dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We analysed the data with Kraken (version 1. 0), a system for ultrafast metagenomic sequence classification using exact alignment  [5]  (http://ccb. jhu. edu/software/kraken/). It relies on the development of "a database that contains records consisting of a k-mer and the LCA (lowest common ancestor) of all organisms whose genomes contain that k-mer"  [5]. We built a reference database with default parameters of Kraken (k=31). Our custom-built database is composed of all the complete Trypanosomatidae genomes collected from the NCBI (http://www. ncbi. nlm. nih. gov/genome/) and complemented by genomes from TriptrypDB release 37 (25 April 2018) (Kinetoplastid Genomics Resource http://tritrypdb. org/tritrypdb/)  [7]. The total number of Trypanosomatidae genomes in the database is 79 (Table  S1 ).
Results & Discussion In a first attempt and to compare the respective limits of the two tools in species assignment, we focused on microorganisms belonging to the Trypanosomatidae family. These were chosen for two reasons, first, because of the presence of lesions observed in mummies by  [1]  and second, because of the endemicity of Chagas disease (WHO www. who. int/chagas/resources/en/) and leishmaniasis in this South American region (WHO www. who. int/leishmaniasis/resources/en/). Trypanosomes and Chagas disease. The genome representativeness of our database is 55% (11 genomes out of approximately 20 Trypanosoma species currently described) (Figure  2A ). The reference database includes trypanosomes responsible for American trypanosomiasis (T. cruzi) and trypanosomes responsible for human African trypanosomiasis and animal trypanosomiasis.
cruzi strain Tula cl2 ( Table  S2 ), which belongs to DTU I. This DTU is widely represented in the genomic database we gathered (5 of the 11 genomes currently available). For FI12, reads that match Tula cl2 are scarce (Table  S2 ). Nevertheless, because of the relatively low proportion (<10%) of reads that match with a genome filed in our database, it is probable that the DTU of the infecting T. cruzi strain is not yet represented. Analysis with Bowtie2 clearly confirmed a T. cruzi infection (53%, 49% and 57% for FI3, FI9 and FI12, respectively) in these mummies (Figure  3C ). A majority of the reads matched the T. cruzi strains Y and Tula cl2 (Table  S3 ) rather than other genomes filed in the database. Concerning the alignment with T. cruzi Y, the reads matched 178 and 203 contigs in FI3 and FI9, respectively, out of 9821 (Table  S4 ).
A large majority of reads (80% in FI3 and 90% in FI9) matched 52 identical contigs for both mummies. For T. cruzi Tula cl2, reads of mummy FI3 and mummy FI9 matched 42 and 39 contigs, respectively, out of 5300, and almost 100% of the reads for both mummies matched 28 identical contigs (Table  S4 ). Overall, this set of results indicates that the infecting T. cruzi strain is probably not in our reference database. A large majority of the genome of Trypanosoma cruzi strains is lacking: currently, out of more than 1,902 strains inventoried to date  (Brenière et al. 2016) , complete genomes are deposited in the NCBI and/or Tritryp databases for only 11. Likewise, the database we used does not contain representative isolates belonging to DTUs III, IV or VII (TcBat). Interestingly, ancient T. cruzi DNA was also identified in human mummies dating from the same period (Chinchorro culture) and in the same geographical region, Southern Peru  [9].
In Bolivia and Peru, strains belonging to the DTU I and DTU V clades are the main T. cruzi strains isolated, followed by strains of DTUs IV, III and VI  [10]. The knowledge of the complex evolutionary history of T. cruzi, which involved genetic exchanges  [11] , and the existence of hybrid DTUs will certainly benefit from the identification of the infecting strains at the DTU level from this ancient DNA material. Insight into the circulating strains in these mummies would generate important elements for the calibration of such a dynamically evolving scenario. Nevertheless, our analysis highlights that the ancient T. cruzi DNA present in the three mummies cannot be assigned to T. cruzi CL Brener (DTU VI). Leishmania and leishmaniasis. All the Leishmania genomes currently available in NCBI or in TRITRYPdb are included in the reference database.
