dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Acquired Resistance Genes The whole panel of genomes was analyzed using the ResFinder 2. 1 application  (Zankari et al. , 2012)  on the CGE server. The threshold for reporting a match between a gene in the ResFinder database and the input S. Derby genome was set at 90% identity over at least 3/5 of the length of the resistance gene. BioNumerics software version 7. 6. 1 (Applied Maths, Sint-Martens-Latem, Belgium) was used to perform a BLAST to localize each resistance gene inside the assembled genome. In order to investigate the implantation of the AMR gene within the genome of S. Derby, SGI-1 coding sequences (NCBI:AF261825. 2) were extracted and BLASTed against the dataset with the BioNumerics BLAST tool. The complete genomic sequence of the SGI-1 was investigated using the BioNumerics alignment and sequence visualization tools.
Salmonella Pathogenicity Islands (SPI) -Identification SPI-1, the two segments of SPI-2, SPI-3, SPI-4, SPI-5, and SPI-23 were tested for within the set of 140 genomes of S. Derby using BLASTn 4  with a cut-off of 90% identity. The SPI reference sequences used for the BLASTs were collected by the NCBI database (accession numbers KP279311. 1, AJ224978. 1, KP258194. 1, AF106566. 1, KP234070. 1, AY144492. 1, and LAZB00000000-project PRJNA270707, respectively). All these sequences correspond to complete SPIs from S. Typhimurium strains with the exception of SPI-23, which corresponds to S. Derby strain 07CR553  (Kerouanton et al. , 2015). In the absence of a reference sequence for SPI-23, the primers defined by  Hayward et al. (2016)  were used to generate the in silico PCR and to extract a complete SPI-23 sequence from the genome of the S.
Derby strain 07CR553 (accession number LAZB00000000), contig 5 (1894692. 1931302)  (Kerouanton et al. , 2015). Both the complete sequence of SPI-23 obtained as described above and the 41 Coding DNA Sequences (CDS) described by Hayward were BLASTed separately against our dataset to verify the results of the in silico PCR.
Fimbriae FimH Allele Characterization The fimbriae FimH allele, identified as host specificity marker by previous studies  (Yue et al. , 2015) , was also sought and characterized within our panel of S. Derby genomes. The fimH gene sequence was isolated from the project PRJNA297164 (NCBI) and compared with the sequence of the dataset using BLASTn. As an element of comparison, 25 fimH sequences from 25 different serotypes of Salmonella enterica subsp. enterica selected among the most frequently isolated in humans, animal, and food  (Weill and Le Hello, 2014; EFSA, 2016)  were added to this study (Supplementary Table  S2 ). Whole FimH alleles were extracted by dataset, annotated using the BioNumerics 7. 6. 1 annotation plugin (Applied Maths, Sint-Martens-Latem, Belgium) and aligned on the reference fimH sequence of the S.
The similar prevalence of Derby in the two animal species, 12. 6% for Gallus gallus and 14. 2% for turkey  (DGAl, 2014 (DGAl, , 2015)) , suggests that the ST71 strains are well adapted to both Gallus gallus and turkey. On the other hand, these data underline the disproportion observed in France between the prevalence of Derby upstream of the food chain (in animals) and the contamination recorded downstream (in food). In our panel representative of the food production chain in France, two strains were isolated from the turkey food sectors and 54 from the Gallus gallus food sector. This discrepancy could be explained by the fact that 81% of the production in the French poultry sector concerns Gallus gallus and only 5% turkey, and that the farm environment is often common for the two species, so that cross contamination should not be excluded.
, 2015). In the whole dataset, 19 different missense substitutions have been detected in the lectin domain and 8 different missense substitutions in the pilin domain compared to S. Typhimurium's SL1344 sequence. Although most of the substitutions were reported previously by  Yue et al. (2015) , 5 new substitutions  (residues 91, 112, 113, 252, and 278)  were identified in this study. The substitution of the residue 91 in the lectin domain was notably specific to the lineages ST40 and ST39. This new substitution was the only variation that differentiates the FimH protein sequence of the S. Derby lineages ST40 and ST39 from that of the S. Typhimurium SL1344. The impact of this mutation on host specificity could be investigated by invasion test on epithelial cells. The lineage ST682 shared four identical substitutions (Q89R, L126R, Y131S, and I317N) with S.
