dataesr/scientific-paragraphs-categorization huggingface dataset:
Daily staff meeting ensured assessing the reliability of the data collected and adjusting medical procedures overtime, in the context of a newly emerging disease that was totally unknown three months before we started our study. In the context of pandemic, such a study allows more flexibility that a randomized controlled trial (RCT) with stringent methodological constraints and is much more economical. It is also more reliable that big-data studies conducted by external investigators dealing with incomplete information retrieved from medical files that have led to recent retraction of papers from the two major medical journals  [9]. Indeed, when inconsistencies appeared in our database of the study, the authorized person in our team was able to return to patients files to reassess the data.
We enrolled all patients including those who started their treatment with delay or stopped it early. Because of the crisis situation we had to face, clinical, virological and radiological data were not documented in 100% patients. However, missing data may also be a limitation of RCT. Furthermore, RCT are not useful in the context of an emerging pandemic when commercially available drugs known to be active in vitro are available for immediate treatment  [38, 39]. Our approach of early diagnosis and care of as many patients as possible results in much lower mortality rates than other strategies. The test-and-treat strategy adopted in Marseille also seems capable of shortening the duration of the outbreak when compared to data from France overall by identifying infected people and reducing their viral shedding duration.
Acknowledgements We thank the 7 reviewers who helped in substantially improving and clarifying the manuscript with their many comments and suggestions. We are thankful to:  Julien Andreani ,  Marion Bechet ,  Amin Ben Lassoued ,  Corinne Blasco ,  C� eline Boschi ,  Laurent Boyer ,  Jean-Paul Casalta ,  Herv� e Chaudet ,  Florian Correard ,  Pierre Dudouet ,  Gr� egory Dubourg ,  Carole Eldin ,  Vera Esteves-Vieira , V� eronique Filosa, L� ea Fuster,  Marc Gainier ,  Lugdivine Ganci ,  C� eline Gazin ,  Stephanie Gentile ,  Clio Grimaldier , V� eronique Jacomo,  Marie-Th� er� ese Jimeno ,  Elisabeth Jouve ,  Alexandra Kotovtchikhine ,  B� eatrice Labric, Marc L� eone, Nicolas Levy, Pierre-Yves L� evy, L� ea Luciani, Cl� ea Melenotte ,  Lucille Pinault ,  Pierre Pinzelli ,  Elsa Prudent ,  Laurence Thomas ,  Joana Vitte ,  Nathalie Wurtz.
3 to 3 •. To avoid these specular reflections, the CALIOP viewing angle was changed from 0. 3 to 3 • in November 2007, and since then CALIOP has been observing clouds almost continuously for 12-13 more years. This has provided more data for a thorough re-evaluation of phase determination and has motivated changes to the CALIOP cloud phase algorithm for Version 4 (V4). The V4 algorithm now excludes over-identification of HOI at 3 • , particularly in cold clouds. The V4 algorithm also considers cloud layer temperature at the 532 nm centroid and has been streamlined for more consistent identification of water and ice clouds. In V4 some cloud layer boundaries have changed because 532 nm layer-integrated attenuated backscatter in V4 has increased due to improved calibration and extended layer boundaries, while the corresponding depolarization has stayed about the same.
, 2010)  on the CALIPSO satellite. CALIOP transmits linearly polarized light at 532 and 1064 nm. The total backscattered 1064 nm return is measured using a single avalanche photodiode. The 532 nm backscattered light passes through a polarization-sensitive beamsplitter, and then two photomultiplier tubes detect components that are polarized in a parallel and a perpendicular sense relative to the polarization plane of the transmitted laser light  (Hunt et al. , 2009). The CALIPSO satellite also carries the Imaging Infrared Radiometer (IIR), which provides radiance measurements at three wavelengths in the IR window channel (8-12 µm) in a swath with the center collocated to the lidar ground track. The IIR data along the lidar ground track provide additional information about cloud particle size using an improved split-window technique  (Garnier et al.
In contrast, internal refraction and reflection in randomly oriented ice (ROI) crystals create a depolarization signal that is mainly independent of cloud optical thickness  (Hu et al. , 2007). Additionally, specular reflections from horizontally oriented hexagonal plates of ice crystals, HOI, create backscatter that can be quite large without causing significant depolarization of the lidar signal. Using simulations with this Monte Carlo model and CALIOP observations,  Hu et al. (2007 Hu et al. ( , 2009) )  demonstrated that the distribution of cloud layer-integrated 532 nm backscatter coefficients and layer-integrated volume depolarization ratios provides a relatively simple and effective threshold method for separating clouds into three categories: ROI, HOI and water. The following section provides an overview of how the cloud thermodynamic phase algorithm fits into the CALIOP data processing stream and describes how these quantities are derived from the 532 nm parallel and perpendicular backscatter signals detected by CALIOP.
