dataesr/scientific-paragraphs-categorization huggingface dataset:
This software is the only one able to deal with very large raw data sets and huge databases. We grouped the identified proteins to produce the shorter list of proteins explaining the mass spectrometry results. We compared the number of peptides and proteins identified with specific metagenomes with those obtained with the MetaHIT gene catalog. To go further in the analysis, we quantified the peptides using two quantification methods: Spectral Counting and eXtracted Ion Chromatogram with the dedicated house made software MassChroQ. A new generation mass spectrometer, a longer column and an efficient analysis software allowed us to anlyze the intestinal metaproteome of an unusually large cohort. Investigations of the microbiome from this cohort, may open new avenues for reducing cardiovascular risk, including the discovery of biomarkers, new targets and new therapeutic molecules.
( 1 )1 1 C. Virgiliou, I. Sampsonidis, H. G. Gika, N, Raikos, G. A. Theodoridis, Development and validation of a HILIC-MS/MS multitargeted method for metabolomics applications, Electrophoresis 36 (2015) 2215-2225. (2) A. Periat, I. S. Krull, D. Guillarme, Applications of hydrophilic interaction chromatography to amino acids, peptides, and proteins, Review Article, J. Sep. Sci. 38 (2015) 357-367. Mots-Clés : Metabolomics, LC, HRMS, RPLC, HILIC, Database * Intervenant † Auteur correspondant: Serge. Rudaz@unige. ch An in-solution digestion method to improve Proteome coverage from small samples of Extracellular Matrix and Membrane extracts [P27] Haddad Iman * † 1 , Anne Joutel 2,3 , Joelle Vinh 1 Spectrométrie de Masse Biologique et Protéomique (SMBP) -CNRS : USR3149, ESPCI Paris, PSL Research University : USR3149 -10 rue vauquelin 75005 Paris, France 2 Genetics and Pathogenesis of Cerebrovascular Diseases (UMR 1161 INSERM) -Université Paris Diderot -Paris 7 -10 avenue de Verdun 75010 Paris, France 3 Hôpital Lariboisière (APHP) -DHU NeuroVasc Sorbonne -10 avenue de Verdun; 75010 Paris, France.
" Chapter 6: Room Temperature Ionic Liquids and Eutectic Mixtures ". In Al- ternative Solvents for Green Chemistry, 118-42, 2009. doi:10. 1039/9781847559524-00118. Acknowledgement: INCa project PLBIO14 and ANR-11-INBS-0010 MetaboHUB. Mots-Clés : Ionic liquids, extraction, optimization, LC/MS, NMR * Intervenant † Auteur correspondant: cyril. jousse@uca. fr 1 Centre des Sciences du Goût et de lÁlimentation [Dijon] (CSGA) -INRA : UMR1324, Université de Bourgogne, Institut National Supérieur des Sciences Agronomiques, de lÁlimentation et de lÉnvironnement, CNRS : UMR6265 -Université de Bourgogne -Bât. CSG -9E Bd Jeanne dÁrc -21000 Dijon, France.
Swiss Centre for Applied Human Toxicology -University of Basel Missionsstrasse 64 4055 Basel, Suisse Analytical Coverage Investigation of a Database of Reference Compounds for Metabolomic Studies [P26] Julian Pezzatti  *  1 , Víctor González-Ruiz 2,1 , Nicolas Drouin 1 , Yoric Gagnebin 1 , Serge Rudaz  † 2,1 1 Analytical Sciences, School of Pharmaceutical Sciences -University of Geneva Michel Servet 1 1211 Genève, Suisse 2 Mots-Clés : extraction, analyse, lipides, lipidomique, spectrométrie de masse Programme *  Intervenant.
1 MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010) 2 Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) -INSA, CNRS : UMR5504, INRA : UMR0792 -135 Avenue de rangueil, 31077 Toulouse cedex 04, France.
Remerciements : INSERM pour le financement du projet PLASTOC (PLastic Additives Study Of the POtential risk for Cancer),PLAN CANCER 2014-2019 Extraction à l'eau subcritique des composés phénoliques de deux espèces du genre Glebionis de Corse [P221] Marion Brunel  *  1 , Franck Renucci  † 1 , Jean Costa  ‡ 1 , Xavier Vitrac  § 2 , Alain Muselli  ¶ 1 1 Université de Corse, UMR-CNRS 6134 SPE, Laboratoire Chimie des Produits Naturels -Université de Corse - Campus Grimaldi, BP 52, 20250 Corte, France 2 Laboratoire PHENOBIO SAS -Laboratoire PHENOBIO SAS -Centre Montesquieu, 1 Allée Jean Rostand, 33650 Martillac, France Mots-Clés : additifs plastiques, quantification, ultra, traces, sang Programme *  Intervenant † Auteur correspondant: emmanuelle. vulliet@isa-lyon. fr.