Emission signal was filtered with a 680 nm short-pass filter (Semrock) to remove infrared reflection and a 525/50 nm band-pass filter (Semrock). Photon detection was carried out with hybrid detectors (Hamamatsu Photonics K. K. , Japan; Leica, Germany). An OKO-lab H101 system was used for temperature control. An Okolab DGT-CO2 system was used to control CO 2 concentration. Raw data movies were made with Fiji  (Schindelin et al. , 2012). Raw data are available online at bioemergences. eu/fabreges-et-al/.
Digital reconstruction Reconstructions were performed on single specimens. A manual region of interest (ROI) was made to enclose each specimen. Signal intensity outside the ROI was set to zero to mask other specimens in the field of view. Embryos were reconstructed with the BioEmergences Workflow as described previously  (Faure et al. , 2016). Nuclear centers were detected with the Difference of Gaussians algorithm, parameters were manually selected every 25 timesteps to best fit the raw data and interpolated over the whole imaging sequence. Tracking was carried out using the Simulated Annealing algorithm. Cell lineages were manually curated with the software Mov-IT  (Faure et al. , 2016)  either until hatching (wt1, wt3, nt1 and nt2) or earlier when the fluorescent signal was too weak (wt2).
Temporal rescaling The time of fertilization of wild-type embryos was estimated as 8 h postcoitum, as measured previously  (Pincus and Enzmann, 1935). The activation time for clones was set at the time of the second electric pulses. Fertilization and activation times were used to define the first fixed point for rescaling (time 0. 0 n. a. ). The overall embryo volume was estimated with the surface enveloping the outermost nuclei (convex hull), calculated using the R geometry package (CRAN. R-project. org/package=geometry). The first decrease of more than 10% in the hull volume from one time-step to the next was taken as the first blastocoel collapse (80. 7 hpf, 77. 7 hpf, 86. 8 hpf, 92. 3 hpa and 96. 0 hpa for wt1, wt2, wt3, nt1 and nt2, respectively) and used as the second fixed point for rescaling (time 1.
Analysis of the reconstructed data CSV files containing information for cell position, temporal links and annotations (validation, inner, outer) were constructed and analyzed with custom R scripts. Annotation of cell divisions (symmetry or asymmetry) was determined based on the identity of mother and daughter cells prior to their next mitosis. If daughter cells did not divide by the end of the imaging sequence, their identity at the last time step was used. When measurements were analyzed from pools of embryos (wild type versus clones), they were resampled over time to obtain the same number of time points. The finest temporal resolution was preserved and data were interpolated linearly if necessary.
Funding V. D. and N. D. are members of  RGB-Net  ( TD 1101 ) and Epiconcept (FA 1201) COST actions. This work was supported by a fellowship from the  Ministere de l'Enseignement Supeŕieur et de la Recherche  and by the  Young Researcher Prize  from  Fondation des Treilles  to D. F. ; by  France BioImaging infrastructure  ( ANR-10-INBS-04  to N. P. ); by  Morphoscope2  ( ANR-11-EQPX-0029  to N. P. ); by  Reǵion Îlede-France InterDIM   ISC11  (to N. P. ); by  CRB-Anim  ( ANR-11-INBS-0003  to V. D. ); and by  Revive  ( ANR-10-LABX-73  to V. D. ). Deposited in PMC for immediate release.
The preliminary choice of materials used for construction today presents an alternative solution to meet its problems. This certainly reduces the energy and environmental impact, but reveals new challenges, as this could alter summer comfort and indoor air quality. In addition, the use of materials with low environmental impact, such as bio-based mortars, already known for their thermal insulation properties, acoustic properties and low environmental impact, is increasingly taking place in the construction industry last year's  [2]. Moreover, the use of these materials is hampered, in particular, by the unavailability of databases relating to their intrinsic properties and by ignorance of their behaviors over time. This use requires taking into account several criteria that make them vulnerable; the problem of swelling-shrinkage at the cement-fiber interface and fungal growth are the most important criteria for assessing the degradation of these materials.
25  clearer application of the absorption doctrine could be found in Portugal v Council, where Portugal questioned the legal basis used to conclude a cooperation agreement with India. Relying on Opinion 1/94, Portugal argued that the legal bases related to trade policy and development policy could not be used to include a general provision on intellectual property protection in the agreement. 26 In this regard, the Court noted that the obligations assumed were very limited. The commitment to facilitate access to databases for instance "ha[d] only a very limited scope and [was] ancillary in nature". 27 The Court therefore concluded that the scope of the resulting obligations was not as such to constitute objectives different from the general objective of development cooperation. 28 While the Court thereby undeniably applied the absorption doctrine it must be noted that, differently from what Advocate General Wahl seemed to imply, there was no exclusive national competence at issue.
