dataesr/scientific-paragraphs-categorization huggingface dataset:
After rinsing, slides were mounted in Dako fluorescent mounting medium (Dako) and examined using a fluorescence confocal laser-scanning microscope (Zeiss LSM 700, Tecnoviv platform). All images were acquired under the same conditions (636, 1. 40 oil DIC M27, pinhole 1. 00 [arbitrary units], 8-bit sampling, average of 4 frames at 140061400 pixels). The resolution obtained was 13,778 pixels/mm. For presentation, images were imported into ImageJ software.
Bioinformatic analysis The presence of a signal peptide was determined by a primary structure analysis performed using SignalP 3. 0. Potential glycosylation sites were predicted using NetNglyc 1. 0 and NetOGlyc 3. 1 (http://www. cbs. dtu. dk/services/). Protein domain searches were performed using SMART (http://smart. embl-heidelberg. de/) and Motif Scan (http://hits. isb-sib. ch/cgi-bin/PFSCAN) software. ahelical and b-sheet regions were identified by secondary structure prediction using Jpred3 and HHpred servers (http://toolkit. tuebingen. mpg. de/hhpred). Sequences were searched for putative transmembrane domains (TMDs) using the PRED-TMBB server  [66, 67]. Prediction of Biomphalysin three-dimensional (3D) structure and alignment with the crystal structure of proaerolysin were performed using I Tasser and TM-align servers  [68, 69].
Phylogenetic analysis To investigate the phylogenetic position of Biomphalysin, we retrieved sequences of aerolysin homologues from a recent study  [71]. Forty-seven sequences (Table  1 ) from organisms belonging to animal, plant, fungi, and bacterial kingdoms were used to construct a phylogenetic tree. Selected sequences were then aligned using the MUSCLE algorithm implemented in CLC Sequence DNA Workbench 6. 6. 2 software (CLC bio). Poorly aligned regions were trimmed using trimAl v1. 4 with automated1 option  [72]. Phylogenetic analyses were performed using Bayesian and maximum-likelihood (ML) inferences. ProtTest v3. 2 was used to select the model of protein evolution (amino acids substitution) that best fit the multiple sequence alignment  [73]. The WAG+F model was selected. A Bayesian analysis was performed using MrBayes 3.
Molecular characterization of Biomphalysin In a previous study designed to characterize the interactome between B. glabrata plasma and S. mansoni primary sporocyst extracts, we identified a partial coding sequence corresponding to a new, putative cytolytic protein from B. glabrata  [30]. Because of its similarities to proteins of the b-PFTs superfamily, the corresponding protein was named Biomphalysin. In the present work, we obtained a full-length cDNA clone of Biomphalysin using the RACE method (Figure  1 ). The complete sequence is 1972 base pairs (bp) in length and displays a 59-untranslated region (UTR) of 47 bp, a 39-UTR of 206 bp, and an open reading frame (ORF) of 1719 bp (GenBank accession number : KC012466). The ORF encodes a precursor protein of 572 amino acid residues, of which the first 17 amino acids correspond to a putative signal peptide, as predicted by the SignalP program.
After signal peptide removal, Biomphalysin displayed a theoretical pI of 6. 2 and predicted molecular weight of 62. 8 kDa. An analysis of putative post-translational modifications using the NetOglyc and NetNglyc server suggested the absence of Oglycosylation and a putative N-glycosylation event at N 530. A BLASTP similarity analysis revealed significant similarities with members of the b-PFT superfamily, including aerolysin-like proteins. The most closely related sequence was a hypothetical protein from Nematostella vectensis (XP_001629482) with 55% similarity and 39% identity (E-value = 2610 2123 ). An analysis with the SMART and MotifScan programs revealed an aerolysin signature (pfam 01117) at residues 178-525 of Biomphalysin (Evalue = 1. 3610 243 ). HHpred (homology detection and structure prediction by HMM-HMM comparison) software predicted a high structural homology with the pore-forming lobe of aerolysin (Evalue = 1610 289 ) and a high proportion of b sheets (31%).
A 3D alignment of the aerolysin domain of Biomphalysin with the proaerolysin template was performed using I-Tasser and TM-align servers. This latter analysis revealed a high degree of similarity between the two structures (TMscore = 0. 92; Figure  3B ). Despite these structural similarities, neither C-type lectin motifs nor a cleavage site were found using motif prediction software  [50, 83]. Biomphalysin displays a structural feature that distinguishes it from other b-PFTs. Indeed, Biomphalysin possesses a second lobe which displays no lectin-like domain as it has been reported for aerolysin  [50]. A phylogenetic tree was subsequently constructed using 46 sequences of aerolysin-like toxins from different kingdoms (Table  I ). As expected, the Biomphalysin sequence in this tree appeared to be closely related to a predicted, uncharacterized protein identified in the cnidarian, N.
