dataesr/scientific-paragraphs-categorization huggingface dataset:
This is based on the conservative assumption that To test whether differences in expression were significant, we performed Student's t-test on the natural logarithms of the average fluorescence  (Beal et al, 2016). High-throughput microscopy (imaging cytometry) An Amnis ImageStreamX (EMD Millipore) imaging cytometer was used to image the cells in high-throughput in brightfield, GFP, and RFP channels. Images were analyzed using the IDEAS â (EMD Millipore) software suite. For each condition, at least 10,000 events were recorded per replicate. Cells that were out of focus or tilted were identified by calculating the average gradient of a pixel normalized for variations in intensity levels (Gradient RMS feature in IDEAS â ). Additionally, we used the Feature Finder script of IDEAS â to remove contaminating particles, images with multiple cells and beads.
Fluorescence and phase-contrast microscopy Fixed cells were transferred to PBS microscopy pads with 1. 5% UltraPure Agarose (Invitrogen) and imaged using an inverted microscope (TI-E, Nikon Inc. ) equipped with a 100× phase-contrast objective (CFI PlanApo LambdaDM100× 1. 4NA, Nikon Inc. ), a solid-state light source (Spectra X, Lumencor Inc. ), a multiband dichroic (69002bs, Chroma Technology Corp. ). mCherry fluorescence was measured using excitation (560/32) and emission (632/60) filters. Images were acquired using a sCMOS camera (Orca Flash 4. 0, Hamamatsu) with an effective pixel size of 65 nm. MATLAB code adapted from the Morphometrics package  (Ursell et al, 2017)  was used to find cell contours from phase-contrast images. Background intensity, uneven illumination, and cell autofluorescence were accounted for in the analysis.
Acknowledgements We thank  T. K. Lee  and  K. C. Huang  for providing strain TKL130,  F. Bendezù  for providing plasmid pFB262,  H. Cho  and  T. Bernhardt  for providing plasmid pHC942, and  D. Mazel  for providing plasmid pSW23t. We thank  J. Fernández-Rodríguez  for providing the P Phlf -sfgfp-phlF DNA fragment. We thank  E. Brambilla  and  E. Oldewurtel  for support in microscopy, as well as  A. Soler  and  M. Hasan  for support regarding flux cytometry and imaging cytometry. We acknowledge the  Technology Core of the Center for Translational Science (CRT) at Institut Pasteur  for support in conducting this study. This work was supported by the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  [Grant Agreement No. ( 677823 ) to DB and No.
( 679980 ) to SvT]; the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) to SvT, DB, and AV; the  Marie de Paris "Emergence(s)" program  and the  Volkswagen Foundation  to SvT; and the  Pasteur-Weizmann consortium  to DB.
The bacterium was a Gram-negative, non-spore forming and nonmotile anaerobic coccus. Strain P4590 was catalase negative and oxidase negative. The colonies could not be identified by our systematic matrix-assisted laser desorption-ionization time-of-flight screening (http:// mediterraner-infection. com/article. php?laref=256&titre=urmsdatabase) on a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  [3]  (Fig. 1 ). The 16S rRNA gene was sequenced using fD1-rP2 primers as previously described, using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France). Strain P4590 exhibited a 95. 5% sequence identity with Helcococcus seattlensis, the phylogenetically closest species with standing in nomenclature (Fig. 2 ), which putatively classified it as a member of the genus Helcococcus within the family incertae sedis in the phylum Firmicutes.
, San Diego, CA) using the paired-end technique coupled with the mate pair technique. The index representation for strain 4858 was determined to be 5. 42%. A total of 683,373 paired-end reads, filtered per the read qualities, were assembled using the SPAdes software (1). The resulting contigs were combined by use of SSPACE (2) assisted by manual finishing and GapFiller (3). This yielded a 5,614,132-bp draft genome with a 68. 24% GC content, composed of 8 scaffolds and 12 contigs. Open reading frames (ORFs) were predicted using Prodigal (4) with default parameters. Functional annotation was achieved using BLASTp against the GenBank database (E value, 0. 001; coverage, 0. 7; identity, 30%) (5) and the Clusters of Orthologous Groups (COG) database  (6). When no search results were found, a second round was done against the nonredundant protein sequence (nr) database using BLASTP with an E value of 1 ϫ 10 Ϫ03 , coverage of 0.