175). Accordingly, although the Union declared that it "takes full responsibility in these proceedings for the actions of its member States [and requested] that the term 'and certain member states' be dropped from the name of the case" (para 7. 171), the Member States were parties to the proceedings together with the EU (see P-J. Kujper, E. Paasivirta, "EU International Responsibility and its Attribution: From the Inside Looking Out", supra note 1, 62-63). This suggests that, before the WTO DS bodies, the EU and its Member States are entitled to affirm their view as far as the proper respondent is concerned: nevertheless, their statement is not binding in the dispute proceedings. 51 The list of agreements requiring declarations of competence is available on the European Union Treaty Office Database, available at http://ec.
3b ). We then assessed cytokine production by the transferred Marilyn cells after in vitro restimulation with DBY Ag. Activated Marilyn cells in the tumor context did not produce IFN-γ or IL 10. Regarding IL-2, we did observe some level of secretion in the TdLN. However, the frequency of IL-2 + cells within the tumor was much lower than in the control (Fig. 3c, d ). To better characterize the anergic Marilyn cells, we analyzed their transcriptome after isolation from the TdLN of mice bearing early or advanced tumors as compared to several control subsets. The anergic Marilyn cells were similar to Marilyn pTregs or to bulk Marilyn cells from our first data set (Fig. 2a ), when compared to naive Marilyn cells or activated Marilyn cells from tumor-free mice receiving either the Mod-LV alone or immunized with DBY + CpG i.
In vivo tumor model and immunohistochemistry analyses (IHC). Lung adenocarcinoma was induced in KP mice by intratracheal injection of 2 × 10 4 lentiviral particles expressing the fusion protein Luciferase-DBY and Cre-recombinase as described  28. In vivo tumor growth was measured by bioluminescence. Briefly, tumor-bearing mice were shaved and anesthetized with Isoflurane (Sigma-Aldrich). D-luciferin substrate (150 mg per kg, Promega) dissolved in PBS was injected i. p. 15 min before imaging in IVIS Spectrum (Perkin Elmer). Photon fluxes were transformed into pseudocolor images using the Living Image software (Perkin Elmer). Early and advanced tumor stages corresponding to 10-12 weeks and 20-24 weeks after lentiviral infection, respectively were validated by IHC analysis. Lung and TdLN were fixed in 4% paraformaldehyde, dehydrated and embedded in paraffin.
Sections were then treated with Serum-Free Protein Block (Dako) and incubated with primary antibodies. Peroxidase ABC system was used for signal amplification (Vectastain ABC Kit, Vector) and DAB detection system for revelation (Sigma-Aldrich) using manufacturer's specifications. For double staining CD3/B220, a secondary peroxidase-conjugated antibody was used (ImmPRESS Reagent Kit, Vector) to detect the CD3 staining combined with an AEC detection system (Peroxidase Substrate Kit, Vector). For B220 staining, a biotinylated secondary antibody was used and the signal amplified with Alkaline Phosphatase ABC system (Vectastain ABC Kit, Vector) and Vector Bleu detection system (Alkaline Phosphatase Kit III Substrate, Vector). Primary antibodies used are detailed in Supplementary Data 4. Slides were scanned and analyzed as above.
Cells were co-cultured for 3 days at 37 °C and proliferation assessed by flow cytometry based on CFSE dilution. mRNA extraction and microarrays. Approximately 5 × 10 4 total Marilyn cells from 3 pools of 5 mice were purified by FACS from TdLN 7 days after transfer. Naive Marilyn cells and activated Marilyn cells from tumor-free B6 mice receiving male splenocytes into the f. p. or inoculated with the LV vector i. t. were used as controls. A second data set (3 pools of 5 mice per condition) was generated with 3000 CD4 + CD25 hi host Tregs and FOXP3-GFP Pos Marilyn pTregs or FOXP3-GFP Neg Marilyn anergic cells purified from TdLN of mice bearing early or advanced tumors. FOXP3-GFP Pos Marilyn pTregs generated in tumor-free mice receiving DBY peptide i. t. and CD4 + CD25 hi host Tregs from unmanipulated mice were used as controls.
