dataesr/scientific-paragraphs-categorization huggingface dataset:
A linear fit forced to go through 0/0 yields the molar heat of crystal dissolution. We find 4. 2 cal/mol and 10. 3 cal/mol at 20 • C and 30 • C, respectively, yielding a value of 0. 61 cal/(mol K) or 2. 6 J/(mol K) as value for the molar heat capacity of crystal dissolution. We fitted Equation 11 through the data points to estimate γ. Note that in this case, we only have data at two temperatures and therefore we cannot estimate the accuracy of the fit. However, this data and analysis does provide a good idea about the magnitude of the enthalpy and heat capacity of GNNQQNY assembly into crystals. α-lactalbumin from the known aggregating region of lysozyme. The estimated aggregating region was TFHT-GINY of α-lactalbumin (Uniprot P00711, region 48-122). In order to cover a wider range of total hydrophobic surface area, we also included thermodynamic data of the self-assembly of a hydrophobic amino acid [phenylalanine, 47] and of a hydrophobic dipeptide [diphenylalanine, 48].
Acknowledgements SA and JvG thank  NWO  for funding received under project number  680-91-112. AKB thanks the  EMBO and Magdalene College, Cambridge  for funding. HM thanks the  CNRS InFinity program  and the  Exploratory Research Space of the RWTH Aachen University  for a Theodore von Kármán Fellowship. We thank  Alexandra Ziemski  for help with the production of α-synuclein.
6×10 6 km 2 , which constitutes a decrease of 53 % compared to the pre-industrial period. The majority (11 out of 16) of models simulate summer seaice-free conditions (≤ 1 × 10 6 km 2 ) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions, although the degree of underestimation varies strongly between the simulations. The simulations with the highest Arctic SAT anomalies tend to match the proxy dataset in its current form better. The ensemble shows some agreement with reconstructions of sea ice, particularly with regard to seasonal sea ice. Large uncertainties limit the confidence that can be placed in the findings and the compatibility of the different proxy datasets. We show that while reducing uncertainties in the reconstructions could decrease the SAT data-model discord substantially, further improvements are likely to be found in enhanced boundary conditions or model physics.
Three reconstructions from  Ballantyne et al. (2010)  at the same location (78. 3 • N, -80. 2 • E) were averaged to avoid oversampling that location. The uncertainties in the reconstructions were derived by  Feng et al. (2017)  and  Salzmann et al. (2013)  from relevant literature. The data-model comparison will be a point-to-point comparison of modelled and reconstructed temperatures estimated from palaeobotanical proxies, which initially does not take the uncertainties of the reconstructions (Table  S1 ) into account. The potential influence of the uncertainties in reconstructions on the outcomes of the data-model comparison will be investigated in a later section. The temporal range of the reconstructions is broad and certainly not resolved to the resolution of the KM5c time slice, unlike the dataset of SST estimates compiled by  Foley and Dowsett (2019)  used for PlioMIP2 SST data-model comparisons by  Haywood et al.
(2020)  and  McClymont et al. (2020). Prescott et al. (2014)  found that peak warmth in the mPWP would be diachronous between different regions based on simulations with differ- ent configurations of orbital forcing. Orbital forcing is particularly important in the high latitudes and for proxies that may record seasonal signatures (e. g. due to recording growing season temperatures). As such, there may be significant biases in the dataset, as the temporal ranges of the proxies include periods with substantially different external forcing than during the KM5c time slice for which the simulations are run. Feng et al. (2017)  investigated the effects of different orbital configurations, as well as elevated atmospheric CO 2 concentrations (+50 ppm) and closed Arctic gateways in PlioMIP1 simulations, and found that they may change the outcomes of data-model comparisons in the northern high latitudes by 1-2 • C.
Further uncertainties arise due to bioclimatic ranges of fossil assemblages, errors in pre-industrial temperatures from the observational record, potential seasonal biases, and additional unquantifiable factors. Ultimately, the uncertainties constrain our ability to evaluate the Arctic warming in the PlioMIP2 simulations substantially. A more detailed description of the uncertainties in the SAT estimates can be found in the work of  Salzmann et al. (2013). The reconstructed temperatures are differenced with temperatures from the observational record to obtain proxy temperature anomalies. Observational-record temperatures are obtained from the Berkeley Earth monthly land and ocean dataset  (Rohde et al. , 2013a (Rohde et al. , , 2013b)) , and the average temperature in the 1870-1899 period was used.