To determine whether the absence of Pyk2 also affected spine morphology, we quantified the spine head diameter and spine neck length. Spine head size did not change between genotypes (Fig. 3h ), whereas spine neck length was decreased in Pyk2 mutant mice (Fig. 3i ). Altogether, these data show that the lack of Pyk2 leads to a decrease in PSD-95 at synapses and a decreased number of PSDs and spines. Adult hippocampal Pyk2 deletion recapitulates the phenotype. Although Pyk2 expression in the hippocampus is mostly postnatal  4  , the severe alterations observed in Pyk2 þ / À and Pyk2 À / À mice could result from developmental effects. To rule out this possibility, we used 3-month-old mice bearing floxed Pyk2 alleles (Pyk2 f/f mice). Mice received a bilateral stereotaxic injection in CA1 of adeno-associated virus expressing Cre recombinase and GFP (AAV-Cre) or expressing GFP alone (AAV-GFP), as a control (Fig.
The arm preference was measured as the time exploring each arm Â 100/time exploring both arms. Animals were tracked and recorded with Smart junior software (Panlab). Viral constructs and stereotaxic injection. For specific deletion of Pyk2 in dorsal hippocampus expression, 4-week-old Pyk2 f/f mice were stereotaxically injected with AAV expressing Cre recombinase and (AV-9-PV2521, AAV9. CamKII. HI. eGFP-Cre. WPRE. SV40 (AAV-Cre)) (from Perelman School of Medicine, University of Pennsylvania, USA). To overexpress Pyk2, we used AAV1-CamKIIa0. 4-GFP-2A-mPTK2B (AAV-Pyk2) with a T2A cleavable link (Vector Biolabs Malvern, PA, USA). As a control, we injected AAVs expressing GFP (AV-9-PV1917, AAV9. CamKII0. 4. eGFP. WPRE. rBG (AAV-GFP) from Perelman). Following anaesthesia with pentobarbital (30 mg kg À 1 ), we performed bilateral injections of AAV-GFP, AAV-Cre or AAV-Pyk2 (2.
Offline analysis of fEPSP slopes was done with Clampfit software (Molecular Devices). Baseline potential was set to zero and recordings were low-pass filtered at 1 kHz with Bessel filter. A 1-ms time-window was manually positioned at the onset of the fEPSP and its initial slope automatically measured. LTP was investigated in AAV-injected R6/1 mice in similar conditions except that mice were 5-month old at the time of the recording. Paired pulse ratio was determined at a 50 ms interval. In all cases, the experimenter carrying out the acquisition and analysis of electrophysiological data was blind to the mice genotype and/or AAV type. Electronic microscopy. Mice were transcardially perfused with a solution containing 40 g l À 1 paraformaldehyde and 1 ml l À 1 glutaraldehyde in 0. 1 M sodium phosphate buffer (PB), pH 7.
Bright-field images of Golgi-impregnated stratum radiatum dendrites from hippocampal CA1 pyramidal neurons were captured with a Nikon DXM 1200F digital camera attached to a Nikon Eclipse E600 light microscope ( Â 100 oil objective). Only fully impregnated pyramidal neurons with their soma entirely within the thickness of the section were used. Image z-stacks were taken every 0. 2 mm, at 1,024 Â 1,024 pixel resolution, yielding an image with pixel dimensions of 49. 25 Â 49. 25 mm. Z-stacks were deconvolved using the Huygens software (Scientific volume imaging, Hilversum, the Netherlands) to improve voxel resolution and to reduce optical aberration along the z axis. Segments of proximal apical dendrites were selected for the analysis of spine density and spine morphology according to the following criteria: (a) segments with no overlap with other branches that would obscure visualization of spines and (b) segments either 'parallel' to or 'at acute angles' relative to the coronal surface of the section to avoid ambiguous identification of spines.
Only spines arising from the lateral surfaces of the dendrites were included in the study; spines located on the top or bottom of the dendrite surface were ignored. Given that spine density increases as a function of the distance from the soma, reaching a plateau 45 mm away from the soma, we selected dendritic segments of basal dendrites 45 mm away from the cell body. The total number of spines was obtained using the cell counter tool in the ImageJ software. At least 60 dendrites per group from at least three mice per genotype were counted. For a more precise description of the dendritic shape changes, the spine head diameter was analysed as a continuous distribution (between 368 and 418 spines per group were analysed) using the ImageJ software. Then, a distribution analysis of head diameter was performed.
