dataesr/scientific-paragraphs-categorization huggingface dataset:
Conflict of interest HS is supported by research grants from The German Center for Infection Research (DZIF), the European Union (MagicBullet, Grant Agreement 278232), Novartis and Pfizer, has received speaking fees from Astellas, AstraZeneca, Gilead, MSD, Novartis, Oxoid and Pfizer, and is an advisory Board Member or consultant to AstraZeneca, Basilea, Cubist, FAB-Pharma, Novartis, SOBI, The Medicines Company, Theravance, and ThermoFischer. S. Hagel reports having received lecture fees from Pfizer, MSD, and Astra Zeneca. S. Harbarth reports having received investigator-initiated research grants funded by Pfizer and B. Braun; he is also a member of the advisory boards of Destiny Pharma, bioMerieux, Novartis and DaVolterra. Other authors: no conflicts to declare.
FlowJo software (Treestar, Ashland, OR) was used to analyze data. Three dimensional microscopy. Purified pDC cells cultured 4 h in presence of siRNA GLO-DY-547 complexed with the different transfection reagents. pDC cells were plated on collagen (ThermoFisher Scientific)-coated slides and then fixed in 4% paraformaldehyde, quenched with 0. 1 M glycine. Cells were incubated in permeabilizing buffer containing 0. 5% saponin with monoclonal antiboby anti-LAMP-1 (Cell Signaling). LAMP-1 was revealed by a Goat anti-rat-Alexa Fluor 594 (LifeTechnologies). Slides were mounted with DAPI Fluoromount-G (SouthernBiotech). Slides were scanned with a Confocal Microscope Zeiss LSM 710 (Carl Zeiss Microscopy) using a 63x Plan-Apochromat objective and images were acquired with the Zen software. Images of purified pDC were analyzed using the ImageJ software (NIH, Bethesda, MD, USA).
Acknowledgements We thank the  Central Service for Microscopy (SCM) of Paris Descartes University  and particularly  Jean-Maurice Petit  for technical assistance and the  Central Service of Cytometry. We also thank  Dr. Jean-Christophe Paillart  for providing us with various RNA and  Dr. Olivier Neyrolles  and  Geanncarlo Lugo-Villarino  for the scientific discussions and input. This work was supported by  SIDACTION  and by the  Agence National de la Recherche sur le SIDA et les Hépatites (ANRS).
In addition, CHD7 overexpression increased cell proliferation in human embryonic stem cell-derived NSPCs (ES-NSPCs). In Chd7 mutant fetal mouse brains, there were fewer intermediate progenitor cells (IPCs) compared to wildtype littermates, indicating that Chd7 contributes to neurogenesis in the early developmental mouse brain. Furthermore, in silico database analysis showed that, among members of the CHD family, CHD7 is highly expressed in human gliomas. Interestingly, high levels of CHD7 gene expression in human glioma initiating cells (GICs) compared to normal astrocytes were revealed and gene silencing of CHD7 decreased GIC proliferation. Collectively, our data demonstrate that CHD7 is an important factor in the proliferation and stemness maintenance of NSPCs, and CHD7 is a promising therapeutic target for the treatment of gliomas.
Chd7 is a downstream target of MIF in mouse NSPCs We first performed qRT-PCR analysis to examine whether Chd7 gene expression changes on MIF treatment of NSPCs. MIF treatment increased the RNA level of Chd7 in NSPCs (Fig. 1a ). In contrast, cell treatment with ISO-1, a MIF antagonist, led to a decrease in Chd7 RNA levels in NSPCs (Fig. 1b ). We have also observed that the CHD7 protein changes correlate closely with changes in its gene expression (data not shown). Together, these results suggest that the Chd7 gene is a downstream target of MIF signaling in NSPCs in vitro.
The gene expression of neural marker (NSE), an astrocyte marker (GFAP), or an oligodendrocyte marker (CNPase) was quantified by qRT-PCR. For the graphs, the data were compiled from three independent experiments. Error bars indicate S. E. values; **P < 0. 01 versus control; Student's t-test CHD7 gene silencing was confirmed by immunocytochemical analysis, and the number of differentiated neurons was decreased in human ES-NSPCs (0. 52 ± 0. 13 fold compared to control) as seen in mouse NSPCs (Additional file 6: Figure  S5 ).
