dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). This is in agreement with the Reactome pathway database, which revealed the overrepresentation for Planar Cell Polarity "PCE/CE pathway" (P = 1. 04E-30), while "AUF1 (hnRNP D0) destabilizes mRNA" (P = 2. 47E-31) term highlights a functional role in RNA degradation. "Regulation of Apoptosis" (P = 1. 29E-30), "Stabilization of p53" (P = 4. 05E-30), "Ubiquitin -dependent degradation of Cyclin D1" (P = 1. 6E-32) are also found highly enriched. Top enrichment terms are shown in Table  1  and Figure  2 , while the full lists can be found in Supplementary Table  S2. As shown in the GO CC terms, also in the Reactome, the terms Cell-Cell communication (P = 6. 04E-05) and Cellcell junction organization (P = 0. 00047817) are still highly significative. Taken together, the functional enrichment analysis from these two distinct sources (GO and Reactome) indicate that the p140Cap interactors exhibit functions relevant to cell adhesion, protein homeostasis, regulation of basic cell features such as cell cycle and apoptosis, which are commonly deregulated in tumor cells.
Community Structure Reveals the Topology-Functional Relationships Within p140Cap Interactome To examine whether the identified functional terms are associated with specific sub complexes within the interactome, we reconstructed the PPI network to perform enrichment analysis over its community structure. Using combined mouse and human PPI data collected from three data sources (see section "Materials and Methods") we built a PPI network for the 374 proteins obtained for the cancer interactome. The network analysis is solely based on our set of PPIs filtered from the Intact, BioGrid and DIP databases: these PPIs are the "direct and physical" human PPIs found from experiments. The resulting network was analyzed with respect to node centrality measures (Supplementary Table  S3 ) including: Degree, Betweenness (Bet), Closeness, Clustering Coefficient (CC), Page Rank (PR), Semi-Local centrality (SL), and mean shortest path (SP).
27E-06), 'Fc gamma receptor signaling pathway" (P = 6. 04E-03), "Ephrin receptor signaling pathway" (P = 1. 77E-02) and "Actin filament bundle assembly" (P = 2. 5E-02) (Supplementary Table  S4 ). Notably, C2 contains 7 proteins shared with synaptic dataset, namely: p140Cap, ERBB2IP, and the cell adhesion proteins such as the Junction plakoglobin (JUP), a common junctional plaque protein, together with the Catenin beta-1 and Catenin delta-1. In addition C2 also contains two proteins involved in intracellular membrane trafficking, the GTPase-activating protein (GAP) RP2 involved in trafficking between the Golgi and the ciliary membrane, and the RANBP9, a protein associated with the small GTP binding protein RAN, which is essential for the translocation of RNA and proteins through the nuclear pore complex.
Comparison of Cancer and Synaptic p140Cap Interactomes Figure  6  shows the generalized functional profiles for both interactomes based on the proteins investment into primary biological function, such as Processing of Genetic Information, Metabolism, Signaling, Transport, etc. , which in turn are subdivided to lower level KEGG categories  (Liebermeister et al. , 2014). Here, size of category/pathway depends on accumulated abundances of proteins, participating in the respective pathways. The categories are equivalent for both interactomes, however, the dominant terms are evidently different. While "Genetic Information Processing" (mainly with "Translation" and "Folding, Sorting and Degradation" terms) makes the largest impact in cancer interactome (Figures  6A, C ), in the synaptic dataset the most prominent category is "Environmental Information Processing", equally distributed between "Signaling molecules and interactions" and "Signal transduction terms" (Figures  6B, D ).
In the ERBB2 cell model chosen for this analysis, p140Cap is causal in impairing in vivo tumor growth and metastasis formation. This model as well the double transgenic mice p140-NeuT are consistent with the overall improved prognosis  Enrichment values for our set of common synaptopathies and cancers for p140Cap PPI proteome networks using combined OMIM/geneRIF/Ensembl variation annotation data mapped into the full Human Disease Ontology (HDO) ontology tree (17,731 genes annotated onto 3457 HDO terms) for the cancer (321 significant genes annotated onto 1346 HDO terms), synaptic (212 significant genes annotated onto 955 HDO terms) networks. Where enrichment values were calculated using the Topology-based Elimination Fisher method  (Alexa et al. , 2006) , and N gives the number of disease genes found in each network.
