dataesr/scientific-paragraphs-categorization huggingface dataset:
WGS generation and analysis WGS was performed at the Wellcome Trust Sanger Institute on an Illumina HiSeq 2500 platform to generate 100 bp paired-end reads. Short read data are available in the European Nucleotide Archive (ENA) database (S3 Table ). Sequence reads were mapped against reference genome V. cholerae O1 El Tor reference N16961 (accession numbers LT907989/LT907990) using SMALT v0. 7. 4 [17]. SMALT was used to index the reference using a k-mer size of 13 and a step size of 6 (-k 13 -s 6) and the reads were aligned using default parameters but with the maximum insert size (i) set as 3 times the mean fragment size of the sequencing library. PCR duplicate reads were identified using Picard v1. 92 [18] and flagged as duplicates. A reference-based alignment was obtained by mapping paired-end Illumina reads to DNA sequenced from APW-enriched specimen spotted filter papers and isolate spotted filter papers to the V.
cholerae O1 El Tor reference N16961. Automated annotation was performed using PROKKA v1. 11  [19]  and a genus-specific database from RefSeq  [20]. Variation detection was performed using SamtoolsMpileup v0. 1. 19 [21]  with parameters "-d 1000 -DSugBf" and bcftools v0. 1. 19 [22]  to produce a BCF file of all variant sites. All bases were filtered to remove those with uncertainty in the base call. The bcftools variant quality score was required to be greater than 50 and the mapping quality to be greater than 30. If all reads did not give the same base call, the allele frequency, as calculated by bcftools, was required to be either 0 for bases called the same as the reference, or 1 for bases called as a single nucleotide polymorphism (SNP) (af1 < 0. 95). The majority base call was required to be present in at least 75% of reads mapping at the base, (ratio < 0.
Assemblies Short reads from the Cameroonian samples were assembled de novo using SPAdes v3. 10. 0  [25] , reordered against the reference sequence with ABACAS  [26] , and then a metaSPAdes  [27]  was performed with parameters-meta -t 8 -m 15. Statistics from assemblies were extracted with metaQUAST with parameters-fast -no-check  [28]. Genome completeness estimates and checks for contamination were performed using CheckM lineage wf with the following parameters -t 8 -x fa-reduced tree  [29]. Kraken v0. 10. 6  [30]  was used to assign taxonomic labels using default parameters and the database Refseq release 72 (27/08/2015). Annotation was performed using the RAST server  [31].
The use of MetaSPAde and MetaQUAST software allowed us to determine with higher accuracy the specific contribution of V. cholerae genome to the assemblies as well as the extent of bacterial diversity found in these samples (Fig  3 ). As expected, genomic diversity appears higher in enriched specimen spotted filter papers compared to culture isolate spotted filter papers. Similarly, V. cholerae genome is present in higher proportion in enriched specimen spotted filter papers compared to culture isolate spotted filter papers, which can be easily explained due to the lower quantity of DNA present in the isolate spotted filter papers (Fig  3).
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  and by the  International Center for Frontier Research in Chemistry (icFRC). The authors gratefully acknowledge financial support from  EU GRAPHENE   Flagship  project (no. 785219 ) and from the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), AcRF tier 2 grant ( MOE2017-T2-2-005 ) from  Ministry of Education of Singapore  and  AME-IRG grant  ( AMEIRG18-0016 ) from  Agency for Science, Technology and Research of Singapore. The authors wish to thank  Cathy Royer  and  Valérie Demais  for help with TEM analyses at the  'Plateforme Imagerie  in vitro' at the  Center of Neurochemistry (Strasbourg, France). Dris Ihiawakrim  and  Belén Ballesteros  are gratefully acknowledged for fruitful discussion.
We measured virulence as the time to kill larvae of G. mellonella. This was done for five representative isolates of each group (isolates G1#21, G1#23, G1#42, G1#44, and G1#51 for group 1, isolates G2#25, G2#29, G2#36, G2#39, and G2#40 for group 2, and all members of group 3) and for three injected doses (corresponding to 20 l of a 1:1,000, 1:100, or 1:10 diluted culture). We conducted two replicate experiments and injected each dose four times. Appropriate dilutions of each injected culture were plated on NBTA to estimate the injected dose. We used the automated procedure described by Parthuisot et al. (40)  to measure time of insect death, and we analyzed data with a Cox proportional hazard model, with variation among replicate experiments and variation among isolates within each group considered a Gaussian random effect.