In addition to these extended near-nadir viewing angle changes, a series of rapid tests were performed at more oblique viewing angles of 10, 14 and 30 • during 2014-2017. This testing is not reported here and is omitted from the analyzed data, as are other short periods of time when the CALIPSO satellite or CALIOP was performing brief special operations. CALIPSO data users can determine the off-nadir viewing angle used for data collection by examining the parameter "Off_Nadir_Angle" in CALIOP Level 1 Profile or Level 2 layer files. Given the sharp peak in reflectance at visible wavelengths near 0 • as measured by POLDER (Fig. 3  of  Bréon and Dubrulle, 2004) , a hypothesis was that the specular reflections would drop off sharply as the viewing angle was increased. Figure  2  shows that the fraction of observed highconfidence HOI drops as the CALIOP viewing angle increases, just as expected from the POLDER observations and by earlier airborne lidar observations  (Noel and Sassen, 2005).
There is an order of magnitude reduction in HOI observed by CALIOP between viewing angles of 0. 3 and 3 • , information that is used to adjust the V4 CALIOP phase algorithm. The primary impacts of specular reflections on CALIOP data are elevated backscatter and reduced depolarization. Figure  3  illustrates these changes as they are influenced by temperature and by viewing angle. The cloud layer temperatures shown in Fig. 3  are cloud layer centroid temperatures  (Garnier et al. , 2015). In V4 these are MERRA-2 re-analysis temperatures interpolated to the 532 nm attenuated backscatter centroid, defined as z cent = base j =top z j β 532 z j base j =top β 532 z j. ( 6 ) Figure  3a  shows the percent of all clouds identified as HOI for a cloud centroid temperature range of 0 to -40 • C. The mean attenuated backscatter (Fig.
Global maps of V4 high-confidence cloud phase occurrence The CALIOP phase algorithm works efficiently to separate ice from water clouds. For example, in 2007 V4 data acquired with a 0. 3 • viewing angle, 73 % of 5 km cloud layer segments were identified as high-confidence ice or water, with 10 % HOI, as determined using the V4 standard 5 km cloud layer product. In 2008, with a 3 • viewing angle, the fraction of high-confidence cloud layers increased to 86 %-87 %, with negligible (< 1 %) HOI. Shown in Fig. 4  are two global maps of the distribution of 5 km high-confidence ice and water cloudy bins detected during April 2008. Figure  6  shows two global maps of the distribution of 5 km high-confidence ice and water cloudy bins during April 2008 from 5 km cloud profiles. Figure  6a  shows the location of high-confidence ROI.
V3/V4 case study comparison In this section we show a case study to illustrate some of the major differences in cloud phase assignment that CALIOP data users who are used to using V3 can expect to see when switching to the new V4 data. The case study chosen is a nighttime overpass of "Superstorm Sandy" that occurred on 29 October 2012 at about 07:15 Z. Sandy was a very large storm system with a combined tropical hurricane and extratropical mid-latitude cyclone  (Kunz et al. , 2013). This storm provides a large dynamic range of backscatter and depolarization, desirable for a meaningful comparison. Figure  11  shows a VIIRS day/night band image of Sandy with the CALIPSO overpass and then the view from the west, with the corresponding CALIOP backscatter image superimposed on top of the VIIRS image.
3 • data than in the 3 • data. However, in V4 between 1 % and 2 % of dusty aerosol layers were identified as ROI in V3, and this is a larger fraction in the 3 • observations. It is worth noting that when multiple features exist in a column, CALIOP can detect underlying features provided the topmost layers are transparent to the lidar beams, which is a unique feature of lidar when compared with passive cloud detection. However, the larger the overhead attenuation becomes, the more uncertain CAD and cloud phase determination also become. Therefore, the topmost layer assignments have the smallest amount of uncertainty. Cloud "fringes", a new class in V4 not determined by the CAD or cloud phase algorithms, also include small amounts of V3 dust, polluted dust and smoke ("other aerosols"), although most V4 cloud fringes were identified as either clear air or ROI in V3.
