dataesr/scientific-paragraphs-categorization huggingface dataset:
The study protocol, its first amendment, and the informed consent and patient information forms were reviewed and approved by the independent Ethics Committee of Pitié-Salpêtrière Hospital (Approved by CPP Ile de France 3 N° ID-RCB: 2010-A00631-38). Written informed consent was obtained from each volunteer before study entry. This study was supported by a grant from the Programme de Recherche, A(H1N1) coordinated by the Institut de Microbiologie et Maladies Infectieuses (IMMI, France). The study sponsor was not involved in the study design, monitoring, or in the data analysis or interpretation. Immunomonitoring analysis was funded by the.
Acknowledgments The Journal of Clinical Investigation http://www. jci. orgVolume 124 Number  7 July 2014  ( Unité des Maladies Infectieuses et Tropicales, Hôpital Pitié-Salpêtrière, AP/HP ),  Christian Perronne  ( Unité des Maladies Infectieuses et Tropicales, Hôpital Raymond Poincaré, AP/HP, Garches, France ),  Christophe Rapp  ( Service des Maladies Infectieuses et Tropicales, Hôpital d'Instruction des Armées Bégin, Saint Mandé, France ),  Dominique Salmon-Céron  ( Pôle de Médecine -Unité de Pathologie Infectieuse, Hôpital Cochin, AP/HP, Paris, France ), and  Daniel Vittecoq  ( Service des Maladies Infectieuses et Tropicales, Hôpital de Bicêtre, AP/HP, Le Kremlin-Bicêtre, Paris, France ). Received for publication December 3, 2013, and accepted in revised form April 10, 2014. Address correspondence to:  Behazine Combadiere ,  INSERM UMR 1135 ,  Centre d'Immunologie et des Maladies Infectieuses -Paris (Cimi-Paris ), 91  Boulevard de l'Hôpital, 75013 Paris, France.
Demographic data and study design The study is based on a collective of twenty representative specimens of each tumour entity (TC, AC, LCNEC, SCLC) for mRNA expression analysis. The initial diagnosis was re-evaluated by two experienced pathologists (JWO, THA). Inclusion criteria were sufficient tumour material and minimum of contamination by lymphocytic and stromal cells of the FFPE tissue. Specimens were collected at the Department of Pathology and Neuropathology at the University Hospital Essen, Germany from 2005 till 2012. TNM-staging was based on the WHO Classification Of Tumours guidelines (2004)  [18]. The study was conducted retrospectively for the identification of biomarkers to distinguish different types of pulmonary neuroendocrine tumours. Patients that received chemotherapy before resection of tumour tissue specimens were excluded.
RNA Extraction and RNA Integrity Assessment Three to five paraffin sections with a thickness of 4 µm per sample were deparaffinised with xylene prior to RNA extraction using the RNeasy FFPE kit (Qiagen, Hilden, Germany) according to the manufacturer's recommendations. RNA concentration was measured using a Nanodrop 1000 instrument (Thermo Fisher Scientific, Waltham, USA). RNA integrity was assessed using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, USA) at the NanoString nCounter Core Facility at the University of Heidelberg, Germany. Smear analysis was performed using the Agilent 2100 expert software to determine the proportion of RNA ≥300 nucleotides (nt) within a given sample.
The Structure of the Fap7-Rps14 Complex Is Conserved from Archaea to Yeast In order to both confirm the structure in solution of yeast Fap7-Rps14 complex and determine the contribution of the nonconserved C-terminal extension of yFap7, we conducted SAXS studies of yFap7 both free and bound to yRps14. The modeling of yFap7 was performed by introducing flexibility on the N-terminal residues of the tag, the LID domains, the b3-b4 loop, and the entire nonconserved C-terminal extension following the a7 helix, and on the b4-a2 loop in yRps14. The yFap7 terminal extension was modeled in the aFap7 conformation bound to aRps14, stacked on the NMP binding domain. The SAXS data recorded on the free yFap7 clearly indicates that the yFap7 structure contains unstructured regions, as the experimentally determined radius of gyration (Rg) of free yFap7 is comparable to that of the Fap7-Rps14 complex (from both yeast and archaea; Table  2 ).