In silico analysis in the present showed the higher expression of CHD7 and CHD9 in brain tumor (Additional file 7: Figure  S6 ). In addition, in TCGA database analysis, the overall survival of high-CHD7 expression patients tended to be shorter compared to that of low-CHD7 expression patients in GBM (data not shown), suggesting that CHD7 may support the proliferation of glioma cells and GICs. In contrast, CHD5 has been reported as a tumor suppressor gene in some tumors, such as in lung cancer  [38]. In addition, intriguingly, lowexpression of CHD7 was correlated to the survival of gemcitabine treated pancreatic ductal adenocarcinoma patients  [39] , suggesting the potential of CHD7 as a tumor suppresser gene like CHD5 in some tumors. Thus, functional analysis and identification of target genes and upstream regulators of CHD7 in CHD7-high expression tumors including liver and colorectal cancer may be of interest in this context.
HIV vaccine protection (RV144 vaccination trial). The dataset from the RV144 trial was obtained from the online repositories provided in Lin et al  3. Here we analyzed 226 volunteers enrolled in the RV144 trial. Volunteers were injected with a combination of ALVAC HIV (vCP1521) and AIDSVAX B/E (only weeks 12 and 24) at weeks 0, 4, 12, and 24, and immune responses at week 26 were evaluated as immune correlates of infection risk. Briefly, systemic IgA and IgG antibody titers to HIV envelope antigen were determined and CD4 + T-cells were Table  1. Meta-analysis of 226 vaccinated volunteers. Distribution of infection events and exclusion of volunteers with undetectable HIV-specific vaccine response. stimulated or not with a peptide pool covering the HIV envelope protein contained in the vaccine and analyzed for their expression of IFN-γ , IL-4, IL-17A, TNF-α , IL-2 and CD40L at the single-cell level by flow cytometry 2.
All volunteers were followed for 42 months following the last vaccination and analyzed for HIV contraction  4. In total, 38 of the 226 vaccinated volunteers contracted HIV. Polyfunctionality analysis. The RV144 datasets included exhaustive boolean combination gating of all functional parameters measured at the single-cell level on stimulated and non-stimulated CD4 + T-cells. Datamining of the 2 n (n being the number of bimodal parameters) boolean combination gates of a given dataset was conducted with the Boolean Datamining component of the Funky Cells ToolBox software (Version 0. 1. 2, www. FunkyCells. com). This was particularly important for segregation of Th1, Th2 and Th17 cells within the RV144 dataset based on the expression of prototypic effector molecules IFN-γ , IL-4 and IL-17a, respectively.
Samples with undetectable HIV-specific CD4 + T-cell responses were identified as those where the frequency of cells expressing at least one of the measured effector molecules post HIV-antigen stimulation was below or equal to the same sample without stimulation. For the Th1, Th2 and Th17 cell subset analysis we finally excluded samples for which the corresponding prototypic effector molecule was below background level (cf. Table  1 ). The Th sub-analysis was conducted on the three effector molecules, CD40L, IL-2 and TNF-α. Scatter plot graphics were accomplished with the "beeswarm" package for R. Polyfunctionality Index analysis. The general polyfunctionality index (1) was previously described  5, 8. Briefly, the general polyfunctionality index was invented to reduce a 2 n -dimensional polyfunctional measure to a one-dimensional value taking into account both individual and combined expressions of effector molecules.
Statistics Statistical analysis was performed using SPSS (Chicago, Illinois, U. S. A. ) and Graphpad Prism (San Diego, California, U. S. A. ). The one-way ANOVA test was used for comparison of the reactivity between multiple groups (Figs 1 and 2B, and S2 Table ), and p values were corrected using the Bonferroni's Multiple Comparison Test. ROC curves were compared using the Delong method. In addition, the Mann-Whitney U test was used for comparison of two individual groups (Fig 2A ), and the Wilcoxon signed rank test was used to compare sets of ordinal data (S2 Table ). Spearman's rank correlation coefficient was used to compare correlations. P values <0. 05 were regarded as significant, unless stated otherwise.
Funding: JD was financially supported by a grant from the  Dutch Arthritis Association  (http://www. reumafonds. nl;grant  09-1-308 ; JvdV). MH was supported by the  German Research Society  (http:// www. dfg. de;DFG;  SFB643-TP-B5 ) and by the  K & R Wucherpfennig-Stifting. SM and JPB were financially supported by the  French Centre National de la Recherche Scientifique  and the  Laboratory of Excellence Medalis  (https://medalis. unistra. fr; ANR-10-LABX-0034 ),  Initiative of Excellence (IdEx), Strasbourg University.
