dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that the same weight is used for all grid points. • om_future: same as om_present except that the models weights are optimized on the projection period. This aggregation method cannot be used in practice since the needed reference dataset in the projection period is unlikely to be available. It serves as a basis to assess the best results one could achieve in terms of bias with a multi-model mean approach (provided all information about the reference are available). • min_bias: at each grid point, we select the value of the model having the smallest absolute bias in the calibration period. The same labeling is kept for the projection period. It corresponds to the result of a GC where only the data energy is minimized. ( 5) C( ) = ∑ p∈P ref p - ∑ f ∈F w f X p (f p ) 2 • gc_present: a GC procedure where the data energy and smooth energy are defined (and optimized) with respect to the calibration period.
• gc_future: a GC procedure where the data energy and the smooth energy are defined with respect to the projection period. Similarly to om_future, this aggregation cannot be used in practice since the reference dataset in the projection period needed for the data energy is unlikely to be available. However, gc_future gives an idea of the best results one could achieve with graph cuts. • gc_hybrid: a GC procedure where the data energy is defined with respect to the calibration period and where the smooth energy is defined with respect to the projection period. This is possible in practice as the smooth energy only depends on the values of the models and not on the reference. The formulation of gc_hybrid can make more sense than the gc_present as we evaluate the degree of spatial continuity in the projection period and not in the calibration period.
Acknowledgements Work by  Mats Garvik  was supported by the  ERC  Grant no. 338965-A2C2  and by the  EUPHEME  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI  Climate and co-funded by the  European Union  (Grant  690462 ). We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their models outputs. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank the  Copernicus Climate Change Services  for making the ERA5 reanalyses available.
Availability of data and material CMIP5 climate model simulations can be downloaded through the Earth System Grid Federation portals. Instructions to access the data are available here: https:// pcmdi. llnl. gov/ mips/ cmip5/ data-access-getti ng-start ed. html, last access: 08 February 2021, (PCMDI, 1989). The ERA5 reanalyses can be downloaded through the Climate Data Store  (Hersbach et al. 2019). Last accessed: access: 08 February 2021.
Fluorescence Image Acquisitions A multi-zoom macroscope MacroFluo Z16 APO (Leica, Germany) was equipped with 4 filter blocks for excitation under UV, blue, green, and red light, and a respective collection of blue, green, red, and NIR emission light (see Supplementary Table  S2  for wavelengths; Figure  2A ). The exposure times were fixed at UV: 1,500 ms, Blue: 1,000 ms, Green: 500 ms, and Red: 2000 ms. The total magnification was obtained by combining the lens Plan Fluor 0. 5x and setting the optical zoom. Images were acquired with a CoolSNAP ES cooled CCD camera (Roper Scientific Photometrics Instruments) with 1392*1040 pixels and a pixel size of 6. 45*6. 45 µm. Metaview software [Universal Imaging Corporation, Molecular Devices] was used for the control of the CCD camera. For simplification, we named the different acquisition channels by their emission light (blue, green, red, and NIR).
e. , a dimensionality of 1,447,680 for the vector space). Three principal components were retained. The derived eigen-vectors were then rearranged back from the 1D vector to reconstruct 3 rectangular eigen-images. Finally, the values were normalized between 0 and 255 and the principal components 1, 2 and 3 considered respectively as the red, green, and blue channels in the RGB system to obtain a merged color image (Figure  2A ). The python code, including the preprocessing and PCA steps, is available on GitHub (https://github. com/paulmonsarrat/ spectral_periodontal_tissues).
Histology on Decalcified Tissues Samples To confirm the identification of tissue structures by spectral analysis, control sections from the same sites were routinely processed. Briefly, all formalin fixed 2-mm sections samples adjacent to fluorescent ones were decalcified with a commercial solution (Decalc ® -Histolab) containing hydrochloric acid at 10%-20% for 10 days and rinsed before paraffin inclusion. After paraffin embedding, 3 µm thickness paraffin sections were dewaxed (successive toluene and descending alcohols bathes) and stained with Haematoxilin and Eosin and Masson trichrome. The stained slides were imaged by light microscopy on a Nikon Eclipse Ci-L microscope with a DS-Fi3 Camera and NIS Elements D software.