10b ) in at least 1 month (in winter) per year (Fig. 10b ), consistent with proxy evidence  (Clotten et al. , 2018; Knies et al. , 2014). The uncertainties in both the SAT and SIE reconstructions are large, and it may not be possible to match both datasets in their current forms. This would require increased Arctic annual terrestrial warming compared to the mean model (Sect. 5. 1) as well as perennial sea in the summer and a large SIE in winter (extending at least into the Iceland Sea). Moreover,  McClymont et al. (2020)  found that the warmest model values in the PlioMIP2 ensemble tend to align best with North Atlantic SST reconstructions, further indicating that strong Arctic warming is required for data-model agreement. If there was no perennial sea ice in the mPWP like most models in the PlioMIP2 ensemble, the different proxy records may be more compatible, but this would be in disagreement with findings from  Darby (2008).
Furthermore, there is some agreement with reconstructions of sea ice in the ensemble, especially for seasonal sea ice. The limited availability of proxy evidence and the uncertainties associated with them severely constrain the compatibility of the different proxy datasets and our ability to evaluate the Arctic warming in PlioMIP2. Increased proxy evidence of different climatic variables and additional sensitivity experiments, Clim. Past, 16, 2325 Past, 16, -2341 Past, 16, , 2020  https://doi. org/10. 5194/cp-16-2325-2020 among other goals, are needed for a more robust evaluation of Arctic warming in the mPWP. Lastly, we find differences in Arctic climate features between the PlioMIP2 ensemble and future climate ensembles that include the magnitude of Arctic amplification and changes in AMOC strength.
These differences highlight that caution has to be taken when attempting to use simulations of the mPWP to learn about future climate change. Data availability. The reconstructions used in this study are available in the Supplement. The model data can be downloaded from PlioMIP2 data server located at the School of Earth and Environment of the University of Leeds, an email can be sent to Alan Haywood (a. m. haywood@leeds. ac. uk) for access. At the time of publication, the data from CESM2, EC-EARTH3. 3, GISS-E2-1-G, IPSL-CM6A-LR, and NorESM1-F can be downloaded through CMIP Search Interface at https://esgf-node. llnl. gov/search/cmip6, last access: 17 November 2020; Department of Energy, 2020. Supplement. The supplement related to this article is available at: https://doi. org/10. 5194/cp-16-2325-2020-supplement.
CESM2 simulates the smallest deviations from reconstructions in the ensemble, with a MAD of 5. 0 • C and a median bias of -2 • C. The five models that simulated the highest Arctic SAT anomalies (CCSM4-Utrecht, CCSM4-UoT, CESM1. 2, CESM2, and EC-Earth 3. 3) simulate the lowest median biases, indicating that the upper end of the range of simulated Arctic SAT anomalies in the PlioMIP2 ensemble tends to better match the proxy dataset in its current form. Future research into the underlying mechanisms for the increased Arctic warming in these 5 simulations, compared to the remaining 11 simulations in the ensemble, may form a way to uncover factors that contribute to improved data-model agreement.
Bone texture analysis Images in the DICOM format (Digital Imaging and COmmunications in Medicine) obtained by CBCT were converted in 256-gray-level images in the BMP format (Bitmap ) using ImageJ 1. 5 (US National Institutes of Health, Bethesda, MD, USA)  27. The DataViewer software (release 1. 5; Brucker microCT, Kontich, Belgium) was used to re-slice the stacks of 2D images: i) at the mandibular condyle, the area of interest was selected on a sagittal plane at the mid-condyle with the largest trabecular bone surface and including the mandibular notch; ii) for alveolar bone, the area of interest was selected on a plane comprising the second premolar, molars, mandibular canal and the retromolar area. Areas of interest were selected manually by one investigator (Fig. 2 ). Texture analysis was performed using MaZda 3D Editor 4.
3D analysis of the mandible After CBCT acquisitions, semi-automatic segmentations of mandible volumes were performed with ITK-Snap v3. 6 open-source software (http://www. itksnap. org)  30. A global threshold was used to segment bone on the whole series of patients  31. 3D models were generated and analyzed with 3DSlicer v4. 8 software (National Alliance for Medical Image Computing NA-MIC; http://www. slicer. org)  32. The Surface Registration tool of the CMF registration module was used to superimpose 3D models of the mandible before (T0) and after BTX injection (T1). This tool is based on a superimposition process using the entire surface mesh of both 3D models. Then, the Model to model distance module of MeshMetric3D plugin (https://www. nitrc. org/projects/meshmetric3d) was used to compute point-by-point distance between two triangular meshes of the superimposed 3D models.