Biomphalysin hemolytic activity Most aerolysins characterized to date display potent hemolytic activity. In order to investigate the cytolytic capacity of Biomphalysin, we produced a recombinant protein flanked by an N-terminal hexa-histidine tag. We encountered some difficulties in producing recombinant Biomphalysin (rBiomphalysin) in our bacteria system. We tested different bacterial strains, including Escherichia coli BL21 (DE3); BL21 (DE3) pLysE; and BL21(DE3) CodonPlus, Rosetta (with and without classical chaperone expression. ). With most of these systems, we obtained a low production level or a cleaved protein (data not shown). Consequently, we decided to produce the rBiomphalysin using an in vitro recombinant expression system based on wheat germ extract and cell free transcription and translation system (RTS).
A BLAST search against the Trace Archive Biomphalaria database confirmed the presence of this intronless gene in the snail genome. Considering the suspected anti-schistosomal role of Biomphalysin, we examined whether Biomphalysin expression was modulated by parasite challenge. In these experiments, snails were infected with S. mansoni miracidia and Biomphalysin transcripts were quantified by quantitative RT-PCR at different times following exposure  (3, 6, 9, 12, 24, 48 and 96 h). Bge cells cultivated in the presence of in vitro-transformed sporocysts were also analyzed at the same time points. As shown in Figure  10B , Biomphalysin expression levels did not significantly change after S. mansoni challenge compared with uninfected snails or naive Bge cells. These data indicated that Biomphalysin can be considered an immunity-related gene that is constitutively expressed and is not modulated by S.
Discussion In order to identify molecular determinants that play a key role in the interaction between B. glabrata and S. mansoni, we recently developed an interactome approach that allowed us to discover a factor in B. glabrata plasma related to the b-PFT superfamily. This molecule was found in the precipitate containing various molecules from S. mansoni (SmPoMucs, glycoprotein k, tetraspanin, chaperone stress proteins, anti-oxidant enzymes) and B. glabrata (FREPs, lectin like protein AIF…)  [30]. In the present publication, we describe the cloning and functional characterization of this b-PFT of B. glabrata, which we have named Biomphalysin. The full-length cDNA of Biomphalysin was 1972 bp encoding a 572-amino-acid protein with a molecular weight of approximately 63 kDa. Biomphalysin displays low similarities toward database b-PFTs at the primary structure level, but contains an aerolysin domain that is a common core of aerolysin-like b-PFTs  [40].
Data collection, structure determination and refinement A native data set of Lsm2-3-Pat1C crystal and singlewavelength anomalous diffraction data set of SeMet-Lsm2-3-Pat1C crystal were collected on the beamline ID23-1 (ESRF, Grenoble, France) using diffraction cartography  [59] , as implemented in the beamline GUI MxCuBE  [60, 61] , to define the best volumes of the crystals. Multiple-wavelength anomalous diffraction (MAD) data sets of Ta 6 Br 12-soaked Lsm2-3-Pat1C crystal were collected at a resolution of 3. 7 Å on the beamline X06SA (PXI, SLS, Switzerland). All data sets were integrated with XDS  [62]  and merged and scaled with Scala from the CCP4 suite  [63]. Phases of the Ta 6 Br 12 -soaked MAD data sets were calculated using the phasing module Autosol from the PHENIX program package  [64].
After density modification, the electron density map was good enough to identify the three Pat1C molecules composed of α-α superhelix surrounding a seven-membered Lsm ring structure arranged in an asymmetrical manner. A poly Ala model derived from Lsm3 (PDB code 3BW1 chain A) excluding loop4 (between β3 and β4) was built into the initial electron density map calculated at a resolution of 3. 7 Å using O  [65]  to generate a poly Ala seven-membered ring model. For Pat1C, a poly Ala model was built manually in Coot  [66]. Through superposition and combination, a poly Ala model consisting of three Pat1C molecules and one seven-member Lsm ring was generated. Due to the non-isomorphism between tantalum derivative data set and native data set, this poly Ala model was first subject to rigid body refinement in REFMAC5  [67]  against the native data set at a resolution of 5 Å.
