dataesr/scientific-paragraphs-categorization huggingface dataset:
As the CHIMERE model is used for both analysis and forecast, particular attention was given to the optimization of computational performance. Numerous improvements were made in the code and are completely transparent for the user: these changes are described in Sect. 2. Section 3 presents the changes in the model geometry, including the vertical mesh, as well as changes in the horizontal coordinate system allowing for the application of the model to hemispheric scale domains. Section 4 presents the improvements in the representation of anthropogenic emissions, including the use of the global HTAP (hemispheric transport of atmospheric pollutants) emission dataset for anthropogenic emissions, and the improvements in modeling mineral dust emissions. Section 5 describes the changes in the representation of various physical and chemical processes in the model, such as inclusion of the SAPRC scheme for gaseous chemistry and inclusion of chlorine chemistry in the model.
This section also presents the evolutions in the modeling of the physical processes affecting aerosols, as well as the implementation of the Fast-JX module for radiative transfers. Another major improvement presented in this section is the ability of CHIMERE-2017 to provide lidar observables as a model output. Section 6 presents the application of CHIMERE-2017 to simulations of 3 winter months and 3 summer months in a domain covering continental Europe at 50 km resolution, and the scores obtained by the model in comparison with back-ground observations of gaseous and particulate species in this configuration. Section 7 presents the application of the new model version to the simulation of the eruption of the Puyehue-Cordon Caulle volcano, in the Chilean Andes, in June 2011. This event provides a good test bed for this new version, since the volcanic plume from this volcanic eruption was dense enough to be observed by satellites all along its circumpolar transport around the South Pole.
4. Vertical transport on this mesh can be calculated using either a slope-limited Van Leer scheme  (Van Leer, 1979)  or a upwind scheme, depending on user's choice, also taking into account turbulent mixing and, optionally, deep-convection fluxes, following the  Tiedtke (1989)  formulation. CHIMERE needs to be forced at least by input meteorological fields, and by anthropogenic emissions. A preprocessor for anthropogenic emissions, named emisurf, is provided to the users. This preprocessor was historically developed for the downscaling and reformatting of the raw emissions from the EMEP (European Monitoring and Evaluation Program) emission inventory at 50 km resolution, but can be adapted by users to any other raw dataset they need to use. The main steps for this are described in  Menut et al.
(2012) : -A first step projects the annual masses from the "raw" EMEP grid to the CHIMERE grid. The spatial emission distribution from the EMEP grid to the CHIMERE grid is performed using proxies like population density, as described by Fig. 5a-d. Proxies used by emisurf for this process include land-use data (either GLCF, USGS or GlobCover), large point source database (such as the EPER database for Europe), etc. -Second, monthly, weekly and hourly profiles are prescribed to convert annual totals to hourly fluxes used as input for CHIMERE. These factors are derived largely from data provided by the University of Stuttgart (IER) as part of the GENEMIS project  (Friedrich and Reis, 2004) , and are available as data files from the EMEP model website, www. emep. int. -A last step consists in converting the species available in the raw data into the model species.
This proxy provides a unitless value for a given cell at 1 km resolution over Europe. It is built by crossing several databases (population, land cover data, roads, etc. ); it consists of a linear regression of several parameters such as population density, length of road, and surface of urban areas in a given fine grid cell. The regression coefficients are calculated over France thanks to the use of the French highresolution bottom-up inventory and applied everywhere over Europe (Fig. 6 ). For the extrapolation at the European level, it uses the best source of information among the following proxies: CORINE land cover (from the European Environment Agency), road data of the ETISplus European project (European Transport policy Information System) for 2010 over Europe. ETISplus combines data, analytical modeling with maps (GIS) and a single online interface for accessing the data.
Default European GIS road data from EuroglobalMap, default worldwide GIS road data from natural Earth data 1  , and population database by  Gallego (2010)  over Europe and data from Center for International Earth Science Information Network (CIESIN) for the rest of the world. All of these data were not available on the whole domain. Therefore, three tiers of information were defined to cover all countries with different levels of confidence: -Countries covered by all the data: Iceland, Norway, Turkey, Bosnia Herzegovinia, Serbia, Montenegro, Kosovo, Macedonia, Albania and all the EU28 except Greece. -Countries without CLC coverage but with ETIS or EuroglobalMap data: Belarus, Ukraine, Moldavia and Greece. -Other countries are only covered by the world road map and population data. For shipping emissions (SNAP 8), a proxy was developed using an inventory of shipping routes obtained from the US National Center for Ecological Analysis and Synthesis.
