dataesr/scientific-paragraphs-categorization huggingface dataset:
Cells were then fixed and permeabilized using the BD Cytofix/Cytoperm and BD Perm/Wash (BD Biosciences). The IL-6 and IL-8 cytokines were detected after incubation for 20 min at 4 C in BD Perm/ Wash containing PerCP conjugated anti-IL-8 (BH0814) and APC conjugated anti-IL-6 (MQ213A5) antibodies (Biolegend). Data from labeled samples were acquired using a BD CantoII cytometer (BD Biosciences) followed by analysis using FlowJo software (TreeStar). Stainings with rat IgG1-APC (Biolegend, RTK2071) and mouse IgG2b-PercP (Biolegend, MPC-11) were used for negative gating controls for anti-IL-6 and anti-IL-8, respectively. Negative gates were defined using unstimulated conditions.
Calcium Imaging 5-to 7-week-old astrocyte cultures were loaded with the calciumsensitive dye Fluo-4. Fluo-4 was prepared according the manufac-turer's instructions from an imaging kit (Life Technologies) and directly added to culture medium. Astrocyte cultures were incubated with 1 mM Fluo-4 for 15 min at 37 C. Cells were washed with medium once and imaged immediately. Live fluorescent imaging was performed using a Zeiss spinning-disk confocal microscope, using a standard fluorescein isothiocyanate filter, and images were taken every 131 ms with the 203 objective for 120 s. Five areas with visibly spread astrocytes were selected for imaging for each line, and experiments were repeated three times. For quantification of change in intensity over time, astrocytes were outlined as regions of interest (ROI) and analyzed using ImageJ software.
The neurons were co-cultured with the astrocytes for the following 48 hr. The co-cultures were then fixed with 4% paraformaldehyde and immunocytochemistry performed with GFP (1:500 chicken anti-GFP; Aves Labs) for quantification of the percentage of survival and the neurite length. Neuronal survival was calculated by counting the number of GFP-positive neurons 48 hr after co-culture and comparing this with the total number of cells plated. In brief, we counted the number of cells that attached in the well and divided it by the number of GFP neurons plated following the cell sorting. We performed the experiment in triplicate. For the neurite outgrowth assays, images were processed using ZEN Imaging software (Carl Zeiss Microscopy). Neurolucida (MBF Bioscience 11) was used to reconstruct individual GFP-labeled neurons after co-culture, and to perform subsequent morphological analyses along with Mirosoft Excel.
ACKNOWLEDGMENTS For the production of the iPSCs, the authors would like to acknowledge financial support from  Janssen Pharmaceuticals. This work was supported by the  Paul G. Allen Family Foundation ,  Bob  and  Mary Jane Engman ,  The JPB Foundation ,  The Leona M. and Harry B. Helmsley Charitable Trust  grant # 2012-PG-MED002 ,  Annette C. Merle-Smith ,  R01 MH095741  (F. H. G. ),  U19MH106434  (F. H. G. ), and  The G. Harold & Leila Y. Mathers Foundation. This work was supported by the  Flow Cytometry Core Facility of the Salk Institute  with funding from  NIH-NCI CCSG :  P30 014195 ; the  Next Generation Sequencing Core Facility of the Salk Institute  with funding from  NIH-NCI CCSG :  P30 014195 ; the  Chapman Foundation  and the  Helmsley Charitable Trust  and by The  Razavi Newman Integrative Genomics and Bioinformatics Core Facility of the Salk Institute  with funding from  NIH-NCI CCSG :  P30 014195.
Spitzer photometry The analysis was done using the Spitzer /IRAC Basic Calibrated Data (BCD) of the 7714 frames. These frames are produced by the standard IRAC calibration pipeline and include corrections for dark current, detector nonlinearity, flat fielding, and conversion to flux units. To extract the light curves, we first find the center of the point-spread function (PSF) of the star to a precision of 0. 01 pixels by computing the weighted centroid of the star. To obtain the photometric measurement, we then used the APER routine of the IDL Astronomy Library 3 to perform a weighted photometry within an aperture of a given radius  (Horne 1986; Naylor 1998). After various tests, we decided to use a radius of 2. 3 pixels, which minimizes the final rms of the light curves. The background level for each image was determined with APER by the median value of the pixels inside an annulus, centered on the star, with an inner and outer radius of 10 and 15 pixels, respectively.
