dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by  Inserm Transversal Programme on Microbiota , the  French National Research Agency  ( ANR-10-LABX-62-IBEID ,  INCEPTION ),  ANR-17-CE17-0014  and  ANR-21-CE15-0006 , the " Fondation ARC" PJA 20191209650 , the  "Ligue Nationale contre le Cancer Subvention de Recherche Scientifique "  RS20/75-63 , the  "Fondation pour la Recherche Médicale" (Equipe FRM   2016 ,  DEQ20161136698 ).
Methods Modeling framework. The CAMS high resolution forecasting system at the European Centre for Medium Range Weather Forecasts (ECMWF)  13, 14, 20  has been used to produce the nature run dataset which includes simulations of CO 2 , CH 4 and CO as illustrated in Fig. 1. It is based on the Integrated Forecasting System (IFS) model cycle 46R1 used to produce the operational weather forecast from June 2019 to June 2020  21. The model has a reduced octahedral Gaussian grid  22  with a resolution of Tco1279 (corresponding to approximately 9 km) and 137 model levels. The simulations have been produced by running a sequence of 1-day IFS forecasts of the carbon tracers and weather. The weather forecasts are initialized with state-of-the-art re-analysis of meteorological fields (ERA5)  23. The atmospheric tracers start from the CAMS re-analysis  24, 25  initial conditions at the initial date of the dataset and from then onwards they are cycled from one forecast to the next in a free-running style.
Model output. The standard parameters available from the CHE nature run dataset are listed in Table  2  and Table  S1  in Supplementary Information file 1. Additional experimental tagged tracers are provided to characterize the atmospheric enhancement associated with the natural surface fluxes and anthropogenic emissions (Table  3 ). The enhancement can be computed by subtracting the concentrations of the background tracer without the specific emission/flux from the tracer concentration with the flux/emission. This assumes that the transport is linear. It is worth noting that artificial negative enhancements can occur in the vicinity of plumes due to numerical oscillations associated with the cubic interpolation of the advection scheme around very steep gradients. This can be considered a numerical error in the simulation.
The CO 2 tagged tracers are simulated without applying any mass fixer in order to ensure the signal comes only from the flux. The tagged tracers provide the enhancement during each 1-day simulation as they are re-initialised every day at 00UTC in order to avoid growing errors associated with the mass conservation  33, 34. This means the flux enhancement is reset to zero at 00 UTC. Detailed information on those tracers is provided in Table  4. Figure  1b  provides an overview of the different types of model output from the CHE nature run dataset and how these can be compared to other datasets including various types of observations  5, 35, 36  as well as atmospheric inversions/simulations of carbon tracers  9. Such a comparison can shed some light on the different components of the uncertainty in the simulations of carbon tracers coming from the surface fluxes, the atmospheric transport and the representativity error associated with the limited model resolution  14.
As satellites have different viewing geometries over land and ocean  16  , it is very important to understand how many of these plumes are located over land, ocean and coastal regions. Moreover, satellite observations only provide total column CO 2 over cloud-free regions. Table  4  provides an example of statistics on the proportion of anthropogenic plumes accumulated over a 24-hour period over land/ocean and the proportion of plumes under cloudy conditions for January and July 2015. These fossil fuel tagged tracers and other tagged tracers associated with the biogenic fluxes, ocean fluxes and biomass burning emissions are all included in the CHE nature run dataset (see Table  3 ). Example: insights into total column variability. The CO 2 , CH 4 and CO observing system is based on in situ observations, at the surface or from tall towers, and remote sensing observations from ground-based stations or satellites providing partial/total column observations.
Data Records The CHE nature run dataset can be accessed through the ECMWF API following the examples provided in  46. The data can be extracted on the native octahedral grid with the original resolution (tco1279, corresponding to approximately 9 km) or on a regular latitude/longitude grid at the required resolution of the user. Both grib and NetCDF formats are available. The dataset extends from 26 December 2014 to 31 December 2015. The list of contents is provided in Table  2. All meteorological and tracer fields and surface fluxes have been archived with 3-hourly time steps with respect to the 00 UTC initialization of the weather forecast. Step 0 of all the meteorological parameters represents the initial conditions taken from ERA5  23. Atmospheric species (CO 2 , CH 4 and CO) at step 0 are equivalent to tracers from the previous day at step 24, because they are free-running from one 1-day forecast to the next as illustrated in Fig.