6c ). For dimers, the highest fluorescence intensity was recorded for the CN 009617V/ SOD1 complex, and the weakest was recorded for the CN 009543V/SOD1 complex. Thus, fluorescence emitted from protein bands indicates that electrophilic intermediates of NBD compounds bind to SOD1 monomers, resulting probably in covalently cross-linked protein subunits, which are highly stable in vivo and in vitro. To gain insight into NBD compound interactions, docking analysis was performed with a high-resolution SOD1 homodimer structure, 4A7U  36. Lipophilic and non-lipophilic NBD compounds were accommodated within the hydrophobic cavity constituted by Val7 Val158 in the dimerization region or interacted with other putative sites involved in protein dimerization (Figs S3, S4 and Table  S1 ). However, docking data for SOD1 dimer stabilizers  37  has not been confirmed in recently obtained co-crystal structures of some stabilizers bound to the protein  36.
Molecular docking. In silico modeling of small compound interactions with SOD1 was performed with the AutoDock Vina software package (http://vina. scripps. edu/index. html)  46. The PDB file 4A7U of the SOD1 dimer  36  was downloaded from http://www. rcsb. org/pdb/home. The molecular model of NBD compounds was created in pdb format with ChemBioDraw Ultra 12. 0 software (http://software. informer. com/getfree-chembi-o3d-ultra-12. 0/). Free energy minimization of compounds was performed with the MM2 Program of ChemBioDraw Ultra 12. 0 software. Putative binding sites formed between small compounds and SOD1 were checked with AutoDock Tools 1. 5. 6rc3. The highest-scoring nine conformations were predicted for each interaction by the Vina scoring function.
Acknowledgements The authors acknowledge the  Drug Synthesis  and  Chemistry Branch  of the  NCI  for providing the compound NSC 228155 (http://dtp. nci. nih. gov/dtpstandard/ChemData) and the  French National Chemical Library  for initially providing the compound CN 009543V (http://chimiotheque-nationale. enscm. fr/index. php). This work was initially funded by the  Agence Nationale de la Recherche  grants  ANR-07-RIB-012 ,  ANR-07-PNANO-051-02  and  CNRS. We are grateful to  M. F. Lecocq  and  I. Prochina  from  ProtNeteomix  for technical assistance. We thank ProtNeteomix for publication fees.
High-throughput RT-qPCR and statistical analysis High-throughput RT-qPCR was performed by the ACO-BIOM to assess the transcriptomic response of juvenile and adult C. gigas to infection with OsHV-1, V. aestuarianus and V. tasmaniensis LGP32. We investigated the transcriptional response of 102 immune-related genes, which consisted of 43 putative anti-viral genes and 59 putative anti-bacterial immune genes. It was expected that a proportion of immune genes would be differentially expressed in response to both OsHV-1 and Vibrio infection. The antiviral immune genes were identified in the genome and transcriptome of C. gigas  [36, 37, 51]  by performing homology searches (BlastP) using known vertebrate and arthropod antiviral proteins. These proteins have a broad relevance to antiviral immunity, including members of a primitive interferon-response (virusrecognition, signaling), anti-viral effectors, program cell death (autophagy and apoptosis) and RNA interference pathways.
Immune genes related to antibacterial immunity were chosen from previous studies investigating the transcriptional response of C. gigas to vibriosis  [18, 19]. These genes have been identified to be differentially expressed in oysters capable of surviving an experimental infection with a virulent Vibrio spp (V. tasmaniensis LGP32 and V. aestuarianus LPi 02/41) versus an avirulent Vibrio sp. (V. tasmaniensis LMG20012)  [18]  or were predictive of the capacity of an oyster to survive a virulent Vibrio infection  [19]. These 59 immune genes belong to six functional categories, including immunity (recognition, signaling and effector molecules), cellular adhesion and differentiation, cytoskeleton reorganization, apoptosis and oxidative stress. Lastly, three housekeeping genes were also included as internal controls for normalising the data (Cg-EF1, GenBank #AB122066; Cg-RPL40, #FP004478; Cg-RPS6, #HS119070).