We also monitored the emergence of variants after injection of G1#23 in G. mellonella. Twenty insects were homogenized after 1, 2, 3, 6, or 10 days of incubation at 28°C. As for in vitro emergence, red and blue colonies were sampled from NBTA plates, and their motility was quantified as described above with two replicate measurements for each colony. In all these experiments, measurements performed on red and blue clones were first averaged by culture before being compared using nonparametric tests. Availability of data and materials. The supplemental files for this article (Additional files 1 to 6) can be found at FigShare (https://figshare. com/s/4cf02d0aa870dd20d5ab). The data sets supporting the conclusions of this article are included within Additional file 6.
ACKNOWLEDGMENTS This work was supported by the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 andANR-11-IDEX-0002-02 ). We are very much indebted to  Frank Gilbert , who helped in running spectrophotometry analyses. We thank  Rémi Peyraud  for discussions about the evolution of major regulators in pathogens. We also thank  David Duneau  for his help in clarifying an earlier version of this manuscript and for his insight on our work. J. -B. F. and A. G. designed the research. S. P. collected the initial collection of variants. N. P. and J. -B. F. performed the experimental evolution experiments. N. P. performed PCR and analyzed sequences. M. C. C. performed virulence and transmission experiments. A. G. and A. L. designed and performed the lrp complementation experiments.
Sea ice has been monitored in terms of concentration and types with microwave satellite observations since the late 1970s. However, it remains an open question as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with two sea ice age (SIA) and three sea ice type (SIT) products using microwave radiometer and scatterometer data from 2000 to 2015. Their performances were evaluated quantitatively with samples that are used for generating tie points, and qualitatively with the RADARSAT imagery. The methods that combined scatterometer and radiometer data have overall better performances on ice type discrimination.
The best methods are ECICE-QSCAT-2 for the years 2000-2009 and ECICE-ASCAT for 2009-2015, both using scatterometer data along with radiometer data. Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions, for instance, homogeneous sea ice regions with little artifact for SIA algorithms to track.
In this study, radiometer and scatterometer data were used in the eight SITC methods, the description of which can be found in Section 3. The retrieved SITC results were thereafter inter-compared with the SIA and SIT products. The radiometer data used in this study includes data from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program. The swath data from January 2000 to December 2015 is taken from the Fundamental Climate Data Record (FCDR) of Microwave Image Radiance, Edition 3, provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Climate Monitoring Satellite Application Facility  (Fennig et al. , 2017). The radiometer ratio parameters to be used in the algorithms (see Equation  1 ) were computed on swath data before the brightness temperatures and the respective parameters were gridded to the Equal-Area Scalable Earth (EASE2) Grid of 25 km spcaing.
The scatterometer data used are from the SeaWinds scatterometer onboard QuikSCAT (QSCAT) satellite and the Advanced Scatterometer (ASCAT) on EUMETSAT's Metop-A and Metop-B satellites. QSCAT is a Ku-band (13. 4 GHz) conically scanning pencil-beam scatterometer, operating from July 1999 to November 2009. It has two beams, each with a wide range of azimuth angles. The inner beam is horizontally polarized at an incidence angle of 46 • , whereas the outer beam is vertically polarized at an incidence angle of 54 •  (Hoffman and Leidner, 2005). Normalized backscatter coefficients of horizontal and vertical polarizations, σ • hh and σ • vv (referred to as BKSH and BKSV, respectively), are resampled to a grid of 12. 5 km in polar stereographic projection. They are provided and archived at CERSAT/Ifremer  (Girard-Ardhuin et al.