The PSF, used for weighting, is estimated as the median of the backgroundsubtracted fluxes. The estimated error on the weighted integrated flux is calculated as the square root of the photonnoise quadratic sum (Horne 1986; Naylor 1998). After producing a time series for each observation, we iteratively select and trim outliers by comparing the measurements to a transit light curve model. Doing so, we remove any remaining points affected by transient phenomena. At the end, we discarded 170 exposures in run #1 and 522 in run #2 ; we ended up with a total of 3168 photometric measurements in run #1 and 3854 in run #2. The decrease of the 2 GO7 program #70031. 3 http://idlastro. gsfc. nasa. gov/homepage. html measured rms when binning the measurements up to 100 pixels showed that the red noise is negligible at least on a timescale of one hour.
Acknowledgements. This work is based on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute Technology  under a contract with  NASA. Support for this work was provided by the  CNES  and by an award from the  Fondation Simone et Cino Del Duca. We acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under program  ANR-12-BS05-0012  " Exo-Atmos ". ASB acknowledges funding from the  European Union Seventh Framework programme  ( FP7/2007-2013)  under grant agreement No. 313014  (ETAEARTH). HD acknowledges support by grant  ESP2015-65712-C5-4-R  of the  Spanish Secretary of State for R&D&i (MINECO).
The list of variables requested by PMIP for the PMIP4-CMIP6 palaeoclimate experiments can be found here: http: //clipc-services. ceda. ac. uk/dreq/u/PMIP. html. This request is presently processed by the CMIP6 Working Group for Coupled Modeling Infrastructure Panel (WIP) into tables, which define the variables included in the data request to the modelling groups for data to be contributed to the archive. The most up-to-date list including all variables requested for CMIP6 can be found at the WIP site: http://proj. badc. rl. ac. uk/svn/exarch/CMIP6dreq/ tags/latest/dreqPy/docs/CMIP6_MIP_tables. xlsx. The last two columns in each row list MIPs associated with each variable. The first column in this pair lists the MIPs, which are requesting the variable in one or more experiments. The second column lists the MIPs proposing experiments in which this variable is requested.
Description of forcing boundary conditions Some of the forcing fields are extensions in time of the "official" CMIP6 data sets for the historical simulations. These are documented in individual contributions to the GMD special issue on CMIP6 and available through the contributors' web sites (see below and Appendices). PMIP4 specific time series and reconstructions are available via the PMIP4 website and specifications on data format and technical implementation are given in the Appendices.
Greenhouse gas forcing GHG time series for concentration-driven simulations are provided by CMIP6 for the period 1 to 2014 CE (Fig. 1 ). The data compilations for surface concentrations of CO 2 , CH 4 , and N 2 O are based on updated instrumental data and ice-core records  (Meinshausen et al. , 2017). Differences between the new CMIP6 data set and previous estimates for CMIP5 are rather small (e. g. for global mean surface mixing rations, see Fig. 9  in  Meinshausen et al. , 2017). The CMIP6 reconstruction offers a better representation of latitudinal and seasonal variations and we recommend using this data set for consistency throughout the CE. GHGs should be implemented as for the CMIP6 historical simulations (see http://www. climatecollege. unimelb. edu. au/cmip6 and Appendix A2).
An important difference compared to previous forcing data sets is that the new EVA2k reconstruction includes a background stratospheric aerosol level, which produces a non-zero minimum AOD in periods of no volcanic eruptions. Like the CMIP6 historical volcanic forcing, the background level is defined as being equal in global mean AOD to the observed AOD minimum in the years 1999-2000 CE  (Thomason et al. , 2017). The reconstruction of volcanic forcing from ice core records carries substantial uncertainties  (Hegerl et al. , 2006; Gao et al. , 2008; Crowley and Unterman, 2013; Stoffel et al. , 2015). At present, different global aerosol models produce a large range of forcing estimates for specified sulfur injections, which motivates ongoing research  (Zanchettin et al. , 2016). The EVA module allows for the production of volcanic forcing time series with varying characteristics, such as the magnitude of the eruptions.