Summary 5. 1 Impact of the near-nadir viewing angle on observations of specular reflections from oriented ice crystal faces Specular reflections from smooth ice crystal faces oriented perpendicular to the lidar beam impact both the total backscatter and the depolarization of the Clouds and Aerosol Lidar with Orthogonal Polarization (CALIOP) elastic backscatter lidar signal. These reflections are observed by CALIOP as expected from theory, from POLarization and Directionality of the Earth's Reflectances (POLDER) measurements and from ground-based and airborne lidar measurements. As predicted by POLDER, the impact of these specular reflections on atmospheric measurements diminishes as the nadir viewing angle is increased beyond 1 • and reduces by an order of magnitude at 3 •. Changing the CALIOP viewing angle to 3 • influences the observation of horizontally oriented plates and limits their observation in the atmospheric data to a very small fraction of observed ice clouds.
CALIOP data granules extend from one terminator to the next, thus dividing each orbit into separate daytime and nighttime segments. Profiles reported in the first 100 daytime and nighttime granules in each month during 2007 and 2008 were used for the comparison matrices shown in Fig. 16. The distributions did not change significantly when more granules were included. As determined from this ensemble of profile data the high phase confidence cloudy volume detected at 5-80 km increased by 5 %-9 % between V3 and V4. The larger end of this range occurs in the data observed at 0. 3 • , and the smaller at 3 •. This is also reflected in the smaller increase in total cloud volume from V3 to V4 in the 3 • data, which has 0. 5 % fewer water clouds and 1. 5 % fewer ice clouds but also has 5 % more dusty aerosol bins than at 0.
6 Plain language summary CALIOP data users will find that there are more cloud layers detected in V4, with edges that may extend further than in V3, for a combined increase in total atmospheric cloud volume of 6 %-9 % for high-confidence cloud phases and 1 %-2 % for all cloudy bins, including cloud fringes and unknown phases. In general, the increase between V3 and V4 cloudy bins is larger for data acquired at 0. 3 • than it is for 3 • data. In V4 there are also fewer cloud layers identified as HOI, particularly in the 3 • observations. There is increased confihttps://doi. org/10. 5194/amt-13-4539-2020 Atmos. Meas. Tech. , 13, 4539-4563, 2020 dence in the V4 cloud phase assignments, due to enhanced V4 backscatter calibrations, a better CAD algorithm, new overhead extinction retrievals and our overhaul of the cloud phase algorithm.
Acknowledgements. Bryan Baum 's thoughtful comments and suggestions caused us to generate a better manuscript. We thank the  Aeris infrastructure and Nicolas Pascal  for IIR code development support and for IIR data processing at the  Aeris/ICARE Data and Services Center. William Straka III  created the combined VI-IRS/CALIOP images shown in Fig. 11. Financial support. This research has been supported by the  NASA Science Mission Directorate, Earth Science Division  ( CALIPSO  Project). Review statement. This paper was edited by  James Campbell  and reviewed by two anonymous referees.
Raw p-values of the limma t-test were used to define sets of regulated genes (p<0. 01). Detection Above Background (dabg) p-values were used to exclude background signals: significant genes were filtered for p<0. 05 in more than half of the samples in at least one group. Array data has been submitted to the GEO database at NCBI (GSE146036). In the arrow graphs, only genes which were up-or down-regulated more than two times compared to non-exposed cells are shown. The signal (x axis) is drawn in logarithmic scale. Expression is normalized to expression of control samples. Preparation and characterization of TiO2 nanotube suspensions TiO2 nanotubes were suspended in nanopure water with 2 % v/v mouse serum (prepared inhouse) to a final concentration of 3. 24 mg ml -1. The suspension was probe sonicated on ice for 16 min with 10 % amplitude.
In vivo data The average hydrodynamic particle size of the TiO2 nanotube in suspension (3. 24 mg ml -1 ) was determined by Dynamic Light Scattering (DLS). The TiO2 nanotube suspension had a bimodal size distribution with a major peak at 60 nm and a narrow peak at 21 nm. [16] The intensity-based average size was 168. 7 nm and the polydispersity index (PI) was 0. 586, indicating some polydispersity in the suspensions. Endotoxin levels were measured using the Limulus Amebocyte Lysate Assay. The level of endotoxins was low in TiO2 tube suspensions (0. 095 endotoxin units (EU) ml -1 ), and in nanopure water with 2 % mouse serum (0. 112 EU ml -1 ).