technical Validation The dataset is based on the state-of-the-art operational NWP and CAMS forecasting system  21, 47  which has been proven to produce reliable and accurate atmospheric CO 2 , CH 4 and CO variability  [13] [14] [15]. The CHE nature run focuses on 2015, a year characterised by a pronounced decrease in the terrestrial carbon sink associated with the. The variability with respect to the mean number is shown by the +/standard deviation. The statistics are also provided for clear-sky conditions, land, ocean and coastal regions, as these considerations are all relevant for satellite observations. Clear-sky model cells are defined with a cloud fraction threshold less than 10% over the 9 km × 9 km model cell; land cells have more than 99% land; ocean cells have less than 1% land and model cells over the coast have land between 1 and 99%.
Example: Simulation and observation mismatch in the total column of CO 2 , CH 4 and CO. The TCCON data  60  which is widely used as a reference to evaluate biases in global measurement of CO 2 , CH 4 and CO total column averages-referred to as XCO 2 , XCH 4 and XCO-from space  16  is used here to assess the inter-hemispheric gradient, seasonal cycle and synoptic day-to-day variability in the nature run dataset (Fig. 4 ). The large-scale patterns of variability on a monthly scale are generally well represented for the three species. The amplitude of the XCO 2 seasonal cycle is underestimated at most TCCON sites, with the summer trough being 1 to 3 ppm higher than observed. This is consistent with the general underestimation of the biogenic sink during the growing season shown in Fig. 3. XCH 4 is overestimated in spring/summer and underestimated in autumn/winter, due to errors in the seasonality of the chemical sink and emissions (e.
5 ppm for XCO 2 , 10 ppb for XCH 4 and 10 ppb for XCO, except at urban sites near emission hotspots such as Pasadena, Tsukuba and Paris. Example: Fine-scale structure in vertical profiles. The vertical profiles of CO 2 , CH 4 and CO are illustrated in Fig. 5  with a comparison to AirCore observations  36, 39  from the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory and the lower-resolution CAMS surface in situ inversion dataset  57, 65, 66. While most global transport models used in atmospheric inversion systems have too coarse horizontal and vertical resolution to be able to represent the fine-scale vertical structure, the CHE nature run is able to capture the small-scale anomalies along the atmospheric column from the surface up to the lower stratosphere (50 hPa).
The profiles on three different consecutive days show the large variability associated with day-to-day synoptic transport, particularly for CO 2. Capturing this type of vertical variability is important because it reflects the ability of atmospheric transport models to represent vertical mixing and long-range transport. Both need  dataset and the MARS data extraction features are freely available through ECMWF API (https://www. ecmwf. int/ en/forecasts/access-forecasts/ecmwf-web-api) following a registration step (https://apps. ecmwf. int/registration/). The data can be accessed using python (https://www. python. org). The commands and steps required are detailed in the Supplementary Information file 1 (S2). ). The nature run is compared to NOAA AirCore (v20201223) observations  36, 39  and the CAMS CO 2 and CH 4 inversion  57, 65, 66  (a-i) during three days in June, depicted by the dashed lines in (j) where the nature run hovmöller plot for CO 2 shows the temporal variability of the vertical profile at Sodankylä over the whole month of June.
acknowledgements The Copernicus Atmosphere Monitoring Service is operated by the  European Centre for Medium-Range Weather Forecasts  on behalf of the  European Commission  as part of the Copernicus Programme (http://copernicus. eu). The  CHE  and  CoCO2  projects have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776186  and No 958927. We also thank the  FLUXNET  and  TCCON PIs  for providing the data used for the validation of the nature run dataset.
Data Processing and Statistical Analysis Due to the complexity of this dataset, data for female and male mussels were treated separately. The raw data were converted into mzXML files with MSConvert freeware (ProteoWizard 3. 0  [71] ). Then, the data were processed using the XCMS package in the R environment. Optimized XCMS parameters were implemented: m/z interval for peak picking was set at 0. 002, the signal-to-noise ratio threshold was set at 10, the group bandwidth was set at 15, and the minimum fraction was set at 0. 5. The data were first filtered, and signals presenting an RSD of QC > 20% and retention time beyond the analysis time range (60-1300 s) were discarded. After that, a Mann-Whitney test was performed daily to highlight signals presenting significant differences between the exposed and the control groups.