Mineral dust emissions Mineral dust modeling is an important process for understanding climate evolution but also for air quality regional modeling. For many regions over the world, it becomes necessary to manage air pollution knowing the relative part of anthropogenic and natural contributions. For this, even over small regions, it is important to have the same level of knowledge for mineral dust emissions as for anthropogenic or biogenic emissions. In this new model version, many improvements were done for mineral dust emissions. They are related to input databases, the emission schemes themselves and additional options to better take into account the impact of meteorological conditions on emissions.
Soil, land use and roughness length For the calculation of mineral dust emissions, several variables have to be known: land use, soil characteristics, aeolian roughness length and erodibility. Originally, CHIMERE used a database limited to North Africa and the Arabian Peninsula. For simulations over Africa or Europe, this spatially limited database was considered adequate, Sahara being the major source in this region. But for this new CHIMERE-2017 version, the goal is to enable calculations of mineral dust emissions anywhere in the world. It is then necessary to change from regional to global databases. A large part of this change was already done in  Menut et al. (2013b)  for land use, soil and roughness length. The soil and land use used are now those from NCAR USGS land-use dataset  (Homer et al.
, 2004)  and STATSGO-FAO soil dataset  (Wolock, 1994). The roughness length is estimated using the global 6 km horizontal resolution "Global Aeolian Roughness Lengths from AS-CAT and PARASOL" dataset  (Prigent et al. , 2012). In addition to these changes, the option to evaluate the soil erodibility based on satellite data was added. Therefore, three options are now available in CHIMERE 2017: 1. Calculate the erodibility from the land-use database: cropland, grassland, shrubland and barren or sparsely vegetated areas, are then considered as partly erodible. This was the only option offered in earlier CHIMERE versions. In this case, constant percentages are applied for each land-use category. 2. Use the global erodibility dataset derived from MODIS  (Grini et al. , 2005) , included and used in CHIMERE as described by  Beegum et al.
Impact of vegetation on dust emissions The vegetation evolves during the year and this variability will impact the mineral dust emissions. Contrarily to the previous model version, more focused on Saharan areas, this version is able to model mineral dust all around the world. For example in areas such as the Sahelian region or Europe, mineral dust are observed but are very dependent on the vegetation variability. To take into account this variability, the vegetation fraction is diagnosed from the USGS 30 s resolution database and acts as a limiter to the erodibility factor.
Modeling strategy CHIMERE-2017 includes the module Fast-JX version 7. 0b  (Wild et al. , 2000; Bian and Prather, 2002)  for the online calculation of the photolysis rates. Fast-JX is a module that solves the equations of radiative transfer in an atmospheric column taking into account the solar zenith angle, the vertical profile of ozone and water-vapor concentrations, the ice and water clouds, the radiative effect of scattering and absorption by aerosols and the surface albedo. Following the recommendations of the Fast-JX developers, the effective size of ice particles is estimated following  Heymsfield (2003)  as R eff i = 164 × IWC 0. 23 , where R eff i (µm) is the effective radius of ice particles, and IWC is the ice content of the atmospheric particles (g m -3 ). Regarding water droplets, their radius is estimated also following the recommendations of Fast-JX developers, as 9.
J. Highwood 3. However, the specification of these parameters is in a parameter file, and can be changed by the user to other values. In the same way, the user can easily introduce more species in the optical treatment for specific studies, e. g. , volcanic ashes. After the preprocessing phase, at each time step and in each model column, the Fast-JX module resolves the radiative transfer in the model atmospheric column, computing the actinic fluxes at each model level and integrating them over N wavelength bins in order to produce accurate photolysis rates. In the configuration adopted for CHIMERE-2017, N is set to 12, which is the value recommended by Fast-JX developers for tropospheric studies. These 12 wavelength bins include the seven standard Fast-J wavelength bins from 291 to 850 nm, as described in  Wild et al.
Online calculation of lidar profiles During the model integration, some additional diagnostic variables are estimated: (i) the clouds optical depth and the aerosol optical depth (AOD) using the Fast-JX module, and (ii) the lidar profiles. The lidar profiles are calculated using the aerosol contributions only, as detailed in  Stromatas et al. (2012). They are proposed as output after a simulation and are designed to be Table  5. Refractive indices for the main aerosol species in CHIMERE at 200, 300, 400, 600 and 1000 nm.
