dataesr/scientific-paragraphs-categorization huggingface dataset:
In our dataset, the smallest ICE was also identified in S. aureus (strain USA300-FPR3757) and was only 11. 5 kb long. The largest ICEs were found in Rhodopseudomonas palustris BisB18 (MPF T ) and Pseudomonas putida S16 (MPF G ) and were around 155 kb long. The distribution of ICE size per MPF type showed that type F (median size of 99 kb) and G (median 80 kb) were the largest, whereas those of type FA were the smallest (median 23. 5 kb) (Figure  2 ). The distribution of the size of ICE in our dataset is close to that of ICEs reported in the literature, even if we could not identify any ICE of a size comparable to ICEMl-SymR71. Such large ICEs may be rare or specific of taxa not sampled in our study. The majority of ICEs were found to be AT-rich compared to their host's chromosome (Figure  2 ).
This is, with some exceptions, a general trend for mobile genetic elements  (64). The distribution of sizes of these 160 families of ICEs is similar to the one for the entire dataset (Supplementary Figure  S6 ), validating our decision to keep all ICEs in our analysis. It was known that the largest plasmids are typically in the largest genomes  (7). We observed a positive correlation between the size of ICEs and that of their host chromosomes ( = 0. 47, P-value < 0. 0001, after discounting the ICE size from chromosome size), even if this is partly because the smaller types of elements are associated with the clades with the smallest genomes (Supplementary Figure  S7 ). Larger ICEs may be disfavored in smaller genomes because they are harder to accommodate in terms of genome organization  (65) , thus leading to higher fitness cost, or because smaller genomes endure lower rates of HGT  (66).
The functional repertoires of ICEs We investigated the functional classification of the genes in ICEs in relation to those in the rest of the host chromosome using the EggNOG database (see Methods). Unknown or unannotated functions accounted for 61% of all genes in ICEs, showing how much remains to be known about the functions carried by these elements. We observed three functional categories that were systematically more frequent in ICEs (P-value<0. 01 with Bonferroni correction for multiple test, Figure  5 ), including typical ICE functions: secretion (genes related to conjugation), replication/recombination/repair (integrases, relaxases, and transposable elements), and to a lesser extent cell cycle control/cell division/chromosome partitioning. The category associated with transcription (gene expression regulation) showed similar frequencies in the ICE and in the host chromosome.
The identification ICEs split in several contigs requires the availability of a very similar ICE for reference, which may bias the study of these elements. The increasing use of long-read technologies to sequence bacterial genomes will soon solve this limitation. Even if our dataset is representative of the diversity of experimentally studied ICEs, we lacked ICEs from types B (Bacteroidetes), of which there are experimental systems  (85) , and C (Cyanobacteria), for which there is no experimental system. Further data will be necessary to study these elements. Another limitation of this work is the assumption that the presence of a certain number of components of the T4SS system and a relaxase are necessary and sufficient to define an ICE. While our previous studies have shown that we are able to identify known conjugative systems accurately, we cannot exclude the possibility that some of the identified ICE are defective for transfer.
This may explain the existence of some elements that lack identifiable integrases. In spite of these limitations, the availability for the first time of large dataset of ICEs having undergone a systematic expert curation allowed to quantify many traits associated with ICEs and confirm, and sometimes infirm, observations made from the small number of well-known ICE models. It also allowed to characterize their genetic organization, and identify common traits. Integration and conjugation are the only functions that we found to be present in most ICEs. Interestingly, even these functions had very different phylogenetic histories, as revealed by the scattered distribution of ICEs per MPF type in the phylogenetic tree of the tyrosine recombinases. A number of other functions were often identified in some types of ICE, notably defense systems, partition, and replication.
ACKNOWLEDGEMENTS J. C. is a member of the  École Doctorale Frontière du Vivant (FdV) -Programme Bettencourt. We thank  Christine Citti  for insightful comments on a previous version of this manuscript,  Fernando de la Cruz  for insights and providing the list of rep proteins of PLACNET,  Bertrand Néron  and  Olivia Doppelt-Azeroual  for setting up the webserver version of CONJscan. Author contributions: J. C. and E. P. C. R. designed the study. J. C. and M. T. produced the data. J. C. made the analysis. J. C. and E. P. C. R. drafted the manuscript. All authors contributed to the final text of the manuscript.