All blots were incubated with the primary antibody overnight at 4 °C by shaking in PBS with 0. 2 g l À 1 sodium azide. After several washes in TBS-T, blots were incubated with secondary anti-rabbit or anti-mouse IgG IRdye800CWcoupled or anti-mouse IgG IRdye700DX-coupled antibodies (1:2,000, Rockland Immunochemicals, USA). Secondary antibody binding was detected by Odyssey infrared imaging apparatus (Li-Cor Inc. , Lincoln, NE). For loading control a mouse monoclonal antibody for a-tubulin was used (#083M4847V, 1:10,000; Sigma). Statistical analysis. Statistical analyses were carried out using the GraphPad Prism 6. 0 software. Data sets were tested for normality distribution with d'Agostino-Pearson and Shapiro-Wilk tests. When distribution was not different from normal, they were analysed with parametric using Student's t-test (95% confidence), one-way ANOVA or two-way ANOVA, with Holm-Sidak post hoc multiple comparisons test.
Acknowledgements This work was supported in part by  Inserm , the  UniversitePierre et Marie Curie  ( UPMC, Paris 6 ), and an  ERC  advanced investigator grant (# 250349 ) to J. -A. G. A. G. was partly supported by the  King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research  award (# OSR-2015-CRG4-2602 ) to J. -A. G. and  Stefan Arold. J. -C. P. lab is supported by grants from the  Human Frontier Science Program  ( RGP0022/2013 ) and the  Fondation pour la Recherche Medicale  ( DEQ20140329539 ). Q. C. and C. S. were recipients of doctoral fellowships of  UPMC. Equipment at the IFM was also supported by  DIM NeRF from Region Ile-de-France  and by the  FRC/Rotary  'Espoir en tete'. Microscopy was carried out at the  Institut du Fer aMoulin Cell  and  Tissue Imaging  facility.
1  and Supplemental Fig. 4 ). Multiple alignment was realized with Clustal Omega  32. This program aligned the predicted transmembrane domains (Uniprot Q12809 for hERG, P51787 for KCNQ1) and the predicted position of the narrowest part of the bundle crossing, also named gating residue  33, 34  , of hERG and KCNQ1. We first designed a hERG S4-S5 L peptide based on the most potent KCNQ1 S4-S5 L (L251-L266  11  ). Since mutagenesis studies suggested a close proximity of D540 in S4-S5 L and R665 in S6 T 6, 13 , we chose two peptides shifted by 3 and 6 amino acids to the N-terminus to include the D540 residue. Names of the peptides were given according to their position along the sequence: S4-S5 L (-3) is A536-F551, S4-S5 L (0) is L539-M554, and S4-S5 L (+3) is Y542-F557. For S6 T peptides, we first selected the hERG sequence which aligned with the most potent peptide of KCNQ1 (I346-K358).
Stain free gel technology (Bio-Rad) was used as loading control for protein normalization: band intensities were first normalized to the intensity of the corresponding stain free membrane lane, and ratios were then normalized to control hERG condition  35. Electrophysiology. The day after splitting, COS-7 cells were mounted on the stage of an inverted microscope and constantly perfused by a Tyrode solution (cf. below) at a rate of 1-3 ml/min. The bath temperature was maintained at 22. 0 ± 2. 0 °C. Stimulation and data recording were performed with Axon pClamp 10 through an A/D converter (Digidata 1440A) using an Axopatch 200B (all Molecular Devices). Patch pipettes (tip resistance: 2-3 MOhms) were pulled from soda lime glass capillaries (Kimble-Chase). Currents were recorded in the whole-cell configuration.
Acknowledgements We thank  Béatrice Leray  and  Aurore Girardeau  for their technical support. We thank  A. Labro ,  I. Baró  and  T. James  for careful reading of the Manuscript. This work was funded by grants from the  Marie Curie European Actions  ( PIOF-GA-2011-298280 ). Olfat Malak  was laureate of the Line Pomaret-Delalande prize of the  Fondation pour la Recherche Médicale  ( PLP20141031304 ; FRM). Olfat Malak wishes to personally thank  Mrs. Line Pomaret  for her generous support. Dr Z. Es-Salah-Lamoureux  was supported by grants from the  Lefoulon Delalande Foundation , the  Fondation pour la Recherche Médicale  (SPF20111223614; FRM) and the  Fondation Génavie.
