dataesr/scientific-paragraphs-categorization huggingface dataset:
Antibodies-HTO were validated by FACS (Supplementary Fig. 8 ). Single-cell RNA-sequencing experiment. FACS-sorted cells were labeled with anti-CD45 conjugated to HTO (Supplementary Table  1 ). Cells were isolated using the Chromium technology from 10× Genomics. Briefly, 5000 cells of each sorted population were pooled, loaded on a Chromium Chip A and droplet-encapsulated with a chromium controller. Single-cell cDNA synthesis and sequencing librairies were prepared with Single-Cell 5′ Library & Gel Bead kit (10× Genomics), according to manufacturer's instructions. Libraries were sequenced using a Next-seq500 and the following parameters, Read1: 26 cycles, i7: 8 cycles, and Read2: 57 cycles. ScRNAseq data analysis. For data preprocessing, mRNA library reads were aligned to the GRCh38 version of the human genome and quantified using Cell-Ranger count (version 3.
0. 1). Antibody counts for cell hashing were quantified using CITE-seq-count (version 1. 4. 3, NYGCtech, https://github. com/Hoohm/ CITE-seq-Count), using default parameters. The produced mRNA and HTO data matrices were imported into R (v3. 5. 3), and downstream analysis were performed with the Seurat package (v3. 0. 1)  22, 27. In order to perform samples demultiplexing, HTOs for each cell were normalized using a centered log ratio transformation across cells. Cells were demultiplexed using the Seurat HTODemux function, and cell doublets and background empty droplets subsequently removed. Before mRNA expression analysis, we filtered out low quality cells from the mRNA matrix (i. e. , cells expressing < 200 genes or >10% of mitochondrial-associated genes). Genes expressed in less than three cells were removed.
Then BAFExtract was used to calculate BAF for each bam, and finally CaSpER was run using physiological cells (clusters 2-5) as control. To infer gene regulatory network responsible for clonal transcriptional changes underlying tumoral evolution, we applied SCENIC method  28  , using the filtered matrix raw UMI counts. Reverse transcription-PCR. RNA from T1-CD19 pos and T2-CD19 neg samples were extracted using RNAeasy mini kit (Qiagen), and bulk cDNA was synthesized using high capacity cDNA reverse transcription kit (Applied Biosystems). Then CD19 transcripts were assessed by PCR using specific primers (Supplementary Table  1 ) and Fig. 1  CD19 neg B-ALL relapse following CAR-T therapy. a Cell sorting strategy. Cells before (T1) and after (T2) CAR-T treatment were gated according to FSC/SSC profile (left FACS plots).
We used CD34 and RPS14 expression as tumoral markers. Indeed FISH analysis revealed a 5q32 deletion, explaining RPS14 lower expression in B-ALL cells. c UMAP visualization of the six main clusters and their corresponding cell types of T1 and T2 sorted samples. d UMAP visualization of the four demultiplexed samples: T1-CD19 neg , T1-CD19 pos , T2-CD19 neg , and T2-CD19 pos. e Agarose gel of CD19 cDNA amplified products using exons-specific primer sets depicted on the top panel. PCR were performed with bulk cDNA from T1-CD19 pos cells and T2-CD19 neg cells. Agarose gel data are representative of two independent experiments. Lane "Lad" is the 1 kb DNA size marker. Schematic representations of PCR products indicated by "a"-"f" arrows are shown below the gel. In the first PCR, CD19 transcript was amplified with "partial Read1" primer (or P12) and CD19 primer P10.
Acknowledgements High-throughput sequencing was performed at the  TGML Platform , supported by grants from  Inserm ,  GIS IBiSA, Aix-Marseille Universite , and  ANR-10-INBS-0009-10. This study was partly supported by research funding from the  Canceropôle PACA, Institut National du Cancer and Région Sud. S. P. received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Grant  Agreement No. 713750 , and from the  Regional Council of Provence-Alpes-Côte d'Azur ,  A*MIDEX  (No. ANR-11-IDEX0001-02 ). D. P. -B. received financial support from  ITMO Cancer of AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the  Cancer Plan  (Project No. C19046S ) and from  CNRS  " Osez l'interdisciplinarité!" program-"DMATh " project.