Differences in the secular variations in TSI (Fig. 3 ) are mainly due to the assumptions made in the irradiance models. The new PMOD- based reconstruction features a LMM-to-present amplitude of 3. 4 W m -2 (about 0. 25 %), whereas the SATIRE-based forcing changes by less than 1 W m -2 (0. 06 %) during this period. Differences between the 14 C-and 10 Be-based reconstructions manifest themselves mainly in the phasing and differences in secular trends, for example in the duration and timing of the LMM. The SATIRE-based solar activity reconstruction is also in good overall agreement with a solar activity reconstruction that is exclusively based on cosmic ray measurement/proxy data via a combination of 14 C and neutron monitor data  (Muscheler et al. , 2016). To achieve a smooth transition from the pre-industrial to modern periods, the reconstructions are combined (see Appendix A4.
g. the ATLAS3  (Thuillier et al. , 2003)  or WHI  (Woods et al. , 2009)  quiet Sun reference spectra. The SSI records provided for the PMIP4 experiments are a combination of the rescaled reconstructions before 1850 CE, shown for the 14 C-based SATIRE reconstruction data set as the cyan solid line in Fig. 4 , and the CMIP6 time series for the historical simulations  (Matthes et al. , 2017) , shown by the red line. Details of the rescaling and adjustment can be found in Appendix 4. 2. Compared to the original reconstruction, the CMIP6 record underestimates the variability in   (Matthes et al. , 2017). TSI (a) and SSI (bd) in three broad spectral intervals (in the UV between 200 and 400 nm, in the visible at 400-700 nm, and in the near-IR at 700-1200 nm wavelength). The blue lines are the original 14 C/SATIRE-based time series, the cyan lines represent the adjusted data, and the red line is the CMIP6 forcing.
the UV after 1850 CE by about 10-15 %, and by more than 35 % if compared to PMOD (not shown), while it overestimates the variability in the visible and IR by about 10-15 % and by more than 40 %, respectively. While adjusting the pre-industrial reconstruction to the CMIP6 historical records yields a smooth transition in 1850 CE, it needs to be kept in mind that the amplitude of the variability in the spectral bands is adopted from the original models (i. e. from isotopebased reconstructions before 1850 CE and the CMIP6 record afterwards) and depends at least partly on the construction of the data set. In addition to the standard (adjusted to CMIP6) 14 C data sets, we therefore also provide the original records for the entire period for testing the climatic effects of the conflation. In summary, PMIP4 provides three reconstructions of TSI and SSI from the most-up-to-date records of cosmogenic radioisotopes 14 C and 10 Be using a chain of models, all of which have been improved and updated since PMIP3.
In contrast to CMIP3, for all provided reconstructions, total and spectral irradiance are computed in a self-consistent manner. In particular, the same model has been used to reconstruct irradiance from each radioisotope to allow an estimate of the uncertainty due to the effect of local conditions on their formation and deposition. Two irradiance reconstructions were obtained from 14 C data using different irradiance models to allow for sensitivity experiments testing the response to the amplitude of the solar forcing. The default forcing for CMIP6-PMIP4 past1000 is the 14 C SATIRE-based reconstruction. The PMOD-based reconstruction provides an upper limit on the magnitude of the long-term changes in irradiance. Since the historical CMIP6 recommendation is an arithmetic average of two conceptually different models with significant differences in the SSI variability, special care has been taken to combine the PMIP4 data sets with the historical forcing.
(2017)  have been calculated with respect to the 10. 7 cm radio flux (F10. 7), which is not available for the PMIP period. Hence we have re-performed the regression of the same ozone fields but with respect to solar UV irradiance averaged over the spectral range from 200 to 320 nm (see Appendix 4. 3 for details). We recommend calculating time-varying ozone input for PMIP4 by scaling these coefficients with the anomaly of the respective UV flux during the simulation period and adding it to the CMIP6 pre-industrial ozone climatology. The UV flux anomaly should accordingly be calculated with respect to the CMIP6 pre-industrial irradiance data  (Matthes et al. , 2017).
Land use changes and anthropogenic land cover changes For the past1000 simulation, land use changes need to be implemented using the same input data sets and methodology as the historical simulations; the CMIP6 land use forcing data sets now cover the entire period 850-2015 CE  (Hurtt et al. , 2017) , which provides a seamless transition between the CMIP6 past1000 and historical simulations. The new land use forcing, Land-Use Harmonization 2 (LUH2), is provided as a contribution of the Land-Use Model Intercomparison Project (LUMIP) to CMIP6 (https://cmip. ucar. edu/lumip). The LUH2 strategy estimates the fractional land use patterns, underlying land use transitions, and key agricultural management information, annually for the period 850-2100 CE at 0. 25 • × 0. 25 • spatial resolution. The estimate minimises the differences at the transition between the historical reconstruction and the conditions derived from integrated assessment models (IAMs).
