dataesr/scientific-paragraphs-categorization huggingface dataset:
Role of the funding sources Work in the Cossart laboratory received financial support from  Pasteur Institute, INSERM  and  INRA , from the  European Research Council (ERC)  advanced Grant  233348 , from  Agence Nationale de la Recherche  ( ANR blancs Mitopatho   12-BSV3-0017-03  and  EpiLis 11-BSV3-003-01 ;  ERA-NET Infect-ERA  ProAntiLis), from  Laboratoires d'Excellence  ( IBEID ANR-10-LABX-62-01 ), from  Fondation Jeantet and Fondation le Roch Les Mousquetaires. P. Cossart is a  Senior International Research Scholar  of the  Howard Hughes Medical Institute ,  F. Stavru  is a CNRS research associate. We declare no conflict of interest.
Phylogenetic Reconstructions Sequence alignments of the sequences listed in Table  S1  were constructed using the alignment editor Seaview 3. 0 and the MUSCLE algorithm. S. canicula sequences were retrieved by tblastn searches in transcriptomic databases obtained by Sanger and Illumina sequencing of embryonic and adult cDNA libraries (stages 8-25 and mixed adult tissues). Maximum-likelihood trees were inferred using the PhyML program version 3, the LG-F+ 12+I substitution model and the SPR algorithm. Posterior probabilities (PPs) supporting groupings were calculated using the aLRT algorithm implemented in PhyML and are displayed as percentages at the corresponding nodes. Only PP >80% are indicated. The trees were viewed and edited using Mega6.
Identification of Catshark Orthologues of the Genes Studied Exhaustive phylogenetic characterizations of the catshark Dlx gene repertoire have been previously published  (Debiais-Thibaud et al. , 2013) , confirming the identity of ScDlx2 and ScDlx5. In order to unambiguously identify the catshark orthologues of Foxg1, Shh, Nkx2. 1, Otp, and Tbr1, we conducted systematic phylogenetic analyses of the corresponding vertebrate gene families, including all the vertebrate classes derived from duplications of a single ancestral chordate orthologue (Figure  1 ). In each case, phylogenies were constructed from alignments containing deduced amino acid sequences of all paralogous sequences retrieved from catshark transcriptomic databases and from a representative sampling of actinopterygians and sarcopterygians.
The trees were rooted using a Branchiostoma floridae sequence, except in the case of Otp which could not be found in the amphioxus Ensembl database. In the case of Foxg1, three strongly supported classes (posterior probability or PP > 90%), each containing a catshark and several osteichthyan sequences, were retrieved, highlighting for the first time the presence of three gnathostome Foxg1 classes (Figure  1A ). These classes were termed Foxg1a, Foxg1b, and Foxg1c, respectively. One coelacanth and several actinopterygian sequences, but no amphibian or amniote sequence, were found in the Foxg1b and Foxg1c classes, suggesting a loss of their representatives in tetrapods. We focused the expression analysis on ScFoxg1a, the catshark orthologue of the only Foxg1 gene retained in all major gnathostome lineages including tetrapods.
Altogether, these data show that the prosomeric model in its latest version provides an adequate reference to describe the molecular organization of the catshark developing hypothalamus, thus highlighting the underlying unity of this complex anatomical structure across jawed vertebrates. Région Centre, Région Bretagne (EVOVERT grant number 049755; PEPTISAN project), National Research Agency (grant ANR-09-BLAN-026201), CNRS, Université d'Orléans and Université Pierre et Marie Curie. GNSD would like to thank Spanish SEPE for its funding support.
Acknowledgments This work was supported by grants from the  Spanish Dirección General de Investigación-FEDER  ( BFU2010-15816 ), the  Xunta de Galicia  ( 10PXIB200051PR ,  CN 2012/237 ),  European Community-Research Infrastructure Action  under the  FP7 "Capacities" Specific Programme  ( ASSEMBLE 227799 ), the.
Quantitative Real-time PCR Quantitative Real-Time PCR was performed with the ABI PRISM 7300 detection system (Applied Biosystems, Foster City, USA) and using SYBRGreen dye. Relative RNA abundance was calculated using the ΔΔCT formula and normalized to the transcript levels of the housekeeping gene β-actin with the help of the Sequence Detection Software v. 1. 4 (Applied Biosystems, Foster City, USA). Assays were performed in duplicate. TaqMan primers for IL23 and IL17 were purchased from Life Technologies (Mulgrave, Australia). Primer sequences used for SybrGreen RT-qPCR were as follows: -actin/for:.