Data availability Fastq raw data are available in SRA (https://www. ncbi. nlm. nih. gov/sra? term=SRP269742). Processed data can be accessed from the NCBI Gene Expression Omnibus database (accession code GSE153697). Human transcriptome reference used for our analysis is available at 10× Genomics website (https://cf. 10xgenomics. com/supp/ cell-exp/refdata-cellranger-GRCh38-3. 0. 0. tar. gz) or in zenodo (https://zenodo. org/ record/4114854/files/refdata-cellranger-GRCh38-3. 0. 0. tar. gz?download=1). The cisTarget database (hg19-tss-centered-10kb-7species. mc9nr. feather) used for SCENIC analysis is available at https://resources. aertslab. org/cistarget/databases/homo_sapiens/ hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-10kb-7species. mc9nr. feather.
We used RKO MICA*008-HA cells and HeLa cells which endogenously express MICA*008  28. Immunoprecipitated proteins were subsequently analyzed by LC-MS/MS. We searched the results for proteins which specifically co-precipitated with US9 in both cell types. These analyses identified Protein Sel-1 Homolog 1 (SEL1L), an essential component of the HRD1 ERAD complex  52  , as a US9 interacting protein (mass spectrometry data available in Supplementary Data 6). Hence, we chose SEL1L for further analysis. First, we performed an anti-His tag IP and immunoblot to validate the interaction between US9 and SEL1L in RKO MICA*008-HA and in Hela cells (Figs. 6a and S6a, respectively). US9 specifically co-precipitated with SEL1L (shown by a red arrow). We also found that US9 co-precipitated with ER-resident MICA*008 (shown by a black arrow).
Cells were then washed, treated for 5 min with DAPI, and covered with coverslips. A confocal laser scanning microscope (Zeiss Axiovert 200M; Carl Zeiss MicroImaging, Thornwood, New York) was used to obtain images. Images were acquired and processed using the Olympus FluoView FV1000 software. Flow cytometry. For flow cytometry, cells were plated at equal densities and incubated overnight. Resuspended cells were incubated on ice for 1 h with the primary antibody at a concentration of 0. 2 μg per well. The cells were then incubated for 30 min on ice with the appropriate secondary antibody at a concentration of 0. 75 μg per well. In all experiments using cells transduced with a GFP-expressing lentivirus or transfected with EYFP, the histograms are gated on the live GFP/EYFP + population. Gating strategy is described in Fig.
S2f. 10,000 live cells were acquired from each sample. Background stainings with a secondary antibody alone or with an isotype-matched control in HCMV-infected cells were performed for all cell types in the experiment. A single representative control staining (identified in the figure legends) is shown for each experiment, and all background stainings were similar to the one shown. Flow cytometry data were analyzed using FCS Express version 6 (De Novo Software, Pasadena, CA). CD107a-degranulation assay. Analysis of CD107a on NK cell surface was previously described  51. Briefly, primary IL-2-activated bulk NK cells were coincubated with the target cells at a ratio of 1:1 at 37 °C for 2 h, in the presence of an APC-conjugated CD107a antibody and a PE-conjugated CD56 antibody (Biotest). CD107a levels on NK cells were then determined by flow cytometry.
Membranes were developed using the EZ-ECL kit (Biological Industries). Images were acquired and quantified using the Image Lab software (Bio-Rad, Hercules, CA), and blot contrast and brightness was adjusted as appropriate for better visualization. Unmodified full blots are included as a separate file. NP40 fractionation. Cells were plated at an equal density, incubated overnight, and lysed in 1% NP40 lysis buffer (150 mM NaCl, 50 mM Tris, pH 7. 4, 1 mM PMSF, 1:100 aprotinin). The lysates were centrifuged at 12,000 × g for 10 min and the supernatants were collected as the detergent-soluble fraction. Pellets were washed three times in PBS and then resuspended in protein sample buffer, boiled for 10 min at 99 °C, and collected as the detergent-insoluble fraction. Cycloheximide chase. For cycloheximide chase, cells were incubated for 4-5 h in the presence of cycloheximide (50 μg/ml, Sigma-Aldrich) in combination with epoxomicin (Merck Millipore) or with DMSO mock treatment.