7ϫ, and 30% identity. Noncoding genes and miscellaneous features were predicted using RNAmmer  (7) , ARAGORN (8), Rfam (9), Pfam  (10) , and Infernal  (11). Of the 5,305 predicted genes, 5,255 were protein-coding genes and 5 were encoded RNAs, including 1 5S rRNA, 1 16S rRNA, 1 23S rRNA, and 47 tRNAs. A total of 4,175 genes (79. 45%) were assigned putative functions (by COG or nr BLAST search), 79 genes were identified as ORFans (ORFs with no detected homology to other ORFs in the database) (1. 5%), and 831 genes (15. 81%) were annotated as hypothetical proteins. The genome of strain 4858 was further analyzed by in silico DNA-DNA hybridization (DDH)  (12) , with genomes exhibiting the closest 16S rRNA gene sequence similarity. The DDH values were estimated using the GGDC (Genome-to-Genome Distance Calculator) version 2.
0 online tool  (13). This analysis yielded a DDH value of 45% with Mycobacterium europaeum CSUR 1344 (GenBank accession number CTEC00000000), 34. 50% with Mycobacterium parascrofulaceum ATCC BAA-614 (ADNV00000000), 25. 60% with Mycobacterium palustre DSM 44572 (LQPJ01000000), 23. 10% with Mycobacterium sherrisii BC1_M4 (MIHC00000000), and Mycobacterium simiae ATCC 25275 (CBMJ000000000) and 22. 20% with Mycobacterium kubicae CIP 106428 (LQPD00000000). These data indicate that Mycobacterium sp. strain 4858 is related to the Mycobacterium simiae complex of mycobacteria, expanding this large complex to its twentieth species. Accession number(s). The Mycobacterium sp. strain 4858 genome sequence has been deposited at EMBL under the accession number OESL01000000.
These data can provide national level estimates of the spatial distribution of the benefits generated by the provision of seafood ES utilized in the WCPO purse seine tuna fishery. To better address societal well-being at a higher resolution i. e. , at the subnational level, data on the types of beneficiaries and associated benefits received by each are required. Although in our analysis we tried to collect this information, it was very scattered, since only a few companies share these data upon request. A detailed overview of all data used and their associated sources is given in the Supplementary material.
We therefore used a qualitative way to rank the countries' production based on the best available data used by the World Bank (2016) and  FAO (2015). We were able to acquire information on the number of jobs created by this industry in the Pacific Island countries, with a total of 12 867 employees in 2010 and another 18 200 predicted to be added in Papua New Guinea (Data [10], Table  1 , Supplementary material). For the rest of the countries it was not possible to directly attribute the jobs created or the economic benefits from the WCPO purse seine tuna fishery. Due to the highly heterogeneous data (in terms of completeness) for this stage of the tuna supply chain, we approached this step of the VCA in a purely qualitative way.
The big picture Looking at the big picture of the WCPO tuna supply chain, a very broad range of costs and benefits is generated and a series of different levels of economic beneficiaries is involved in the various steps of the chain, making mapping extremely complex. We can identify three types of ES spatial flows within the WCPO purse seine tuna fishery socialecological system: (i) one-to-one flows; (ii) closed loop flows; and (iii) open loop flows. All are represented within the map shown in Fig. 7. It is important to note that for some countries data were incomplete, so we could not accurately account for all spatial flows emerging from the fishery. These countries are only indicated in the final map, since they are part of the system, but benefit flows are not directly indicated. The observed flows are explained below.
The distance between the center of the Pn. p and AC nuclei was measured in pixels using a Photometrics CoolSNAP ES camera and the Nikon NIS-Elements software (version 3. 0. 1). To avoid measurement errors due to the animal curvature, the distance between each Pn. p cell (except P6. p) and the AC was calculated via a Pythagorean formula. For example, the distance between P4. p and the AC is equal to: 𝑃6. 𝑝 %& ' + (𝑃5. 𝑝 +,. -+ 𝑃6. 𝑝 +. -) ' Where 6. %& is the distance between P6. p and the AC, 5. +,. -is the distance between P5. p and P4. p, and 6. +. -is the distance between P6. p and P5. p. Non-normalized measurements can be found in Table  S4.