, 2008) , available on CERSAT/Ifremer portal (http://cersat. ifremer. fr). ASCAT is a C-band scatterometer with three vertically polarized antennas transmitting pulses at 5. 255 GHz, operating from May 2007 to present. The fan-beam antennas are oriented at 45 • , 90 • and 135 • with respect to the satellite track. The incidence angle varies between 25 • and 65 •. Backscatter coefficients from ASCAT (σ • a , referred to as BKAS) are normalised to the backscatter at a constant incidence angle of 40 • and resampled to a polar stereographic 12. 5 km resolution grid. The gridded data is built from swath data (from EUMETSAT Level 2B product) averaged over one day by CERSAT/Ifremer. The QSCAT data from January 2000 to November 2009 and ASCAT data from October 2007 to December 2015 (data from Metop-A only) were used for sea ice type concentration retrieval in this study.
1. The C3S-SIT dataset is provided for the winter months (October to April) on a 25 km EASE grid, available through the C3S Climate Data Store (https://cds. climate. copernicus. eu). The KNMI-SIT product uses a maximum likelihood class discrimination approach  (Rivas et al. , 2018)  based on probabilistic distance to ocean wind and sea ice geophysical model functions (GMFs). GMFs describe the behaviour of backscatter as a function of observational geometry (i. e. , incidence and azimuth angles) and geophysical variables such as wind speed and direction or sea ice type. This dataset is available in a polar stereographic 12. 5 km grid for the whole period of satellite scatterometer missions (ERS, QSCAT and ASCAT), extending from 1992 to present. Two KNMI-SIT products were used in the study, one based on QSCAT data (referred to as KNMI-QSCAT-SIT product, from 2000 to 2009), the other with ASCAT data (referred to as KNMI-ASCAT-SIT product, from 2007 to 2015).
Other data Four RADARSAT-1 and two RADARSAT-2 synthetic aperture radar (SAR) images were used for validation. RADARSAT-1 and RADARSAT-2 are two Canadian Earth Observation Satellites. The first one was operational from March 1995 to March 2013 and the second from December 2007 to present. RADARSAT-2 is a follow-on satellite mission of RADARSAT-1, both with SAR instrument working at 5. 3 GHz (C-band). All the SAR images used in this study are ScanSAR Mode data, with a pixel size of 50 m and resolution of around 100 m. They were radiometrically calibrated and projected to the UTM projection with a pixel size of 100 m. The geographic locations of the SAR images and the dates are shown in Figure  1. The data was acquired from the Alaska Satellite Facility (ASF) and RADARSAT-2 portal in MacDonald Dettwiler and Associates (MDA).
Validation with SAR This section presents qualitative comparisons of the MYI concentrations from different SITC methods against information retrieved from visual analysis of the RADARSAT-1 and RADARSAT-2 SAR images. Six case studies are addressed using   (NSIDC-SIA is a weekly product). In this case, although the use of scatterometer data helps to improve the estimation of MYI concentration, it tends to misidentify young ice as MYI when backscatter from the former is high. The East Siberian Sea is one of the regions that have the most extensive coverage of landfast ice in the Arctic  (Dammann et al. , 2019). and third best performance. This case shows that using scatterometer data in SITC methods favors the identification of MYI in highly dynamic regions. Moreover, the performance is better with QSCAT (Ku-band) than ASCAT (C-band).
Conclusions Results from the eight SITC methods are inter-compared with two SIA products and three SIT products, using microwave radiometer and scatterometer data spanning the winters (October -April) from 2000 to 2015. Performances of the SITC methods are evaluated quantitatively and qualitatively with samples that are used to generate tie points as well as SAR images. Overall, the results can be summarized as follows: -Using scatterometer data along with radiometer data helps the discrimination of FYI and MYI. The most appropriate approach to reproduce sea ice type distributions in the Arctic is the combined method: ECICE-QSCAT-2 for the years 2000-2009 and ECICE-ASCAT-2 (or ECICE-ASCAT-1) for 2009-2015. -In the SITC methods, Ku-band scatterometer works better than C-band scatterometer on identifying MYI due to the higher sensitivity to volume scattering in MYI.
The SICCI-SIA product, on the other hand, seems to underestimate MYI concentration in homogeneous regions with little artifacts to track. -Among the three SIT products, the eight SITC methods have the best agreement with the KNMI-QSCAT-SIT product. Besides, all the SITC methods agree better with the SIT products in mid-winter than transition seasons. -Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions.