Model configuration The meteorological simulation has been performed using the WRF meteorological model, version 3. 5. 1, on a simulation domain covering most of the Southern Hemisphere at a resolution of about 55 × 55 km at 45 • S. with 20 vertical levels from the surface to 100 hPa. For the gaseous chemistry, the MELCHIOR-2 chemical mechanism has been used. The horizontal domain is composed of 250 × 250 cells and is centered at the South Pole and covering the entire extratropical Southern Hemisphere. The horizontal resolution varies with latitude: 65 × 65 km (at the pole), 55 × 55 km (at 45 • S) and 36 × 36 km (at 25 • S). The anthropogenic and biogenic emissions are taken into account and produced from the HTAP dataset and MEGAN model, respectively. Mineral dust emissions have not been included in this simulation, since the focus of this test bed study was in the circumpolar transport of ash emissions from the Puyehue volcano.
Analysis of the circumpolar transport The simulation is initialized by climatological concentrations for aerosols and trace gases from the LMDZ-INCA chemistry-transport model. These two datasets are also used to provide the top and lateral boundary conditions during the simulation. The simulation itself, covering the 15 May through 30 June, can be divided into two successive phases; first, from 14 May to 4 June, the model undergoes a spinup period, with the concentrations of gaseous and particulate species building up due to the emissions of sea-salt and anthropogenic contaminants (Fig. 9a ). At the end of this spinup period, significant AOD values, from 0. 05 to 0. 20 appear over the Southern Ocean from 30 to 70 • S, mostly due to seasalt emissions, consistent with the findings of  Jaeglé et al.
9f ) and the report of Global Volcanism Program (2013), which indicates that part of the plume was reaching South America from the Pacific ocean at that time between 35 and 50 • S while other parts of the ash plume were located further to the south, close to the Antarctic Peninsula, consistent with Fig. 9f. On 14 June and during the following days, the plume from the initial explosion of 4 June and the following days is passing over the Puyehue volcano again, a fact that is correctly captured by the CHIMERE model.
Acknowledgements. For anthropogenic emissions, Euroglob-alMap products include Intellectual Property from European National Mapping and Cadastral Authorities and is licensed on behalf of these by EuroGeographics. Original product is freely available at www. eurogeographics. org. Terms of the license available at http://www. eurogeographics. org/form/topographic-data-eurogeographics. The MACC boundary conditions dataset was provided by the  MACC-II  project, which is funded through the  European Union   Framework 7 program. It is based on the MACC-II reanalysis for atmospheric composition; full access to and more information about this data can be obtained through the  MACC-II  web site http://www. copernicus-atmosphere. eu. We acknowledge  C. Prigent  for providing the global highresolution aeolian aerodynamic roughness length.
Red algal sequences lie on red branches and triangles group sequences in Viridiplantae (green), stramenopiles (brown), Cyanobacteria (turquoise), Bacteria (pink), and Amoebozoa (gray). The proteins were aligned using MAFFT with the L-INS-I algorithm and the scoring matrix Blosum62 and then manually refined with BIOEDIT. The phylogenetic tree was calculated using maximum likelihood in MEGA 6. 06. See SI Appendix, Table  S42  for full names and GenBank accession numbers. Bootstrap values ≥ 65% are shown at nodes. the transition to land when plants evolved true roots (Fig. 5 ). Other inorganic and organic nutrient uptake and conversion capabilities appear similar to those described for other red algae (e. g. , Fig. 5  and SI Appendix, Fig. S51  and Tables S49, S52, and S53), except that the number of ammonium transporters (seven genes) (SI Appendix, Table  S49 ) and intracellular copper transporters [P 1B -type ATPases; six genes (MTA1-6)] (SI Appendix, Fig.
Methods P. umbilicalis was isolated as an unialgal culture from a blade growing at Schoodic Point, Maine (44°20′1. 68′′ N; 68°3′29. 14′′ W) on  April 3, 2008. This single isolate  (103)  was cloned by culture of progeny from its asexual neutral spores, followed by DNA extraction, and assembly of the genome from sequences produced using Illumina and Pac-Bio sequencing platforms (see SI Appendix, Methods for full details). This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under accession no. MXAK00000000. The version described in this paper is version MXAK01000000. Phylogenetic profile of FTR1 and FEA/ISIP2a homologs in photosynthetic eukaryotes. Whether each organism is an alga, is multicellular, or lacks true roots is indicated with a colored square, and the presence of a FTR1 or FEA/ISIP2a homolog in that organism is indicated with a colored circle.