Summary Given the interest in emotive-factive predicates and the large variety of theories proposed to account for their properties, the main goal of this project was to gather quantitative data in a theory-neutral perspective (as much as possible). We tested which types of questions can be embedded under emotive-factives, what (exhaustive) readings these embedded questions would carry, and what monotonicity properties are associated with emotive-factives. We focused on testing various widespread claims from the literature rather than predictions of specific theories, but this will not prevent us from pointing out certain precise theoretical consequences of our data along the way. 2 Experiment 1: Selectional properties of attitude predicates.
Statistical methods The data and analysis script for each experiment are available online at http:// semanticsarchive. net/Archive/GRhZmM4N/Cremers-Chemla-ExpEmotiveFactives. html. Results were analyzed with R and the lme4 package (R Core Team 2014;  Bates et al. 2015b ) and plotted with ggplot2 (Wickham 2009). To compensate for differences in how participants used the continuous scale, responses were centered and normalized by participant before any analysis. We followed the procedure proposed by  Bates et al. (2015a) , who suggest to first try to fit maximum random effects structures by participants and by item (as suggested in  Barr et al. 2013 ) and then eliminate useless components in the random effects structure to avoid over-parametrization. For each relevant parameter, we report the 95% confidence interval (CI) rescaled to the original 0-100 scale used in the graphs.
Conclusions for Experiment 1 We collected naive speakers judgments pertaining to the selectional properties of embedding predicates. The results confirmed most judgments from the theoretical literature. New observations include the contrast between the acceptability of whether-questions under emotive-factive and anti-rogative predicates, a small difference between polar and alternative questions (which may be explained by dialectal differences), and data on adverbs of quantity which may inform the debate on QVE with rogative predicates. In the remainder of this paper, we move on to more semantically oriented investigations, focusing on constructions that were judged grammatical in this experiment. We will first investigate the monotonicity of emotive-factive predicates when they embed straight declarative complements.
More precisely, we always fitted models with maximal by-subject and by-item random structures, although they often did not converge. Then we used the function rePCA provided in the package RePsychLing to remove all components which explained less than 0. 1% of the variance explained by the main component. This was done independently for the subject and item random effects structures, because random effects associated with items tend to be smaller. We also removed correlations between random effects in the updated "parsimonious" model. A second pass ensured that no component under the threshold remained in the new model (this could happen when the PCA had been done on a maximal model which had not.
Though protists are a widespread and diverse group, scientists know little about them. One reason for this is the lack of high-throughput ways to recognize and count protists in environmental samples. Colin, Coelho et al. set out to tackle this blind spot in ecology and cell biology by developing an automated imaging system. The system needed to image many kinds of protist cells in enough detail to see the features inside. The end-result was a 3D-imaging technique called e-HCFM -which is short for "environmental high content fluorescence microscopy". Colin,  Coelho et al. went  on to use the technique on 72 samples collected on an expedition across the world's oceans. This allowed them to automatically image, recognize and classify over 330,000 organisms. This approach and new dataset will benefit researchers working in many fields, from cell biology to ecology, computational biology and beyond.
The segmentation performance of each particle is enhanced by the sharp contrast generated by the fluorescent signals. In particular, the thin transparent biomineral and/or organic structures shaping many groups of eukaryotic taxa can be precisely detected (Figure  1 -figure supplement 5), generating more accurate computed features. We then generated a reference training set to build an automated classifier. A total of 18,103 objects (5. 4% of the whole dataset) were manually curated and classified into a 4-level hierarchical framework including 155 categories (taxonomic lineage for organisms and morphological types for other particles, Figure  3a ; Figure  3 -source data 1). A random forest classifier was empirically determined to perform the best, combining high accuracy and fast computation time.
, 2014; de Vargas et al. , 2015) , thus filling the gap between cell biology, evolution, and ecosystem structure and function. A key future challenge will be to combine automated, 3D multicolor fluorescent imaging of complex natural assemblages of eukaryotic cells with online detection of cells of interest and their isolation for single-cell -omics  (Kolisko et al. , 2014). This will allow bridging the deluge of novel environmental eukaryotic genetic data to the complex cellular structure, shape, and behaviour of eukaryotic biota, while scaling up the information, through global meta-omics datasets, to the level of the ecosystems.