Total RNAs were extracted using RNeasy micro Kit (Qiagen) according to manufacturer's instructions. Total RNA concentration and RNA integrity was monitored by electrophoresis (Agilent Bioanalyzer, RNA 6000 Pico Assay). Gene expression analysis was conducted according to Affymetrix recommendations using Mouse Gene 2. 1 ST arrays. Briefly, 1 ng (data set 1) or 100 pg (data set 2) of total RNA were processed in parallel with an external Mouse Universal Reference RNA to control robustness and reproducibility of enzymatic steps. Amplified and labeled molecules were monitored in order to hybridize arrays with 2. 25 µg of labeled DNA. Raw data were generated and controlled with Expression console (Affymetrix) at the Institut Curie Genomic facility. Human affymetrix data were generated from chCD4 + T cells (CD3 + CD4 + CD127 -CD25 -) and Tconv cells (CD3 + CD4 + CD127 + CD25 ± ) purified by FACS from the blood of healthy donors (n = 2) or patients with metastatic uveal melanoma (n = 3), as previously described  11.
Total RNAs were extracted using RNeasy micro Kit (Qiagen). Samples were processed as described for the mouse samples and the hybridization was performed on human Gene 2. 1 ST arrays. Statistical analysis. Results were expressed as mean±SEM. Data analysis was performed on Prism software (GraphPad Prism v6) using unpaired student t-test or Mann-Whitney non-parametric test, as appropriate. Significances were indicated as follow *p < 0. 05, **p < 0. 01, ***p < 0. 001, ns as non-significant. Transcriptome analysis. Raw data files were processed: background corrected, normalized using the quantile method and analyzed using R with Bioconductor and packages Limma 48 and Oligo  49. Genes were averaged using ProbeID and GeneName and transcripts were filtered using the refseq mRNA database. Principal component analyses on most differentially expressed genes, heatmaps and hierarchical clustering were performed using Qlucore Omics explorer 3.
1. Significance threshold was defined for q < 0. 05. Gene set enrichment analyses were performed, as previously described 50,51 using gene sets described previously (MSigDB database, Broad institute  32  ). Data availability. The affymetrix data supporting this study have been deposited in the GEO database under the accession numbers GSE113623, GSE113624, and GSE113625. The authors declare that the data supporting the findings of this study are available within the article and its Supplementary Information files, or are available upon reasonable requests to the authors.
Acknowledgements We thank  Virginie Dangles-Marie ,  Celine Daviaud ,  Isabelle Grandjean , and  Mickael Garcia , the mouse facility technicians and the flow cytometry core at  Institut Curie. We thank the  NIH tetramer core facility  ( Emory university ) for providing DBY:I-A b tetramers. We thank  Dr. Tyler Jacks  and  Dr. Michel Dupage  for providing the KP mice, tools and methodological support. We thank  Dr. Sebastian Amigorena  and  Dr. Claire Hivroz  for discussions and reviewing the manuscript. R. A. was supported by  Cancéropôle Ile de France ,  Fondation ARC pour la Recherche sur le Cancer and Fondation Trouver. This work was supported by the  Institut National de la Santé et de la Recherche Médicale ,  Institut Curie ,  Fondation Trouver ,  Agence Nationale de la Recherche (ANR)  ( Labex DCBIOL and PACRI ).
Study design Data collection was conducted from May to October 2016, involving participation of 14 adult volunteers. No monetary reward was offered for the participation to attract people that are highly motivated. Invited to participate were independent/healthy adult office-workers, aged 21 and over (male or female), who did not require any special care, worked full-time and whose residences or common places of activity were spatially diverse and within the Brisbane Metropolitan Area (BMA) in Queensland, Australia. Recruitment was through advertisements at the Queensland University of Technology (QUT). QUT has over 12,000 staff members and its campuses are located within the BMA (statistics for the 2014-2015 financial year; https://www. qut. edu. au/about/our-university/qut-at-a-glance). The research was also promoted to non-QUT community through word of mouth and advertising on social media.
5 concentrations, temperature and relative humidity together with the geolocations using the GIS data captured by the smartphone. The realtime instantaneous data can be viewed on the Smartphone using the Aircasting App, together with the historical measured data as a heatmap. Four Airbeams were used in this study and they were programmed to record the averaged data every 5 minutes. In this paper, we used the measured estimates of PM2. 5 concentrations and geolocation time-series. The results of preliminary measurements to evaluate the Airbeam performance in measuring PM2. 5 concentrations at ambient levels of Brisbane that are generally lower than 10 µg m -3 (Queensland Department of Environment and Heritage Protection: https://www. ehp. qld. gov. au/air/data/search. php) showed that they were able to pick up the microscale variation in the PM2.