Additional file 1 provides the GenBank accession number for each target gene, designated immunological function and the nucleotide sequences for each primer pair. The mRNA expression levels of the chosen target and internal reference genes were determined in juvenile and adult C. gigas at 1, 2, 3 and 4 days post-inoculation. The total qPCR reaction volume was 0. 5 μL and consisted of 0. 25 μL of cDNA (ng) and 0. 25 μL of LightCycler ® 1536 DNA Green Master Kit (Roche) containing 0. 55 μM of PCR primer (Eurogenetec). Pipetting into the 1536 well-plate (Roche) was performed with Labcyte Acoustic Automated Liquid Handling Platform (ECHO). The LightCycler ® 1536 Instrument (Roche) was used with the following program: enzyme activation of 95 °C for 1 min followed by 45 cycles of denaturation (95 °C, 2 s) and hybridization-elongation (60 °C, 30 s).
A subsequent melting temperature curve of the amplicon was performed to verify the specificity of the RT-qPCR reaction. The amplification efficiency of each primer pair was previously validated using a serial dilution of cDNA and only primer pairs with efficiency of 2 ± 0. 1 were used for gene expression analysis. The RT-qPCR data was normalised using the 2 (ΔΔCT) method  [38]  using elongation factor 1 (GenBank AB122066) as the internal reference gene. Statistical analysis of qPCR data was performed separately for juvenile and adult oysters. Two-way analysis of variance (ANOVA) was conducted to individually assess expression levels of the 102 target genes using the univariate general linear model (GLM) with post hoc Tukey's HSD test in IBM SPSS Statistics v 20. 0. The two factors analysed were "PATHOGEN" with four levels (OsHV-1, V.
tasmaniensis LGP32, V. aestuarianus & seawater) and "TIME" (1, 2, 3 and 4 days). When interactions between these factors were non-significant, these terms were removed from the model to test for single-order effects alone. Hierarchical clustering was performed on target genes identified to be differential expressed using Multiple Array Viewer software (version 4. 6. 2). Clustering analysis was performed with Pearson correlation based distance.
Acknowledgements The authors wish to thank  Marc Leroy  for laboratory assistance at IHPE as well as  F. Ruelle  for technical assistance at the Palavas-les-Flots Ifremer station. The authors are grateful to  Delphine Destoumieux-Garzón  for fruitful discussions and mentorship. The authors acknowledge the IFREMER hatchery team (LGPMM) in  La Tremblade  and the nursery team (LSPC) in Bouin for the production of juvenile oysters. The authors are also thankful to the  Acobiom company team  ( D. Piquemal ,  F. Noguier ,  R. Bruno ) for technical support, experimental design assistance and statistical support. The authors are finally thankful to  Philippe Clair  from the qPHD platform/Montpellier genomix for.
Galectin-3 is an important protein in molecular signalling events involving carbohydrate recognition, and an understanding of the hydrogen-bonding patterns in the carbohydrate-binding site of its C-terminal domain (galectin-3C) is important for the development of new potent inhibitors. The authors are studying these patterns using neutron crystallography. Here, the production of perdeuterated human galectin-3C and successive improvement in crystal size by the development of a crystal-growth protocol involving feeding of the crystallization drops are described. The larger crystals resulted in improved data quality and reduced data-collection times. Furthermore, protocols for complete removal of the lactose that is necessary for the production of large crystals of apo galectin-3C suitable for neutron diffraction are described.
Lactose is a natural ligand. Although the affinity of glycerol for galectin-3C is too weak to measure by isothermal titration calorimetry at room temperature, it has been observed to bind to galectin-3C at high concentration and to mimic part of the galactose ring  (Saraboji et al. , 2012). Comparison of the hydrogen-bonding patterns of these two ligands and with that of water molecules in the apo structure may be instructive. We describe our experience of data collection from crystals of various volumes at four different neutron sources. Furthermore, we present the merging of data sets from two different sources into one, more complete, data set. The methods that we have used may be of value for the study of other systems.
The wavelength range was 3. 35-4. 35 A ˚. A data set to 1. 9 A resolution was collected from 30 exposures of 16 h and 11 low-resolution exposures of 2 h in three different crystal orientations (Table  1 ). The crystal was rotated by 7 between exposures and the total data-collection time was 21 d. Data were indexed and integrated using LAUEGEN  (Campbell et al. , 1998) , wavelength-normalized using LSCALE  (Arzt et al. , 1999)  and scaled and merged using SCALA  (Evans, 2006).