The modeling of yFap7 with these data clearly shows that a conformation of the C terminus packed on the globular domain is not compatible with the SAXS data (Figure  2C ). The most representative structure fits well to the data and shows that the Cterminal extension is unstructured and does not form any contacts with the yFap7 core and NMP domains (Figure  2C ). Because the initial conformation was chosen to ''favor'' the compact conformation during the modeling of the SAXS data, the unwinding of the C-terminal region in order to fit the SAXS data cannot be attributed to an artifact of the modelization protocol. Modeling of the structure of the yFap7-yRps14 complex against the SAXS data shows that the complex adopts the same overall structure as its archaeal homologue, with both the same binding surfaces involved and the same orientation between the yFap7 and yRps14 proteins (Figure  2B ).
The archaeal complex can therefore be used with confidence to model the yeast complex. Surprisingly, the data suggest that the yFap7 C-terminal extension comprising helix g8 adopts the same conformation as the archaeal complex. Firstly, the Rg measured from the SAXS data recorded on the yFap7-yRps14 complex is comparable to that of the archaeal complex, indicating the absence of additional unstructured regions in the yeast complex. In addition, even though the conformation of the C-terminal extension was considered as flexible in the structure calculations, it remains in the same conformation as the aFap7 Cterminal extension in all calculated structures (Figure  2B ). This indicates that despite the poor conservation of this region, the yFap7 C terminus participates in yRps14 binding in the same region as the archaeal complex.
Crystallization, Data Collection, and Processing The crystallization trials were performed using the hangingdrop vapor diffusion technique in 1 ml drops (with a 1:1 protein:precipitant ratio) equilibrated against 500 ml reservoir solution at 18uC. The two different crystal forms were obtained in 0. 2 M MgCl2, 0. 1 M Tris-HCl pH 8. 5, 25% (w/v) polyethylene glycol 3350 with the aFap7-aRps14 complex at 3 mg ml 21  supplemented with 1 mM MgCl 2 and either 1 mM ADP or 1 mM ATP. Crystals were cryoprotected using three successive soaking steps with the reservoir solution containing 10%, 20%, and 30% ethylene glycol. X-ray data were collected at the Soleil Synchrotron (Saint-Aubin, France) on Beamline Proxima1 and at ESRF (Grenoble, France) on ID23-1. For phasing, crystals were soaked in a solution composed of the reservoir solution supplemented with 3 mM potassium tetrachloroplatinate(II).
Data were collected at the absorption threshold of Platinum (1. 0716 A ˚). Native and SAD datasets were indexed using the program XDS  [53] , and experimental phasing and molecular replacement were carried out with the program Autosol and Phaser  [54]  from PHENIX  [55]  and described elsewhere (Loc'h et al. in preparation). Initial rebuilding was carried out with Buccaneer from the CCP4 program suite  [56]  and subsequent rebuilding and refinement with COOT  [57]  and the Refine module from PHENIX. The ATP crystal form was solved by molecular replacement with the ADP bound model of the complex using PHENIX. PDB coordinates were deposited with codes 4cwn and 4cw7 for the ADP-and ATP-bound structures.
SAXS SAXS data were collected on Beamline SWING at the Soleil Synchrotron at an electron energy of 13. 32 keV. Protein samples were centrifuged at 13,200 g for 10 min before data acquisition, and concentration was measured with Nanodrop spectrophotometer (Thermo). Two modes of data collection were used: direct injection and size exclusion chromatography separation prior to data collection. For direct injection, samples at known concentration were loaded at a flow rate of 200 mL min 21 in the SAXS capillary flow cell. For size exclusion chromatography, samples were loaded on an Agilent SEC-3 size-exclusion chromatography column connected to an Agilent HPLC system at a flow rate of 200 mL min 21. In each case, 100 frames, with exposure times of 1 s per frame, were collected at 288 K in the flow cell connected to the HPLC system.
The same buffer was used for all data collection (Tris-HCl 50 mM, NaCl 200 mM, pH 7. 5). Buffer scattering was collected in the same conditions or using the gel filtration profile before the void volume. Data reduction-that is, image conversion to 1D profile, absorption correction, scaling to absolute scale, buffer subtraction, averaging, and peak analysis-was carried out using the program FOXTROT, available on Beamline  [58]. Further processing and data analysis was done using the programs of the ATSAS suite  [59]. Guinier plot analysis was performed using PRIMUS  [60]  with scattering data at low angle regions with qmax*Rg,1. 3. Pair distribution function was calculated with GNOM  [61]. Ab initio analysis was performed with DAMMIF  [62]. The 50 DAMMIF calculations were averaged with DAMAVER  [63]  to produce averaged and filtered shape.
