dataesr/scientific-paragraphs-categorization huggingface dataset:
It remains to be seen how we may use realistic shapes of the spectrum for the unresolved part of the spectrum  (Elfouhaily et al. , 1997; Yurovskaya et al. , 2013 ) so that the model may produce realistic mean square slopes and drag coefficients in particular for high wind speeds  (Janssen & Bidlot, 2023). The present work was limited by the availability of large datasets with detailed directional wave measurements and reliable measurements of the short wave energy level. In particular we have made no attempt to tune the spectral level to an elusive reference and only used stereo-photo and stereo-video measurements as a weak guideline for average wind conditions  (Banner et al. , 1989; Leckler et al. , 2013; Peureux et al. , 2018). The tail level may vary widely depending on the choice of cumulative terms.
Data Availability Statement In agreement with Fred Duennebier's family and colleagues the bottom pressure spectral data is available at https://doi. org/10. 17882/92104. The CDIP wave buoy data are available at cdip. ucsd. edu, ERA5 reanalysis available from https://cds. climate. copernicus. eu and altimeter data from the CNES/NASA/Eumetsat/NOAA mission Jason-2, reprocessed by ESA and available at dx. doi. org/10. 5285/8cb46a5efaa74032bf1833438f499cc3. The WAVEWATCH III ® model (The WAVEWATCH III Development Group, 2019) can be downloaded from https://github. com/NOAA-EMC/WW3. The particular modifications used here for ST4 and GQM are available at https://github. com/umr-lops/WW3/tree/GQM_with specific example test cases in regtest/ww3_ts1/ input_10ms.
Flow Cytometry Analysis of Cell Phenotype Cells were washed with ice-cold PBS, incubated with Fc block (Human TruStain FcX, Biolegend, London, UK, 1/20) for 15 min, incubated with antibodies for 20 min at 4 • C and washed before analysis using a BD LSRFortessa X-20. Antibodies were AlexaFluor700-CD80 (#561133, BD, Franklin Lakes, NJ, USA), PE/Cy7-CD86 (#561128, BD), APC/Alexa750-CD71 (#A89313, Beckman coulter, Brea, CA, USA), ECD-CD62L (#IM2713U, Beckman coulter), Krome orange-CD14 (#B01175, Beckman coulter), Pacific Blue-CD16 (#A82792, Beckman coulter), APC-CD1a (#IM3645, Beckman coulter), PE-CD163 (#556018, BD) and FITC-CD115 (#FAB329F, R&D Systems). The data were analyzed with FlowJo software v. 10. 0. 00003.
Confocal images were acquired on a Leica SPE Confocal system, sequential acquisition using 63X/1. 3 oil immersion. For 3D pictures, we used Bitplane Imaris software v. 7. 7. 2. Lines intensity profiles were realized using the LAS AF software v. 2. 7. 3. 9723. Fluorescence quantification and co-localization quantification were calculated using ImageJ 2. 0. 0-rc-69/1. 52p.
RNA Sequencing RNA extracted using the RNeasy mini kit (QIAGEN) was processed using SureSelect Automated Strand Specific RNA Library Preparation Kit and sequenced on Illumina Novaseq 6000 in paired-end 100bp mode in order to reach at least 40 million reads per sample. Raw reads cleaning was done using Trimmomatic (v0. 32) with the following parameters: IL-LUMINACLIP:2:30:10 LEADING = 3 TRAILING = 3 SLIDINGWINDOW:8:20 MINLEN:30. Cleaned reads were pseudo-mapped to genome with Salmon (v0. 9. 2) and wasabi (v0. 2) with the following parameters: -numBootstraps 100 -gcBias -seqBias -libType A. The differential analysis was performed with Sleuth (v0. 29), in gene aggregation mode on Gencode HG38, with qvalue below 0. 05 and fold change above 1. 3. Graphs and clustered heatmap were performed with euclidian distance and ward criterion under in-house python (v3.
5. 2) scripts. RNA sequencing data are in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5752 (Username: Reviewer_E-MTAB-5752; Password: y9PIG6yd). Gene Set Enrichment Analysis (GSEA) was performed with the java version gsea2. 0 of the Broad Institute installed on a local platform and the Molecular Signatures Database v6. 2 as reference. Primers used to validate gene expression changes by qRT-PCR are listed in Table  S2.