Acknowledgements. This work was supported by the  Area of Advance at Chalmers University of Technology, Gothenburg, Sweden. We thank the  ASF  and  MDA  for providing RADARSAT-1 and RADARSAT-2 images. The contribution of C. M. was supported by the  Deutsche Forschungsgemeinschaft (DFG)  in the framework of the priority programme " Antarctic Research with comparative investigations in Arctic.
Patients and methods This is a retrospective monocentric cohort study including all patients admitted for AECOPD to the Rouen University Hospital department of pulmonary and intensive respiratory care, between November 2015 and January 2017. Patients were identified using hospital electronic database. They were included if they were admitted for an AECOPD and if they had a lung function test confirming the diagnosis of COPD according to GOLD guidelines. Patients admitted for another cause than AECOPD, patients who died during inpatient stay, or who were discharged to a rehabilitation and aftercare facility were not included. Medical history and management during inpatient stay were collected using the electronic medical record. Frequent exacerbators were defined as patients having two or more AECOPD within the last 12 months or having one severe AECOPD within the last 12 months.
Severe AECOPD was defined as an AECOPD requiring hospitalization. Data regarding follow-up and participation to the PRADO program were collected from the social welfare database. Referral to PRADO discharge bundle was decided by the senior clinician in charge of the patient. Hence, patients were allocated to the PRADO group, and were referred for the discharge care bundle, or were allocated to the control group in which they received only standard care. This study was approved by Rouen University Ethical Board (CERNI -approval E2018-65), and patients gave a written informed consent to participate. All patients' data were anonymized. Primary outcome was defined as readmission or death within 28 days following discharge for an AECOPD. Readmission for another cause than AECOPD was not considered.
Survival data were analyzed using the Kaplan-Meier method and Logrank test. All tests were two-sided with type I error rate was set at 0. 05. The analyses were performed using GraphPad Prism 6 ® for Mac OS X ® (GraphPad Software, Inc. , La Jolla, CA, USA), IBM SPSS ® Statistics v20. 0 (IBM Corporation, Armonk, NY, USA) and R (version 3. 5. 0, R Foundation for Statistical Computing, Vienna, Austria). As this study was retrospective and monocentric, the sample size was not controlled. A post hoc power analysis was conducted. Assuming a 16% readmission rate in the control group, with 62 patients in the PRADO group and 202 patients in the control group, a 5% two-sided type I error rate, the statistical power to detect a 5% absolute readmission risk reduction (31% RR reduction) would have been 18% (normal approximation power analysis).
We measured the fluorescence intensity of the multicellular system in each input state and were able to clearly distinguish expected ON and OFF states for all systems. We did observe differences in ON state intensities, some directly related to the differences of ON level of the separated devices, others to differences in growth rates between strains having different GFP or integrase expression status (Supplementary Fig. 9 ). We also obtained multicellular systems with more constant output levels by using devices having lower transcription input signals, albeit at the cost of a lower fold change (Supplementary Fig. 10 ). Taken together, these data unambiguously demonstrate the feasibility of composing strains containing various recombinase devices to implement complex Boolean functions at the multicellular level.
RFP excitation was performed by a 561 nm laser and filter 615/25 nm (YL2). Voltages used were FFS: 440, SSC: 340, BL1: 360, for all experiments except with BP and LR targets, and BL1: 400 and YL2: 400, for experiments with BP and LR targets. Data were analyzed and presented using the Flow-Jo (Tristar) software.
Characterization of elements. A glycerol stock from each construct was streaked on LB agar plate supplemented with kanamycin. 96 deep well plates filled with 500 µL of LB with kanamycin antibiotic were inoculated with three clones from the freshly streaked plates. For all experiments, three clones of the negative control strain corresponding to RBS-GFP without promoter and the positive control strain corresponding to P7-RBS-GFP were inoculated. Plates were grown 16 hours at 37 °C. Cultures were diluted 40 times on Focusing Fluid and measured on flowcytometer. Three experiments with three replicates per experiments were performed for elements, integrase sites, and terminators characterizations. Data were analyzed using Flow-Jo. Bacteria events were gated to remove debris from the analysis by plotting FSC-H over SSC-H.