It is based on new estimates of gridded cropland, grazing lands, urban land, and irrigated land, from the Historical Land Use Data Set for the Holocene (HYDE3. 2, Klein Goldewijk, 2016). Within HYDE3. 2, grazing lands are now sub-divided into managed pasture and rangeland categories, and irrigated land also includes a subcategory of land flooded for paddy rice. Within LUH2, cropland area is sub-divided into five crop functional types based on data from  Monfreda et al. (2008)  and from the Food and Agricultural Organization of the United Nations (FAO). The temporal evolution of the various types is displayed in Fig. 5. LUH2 includes a new representation of shifting cultivation rates and patterns and also includes new layers of management information such as irrigated area and industrial fertiliser usage.
, 2011). In turn, the lower estimate of early agricultural area at the beginning of the last millennium implies larger land-use-induced land cover changes over time to match the land cover distribution of the industrial era (see  Schmidt et al. , 2012). To allow an assessment of the substantial uncertainties associated with reconstructing historical land use, while at the same time remaining consistent with the format of the default data set, maximum and minimum alternative reconstructions of the LUH2 data set will also be provided during the course of PMIP4. In particu-lar, both upper-and lower-bound scenarios will be created in order to provide a range of wood consumption scenarios. The upper scenario is identical to the baseline scenario but without the national-scale factors based on  Smil (2010).
Multiple realisations will be available for a larger subset of models, enabling improved assessments of the relative contributions of internal climate variability and externally forced changes towards the evolution of the climate system over the last millennium. The wealth of proxy-based reconstructions together with the multi-model, multi-realisation database provided by PMIP4 simulations will refine investigations of the response to external forcing, allow studies of regional versus global changes, and improve process understanding. Dedicated sensitivity studies will, in addition to the default past1000 simulation, allow individual groups or clusters of researchers to investigate uncertainty in reconstructions and the representation of the forcing agents in the models. In particular, a broader evaluation of the PMIP4 simulations of the last millennium is expected due to the increasing attention on processes and variables other than temperature, such as the hydrological cycle and climate extremes.
Acknowledgements. The work by  Ilya G. Usoskin  was partly done in the framework of  Center of Excellence ReSoLVE  (project no. 272157  of the  Academy of Finland). Bette I. Otto-Bliesner  acknowledges funding by the  U. S. National Science Foundation (US-NSF ) of the  National Center for Atmospheric Research  and support by  US-NSF   EaSM2  award  AGS 1243107. Julia Pongratz  is supported by the  German Research Foundation 's  Emmy Noether Program  ( PO 1751/1-1 ). Eugene Rozanov  and  Tania Egorova  have been partially supported by the  Swiss National Science Foundation  under grant  CRSII2-147659  ( FUPSOL II ). Christoph Nehrbass-Ahles and Fortunat Joos acknowledge support by the  Swiss National Science Foundation. Steven J. Phipps  was supported under the  Australian Research Council's Special Research Initiative for the Antarctic Gateway Partnership  (project  ID   SR140300001 ).
Kees Klein Goldewijk is supported by  Dutch NWO VENI  grant no. 016. 158. 021  and endorsed by the  PAGES LandCover6k group. Alexander I. Shapiro  acknowledges funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. 624817. Andrew Schurer  was supported by  ERC -funded project  TITAN  ( EC-320691 ). J. Fidel González-Rouco  acknowledges project  ILModelS   CGL2014-59644-R  and  Raimund Muscheler  received support from the  Swedish Research Council  (grant  DNR2013-8421 ). Jason E. Smerdon  was supported in part by  U. S. National Science Foundation  grants  AGS-1401400 ,  AGS-1243204 , and  AGS-1602581  (LDEO contribution no. 8150 ). Open-access charges have been covered partly by the  European Union  post-grant OA pilot and partly by the  Max Planck Society.
The dark ocean microbiota represents the unknown majority in the global ocean waters. The SAR202 cluster belonging to the phylum Chloroflexi was the first microbial lineage discovered to specifically inhabit the aphotic realm, where they are abundant and globally distributed. The absence of SAR202 cultured representatives is a significant bottleneck towards understanding their metabolic capacities and role in the marine environment. In this work, we use a combination of metagenome-assembled genomes from deep-sea datasets and publicly available single-cell genomes to construct a genomic perspective of SAR202 phylogeny, metabolism and biogeography. Our results suggest that SAR202 cluster members are medium sized, free-living cells with a heterotrophic lifestyle, broadly divided into two distinct clades.
