dataesr/scientific-paragraphs-categorization huggingface dataset:
(  2019 )): phytoplankton, protozooplankton or mixoplankton. Phytoplankton are defined as protists that obtain their nourishment via photo(auto)trophy and osmo(hetero)trophy. Irrespective of seasonality or environmental conditions, phytoplankton are always incapable of phago(hetero)trophy. Protozooplankton are defined as protists that obtain their nourishment via phago(hetero)trophy and osmo(hetero)trophy. Irrespective of seasonality or environmental conditions, protozooplankton are always incapable of photo(auto)trophy. Mixoplankton are defined as protists that obtain their nourishment by combining photo(auto)trophy, osmo(hetero)trophy and phago(hetero)trophy, so called mixotrophy. Furthermore, for each mixoplankton, the type of mixotrophy is assigned as a trait. In the dataset, the type of mixotrophy is defined by assigning CM, GNCM, pSNCM or eSNCM to the mixoplankton, according to the types identified in  Mitra et al.
Sampling methods Study extent: This dataset focuses on the trophic modes of aquatic protists. It combines data from five different sources: routine monitoring (HELCOM 2019, Rijkswaterstaat 2019), scientific cruises (John 2020, Martin 2020), scientific papers (primary literature), review papers and a book chapter (secondary literature). Sampling for routine monitoring and on scientific cruises was performed using Niskin bottles, followed by inspection of the samples using light microscopy. In the case of the scientific cruises, metabarcoding was employed for further validation of the microscopic data. Suppl. material 1 lists all sources and their complete references. Sampling description: This dataset (Suppl. material 2) covers 1296 taxa. Fig. 1  shows that the bulk of the taxa stem from routine monitoring (72%) and are mainly labelled as phytoplankton (89%).
Both of these groups are known to contain mixoplankton  (Haraguchi et al. 2018, Stoecker and Lavrentyev 2018). Furthermore, mixotrophy must be proven in phyto-and protozooplankton by observing either feeding or utilisation of chloroplasts from prey or symbionts. It must be assumed that taxa remain labelled according to the historical dichotomy until proven otherwise. This remains an issue also for this dataset, of which the user should be aware. It can only be remedied by continually updating the dataset as new mixoplankton taxa are empirically determined.
Quality control: In order to ensure consistent taxonomy over the various data sources, each data source was matched against the WoRMS taxonomic database using the WoRMS "Match taxa" tool. This ensured that each taxon was given the currently-accepted scientific name and referenced with an AphiaID. Data sources were tidied  (Wickham 2014 ) and joined into one dataset. If the various sources disagreed with each other on the trophic mode of the taxon, two decision pathways were possible. Firstly, mixoplankton data sources were always given precedence over other sources. Secondly, if the data sources disagreed on non-mixoplankton, then expert knowledge and literature was used to assign the trophic mode of that organism. Lastly, the list was checked by expert witnesses to ensure correct trophic classifications.
Taxonomic coverage Description: Fig. 4  visualises the contribution of each class to the total dataset (Fig. 4a ), as well as the contribution of trophic modes across those classes (Fig. 4b ). It should be noted that only those classes are displayed that make up 90% of the total dataset. The largest class, represented in the dataset, is Bacillariophyceae, followed by Dinophyceae and Cyanophyceae. In terms of trophy, the classes which contain the most phytoplankton are Bacillariophyceae, Cyanophyceae, Chlorophyceae and Trebouxiophyceae. The classes which contain the most mixoplankton are Dinophyceae, Polycystina, Oligotrichea, Globothalamea, Prymnesiophyceae, Acantharia and Cryptophyceae. Protozooplankton are represented in the classes Dinophyceae, Polycystina, Oligotrichea, Globothalamea and Acantharia, in which they contribute between 5% and 30%.
Traits coverage This dataset focuses on the trophic mode of aquatic protists. As mentioned, aquatic protists can be divided into phytoplankton, protozooplankton and mixoplankton. This next section will give more detail on these different functional groups and their impact on aquatic ecosystems. Phytoplankton are defined as those protists that perform photosynthesis and are incapable of phagotrophy. The most prominent examples of phytoplankton groups are cyanobacteria, diatoms and green algae. Attributing to their need of light for photosynthesis, phytoplankton are found in the euphotic zone, where light is available. It is estimated that aquatic photosynthesis by phytoplankton totals about half of the total primary production on Earth  (Falkowski 1994 , Field 1998 ). Phytoplankton in marine ecosystems play an important role in major biogeochemical cycles.
