dataesr/scientific-paragraphs-categorization huggingface dataset:
CLADE: closer sequences for annotations directed by evolution CLADE is a computational approach that highly increases the sensitivity of domain prediction. It is a multi-source approach where several hundred probabilistic profiles are used to represent each domain, instead of one as for mono-source strategies, employed by methods like HMMer  [9, 10]  and HHblits  [12]. CLADE predicts domains based on two classes of probabilistic profiles. The first is the profile library available in the Pfam database (version 27). There are 14,831 profiles, one for each domain. These profiles capture the consensus of homologous sequences, and the idea behind them is that homologous proteins should share common physico-chemical and structural features that could be described by consensus on the entire set of homologs.
The second class of profiles is constituted by hundreds of probabilistic models, associated to each Pfam domain. They are constructed starting from homologous sequences spreading a large panel of taxonomic origins, to guarantee that the phylogenetic tree is well represented. For each selected homologous sequence, a specific profile, named "clade-centred model" (CCM), is constructed. To construct a CCM, the selected homologous sequence is used as a query to search for close homologs within the non-redundant protein database (NR) with PSI-BLAST  [17]. The CCM is the resulting PSI-BLAST profile. Note that PSI-BLAST constructs the CCM profile from NR sequences detected with an E-value threshold set to 1e-3 by default. CLADE uses this PSI-BLAST default E-value (1e-3) and sets the number of PSI-BLAST iterations to 5.
Data availability Plasmobase website provides access to downloadable xls files containing the full list of annotations for the 11 Plasmodium species. Each file contains, for each domain hit, the PlasmoDB accession number of the sequence where the hit is identified, its starting and ending position, the Pfam domain name, the Pfam accession number, the E-value, CLADE SVM probability, the model identifier in the CLADE model library (either a clade-centred model or a Pfam consensus model), the start and the end position of the hit either in the domain sequence used to construct the clade-centred model or in the Pfam consensus model, the clade name (if any) and the organism name of the sequence originating the clade-centred model used for the domain identification. Note that if a protein sequence is annotated by several domains, the file contains several rows, one for each domain annotation.
The Plasmobase platform The Plasmodium database Plasmobase is a friendly interface allowing users to search by domains (with a Pfam accession number or a Pfam description), proteins (with a protein accession number or a protein annotation) or domain architectures (with a list of Pfam accession numbers) on all Plasmodium species or on a specific one. As a result, it provides the list of corresponding proteins with their accession number, Plasmodium species name, PlasmoDB annotation, a list of domains forming the predicted architecture (where new domains are highlighted), and the accessibility ("Look up" link) to a graphical interface providing comparative information on the predicted architecture. Each protein in the list is linked to its Plas-moDB description, and each domain forming the protein architecture is linked to its Pfam description.
The "Pfam-27 architecture" box shows the annotation proposed by the Pfam database version 27, where domains are predicted by HMMer and the architecture is obtained by employing a simple strategy that considers highest confidence domains without overlapping. For PF3D7_1369500 no domain was predicted by HMMer, and an empty box is shown. This display is accessible by clicking on the "show" link. (In Fig. 1 , the button "hide" allows hiding the display). Orthologs and paralogs according to PlasmoDB can also be displayed by clicking on the "show" link. Note that all PF3D7_1369500 orthologs are identified by PlasmoDB as "conserved Plasmodium protein, unknown function" or "hypothetical protein". The list of all overlapping domains identified by CLADE, but not selected by DAMA to belong to the proposed architecture, is shown in the "All CLADE domains" box.
1 ). Each protein accession number is provided together with the species name, the length of the sequence and the PlasmoDB annotation. The display of the associated Plasmobase architectures can be obtained by clicking the "Compare orthologous/paralogous architectures" button. Figure  2a  (left) shows all the Plasmodium species having protein sequences with a similar architecture to PF3D7_1369500 in Plasmobase. The proportion of proteins including the same co-occurring domains is shown by a pie chart. There are 10 orthologous proteins, in 10 Plasmodium species. The only exception is P. cynomolgi where MIF4G_like_2 was not identified. Architectures can be compared in more details by ticking some or all organisms and pressing the button "Compare architecture in Plasmobase". In this example, P.
Improvement over PlasmoDB Table  3  compares the number of domains, found in each species, reported in PlasmoDB and Plasmobase. Plasmobase identifies a large number of Pfam domains in each Plasmodium species when compared to Plas-moDB predictions. These latter are based on HMMer (hmmscan)  [9, 10]  using the Pfam model library. The Fig. 2  Proteins with similar architectures explored in Plasmobase/UniProt. a All Plasmodium species contain a protein sequence sharing the same CLADE architecture as PF3D7 1369500. A selection of these species allows to explore these protein sequences in Plasmobase, and verify information for domain architecture identification in other species. The whole list of domains is reported (in this specific example, there is only one architecture per species). b Plasmobase allows to explore the UniProt database for architectures that are similar to the one identified by CLADE for PF3D7 1369500.
Comparison with EuPathDomain The EuPathDomain  [20]  is a protein domain database dedicated to ten eukaryotic human pathogens, including three Plasmodium species: P. falciparum 3D7, P. vivax Sal-1 and P. yoelii yoelii 17XNL. It uses hmmscan with permissive E-values to generate a set of potential domains, CODD  [6]  is then used to predict domain architectures based on domain co-occurrence. Plasmobase improves over EuPathDomain in several manners. When compared with EuPathDomain, Plasmobase: 1. considers 11 Plasmodium species while EuPathDomain considers just 3 organisms,  All information contained in the database can be downloaded in xls files, one for each species, permitting the user to process the information in alternative ways.