For the sorting of monocytes, the antibodies used were the following: APC-CD11b (#17-0112-83, Thermo Fisher Scientific), PE/Cy7-CD11c (#561022, BD), FITC-NK (#553164, BD), PerCP/Cy5. 5-Ly6C (#560525, BD), AlexaFluor700-Ly6G (#561236, BD), biotin-CD115 (#135507, Biolegend), PE-Streptavidin (#554061, BD). The data were analyzed with FlowJo software v. 10. 0. 00003.
Fibrosis Quantification To quantify the extent of collagen fibers, left lungs were fixed in 4% formaldehyde, paraffin embedded, cut into 4-µm sections, stained with Sirius Red, scanned using a microscopy virtual slide system (Olympus VS120), and analyzed using ImageJ 1. 50b software. To quantify airspace number, tissue sections 4-µm stained with Sirius Red were scanned using a NanoZoomer-SQ (Hamamatsu Corporation, Shizuoka, Japan). Images of entire lung sections were recorded by means of NDP. view. 2 software (Hamamatsu Corporation, Shizuoka, Japan) and analyzed at ×20 magnification with a pixel size of 0. 452 µm. To quantify fibrosis, we used a numerical program that allows a fully automatic selection of airspaces (alveoli and ducts) from the entire lung sections, without the large bronchi and vessels.
The data were analyzed with FlowJo software v. 10. 0. 00003. Interstitial macrophages were selected according to their larger size (FSC) and granularity (SSC), on CD45 expression to select leukocytes and GR1-positive cells will be excluded to remove neutrophils, they are equally CD11b high, SiglecF negative, IA-IE positive and CD24 negative.
Acknowledgments: We acknowledge the Cell and  Tissue Imaging Platform  ( PICT-IbiSA ), member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ) for helping in microscopy image quantifications. We thank  Karine Bailly  of the  Cochin Cytometry and Immunobiology Facility. We thank  Philippe Rameau ,  Yann Lecluse , and  Didier Métivier  for outstanding technical assistance,  Philippe Dessen ,  Thibault Dayris ,  Serge Koscielny ,  Jane Merlevède ,  Matthieu Duchmann  and  Khadija Diop  for their advices in biostatistics and bioinformatics,  Olivier Hermine  for providing blood samples from NOX2-deficient patients,  Alexandre Boissonnas  for helping in lung cells analysis.
SPI data analysis method At high energies, focusing photons becomes a hard exercise. Wolters telescopes, such as XMM-Newton, can be used up to keV energies, but for MeV and GeV photons, telescopes look like particle detectors. SPI is an instrument with a coded-mask made of transparent pieces that let the photons come in and opaque pieces absorbing them. On the germanium detector, located below the coded-mask, each source emitting photons casts a coded shadow, but the detected photons are not directionally tagged and this impacts the data analysis. One analysis method is to assume spatial models for the photon emission, simulate what would SPI see through the coded-mask and compare the results to the SPI raw data, i. e. numbers of photons per observation, per energy and per detector. The best model is chosen with a maximum likelihood fit performed in each energy bin independently, which finally allows one to construct the total spectrum.
optimize the model and then extrapolate OCpetro oxidation rates (Extended Data Fig. 1 ). Model simulations were implemented at 1-km grid scale (Mollweide projection, WGS84 datum) in the Python programming language. R and python code, their environments and the necessary data files to run these are all deposited in a Zenodo depository (https://doi. org/10. 5281/zenodo. 8144244) and additionally code is available from Github: https://github. com/jessezondervan/Global_OCpetro_Oxidation.
In the large river dataset 9 some large rivers are noted to have markedly increased Re concentrations and fluxes, with the conclusion that this was due to anthropogenic Re inputs. In a first order catchment of the Mississippi Basin, this has been confirmed by a detailed Re mass balance  52. A study of Re across Indian catchments suggests that whilst Re in Himalayan catchments and the mainstem Ganges and Brahmaputra behave conservatively, peninsular lower relief catchments with denser populations and industrial activity suggest anthropogenic inputs  53. For this study's purposes to quantify weathering reactions, we only use Himalayan rivers and the mainstem Ganges and Brahmaputra in India and we have further excluded Re data from the Danube, Mississippi, and Yangtze rivers from our analysis.