ACKNOWLEDGMENTS. We are grateful to the anonymous reviewers and editor for their insightful and constructive suggestions. We thank  Dr. Lilibeth Miranda ,  Charlotte C. T. Quigley , and  Charlotte Royer  ( University of Maine ) for their major assistance in maintaining cultures of the genomic strain during the project period;  Drs. Joyce Longcore  ( University of Maine ) and  M. Blackwell  ( Louisiana State University ) for discussions about fungi;  Dr. Sarah Tepler Drobnitch  for discussion about kelp sieve elements; and the many colleagues who provided useful discussion as participants in the  Porphyra Research Coordination Network  (supported by  NSF   RCN 0741907  [to S. H. B. , A. R. G. , and J. W. S. ]), especially to  Elisabeth Gantt  ( University of Maryland ), an original co-PI of the NSF RSN and DOE JGI contract.
The work conducted by the  US Department of Energy (DOE) Joint Genome Institute , a  DOE Office of Science User Facility , was supported by the  Office of Science of the US DOE  under Contract  DE-AC02-05CH11231  (to S. H. B. , E. G. , A. R. G. , and  J. W. S. ). Other  major research support was provided by  NSF   0929558  (to S. H. B. and A. R. G. );  National Oceanic and Atmospheric Administration (NOAA)  Contract  NA060AR4170108  (to S. H. B. );  German Research Foundation  Grant  Mi373/12-2  of  FOR1261  (to M. M. ); the  French National Research Agency  under  IDEALG  Grants  ANR-10-BTBR-04-02  and  04-04  " Investissements d'avenir, Biotechnologies-Bioressources " (to J. C. , E. F. -B. , G. M. , and S. M. D. ); the  New Hampshire Agricultural Experiment Station , Scientific Contribution No.
2694 , supported by the  US Department of Agriculture/National Institute of Food and Agriculture Hatch  Project  1004051  (to A. S. K. and Y. C. ); the  Biotechnology and Biological Sciences Research Council  ( BBSRC BB/1013164/1 ) of the  United Kingdom  and  European Union FP7 Marie Curie ITN Photo. Comm 317184  (to A. G. S. and K. E. H. ); the  Office of Biological and Environmental Research of the US DOE  (C. E. B. -H. ); the  Connecticut Sea Grant College Program  ( R/A-38 ) and the  NOAA National Marine Aquaculture Initiative.
The S-D-Bact-1522-b-S-20 primer was labeled at the 5'-end with the IRD800 dye fluorochrome (MWG SA Biotech, Ebersberg, Deutschland) to allow for the detection of the PCR fragments by the LiCor DNA sequencer system (ScienceTec, Les Ulis, France). Data obtained from the 1D-Scan software (Sciencetec) were converted into a table summarizing band presence (that is, peaks) and intensity (that is, height or area of peak) using the PrepRISA program 58 ; 100 peaks, 2 bp resolution and Gaussian peak area were used to provide a robust analysis of bacterial communities. The resulting bacterial-ARISA data matrix (bacterial communities as rows and bands as columns) accounted for the presence/absence and relative intensity of bands. Statistical analysis and modelling. The cumulative respiration curves were described by fitting a two-compartment first-order model (Eq.
In order to determine the effects of soil, micro-environment (matric potential) or microbial community origin on microbial community structure, a between-group analysis (BGA) was carried out on the B-ARISA profiles using the ade4 package  60  in the R environment (Studio Team, 2015; RStudio: Integrated Development for R. RStudio, Inc. , Boston, MA. URL http://www. rstudio. com/). BGA is a supervised classification method that is based on carrying out an ordination of groups of samples rather than of samples. Whilst principal component analysis (PCA) maximises the variance amongst individual samples, BGA maximises the differences among the centroids of groups of samples. The individual sample locations are subsequently projected onto the resulting axes. The B-ARISA profiles were grouped according to treatment combination and day of analysis.
3% Triton ×100 and 0. 5% milk. A dye-conjugated secondary antibody was then incubated with the sections at room temperature for 1 h in PBS supplemented with 0. 3% Triton ×100 and 0. 5% milk. Images were obtained with an Imager. Z1 ApoTome AxioCam (Zeiss) microscope and processed with Axio Vision Software. The percentage of Ki-67-positive cells was determined by counting the total numbers of ductal epithelial cells and Ki-67-positive cells using ImageJ software. Cell culture and reagents MCF-7 cells were maintained in DMEM, 4. 5 g/L glucose supplemented with non-essential amino acids (NEAA) (Invitrogen) and 10% fetal bovine serum (FBS) (Biowest). T47D cells were maintained in RPMI 1640 supplemented with NEAA, sodium pyruvate (Invitrogen) and 10% FBS (Biowest). HC-11 cells were maintained in RPMI 1640 supplemented with 2 mM L-glutamine, 5 μg/mL insulin (Invitrogen), 0.