The database genome representativeness is 38%, with 21 genomes out of 54 Leishmania species currently identified  [12]. The representativeness of the database appears to be far better for microorganisms belonging to the subgenera Leishmania and Viannia (Figure  2B ). Concerning human pathogenic Leishmania species, the representativity is far better than 70% (14 genomes out of 20), with the subgenera Leishmania and Viannia well represented (80 and 83%, respectively). However, no genome available concerning the subgenus Porsicia (Paraleishmania section) is currently available (Table  1  below). This includes some human pathogenic Leishmania sp. , such as L. colombiensis. The NGS reads analysed with the metagenomic sequence classifier Kraken highlight a probable co-infection with Leishmania parasite for the 3 mummies FI3, FI9 and FI12 (Figure  3B ), with 3%, 3% and 10% of the reads attributed to Trypanosomatidae that belong to the genus Leishmania, respectively.
Leishmania Nevertheless, the occurrence of an infection by multiple Leishmania sp. cannot be ruled out. From these results, it is clear that to be accurate in its species assignment, the reference database used by Kraken needs to be highly representative of the described species and strains. The more exhaustive the database, the better the assignment accuracy will be. The alignment program Bowtie2 detects the presence of Leishmania in the three mummies (FI3, FI9 and FI12) (Figure  3C ). But unlike the mummy FI12, in the mummies FI3 and FI9, the reads match a large panel of Leishmania sp. , especially in the mummy FI3 ( Table  S3 ). This differs from the results performed with Kraken. However, it is not possible to unequivocally assign DNA to a specific Leishmania species and L. donovani in particular.
Our analysis reveals that the genome of the infecting Leishmania species, whose DNA is detected in these ancient remains, is probably not present in our reference database. We may consider the occurrence of an ancient Leishmania species. Nevertheless, since our reference database is not exhaustive, we cannot rule out the presence of Leishmania sp. causing cutaneous or muco-cutaneous lesions or the co-infection of multiple Leishmania species. Still, the detection of Leishmania DNA is clearly not the result of an artefact or a contamination. Other Leishmaniinae (Crithidiatae). Trypanosoma and Leishmania are dixenous parasites, meaning that their life cycle includes an invertebrate as a first host and a vertebrate as a second host. Several genomes of (one-host) Trypanosomatidae are available and belong to the genera Angomonas, Crithidia, Leptomonas, Lotmaria and Strigomonas ( Table  S1 ).
The database genome representativeness for Leptomonas is 5%, with only 2 genomes available out of 39 Leptomonas species currently described (Figure  2C ). The analysis performed on the three mummies revealed that a relatively high proportion of reads attributed to the family Trypanosomatidae are reminiscent of the genus Leptomonas (13%, 18%, and 13% for FI3, FI9 and FI12, respectively) (Figure  3B ). A large majority of these reads are assigned to Leptomonas seymouri and not Leptomonas pyrrochoris after an analysis with Kraken (Figure  4 ) (Table  S2 ). Nevertheless, because of the lack of available genomes for 37 out of 39 Leptomonas sp. , the assignment to L. seymouri has to be taken with caution, and an assignment at the strain level cannot be made. Bowtie2 also confirms the recurrent occurrence of Leptomonas seymouri in all mummies (Figure  3C ), with a high number of reads matching the Leptomonas seymouri strain BHU-1095 (Table  S3 ) in mummies FI3 and F9 (56245 and 125577, respectively).
Conclusion The strength of our approach is to work also with the most exhaustive reference database of Trypanosomatidae genomic sequences possible and two programs with complementary approaches in metagenomic analysis (k-mer searching vs local alignment) to gain insight into the identity of the infecting Trypanosomatidae. Analysis using the sequence classifier (Kraken) unambiguously confirmed Trypanosoma cruzi infection and undoubtedly Leishmania infection in the three Andean mummies. In addition, our analysis provides new information on coinfection by at least two human pathogenic trypanosomatids, Leishmania species and T. cruzi, in all mummies with available metagenomes. This type of co-infection is known to occur in human individuals as well in some wild mammals  [18]   [19]. Unfortunately, it is not possible to go further in the assignment to the species level for Leishmania and DTU level for T.
cruzi. This may be due to the lack of a number of Leishmania genomes, particularly those of the Paraleishmania section, and of members of T. cruzi belonging to some DTUs. It also highlights a pattern of poly-infection coupled with an opportunistic trypanosomatid, i. e. , Leptomonas seymouri  [20]. Therefore, future studies with an exhaustive reference database are necessary to better understand the interrelationships that shape the microbial community and play a role in the evolution of the parasitome. Figure Legends.