Estimation of Re source and fc To estimate the fraction of dissolved Re sourced from OCpetro, fc, for the rivers in the Re flux dataset  9  , we follow a previously used forward model mixing approach  34, 25  : ReOC = Retot × fc = Retot -Resulf -Resil (Eqn. S2) Where ReOC is the rhenium concentration of OCpetro-derived Re in the dissolved load, Retot is the measured concentration, Resulf and Resil are the concentrations derived from weathering of sulfide and silicate minerals respectively. These unknowns can be quantified as: 𝑅𝑒 /012 = 𝑆𝑂 3 × $ *$ 4! & % /012 (Eqn. S3) 𝑅𝑒 /-1 = 𝑁𝑎 × $ *$ 56 % /-1 (Eqn. S4) where the element ratios of the end members for silicate, $ *$ 56 % /-1 , and sulfide $ *$ 4! & % /012 are defined and with the assumption that the dissolved sulfate (SO4) and sodium (Na) respectively derive only from sulfide oxidation and silicate weathering and are conservative.
Constraints on ([OCpetro]/[Re])i A recent compilation 54 provides measurements of [OCpetro]/[Re]i from rock samples of different ages around the world. However, most of these measurements were made on black shales, with OCpetro contents > 1% which occur on only 0. 3% of the Earth surface (Fig. S5 ). Riverbed material sediments from erosive catchments provide an alternative way to capture landscape-scale average rock composition, albeit with some potential for weathering to alter the primary signal. Here we compile measurements of [OCpetro]/[Re] on bed materials from rivers around the world (Supplementary Table  S3 ; Extended Data Fig. 2 ) and supplement this dataset with additional samples from mudrocks of the Eastern Cape, New Zealand, and the Peruvian Andes measured using methods described in detail previously  25.
We find that regions with lower OCpetro concentrations that are more typical of sedimentary rocks at the continental surface -units including fine-grained sedimentary rocks make up > 35 % of Earth surface (Extended Data Fig. 5 ) -have lower and more consistent ratios of OC and Re in their rocks. The samples from the Peel River in the Mackenzie River basin  34  , overlap the lower end of the published black shale values. Since this is the catchment with the highest proportion of black shales in our Re dataset, these samples allow us to capture the imprint of this important marginal lithology at the landscape-scale. The bedrock composition in the catchments of rivers studied in the Re flux dataset 9 are not reported. However, we note the good geographic coverage and number of samples that we have from riverbed materials from erosive settings around the world.
OCpetro oxidation yields and uncertainties Equation 1 is used for each basin in the Re flux dataset  9. Uncertainties in fc derive from the range of values used in the sulfide and silicate end member compositions (Eqns. S3, S4). For ([OCpetro]/[Re] ) we use the range of values discussed in Section 1. 2. 2 (Extended Data Fig. 3 ). A Monte Carlo uncertainty propagation is used on these variables, with 10,000 randomly selected combinations of input values (with uniform sampling) are used to estimate JOCpetro-ox for each basin. The median value of the Monte Carlo simulation and the interquartile range are reported (Supplementary Table  S1 ).
Geospatial catchment boundaries To derive the catchment outlines and areas of Re-proxy samples in our compiled dataset, we used the HYDROSHEDS flow direction grid at 3 arc-second resolution  55  , and ArcGIS Pro 56. For catchments in Iceland and outside the latitudinal cover of HYDROSHEDS, catchments were derived from ALOS AW3D using TauDEM functionality in OpenTopography 57 and from the HYDRO1K flow direction grid respectively  58. Whilst most published sample coordinates Supplementary Table  S1 ) give the correct location on the cited drainage systems, in a handful of cases coordinates had to be amended by up to a few km, which may reflect errors in transcribing (e. g. , Kikori and Purari 9 ). Final quality control included a comparison of the extracted drainage basin areas and those published, with good agreement overall (< 2% residual).
However, some drainage areas cited in the Re flux dataset 9 refer to the river mouth, rather than the river catchment upstream of the Re sample location. In these cases, we use the Re sample location and its upstream catchment. Finalised coordinates of Re samples snapped to the drainage system are given in Supplementary Table  S1 , together with the Re locations' upstream drainage area. Spatial files of upstream drainage boundaries and Re sample locations are available on Zenodo (Available from: https://doi. org/10. 5281/zenodo. 8144244). To convert dissolved Re concentrations into Re fluxes, average annual water discharge was calculated using published numbers at gauges (Supplementary Table  S1 ) and scaled to the upstream drainage area of Re sample locations. In addition to spatial catchment boundaries for the Re proxy dataset, we compare our spatial model output to published estimates of silicate weathering 10 that use the GRDC dataset of Major River Basins of the World 59.