Trophy Gives the trophic mode of taxa as either "phytoplankton", "protozooplankton" or "mixoplankon". typeMX Gives the type of mixotrophy as either "CM" (Consitutive Mixoplankton), "GNCM" (General Non-Constitutive Mixoplankton), "eSNCM" (endosymbiotic Specialist Non-Constitutive Mixoplankton) or "pSNCM" (plastid Specialist Non-Constitutive Mixoplankton). If the type of mixotrophy does not apply (because the organism is labelled as phytoplankton or protozooplankton), the type of mixotrophy is labelled with "NA". source Gives the reference for the assigned trophic modes. Refers to primary literature or secondary literature (book, review papers or published datasets from routine monitoring). dataType Denotes the origin of the data point. Can be either "book", "paper", "review", "routine monitoring" or "scientific cruise".
Use license: Creative Commons Public Domain Waiver (CC-Zero) Data resources Data package title: Trophic modes of aquatic protists Number of data sets: 2 Data set name: List of trophic citations Description: List of trophic mode references giving the short citation form (used in the dataset) and the full reference. the reference source used in the dataset Full citation Full APA citation of the reference source Data set name: Trophic modes of aquatic protists Description: Dataset listing the trophic modes of aquatic protists (with reference and data source) along with their accepted scientific name, AphiaID and taxonomic hierarchy.
Acknowledgements The authors want to thank  Salvador Fernández  for reviewing the manuscript and the staff at  WoRMS  for their quick and efficient handling of the dataset submission. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  766327  and  EMODnet Biology  ( EC Service contract -EASME/EMFF/2016/1. 3. 1. 2/Lot5/SI2. 750022 ).
2 × 10 4 μg/mL hAlb) repopulated huFNRG mice showed that our isolation/purification protocol yielded predominantly human hepatocytes independent of the extent of liver humanization (Fig. 2B ). Collectively, we perfused 84 huFNRG livers and after purification, analyzed the cells by flow cytometry to measure the expression of human and murine markers. In 78 mice (93%), this protocol yielded mostly human cells (Fig. 2C ). To obtain high yields, we perfused livers from retrorsine preconditioned highly chimeric huFNRG mice based on hAlb serum levels around 10 4 μg/mL. Liver perfusions of 70 huFNRG mice yielded on average 75 million mpPHH per liver, ranging from 0. 4 to 240 million (Fig. 2D ). These data show that our isolation/purification protocol results in consistently large numbers of predominantly human hepatocytes isolated from highly chimeric huFNRG mice.
To determine mpPHH permissiveness to Pf, we used parasite entry assays and staining with Pf-specific antibodies with or without cell permeabilization. This allowed us to distinguish parasites that only bound to the cell surface from those that invaded the cells. On average, 11% of Pf parasites successfully invaded hepatocytes (Fig. 4E ). We then monitored parasite growth over time by measuring the intracellular area occupied by individual parasites and found that parasites increased in size from days 2 to 4 postinfection (Fig. 4F ). Together, these data showed that mpPHHs support efficient infection and development of Pf parasites. The chronic phase of HBV infection cannot readily be recapitulated in cell culture due to the short lifespan of current PHH systems. We took advantage of the longevity of mpPHH cultures to monitor HBV infection parameters for up to 8 wk postinfection.
0580-250; Standbio Labs). Flow Cytometry. Cells were fixed and stained for human and mouse CD81; human leukocyte antigen (HLA)-A, B, C (BD Biosciences) and RFP expression were measured on an LSRII flow cytometer (BD Biosciences) as described previously  (29). Data were analyzed using FlowJo software. Fluorescence Staining and Imaging. For immunofluorescence staining, mpPHH cultures were fixed and stained using as primary antibodies a rabbit polyclonal anti-NuMa antibody (catalog no. ab97585; Abcam) and a mouse monoclonal anti-CK18 antibody (catalog no. ab82254; Abcam). AlexaFluor 488 and 594 (Thermo Fisher) of the corresponding species were used as secondary antibodies. Nuclei were stained with DAPI. Cells were imaged using a Nikon Eclipse TE300 fluorescent microscope and processed using ImageJ.