Acknowledgments We thank the beamline scientists at  ID14-4  and  ID23-1  ( ESRF, France) ,  PXI (SLS, Switzerland) ,  BL13B1  ( NSRRC, Taiwan)  and  BL17U  ( SSRF, Shanghai, China ) for assistance and access to synchrotron radiation facilities. This work was financially supported by the  Agency for Science, Technology and Research in Singapore (HS)  and partly by the  National Natural Science Foundation of China  ( 31270816 ) and  Zhejiang Provincial Natural Science Fuoundation of China  ( LZ12C05001 ).
In addition, integration of the peak intensities over time revealed differences in intensity between the peak constituents of double signals (Figure  2a, c, d ). These variations indicated two-to threefold up-and downregulation    of various MHC I peptides and provided evidence of more subtle changes in the presentation of MHC I antigens associated with the expression of the protein components mediating rapamycin-inducible transcription. To obtain information about the sequences of peptides presented at the cellular surface by MHC I, aliquots of labeled peptides from transduced and wt cell populations were analyzed individually by liquid chromatography linked to electrospray ionization tandem mass spectrometry (LC-ESI-MS/ MS). The results were first screened for peak constituents of double signals on the basis of chromatographic retention time and total mass data from LC-ESI-MS analysis.
As could be expected from the labeling protocol (see Materials and Methods section), the peak constituents that corresponded to the lower masses within double signals were from wt cells, whereas peak constituents corresponding to the higher masses were from transduced cells. For a significant number of double signals, MS/MS fragment spectra of both peak constituents were analyzed and gave identical peptide sequences. This confirms that the double signals represent peptides presented at the surface of both transduced and wt cells. We next identified MHC I peptides that appeared or disappeared after expression of the transgenic proteins. Information on chromatographic retention time and total mass was again used to screen the LC-ESI-MS/MS data for the single signals detected by mass spectrometry in combined aliquots of labeled peptides from transduced and wt cells.
LC-ESI-MS and LC-ESI-MS/MS analyses. The modified peptide extracts were analyzed as described  12  using a reversed phase nanoLC-2D system (Eksigent, Darmstadt, Germany), coupled to a hybrid quadrupole orthogonal acceleration timeof-flight MS/MS (Q-TOF Ultima; Micromass/Waters, Saint-Quentin en Yvelines, France) equipped with a micro-ESI source. Results for mixed transduced and wt samples were recorded in an LC-ESI-MS experiment without fragmentation. For sequence analysis, results for transduced and wt samples were recorded separately in individual LC-ESI-MS/MS experiments. Fragment spectra were analyzed manually and database searches (Proteomics Department at the Hammersmith Campus of Imperial College London, National Center for Biotechnology Information, Expressed Sequence Tag) involved using the Multiple Alignment System for Protein Sequences Based on Three-way Dynamic Programming (MASCOT, http:// www.
Before incubation with peripheral blood mononuclear cells, target cells were treated with mitomycin C (50 μg/ml) for 30 minutes. After 6 days of coculture, cells were stained with eFluor780-conjugated anti-CD3 (UCHT1; eBiosciences, Paris, France), phycoerythrin-conjugated anti-CD25 and allophycocyanin-conjugated anti-CD4 antibodies, and with 7-aminoactinomycin D to exclude dead cells (BD Biosciences, Le Pont de Claix, France). T cell activation and proliferation was then monitored by flow cytometry using a Canto II flow cytometer (BD Biosciences). Results were analyzed with BD Diva software (BD Biosciences) and are reported as mean values ± SE of at least three independent experiments. One-way analysis of variance followed by the Student-Newman-Keuls test (SigmaStat software; Systat Software, Chicago, IL) were used for statistical analyses and a P of <0.
Acknowledgments. We thank  ARIAD Inc. (Cambridge, MA)  for providing the regulation systems allowing rapamycin-inducible transcription and secretion. We thank the " Association Française contre les Myopathies (AFM)" , the  "Institut pour la Recherche sur la Moelle Épinière (IRME)", and "Rétina France"  for financial support. The authors declared no conflict of interest.
In light of this limited success, it was hypothesized that deleterious de novo mutations in any of several different genes could explain the high global incidence of SCZ despite a reduced reproductive fitness. Our group first reported the presence of a significant excess of potentially deleterious de novo mutations in 401 synaptic genes using Sanger sequencing in a cohort of SCZ and autism patients  [5]. We have later confirmed an excess of exonic de novo mutations and more particularly of nonsense variants in 15 SCZ trios (probands and parents) using exome sequencing. Interestingly, 4:11 ratio of nonsense to missense mutations is significantly higher than the expected 1:20 ratio (P = 0. 005467), and according to the Human Gene Mutation Database, the expected ratio of nonsense to missense among all mutations reported to cause Mendelian diseases is 1:4 which in line with what we found (P > 0.