Drainage areas used by  10  have slight discrepancies with those found in the GRDC dataset. We account for these in our analysis of major river basin net weathering flux (Supplementary Table  S4 ). as the spatial derivative of elevation -decreases, resulting in an artificial flattening effect  16. As our Monte Carlo framework is computationally intensive, using a 90-m resolution global raster input would not be feasible. However, we use a 90-m resolution slope dataset to run regression curves shown in Extended Data Fig. 6 , after which we output a 90-m resolution raster dataset of estimated denudation rates using the median regression curve. By resampling the raster dataset of estimated denudation rates to 1-km resolution after conversion from slope values, we avoid the bias that can lead to an underestimation of denudation by the flattening effect.
Section 3. Limitations and uncertainties There is a temporal mismatch between CRN denudation data which informs our probabilistic Results of model vs Re-predicted OCpetro oxidation fluxes help us to assess the potential for anthropogenic Re input to impact our estimates (Extended Data Fig. 7 ). We have considered anthropogenic Re inputs by removing three large river basins from a previous compilation  9  and by adding carefully selected river catchment sites to our Re dataset (Section 1. 2). In addition, our conversion of Re fluxes to OCpetro oxidation is conservative because we uniformly sample the range of Re/OC ratios starting at the lowest measured Re/OC ratio (Section 1. 2. 2). This leads to error bars within our estimates that are conservatively large. Most notably, the model outputs of OCpetro oxidation vs the Re-estimated fluxes for each basin (Extended Data Figure  7 ) show a tendency for the model to underpredict smaller catchments more than larger catchments.
Our confidence in the weathering signal from Re in the small upland catchments is highest, and the upland, high erosion rate regions that these catchments sample contribute a dominant proportion of the global OCpetro flux in our model. Consequently, whilst we cannot completely deconvolve the effect of anthropogenic Re in our constraints, we have confidence that the effect is unlikely to result in a significant overprediction of global OCpetro flux estimates. The global extrapolation of OCpetro oxidation proxy data attempts to account for the dataset's underlying heterogeneities in denudation and OCpetro stocks. However, it does not consider variability in temperature or precipitation which may control weathering -as seen in small scale field measurements of OCpetro oxidation at sites of high denudation  30, 31.
This is primarily due to the size of the Re-proxy catchment database, its spatial coverage and uncertainties inherent in any proxy approach. While the Re-proxy dataset is latitudinally variable (Fig. 1a ) the model misfit minimization procedure shows the first order controls on flux by OCpetro stock and denudation (Extended Data Fig. 7 ) meaning that any climatic controls on weathering could be not resolved at the global scale. We note that any bias introduced by extrapolating the global Re-proxy data without including climatic spatial controls on weathering intensity is likely to be minimal, because the underlying dataset spans from the tropics to Arctic locations. Previous work has suggested that OCpetro oxidation and cycling of other OC pools can take place during floodplain transport in large fluvial systems  64, 65.
While the Re-proxy dataset includes large basins with extensive floodplain areas (Fig. 1a ), our model-data misfit approach may attribute the downstream fluxes to any higher denudation parts of the catchment. When Finally, our model includes implicit assumptions and features of the datasets which must be acknowledged. First, the model assumes a steady state, which might not accurately describe OCpetro oxidation in regions responding to changes in uplift, deglaciation, or human activities, which may yet to have reached steady-state conditions. Second, the catchment-scale CRN denudation data used in our model generally derive from lithologies that are quartz-rich and coarse-grained. These typically have lower erodibilities, potentially leading to an underestimation of denudation rates of softer shales which contain the majority of OCpetro stocks.
Acknowledgments The authors would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC) facility  in carrying out this work. http://dx. doi. org/10. 5281/zenodo. 22558. In addition, this work is based on digital elevation products and high-performance topographic analysis services provided by the OpenTopography Facility with support from the  National Science Foundation  under  NSF  Award Numbers  1948997 ,  1948994  &  1948857. This study was supported by the  European Research Council   Starting Grant   ROC-CO2 678779  (R. G. H. ).