3D confocal fluorescence imaging Immunofluorescent staining of crypts was detected with an Olympus FV1000 Spectral Confocal attachment to an Olympus IX-81 microscope on either a 60x water immersion (NA 1. 2). The crypts were imaged using standard filter sets and laser lines, acquiring single labeled images. DAPI, β-catenin and E-cadherin fluorescence were excited with the 405nm, 488nm and 546nm laser lines, respectively and the emission wavelength were measured at wavelengths 405nm, 473nm and 559nm, respectively. The images were captured using Olympus FluoroView software (Version 1. 7c). 3D image stacks were acquired, which encompass the entire depth of the crypt(s) in the field of view. The entire depth of the sample was acquired as 3D image stacks at approximately 20µm thickness for each optical section.
For quantitative spatial analysis of key proteins in isolated crypts, cubic voxels were acquired for each image stack. The output analogue signal, representing the fluorescence intensities was digitized to 16 bits resolution at 65536 levels of grey and saved as an Olympus Image Binary (OIB) image. The Olympus Image Binary (OIB) image files from the fluorescently stained individual whole mount crypts were imported into the ImageJ/Fiji software  (Schindelin et al. , 2012)  for processing and display. 3D image stacks were extracted, and imported as "tiff" files into MATLAB (MathWorks, Natick, Massachusetts, USA), for analysis. Quantitation of β-catenin and E-cadherin was conducted as described  (Tan et al. , 2013).
Acknowledgments The work in the laboratory of ME is supported the  Ludwig Institute for Research , the  Victorian State Government Operational Infrastructure Support , the  IRIISS scheme of the National Health and Medical Research Council Australia (NHMRC) , and  NHMRC  grants # 487922 , # 433617 , # 603122  and # 1064987  (to MB and ME). ME is an  NHMRC Senior Research Fellow. RHD is supported by  NIH   P01  grant  CA090890  and a Chancellor's  Research Initiative from Texas A&M. Work in the laboratory of K-PJ  is supported by grants from the  Wilhelm-Sander Stiftung , the  KKF (Kommission fuer klinische Forschung, Medical Faculty, TUM) , and by a grant from  DAAD/INCA Joint Translational Research Program on Cancer  (to GO and K-PJ). The authors thank the staff of the animal house, histology and microscopy facilities at  TUM  and  WEHI  for expert technical assistance.
Two longstanding obstacles to documenting sexual events in Alexandrium blooms have been the difficulty of differentiating their vegetative, gamete and planozygote stages and also the necessity of following single populations through time. A previous study advanced an approach that addresses both challenges through a combination of imaging flow cytometry, quantitative DNA content measurements, and repetitive sampling of single, localized populations  (Brosnahan et al. 2014). Here, we have expanded upon that work through an in situ deployment of an Imaging FlowCytobot (IFCB), a submersible flow cytometer that captures high quality images of phytoplankton 10-100 lm in length at rates up to 12 s 21  (Olson and Sosik 2007 ). An accompanying suite of image analysis and classification software makes it possible to quantify the affinity of images to genus and speciesspecific classes  (Sosik and Olson 2007).
Cable-free mooring deployment of the Imaging FlowCytobot The IFCBs deployed in Salt Pond were second-generation prototypes that incorporated several changes from the original system (described by  Olson and Sosik 2007). The purpose of these modifications was to reduce both the size and power consumption of the original IFCB. Changes included the following: (1) replacement of the camera with a new, smaller GigE camera; (2) replacement of the PC1041 computer with a new Intel Atom processor based computer; (3) replacement of the flash lamp module and power supply with a smaller, low-power version; and (4) installation of a custom optomechanical system that incorporated a more compact syringe pump and reduced the number of control and data acquisition boards. The updated design is approximately half as heavy, uses 1/3 the power, and is more stable and simpler to operate than the original version.