All coding regions and splice site junctions of LRP1, KPNA1, ALS2CL and ZNF480 genes were amplified and then sequenced using Sanger sequencing technology. Variant detection analysis was done using Mutation Surveyor (v. 3. 23, Softgenetics) and rare exonic variants were confirmed by reamplification of the relevant fragment in both probands and parents (when available) using forward and reverse oligos. The first gene, LRP1 (NM002332, 89 exons) encodes for Homo Sapiens low density lipoprotein receptorrelated protein 1 and is located at 12q13-14. We identified four rare LRP1 variants (S278I, R379H, M1795I and G3948D) absent from public databases (dbSNP [7], 1000 genome project  [8]  and Exome Variant Server (EVS)  [9] ) and two additional variants (G169D and A2160T) only reported in EVS at a very low frequency (Table  1 ).
Interestingly, the missense G169D is predicted to affect protein function by SIFT (0. 01) and Polyphen (2. 337) predicting softwares  [10, 11]. Unfortunately the parents' DNAs were not available for an inheritance study. We also identified eight other LRP1 variants that were previously deposited in EVS database and their minor-allele frequencies ranged from 1 to 159 / 10 755 to 10 599 total alleles. Two of these, G3725E and R1993W, are predicted to affect protein function despite minor-allele frequencies of 12/ 10 746 and 25/ 10 733, respectively. However, the fact that G3725E is transmitted by an unaffected mother, suggests it is unlikely to play a major role in SCZ. Unlike the original published findings, we did not find any highly damaging variant (ie. nonsense or insertion/deletion) in LRP1 gene.
The KPNA1 gene screening (NM002264; 14 exons), which encodes for karyopherin alpha 1 protein and is located at 3q21, did not lead to the identification of rare variants in our SCZ cohort. The sequencing of ALS2CL (NM147129; 26 exons), encoding for ALS2 C-terminal like protein and located at 3p21. 31, revealed one nonsense mutation (E65X) in one SCZ patient. Because of this ALS2CL variant, we opted to screen the full gene in 286 additional SCZ patients and 189 control individuals. We observed a minor-allele frequency of 4/475 SCZ patients and of 1/189 controls for E65X. This variant has been lately added to dbSNP (rs139496961) and EVS databases with a minor allele-frequency of 20/10 738. The presence of E65X in a control individual from our ethnicallymatched control cohort makes ALS2CL gene less likely to predispose to SCZ.
We also observed three additional rare ALS2CL variants in our 189 initial cases (T268S, T460M and P580S) but their pathogenicity is unlikely since none of the prediction software predicted them to be deleterious. Finally, the screening of ZNF480 (NM144684; 4 coding exons) a zinc finger protein which is located at 19q13. 41, led to the identification of two nonsense variants in one SCZ patient and one control individual (R276X and R500X, respectively). During our gene screening process, five nonsense variations (R304X, R360X, R416X, K434X and Q528X) were deposited in the EVS database by other groups with frequencies ranging from 1/10 755 to 2/10 756. Although we cannot rule out that schizophrenic or borderline personalities can be found in public databases such as EVS, the fact that five additional nonsense variations were found in the C-terminal region of this gene is not in favor of a deleterious effect of a de novo nonsense mutation in this region.
[6]]tions identified in LRP1, ALS2CL, ZNF480 and KPNA1 and occurrence in SCZ and CTR cohorts (Continued) according to build Hg19; b variant position according to Mutalyzer 2. 0. beta-20[12]and to Genbank accession number NM_002332. 2 for LRP1, NM_147129. 3forALS2CL, NM_144684. 2forZNF480 and NM_002264. 3forKPNA1; c MS: missense and NS: nonsense; d nonsense mutations previously identified by exome sequencing[6]; e rs number as obtained in dbSNP database [7]; f observed allele counts according to Exome Variant Server in all populations.