Each exposure was separated by a ' step of 10. At the time of data collection MaNDi was equipped with 30 of 46 possible Anger cameras. The diffraction images were processed using the Mantid software  (Arnold et al. , 2014)  and scaled for detector response using the Argonne National Laboratory variable-wavelength datareduction program ANDREV  (Schultz et al. , 1984). The two data sets from deuterated galectin-3C in complex with lactose, from LADI-III and MaNDi, were later merged and analysed using XPREP (Bruker). Monochromatic neutron diffraction data were collected from two crystals of deuterated galectin-3C measuring around 1. 0 mm 3 , one in complex with glycerol and the other in complex with the synthetic thiodigalactoside inhibitor ALN712, at the BIODIFF instrument of the FRM-II reactor in Garching, Germany, which was equipped with the same type of imaging-plate detector as at LADI-III  (Niimura et al.
X-ray data collection and processing Room-temperature X-ray data were collected for each of the crystals from which neutron data were obtained. The lactose-2, inhibitor and glycerol complex data sets were collected at station I911-3 of the MAX-II synchrotron in Lund, Sweden  (Ursby et al. , 2013)  using a 225 mm MAR Mosaic CCD detector. For all crystals, both a low-resolution and a high-resolution data set were collected. Owing to the large size of the crystal, the low-resolution data-set exposure time was very low to avoid oversaturated spots. The highresolution data were collected using helical data collection in order to spread out radiation damage. Data were integrated using XDS  (Kabsch, 2010)  and merged using AIMLESS  (Evans & Murshudov, 2013)  from the CCP4 package  (Winn et al. , 2011).
Refinement All structures were refined with phenix. refine from the PHENIX package  (Adams et al. , 2010). The starting model was the 0. 86 A ˚resolution lactose-bound galectin-3C X-ray structure without water molecules and ligands. The structures were first refined using only X-ray data. The ligand molecules and restraints were built in PHENIX  (Adams et al. 2010). Water molecules were added manually. After satisfactory R factors were obtained, we started joint neutron/X-ray refinement  (Afonine et al. , 2010). The weighting between the X-ray and neutron terms was determined automatically. The optimal weighting between geometric and X-ray terms, as well as the weighting of B-factor restraints, was determined by multiple refinements with different weights. The neutron and electrondensity maps were visualized and minor manual model rebuilding was carried out using Coot  (Emsley et al.
Obtaining a structure free of lactose is timeconsuming, requiring extensive soaking or dialysis of the crystals in lactose-free solutions for up to three months, as also shown by others  (Collins et al. , 2007). This may be owing to a highly reduced off-rate owing to the same diminished protein dynamics. The problem is presumably exacerbated by the large size of the crystals for neutron diffraction, and we found that soaking crystals for several weeks in lactose-free solution still did not remove lactose completely (Figs. 4a  and 4b ). In fact, we used a crystal treated in such a way to collect the lactose-3 data set at MaNDi. Thus, we developed a two-step method to circumvent the problem of lactose persistence, in which lactose was first replaced by glycerol and then by water. Crystals of apo galectin-3C where 20% glycerol ($2 M) was used as a cryoprotectant revealed glycerol in the binding site Improvement in diffraction from LADI-III.
Full analysis of the apo structure (neutron and X-ray) will be presented elsewhere. reducing the losses from storage-phosphor decay in the imaging plate  (Wilkinson et al. , 2009). Data to even higher resolution (1. 6 A ˚) were obtained at MaNDi in 14 d from a crystal of 1. 0 mm 3 , although the completeness and multiplicity were lower than at LADI-III. This shows the advantage of the time-of-flight Laue technique, which has a lower background, for observing the weaker reflections at high resolution. The crucial difference between the two experiments at the spallation neutron sources MaNDi and PCS was the combination of the higher flux at MaNDi and a larger crystal, allowing the reflections to exceed the detector noise threshold. The lower completeness obtained could be partly explained by the fact that not all of the MaNDi detectors were available when the data were collected, thus reducing the reciprocal-space coverage.
The data collected at BIODIFF, which is a monochromatic instrument, show excellent completeness, both overall and in particular at the highest resolution, owing to the avoidance of the spatial and harmonic overlaps in the diffraction pattern that are problematic for pseudo-Laue data. The resolution for the glycerol complex rivalled that from LADI-III and MaNDi for a crystal of only 1 mm 3. However, the relatively low flux achieved from implementation of the monochromatic method means that lower multiplicity is achievable in a given datacollection time. It is notable that the higher signal-to-background ratio at a monochromatic diffractometer allows data collections in comparable times to a reactor Laue instrument given that the crystal size is sufficient. The data set from MaNDi was intended to be from apo deuterated galectin-3C, but problems in soaking out lactose from large crystals (which were eventually overcome) resulted in a lactose complex with a lactose occupancy of $0.