However, 39 proteins are common. When compared to the total mouse genome 24,402 (genes with protein sequence data, taken from MGI website) the probability of observing an overlap of this size from two independent datasets is very low (P = 2. 68E-22). However, one may argue that datasets are not independent as they have common bait -p140Cap. If we try to estimate the probability of cancer set given we know the neural one: P (cancer set | neuronal set) = P (cancer set AND neuronal set)/P (neuronal set), we end up with P = 5. 63E-10, which is again, very significant. Overall, comparing the list of cancer interactors of p140Cap with the full list of proteins identified in synapse, there are about 160 interacting proteins in cancer that were not identified in synapse, indicating that the cancer interactome could be celltype specific.
FUNDING This work was supported by  MIUR (Ministero Università Ricerca ,  PRIN 2015  to PD),  AIRC  ( Associazione Italiana Ricerca Cancro)  to PD ( IG-20107 ), and  Fondazione   CRT 2017. 2054  to PD. AAd and YC thank the  ProFI Infrastructure  ( ANR-10-INBS-08-01 ) for support. EM and MM received funding from  Cariplo 2012  grant agreement  0560 ,  Fondazione Vodafone   2017-2018 ,  Regione Lombardia-CNR   2017-2019 , and  Italian Ministry of Health   2015-2017. OS, CM, and JA received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under Specific Grant Agreement No. 785907  ( Human Brain  Project  SGA2 ).
Acknowledgement: This research was supported by the  Ministry of Science and ICT (MSIT) ,  Korea , under the  Information Technology Research Center (ITRC) support program  ( IITP-2019-2015-0-00448 ) supervised by the  Institute of Information & Communications Technology Planning & Evaluation. Portions of this work were presented at the  SPIE Photonics West 2018 Conference, San Francisco, USA.
32 To generate the initial structure for the silver(I)complexed cage, one of the most open conformations generated along the MD simulation of the uncomplexed cage was taken and the four Ag + ions were placed between the two coordinating nitrogen atoms of the linkers; then, this structure was energy minimized using the steepest descent algorithm. The initial structures for both complexed and uncomplexed forms of cage 1 were taken from the crystal structure of the complexed cage (for the uncomplexed simulations, the four Ag + ions were removed). 37 imulations were performed with the GROMACS software package using the GROMOS96 [36] force field. The GROMOS force field parameters for uncomplexed cages 1 and 2 were obtained from the Automated Topology Builder (ATB) database, 52 with the exception of the partial charges set, obtained from QM calculations (mentioned earlier).
QM-derived ESP charges for the silver(I)-complexed linkers were used. The remaining force field parameters were obtained by analogy with similar already parametrized chemical groups. The four cages were placed in a dodecahedral box large enough to contain the molecule and at least 1. 0 nm of solvent on all sides with a proper number of DCM molecules to reproduce the density of DCM at 300 K and 1 bar (1. 33 g/ cm 3 ). A proper number of DCM molecules was then substituted with the corresponding number of MeOH molecules to reproduce the experimental DCM/MeOH (90:10) proportion. The force field parameters for DCM and MeOH were taken from the GROMACS topology database.
The quality of each sequence was checked manually and the closest match in NCBI databases was determined by BLAST  (Altschul et al. , 1990). Further, sequences were aligned in Muscle, as implemented in MEGA 7. 0  (Edgar, 2004; Kumar et al. , 2016). Alignments were reviewed manually to verify mismatches, and a phylogenetic tree was constructed by maximum likelihood using the K2, G+I model. The reliability of each node in the tree was assessed by bootstrapping over 500 replicates.
22 µm. To obtain fungal extracts, 43 fungal isolates were grown for 3 weeks at 19 • C in MEA/ASW medium, and extracted three times with ethyl acetate. A detailed recipe of the medium is provided in Supplementary Table  S1. Extracts were tested at a final concentration of 250 µg/mL, with final concentration of DMSO 2. 5%. Bacterial supernatants and fungal extracts were tested for the production of molecules interfering with AI-2 quorum sensing. Briefly, 20 µL of test samples and corresponding controls were mixed with 180 µL of V. campbellii MM32 diluted 1:5,000 and incubated at 30 • C and 100 rpm. Luminescence and cell density (OD 620 ) were measured after 24 h. Data were collected in triplicate, and luminescence change was calculated as (lumi SN/E -lumi Control )/lumi Control , where lumi SN/E is bioluminescence (normalized to cell density) from the reporter strain in the presence of supernatant or extract, and lumi Control is bioluminescence (normalized to cell density) in the presence of either marine broth (when supernatants are tested) or DMSO (when extracts are tested).