Acknowledgements We thank the families involved in our study. We thank  Anne Noreau  and  Hussein Daoud  for scientific advices. We are thankful for the efforts of the members of the  Genome Quebec Innovation Centre Sequencing and Bioinformatics groups. Guy A. Rouleau  is grateful for the support received through his positions as  Canada Research Chair in Genetics of the Nervous System and Jeanne-et-J. -Louis-LevesqueChair for the Genetics of Brain Diseases. We would also like to thank the  NHLBI GO Exome Sequencing Project  and its ongoing studies which produced and provided exome variant calls for comparison: the  Lung GO Sequencing Project  ( HL-102923 ), the  WHI Sequencing Project  ( HL-102924 ), the  Broad GO Sequencing Project  ( HL-102925 ), the  Seattle GO Sequencing Project  ( HL-102926 ) and the  Heart GO Sequencing Project  ( HL-103010 ).
Satellite remote sensing of surface POC concentrations A time series of 290 level-1b satellite images (free or partly free of clouds) of the Mackenzie Shelf region collected with the Medium Resolution Imaging Spectrometer (MERIS) onboard the Envisat platform over the period of May-September 2009 were acquired from the ODESA website (http://earth. eo. esa. int/odesa/). All images were processed to level 2 with the ODESA CFI software and using the alternative atmospheric correction of  Babin and Mazeran (2010). For more information about the standard process of MERIS level-2 products, please see ESA  (2011). Monthly (May, September) and semi-monthly (June-August) composites of MERIS images (1 km resolution) were generated using the mosaic algorithm of the Beam/VISAT opensource software (http://www.
brockmann-consult. de/beam/). The sea ice adjacency mask of  Bélanger et al. (2007)  to detect pixels potentially contaminated by the presence of sea ice was implemented in the mosaicking processing chain. A regional POC algorithm based on the empirical relationship between in situ surface POC concentration and the blueto-green ratio of remote-sensing reflectance (490, 560 nm) measured during the Canadian Arctic Shelf Exchange Study (CASES) 2004 and Malina 2009 field campaigns was applied to level-2 images during the process. For a complete methodology on the development of the regional POC algorithm, see Appendix B. Monthly and semi-monthly maps of surface POC concentrations for the year 2009 were produced using the Generic Mapping Tools software package (http://gmt. soest. hawaii. edu/).
Ship-based measurements of physical and biological variables During the ArcticNet-Malina campaign, a caged rosette profiler equipped with a conductivity-temperature-depth system (CTD, Seabird SBE-911+) connected to a fluorometer (Seapoint) and a transmissometer (Wetlabs C-Star, path 25 cm) was deployed from once to several times at each sampling station (Fig. 1 ). The CTD data were verified and calibrated following the Unesco technical papers  (Crease, 1988). Water samples were taken onboard for salinity calibration using a Guildline Autosal salinometer. Fluorescence data were calibrated against in situ Chl a concentrations using linear regressions specific to the period 18 July-11 August (r 2 = 0. 96, p<0. 01, n = 71) and 11-24 August (r 2 = 0. 90, The rosette profiler was also equipped with an Underwater Vision Profiler 5 (UVP5) allowing routine recordings of particle size distributions (i.
e. , both nonliving particles and zooplankton). Full details of the technical specifications and processing operations of the UVP5 can be found in  Picheral et al. (2010)  and in  Forest et al. (2012). Briefly, the UVP5 aims at recording and measuring all objects >80 µ m ESD (i. e. , inferior limit of the lowest size class) in real time during deployment. The size and grey level of every object are calculated in situ, but only images of objects >600 µ m are stored on a memory stick for further processing. The Zooprocess imaging software (http://www. zooscan. com) was used to identify major zooplankton groups (>600 µm) with the Plankton Identifier (PkID)  (Gorsky et al. , 2010). The prediction of organisms obtained from the PkID files was exhaustively post-validated to obtain an accurate dataset of abundance and biovolume for zooplankton larger than 600 µ m from the UVP5.
The zooplankton dataset acquired with the UVP5 during the ArcticNet-Malina campaign was further compared with zooplankton collected in situ with 29 vertical net tows, which showed very good agreement (see  Forest et al. , 2012 for details). This enabled us to use the zooplankton biovolume dataset obtained with the UVP5 to estimate zooplankton biomass over a fine-scale spatial grid. Zooplankton biovolume was converted into carbon biomass using various conversion factors gathered from the literature. We are confident that the use of the UVP5 dataset provided reliable estimate of zooplankton biomass since large organisms dominate this biomass in the Beaufort Sea (e. g. ,  Darnis et al. , 2008; Forest et al. , 2011). For copepods we used the regional relationship established by  Forest et al. (2012).