Acknowledgments We are very grateful to the High-throughput Sequencing facility of the I2BC (http://www. i2bc. paris-saclay. fr/spip. php?article399&lang=en,  CNRS, Gif-sur-Yvette, France ) for the realization of libraries and sequencing for MFA studies, and particularly to  Maud Silvain  and  Cloelia Dard-Dascot  for their very generous help in genome analyses. We are very thankful to  Pr. David Sherratt  ( Oxford University ) and to  Dr. Meriem El Karoui  ( University of Edinburgh ) for their skilful reading of a previous version of this manuscript, and to all members of Frederic  Boccard (I2BC) laboratory  for useful and stimulating discussions.
Work in BM laboratory is supported by the  Agence National de la Recherche ,  ANR  grant # 11 BSVS5 006 01. Work in FXB laboratory is supported by the  European Research Council  under the European Community's  Seventh Framework Programme  [FP7/2007-2013 Grant Agreement no. 281590 ]. Work in DRFL laboratory is supported by grant  MR/M019160/1  from the  Medical Research Council (UK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 Published by Copernicus Publications on behalf of the European Geosciences Union. 5050 C. Vigouroux et al. : Harmonized formaldehyde time series from 21 FTIR stations and 11 × 10 14 molec cm -2 , respectively. The median values among all stations are 13 % and 2. 9 × 10 14 molec cm -2 for the total systematic and random uncertainties. This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles.
The dominant source of systematic uncertainty being the spectroscopic parameters, it is crucial that all stations use the same spectroscopic database. We use the compilation from Geoffrey Toon (JPL), the atm16 line list, which is available at http://mark4sun. jpl. nasa. gov/toon/ linelist/linelist. html (last access: 5 September 2018). In this atm16 line list, the HCHO and N 2 O lines correspond to the HITRAN 2012 database  (Rothman et al. , 2013). This HITRAN 2012 database includes the latest improved HCHO parameters (broadening coefficients,  Jacquemart et al. , 2010) , which complement the release in HITRAN 2008  (Rothman et al. , 2009)  of new HCHO line intensities from the same group  (Perrin et al. , 2009). The spectroscopic parameters for the lines of water vapour and its isotopologues in atm16 are from Toth 2003 1  ; some lines from the other strong absorbing gases in the vicinity of HCHO (O 3 and CH 4 ) have been empirically adjusted or replaced with older HITRAN versions in atm16 when obvious problems were found in the HITRAN 2012 database.
The comparison of these two line lists shows the crucial need for good spectroscopic parameters in order to obtain precise amounts of atmospheric gases. As seen in Fig. 3  (right panel), the residuals are not perfect and there is still room for further improvement in the forward-model parameters. The atm line list created by Geoffrey Toon (JPL) is updated every 4 years, when HITRAN provides a new release, so that when the HI-TRAN line list is improved and provides either similar or better residuals than the atm line list, the empirical parameters of atm are changed by the preferred official database. In SFIT4 and PROFFIT retrieval codes, based on optimal estimation, a priori information (profile and regularization matrix) needs to be provided. In this work, the a priori HCHO profile, as well as all interfering species except water vapour and its isotopologues, were provided for each station from the v4 of the model WACCM  (Garcia et al.
HCHO diurnal cycle As explained in the introduction, to reconcile the different results obtained using satellites observing at different times (e. g. SCIAMACHY and GOME-2 measuring in the morning and OMI in the afternoon), it is crucial to have groundbased observations of the HCHO diurnal cycles  (Barkley et al. , 2013; De Smedt et al. , 2015; Stavrakou et al. , 2015). The diurnal cycle is also important for model validation, since emissions, chemistry and other processes depend on the time of the day. Our FTIR data set is now able to provide the diurnal cycles at 21 different locations. To separate the effect of the strong seasonal cycle (shown in the next section), we give the diurnal cycle in four different seasons in Fig. 6  for a selection of the sites, while the other ones are provided in the Supplement (Fig.