It is also essentially identical to the version that is now commercially available through McLane Research Laboratories (Falmouth, Massachusetts). The basic functionality of the second generation IFCB and its accompanying software suite is essentially the same as earlier versions that have been deployed for in situ monitoring  (Olson and Sosik 2007; Campbell et al. 2010; Peacock et al. 2014). One modification to the instrument software was addition of an alternating acquisition capability that enabled switching between high-and low-sensitivity cell detection modes. This was an effort to record the fullest diversity possible of red fluorescent cells and particles in Salt Pond while also improving the quantitation of A. fundyense. The volume of seawater analyzed by the IFCB is inversely related to its triggering rate because the system does not record new images while processing camera data.
Data bandwidth was adequate to transfer data from the instrument to a shore-based server in near real time while also controlling the IFCB acquisition software. The system consumed about 1/3 gallon gasoline d 21 during continuous operation of the IFCB and was refueled every 7-10 d. The raft itself was secured over the deepest area of the pond (8-9 m deep) by three mooring anchors. These anchors both fixed the location of the raft and also prevented tangling of the anchor lines and the IFCB. The support raft was deployed 15 March and recovered 22 June 2012. One of two prototype IFCBs was deployed during this period except for two gaps (17-20 March and 14-21 May) when maintenance was needed for the installed instrument and the second was unavailable. Additional data gaps occurred due to malfunctions in developmental versions of the IFCB software.
Weekly NMS surveys and other sampling in Salt Pond during the raft deployment Weekly surveys of NMS physical and biological conditions were conducted during the IFCB deployment as described elsewhere  (Crespo et al. 2011; Ralston et al. 2014 ). These activities are outlined again here with emphasis on data collection within the confines of Salt Pond. An additional, intensive survey of the A. fundyense parasite Amoebophrya was also conducted  (Velo-Su arez et al. 2013 ) and data from that study were used for comparison to IFCB-based estimates of infection prevalence. NMS surveys coincided with daytime high tides and were begun 14 February and repeated approximately weekly through 8 May, about 2 weeks after the last A. fundyense cells were detected by IFCB. Survey stations included one adjacent to the IFCB raft and three around the pond's periphery, one each near the pond's northern, eastern and western shores, but only data from the IFCB raft station were compared directly to the IFCB record.
Image classifier development Development of Salt Pond-specific image classifiers was undertaken using a suite of publicly available MATLAB-based tools (https://beagle. whoi. edu/svn/ifcb/trunk). These tools create a classification "machine" that compares an image's features (image dimensions, geometry, cell shape, texture, etc. ) to those from example image sets defining species-and genus-specific classes. An early version of this software was described by  Sosik and Olson (2007)  and subsequent development has led to numerous improvements including integration with a web-based interface for data access in standardized formats, interactive browsing of time series images via a web "dashboard" (see http://ifcb-data. whoi. edu/ saltpond/), and use of a "random forest" based machineclassification scheme.
fundyense singlets, doublets, triplets, and quadruplets; fusing gamete cells; dividing vegetative cells; combinations of singlet, doublet, and triplet cells captured in single images; cells with mature grade Amoebophrya infections; and lysed A. fundyense singlets (Fig. 2 ). Classes describing paired cells (dividing vegetative cells, vegetative doublets, and fusing gametes) were differentiated based on the orientation of the constituent cells' cingular grooves (parallel in vegetative cells and oblique in fusing gamete pairs), their relative size (fusing gametes often being unequal), and the length of the cells' contact area. Paired cell images in which these features are obscured were classified as vegetative doublets. Preliminary Salt Pond random forest classifiers were built using limited sets of training images and then applied to the dataset.
PI fluorescence was recorded from 700 to 1200 A. fundyense cells per sample using CellQuest Pro software version 5. 2. 1 on a Macintosh computer running operating system version OSX 10. 4. 11. Acquisition scaling of PI fluorescence was set to linear with voltage and threshold settings adjusted to capture the complete distribution of 1c-4c A. fundyense cells. All other light scattering and fluorescence parameters were acquired with logarithmic scaling. System and sample stability were assessed through analysis of a culture control immediately before and after analysis of the field samples. To identify and compare the relative abundance of DNA content populations, data were imported to MATLAB and analyzed using mmvn_toolkit for Gaussian mixture modeling (GMM;  Boedigheimer and Ferbas 2008).