Homology models were made with MODELLER  [64]. Modeling with SAXS data was done with DADIMODO  [65]. Analysis of the fit between model and experimental data was done with CRYSOL  [66]. The x2 value (Chi) against the experimental SAXS profile and the residual are computed with the Crysol program and defined as: x 2 ~X Q k~1 I exp (q k ){c:I m (q k ) s exp (q k ) , q k is the momentum transfer, I exp the experimental intensity data, I m the intensity calculated from the model, s exp the experimental error, and c a scaling parameter. The residual is defined as: I exp (q k ){I m (q k ) s exp (q k ) : The data collection and processing statistics are reported in Table  2.
RNA Binding Assays Filter binding assays were performed and adapted as described  [36]. The labeled RNA was heated at 65uC for 2 min and immediately placed on ice for 10 min and diluted in binding buffer containing 30 mM Tris-HCl pH 8. 0, 150 mM KCl, 6 mM MgCl2, and 60 mg of E. coli tRNA per ml. Binding reactions consisted of 12 ml of RNA (10 fmol) and 8 ml of proteins (final concentration from 0. 0036 to 3. 6 mM). Binding reactions were incubated at 20uC for 15 min and then applied directly to filters containing the two membranes under gentle vacuum. Before and after application of the binding reactions, 200 ml of binding buffer without tRNA was used to equilibrate and rinse the system. Binding was quantified using a Molecular Dynamics phosphorimager and Image Gauge program (Fujifilm). The Intensity was corrected for background and fit for K d using GNUPLOT (Thomas Williams, Colin Kelley et al.
1 mM) in a final volume of 20 ml. The interaction was tested in the absence and presence of 5 mM MgCl 2 and with addition of AMP-PNP at 0. 1 mM and 1 mM as the final concentrations. Interactions were analyzed on 8% acrymamide-bis-acrylamide native gel, and binding was revealed and quantified using a Molecular Dynamics Phosphoimager and Image Gauge Program (Fujifilm). (C) Betascope image of nitrocellulose filters and 10 fmol per well of helix 23 18S rRNA was incubated with yRps14 (a), yRps14-yFap7 complex (b), and yFap7 (c). Wells 2-8 contained progressively higher concentrations of proteins in steps ranging from 0. 0036 to 3. 6 mM. (D) Filter binding assays. yFap7 competes with RNA (radiolabeled Helix 23 of 18S rRNA) for yRps14 binding using yRps14-MBP and yFap7-His proteins. Binding buffer contains 20 mM HEPES pH 8.
Acknowledgments Work at UPD was supported by  CNRS, University Paris Descartes , the RNPGenesis grant from the  Agence Nationale de la Recherche  ( ANR-11-JSV8-0004 ), the  Institut Universitaire de France  (to N. L. ), and  The foundation Descartes  (to J. J. ). S. L. and D. T. were supported by the  Wellcome Trust  ( 077248 ). We thank  N. Ulryck  and the  B. Sargueil group  for help with the filter binding assays,  S. Granneman  and  R. Hector  for constructs and comments, and  F. Guillonneau ,  P. Mayeux ,  M. Ries , and  M. Leduc  ( Plateforme Proteomique UniversiteParis Descartes (3P5), Paris, France ) for mass spectroscopy analysis and discussions.
MATERIALS AND METHODS Three types of aerial imagery were used in the stage of photogeological interpretation which preceded field work: Nine stereographic pairs (lateral aerial view photos) in the 1:70. 000 scale from a 1968 flight; LANDSAT-7 2000 30-m resolution radar imagery; and a digital terrane elevation model generated using Golden Software Surfer 8 © , using topographic data from the SRTM mission (http://seamless. usgs. gov), with a resolution of 3'' (90 meters). The Rio Preto fold belt (Figure  2 ) is composed by the Cristalândia do Piauí Complex to the north, representing the basement of the fold belt, and the Formosa and Canabravinha formations, which represent Proterozoic metasedimentary sequences of the supracrustal structure of the fold belt  (Egydio-Silva 1987 , Egydio-Silva et al.