For both experiments, 96 deep wells plate filled with 500 µL of LB per well were inoculated with three clones per co-transformation and three clones per control corresponding to the BP target and LR target strains. For integrase characterization with Plac-integrase plasmid and dual controller plasmid, LB was supplemented with 100 µM of IPTG for co-transformation with Plac-integrase and 1% of Arabinose for co-transformation with the dual controller for expression of Tp901. Plates were grown 16 hours at 37 °C. Cultures were diluted 40 times on Focusing Fluid and directly measured on flow-cytometer according to previously described methods. Data analysis was performed using Flow-Jo. Bacteria events were gated to remove debris from the analysis by plotting FSC-H over SSC-H. Data were represented using a density plot of BL1-H over YL2-H, corresponding to the GFP fluorescence intensity over the RFP fluorescence intensity.
The proportions of bacteria in BP or LR states were obtained using BL1-H over YL2-H plot by gating the population corresponding to the BP or LR target strain. Data represented in the heatmap correspond to the mean of the proportion obtained for the three replicates in one experiment. Details about the flow-cytometry gating strategy can be found in supplementary Fig. 11. Characterization of recombinase logic devices. Each device was co-transformed with each integrase cassettes corresponding to its input number. For each transformation, three clones were picked and inoculated in 500 mL of LB in 96 deep well plate. Additionally, the negative control (RBS-GFP without promoter) strain and positive control (Promoter-RBS-GFP) strain were streaked from glycerol stocks and three clones were picked and inoculated.
Plates were grown 16 hours at 37 °C. Cultures were diluted 40 times on Focusing Fluid and measured on flow-cytometer. Two experiments with three replicates per experiments were performed. Data were analyzed using Flow-Jo using the same procedure than the one detailed previously for element characterizations. Multicellular logic system prototyping. Devices were co-transformed with corresponding constitutive integrase cassettes. Three clones per transformation were inoculated in 500 µL of LB in 96 deep well plate. Plates were incubated during 16 hours at 37 °C to reach stationary phase. From the stationary phase culture, cells were mixed in identical proportions and diluted 1000 times for growth in 500 µL of LB in 96 deep well plate. Plates were incubated 16 hours at 37 °C. The co-cultures were diluted four times in PBS and analyzed using a plate reader for measurement of bulk fluorescence intensity.
Acknowledgements We thank members of the synthetic biology group and of the CBS,  P. Lemaire  and  P. Hersen  for fruitful discussions. Support was provided by an  ERC   Starting Grant  "Compucell", the  INSERM Atip-Avenir program  and the  Bettencourt-Schueller Foundation. S. G. was supported by a Ph. D. fellowship from the  French Ministry of Research  and the  French Foundation for Medical Research (FRM). The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
One important finding relies on a pronounced contrast between a positive LSt O 3 response to ENSO in the extratropics and a negative one in the tropics, with a delay of 3 months, which supports a stratospheric pathway for the ENSO influence on lower stratospheric and tropospheric O 3. In terms of trends, we find an unequivocal O 3 recovery from the available period of measurements in winter-spring at middle to high latitudes for the two stratospheric layers sounded by IASI (>∼ 35 • N-S in the MUSt and >∼ 45 • S in the LSt) as well as in the total columns at southern latitudes (>∼ 45 • S) where the increase reaches its maximum. These results confirm the effectiveness of the Montreal Protocol and its amendments and represent the first detection of a significant recovery of O 3 concurrently in the lower, in the middle-upper stratosphere and in the total column from one single satellite dataset.
Although recent papers based on observational datasets and statistical approaches agree that we are currently progressing towards an emergence into ozone recovery (e. g. Pawson et al. , 2014; Harris et al. , 2015; Steinbrecht et al. , 2017; Sofieva et al. , 2017; Ball et al. , 2018; Weber et al. , 2018) , trend magnitude and trend significance over the whole stratosphere substantially differ from one study to another and, consequently, they are still subject to uncertainty  (Keeble et al. , 2018). A clear identification of the onset of O 3 recovery is very difficult due to concurrent sources of O 3 fluctuations (e. g. Reinsel et al. , 2005; WMO, 2007 WMO, , 2011)). They include changes in solar ultraviolet irradiance, in atmospheric circulation patterns such as the quasi-biennial oscillation (QBO;  Baldwin et al.