Samples were then desalted with two volumes of water using Sep-Pak C18 SPE cartridges (Waters, Beverly, MA, United States), and eluted with two volumes of acetonitrile. Subsequently, samples were analyzed by LC-MS/MS on a Dionex Ultimate 3000 HPLC system coupled to a Q Exactive TM Focus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, United States) and fitted with an electrospray ionization source and a Hypersil GOLD C18 column (2. 1 mm × 150 mm, 1. 9 µm particle size; Thermo Scientific, Waltham, MA, United States) operating at 20 • C. In this system, eluates are introduced directly into the mass spectrometer. LC-MS parameters are detailed in Supplementary Table  S1. Data were collected using Xcalibur, in parallel reaction monitoring mode targeting the precursor ion at m/z 233. 1285.
Extracts were tested for their impact on quorum sensing at a final concentration of 250 µg/mL, with final concentration of DMSO 2. 5%. Briefly, 20 µL of extract were mixed with 180 µL of V. campbellii MM32 diluted 1:5,000 and incubated at 30 • C and 100 rpm. Luminescence and cell density (OD 620 ) were measured after 24 h. Data were collected in triplicate, and luminescence change was calculated as (lumi E -lumi Control )/lumi Control , where lumi E is bioluminescence (normalized to cell density) from the reporter strain in the presence of extract, and lumi Control is bioluminescence (normalized to cell density) in the presence of DMSO. To assess the viability of the biosensor, 100 µL of culture was reacted with 30 µL of 0. 01% resazurin  (Graça et al. , 2013)  immediately after measurement of luminescence, and fluorescence (λ ex : 530 nm, λ em : 590 nm) was measured after incubating for 4 h at 30 • C with agitation.
Co-culture Experiment: LC-MS-Based Metabolomic Analysis Dried extracts were solubilized in methanol at 0. 5 mg/mL, and analyzed by HPLC-MS in one batch and in a random sequence. Samples were loaded onto a Dionex Ultimate 3000 HPLC system fitted with a C18 Acclaim TM RSLC PolarAdvantage II column (2. 1 mm × 100 mm, 2. 2 µm pore size; Thermo Scientific, Waltham, MA, United States) operating at 40 • C, and coupled to a Maxis II TM QTOF mass spectrometer (Bruker, Bellerica, MA, United States) with an electrospray ionization source. Data were acquired with Data Analysis software. LC-MS parameters are listed in Supplementary Table  S1. Raw LC-MS data were calibrated and converted to netCDF format using Data Analysis software (Bruker), and processed using the R package XCMS  (Smith et al. , 2006).
Based on analytical conditions and raw data characteristics, final peak picking parameters were method = 'centWave, ' ppm = 10, and peak width = c(5,20), while final grouping parameters were bw = 5, mzwid = 0. 015, and retention time correction method = 'obiwarp. ' Other parameters were set to default values. To limit noise from compounds already present in culture media, the dataset was filtered with an in-house script to retain only those features with intensity in at least one sample more than fivefold its average intensity in blank samples.
Production of AI-2 Compounds by Bacterial Endophytes Most (86%) bacterial endophytes isolated from brown algae elicited an increase in luminescence from V. campbellii MM32, a quorum sensing reporter strain (Table  1  and Supplementary Data S1). Strikingly, 10% of isolates boosted luminescence by over 50%, including a Kocuria isolate, six Bacillus isolates, and 13 Proteobacteria. Indeed, a Marinomonas isolate and five Cobetia isolates increased luminescence by at least 100%. In contrast, all but one Pseudoalteromonas isolate increased luminescence by less than 50%. To confirm these results, DPD production by Marinomonas 424 and Cobetia 352b, two of the strongest inducers of luminescence, was quantified by LC-MS/MS. For comparison, DPD production was also quantified in Pseudoalteromonas 352a, which was co-isolated with Cobetia 352b and is a weak inducer of luminescence.
ACKNOWLEDGMENTS We acknowledge the  Scottish Association for Marine Science (SAMS)  and the  Biological Station of Roscoff  for hosting the field missions. We would like to greatly thank  Philippe Potin  ( Marine Biology Station of Roscoff, France ) for taxonomic identification of the brown macroalgae species during the field mission. We are grateful to the Bio2Mar platform (http://bio2mar. obs-banyuls. fr)for providing technical help and support.
ACKNOWLEDGEMENTS We are thankful to the  French Interprofessional Organisation for Seeds and Plants (GNIS)  which enabled this scientific work by funding the  Mites Against Diabrotica  (M. A. D. ) project. We also acknowledge all the partners in this project:  Axereal Serbia ,  French National Institute for Agricultural Research (INRA)  and with a more specific attention Arvalis, which provided Agriotes sordidus eggs and larvae, essential for this study.