Acknowledgements CJK and SMN acknowledge support from  Fellowships and Discovery Project  funding from the  Australian Research Council. GC and JFL thank the  Aquitaine Region  for supporting the development of the international platform of photomagnetism. Access and use of the facilities of the  Advanced Photon Source (APS)  was supported by the  USA Department of Energy, Office of Science, Office of Basic Energy Sciences  (Contract No. DE-AC02-06CH11357 ). Travel to the APS was funded by the  International Synchrotron Access Program (ISAP)  managed by the  Australian Synchrotron  and funded by the  Australian Government.
The results obtained here are a significant improvement in current methodologies and provides new insight into the organization of alpha cells and their cellular interactions within the islet's cellular framework. With the development of new imaging techniques, such as single-and two-photon scanning laser microscopy and single plane illumination microscopy, the acquisition of volumetric image data from thick tissue samples is more common 1. Though a lot of effort has been done on the automated analysis of cells or nuclei in microscopic images, the tools to analyze the spatial organization of tissues are limited. Analyzing the 3D organization of cells in tissue datasets is not common, and the measurements are mostly done on individual cells 2-4 or with the tissue as a whole 5. Tissue analysis requires the identification of different cellular components and the computation of the physical interactions between them.
In most cases the components are the cells themselves. Towards this goal, scientists first need to identify the location and identity of cells that make up a given tissue. Since clear cytoplasmic or membrane labelling is usually difficult to obtain in thick tissue samples, most studies rely on a nuclear labeling (e. g. DAPI) as a cellular identification approach. However, nuclei segmentation, especially in large 3D image datasets, is not trivial and remains an active research area among bioimage informaticians  [6] [7] [8]. Furthermore, whole tissue analysis poses an additional challenge when segmenting cells within a crowded cellular environment. In this case, commonly used techniques for segmenting nuclei or cells are based on a "region-growing approach" 9-14 and where FARSIGHT is the best example 9.
Datasets. We applied our methodology to analyze 3D confocal image stacks of islets of Langerhans in whole or serial histological pancreas sections from two different species: C57/BL6 mouse (8 weeks of age, n = 6 animals and 8 stacks) and monkey (Macaca fascicularis, 9-12 years of age, n = 6 animals and 6 stacks). Detailed information for the islets used as testbeds for validation of our toolbox can be found in Table  1. Additional information about the sample preparation and imaging protocols is shown in the methods section. Besides thick tissue sections, we also analyzed 2 whole mouse islets (Mouse 4 and Mouse 6) that were obtained from optically cleared tissues using SeeDB and imaged with 2-photon microscopy  32.
Nuclei segmentation. In homogeneous tissues, cells and nuclei tend to have roughly the same shape and size, while also appearing in a tightly packed organization with proximity between nuclei. In this work, we used DAPI staining to label the cell nucleus. First, the nuclear label signal was filtered to reduce noise and enhance structures based on their size. We then used our spot segmentation procedure to segment the nuclei (Fig. 1  and methods), where the local maxima of the filtered image are detected and used as seeds for local segmentation  33. This nuclei segmentation approach allowed us to identify and segment correctly approximately more than 97% of total nuclei in all datasets (Fig. 2A-C  and Table  2 ). In addition, we observed a low false-positive rate of 106 objects out of 8837 nuclei and a low false negative rate of 257 objects (Table  2 ) for our validated dataset (Table  2 ).
Cell phenotype computation. Due to the limited number of possible markers that could be used in a single immunofluorescence experiment, we did not stain the plasma membrane in our study. Instead, we used the nuclear volume as the theoretical core of the cell and then predicted the maximum likelihood of the cell "zone" by extending the nuclei boundaries according to an average cell size (4 μm, Fig. 2D ). To avoid the potential overlap of two close nuclei, we added a watershed procedure to ensure the exact separation between two nuclear volumes (Fig. 2B, D ). For our application, the islets of Langerhans is composed of three main types of endocrine cells, namely alpha-, beta-and delta-cells. Therefore, our mouse and monkey datasets were composed of three different channels that represent glucagon, insulin and somatostatin markers (Figs  1  and 2A ).
All three markers are cytoplasmic and are sufficient to determine the cell identity. The labelling is quite dense throughout the cytoplasm and no clear separation between the different cells was visible (Fig. 2A ). To reduce the complexity of the number of voxels in each dataset, we clustered the voxels from the different images containing the specific cell markers (i. e. insulin, glucagon and somatostatin) into regions and combined this information into one single image. This was achieved using a supervoxels clustering approach  34  resulting in a single 3D image where islet-cells are represented by regions containing voxels of similar values for all three channels (Figs  1  and 2E ). Next, each region was assigned a cell phenotype based on the most abundant label with the highest average median intensity value.