The potential signal from Hoechst was finally not subtracted because the AF546 fluorescence was mostly used to delineate cell edges. The laser power and exposure settings were tuned to reach a tradeoff between the dimmest and brightest object of interest whereas the limited dynamic range of our photomultiplier tube detector (PMT) cannot avoid some signal saturation (the variability of environmental samples cannot be anticipated). Signals were not averaged. The automation was piloted through the HCS A module of the LASAF software (Leica Microsystem). We used the water immersion lens HC PL APO 40x/1,10 mot CORR CS2. The scanning was bidirectional with a speed set at 600 Hz. The pinhole was adjusted to 1 Airy unit for all channels. Each square field of view (fov) is 385. 62 mm wide and 21. 80 mm thick.
Monitoring of the microscope performance Since we processed the Tara Oceans samples sequentially in multiple runs, we monitored the stability of the illumination (laser sources), optics and the PMTs by using fluorescent beads (Invitrogen I7225, InSpeck deep-red, lot1267301; Thermo Fisher Scientific, MA, USA) as internal calibration standards which were included in each multi-well plate prior to running image acquisition (see mounting details above). We show in the Figure  1 -figure supplement 6 (see also Figure  1 -figure supplement 6-source data 1) the distribution of fluorescence intensity for 3 types of beads (0. 32%; 1. 41%; 4. 10% fluorophore concentration) emitting in the chlorophyll channel (average intensity after sum projection of corresponding z-stacks, segmentation and size filtering).
6 mm 2 as this corresponds to a circle of 5 mm diameter). Objects touching any of the four image borders are also removed. Finally, because the imaging was performed in overlapping fields, it is possible to detect the same object in multiple fields, thus we assign it to its canonical field based on the geometry and remove it from all other fields (see Figure  1 -figure supplement 4a). Image analysis was performed using the mahotas package  (Coelho, 2013).
Object classification A hand-labeled dataset of 18,103 objects (5. 4% of the whole dataset) was used for training an automated classifier. For maximizing the diversity of the training set, the entire dataset was explored, similar objects were categorized and 155 categories with more than 30 specimens were kept. We did not overweight classes of very abundant object and considered that few hundreds of representative objects were enough to define a class statistically. These categories were organized into a fourlevel hierarchical framework (Figure  3a ; Figure  3 -source data 1) reflecting taxonomic lineage for organisms and particle types for other objects. The goal of this hierarchical pattern of classification was to evaluate the classifier performance at different granularities. A random-forest classifier, using 500 trees  (Breiman, 2001)  was used for classification.
This classification method was chosen empirically as the best combination of accuracy and computational performance among several methods tested (support-vector machine with diverse set of kernels, logistic regression, and random forests). This step of the pipeline was implemented using the scikit-learn package  (Pedregosa, 2011). At classification time, the software returns, for each object, a probability estimate for each class. The object is assigned to the class with the highest probability; a confidence score is defined as the difference between the highest and the second-highest probability values. Feature importance was computed by the scikit-learn package as the Gini importance  (Breiman, 2001). We report a normalized version where the reported importance values were divided by the average importance so that values above one correspond to features with higher than average importance and values below one to features less important than average (Figure  1 -source data 2).
Computational costs and distributed computation In total, it takes circa 40 CPU hours to process one acquisition (which consists of two wells, 768 data fovs, and 48 control fovs). However, after the background estimation step, the process is embarrassingly parallel as each fov can be processed in parallel (a computationally inexpensive post-processing of the data relabels all objects so that they are numbered sequentially within each sample). Our software is designed to take advantage of a multi-core machine computer cluster by automatically splitting the computation across available CPUs. Image analyses of one microscopy run are performed in 20 min with sufficient resources.