Micro-CT Imaging Micro-CT acquisitions were performed using the Zeiss Versa 510 laboratory X-ray microscope (Carl Zeiss Meditec, France) at 40 kV and 3 W acquisition settings for an isotropic voxel size of 18. 7 µm (0. 4× lens) with a LE1 tube output filter. A targeted acquisition at higher magnification on the alveolar bone crest was also performed (40 kV, 3 W, 1. 5 µm voxel size, and LE1 output filter). 1601 projections were acquired, for a total of 3. 3 GB raw data for each acquisition, and volumes automatically reconstructed then visualized using ImageJ ® 1. 53 software.
Fluorescence multispectral datasets (Figure  2B ) were acquired using sequential imaging with several excitation wavelengths, combined with optical filtering of the fluorescence signal by ease rotating a filter wheel in front of a CCD camera. Natural autofluorescence contribution, requiring high exposure time, was significantly lower than fluorescence labelling in all the channels, and did not sustain appropriate discrimination between tissular componants (Supplementary Figures S1). Single-labeling showed fluorescence spectral overlap between the different dyes used (Supplementary Figures  S2, S3 ). Supplementary Figure  S2  illustrates the differential tissue staining between Calcein-AM and CFDA-SE dyes with a clearer contrast of mineralized tissue compared to soft tissue, particularly brought out by CFDA-SE.
ACKNOWLEDGMENTS We are grateful to the  RESTORE-TRI-GenoToul imaging platform and Fabrice Schmitt (Carl ZEISS SAS, RMS division ). We gratefully acknowledge the  Programme d'Investissements d'Avenir , and the  Agence Nationale pour la Recherche  for the support of the national infrastructure "ECELLFrance: Development of mesenchymal stem cell-based therapies" ( PIA-ANR-11-INBS-005 ) and for the grant  ANR-16-CE18-0019-01  (https://doi. org/ 10. 13039/501100001665 ).
Protein structure prediction Swiss-Model was used to identify the boundaries between the bactofilin domain and the terminal regions of CcmA. The primary amino acid sequence of CcmA from the H. pylori G27 genome (GenBank: CP001173. 1) was submitted to Swiss-Model (https://swissmodel. expasy. org/  (Waterhouse et al. , 2018) ) where the structure of BacA from C. crescentus  (Vasa et al. , 2015)  was used to model CcmA. RoseTTAFold was used to predict the structure of CcmA; the same primary amino acid sequence of CcmA was submitted to the Robetta protein prediction service (https://robetta. bakerlab. org/  (Baek et al. , 2021) ). Models generated in RoseTTAFold were modified in Pymol (The PyMOL Molecular Graphics System, Version 2. 4. 2 Schrödinger, LLC. ).
Western blot densitometry Western blot densitometry was performed using Image Lab software version 6. 0. 1 (Bio-Rad). The lanes on each Western blot were defined, then the adjusted total lane volume was calculated by the software. To calculate relative Csd1 levels, the signal detected by the ɑ-Csd1 antibody for each sample was divided by the signal detected by the ɑ-Cag3 antibody from the same sample on the same membrane. After calculating relative Csd1 levels, they were normalized by dividing the signal by the relative Csd1 level from the SSH51A strain (rdxA::csd1).
Phase contrast microscopy and quantitative morphology analysis Phase contrast microscopy and image analysis was performed as previously described  (Sycuro et al. , 2010). Briefly, cells were grown in liquid culture to mid-log phase (0. 3-0. 7 OD600), centrifuged, and resuspended in 4% paraformaldehyde (PFA) in phosphatebuffered saline (PBS) and mounted on glass slides. Cells were imaged with a Nikon TE 200 microscope equipped with a 100X oil immersion lens objective and Photometrics CoolSNAP HQ charge-coupled-device camera. Images were thresholded with FIJI  (Schindelin et al, 2012)  and CellTool software  (Pincus & Theriot, 2007)  was used to measure both side curvature and central axis length.
Negative stain and Transmission Electron Microscopy (TEM) After purification of 6-his CcmA samples were dialyzed against 25 mM Tris pH 8 for 24 hours. Samples were then normalized to 1 mg/ml and placed on carbon-coated grids after glow-discharging. Samples were fixed in ½ X Karnovsky's fixative and washed with 0. 1 M Cacodylate buffer and water, then stained with Nano-W (Nanoprobes) for 60 seconds, wicked, then stained for another 60 seconds. Images were acquired on a kV ThermoFisher Talos 120C LaB6 microscope equipped with a Ceta Camera using Leginon software  (Suloway et al, 2005). The width of bundles and filaments were measured using FIJI  (Schindelin et al. , 2012).