If the intracellular signal intensity for all three channels was below a predefined threshold, the region was classified as "unlabeled" (Figs  1  and 2F ). In the case of small regions that are labelled (but not belonging to the "unlabeled" type) inside a cell zone, and below a predefined minimum threshold, the cell was also assigned to the "unlabeled" category (Table  3 ). The application of this algorithm on mouse and monkey islet image datasets correctly identified cells with an accuracy of more than 97% (Table  3 ). Next we applied our toolbox to mouse and monkey islets and investigated their endocrine cell composition. Here we observed a mean percentage of alpha-cells (P α ) is 18 beta-cells, the percentage (P β ) is 76. 4% in mouse and 66. 9% in monkey islets, and for delta-cells (P δ ) is 5.
Analysis of direct cell-to-cell interactions. Once all islet-cells were properly segmented and identified, we built a network of interacting cells using their cell zones. If two cell zones are touching we build an link between the two cells and define them as interacting. First, when analyzing intact mouse islets (i. e. mouse4, and mouse6 in Tables  1, 2 , 3, 4 and Supplementary Table  1 -5), we observed a higher number of direct interactions per islet-cell (mean of 7. 48, 8. 98, 9. 94 interacting cells of alpha, beta and delta cells respectively -Supplementary Table  1 ) what suggests that analyzing thin and individual histological sections may underestimate the number of connections in a tissue dataset. Second, we computed the relative proportion of different cell types interactions, defining homotypic interactions between similar cell types (alpha-alpha (P αα ), beta-beta (P ββ ) and delta-delta (P δδ )), and heterotypic interactions between two different cell types (alpha-beta (P αβ ), … ).
We found more homotypic beta-beta interactions in mouse datasets than in monkey datasets, and approximately same proportion of alpha-alpha interactions in mouse and monkey datasets (Supplementary Table  2 ). In order to investigate further the different organisation of islets of Langerhans in mice and monkeys we restrained our analysis on the two major cell types alpha and beta, and added more datasets for this analysis (Fig. 3 ). Due to the heterogeneous organization of primate islets with a lower relative number of beta cells, we observed a significantly lower P ββ of 49. 4% (p < 0. 05 vs mouse, Fig. 3A ). P αα was not significantly different from mice at 8. 6% (Fig. 3A ). Next, we investigated the number of direct contacts between alpha-and beta-cells in mouse and monkey islets. Here we observed that monkey islets have a significantly higher percentage of P αβ than mouse islets (Fig.
Analysis of indirect cell-to-cell interaction. Next, we investigated the framework of the indirect cellular interactions in mouse and monkey islets. Briefly, this was calculated with the following logic: when a given islet-cell "X" contacts directly a predetermined "reference" cell, "X" will be at distance equal to 1 from the "reference" cell (Supplementary Fig. 1 , white cell). If another islet-cell "Y" contacts islet cell "X", "Y" will then be at a distance value of 2 from the "reference" cell (Supplementary Fig. 1 , dark purple cells). This mechanism allows the computation of relative distance maps for any cell or group of cells within our dataset in 3D. Using alphaand beta-cells as test platforms, we computed the cell distances between beta-cells and the closest alpha-cell. We observed that, in both mouse and monkey islets, most of the beta cells are less than 4 cells away from the closest alpha-cell (Supplementary Table  3 ).
When comparing the distances from observed and randomised data we observed that in a random organisation of alpha-cells, beta-cells tend to be closer to alpha-cells. This indicates that alpha-cells tend to be clustered. Following the application of the randomization procedure to alpha-cells, we performed a more complex analysis by comparing the number of indirect beta-alpha interactions and of cell distances in both observed and randomized patterns (Supplementary Table  3 ). For both mouse and monkey islets, we observed a higher number of beta-cells with large cell distances to alpha-cells in the observed datasets than in our randomized models for distance greater than or equal to 2 (Supplementary Table  3 ). This observation supports the concept that alpha-cells are organized in a cluster-like pattern.
Therefore, in this scenario the observed values are not significantly different from a random organization pattern. To investigate further the spatial organization of alpha-cells, we applied this concept on mouse and monkey islet datasets. We observed SDI values of F-and G-function in mouse (Supplementary Fig. 2A, B ) and monkey islets (Supplementary Fig. 2E, F ) outside the 0. 05-0. 95 interval (Supplementary Table  4 ), which indicates that alpha-cells are organized in a non-random distribution pattern. These results also support our previous observations (Supplementary Table  3 ) that alpha-cells are organized in clusters in both mouse and monkey islets. Of note, our simulations of a randomized organization generated expected results with SDI values within the 95% confidence interval (Supplementary Table  4  and Supplementary Fig.