Metabolite Annotation and Identification Metabolite annotation was completed by mass-matching with 0. 002 Da precision using the online Human Metabolome Database (HMDB, http://www. hmdb. ca/, 14 June 2021). The identification of annotated metabolites was based on two confirmation levels. The annotation levels are listed in Table  2  on the basis of the results reported by  Sumner et al. 2007 [72]. Level 1 corresponds to identification confirmed by injection of the analytical standard (5 ng of standard) on the same analytical platform and under the same conditions (validation based on both accurate mass and retention times). Each standard was also added to QC samples to confirm the retention time within the matrix. In order to confirm the compound annotation, MS/MS acquisitions were also performed on QC samples with a high energy collision dissociation (HCD) cell set at 20 eV and a mass resolving power of 17,500.
The MS/MS spectra observed after fragmentation of the signal of interest were compared to an in-house database. A metabolite's identity was confirmed when most of the major fragments were in common. Level 2 metabolites were annotated on the basis of public databases (m/z matching and consistent physicochemical properties). The identified and annotated metabolites were assigned in metabolic pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www. genome. jp/kegg/, 16 June 2021) and the pathway analysis online tool from MetaboAnalyst (https://www. metaboanalyst. ca/, 16 June 2021).
Data Acquisition The analyses were run on the same equipment as for the metabolomics with a flow rate of 0. 35 mL/min and a binary gradient of ACN (A) and water (B), both containing 0. 1% FA as follows: 10% A at 0 min, 50% A at 4-6 min, 60% A at 8-10 min, 70% A at 14-16 min, 80% A at 17-19 min, 10% A at 21 min, and a stop time at 27 min. The data acquisition was performed simultaneously in ESI+ and ESI-with the parameters as follows: 275 • C capillary temperature; 200 • C heater temperature, and the electrospray voltage was set at 4. 0/-4 kV. The full scan data were acquired at a mass resolution of 35,000 and an m/z scan range of 50 to 750. Using a mass inclusion list composed of the mass of the precursor ion of the compounds of interest, MS2 was achieved. A total of 10 eV absolute collision energy and 17,500 FWHM resolution were used.
Identification and Quantification of VLF Each compound was confirmed by comparison of its retention time and m/z with our in-house database. In order to quantify VLF in mussel soft tissues, calibration curves were plotted for blank mussel tissues by adding a fixed amount of the internal standards: VLF-d6 (500 µg/kg dw) and increasing quantities of VLF from 0 to 3000 µg/kg dw before extraction. In addition, a seawater calibration curve was plotted by adding a fixed amount of internal standard VLF-d6 (at 1 µg/L) and increasing quantities (0-15 µg/L) of the targeted VLF analyte in blank seawater.
We sequenced an additional 13 PDOs using the QIASeq Targeted DNA panel. Clinically relevant mutations identified in 11 PDOs were compared with mutations in the corresponding primary tumor (when tumor sequencing data were available) and found to be highly conserved (Figure  2B ), with an 88% concordance rate between PDO and donor tumor for these mutations. Using comparative genomic hybridization (CGH), we demonstrated that the copy number variants (CNVs) of PDO were stable from passage 1 to passage 5 (Figure  2C  and Supplemental Figure  4, A-C ), showing that our PDOs retained the genomic landscape and mutational patterns of their primary tumor source across multiple passages. Genomic and transcriptomic profiling of PDOs provides insights into drug sensitivity. Based on targetable mutations identified in the PDOs, we tested whether our models could predict response to therapy using genomic profiling.
To elucidate this difference in response, we evaluated transcriptomic data from these 5 PDOs in both media. Differential gene expression analysis of RNA-seq data demonstrated a high level of variance between WNT and PaTOM media for each PDO (Supplemental Figure  10A ) and distinct clustering of significantly differentially expressed genes between media (Figure  4A  and Supplemental Figure  10B ). GSEA of PDO HO44 showed enrichment in WNT media for EMT markers and TGF-β signaling (Figure  4B  and Supplemental Table  5G ). Increased TGF-β signaling is associated with EMT and tumor resistance to 5-FU and oxaliplatin  (29) (30) (31). PaTOM media showed an increase in expression of MTORC1 signaling and steroid hormone biosynthesis (Figure  4B  and Supplemental Table  5 , G and H). PaTOM medium contains hydrocortisone, which is part of the cholesterol pathway for steroid biosynthesis.
