics committees approved the EURO-SKI protocol (ClinicalTrials. gov NCT01596114), as well as the AFTER-SKI follow-up. Six years after inclusion in EURO-SKI data from consecutive patients in TFR at month 36 was collected: (1) patient alive or not, and if dead, cause of death, (2) number of RQ-PCRs performed per year, and (3) level of BCR-ABL1 (IS) at months 36, 48, 60, and 72. Whether the patient had continuously undetectable, occasionally detectable, or fluctuating levels of BCR-ABL1 was also captured. For patients who lost MMR, the time point, BCR-ABL1-value, restart of TKI, and reachievement of MMR and MR4 were reported. The RT-qPCR testing of BCR-ABL1 in clinical practice after 36 months was assessed for all participating patients.
First, the nucleoprotein filament is formed by binding of singlestranded DNA (ssDNA) to site I (step1). Then, the filament is activated by binding of the cofactor ATP to the ATP binding site (step 2). Next, RecA performs a homology search to find a homologous double-stranded DNA (dsDNA). In this process, dsDNA interacts first with the NTD (step 3) and then with the CTD (step 4), by which it can move to site II, where it binds (step 5). If the bound dsDNA is homologous to the ssDNA, strand exchange is performed; if it is not, the dsDNA is released (step 6). During these steps, the nucleoprotein filament presents hydrolytic activity, carried out by the [KR] × [KR] hydrolysis motif containing Lys248 and Lys250, which cooperate with Glu96 on the other monomer. The figure was generated using the PyMol program  18  and models provided by Prentiss and Prevost  19.
In the case of cells producing DrRecA, EcRecAV1 and EcRecAV2, growth was even more altered, with a significant lag No difference is made between black and red characters in this case because this feature is applied for other usages. At the top of the first sequence, the 2D structure of E. coli RecA is shown: the squiggles represent α-helices; the arrows, β-strands; the TT letters, strict β-turns; and the star, a residue with multiple conformations revealed by crystallography. The 2D structure is from the PDB entry 4TWZ. Additionally, the different domains of RecA are indicated in boxes: in red, the N-terminal domain (NTD); in pink, the ATP binding site; in blue, the hydrolytic residue and the hydrolytic motif; in orange, site I; in cyan, site II; in yellow, the dsDNA gateway in the CTD; and in green, the C-tail and the Mg 2+ binding sites.
10 , Supplementary Fig. 4 ). Note that in the case of DdRecA, binding was assessed after 20 min, rather than 10 min, due to the observed slow binding kinetics (results not shown). In the presence of ATP, binding was at least fivefold lower than binding in the non-dissociating conformation for EcRecA, DdRecA, PaRecA and DrRecA and twofold lower for the EcPa variant. However, for the EcRecAV1 and EcRecAV2 variants, contrary to expectations, the binding of ssDNA was higher in the presence of ATP than in the presence of ATPγS. These data highlighted an altered cofactor interaction for these two RecA variants.
The bands were quantified with Image Lab software from Bio-Rad. ssDNA binding reaction. Twenty microlitres of the reaction mixture containing the selected buffer, 500 nM 35-nt 5'-FAM-labelled oligonucleotide, 10. 5 µM RecA (RecA in excess) and 3 mM ATP were incubated at 30 or 37 °C for 10 min. The non-dissociating conformation was analysed by adding 0. 3 mM ATPϒS instead of.
Next, RecA performs a homology search to find a homologous double-stranded DNA (dsDNA). In this process, dsDNA interacts first with the NTD (step 3) and then with the CTD (step 4), by which it can move to site II, where it binds (step 5). If the bound dsDNA is homologous to the ssDNA, strand exchange is performed; if it is not, the dsDNA is released (step 6). During these steps, the nucleoprotein filament presents hydrolytic activity, carried out by the [KR] × [KR] hydrolysis motif containing Lys248 and Lys250, which cooperate with Glu96 on the other monomer. The figure was generated using the PyMol program 18  and models provided by Prentiss and Prevost 19.