, (2015). The software used was X-Correl. This method allows the displacement calculation in the presence of single and or crossed cracks in subsets. It can give accurately the displacements near a crack, without knowing the crack distribution or position. In this study, displacements were calculated in subsets from 32 to 64 pixels, which correspond to 70. 4 to 140. 8 µm, respectively. Strains were calculated by displacement derivation for steps of 2 pixels  (Hédan et al. , 2014; Fauchille et al. , 2016a). In this study, results are presented for main strains ε and ε (Equations 1,2). ε , = ε , + ε , 2 + ε , sin 2 (1) ε , = ε , + ε , 2 - ε , sin 2 with θ = , , , (2) ε , , ε , and ε , are calculated by displacement derivation on neighbour subsets. Strain fields were determined with an increment of 1 pixel between two subsets.
The electron beam drift was taken into account to assemble correctly all images  (Fauchille, 2015). The image acquisition was performed on SEM JEOL 5600 LV equipped by Brukher Esprit software. The acquisition parameters were a voltage of 15 kV, a magnitude x200, and a working distance of 16. 3 mm. BSE images are composed of 256 different grey levels (8bits), in which each grey level represents a mean atomic number and a local mean mineralogical composition. The image acquisition was performed with an optimized BSE contrast with a well-defined histogram of grey levels to facilitate the further phase segmentation. At the resolution of 0. 625 µm. pixel -  , this mode allows a well distinction of clay matrix and micrometer-scale clasts (Appendix 2). The clay matrix is defined at the mescoscopic scale as the homogeneous ground mass of the clay rock, mainly composed of fine clay minerals and clasts smaller than 0.
Phase segmentation The mineralogical phases were segmented on the reconstructed mosaic of BSE images by the in-house software MicroPhaseMap  (Prêt et al. , 2010a,b) , according to the histogram of 8 bit grey levels and the variance filter. The clay matrix, clasts composed of quartz, carbonates and heavy minerals, and macropores were considered (Appendix 3). Note here that the clay matrix included small clasts smaller than 0. 625 µm because it depends on the scale and the resolution of the study.
Quantitative parameters From the segmented mosaic of BSE images, quantitative parameters were calculated to describe the microstructure. The area, length ratio and orientation of each type of clast were calculated by ImageJ software and in-house codes  (Fauchille et al. , 2018). The area of a clast corresponds to the total number of its pixels. In the same subsets than those previously used for DIC, the area fraction 0 * (Equation  4 ) is calculated: 0 * , = 1 # + 1 ² % % 1 2,3 3, ) * 3, * 2, ) * 2, * (4) 1 2,3 is the indicator function and equal to 0 or 1 (1 when the pixel 4, 5 corresponds to the segmented phase and 0 else). # is the size of the subset. The area fraction of a chosen phase p in a square subset of # × # pixels is the ratio of pixels which correspond to the chosen phase by the total number of pixels in the subset.
The comparison of strain values with 6 ̅ * and 0 * requires an excellent superimposition between the optical image at t0-2 used for DIC (Figure  1b ), and the mosaic of SEM images. To quantify the error of superimposition between these images, the distances between the center of 4 markers (well-visible clasts and macropores) were measured on both images with Deftac software. The mean error of clast position is in the order of 2. 3 µm, that is to say that a clast localized on the reference optical image can be in theory at a distance of ± 2. 3 µm around on the SEM images.
Lineage tracing in zebrafish Cell lineage tracing with fluorescein was performed as described by  Staudt and Houart (2007). For time-lapse cell tracking experiments, zebrafish embryos were injected with Kaede and H2B-GFP RNA at the one-cell stage. At bud stage, cells were photo-converted at 405 nm using a Leica SP5 confocal microscope, and embryos were imaged with a Zeiss Z. 1 light-sheet fluorescence microscope for 12 h with a time step of 6 min. Images were processed with the ZEN and Arivis softwares, and cells were tracked manually in ImageJ using a custom-made plugin.
Background The intestinal mucosa is home to a dense immune cell network that delicately needs to balance responses to microbiota in the gut lumen. In inflammatory bowel disease (IBD), a complex and multifactorial disease, this crucial balance in the immune system is distorted. Although several therapeutic options are available, not all patients respond to treatment. Recent trials have taken an original approach for immune suppression in IBD based on the recognition of the nervous system as a negative regulator of inflammatory processes. The vagus nerve has been identified to negatively regulate inflammation in multiple models through the cholinergic anti-inflammatory pathway  (Tracey 2002). Vagus nerve stimulation improves colitis in rats  (Meregnani et al. 2011 ). Furthermore, current pilot studies of vagal nerve stimulation are undertaken to support a role for vagus nerve activity in active Crohn's disease (NCT02311660 and  Bonaz et al.