Analysis of intensity of CcmA-HaloTag signal at cell surface and within cells A custom Matlab script was used to compute the relative intensity of CcmA-HaloTag signal at the cell surface and at surfaces generated by shrinking the surface of interest toward the centerline of the cell (negative offsets) or increasing the surface away from the centerline (positive offsets). For each of these shells, or surface offsets, we calculated the average WGA signal and the average CcmA-HaloTag signal. To account for errors in spatial alignment between the two color channels, before calculating the intensity at each shell, we performed a rigid body alignment procedure to maximize the intensity of the signal contained within the nominal reconstructed surface (0 nm offset). Consistent with the fact that we utilized the WGA channel for shape reconstructions, this alignment algorithm returned offsets of less than one pixel for the WGA channel.
Description and Pre-processing of Historical Data For this study, historical data from yield trials conducted under normal (non-stress) and drought conditions (at the reproductive stage) at IRRI, Philippines from 2003 to 2019 (17 years) were used. The trials in each year were conducted in two seasons-dry (from January to April) and wet (from late June to September) season. The combination of year, season and treatment were treated as a trial or environment. In total the historical data harbored 19,916 data points with 2,497 unique lines. The data was pre-processed, and the quality of phenotype records was checked initially to ensure high-quality trials and phenotypes are retained for downstream analysis. The data was checked for extreme or unexpected values, missing percentages, and valid experimental design.
Pedigree Data Extraction The pedigree data of 2,490 unique lines were extracted from the breeding data management system (Breeding 4 Results (B4R), 2021) which has the passport to comprehensive information of the genotype, phenotype, and pedigree data of breeding lines. Additional information on the parents and grandparents up to seven generations, cross-type for each line, and breeding strategy were extracted using the IRRI genealogy management system  (McLaren et al. 2005; Collard et al. 2019 ) using customized R scripts.
The model used was: 𝑦 𝑖𝑗 = 𝜇 + 𝑔 𝑖 + 𝑦𝑒 𝑗 + 𝜀 𝑖𝑗 (4) where   is the weighted BLUE value for ith observation in jth year, μ is the overall mean,   is the random effect of ith genotype with gi ∼ N (0, Aσ 2 g) where σ 2 g is the genetic variance and A is the additive genetic relationship matrix based on pedigrees,   is the random effect of year, and   is the residual error, with  ∼ N(0, Rσ 2 ε), where R is the identity error covariance matrix and σ 2 ε is the error variance. The reliability of the breeding values  (Isik et al. 2017)  of each genotype was estimated using the following equation: 𝑟 = 1 - 𝑃𝐸𝑉 𝜎 𝑔 2 (5) Two-stage mixed model data analysis was performed in the R software (R Core Team, 2020). using the ASReml-R 4 package  (Butler et al. 2017 ). The R package AGHMatrix was used for constructing the pedigree A-matrix  (Amadeu et al.
Estimation of the Genetic Trends The genetic gain was estimated separately for three conditions: a) non-stress (normal conditions) trials, b) drought trials, and c) combined data (adjusted means across non-stress and stress trials). For the genetic gain trend, breeding values were regressed on the year of origin of the line. The genetic trend was also estimated for released varieties and checks by regressing the breeding value of checks on the year of origin in non-stress trials, drought trials, and combined data.
Identification of Breeding Panel Adjusted breeding values obtained from mixed-model analysis across non-stress and stress data were used for the identification of elite genotypes as a future breeding resource. A total of 200 lines were selected from the 2,490 unique historical lines based on the higher breeding values and prediction accuracy of >0. 4. In addition to the lines with higher breeding values and prediction accuracy for grain yield, lines with key QTLs for key biotic and abiotic stresses. To make sure genotypes selected are diverse and represent the whole collection of lines in historical lines, the pedigree matrix was used in the analysis to account for similarity among the lines. The similarity and diversity among the selected lines in comparison to the whole collection were visualized through bi-plot.