29 Microarray mRNA analysis was performed using Agilent 8 × 60 K oligonucleotide microarrays (Agilent Technologies Inc. , Mississauga, ON, Canada) as described previously  [39]  with six replicas for each condition. Bioinformatics analysis of the row data: signal intensities were Loess normalized using the limma package in R/Bioconductor. [40] Analysis of differentially expressed genes (DEGs) was performed using the limma package. The genes were considered as significantly differentially expressed if the BH-adjusted p-values were less than or equal to 0. 1. Statistical analysis is same as for the in vitro transcriptomics above.
12 nm and cubic interpolation. Long range dispersion corrections are applied to both energy and pressure. Velocity rescaling thermostat  [45]  is used to control the temperature, which is set to 303 K with the relaxation time of 1 ps. All bonds with hydrogen atoms are constrained using the LINCS algorithm. [46] Atom coordinates and energies are saved every 5 ps. All simulations were performed by the Gromacs 2019 software package. [47] Visualization of the simulations is done by VMD. [48] dels used Lipids are described by the Slipids force field  [49]. For TiO2, we use parameters optimized to fit results on charge density distributions and water-TiO2 surface coordination obtained in abinitio simulations of TiO2-water interface. [50] These parameters are listed in tables in SI section S2e. Water molecules are represented by the TIP3P model  [51]  , and for Na + and Cl -ions Yoo and Aksimentiev ion parameters is used.
Separate in vitro models were obtained from the in vivo model by swapping the macrophage influx with macrophage replication and leaving out non-existent cells for monocultures. The system of equations was solved numerically using Wolfram Mathematica 12. 0, license L5063-5112 to obtain the time evolution and final state of the model. The same software was also used for visualization of the results. The phase space was scanned by calculating the time evolution of the appropriate system of equations from chapter S5b for a set of nanomaterials with appropriately interspaced parameters: toxicity (tox), cauliflower formation (cff) and signaling efficiency (signalEff). For each parameter, 30 logarithmically-equally-spaced values in a sensible range were chosenthe total amount of values in the grid was thus 30 x 30 x 30 = 27.
Acknowledgments We are grateful to the team at  TeScan  for FE-SEM measurements and would like to thank  G. Hlawacek  and  N. Klingner  for assistance on HIM. We thank  K. Richter  for excellent technical assistance for the transcriptomics analysis and  J. Birkelund Sørli  with in vivo experiments. We kindly thank  JRC Nanomaterials Repository  for providing us with various nanomaterials and the team from  Syglass  for their support.
Funding Sources This research was funded by the  EU Horizon 2020  Grant No. 686098  ( SmartNanoTox  project),  Slovenian Research Agency  (program  P1-0060 ),  Slovenian Research Agency Young Researcher Program  ( H. Majaron ),  Slovenian Research Agency Young Researcher Program (A. Sebastijanović) , the  Helmholtz Alliance 'Aging and Metabolic Programming, AMPro' (J. Beckers) ,  Genomics Research and Development Initiative and Chemicals Management Plan of Health Canada (S. Halappanavar) ,  China Scholarship Council  ( CSC  fellowship  201806240314 ,  Q. Zhou ), and  Science Foundation Ireland  (grant  16/IA/4506 ,  V. Lobaskin ).
It also enables sociologists, who generally have a great deal of information on nodes, to understand what underlies certain individuals' betweenness. To begin, we explain our choice of this indicator by discussing studies that present the comparative advantages of the various centralities. We then describe the data we used to construct this experimental method, giving a few examples of the graphs produced by it. In the next section, the original algorithm developed to increment the deconstruction process is presented. A number of cases are then presented in order to bring the structural method into dialogue with the sociological data. The descriptions of the alters that emerge from the deconstruction process are examined and then combined with sociological variables whose effects on personal networks are well known (type of tie, age, marital status, social class).
simplex from Ibity in Madagascar (accession number 226251) and Burkina Faso in mainland Africa (accession number 184113) were provided by the Millennium Seed Bank Partnership (MSB, Royal Botanic Gardens, Kew) and germinated. Nuclear DNA content was estimated for one individual of each accession following Doležel, Greilhuber & Suda (2007), using a Partec Cyflow SL3 flow cytometer (Partec GmbH, Münster, Germany). Three replicates from each individual were measured separately and Petroselinum crispum (Mill. ) Fuss (2C = 4. 5 pg) was used as the calibration standard. The mean value of the genome size (2C) of the three replicates was calculated.