For proteomics, the same anti-His immunoprecipitation protocol was used, except that two controls were used: empty-vector-transduced cells immunoprecipitated with an anti-His antibody, and US9-HIS-transduced cells immunoprecipitated with an isotype control. Uneluted beads were stored at -80 °C and shipped to the Smoler Proteomics Center, Department of Biology, Technion Institute of Technology, where LC-MS/MS was performed on Orbitrap XL (Thermo Fisher Scientific), and results were analyzed by the MaxQuant software. All the identified peptides were filtered with 1% FDR threshold and minimum of two peptides. Known contaminants were also excluded. For anti-HA tag co-immunoprecipitation, covalently attached anti-HA agarose beads (clone HA-7, Sigma-Aldrich) were used according to the manufacturer's instructions.
EndoH (New England Biolabs) treatment was performed as recommended by the manufacturer. Prior to loading onto a SDS-PAGE immune complexes were dissociated at 95 °C for 5 min in a DTT (40 mM) containing sample buffer. Fixed and dried gels were exposed overnight to a phosphor screen, scanned by Typhoon FLA 7000 (GE Healthcare). For better visualization of the results, contrast and brightness were adjusted. A long exposure to X-ray film was used for autoradiography. Images were quantified using the ImageJ software  83. Statistical methods. A one-way ANOVA was used to compare the effect of the various US9 mutants and chimeras on MICA surface expression (measured as normalized median fluorescent intensity). All mutants sharing the same control groups (e. g. , all single US9 mutants, all double US9 mutants, all EYFP chimeras, etc.
) were grouped into the same analysis to correct for the relevant multiple comparisons. EV controls were not included in the analyses. Where the ANOVA was statistically significant, a post hoc Dunnett's test was conducted to determine which mutants differed significantly from the control group. A two-way ANOVA was used to compare the effects of SEL1L knockdown and the various US9 constructs, including EV controls, on MICA surface expression, and where the ANOVA was statistically significant, a post hoc Tukey test was conducted to determine which mutants differed significantly from each other. ANOVAs and post hoc tests were considered statistically significant when p < 0. 05. Full statistical details including p values, F values, degrees of freedom, and effect sizes (Cohen's d) are presented in the relevant figure legends and/or in the Source data file.
Transduction was performed in the presence of Polybrene (6 μg/ml). Transduction efficiency was assessed by fluorescence levels, and only cell populations with >90% efficiency were used for experiments. Repeat transduction was performed if required to obtain the desired efficiency. MICA genotyping. Genomic DNA was extracted from MRC-5 fibroblasts using the AccuPrep genomic DNA extraction kit (Bioneer, Daejeon, South Korea) according to the manufacturer's instructions. MICA Exons 2-5 were amplified using previously described primers (FW: CGTTCTTGTCCCTTTGCCCGTGTGC and REV: GATGCTGCCCCATTCCCTTCCCAA)28,82, and the resulting PCR products were then ligated into pGEM T-easy plasmids (Promega, Madison, WI) and sent for sequencing. Sequences were compared to all known MICA alleles in the IMGT/HLA database (http://www.
Data availability The data underlying Figs. 1c, 3b, 3d, 4d, 5c, 7c, 7e, 8a, 9c, 9e, and Supplementary Figs. 4d, 7d, 8d and 8f, including full statistical analyses, are provided as a Source data file. Remaining data are available in the article, Supplementary data files or available from the authors upon request.
A new generation of solutions is then formed either by making random changes to a single parent (called mutation) or by combining the vector entries of a pair of parents (called crossover). The process will be repeated until reaching the stopping conditions, which involves either the number of iterations reaching 50 or the stall generations (generation with no significant change in the fitness function) reaching 20. The GA is conducted using the genetic algorithm solver in the MATLAB R2019a Global Optimization Toolbox (The MathWorks, Inc). 3 Modeling approach 3. 1 Regional chemical transport model CAMx The regional model CAMx version 6. 50  (Ramboll, 2018)  was used to model organic aerosol in Europe (15 • W-35 • E, 35-70 • N) for the whole year of 2011, with a horizontal resolution of 0. 25 • × 0.