Descriptive Features of Historical Drought Data The three main traits grain yield, PH, and DTF grown under normal and drought conditions were used for analysis. The difference in phenotypic trait values for all three traits was observed in normal and drought conditions. The difference in trait value was also evident across the years. The raw mean grain yield under the normal conditions ranged from 3000-12000 (kg/ha) as compared to the drought conditions 1360-5600 (kg/ha) (Fig. 1a ). In each trial, lower yield values were observed under drought conditions as compared to trials under normal conditions indicating the impact of drought on the phenotypes. For DTF we observed higher values under drought conditions (63-129 days) as compared to the trials under normal conditions (60-119 days)  (Kazan and Lyons 2016; Shavrukov et al.
S2 ). The dataset has ample diversity among the tested genotypes having a diverse range for PH between 40 cm to 195 cm. Low plant height was observed under drought conditions, consistent with the previous reports  (Ahmadikhah and Marufinia 2016; Mishra and Panda 2017; Hussain et al. 2018; Panda et al. 2021). Heritability for grain yield estimates ranged between 0. 20-0. 94 under the drought trials and 0. 43-0. 83 under normal growing trials for the non-stress trials (Fig. 1b ). Lower heritability was observed in most of the drought trials. Reduction in heritability under drought conditions is a common phenomenon  (Henry et al. 1997 , Kumar et al. 2007 ) which indicates that genotypes are not able to express the higher genetic potential for grain yield.
Historical Data Connectivity Historical data sets usually have very low connectivity as new lines are being tested every year, and only a limited number of times the new lines are being tested. In the current data set, we observe appropriate connectivity of the different genotypes across the years (Fig. 1c ), and this connectivity was mainly created by long-term checks (IR64, Swarna, Sahbhagi Dhan, IRRI 154) across the years. Further, over the breeding cycles and years, the superior genotypes were forwarded and re-tested in the succeeding years which made the dataset well connected to previous years (Additional file 1: Fig. S3 ) Further to ensure good connectivity and get reliable estimates of the breeding values, relationship matrix (Additional file 1: Fig. S4 ) based on pedigrees of 2,970 unique lines was incorporated in the second stage of mixed model analysis.
Acknowledgements We express profound gratitude to the  Bill and Melinda Gates Foundation  to fund this research study. Special thanks to  Hans Bhardwaj  ( Platform Leader ,  Rice Breeding Innovations, IRRI ) for his guidance and all the support. Authors also thank IRRI rainfed breeding program breeders  Gary Atlin ,  Arvind Kumar  and  Shalabh Dixit  who all had conducted these trials since 2003 and generate the data used in this study. We also thanks  Jessica Rutkoski  a former IRRI biometrician who initiated the genetic gain study in the  IRRI's drought breeding program  and we adopted some of the R scripts from her work.
Past studies that have proposed parent terrains for the unique NWA 7034 regolith breccia all agree that it must come from the heavily cratered southern Noachian highlands of Mars  7, 11, 13, [23] [24] [25] [26]  based on their notable geophysical and geochemical affinities, such as the elevated concentrations in potassium (K), thorium (Th), and iron (Fe) in the meteorite  8, 9, [23] [24] [25] [26]  , the ages of the oldest minerals found in the breccia 1,5,8-10 , its unique magnetic signature  7  , and its visible-infrared reflectance spectra  24. In this work, we search for the most likely site of ejection of the regolith breccia by using four criteria based on its geochemical and geophysical properties as well as its geochronological records (Methods), which we compare to potential sites based on their known properties and geological context: (1) high magnetic field intensity and remanent magnetization at the surface from up-todate orbital dataset  27  (Methods, Fig.
2b, c ); (2) high elemental Th and K concentrations  28, 29  (Methods, Fig. 2d, e ) of the areas surrounding each crater candidate; (3) superposition on a Noachian geological unit  30  and (4) connection with material from an Early Amazonian impact. We show that only one crater candidate characteristics match with the meteorite properties. The oldest clasts of NWA 7034 and paired stones were excavated ~1. 5 Ga Fig. 1  Summary of NWA 7034 and paired stone radiometric ages, and chronology of major events experienced by the breccia. Dates from each study are reported in Supplementary Data 1. The ejection event is constrained from  22  Ne/  21  Ne cosmic ray exposure ages  12, 13. Whole rock (w. r), zircon and or baddeleyite (zr. b), apatite (ap), and augite (aug) on which ages have measured are also mentioned.