RNA Extraction and Library Preparation. Total RNA was isolated using TRIzol (catalog no. 15596026; Thermo Fisher) followed by cleaning with RNeasy kit (catalog no. 74104; Qiagen) coupled with on-column DNase I treatment (catalog no. 79254; Qiagen); 250 ng of the DNase-treated RNA was used to generate strand-specific sequencing libraries using TruSeq Stranded mRNA Library Prep (catalog no. 20020594; Illumina). Multiplexed libraries were sequenced as 50-nt single-end reads on a HiSeq2500 system (Illumina) at the Rockefeller University Genomics core facility. RNA-sequencing Bioinformatics Pipeline. Transcript alignment and quantification were done using Kallisto  (33). Transcript indices for Kallisto were generated from Ensembl release hg38 and included all annotated complementary DNA and noncoding RNA transcripts.
Differential expression analysis was then done using Sleuth  (34). Gene names and attributes were queried from Ensembl using biomaRt. For genes containing multiple transcripts, we chose the transcript with the highest mean expression across all samples as the representative example for that gene. Heat maps were generated in R Studio. Statistical Analyses. Groups were compared by the Student t test. Correlations were calculated using the Spearman correlation coefficient. All statistical analyses were done using Prism 8 software (Graphpad). Data Availability. Datasets supporting the findings of this study have been deposited in a publicly available database (Gene Expression Omnibus accession no. GSE130219).
ACKNOWLEDGMENTS. This work was supported by  NIH  Fellowship  F32DK107164  (to E. M. ); fellowships from the  German National Academic Foundation  and the  German Center for Infection Research  (to P. P. ); and  NIH  Grants  R01HL131093  (to R. W. H. and Y. P. d. J. ),  R01AI091707  (to C. M. R. ),  R01DK085713  (to C. M. R. ), and  K08DK090576  and  R01AA027327  (to Y. P. d. J. ). K. V. was supported by a Fellowship of the  Belgian American Educational Foundation. Further support for this project was provided by grants from the  Robertson Foundation  (to E. M. ). The project was cosponsored by the  Center for Basic and Translational Research on Disorders  of the Digestive System through the generosity of the Leona M. and  Harry B. Helmsley Charitable  trust (E. M. ). This work was supported in part by  Bill & Melinda Gates Foundation  Grant  OPP1023607  (to S.
A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database.
85 These radiative transfer models also fundamentally depend on spectroscopic databases containing the parameters enabling to compute the atmospheric gas absorption. The previously mentioned retrieval algorithms mainly rely on the HITRAN spectroscopic database that evolved over the years: WFM-DOAS uses HITRAN 2008  (Rothman et al. , 2009)  as does the UoL-FP (with some updated CO 2 , H 2 O and CH 4 spectroscopic lines). RemoTeC and GOSAT  !!! retrievals use HITRAN 2008 combined with an O 2 A-band line absorption spectroscopic model taking into account line-mixing and collision-90 induced absorption (CIA)  (Tran and Hartmann, 2008)  as well as another line-mixing model for CO 2 lines  (Lamouroux et al. , 2010). BESD relies on ABSCO v4. 0 (computed by ACOS for OCO-2 processing), as does FOCAL for H 2 O  (Thompson et al.
For instance, TCCON datasets were used to validate SCIAMACHY  (Reuter et al. , 2011) , GOSAT  !!! retrieved by the ACOS  (Wunch et al. , 2011a)  and NIES algorithms  (Inoue et al. , 2016)  and OCO-2  !!! produced by ACOS  (O'Dell et al. , 2018; Wunch et al. , 2017) , RemoTeC 110  (Wu et al. , 2018)  and FOCAL  (Reuter et al. , 2017b). These three last algorithms exhibit different biases with regard to TCCON, depending on their respective forward modelling and bias correction strategies: 0. 30 ± 1. 04 ppm, 0. 0 ± 1. 36 ppm and 0. 67 ± 1. 34 ppm for OCO-2 nadir land soundings, respectively. In this paper, we present the Adaptable 4A Inversion (5AI) that relies on the OPerational version of the Automatized 115 Atmospheric Absorption Atlas (4A/OP) radiative transfer model  (Scott and Chédin, 1981; Tournier, 1995; Cheruy et al.