(B) CT scans of donor patient for PDO HO107 before and after treatment with progressive disease (PD), where a metastatic liver lesion of 24. 2 mm developed following treatment with FOLFOX. (C) Relative AUC values for PDO HO107 in response to 5-FU and oxaliplatin in WNT media. Each dot represents the relative AUC for 1 replicate derived from the line graphs in Supplemental Figure  14A ; n = 12. (D) Relative AUC values for PDO HO107 in response to 5-FU and oxaliplatin in PaTOM media. Each dot represents the relative AUC for 1 replicate derived from the line graphs in Supplemental Figure  14B ; n = 6. The data in this figure for HO17, HO44, HO163, HO219, and HO228 were derived from Supplemental Figure  8. PDO response was determined as intermediate, sensitive, or resistant using Jenks Natural Breaks.
The universal amplification reaction was purified, and the final library quantified by Qubit (Invitrogen), Fragment Analyzer Standard Sensitivity NGS Fragment Analysis kit (Advanced Analytical), and qPCR-KAPA Library Quantification Kit (Roche). Targeted DNA libraries were sequenced at 12 samples per lane using the Illumina cBot and HiSeq 3000/4000 PE Cluster Kit. The flow cells were sequenced as 150 × 2 paired-end reads on an Illumina HiSeq 4000, with the QIAseq Read 1 Primer custom sequencing primer, using the HiSeq 3000/4000 sequencing kit and HCS v3. 3. 52 collection software. Base calling was performed using Illumina's RTA version 2. 7. 3. Reads from PDO sequence data for both panels were aligned against the same Hg19 reference using bwa-mem  (54). Variants were called using Mutect2 software.
Variants were filtered and annotated using Ingenuity Variant analysis software (Qiagen) with default filters. Pathogenic and likely pathogenic variants that were detected with a variant allele frequency of at least 5% were considered for further analysis. Detected candidate variants were plotted using OncoPrint  (55). PDO variants were compared to variants found in the donor tumor sequence by the Tempus Targeted Panel, when available. CGH (CNVs). DNAs were digested for 30 minutes with DNase I prior to Klenow-based labeling. In each case, 1 μL of 10× DNase I reaction buffer and 2 μL of DNase I dilution buffer were added to 7 μL of DNA sample and incubated at room temperature and then transferred to 70°C for 30 minutes to deactivate DNase I. Sample and reference templates were then labeled with Cy-3 dUTP and Cy-5 dUTP, respectively, using a BioPrime labeling kit (Invitrogen) according to our published protocol  (56).
All labeling reactions were assessed using a Nanodrop assay (Nanodrop) prior to mixing and hybridization to CGH arrays (Agilent Technologies) for 40 hours in a rotating 65°C oven. All microarray slides were scanned using an Agilent 2565C DNA scanner and the images were analyzed with Agilent Feature Extraction version 11. 0 using default settings. The CGH data were assessed with a series of QC metrics then analyzed using an aberration detection algorithm (ADM2)  (57). Transcriptomic analysis. RNA was extracted using the RNeasy Micro Kit (Qiagen, 74004). At the Robarts sequencing facility (Robarts Research Institute, London, Ontario, Canada), the quality of RNA was assessed using the Agilent 2100 Bioanalyzer, followed by RNA reduction, creation of indexed libraries, and sequenced on the Illumina NextSeq.
Libraries were sequenced using Illumina NextSeq High Output 75 cycle sequencing kits for single-end sequencing. RNA-seq FASTQ files were aligned using STAR version 2. 7. 8a (Galaxy) to the human reference genome GRCh38. Gene-level counts were obtained using HTseq-count v. 0. 9. 1 (Galaxy). Differential gene expression analysis was performed with R package JCI Insight 2022;7(22):e158060 https://doi. org/10. 1172/jci. insight. 158060 DESeq2 version 2. 11. 40. 7 (RStudio). Genes with a base mean of 1 or greater, log 2 (fold change) greater than 1 or less than -1, and an adjusted P value of less than 0. 05 were considered significantly differentially expressed. GSEA (http://software. broadinstitute. org/gsea/index. jsp) was performed using the Hallmark and KEGG gene sets. Heatmaps were created using the R package ggplot2 version 3.