17 ). Hence, the large majority of the ejected debris capable of escaping martian gravity following the formation of Karratha are from the Khujirt ejecta blanket, not from the underlying material related to the Dampier crater (Fig. 3 ). Moreover, orbital datasets indicate that the Karratha crater is associated with particularly high concentrations of K and Th and   18  (Supplementary Table  1 ) and location of provinces and rovers (yellow triangles) referred to in the present study. Background: Mars Orbiter Laser Altimeter (MOLA) shaded relief (https://astrogeology. usgs. gov/search/map/Mars/GlobalSurveyor/MOLA/Mars_MGS_MOLA_DEM_mosaic_global_463m). b, c: Magnetic field intensity and remanent magnetization at the surface from ref. 27. d, e Potassium and Thorium concentration at the surface from ref.
1 ), while the ejection took place ~5 Ma ago  12, 13  (in yellow in Fig. 1 ). Constraints from orbital dataset. We compare the abundance of K and Th as well as the magnetic field intensity and the magnetization of the surface of Mars derived from orbital measurements at the immediate vicinity of each crater candidate with those of the breccia. The concentrations in K and Th are from the Gamma-Ray Spectrometer (GRS) aboard the Mars Global Surveyor (MGS)  28, 29. Although the spatial resolution of the data is low (5°x5°, corresponding to ~296 km at the equator), this dataset offers a consistent method to compare the concentration of both elements qualitatively between several regions where the crater candidates are located  63. We report here both the pixel value of the concentration of K and Th at the crater location (Supplementary Table  1 ) and the bilinear interpolation computed with a radius of 296 km around each crater centroid (Supplementary Table  2 ).
3 ) and (2) a bilinear interpolation computed from a 100 km buffer (resolution of the model) computed around each crater centroid (Supplementary Table  2 ). In order to distinguish crater candidates associated with a host terrain exhibiting relatively high values of the four orbital datasets considered here, we compute the first standard deviation from the kernel density distribution of the data. Values higher than +1σ are considered relatively high (Supplementary Fig. 2 ) and used to discriminate areas with elemental concentration and magnetic properties that might be consistent with the exceptional characteristics of the breccia qualitatively (Supplementary Fig. 3 ). Enrichment in K and Th in the meteorite being related to magmatic processes, we chose to discard the northern lowlands, and more generally areas above the dichotomy, to compute the distribution of the K and Th concentration due to the weathered basaltic surface contribution  64  that may not represent the bedrock chemistry on those provinces.
Supplementary Fig. 3  presents the chemical and magnetic signatures for each crater candidate (in pixel values). The high values range associated with each dataset are represented by the white area and impact craters are color-coded as followed: craters located on Noachian geological unit  30  and superposed on the material of an Amazonian impact crater (cavity or ejecta) are in green. Those that are only located on Noachian material are in orange, and craters superposed on the ejecta of an Amazonian impact crater are in red. Finally, if none of these two criteria are respected, craters are shown in grey. Model age derivation of impact events. Karratha crater is superposed on the ejecta blanket of a 40 km crater (Khujirt, in blue in Fig. 3 ) that filled the cavity of an older crater, most likely of Noachian age (Dampier, in orange in Fig.
3 ). Using the Context Camera (CTX) mosaic  21  , we mapped impact craters superposed on both the 40 km crater cavity (for D > 100 m) and its ejecta blanket (for D > 500 m) to estimate the age of the material surrounding Karratha, i. e. , the ejecta blanket of the 40 km crater (Supplementary Fig. 4c, d ). For this, crater mapping is performed by using the CraterTools software  65  and Crater-Size Frequency Distributions (CSFD) are loaded into CraterStats II  66  and fitted with an isochron using a standard chronology model  22. We derived all model ages using the differential representation  67, 68. Compared to a cumulative plot, the differential representation allows easier recognition of any resurfacing event contribution, the presence of potential overprinting craters formed prior to the ejecta blanket in the population of mapped craters, or secondary craters  67.
Acknowledgements The authors acknowledge  Benoit Langlais  for providing guidance and magnetic field data, and  Denis Fougerouse  for discussions and guidance regarding age measurements used in this study. We gratefully acknowledge the developers of iSALE-2D, including  Gareth Collins ,  Kai Wünnemann ,  Dirk Elbeshausen ,  Tom Davison ,  Boris Ivanov  and  Jay Melosh. This research was funded by the  Australian Research Council  grants  DP170102972 ,  DP210100336 ,  DP180100661 ,  DE180100584 , and  FT210100063 ,  Curtin University , the  Western Australian Government , and the  Australian Government.