, 1995)  (https://4aop. aeris-data. fr) and the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database  (Jacquinet-Husson et al. , 2016)  (http://cdsespri. ipsl. fr/etherTypo/?id=950). Here, version 2015 of GEISA is used. The 5AI scheme is applied to retrieve  !!! from (1) OCO-2 cloud-free target session soundings between 2014 and 2018 and (2) a sample of two years of OCO-2 nadir clear sky 120 measurements with a global land coverage. We compare 5AI retrieval results to TCCON, and to ACOS and FOCAL v08 results over identical sets of soundings in order to assess the reliability of 5AI as a Bayesian optimal estimation implementation. This paper is organized as follows: Sect. 2 describes the 5AI retrieval scheme and its current features, as well as the 4A/OP 125 radiative transfer model, the GEISA spectroscopic database and the empirically corrected O 2 A-band absorption continuum on which it relies.
Section 3 presents the OCO-2 and TCCON data selection. Section 4 presents the a posteriori filters used for this work and shows the 5AI  !!! target and nadir retrieval results which are compared to TCCON, ACOS and available FOCAL v08  !!! products. Section 4 finally underlines the critical importance of forward modelling differences to explain systematic differences between different  !!! products through an average calculated -observed spectral residual 130 correction. Section 5 highlights the conclusions of this work. https://doi. org/10. 5194/amt-2020-403 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4. 0 License.
Forward modelling: 4A/OP and GEISA spectroscopic database The 5AI retrieval scheme uses the OPerational version of the Automatized Atmospheric Absorption Atlas (4A/OP). 4A/OP is an accurate line-by-line radiative transfer model that enables a fast computation of atmospheric transmittances based on atlases containing pre-computed monochromatic optical thicknesses for reference atmospheres. Those are used to compute atmospheric transmittances, for any input atmospheric profile and viewing configuration, that enable to solve the radiative 145 transfer equation and yield radiances and their partial derivatives with regard to the input geophysical parameters at a pseudo-infinite spectral resolution (0. 0005 cm -1 best) or convolved with an instrument function. 4A/OP is the reference radiative transfer model for the Centre National d'Études Spatiales (CNES) / EUMETSAT IASI Level 1 Calibration/Validation and operational processing, and it is used for daily retrieval of mid-tropospheric columns of CO 2  (Crevoisier et al.
250 OCO-2 high-resolution spectra are analysed by the ACOS team in order to retrieve  !!! and other geophysical parameters from them. Two different  !!! values are provided by the ACOS team: raw and posterior bias-corrected  !!!. Raw  !!! is the direct output of the ACOS algorithm following the full physics retrieval: its most recent version is distributed within the B8 retrospective (B8r) ACOS data release  (O'Dell et al. , 2018). Posterior bias-corrected  !!! is an empirically corrected  !!! that has reduced averaged bias with regard to different "truth-proxies"  (O'Dell et al. , 2018). The last available version 255 of this product is distributed within the B9 retrospective (B9r) ACOS data release. It corrects the impacts of footprint geolocation errors and erroneous prior surface pressure temporal sampling directly in the bias correction procedure applied to the B8r raw  !!! product, without a complete full-physics reprocessing of all OCO-2 data  (Kiel et al.
260 In addition to ACOS products, we also compare our results with OCO-2 FOCAL v08 data produced at the University of Bremen with the FOCAL algorithm  (Reuter et al. , 2017a)  that includes an empirical posterior bias correction directly on the top of the full-physics retrieval  (Reuter et al. , 2017b). Only the posterior bias-corrected  !!! is included in FOCAL v08 data. In this work, we compare  !!! retrieved from OCO-2 spectra to TCCON data. The TCCON network uses ground-based high 265 resolution Fourier Transform Spectrometers to measure NIR and SWIR spectra that enable the retrieval of the columnaveraged dry-air mole fractions of greenhouse gases. These retrievals are performed by GGG2014  (Wunch et al. , 2015)  and their results are available on the TCCON Data Archive (https://tccondata.