More investigation is needed to understand the different diurnal cycles at these two mountain sites. We see from Fig. 6  that the FTIR measurements at Porto Velho do not show a clear pattern, in particular if one is interested in the 09:30 and 13:30 LT differences between the overpasses of two different satellites  (De Smedt et al. , 2015). From the 1-year data available at present at this new site, it seems that the diurnal cycle cannot help reconciling the differences observed over Rondônia between GOME-2 and OMI  (De Smedt et al. , 2015). In contrast, the diurnal cycles observed over cities confirm that the observation of a positive bias between OMI (13:30 LT) and GOME-2 (09:30 LT) over urban areas can be indeed explained, at least partly, by the diurnal cycle.
IMAGES model description The IMAGESv2 global model calculates the distribution of 170 chemical compounds gases with a time step of 6 h at 2 • × 2. 5 • resolution, with 40 hybrid (σ pressure) levels in the verticals between the surface and the lower stratosphere (44 hPa level). The model calculates daily averaged concentrations of chemical compounds. The effect of diurnal variations is accounted for through correction factors on the photolysis and kinetic rates obtained from a full diurnal cycle simulation using a time step of 20 min. The same model simulation also stores the diurnal shapes of formaldehyde columns required for the comparison with FTIR data on files. Meteorological fields (winds, temperature, humidity, 3-dimensional cloud cover, solid and liquid cloud water content, large-scale and convective precipitation rates, visible downward radiation, convective updraught fluxes, boundary layer diffusivities, snow depth, sea ice fraction, surface roughness lengths, surface sensible heat flux, friction velocity, etc.
) are obtained from ERA-Interim analyses of the European Centre for Medium-range Weather Forecasts (ECMWF). Anthropogenic emissions of NO x , CO, SO 2 , and NMVOC are provided by the Hemispheric Transport of Air Pollution data set version 2 (HTAPv2)  (Janssens-Maenhout et al. , 2015) , with the NMVOC speciation provided by the emission inventory of the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)  (Lamarque et al. , 2010). Emissions from open vegetation fires are taken from the last version of the Global Fire Emissions Database, GFED4s, which includes the contribution of small fires  (Randerson et al. , 2012; Giglio et al. , 2013). The GFED data are available at daily frequency at 0. 25 • × 0. 25 • from 1997 to the present (http://www. globalfiredata. org, last access: 5 September 2018).
The median correlation between FTIR and IMAGES for the 21 stations is very high (0. 81), with weaker values at the Mexican stations (0. 4/0. 5) and at Mauna Loa (0. 10). The median standard deviation for all comparisons is 25 % (ranging from 11 % to 41 %). This agreement is good considering the FTIR variability (i. e. the SD) of HCHO monthly means (median of 35 %). The standard deviation of the comparisons can be explained partly by the lower variability of the model monthly means (31 %) compared to FTIR, as seen in Fig. tion, the variability of the model data within a month is also much smaller (median of about 11 %; this SD within a month is shown as magenta error bars in Fig. 7 ) than the FTIR one (median of about 28 %). The median of IMAGES and FTIR differences is small (-15 %) and within the FTIR systematic uncertainty estimated at 11 %-26 %.
However, the biases range from -64 % to +51 %, which requires an investigation of their possible causes. The main source of systematic uncertainty is the spectroscopic parameters, which have been harmonized in this work, with each station using the same line parameters database, and the same spectral MWs. Therefore, it is expected that all FTIR stations should provide consistent HCHO total columns within 5 %-17 % (systematic errors due to other sources than spectroscopic ones). To check this, we divide the FTIR stations according to their concentrations levels and latitudes, and use the model for comparisons.
High-mountain sites The mountain sites are more difficult to model, especially when they are close to cities. They are often very clean sites, but the model cannot reproduce this at the current resolution (2 • × 2. 5 • ) when they are surrounded by emission sources in the same pixel. This seems to be the case at Altzomoni, which lies in the same model pixel as Mexico City, lead- ing to an overestimation of 26 %, much larger in summer (+49 %), and at the European station Zugspitze, where the model overestimates the HCHO levels by +41 %. Note that, in the study of  Franco et al. (2015) , a negative bias (-13 %) was observed between FTIR at Jungfraujoch (47 • N, 8 • E) and IMAGES, but the retrieval settings used were different than in the present study. Only a change in the spectroscopic database, from HITRAN 2008 to HITRAN 2012, led to lower HCHO columns by 49 % at Jungfraujoch  (Franco et al.