Accession URLs for software and data sharing purposes The software code is made available under an open source license (https://git. embl. de/coelho/ eHCFM). All raw images and preprocessed thumbnails are publicly available at EBI-BioStudies (accession ID: S-BSST51, https://www. ebi. ac. uk/biostudies/studies/S-BSST51). Images can also be explored through the Image Data Resource platform  (Williams et al. , 2017 ; https://doi. org/10. 17867/10000108). The preprocessed thumbnails will be released for online expert annotations at EcoTaxa (http://ecotaxa. sb-roscoff. fr), and taxonomically and ecologically informed thumbnails will be available for public exploration. The inventory of experiment metadata, samples and their associated contextual data from the Tara Oceans expedition are available at PANGAEA (Tara Oceans Consortium, Coordinators and Tara Oceans Expedition, Participants, 2017; https://doi.
4% of the dataset) classified into 155 categories, was used for automated recognition (Random Forest). (b) Examples of e-HCFM 3D-images and movies from a wide phylogenetic diversity of planktonic eukaryotes (see also Figure 1-figure supplements 1 and 2). Left panel: a chain of diatoms (Asterionellopsis sp. , Heterokonta) (Figure 1-video 1); right panel, top left to bottom right: radiolarian (Rhizaria), ciliate (Alveolata), amoeba (Amoebozoa), choanoflagellate (Opisthokonta), dinoflagellate (Alveolata), coccolithophore (Haptophyta). Key cellular features are labelled with various dyes: DNA/nuclei (blue, Hoechst33342); (intra)cellular membranes (green, DiOC6(3)); cell covers and extensions (cyan, PLL-AF546, a home-made conjugation between a-poly-L-lysine (PLL) and Alexa Fluor 546 (AF546)); chloroplasts (red, chlorophyll autofluorescence).
Acknowledgements We thank the following sponsors for their commitment: the  French Ministry of Research and Government  through the  'Investissements d'Avenir' program OCEANOMICS  ( ANR-11-BTBR-0008 ); The  ANR  program  POSEIDON  ( ANR-09-BLAN-0348 ),  CNRS  (in particular, the GDR3280); EMBL; UPMC; and  VEOLIA  for providing seed-funding at early stages of this project. We thank  L Santangeli  and  A Tanaka  for sample preparations and help,  V Hilsenstein ,  C Conrad , and  S Terjung  from the  EMBL Advanced Light Microscopy Facility  for precious assistance at the microscope,  Yan Yuan  for IT support, EMBL IT facility and  Christian Boulin  for providing secured data storage. This article is contribution number  60  of  Tara Oceans.
Acknowledgments We are very grateful to  Zach Harmany  who made available the SPIRALTAP software under an open source license and to  Moran Mordechay  for useful discussions on further optimization of the imaging scheme. We would like to thank the whole  IMOD lab and Darzacq labs  for insightful discussions and suggestions to this work. This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris.
All experimental protocols were approved by the local ethics committee and are following the French and European regulations on care and protection of the Laboratory Animals (EC Directive 2010/63). Antibodies and Flow Cytometry Analysis-The fluorescent conjugated antibodies used for flow cytometry were purchased from eBioscience (San Diego, CA) and BD Biosciences (San Jose, CA): anti-TCR (H57-597), anti-CD4 (RM4 -5), anti-CD25 (PC61), anti-CD62L (MEL-14), anti-CD45RB (16A), anti-CD39 (Duha59), anti-CD73 (TY/ 23), anti-PD-1 (J43), anti-CTLA4 (UC10 -4F10 -11), anti-GITR (DTA-1) and anti-CD127 (SB/119). Intracellular staining was performed using the Foxp3 staining buffer kit (eBiosciences) according to the manufacturer's instructions before data acquisition on a BD LSRII flow cytometer and analysis with the FlowJo software.
Five microliters of each sample were loaded onto a C-18 precolumn (300-m inner diameter ϫ 5 mm, Dionex) at 20 l/min in 5% acetonitrile, 0. 05% Trifluoroacetic Acid (TFA). After 5 min of desalting, the precolumn was switched online with the analytical C-18 column (75 m inner diameter ϫ 50 cm; in-house packed with Reprosil C18) equilibrated in 95% solvent A (5% acetonitrile, 0. 2% formic acid) and 5% solvent B (80% acetonitrile, 0. 2% formic acid). The peptides were eluted using a 5 to 50% gradient of solvent B at 300 nl/min flow rate, with gradient length as specified in the text and supplemental Fig. S2B. The mass spectrometer was operated in data-dependent acquisition mode with the XCalibur software. For LTQ-Velos Orbitrap runs, MS survey scans were acquired in the Orbitrap on the 300 -2000 m/z range with a resolution of 60,000 and the 20 most intense ions per survey scan were selected for CID fragmentation and analysis in the linear trap.