Data selection We intend to compare 5AI results with regard to TCCON against ACOS and FOCAL results for corresponding sets of 270 soundings. First, we select all the OCO-2 target soundings between 2015 and 2018 with low ACOS retrieved total AOD (<0. 5) and ACOS cloud, sounding quality and outcome flags at their best possible value. As FOCAL v08 uses prior and posterior filtering techniques that are different from ACOS, only a fraction of this first selection intersects with available FOCAL data. In order to increase this fraction, we add all OCO-2 points with the best ACOS cloud and sounding quality flags intersecting the available FOCAL v08 data points, whatever ACOS outcome flag and retrieved AOD. This composite 275 sample set includes 48,885 OCO-2 target soundings and the fraction of available intersecting FOCAL data is shown in Fig.
Sensitivity of raw retrieval results to forward modelling A difference of about 3 ppm is found between 5AI and ACOS raw  !!! retrieved from OCO-2 for both nadir and target observations. As mentioned in Sect. 1 and 2, 5AI and ACOS retrieval schemes rely on different radiative transfer models and spectroscopic inputs, and their respective retrieval setups are also quite different. In order to quantify the impact of these 415 differences, we perform an average 'calculated -observed' spectral residual analysis (hereafter 'calc -obs'), where the calculated spectrum (convolved to OCO-2 Instrument Line Shape) is generated by the forward model 4A/OP using GEISA spectroscopic database and the ACOS retrieval results (posterior pressure grid, temperature, H 2 O and CO 2 profiles as well as albedo and albedo slope), and is compared to the corresponding OCO-2 observation.
430 In order to compare 5AI retrievals with ACOS products while attenuating the impact of the forward modelling differences, the obtained averaged calc -obs residual is added to every OCO-2 measurements within the complementary half of 2016 selected nadir soundings (6,799 in total) to compensate for the systematic radiative model differences between 4A/OP and ACOS. We then apply the 5AI inverse scheme on this new dataset. Figure  11  compares the distributions of 5AI -ACOS 435 retrieval results obtained with and without the calc -obs adjustment. The systematic differences between 5AI and ACOS https://doi. org/10. 5194/amt-2020-403 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4. 0 License. results for  !!! ,  !!! , surface pressure and global temperature profile shift are fully removed when adding the spectral residual adjustment to OCO-2 measurements.
165 The 4A/OP radiative transfer model can be used with monochromatic optical thickness atlases computed from any spectroscopic database. For this present work, the atlases are computed using the GEISA 2015 (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database. Being the base of many work since the beginning in the astronomical and astrophysical communities, GEISA has been also used since the 2000's for the preparation of several current and future spatial missions, as to be chosen by CNES as 170 the reference spectroscopic database for the definition of IASI-NG, MicroCarb and MERLIN. Due to imperfections in the Tran and Hartmann (2008) line mixing and CIA models, an empirical correction to the absorption continuum in the O 2 Aband, fitted from Park Falls TCCON spectra following the method described in Drouin et al.
Acknowledgements This work has received funding from  CNES  and  CNRS. M. Dogniaux  is funded by  Airbus Defence and Space  in the framework of a scientific collaboration with École polytechnique. The authors would like to thank the  ACOS team  and  NASA  for OCO-2 data availability as well as  Maximilian Reuter  and the  University of Bremen  for FOCAL v08 data 485 availability. The TCCON site at Réunion Island is operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC1  to  FR/35/C5 ) and local activities supported by  LACy/UMR8105 -Université de La Réunion. The TCCON stations at  Tsukuba  and Burgos are supported in 490 part by the  GOSAT  series project. Local support for Burgos is provided by the  Energy Development Corporation  (EDC, Philippines).
The Paris TCCON site has received funding from  Sorbonne Université , the  French research center CNRS , the  French space agency CNES , and  Région Île-de-France. The Ascension Island TCCON station has been supported by the  European Space Agency (ESA)  under grant  4000120088/17/I-EF  and by the  German Bundesministerium für Wirtschaft und Energie (BMWi)  under grants  50EE1711C  and  50EE1711E. We thank the  ESA Ariane Tracking Station at North East Bay, 495 Ascension Island , for hosting and local support. N. Deutscher  is funded by  ARC   Future Fellowship   FT180100327. Darwin and Wollongong TCCON stations are supported by  ARC  grants  DP160100598 ,  LE0668470 ,  DP140101552 ,  DP110103118  and  DP0879468  and  NASA  grants  NAG5-12247  and  NNG05-GD07G.