Data and reagent availability Supplementary Tables are available throguh the FigShare portal: -Table  S1. List of strains used in this study. -Table  S2. Sequences of DNA primers used in this study. Sequencing primers to verify by Sanger sequencing the mutations identified by the mapping by sequencing approach, and to identify the molecular lesion in additional alleles. -Table  S3. Sequences of smFISH probes used in this study. The fluorophore coupled to each probe is noted at the end of the set name. -Table  S4. smFISH quantifications, distance measurements and vulval cell fates used in this study. Data and strains are available by contacting Marie-Anne Félix (felix@biologie. ens. fr). Code for mutant identification is available at https://github. com/fabfabBesnard/Andalusian_Mapping.
Acknowledgements We thank  Aurélien Richaud  for performing a smFISH experiment,  Joao Picao Osorio  for comments on the manuscript,  Michalis Barkoulas  for his advice on the Oscheius smFISH,  Mark Blaxter 's lab for their continuous support of nematode genomics as well as for maintaining the Caenorhabditis website (http://www. caenorhabditis. org/), Benjamin Podbilewicz  for advice on the immunofluorescence experiments, and  Sana Dieudonné ,  Aurélien Richaud  and  Clément Dubois  for advice and help in the use of the mapping by sequencing technique. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). We thank Wormbase.
Observational data Measurements of the interaction effects of temperature and moisture on soil respiration fluxes were obtained by incubating a crop field soil at several fixed levels of soil moisture and variable levels of temperature over a period of ca. 6 months, as detailed in the following. Soils from 0 to 20 cm depth were sampled at Versailles, France, from the "Le Closeaux" experimental field plot, cultivated with wheat until 1992 and with maize since 1993. Mean annual temperature and rainfall are 10 • C and 640 mm, respectively. The soil is classified as Luvisol (FAO) silt loam (26 % sand, 59 % silt, 15 % clay) containing no carbonates. Organic carbon contents at the start of the incubation were 1. 2 % in weight. Soil samples were prepared for elemental analysis (C, N) using a planetary ball mill (3 min at 500 rpm).
Temperature effects Reaction rates (V U , V D , K U , andK D in Eqs. 6-9), decay and respiration rates (r ed , r md , r mr ) are temperature sensitive and calculated from their reference values following an Arrhenius type temperature response: r = r ref exp - E a R 1 T - 1 T ref , ( 20 ) where r is the temperature-modified rate, r ref the reference rate at temperature T ref , T temperature in kelvin, E a the activation energy, and R the universal gas constant. Three parameters were used for E a : E a_m and E a_e for microbial and enzyme decay rates, respectively, and E a_V for other reaction rates. Temperature thus affects the rates of decomposition and uptake, the half-saturation constant in the Michaelis-Menten equation, and the rates of microbial and enzyme decay. Apparent activation energies -describing the observed temperature relationship, both in measurements and model data -were obtained by fitting an Arrhenius equation to the temperature-flux relationship at each level of moisture.
2. Each model was calibrated by fitting a single set of parameters simultaneously to all the incubation data (Table  S3 ). For this, the model was run to reproduce each sample treatment, i. e. the applied incubation times and temperatures for each level of moisture (Fig. 1 ). Accumulated soil respiration amounts were then calculated to match those from the observed data. Measured and simulated data from all samples were then combined, and an overall model cost was calculated using the root mean square error (RMSE) and a weighting term, as described below. Parameter optimization was carried out in two steps. We first explored parameter spaces using a Latin hypercube of parameter values. For this we randomly selected unique parameter sets from a uniform distribution over each parameter range (R function randomLHS, package lhs;  Stein, 1987)  to obtain 30 000 parameter sets.