1989 , Caxito 2010 , Caxito et al. 2012a ). The Cristalândia do Piauí Complex is composed mainly For the microstructural description of the rock units, 50 thin sections from selected samples were made. Samples were oriented and collected in the field according to the procedures of  Passchier and Trouw (1996). The lattice preferred orientation (LPO) analysis of two quartzite oriented sections was performed at Université de Montpellier II, France, through the electron backscatter diffraction (EBSD) technique, using a JEOL JSM 5600 scanning electron microscope equipped with an Oxford Instruments/HKL Nordlys EBSD detector and Channel 5 ® software.
ACKNOWLEDGMENTS We are grateful to  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  (Process nº  475510/2008-9 ) and  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)  ( CRA 505-06 ) for the financial support, and the latter for providing scholarship for the first author Master's. We would also like to thank geologist  Felipe Rodrigues Martins  for his assistance in field.
According to the Protein Database (PDB) where all known 3D structures of proteins are stored, the most observed quaternary structure in all taxa is the dimer (Fig. 1A ). A taxon is a set of living organisms grouped because of some shared characteristics. Besides dimers, there exists a large variety of assemblies in terms of quaternary structures and point group symmetries (Fig. 1 ). In forming a protein oligomer, subunit association has to be considered in addition to folding. Folding involves the formation of interactions/bonds between atoms of the amino acids of a single chain. These are intramolecular (within a single molecule) amino acid interactions (Fig. 2A ). Chain association involves the formation of interactions/bonds between atoms of the amino acids provided by at least two individual chains.
Intermolecular amino acid interactions Protein interfaces have been extensively investigated  [14, [30] [31] [32] [33] [34] [35]. But because protein interfaces are large and rather flat in nature, they lack the spatial constraints achieved by a limited number of sequences. Thus the sequences of protein interfaces rarely share trivial profiles or patterns, in contrast to protein-small molecule interfaces or residues involved in enzymatic active sites  [36, 37]. Thus, it has been clear very early on that looking at the 3D structures of protein interfaces was a necessary alternative to sequence analysis  [34, [38] [39] [40] [41] [42] [43]. The increasing number of 3D-structures available for oligomeric proteins has also favored such investigations and the development of computational methods to identify and study the amino acids at protein interfaces on large scale datasets.
Identification of the intermolecular contacts at protein interfaces Many algorithms are available to identify the amino acids involved in intermolecular interactions from the x-ray coordinates of the 3D-structure of a protein oligomer (reviewed in  [13, 21, 45] ). The coordinates are accessible at the protein database (PDB, http://www. rcsb. org/pdb/ home/home. do) [46]. There are databases were interfaces have been classified according to their 3D organization, residue conservation and residue types  [47]. Some databases are used to complement cellular networks (interactomes) with structural information on the binding modes between cellular partners  [12, [18] [19] [20] 48]. The classical algorithms are based on three different measures: (i) accessibility surface area, (ii) voronoi cells and (iii) arithmetic distances.
Accessible surface area (ASA). The first method calculates the solvent accessible surface area by rolling a probe of a given radius around the Van der Waal's surface of the protein atoms whose centre is the accessible surface  [49]. Typically, the probe has the same radius as water (1. 4 Å) and hence the surface described is referred to as the solvent accessible surface. The ASA are calculated for the monomer and for the oligomer and the interface residues are obtained by the difference in their ASA. ASA is currently used to discriminate biological contacts (large ASA, 1600 ± 400 Å 2 )) from crystal ones (small ASA <400 Å 2 )  [50, 51]. The PDB entries are now processed accordingly and provide both biological assembly and asymmetric unit coordinates. The biological assembly entry includes a remark to explain whether the oligomeric state is "author provided" (experimentally shown to be an oligomer) or "software determined" or both.
Hotspots-In the mid-nineties, the specific energetic contribution of the side chain atoms to protein interfaces was investigated using Alanine Scanning Mutagenesis because the mutation by alanine reduced the interactions to backbone atoms  [66]. It was found that only a small subset of interfacial residues were sensitive to an alanine mutation indicating that the energetic contribution of the interfacial residues was not distributed uniformly. Some key 'hot spot' residues contributed dominantly to the binding free energy. Thorn and Bogan deposited hot spots from alanine scanning mutagenesis experiments in the ASEdb database (http:// nic. ucsf. edu/asedb/) [67]. BID (The Binding Interface Database) is another database of experimental hot spots, which collects all available experimental data related to hot spots in protein interfaces(http://tsailab.