Finally, the combined image was analyzed with the Shape population viewer v1. 4 software (https://www. nitrc. org/projects/shapepopviewer) to create color maps showing mandibular surface differences expressed in mm. The lookup table (LUT) provided a color bar and was set with three colors: blue indicated a negative difference corresponding to a bone loss, red corresponded to a positive difference (bone apposition) and green stood for a steady state. 3D superimposed mandibles were analyzed on seven ROI: condylar processes, coronoid processes, mandibular angle region and the lingual side of the symphysis. Condylar processes were analyzed on three areas: anterior, upper and posterior. Because Chang et al. , reported that differences less than 1mm were observed after 3 months with the same technique, here, a difference greater than one millimeter was considered as significant  33.
, 2005 ; Figure  6B ). We found that Col-0 pollen tubes grew with almost equal extD and intD values in Col-0 cell wall. However, the ratio between extD and intD was about three when Col-0 pollen tubes grew in ktn1-5 papilla cells (Figure  6B-D ). These quantitative data were consistent with the observation that pollen tubes appeared more prominent on ktn1-5 stigmatic cells using SEM (Figure  5B ; Figure  5 -figure supplement 1B). Assuming that stigmatic cells are pressurized by their turgor pressure, this may reflect the presence of softer walls in ktn1-5 papillae. To test this hypothesis, we assessed the stiffness of Col-0 and ktn1-5 papilla cell walls using Atomic Force Microscopy (AFM) with a 200 nm indentation. We found that cell wall stiffness in ktn1-   5 papilla cells was about 30% lower than that in stage 13 WT cells (Figure  6E ,F).
Live imaging Flowers at stage 13 and 15 were emasculated and pollinated on plants with mature pollen from the pACT11::RFP line. Immediately after pollination, stigmas were mounted between two coverslips maintained separated by four grease plugs placed at each coverslip corner. To maintain a constant humidity without adding liquid directly on the stigma surface, we used a wet piece of tissue in contact with the base of the stigma. Pollinated stigmas were observed under a Zeiss microscope (Axi-oObserver Z1) equipped with a spinning disk module (CSU-W1-T3, Yokogawa) using a 40x Plan-Apochromat objective (numerical aperture 1. 1, water immersion). Serial confocal images were acquired in the entire volume of the stigma every 1 mm and every minute. Images were processed with Image J software and pollen tube lengths were measured.
The deflection sensitivity of the cantilever was calibrated against a sapphire wafer. All experiments were made in ambient air at room temperature. Matrix of 10 Â 10 measurements (step 500 nm) was obtained for each papilla, with a 1mN force. The Young's Modulus was estimated using the Nanoscope Analysis (Bruker) software, using the Sneddon model with a < 200 nm indentation.
Environmental Scanning Electron Microscopy (SEM) Flowers from stages 12 to 15 were emasculated and pollinated on plants with mature WT pollen. One hour after pollination, pistils were cut in the middle of the ovary, deposited on a SEM platform and observed under Hirox SEM SH-3000 at À20˚C, with an accelerating voltage of 15kV. Images were processed with ImageJ software and pollen tube direction was quantified by counting the number of turns made by the tube, only on papillae that received one unique pollen grain.
Anisotropy estimation Flowers from MAP65 lines were emasculated and stigmas were observed under confocal microscope. Images were processed with ImageJ software and quantitative analyses of the average orientation and anisotropy of CMTs were performed using FibrilTool, an ImageJ plug-in  (Boudaoud et al. , 2014). Anisotropy values range from 0 to 1; 0 indicates pure isotropy, and one pure anisotropy.
Membrane deformation estimation Flowers from LTI6B lines were emasculated and pollinated with mature pollen from the pACT11:: RFP line. 20 min after pollination, stigmas were observed under confocal microscope. Serial confocal images every 1 mm encompassing the entire volume of the stigma were recorded and processed with ImageJ software. Plasma membrane deformation was estimated by choosing the slide from the stack that corresponded to the focus plan of the contact site with the RFP-labelled pollen tube. On the bright field image corresponding to the selected slide, a line was drawn connecting the two ridges of the invagination of the papilla. Two perpendicular lines, one toward the exterior (ExtD) of the papilla to the maximum point of deformation, and the other toward the interior (IntD) on the GFP image were measured, respectively.