Model costs were then obtained by running models with each set. In the second step we used the Nelder-Mead algorithm (as implemented in the function modFit in package FME of the R programming language; R Development Core  Team, 2016; Soetaert and Petzoldt, 2010)  with initial parameter values being the set from the previous step with the lowest model cost. For the cost calculations we used an error term ("err" argument to FME function mod-Cost) to weight the residuals. The error was calculated as the normalized (0-1) standard deviation of measured values at each combination of temperature and moisture, with 0. 1 added to avoid an unreasonable weighting of measurements with near-zero errors. For a visual inspection of the model-data fits, we plotted both the measured and the model relationship between soil respiration and moisture, soil respiration and temperature, and apparent activation energy (E a ) and moisture content.
Model steady state, sensitivity analysis, and validation Equations for steady state were derived by setting the rate of change in the state variables to zero in Eqs. (1)-(  5 ) (where the flux terms are replaced by their respective equations) and then solving for the state variables. This was performed in Python using the "SymPy" package  (Meurer et al. , 2017). A sensitivity analysis was carried out on model parameters using the "sensFun" function from the R package FME, which perturbs each parameter individually by a small amount. We ran the model as above, i. e. simulating the incubation and using daily output. Daily sensitivities were then averaged to obtain an overall value. Sensitivity values were calculated for the C P pool alone, as this pool represents the largest fraction of soil C.
Acknowledgements. This study was partially supported by funds from the  R2DS  projects " Le carbone stable des sols: processus de stabilisation et vulnérabilité" and "Vulnérabilité du C stable et labile du sol aux changements climatiques: mise en évidence, facteurs explicatifs et intégration dans un modèle de biosphère spatialisée ". This open-access publication was funded by the  University of Göttingen. Edited by: Andreas Ibrom Reviewed by:  Thomas Wutzler  and one anonymous referee.
Code and data availability. All code and data used for this analysis are available at https://doi. org/10. 5281/zenodo. 1290716 (Moyano, 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-5031-2018-supplement. Author contributions. FEM developed the model, analysed the data, and wrote the paper. NV carried out the lab experiments and revised the paper. LM contributed to model optimization, discussions, and revisions.
2) can be achieved using SDSS fiber spectroscopy, once corrected for aperture effects using SDSS photometry (e. g. , cmodel -fiber colors), both quantities being available for hundreds of thousands of local galaxies thanks to the SDSS. The paper is structured as follows: in section 2 we describe the sample, in section 3 we outline the data reduction, in section 4 we present the results, and in section 5 the discussion and conclusions. A description of the tables provided is given in the appendix. Standard cosmology is assumed, with H 0 = 73 km/s/Mpc. The spectroscopic data presented in this work, as well as those collected at other frequencies, are available to the community through the HeDaM database (http://hedam. lam. fr/HRS/).
Data from the literature Most of HRS nuclear optical spectra were found in the literature. In particular, 163 spectra were downloaded in FITS format from the SDSS DR13 and DR12 database  (Albareti et al. 2016) ; 114 spectra were found in the NASA/IPAC Extragalactic Database (NED) and 104 of these are taken from  Ho et. al (1997). For these galaxies, the double spectrograph on the Hale 5m telescope at Palomar Observatory yielded simultaneous spectral coverage of ∼4230-5110 Ȧ and ∼6210-6860 Ȧ, with a spectral resolution of ∼4 Ȧ in the blue and ∼2. 5 Ȧ in the red. We joined the blue and red spectra by projecting them on a common wavelength solution and by performing a linear interpolation between the average flux of the last ten pixels of the blue spectrum and the first ten of the red spectrum.
We homogeneously corrected the 26 spectra that we took at Loiano with the G7+G8 grisms, the spectra downloaded from SDSS, the spectra observed by  Ho et al. (1997)  and the spectra downloaded from the NED database using the GANDALF fitting code (Gas AND Absorption Line Fitting,  Sarzi et al. 2006; Falcon-Barroso et al. 2006 ) complemented by the Penalize Pixel-Fitting code  (Cappellari & Emsellem, 2004)  to simultaneously model the stellar continuum and the emission lines in individual pixels. Consistently with B15, we used the MILES stellar library  (Vazdekis et al. 2010). GANDALF is a simultaneous emission and absorption line fitting algorithm designed to separate the relative contribution of the stellar continuum from that of the nebular emission in the spectra of nearby galaxies, while measuring the gas emission and kinematics.