125 • and 14 terrain-following vertical layers from ∼ 20 m above the ground up to 460 hPa. The Carbon Bond 6 Revision 2 (CB6r2) gas-phase mechanism (Hildebrandt Ruiz and Yarwood, 2013) was selected. The gas-aerosol partitioning of inorganic aerosols was simulated by the ISORROPIA thermodynamic model  (Nenes et al. , 1998). For organic aerosols, several OA schemes, including both the traditional two-product approach (SOA chemistry partitioning scheme, SOAP) and the VBS scheme with different parameterizations, were applied (see Sect. 3. 2). The meteorological parameters were prepared with the Weather Research and Forecasting model (WRF version 3. 7. 1; Skamarock et al. , 2008)  based on the 6 h European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis global data  (Dee et al. , 2011).
The meteorological parameters were evaluated and reported in a previous study  (Jiang et al. , 2019a) , which showed that most of the meteorological parameters met the criteria for meteorological model performance by  Emery (2001). The initial and boundary conditions were obtained from the global model MOZART-4/GEOS-5  (Horowitz et al. , 2003). Inputs of ozone column densities were produced based on the Total Ozone Mapping Spectrometer (TOMS) data by the National Aeronautics and Space Administration (NASA, https://acd-ext. gsfc. nasa. gov/anonftp/toms/omi/data/Level3e/ozone/, last access: 17 March 2021), and the photolysis rates were then calculated by the Tropospheric Ultraviolet and Visible (TUV) Radiation Model version 4. 8 (NCAR, 2011). The sourcespecific anthropogenic emissions were based on the European emission inventory TNO-MACC-III (Monitoring Atmospheric Composition and Climate)  (Kuenen et al.
Details about each OA scheme are introduced below. -SOAP. The SOAP (SOA chemistry partitioning) module is a semi-volatile equilibrium scheme based on the traditional two-product approach. POA emissions are assumed to be inert in SOAP. The updated parameterization of SOAP2. 1 in CAMx v6. 50 used the aerosol yield data that correct for vapor wall losses in smog chamber experiments based on  Zhang et al. (2014). -VBS_BASE. The VBS_BASE used the standard VBS parameterization in CAMx v6. 50. The IVOC emissions from different sources were calculated based on the literature. IVOCs from gasoline and diesel vehicles were calculated as 25 % and 20 % of NMVOC emissions from gasoline and diesel vehicles, respectively  (Jathar et al. , 2014). IVOC emissions from residential biomass burning were estimated as 4.
This approach has been widely used in modeling studies  (Ciarelli et al. , 2016 (Ciarelli et al. , , 2017a;; Shrivastava et al. , 2011; Tsimpidi et al. , 2010). All the other parameters were kept the same as the standard VBS parameterization in CAMx v6. 50. The VBS_BASE IVOC emissions were adopted here. -VBS_WLS. The VBS_WLS used the optimized parameters by the VBS box model, including the emissions and vapor-wall-loss-corrected yields for IVOCs from residential biomass burning, and the H vap of the oxidized products. The modified parameters for volatility-bin-specific yields and H vap of the oxidized products from IVOCs can be found at https://doi. org/10. 5281/zenodo. 3998342 (Jiang, 2020). The optimized mass yields in the box model were converted to molar yields using the default molecular weights in CAMx (Table  1 ).
, 2020). For Zurich and SIRTA, only data collected from late autumn to early spring (January, February, March, November, and December) -when emissions from biomass burning are relatively high -were used for the statistical analysis, although the observations covered longer time periods. The spatial distribution and observation periods of each station are shown in Fig. S2. Statistical metrics, including mean bias (MB), mean error (ME), root mean square error (RMSE), mean fractional bias (MFB), and mean fractional error (MFE), between modeled and observed primary and secondary OA were calculated.
Code and data availability. The source code of the standard CAMx model is available at the RAMBOLL website (http://www. camx. com, last access: 17 March 2021). The modified CAMx codes and the source code of the MATLAB-based VBS box model are available online at https://doi. org/10. 5281/zenodo. 3998342 (Jiang, 2020). Data in the figures are available at https://doi. org/10. 5281/zenodo. 4267890 (Jiang, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-1681-2021-supplement. Author contributions. JJ and IEH conceived the study. JJ carried out the model simulation and data analysis. GS and AB conducted the chamber measurements. NM, FC, JEP, OF, and SG provided the measurement data. SA, ASHP, and UB supervised the entire work development.