Deployment at Salt Pond also enabled comparison to weekly surveys of A. fundyense that were ongoing through other parallel projects within the NMS  (Velo-Su arez et al. 2013; Ralston et al. 2014 Ralston et al. , 2015;; Petitpas et al. 2015). Comparison of the IFCB time series to data collected during weekly surveys of A. fundyense abundance (and sometimes daily estimates of Amoebophrya infection) showed reasonable correlations and no bias between sampling methods. The most obvious difference between the datasets was the lack of temporal, physiological and ecological richness from weekly surveys compared to the IFCB record. One exception to this generalization was the sometimes daily record of Amoebophrya dinospores and host infections from FISH-TSA analysis since only the FISH-TSA approach characterized the dynamics of the free-swimming and early-stage host infections of Amoebophrya.
Both the dividing and doublet subclasses were still informative as markers of division in the population. The frequency of doublets oscillated with a diel cycle similar to mean singlet cell volume throughout the bloom's development phase (about 20% of A. fundyense images), then declined steadily to 0% by the end of the bloom's gametic phase. Dividing cells similarly declined to 0% after their surge in abundance late in the bloom's development. A recent version of the size-structured, population matrixmodel has been applied to the Salt Pond dataset but its full development is planned for a future study. One of the advantages of this approach is its ability to account for periods of simultaneous division and cell growth as is likely to occur during unphased divisions  (Sosik et al. 2003). The primary advantages of the mean volume calculation employed here are its simplicity and its conservatism, the latter making its unusually high division estimates especially noteworthy.
Acknowledgments Special thanks to  Bruce Keafer ,  Kerry Norton ,  David Kulis ,  Yan Gao  and other staff and student members of  D. Anderson's laboratory  for assistance in sample collection and preparation of cultures, to  Micheil Boesel  for technical assistance during the design and construction of the IFCB support raft, to  Krista Lee  and staff at the  Cape Cod National Seashore Salt Pond Visitor Center  for assistance in setting up the raft communication system, and to  Linda Amaral-Zettler ,  Leslie Murphy , and  Julie Reveillaud  who assisted with Amoebphrya surveys at Salt Pond. This work was supported by the  National Science Foundation  ( OCE-0430724 ,  OCE-0911031 , and  OCE-1314642 ) and  National Institutes of Health  ( NIEHS-1P50-ES021923-01 ) through the  Woods Hole Center for Oceans and Human Health , by  National Park Service (NPS)  Cooperative Agreement  H238015504 , by the  Gordon and Betty Moore Foundation  (Grant # 2649  to HMS), and by an award from the  Access  to the  Sea program  at the  Woods Hole Oceanographic Institution.
Data treatment The results obtained were analyzed using two-way ANOVA (GraphPad Prism, Version 4. 01, GraphPad Software Inc). Bonferroni multiple comparison post-test was used to determine significant differences between samples or storage times. The significance level applied was 95% (p < 0. 05). When a significant difference was observed between samples, it is denoted with different superscripts in the text. The lag phase for the different samples in the different oxidation parameters was defined as no significant difference with time 0. The antioxidant inhibition percentages were calculated according to the following equation: Inhibition Emulsion Emulsion Emulsion Control Antioxidant C % [ ]= - o ontrol ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ × 100 EmulsionControl and EmulsionAntioxidant are emulsions without antioxidant and emulsion with antioxidant added, respectively.
Acknowledgments We thank  Thi Thu Trang Vu  for skillful work in the laboratory and Maritex (subsidiary of  TINE BA, Norway ) for providing the fish oil. The study is a part of the project entitled "Phenolipids as antioxidants in omega-3 model and real food systems-Effect of alkyl chain length and concentration" with project no  10-093655  financed by the  Danish Research Council, Technology and Production.
Oryza sativa We used the Release 6. 1 of the MSU Rice Genome Annotation Project (ftp://ftp. plantbiology. msu. edu). No confidence score similar to the one existing in Arabidopsis is provided. However, when expression data such as ESTs (expressed sequence tags) or Full Length cDNA (FLcDNA) are available, it is indicated. We kept only gene models supported by full length cDNA. The same data as for A. thaliana were recorded for each selected gene model.