We present the first evidence of vertical stratification of these microbes along the meso-and bathypelagic ocean layers. Remarkably, two distinct species of SAR202 cluster are highly abundant in nearly all deep bathypelagic metagenomic datasets available so far. SAR202 members metabolize multiple organosulfur compounds, many appear to be sulfite-oxidizers and are predicted to play a major role in sulfur turnover in the dark water column. This concomitantly suggests an unsuspected availability of these nutrient sources to allow for the high abundance of these microbes in the deep sea.
In this work, we reconstructed several SAR202 genomes from deep marine and brackish metagenomics datasets from the Ionian, Aegean and the Caspian Sea as well as Pacific, Atlantic and Indian oceans. By screening publicly available unclassified Chloroflexi genomes we also identified another SAG, Atl-SCGC-AB-629-P13, (obtained from a depth of 553 m in the mid-Atlantic) as belonging to the SAR202 lineage. These metagenome-assembled SAR202 genomes (MAGs), together with the two SAGs provide a first insight into the metabolic capabilities of this group and suggest a significant role in the deep-ocean sulfur cycle. Moreover, metagenomic fragment recruitment analyzes reveal that highly related SAR202 genomes are prevalent at geographically distant locations (from the Mediterranean to the Pacific) implying their global distribution as a deep stratified cluster with a sizeable contribution to global ocean biogeochemical cycles, particularly the sulfur cycle.
Metagenomic datasets used in this study The metagenomic samples from the Caspian Sea were taken at the peak of stratification in October 2013 at three depths (15 m, 40 m and 150 m, bottom depth 230 m). Sample collection, DNA extraction, sequencing and assembly of these datasets are described in more detail in Mehrshad et al. Mehrshad et al. [32]  and the sequence data are available from NCBI SRA (Bioproject PRJNA279271). The two deep sea samples from the Mediterranean were collected in the Aegean Sea (600 m, bottom depth 699 m) and Ionian Sea (3500 m, bottom depth 3633 m) in October 2010. These datasets and additional deep chlorophyll maximum samples used in this study are available in NCBI SRA (Bioprojects PRJNA305355 and PRJNA257723). Three representative deep, marine metagenomes from the MALASPINA expedition were also used for assembly (SRR3965592, SRR3963457, and SRR3961935).
The Caspian and the Mediterranean metagenomes from all samples analyzed in this study are from the 0. 22-5. 0 µm fraction and were sequenced by HiSeq2000 (paired end reads of length 100 bp). The MALASPINA datasets are from the 0. 2 to 0. 8 µm fraction and were sequenced using HiSeq2000 (paired end reads of length 150 bp). Basic metadata (sampling date, latitude, longitude, depth, bioproject identifiers, SRA accessions), sequence statistics (number of reads, read length, dataset size) and % of Chloroflexi assigned 16S rRNA reads identified in all metagenomes used for assembly are provided in Supplementary Table  S1.
Unassembled 16S rRNA read classification A non-redundant version of the RDP database  [33]  was created by clustering its ca. 2. 3 million 16S rRNA gene sequences into approximately 800,000 sequences at 90% nucleotide identity level using UCLUST  [34]. A 20 million subset of the reads from the Illumina datasets was compared to this reduced set and an e-value cutoff of 1e-5 was used to identify candidate 16S rRNA gene sequences. The candidate sequences were further examined using ssu-align, to separate them into archaeal, bacterial, and eukaryotic 16S/18S rRNA or non-16S rRNA gene sequences  [35]. Only bona fide 16S rRNA sequences were finally compared to the complete RDP database and classified into a high level taxon if the sequence identity was ≥ 80% and the alignment length was ≥ 90 bp.
Metagenome assembly and annotation All three datasets retrieved from depth profile of the Caspian Sea (15 m, 40 m and 150 m) were assembled together (using IDBA_UD)  [38]  as described previously  [32]. The Mediterranean DCM datasets, the Deep Mediterranean and the MALASPINA datasets were quality trimmed using sickle (https://github. com/najoshi/sickle, default parameters) and assembled using the megahit assembler (--kmin 39 --k-max 99 --k-step 10 --min-count 2)  [39]. The MALASPINA datasets were chosen because they had a high percentage of Chloroflexi 16S rRNA reads (9-11%) (Supplementary Table  S1 ), and also because they represented geographically distant locations (South Atlantic, Indian Ocean and North Pacific). Prodigal (in metagenomic mode) was used for predicting protein coding genes in the assembled contigs  [40].