3. 3. PDAC transcriptomic subtyping was performed using a subset of genes from Moffitt et al. (40)  to cluster the samples with R package ConsensusClusterPlus version 1. 52. 0 (PMID 20427518). Immunofluorescence, H&E, and immunohistochemistry. PDOs were grown in 2-well chamber slides (Nunc, 154526) for paraffin block preparation. Once the PDOs were ready for embedding, organoids were fixed in 4% paraformaldehyde (PFA) (Electron Microscopy Sciences, 15710) for 2 hours at room temperature. Following fixation, PFA was aspirated and the chamber slides were washed with deionized water. After the chamber slides were dissembled, a no. 22 surgical blade (Bard-Parker, 371122) was used to scrape the PDOs in Matrigel into cryomolds (Tissue-Tek, 4565). Four hundred microliters of Histogel (Thermo Fisher Scientific, HG-4000-012) was added to each cryomold to completely envelope the organoids.
Acknowledgments This work was supported by the  Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research (CA265050) ,  GI Cancer program  of the  Mayo Clinic Cancer Center ,  Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine  ( Mayo Clinic ),  Department of Laboratory Medicine and Pathology (Mayo Clinic ) to MEFZ,  Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE  to MEFZ and MTB, a  University of Minnesota-Mayo Clinic Partnership grant ,  Early Therapeutic program  ( Department of Oncology, Mayo Clinic ) to MEFZ and WWM, and a 2022  Conquer Cancer Foundation of ASCO Young Investigator Award  to RMC. Address correspondence to:  Martin E. Fernandez-Zapico ,  Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota 55905, USA. Phone:  507.
Black carbon (BC) from incomplete combustion of biomass or fossil fuels is the strongest absorbing aerosol component in the atmosphere. Optical properties of BC are essential in climate models for quantification of their 20 impact on radiative forcing. The global climate models, however, consider BC to be spherical particles which causes uncertainties in their optical properties. Based on this, an increasing number of model-based studies provide databases and parametrization schemes for the optical properties of BC using more realistic fractal aggregate morphologies. In this study, the reliability of the different modelling techniques of BC was investigated by comparing them to laboratory measurements. In the first step, the modeling techniques were examined for bare.
In the "coated aggregate" representation, a layer of organic carbon was present around each primary particle comprising the soot aggregate. The thickness of this layer of organic carbon is the difference between the outer radius of the primary particle (ao), and the inner radius of the primary particle (ain). Following equation (2), the https://doi. org/10. 5194/egusphere-2022-573 Preprint. Discussion started: 2 August 2022 c Author(s) 2022. CC BY 4. 0 License. relationship between the fraction of organic carbon (foc), the outer radius of the primary particle (ao) and the inner radius of the primary particle (ain) were determined. It must be noted that in the "coated aggregate" representation, the size of the primary particles in the aggregates generated from the Diffusion Limited Aggregation (DLA) software is equal to the outer radius of the primary particle (ao).
Tools for modelling soot optical properties Aggregation of soot agglomerates to form a larger soot fractal aggregate is described by the process of diffusionlimited cluster aggregation  (Witten and Sander, 1983). Based on this principle, various Diffusion-limited algorithms (DLAs) have been developed. The tunable diffusion limited aggregation (DLA) software (Woźniak, https://doi. org/10. 5194/egusphere-2022-573 Preprint. Discussion started: 2 August 2022 c Author(s) 2022. CC BY 4. 0 License. 2012) was used in this study to simulate the "aggregate", "coated aggregate", and "aggregate and sphere" soot representations. This algorithm preserves fractal characteristic of the aggregate, by iteratively adding each primary particle one by one. The Multi-Sphere T-matrix Method (MSTM) code  (Mackowski et al.
, 2013)  and the Lorentz-Mie theory  (Hergert and Wriedt, 2012; Bohren and Huffman, 1983)  were used to model the optical properties of simulated 325 soot particles. The optical properties were calculated in the visible spectrum, for l equal to 467, 530, and 660 nm. It must be noted that the range of 𝜆 was limited as only refractive index at the wavelengths 467, 530, and 660 nm were available  (Kim et al. , 2015). For the "sphere", the "homogeneously mixed sphere" and the "coated sphere" representations, the Python Mie Scattering package PyMieScatt  (Sumlin et al. , 2018)  based on the Lorentz-Mie theory was used. The MSTM code 330 was used for the "aggregate", "coated aggregate", and "aggregate and sphere" representations. The MSTM code contains a FORTRAN based algorithm that calculates the optical properties of a set of arbitrary spheres  (Mackowski and Mishchenko, 2011; Mishchenko et al.