A first look at the Lauder comparison gives a similar annual bias (-25 %), which remains constant over the year, as seen in Table  4  and Fig. 8. However, Fig. 7  shows that, during the austral winters (JJA) 2012 to 2015, the FTIR time series presents unusually high columns. By limiting the comparison to the first years of the period, a better agreement with the model in winter is obtained at Lauder, as for many other sites. Since the time series at Saint-Denis, Wollongong and Lauder have been published in the past using different retrieval strategies  (Vigouroux et al. , 2009; Jones et al. , 2009; Zeng et al. , 2015) , here we report the bias observed at these stations between the previous and present data sets. The bias is only 1. 4 % at Saint-Denis, the present HCHO columns being smaller than the previous data set in  Vigouroux et al.
(2016). However, for a few sites, the behaviour of the model remained unexplained (positive biases at Kiruna and Sodankyla, the too-low model variability at Mauna Loa). These HCHO time series, harmonized and well characterized, provide an important data set for past and present satellites, and model validation. They are continuously extended by new measurements and will be used in the coming years for the validation of new satellites, such as Sentinel 5P and Sentinel 4. Data availability. The FTIR data sets can be provided in the public NDACC repository (ftp://ftp. cpc. ncep. noaa. gov/ndacc/station/, last access: 5 September 2018) depending on each PI decision. Please pay attention to the NDACC data policy. The whole data set used in this publication can be provided upon request to Corinne Vigouroux (corinne.
Acknowledgements. This study has been supported by the  ESA   PRODEX  project  TROVA  ( 2016 )(2017)(2018) funded by the  Belgian Science Policy Office (Belspo). NCAR  is supported by the  National Science Foundation. The NCAR FTS observation programmes at Thule and  Mauna Loa  are supported by a contract with the  National Aeronautics and Space Administration (NASA). The Thule work is also supported by the  NSF Office of Polar Programs (OPP). We wish to thank the  Danish Meteorological Institute  for support at the Thule site and  NOAA  for support at the Mauna Loa site. Eureka measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  under the CANDAC and PAHA projects led by  James R. Drummond , and in part by the  Canadian Arctic ACE/OSIRIS Validation Campaigns , led by  Kaley A.
Walker. Funding was provided by  AIF/NSRIT ,  CFI ,  CFCAS ,  CSA ,  ECCC ,  GOC-IPY ,  NSERC ,  NSTP ,  OIT ,  PCSP , and  ORF. Logistical  and operational support was provided by  PEARL Site Manager Pierre Fogal , the CANDAC operators, and the  ECCC Weather Station. Toronto measurements were made at the  University of Toronto Atmospheric Observatory , supported by  CFCAS ,  ABB Bomem, CFI ,  CSA ,  ECCC ,  NSERC, ORDCF, PREA , and the  University of Toronto. The measurements at.
Acknowledgements: The research leading to these results has received funding from the  H2020 program  of the  European Union  (project  Euro-Sequences ,  H2020-MSCA-ITN-2014 , grant agreement no. 642083 ). The doctoral position of JL is supported by the project  Euro-Sequences. The  CNRS  and  PSA Groupe  are also acknowledged for financial support. TGA data were obtained at the polymer characterization facilities of the  Institut Charles Sadron. The authors thank  Emeric Wasielewski  ( Cronenbourg NMR platform, UMR 7509 ) for the NMR measurements on the 500 MHz spectrometer,  Stéphanie Kouaho  and  Hélène Nierengarten  ( Service of Mass Spectrometry, GDS 3648 ) for the ESI-MS measurements and  Mélanie Legros  ( Institut Charles Sadron ) for the thermal analyses.
Développement de CAR-T TGFβ-RII négatif ou dominant négatif Le TGFβ est produit par de nombreux types tumoraux. Le TGFβ inhibe les fonctions des lymphocytes T et semble jouer un rôle majeur dans les phénomènes d'exclusion immune des cellules T  [32]. Différentes stratégies peuvent être utilisées pour contrecarrer le TGFβ : association avec un anticorps anti-TGFβ, utilisation de CAR-T KO pour le TGFβ Récepteur II ou CAR-T exprimant un dominant négatif du TGFβ Récepteur II  [21]. Suite à la preuve de concept réalisée en préclinique dans un modèle de prostate  [33] , un essai de phase I est actuellement en cours dans le cancer de la prostate avec un CAR-T PSMA-TGFβ Récepteur II dominant négatif (NCT03089203).