RNA extraction and real-time PCR MCF-7 cells (250,000 cells/well) were plated in 6-well plates. After 30 h of serum and steroid deprivation, the cells were treated with solvent as a control, with 10 -9 M E2 or with different concentrations of glyceollin I or II. Total RNA was extracted using a RNeasy mini kit (Qiagen) according to the manufacturer's instructions. Then, the RNA was reverse-transcribed using an M-MLV RT kit (Invitrogen) according to the manufacturer's instructions. For realtime PCR, 5 ng of cDNA was used with 150 nM primers (Table  2 ) and iTaq Universal SYBR Green Supermix (BioRad). Real-time PCR was performed on a CFX 384 apparatus, and the results were analyzed with CFX Manager software (BioRad).
Microarray data analysis and gene filtration Data analysis was performed using the AMEN suite of tools  [19]. Briefly, probes showing a signal higher than a given background cutoff (median of the normalized dataset, cutoff 5. 48) and at least a 2-fold change in at least one pairwise comparison were selected. To define a set of 1852 transcripts displaying significant statistical changes across comparisons, the LIMMA (linear models for microarray data) package was used (F-value adjusted with the false discovery rate method, p ≤ 0. 05)  [20]. The resulting probes were then partitioned into eight expression patterns (termed P1-P8) using the k-means algorithm.
Animals C57BL/6J wild-type (WT) mice were purchased from Charles River Laboratories Japan. Siglech dtr/dtr mice on a C57BL/6J background are described in our previous study (B6. Cg-Siglech <tm1. 1Ksat> mice; deposited in RIKEN BioResource Center [accession number: RBRC05658])  (Takagi et al. , 2011). Although an internal ribosome entry site (Ires)-diphtheria toxin (DT) receptor (Dtr)-enhanced green fluorescent protein (Egfp) cassette was inserted into the 3′ untranslated region of the Siglech gene in Siglech dtr/dtr mice, EGFP was not expressed in microglia in any CNS region due to unknown mechanisms (data not shown). This is consistent with the lack of EGFP expression in pDCs described in our previous study  (Takagi et al. , 2011). C-C chemokine receptor type 2 (Ccr2) RFP/RFP knock-in mice on a C57BL/6J background were obtained from The Jackson Laboratory (stock number: 017586)  (Saederup et al.
Spinal sections were immunoreacted with anti-Iba1 and anti-PKCγ antibodies to stain microglia and the inner lamina II of the dorsal horn  (Malmberg, Chen, Tonegawa, & Basbaum, 1997) , respectively. The number of Iba1 + cells in lamina I and outer lamina II (I/IIo) was counted and is shown as microglial numbers because pvMΦ were rare and their numbers were negligible in the dorsal horn. The sciatic nerve was stained with anti-Iba1 antibody to identify monocytes/MΦ. Images taken by a confocal microscope were acquired using the same laser power and sensitivity, and Iba1 + areas were measured using Image J software version 10. 2 (NIH, RRID: SCR_003070). A total of 12 images (1 field/section, 4 sections/animal, 3 animals) were analyzed. For developmental stages, DT was administrated to P7 Siglech dtr/dtr mice.
Acknowledgments All authors have no conflict of interest (COI) to declare. This work was partly supported by  KAKENHI  ( Grants-in-Aid for Scientific Research on Priority Areas "Brain Environment" 231111007 and "Grant-in-Aid for Scientific Research  (B)"  16H05117  to  H. Kiyama , and " Grants-in-Aid for Young Scientist (B)" 25830050 and "Grant-in-Aid for Scientific Research (C) "  16K07055  to  H. Konishi ) from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan , and grants from  The Hibino Foundation , The  Ichiro Kanehara Foundation  and  The Hori Sciences and Arts Foundation  (all to  H. Konishi ). We are grateful to  Y. Bando  ( Asahikawa Medical University ) for advising EAE model,  Y. Itai  and  M. Okamoto  for their technical assistance, and  Ms. A.