Acknowledgements. We are grateful to the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for the meteorological data, the  National Aeronautics and Space Administration (NASA)  and its data-contributing agencies (NCAR,  UCAR ) for the TOMS and MODIS data, the global air quality model data, and the TUV model. We thank  RAMBOLL  for support with CAMx. Simulations with WRF and CAMx models were performed at the  Swiss National Supercomputing Centre (CSCS). We thank the  Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS)  and the Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL) cost action ( CA16109 ) for support and harmonization within OA measurements and data treatments. Financial support. This research has been supported by the  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  (grant no.
Methods In order to carry out an economic analysis of a given product of interest, it is important to obtain market data for the base country, where the industrial plant can be implanted, as well as for other countries, for assessment of international trade, threats and opportunities. The market parameters of glycerol and each of its derivatives to be evaluated were obtained from the International Trade Center (ITC) portal and the Comex Stat portal. The future scenarios were obtained on the websites of consulting companies. This information must be carefully analyzed due to the differences in the assessment among different companies. ITC is the joint agency of the World Trade Organization and the United Nations that aims to promote business between countries, mainly developing countries, simplify the economy [9], and provide several market analysis tools, covering 220 countries and territories and 5300 products found in the Harmonized System (HS).
The Trade Map is one of the most important and provides, through tables, graphs and maps, performance indicators for imports and exports, international demand, alternative and competitive markets, in addition to a directory of importing and exporting companies  [10]. Comex Stat is a portal to access statistical data of foreign trade in Brazil. Every country has a free access consultation tool that quantifies national imports and exports, e. g. Data Web (United States -https://dataweb. usitc. gov/), Stat Can (Canada -https://www5. statcan. gc. ca/cimt-cicm/homeaccueil?lang=eng) and ETCN (China -http://www. e-to-china. com). The chemical products are found on the Trade Map and Comex Stat by using the Harmonized Commodity Description and Coding System, or simply Harmonized System (HS), and the Mercosur Common Nomenclature (NCM), in the specific case of Brazil.
The MCN has been adopted by the countries that integrate the Argentina, Brazil and Uruguay Block to foster international trade growth, make the creation and comparison of statistics easier, in addition to elaborating freight tariffs and providing other relevant information to international trade. The ITC Trade Map uses a classification of up to 6 digits, while Comex Stat uses not only the SH, but also the NCM, with 8 digits, specifying more the product under analysis. The availability of the market parameters in these databases allows the user correlating and interpreting the information in order to economically analyze the product under evaluation. Import and export values, volumes, growth rates, market shares, etc. are obtained from the Trade Map/ITC (worldwide) and the Comex Stat (for Brazil), while the growth forecasts are obtained from the websites of market consulting companies.
According to Figure  2 , crude glycerol is included in SH6 1520. 00, which covers also water and glycerol lyes. In the example, the output data were obtained for the world, specifying the values in dollars of the exporting countries annually. In "Map", the world map shows the expressiveness of each country in different color scales, depending on the commercialized values of the product. In "Graph", the output data can be generated in graphs. In "download", the table of values can be exported to a spreadsheet or text files.
Search in the Comex Stat The tool Comex Stat is accessed at "General Exports and Imports" through the link: http://comexstat. mdic. gov. br/pt/home. Results are shown in Figure  3 , according to the type of operation (exports or imports), period of time and filters of interest. The information may be filtered according to the product code (SH or NCM), country or economic region, means of transport used and how the results should be displayed. If no input is given related to region, the search is carried out all over the world, The option "Net Weight" in Values filter shows the US$ FOB (freight on board) value, which is used to indicate when liability and ownership of goods is transferred from a seller to a buyer, and also the amount commercialized in kilogram. As an example of using COMEX STAT, information for pure glycerol (NCM: 29054500) is evaluated.
The price difference between crude glycerol and propylene opens up a great opportunity for the production of these derivatives from glycerol  [16]. 1,3-propanediol is obtained mainly from glucose, by fermentative processes, and ethylene oxide, by catalytic routes  [17] , whose prices, on average, were US$ 460/ton and US$ 1200/ton, respectively, in 2019 [10]. Thus, there is a good opportunity for developing an economically competitive and sustainable industry, using glycerol as raw material in an integrated biorefinery. Figure  5  shows the exported values and volumes of crude glycerol considering the five largest exporters: Indonesia, Brazil, Spain, Germany and France. The data were obtained using SH6 1520. 00, which includes crude glycerol, water and glycerol lye. The amount of crude glycerol (SH6 1520.