Data availability All dissolved rhenium sample data are available in the Supplementary Materials Tables S1-S5, which in addition to geospatial data including those for Figure  1  are deposited in a Zenodo repository (https://doi. org/10. 5281/10. 5281/zenodo. 8144244). Source data for Figure 2 and Extended Data Figures 4 -6 are also available in separate Excel and CSV files respectively (see Figure legends).
climate regimes (Fig. 1), excluding river basins with high Re pollution levels such as the Danube, Yangtze and Mississippi 9 (see Methods). The total OCpetro weathering flux constrained from the Re-proxy across the drainage area of river basins in the dataset is 18 MtC yr -1 (17 -23 MtC yr -1 within one standard deviation). The river basins in this study cover 18% of the Earth's continental surface, and this flux would thus scale to MtC yr -1 globally. However, OCpetro stocks are spatially heterogeneous. To obtain a robust representative total OCpetro weathering flux, in the next section, we use a spatial extrapolation model that considers patterns in OCpetro stock and denudation rates.
Using our spatial model, we estimate that oxidative weathering of OCpetro releases MtC yr -1 as CO2 from the land-surface environment. The flux is lower than our spatially uncorrected extrapolation of Re-proxy measurements ( MtC yr -1 ), consistent with the slight bias towards high denudation rate settings in the river basin dataset. The best estimate of the oxidative weathering flux is higher than an independent estimate of OCpetro erosion and river transport (i. e. , the export of OCpetro that has not been weathered) of MtC yr -1 based on river solid load composition and flux 19 , even though the uncertainties overlap. While a direct comparison of these estimates is difficult based on their quantification from dissolved vs particulate river chemistry and flux, they suggest an average weathering intensity of OCpetro of ~60%, which is consistent with studies from large river basins 19 and intensities measured in soils 8,42.
However, pp-KPFM features a severely limited bandwidth. In the case of our last implementation of pp-KPFM  [4] , several tens of seconds (to minutes) are needed to record a single spectroscopic curve, and tens of hours to several days are needed to map a 2D matrix of data. Last, G-mode KPFM requires analysis of large datasets acquired with high-speed data capture  [12]. A few years ago, Borgani and Haviland presented an interesting alternative for time-resolved SPV measurements  [16] , which consists in analysing the intermodulation products between the mechanical oscillation of the cantilever and the photogenerated surface potential. In short, intermodulation spectroscopy allows working in the frequency domain (instead of the time domain) by extracting, during a single measurement, quantities that are directly proportional to the Fourier coefficients of the time-periodic surface photovoltage.
This is done by sequentially sweeping the signal from the second channel of the AWG (i. e. , nω mod with n = 1, n = 2, n = 3, etc. ). To that end, a pre-programmed list of waveforms is played, and the triggering is done by TTL pulses generated by the AFM controller. The AWG waveform playlist is programmed by using the Keysight benchlink pro software, and the TTL pulses are generated during the course of the spectroscopic ramp by using a user-available high speed digital output of the Mimea unit. To measure the "dc" components of the Fourier spectrum (V 0 and the reference phase Φ 0 ), the sideband generation is switched during a dedicated time window within the spectroscopic ramps (i. e. , from DHe-KPFM to standard AM-heterodyne KPFM configuration). To do so, a Python routine is used to toggle the output configuration of the HF2LI modulation module (see Supporting Information File 1), as a function of a trigger signal sent by the Mimea unit.
Both the phase and amplitude spectra can be adjusted using fit functions, which are obtained from an analytical calculation of the Fourier components for a time-periodic SPV, whose decay dynamics between light pulses follows an exponential time dependence (with a time constant τ d ). An initial approach to the problem consists in neglecting the photocharging dynamics (i. e. , making the approximation of an instantaneous sample photocharging as soon as a light pulse is applied). The calculation of these functions and of the Fourier coefficients is given in a dedicated section of Supporting Information File 1. The results of this data-cube DHe-KPFM experiment and of the post-data acquisition data treatment (automated adjustment of the matrix of spectroscopic curves by batch processing with OriginPro software) are presented in Figure  4 , along with the topographic image recorded simultaneously pixel by pixel with the spectroscopic curves.