Final Data Sets We restricted our analyses to gene models with both UTRs described and supported by expression data, ending up with a similar number of genes in both species  (18,134 in A. thaliana and 17,862 in rice). Gene models for which at least one element was less than 10 nt long were discarded to avoid to compute GC-content on too few nts. In the two studied species, most of the genes do not have intron inserted in UTRs (around 79% in A. thaliana and 75% in rice). For those genes, the distribution of intron number within coding regions was similar between species (supplementary table  S1 , Supplementary Material online) and we decided to use these gene sets as reference. Two additional data sets were formed within each species, a first one made of the genes having any number of introns inserted within their coding regions plus a single intron inserted within their 5 0 -UTR (5 0 -UTR sets), and a second containing genes having any number of introns inserted within their coding regions plus a single intron inserted within the 3 0 -UTR (3 0 -UTR sets).
Although the 5 0 -UTR data sets comprise roughly the same number of genes in the two species, intron presence in 3 0 -UTRs in rice is more than twice as frequent as in A. thaliana (supplementary table  S1 , Supplementary Material online). In both species, the remaining set of genes (4,024 in A. thaliana and 4,438 in rice) displays a high number of different combinations of intron insertion within UTRs and was discarded. Finally, to ensure that intron number classes comprise in general more than 100 genes for the reference data sets and the 5 0 -UTR data sets, we restricted our analyses to genes with less than 15 introns in the two reference sets and less than 9 introns in the UTR's data sets. We ended up with six data sets which composition is shown in supplementary table  S1 , Supplementary Material online.
GC-Content Measurements In each species, genes were distributed into classes according to their intron number. Let i be the intron number of the genes and N i the number of genes with i introns (i 2 f0;. ; 14g). Within classes, genes were denoted by the indices g (g 2 f1;. ; N i g) and CDS parts by the indices j (j 2 f1;. ; i þ 1g). In both species, for each of the selected gene model, gene sequences were retrieved and analyzed using R and the bioconductor package  (Gentleman et al. 2004; R Core Team 2013). After sorting genes according to intron number, we explored GC-content pattern of variation at different scales. To study transitions between coding regions and introns, we computed GC-content on 50 nt spanning both sides of each exon-intron junction, sequences being aligned on the consensus 5 0 and 3 0 splice sites.
Intraspecies Comparisons between Genes with or without Intron Inserted within UTR In each species, we compared the reference data set composed of genes with no intron inserted in UTRs with each of the two data sets composed of genes having an intron inserted in their either 5 0 -UTR or 3 0 -UTR. Comparisons between the data sets were made between genes having the same number of introns inserted within their coding regions, the two data sets differing by the presence/absence of a single intron located either within the 5 0 -or 3 0 -UTRs. Within the different intron number classes, we used Welsh two-sample student tests on first (respectively, last) CDS part to test for the existence of significant differences in GC-content. In addition, to investigate for an intron specific effect, we took both the 5 0 -UTR data sets and the intron-free UTR data sets and sorted genes into bins according to the distance between the transcription start site and the translation start site.
To gauge the link between CDS part GC-content, intron number, and rank along genes, we fitted a beta regression model appropriate for the analyses of proportions using the R package betareg (Cribari-Neto and Zeileis 2010). Betareg is based on an alternative parameterization of the beta density in terms of the mean and a precision parameter f estimated by maximum likelihood. It permits to perform inference with variable dispersion model by fitting in a similar fashion two submodels, one for the mean and one for the dispersion parameter. Our assumption is that for a given intron number i and a given rank j along the gene, CDS part GC-content follows a beta law GC cds ij ~Bðm ij ; f ij Þ of mean 0 < m ij < 1 and precision f ij > 0. The beta regression model was fitted using the model (denoted model M1) GC cds ijg ~m þ f ij þ e ijg ð9Þ treating the combination of intron number i and rank j as a factor f ij (0 i 14 and 1 j i þ 1) both for the mean and the precision submodels.
Results Genomic data from two contrasted plant species, the GC-rich monocot O. sativa and the GC-poor eudicot A. thaliana, were used for a comparative study of the effect of intron number on gene GC-content. Genes with no intron located in UTRs represent more than 75% of the genes in both species and were used to describe patterns of variation linked with intron presence within CDS. We also studied two other gene subsets, composed of genes with any number of introns within CDS and a single additional intron present in either the 5 0 -or the 3 0 -UTR. These subsets were used to investigate the consequences of intron presence outside coding regions on GC-content of both introns and coding regions. We focused our analyses on exon coding parts (see Materials and Methods) and to avoid terminology confusion we discarded the term "exons" and used the term "CDS parts" for coding regions.