Statistical analysis Graph and statistics were obtained with R software or Excel. Statistical tests performed are specified in figure legends. for TEM microscopy. We also thank the BioMeca society and Pascale Milani for the AFM measurements. LR was funded by a fellowship from the French Ministry of Higher Education and Research. The work at Laboratoire de Reproduction et Developpement des Plantes was supported by Grant ANR-14-CE11-0021 and the one carried out at Institut Jean-Pierre Bourgin by Saclay Plant Sciences-SPS (ANR-17-EUR-0007), ANR-11-BTBR-444 0006-BFF and by Bio-Based Industries Joint Undertaking No 745012, GRACE. Additional information.
Acknowledgements We thank  O Hamant  for critical reading of the manuscript and fruitful discussion,  A Boudaoud  and  V Battu  for advice on AFM experiments, the  Sice and MechanoDevo team members for discussion, P Bolland ,  A Lacroix  and  J Berger  for plant care and the PLATIM imaging facility of the  SFR Biosciences Gerland-Lyon Sud. We thank the  Bordeaux Imaging Centre  especially  L Brocard  and  B Batailler.
Patient cohort Patient data were selected from the Institut Curie Hospital Group (IC) BC database. 57,665 patients with in situ or invasive BC treated at IC, at the Paris or Saint-Cloud sites (France), from 1980 to 2010 were considered for this case-control study. We selected 3739 female patients with unifocal invasive BC, T1b, or T1c N0M0, expressing estrogen receptor (ER ≥ 10%) without overexpression of HER2 (HER2 0, 1+, or 2+ without gene amplification in in situ hybridization, as defined by the American Society of Clinical Oncology/College of American Pathologists Clinical practice guidelines  [34] ). Patients had been treated at IC by primary surgery between 2003 and 2010 (see flow chart in Additional File 1: Fig. S1 ). Among this population, there were 52 patients with distant relapse (excluding local, contralateral, or axillary lymph node relapse)-then called "cases"-and available samples (Additional File 1: Fig.
Both stained cells quantification and tumor infiltrating lymphocytes (TILs) on HES stained slides were evaluated according to the International TILs Working Group  [35]. For quantification of vascularization, we analyzed eight fields of 1596 mm 2 per tumor (× 10 magnification). We used ImageJ software in order to evaluate the percentage of the surface of the field stained with the CD31 antibody. Each percentage of CD31 + area was normalized on the percentage of stroma in the same photograph. Finally, we expressed results as the mean of the percentage of CD31 + area normalized by the stromal proportion. Decision tree algorithm for prediction of CAF subset identity CAF identity of each tumor was determined using an algorithm developed by the team  [22, 23]. In brief, the algorithm takes as input histological scores of CAF markers.
Maps of CAF subsets at cellular level IHC staining from consecutive sections were scanned on Philips Ultra-Fast Scanner. The × 10 images of CAF markers used in the decision tree algorithm (CD29, FAP, SMA, FSP1) were analyzed from the same areas of representative tumors (considering controls and cases). Images were aligned using elastic transformation from Fiji software plugin (bUnwarpJ). This plugin uses landmarks manually defined on hematoxylin and eosin (H&E) staining of the sections to compute the optimal correlation between original images and alignment at cellular level by elastic transformation. Images were divided into tiles of 225 μm 2 to mimic the approximate size of one fibroblast, and each tile was annotated according to its position in the section. Aligned and annotated images of the CAF markers were then submitted to color deconvolution, and the intensity of each DAB staining was measured by densitometry analysis using ImageJ software.
After a washing step, all samples were resuspended in 50 μl of PBS+ containing 1:100 carboxylate beads (Polyscience #18133) and 5 μg/ml DAPI solution (Thermo Fisher scientific #D1306). Flow cytometry analyses were performed on LSRFortessa™ analyzer (BD biosciences). As FACS did not allow the analysis of the entire suspended sample, at least 5 × 10 5 events were recorded and precision beads were used to normalize viable BC cell (DAPI -EPCAM + FAP -) and CAF (DAPI - EPCAM -FAP + ) counts. Compensations were performed using single staining on anti-mouse IgG and negative control beads (BD bioscience #552843) for each antibody. Data analysis was performed using FlowJo version 10 (LLC, USA). The clustering strategy used was as follows: (i) cells were first gated based on their size (FSC-A) and their granulosity (SSC-A) to exclude debris and the number of beads was counted; (ii) cells were then gated on EPCAM and FAP expression to distinguish cancer cells (EPCAM + , FAP -) from CAF (EPCAM -, FAP + ); (iii) finally, the live/dead cells were distinguished by DAPI staining.