V. ) on a LightCycler 480 II (Roche Applied Science) to analyse expression levels of murine interleukin (IL)-1β, IL-6, IL-10, IL-12, IL-22, tumour necrosis factor (TNF)-α, nitric oxide synthase 2 (Nos2), arginase 1 (Arg1), regenerating islet-derived protein 3γ (Reg3γ), defensin α (DefA) and mucin 2 (Muc2) using Lin-RegPCR software  (Ruijter et al. 2009 ). For normalization murine reference genes β-actin, ubiquitin, hypoxanthine phosphoribosyltransferase (HPRT), cyclophilin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were selected, after analysis for stability in geNorm (  Vandesompele et al. 2002). In human macrophages, expression levels of human IL-1β, IL-6, IL-10, IL-12, TNF-α were measured and normalized using human reference genes β-actin and β2 microglobulin, selected after analysis for stability in geNorm.
We used Seahorse XFe96 Analyzer (Agilent Technologies, Amstelveen, the Netherlands) for the assay and Wave Desktop Software version 2. 3 (Agilent) to calculate the OCR and ECAR. (See figure on previous page. ) Fig. 1  Lipopolysaccharide (LPS)-exposed bone marrow derived macrophages (BMDM) produced less pro-inflammatory cytokines after pretreatment with norepinephrine or salbutamol. (A&C) mRNA expression levels of interleukin (IL)-1β, IL-6, IL-10, IL-12 and tumour necrosis factor (TNF)-α in cell lysate of BMDM after treatment with 100 ng/ml LPS for 18 h and pre-treatment with different concentrations of norepinephrine (a) or salbutamol (c) together with propranolol, normalized for reference genes β-actin and Ubiquitin. Expression is relative to mRNA expression in BMDM not exposed to any treatment.
pneumoniae, and displays high sequence identity across the family. There are currently three X-ray crystal structures available for COG2384 family members (Protein data bank (PDB): 3LEC, 3GNL, 3KR9)  (25)  with no further data published on these. Here, we studied the relationship between the structure and the function of the B. subtilis m 1 A 22 MTase TrmK ( Bs TrmK) with special focus on the enzyme-tRNA recognition. We investigated the properties that establish tRNAs as substrates of Bs TrmK, and we report how a single point mutation in tRNA can convert a non-substrate into a substrate. The identified nucleotide playing a central role in Bs TrmK substrate definition is involved in base-pairing with A 22 , the target site of methylation by Bs TrmK. We also solved the crystal structure of Bs TrmK and used NMR spectroscopy to gain insight into the recognition mode of B.
Steady-state kinetic analysis of Bs TrmK [Methyl-3 H]-SAM (15 Ci/mmol, Perkin Elmer) was mixed with non-radioactive SAM (Sigma) to achieve a specific radioactivity of about 500 cpm/pmol. The methylation kinetic assays were performed in 50 mM Tris-Cl pH 8. 0 with 5 mM MgCl 2 at 37 • C. Aliquots (300 l) were removed after different incubation times and transferred into 5 ml of 5% (w/v) trichloroacetic acid (TCA) at room temperature for 10 min in order to quench the reaction and to precipitate the tRNA. The precipitates were collected by filtration using GF/C filters (Whatman). The filters were washed with ethanol, dried, and the radioactivity was measured by liquid scintillation counting for 2 min, resulting in a counting error below 4%. Data were corrected by subtracting the background radioactivity determined from a control without enzyme.
For the determination of the K M and V max of Bs TrmK for tRNA Ser , the reaction mixtures contained Bs TrmK (100 nM), tRNA Ser (10-2000 nM) and 3 H-AdoMet (90 M). Initial rates (v i ) for each substrate concentration (tRNA Ser ) were determined from the slope of linear fittings of time course data points  (5, 10, 15, 20, 30 min). Enzyme parameters were obtained by non-linear least square fitting using Equation (1) for Michaelis-Menten kinetics. Confidence limits of the parameters at 90% were estimated by Monte-Carlo sampling using the MC-Fit program  (27). v i = V max 2E 0 K M + S 0 + E 0 -(K M + S 0 + E 0 ) 2 -4S 0 E 0 (1).