The 2D structure is from the PDB entry 4TWZ. Additionally, the different domains of RecA are indicated in boxes: in red, the N-terminal domain (NTD); in pink, the ATP binding site; in blue, the hydrolytic residue and the hydrolytic motif; in orange, site I; in cyan, site II; in yellow, the dsDNA gateway in the CTD; and in green, the C-tail and the Mg 2+ binding sites. The first methionine is present in the alignments to allow the program to perform well. Thus, the numbering of each amino acid is shifted by one in the figure compared to the text. For example, in EcRecA, L29 is in the 30 th position in the figure. The figure was generated using the ENDscript server 27.
Acknowledgements The authors acknowledge  Mara Prentiss  ( Department of Physics, Harvard University, Cambridge, MA 02138, USA ) and  Chantal Prevost  ( Laboratoire de Biochimie Théorique ,  CNRS UPR 9080 ,  Univ Paris Diderot, Sorbonne Paris Cité, IBPC, Paris, France)  for sending RecA models. The authors acknowledge  Ivan Matic  ( Université de Paris Descartes, INSERM U1001, Paris, France ) for sending E. coli strains. The authors acknowledge  Ivan Matic  and  Xavier Robert  ( Université de Lyon, UMR5086 CNRS, Lyon, France ) for their invaluable advice. The authors acknowledge American Journal Experts for English editing.
5 M EDTA (pH 8. 0) for a week or a month (depending on the mice's age), dehydrated in graded ethanol solutions, cleared in xylene, and embedded in paraffin. Five-micron sections were cut, stained with Safranin O, and immunohistochemically stained using standard protocols. Antibodies against Collagen type X (1:50 dilution; BIOCYC, Luckenwalde, Germany), Phospho ERK1/2 (1:500 dilution, Cell Signaling, USA) and Ki67 (1:3000; Abcam, Cambridge, MA, USA) and the Dako Envision kit (Dako North America, Inc. , CA, USA) were used. Images were acquired with an Olympus PD70-IX2-UCB microscope (Olympus, Tokyo, Japan), and analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, USA). Mean areas of individual HC and number of HC were measured from collagen X labelled sections, within multiple 8500 m 2 ROIs on sections immunostained for Collagen type X.
Disease Models & Mechanisms • DMM • Accepted manuscript X-rays and micro computed tomography (µCT) analyses. Osx-cre, Osx-Fgfr3 and Col1-Fgfr3 mice and their control littermates were sacrificed at 3 weeks or 3 months of age. The mice were measured and whole-body X-rays were performed with a digital cabinet X-ray system (LX-60, Faxitron, Tucson, AZ, USA). The anatomic features on the X-rays were measured with ImageJ software. The femurs and whole heads were dissected and stored in 70% ethanol. The head was imaged using an lCT40 Scanco vivaCT42 (Scanco Medical, Bassersdorf, Switzerland), with the following settings: integration time: 300 ms, 45 E(kVp), 177 mA. Three-dimensional images were reconstructed using OsiriX software (64-bit version; Pixmeo, Bernex, Switzerland). Femurs were imaged using a Skyscan 1272 system (Bruker, USA), with the following scanning parameters: image pixel size: 5 µm; X-ray voltage: 60 kV; X-ray current: 166 µA; filter: A1 0.
71 mm; exposure: 1800 ms; rotation step: 0. 71°; frame averaging: 3; tomographic rotation: 180°. Cross-sections were reconstructed using NRecon software (Bruker), with the following parameters: smoothing: 0; ring artefact reduction: 2; beamhardening correction: 40%. The Dataviewer, CTAn and CTVox software packages (Skyscan) were used to visualize and determine bone histomorphometric variables. For each variable, the volume of interest was proportional to the length of the femur. Cortical bone was analyzed in the midshaft of the femur. The volume of interest ranged from 5% above the midshaft to 5% below. Trabecular bone was analyzed at the distal femoral metaphysis. The region of interest was delimited manually between the end of the primary spongiosa and a site 15% lower. Isolation of osteoblastic cells from calvaria.