Next, inserts were immersed for 30 min in 4% Violet Cristal (sigma #C0775-100G) and 20% Ethanol (analaR #20821. 310) for fixation and coloration. Five representative pictures per insert were taken at × 5 magnification. Pictures were then analyzed with ImageJ software. We first performed Transwell assays using only BC cells or only CAF-S1 fibroblasts on the upper chamber. By this way, we confirmed that we could easily distinguish BC cells from CAF after their migration through the Transwell, according to their respective morphological characteristics. Thus, we considered that upon cocultures BC cells and CAF-S1 displayed different morphologies that enabled us to distinguish them, as shown in Fig. 4  for cancer cells, and Additional File 8: Fig. S6  for CAF.
5 × inter-quartile range from the hinge. Data from in vitro experiments were shown using mean ± SEM (unless otherwise specified) from at least three independent experiments. Survival curves were constructed using the Kaplan-Meyer method implemented in R package survival. Distant progression-free survival was defined as the time between treatment and diagnosis of distant metastasis (excluding local or regional lymph node recurrence). Survival analyses were done according to the Cox model with Log-Rank test. Explanatory variables with a p value < 0. 20 in univariate analyses were kept for the multivariate analyses using the Cox model. Two-sided tests with p value < 0. 05 were considered statistically significant.
Cadherin 11 expression by CAF-S1 enhances cancer cell pro-tumorigenic properties As the CAF-S1 subset is one of the main indicators of relapse in luminal BC, we set about identifying the molecular mechanism involved. We first used RNA sequencing analysis from CAF subsets isolated from BC (data deposited at the European Genome-phenome Archive under accession number EGAS00001002508)  [22]  and found that CDH11 (cadherin 11, also referred to as osteoblast cadherin) was significantly up-regulated in CAF-S1 subset compared to the other CAF subsets (Fig. 4a ). CDH11 is involved in bone development and is expressed in cells with a mesenchymal phenotype  [43] [44] [45] [46] [47]. Luminal BC mainly relapse in the bone  [48, 49]. This was confirmed in our cohort with 41 cases (78. 8%) showing bone metastases during their evolution.
Funding C. B. was supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm, Plan Cancer -Formation  à la recherche translationnelle), A. E. by the association d'aide en  Cancérologie de Saint-Cloud (ARCS) , and Y. K. by the  Institut National du Cancer ,  INCa  ( INCa-DGOS-9963; INCa-11692 ),  SIRIC (INCa-DGOS-4654 ), and the  Fondation pour la Recherche Medicale (FRM). The experimental work was supported by grants from the  Ligue Nationale Contre le Cancer (Labelisation) ,  Inserm  ( PC201317 ),  Institut Curie  ( Incentive and Cooperative Program Tumor Micro-environment PIC TME/T-MEGA ,  PIC3i CAFi), ICGex  ( ANR-10-EQPX-03 ),  SIRIC (INCa-DGOS-4654 ), and  INCa  ( STROMAE INCa-DGOS-9963 ,  CaLYS INCa-11692, INCa-DGOS-Inserm-12554 ). F. M-G acknowledges both the Association "Le cancer du sein, Parlons-en" and the  Simone and Cino del Duca Foundation  for attribution of their "Grand Prix".
Cloning and mutagenesis Candidate FP-encoding transcripts were identified by BLAST homology searching using avGFP as the query against the assembled transcriptome databases as well as intermediate assembly files created by the Trinity workflow. Searching through intermediate assembly files allowed us to identify potential alternative transcript sequences and those that were (possibly incorrectly) collapsed into single contigs by Trinity. Putative FP-encoding transcripts were validated against raw read data and reconstructed as necessary (see below for detailed methods, results, and discussion). Sequence alignments were performed using Clustal Omega  [32]. For each avGFP homolog identified, the coding region was identified and a synthetic gene was designed to produce the encoded polypeptide sequence using codons optimized for both human and Escherichia coli expression using an in-house BioXp 3200 instrument (SGI-DNA, La Jolla, CA) or ordered as a gBlock double-stranded gene fragment (Integrated DNA Technologies, San Diego, CA).