15 μL GeneScan 500 ROX Size Standard (Applied Biosystems) and denatured at 94 °C for 3 min. GeneMapper Software 5 (Applied Biosystems) was used for allele calling, and results were visually inspected. Peaks differing by one nucleotide were rounded to the closest allele size to avoid the overestimation of genetic variation due to stuttering. The ploidy of populations was also estimated based on the maximum number of alleles (MNA) at a locus. The power of this method, however, depends on the number and frequency of alleles revealed at each locus  (Besnard & Baali-Cherif, 2009). Considering the Drakensberg samples diploid (see results), we estimated allele frequencies for the five loci (LS2, LS4, LS5, LS7 and LS10) for which at least three alleles were revealed in the uKahlamba Drakensberg Park population (31 individuals).
Plastid microsatellites We analysed polymorphisms from the maternally inherited plastid genome. Because low polymorphism was revealed in plastid DNA barcodes (rbcL and matK; data not shown), we decided to analyse 12 microsatellite loci (plastid SSR) that are more variable (Table  2 ). Based on a complete plastid genome of L. simplex (MF563366;  Piot et al. , 2018) , we defined primers in regions flanking a mononucleotide stretch with a minimum of ten repeats. Four loci were simultaneously amplified following the PCR protocol described in  Besnard et al. (2011)  and using the universal M13 primer labelled with the YAK, 6-FAM or AT550 fluorochrome (Table  2 ). PCR products were then multiplexed together with GenScan-600 Liz (Applied Biosystems) and separated on an ABI Prism 3730 DNA Analyzer (Applied Biosystems).
genetic diverSity and population differentiation To understand genetic differentiation among populations, Bayesian clustering of individuals was performed using the software STRUCTURE v. 2. 3. 4   (Pritchard, Stephens & Donnelly, 2000; Falush, Stephens & Pritchard, 2003; Hubisz et al. , 2009) , which has been shown to be the least biased clustering method in mixed-ploidy populations  (Stift, Kolář & Meirmans, 2019). We used a combination of the RECESSIVE-ALLELE model and the ADMIXTURE model; the RECESSIVE-ALLELE model is appropriate when the information about allele dosage is incomplete (i. e. the number of copies of each allele is not known for all the genotypes;  Falush, Stephens & Pritchard, 2007). The admixture model assumes the genome of each individual to consist of fractions of every genetic cluster, which was the most appropriate ancestry model for our dataset available in STRUCTURE.
Analyses were run with a burn-in period of 5 × 10 5 and subsequent 5 × 10 5 Markov chain Monte Carlo replicates, testing 1 ≤ K ≤ 5 using GNU parallel  (Tange, 2011). The number of genetic clusters (K value) best fitting the data was selected according to the method of  Evanno, Regnaut & Goudet (2005)  by using the loglikelihood values and the second-order rate of change of the likelihood distribution of the K values (ΔK) inferred with the online tool STRUCTURE HARVESTER  (Earl & von Holdt, 2012). The STRUCTURE analysis was performed on the total dataset and on the dataset containing only individuals from Madagascar, using the same parameters. The assignment of individuals to genetic clusters and their membership coefficients were computed using the program CLUMPP v. 1. 1. 2 (Jakobsson & Rosenberg, 2007) , applying the Greedy algorithm.
Therefore, the assignment of samples to populations was not taken into consideration. The software DISTRUCT v. 1. 1  (Rosenberg, 2004)  was used to display the clustering results and the individual membership coefficients graphically. GenoDive v. 2. Ob27  (Meirmans & Van Tienderen, 2004 ) was used to account for the different ploidies in our dataset. We calculated genetic diversity indices  (Nei, 1987)  for the individuals occurring in mainland Africa and Madagascar, by applying the maximum likelihood method to correct for the unknown dosage of alleles  (Meirmans & Van Tienderen, 2004). The following indices were calculated: number of observed alleles (N A ), effective number of alleles (Eff-N A ), expected frequency of heterozygotes (H S ) and total heterozygosity (H T ). Results with a P value < 0.
We also performed a pairwise population differentiation analysis within groups with the same ploidy with 999 permutations. We used the R package POLYSAT v. 1. 7-3 (Clark & Jasieniuk, 2011)  to calculate a pairwise matrix of genetic distances (using the Bruvo distance;  Bruvo et al. , 2004)  and to carry out principal component analyses (PCA) on the matrix. Finally, plastid DNA data were analysed separately. The probability that two L. simplex individuals taken at random from Malagasy or South African populations display a different plastid haplotype was computed as Downloaded D = 1 -Σ p i 2 , where p i is the frequency of the plastid haplotype  (Nei & Tajima, 1981; Nei, 1987). We then investigated plastid haplotype relationships using minimum spanning networks based on Bruvo's genetic distance  (Bruvo et al.