2004 ) and the f iber magnitude (the magnitude in 3-arcsec-diameter SDSS fiber radius). The global SFR is obtained by adding the external to the nuclear SFR. Both the stellar mass and SFR in B04 are computed assuming a  Kroupa (2001)  IMF. B04 data are available through the SDSS database (galS pecExtra table) for 205186 galaxies with z¡0. 1 in the sky region corresponding to the one covered by the HRS. Figure  10  represents the relation between the stellar mass and the log(SFR total ) for the aforementioned SDSS galaxies (see contours). The appearance of the galaxy main sequence is evident, separated by a cloud of quenched galaxies, a factor of one hundred less star forming. Among galaxies listed in galSpecExtra we found the data for 38 HRS galaxies. For these we plot the value derived by B04 with open symbols.
Acknowledgements. This research has made use of the GOLDmine database (Gavazzi et al. 2003(Gavazzi et al. , 2014b) ) and of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. Funding for the Sloan Digital Sky Survey (SDSS)  and  SDSS-II  has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbuk agakusho , and the  Max Planck Society , and the  Higher Education Funding Councill for England. The SDSS Web site is http://www. sdss. org/. The SDSS is managed by the  Astrophysical Research Consortium (ARC)  for the Participating Institutions.
BACKGROUND: Paraneoplastic neurological syndromes are rare conditions where an autoimmune reaction against the nervous system appears in patients suffering from a tumour, but not linked to the spreading of the tumour. A break in the immune tolerance is thought to be the trigger. METHODS: The transcriptomic profile of 12 ovarian tumours (OT) from patients suffering from paraneoplastic cerebellar degeneration (PCD) linked to anti-Yo antibodies (anti-Yo PCD OT) was compared with 733 ovarian tumours (OT control) from different public databases using linear model analysis. RESULTS: A prominent significant transcriptomic over-representation of CD8+ and Treg cells was found in anti-Yo PCD OT, as compared to the OT control. However, the overall degree of immune cell infiltration was similar, according to the ESTIMATE immune score.
7 To date, only few studies have directly explored the tumoural tissue, and it is generally accepted that PNS occurs at the fulcrum between the patient's immune system and the tumour, where genetics and environment may play a role. On the patient scale, only human leucocyte antigen (HLA) have been explored, with negative results. 8 However, very recently, a potential HLA genetic predisposition to anti-Yo PCD in the context of a specific cancer has been pinpointed. 9 e hypothesise that antigen ectopic expression may contribute in the breakdown of immune tolerance. Therefore, transcriptomic analysis of anti-Yo PCD OT tissue may provide an insight of the pathways associated with this rare disease. We propose here a comparative transcriptomic analysis of ovarian tumoural tissue from patients suffering from anti-Yo PCD OT, compared to a large series of different datasets of transcriptomic studies of ovarian cancer.
Public data selection 3] [14]  Because PCD affects less than 1 per 1000 ovarian cancer,  15  we considered all the samples from the public data as non-PCD associated. Accordingly, transcriptomic data could classify our anti-Yo PCD OT samples correctly with an accuracy of 100% (95% confidence interval of 98-100%), supporting that transcriptomic data from the public dataset were composed of the OT controls. Further details on this analysis are provided in the Supplementary methods. Furthermore, we also used 16 additional transcriptomic datasets with clinical data to analyse the potential clinical value of CDR2 and CDR2L expression on the survival of OT using curatedOvar-ianData Bioconductor package. 16 Further details on the different public transcriptomic datasets are provided in the Supplementary data.
Selection of datasets Samples from TCGA, 25 GSE51373, 26 GSE69207, 27 GSE66957 (unpublished data) and GSE63553 (unpublished data) were then included in the analysis (Supplementary Table  2  and Supplementary Figure  5 ). All samples were obtained before chemotherapy was administered. Because all datasets used for comparison were fresh frozen (FF) samples, we decided to remove the genes that were known to be differentially expressed within the paired samples from FFPE and FF origins. 28 Our data confirmed these data, but also showed specific differential information unrelated to the samples' origins, since 7. 02% of first differentially expressed genes matched with the gene set in the literature (Supplementary Figure  6 ). After removal and normalisation, the final expression matrix had 9939 genes (Supplementary Table  3 ) and 745 samples.