For Q-ExactivePlus runs, survey MS scans were acquired in the Orbitrap on the 350 -2000 m/z range with a resolution of 70,000, the 10 most intense ions per survey scan were selected for HCD fragmentation and resulting fragments were analyzed at a resolution of 17,500 in the Orbitrap. Protein Identification and Quantification-Raw MS files were analyzed by MaxQuant version 1. 5. 2. 8. Data were searched with the Andromeda search engine against Mouse entries of the Swissprot protein database (UniProtKB/Swiss-Prot Knowledgebase release 2014_09, Mouse taxonomy, 16,699 entries) and a list of potential contaminant sequences provided in MaxQuant1. 5. 2. 8. The search included methionine oxidation and protein N-terminal acetylation as variable modifications, and carbamidomethylation of cysteine as a fixed modification.
Validation was performed through a false discovery rate set to 1% at protein and PSM level determined by target-decoy search in MaxQuant (with a minimum length of 7 amino acids and a minimum Andromeda score of 20 as additional filters for peptide validation). Specificity of trypsin digestion was set for cleavage after lysine or arginine, and two missed cleavages were allowed. The precursor mass tolerance was set to 20 ppm for the first search and 4. 5 ppm for the main Andromeda database search. The mass tolerance in MS/MS mode was set to 0. 8 Da for LTQ-Velos data and 20 ppm for Q-Exactive data. For label-free relative quantification of the samples, the "match between runs" option of MaxQuant was enabled to allow cross-assignment of MS features detected in the different runs. The minimal ratio count was set to 1 for calculation of LFQ intensities.
Experimental Design and Statistical Rationale-The data set contains mass spectrometry results from the analysis of 7 biological replicates of Treg/Tconv cell samples purified by flow cytometry, each experiment performed from a pool of 5-7 mice. Several MS injection replicates were performed, leading to 27 raw files considered for statistical analysis. The detailed description of each analysis (raw file name, sample type, biological replicate number, MS technical replicate number, analytical conditions) is given in supplemental Fig. S2B  and supplemental Table  S1. The quantitative proteomic analysis was performed using the statistical package R (R Development Core Team, 2012; http://www. R-project. org/) on the normalized LFQ intensities from the "proteinGroups" table of MaxQuant. Protein entries identified as potential contaminants by MaxQuant were eliminated from the analysis, as well as proteins identified with less than 2 unique peptides.
After log2-transformation of the data, missing values were independently replaced for each run by its 1% quantile. For each analyzed sample, the values of the technical replicates were averaged and only the proteins quantified in a minimum of 3 biological replicates were subjected to the statistical analysis. We performed a two-sided paired t test between Treg and Tconv cells. The p values obtained for the proteins presenting a minimum 2-fold change (minimum log2 transformed fold change of 1) between the two cell populations were adjusted (Benjamini-Hochberg). Proteins were considered significantly regulated between Treg and Tconv cells when their absolute log2-transformed fold change was superior or equal to 1 and their adjusted p value under or equal to 0. 05. R scripts related to the analysis can be found online ͑https://github.
com/mlocardpaulet/ Duguet_TregTconv_2016 and associated scripts and tables͒ and a simplified outline of the analysis is presented supplemental Fig. S3. Comparison of Proteomic and Expression Data Sets-To validate our approach and build an integrated list of Treg marker proteins, our proteomic data set was compared with a "transcriptional signature" of Treg  (15)  and a proteomic data set of Treg and Tconv populations separated based on their CD25 expression levels  (11). The expression data set was downloaded from the gene expression omnibus webpage (http://www. ncbi. nlm. nih. gov/geo/; GSE7460). Gene identities were retrieved from the moe430 -20 Affymetrix Mouse Genome 430 2. 0 Array (Annotation: GPL1261) in December 2015. We calculated the log2-transformed (Treg/Tconv) ratio using normalized quantities and could plot them against our proteomic log2-transformed fold changes for the 3657 unique genes present in the two data sets.