Personal exposure and exposure intensity Main analysis of this paper is based on estimating and apportioning personal PM2. 5 exposures (defined as average PM2. 5 concentration times corresponding duration of activity or time spent in a microenvironment) and exposure intensities (defined as the portion of the 24-hour PM2. 5 exposure received in a microenvironment divided by the portion of the day spent in that microenvironment)  (Buonanno et al. 2011 , Buonanno et al. 2012 , Mazaheri et al. 2014 , Wangchuk et al. 2015). Data collection was carried out through volunteer participation and involved (a) completing a multiple-choice study questionnaire that took approximately 10 minutes and contained information on the participants' age and gender, socio-economic status (highest qualification) and characteristics of the indoor and outdoor environments during weekdays and weekend, such as age for their residence, ventilation conditions at home and work, type of cooktop at home and commuting preferences; (b) using the Aircasting App coupled with their own Android-based smartphone and carrying a Airbeam personal monitor using a dedicated clip or keeping it within their close proximity, ensuring the unit is exposed to open air at all times for one week (five weekdays and one weekend); and (c) completing daily activity diaries while using the personal monitor to collect information on the potential sources or drivers of air pollution.
The participants were instructed to note duration of time spent at home (indoors, outdoors, eating-cooking, sleeping and other), office (indoors, outdoors and other), other (indoors and outdoors), commuting (comprising of any travel taken by walk, bus, train, car, bicycle, motorbike or other). The participants used their own smartphone, giving them the freedom to monitor their exposure at any point in time through the Aircasting platform. They were asked to charge the Airbeam and their smartphone when possible, such as during office-indoors and sleeping hours, to avoid data loss due to low battery issues. The individuals' data were included if at least 23 hours of data on a given day were available. Time-series of the measured PM2. 5 concentrations were used to estimate exposure and exposure intensities.
Analysis of traffic and land use characteristics Previous studies have shown the importance of the distance to road on the exposure to ultrafine and fine particles in the BMA  (Salimi et al. 2013 , Crilley et al. 2016 , Mazaheri et al. 2016 , Yeganeh et al. 2017). The effect of traffic on the PM2. 5 concentrations was examined using traffic information derived from the South-East Queensland Strategic Transport Model (SEQSTM). SEQSTM was used to calculate the daily average traffic volume (number of vehicles per day) within the transport network and to extract the essential road-related information, including geographical location and speed limit for the roads of interest. The major and minor roads used by the participants for commuting were identified using the recorded geolocation data, and the relevant information extracted from the database provided by SEQSTM for inclusion in the analysis  (Ryan et al.
2008). Distances between the participants and the nearest road were calculated using "Near" geoprocessing tool from ArcGIS. The effect of land-use on PM2. 5 concentration was evaluated according to urban characteristics of the study area based on four categories of "commercial", "industrial", "residential" and "open space" areas from open access datasets provided by Australian Bureau of Statistics (http://www. abs. gov. au/). The open space areas comprised of the sum of data subsets for "agricultural land", "parks" and "water bodies"  (Rose et al. 2011). "Select" geo-processing tool from ArcGIS was employed to calculate the average PM2. 5 concentration for participants who were in outdoor microenvironments and commuting by different modes of transport in the Statistical Areas Level 4 (SA4s), which are defined as "largest sub-State regions in the Main Structure of the Australian Statistical Geography Standard" by the Australian Bureau of Statistics with a minimum population of 100,000 persons.
The analysis used the extracted PM2. 5 concentrations, which were averaged over the time spent in each microenvironment for each participant. Length of the road network, and the percentages of open space areas were calculated for each identified suburb, using "Zonal Statistics" geo-processing tool. A map of the statistical areas investigated in this study is provided in the SI document (Figure  S1 ). R v. 3. 2. 3 (R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses and ArcGIS version 10. 2 (Esri 2013) for spatial data processing. The generalised additive model  (Wood, 2011)  is used to investigate the relationship between PM2. 5 concentrations, humidity, temperature, spatial location, hour of the day and the GIS derived variables of distance to the nearest road and the percent of land classified as open space within the suburb in which the measurement take place.