Features of the intermolecular contacts at protein interfaces To infer the features of protein interfaces, the method is simple: a dataset of protein oligomers/ protein interfaces is built, an algorithm is applied to each one of the interfaces to identify intermolecular contacts and the features of the intermolecular contacts are analyzed using statistics. Classically the parameters to describe a protein interface are: (i) interface size, expressed either in number of amino acids or in ASA, (ii) the number of regions of interfaces over the full-length chain, (iii) the chemical properties of the amino acids (amino acid frequency, the interface propensity, namely the frequency of a residue a i in the protein interface divided by its frequency in a reference set, generally the full-length chain).
Dataset based on features of the full-length protein Many dataset are built on proteins sharing properties at the level of their full-length chains (function, organism, superfamily, folds, and quaternary structures) but without necessarily sharing features at the level of their interfaces  [39, [87] [88] [89]. In particular, the geometries of the interfaces are not necessarily looked at and therefore interfaces with different geometries are often compared. But it is generally assumed that proteins related in terms of folds or functions associate in similar ways. However, a screen over a large dataset of dimers, performed by Keskin et al. has shown that a non-negligible amount of protein oligomers have interfaces sharing features although they have different folds and functions  [90]. This set is referred in the paper as "type II", following the term "type I" used for protein interfaces sharing features and derived from protein oligomers having similar fold and/or functions.
To establish the determinants of the construction of an interface it is simpler to look at type II interfaces because the pressure of evolution over the fold and the function of the protein chain is alleviated compared to type I interfaces. Globally, the results of studies on protein interface dataset (mainly type I) revealed the importance of hydrophobic interactions in the formation of protein interfaces, greater residue conservation and chemical property similar to surface residues but packing like core residues  [34, 38]. The two latter properties are coherent with the fate of a protein interface. The topology of a soluble protein is defined by surface residues which are accessible to the solvent and core residues which are, on the contrary, buried and inaccessible to the solvent. The amino acids of a protein interface have the solubility requirement of surface residues because the domains of the interface are initially accessible to the solvent to allow binding.
To have stable binding, the domains need to minimize void and maximize packing as for the core residues. If the role of hydrophobic residues is consistent over any dataset, the importance of polar and charged residues in interfaces varies very much between datasets. Altogether this indicates that hydrophobic residues are involved in promiscuous interactions while polar and charged residues yield alternative recognition modes and hence provide each type of interfaces its specificity. Up to date, there is no single property sufficiently unambiguous to identify the protein interface from the rest of the protein, and considerable disagreement exists on which properties are actually useful. Conservation is an excellent example of a property both widely used and widely debated. De Vries and Bonvin as well as Neuvirth raise the matter of having so many algorithms and the absence of consensus on the parameters truly relevant to the formation of a protein interface  [45, 91].
Their formation depends on interactions between atoms of the backbone, result which has led to the proposal that aggregation is a generic property of the polypeptide chain  [100, 101]. They adopt a cross β structure which can be predicted from sequences by the PIRA (Parallel 'In Register' Arrangement) model, a network made of single pairs of residues  [102] [103] [104] [105] [106] [107]. Different predictors of the aggregationprone sequences involved in the fiber formation are now available  [96, 98, [108] [109] [110] [111]. We have studied a dataset of 1056 interfaces present in 755 protein oligomers not known to be involved in conformational diseases  [59]. As others, we found no specificity at the level of individual hot spots. The chemical properties of the individual hot spots and their distribution on the sequence characterize only the secondary structure and the solubility of the β-strands.
In contrast the interaction pairs provide the interface some specificity. Interestingly, the interfaces are best described by two sets of interaction pairs, pairs involving backbone atoms made essentially of hydrophobic and/or small residues and pairs involving at least one atom of the side chain, preferentially made of charged, polar, long and medium residues. The backbone pairs have properties common to intramolecular β-strand interactions and intermolecular β-strands involved in fiber formation in terms of amino acid preferences. Thus hydrophobic amino acids whether in pairs or as individual are not giving any specificity to interfaces. That explains that they always appear in any dataset. On the other hand the side chain pairs have particular geometrical characteristic in terms of number of atoms, branching and length.