Plasmobase contribution to new protein annotations In Plasmobase, the user can explore and compare a large number of new domain annotations helping to the identification of a protein function. The protein PF3D7_1369500 illustrated in Fig. 1 , for instance, contains three domains MIF4G/MIF4G-like obtained with E-value 1e-21, 1e-19 and 1e-8 and known to co-exist within protein sequences of metazoan, fungi and viridiplantae. In Plasmobase, this information, coming from UniProt, is accessible. The 96 UniProt metazoan sequences can be listed and their UniProt description can be looked up by clicking the protein accession number. By so doing, the user discovers that for these sequences, the architecture suggests the involvement of the protein in RNA metabolism and its role of RNA binding protein, but that its annotation goes from "uncharacterized protein" to "nuclear cap binding protein sub unit 1 (NCBP1)" inferred by homology, by experimental evidence at transcript level (in Mustela pitorius furo et Xenopus laevis) and by experimental evidence at protein level (in Mus musculus and Homo sapiens).
Funding This work undertaken (partially) in the framework of CALSIMLAB is supported by the public grant  ANR-11-LABX-0037-0  from the " Investissements d' Avenir" programme  ( ANR-11-IDEX-0004-02 ). Experiments were carried out using the  UPMC MESU machine  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the " Investissements d' Avenir" program. Funds from the  Institut Universitaire de France (AC).
Complexes 1 and 2 are luminescent, whereas complex 3, comprising of a quenching ferrocenyl moiety, does not  [38]. Accumulation of 1-3 in mitochondria is observed for HeLa cells via confocal microscopy. Upon prolonged laser irradiation, 2 displays photocytotoxic activity. Comparative analysis of the data collected for the three compounds and results obtained for the adducts of 1 and 3 with model proteins enables us to propose a mechanism explaining the molecular basis of the mitochondrial accumulation of 1-3.
Moreover, for several compounds photocytotoxic and/or cytotoxic activity has been observed  [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37]. Herein we report the synthesis and characterization, including X-ray structure analysis, of three new fac-[Re(CO)3(1,10-phenantroline)L] complexes, hereafter called 1-3, with different ancillary ligands: L = heptanoic acid (1), hept-6-ynoic acid (2), and 4-(1-ferrocenyl)-butanoic acid (3), respectively. Complexes 1 and 2 are luminescent, whereas complex 3, comprising of a quenching ferrocenyl moiety, does not  [38]. Accumulation of 1-3 in mitochondria is observed for HeLa cells via confocal microscopy. Upon prolonged laser irradiation, 2 displays photocytotoxic activity. Comparative analysis of the data collected for the three compounds and results obtained for the adducts of 1 and 3 with model proteins enables us to propose a mechanism explaining the molecular basis of the mitochondrial accumulation of 1-3.
Single-Crystal X-ray Structural Analysis of Complexes 1-3 The molecular structures of 1-3 were determined by single-crystal X-ray analysis. Crystals of 1 and 3 that were suitable for X-ray diffraction analysis were obtained from dimethyl sulfoxide-water mixture, whereas crystals of 2 were obtained by slow diffusion of n-pentane into a dichloromethane solution of 2. It turned out that the investigated compounds crystallize in the P2 1 /n (1 and 2) or P2 1 (3) space group of the monoclinic crystal system with one (1 and 2) or two molecules (3) of the given compound in the asymmetric unit, respectively. The Oak Ridge thermal ellipsoid plot (ORTEP) drawing of 1, 2, and 3 is shown with atom numbering in Figure  2. Details of crystallographic data and refinement parameters are summarized in Table  S1.
X-ray Crystallography Good quality single-crystals of 1-3 were selected for the X-ray diffraction experiments at T = 100(2) K. They were mounted with paratone-N oil at the MiTeGen micro-mounts. Diffraction data were collected on the Agilent Technologies SuperNova Dual Source diffractometer with MoKα radiation (λ = 0. 71073 Å) using CrysAlis RED software (CrysAlis CCD and CrysAlis RED, Oxford Diffraction, Oxford Diffraction Ltd. : Yarnton, UK, 2008). The analytical numerical absorption correction using a multifaceted crystal model based on expressions derived by R. C. Clark & J. S. Reid  [59]  (1 and 2) and numerical absorption correction based on gaussian integration over a multifaceted crystal model (3) implemented in SCALE3 ABSPACK scaling algorithm, were applied  [60]. The structural determination procedure was carried out using the SHELX package  [61].
The structures were solved with direct methods and then successive least-square refinement was carried out based on the full-matrix least-squares method on F 2 using the XLMP program  [61]. All H-atoms were positioned geometrically, with C-H equal to 0. 93, 0. 96 and 0. 97 Å for the aromatic/methylidyne, methyl and methylene H-atoms, respectively, and constrained to ride on their parent atoms with U iso (H) = xU eq (C), where x = 1. 2 for the aromatic, methylidyne and methylene H-atoms, and x = 1. 5 for the methyl H-atoms. The figures for this publication were prepared using ORTEP-3 program  [62].
ESI MS measurements were carried out using an LCQ DECA XP Ion Trap mass spectrometer (ThermoElectron, Milan, Italy) equipped with an OPTON ESI source (operating at 4. 2-kV needle voltage and 320 • C) in positive mode. Multicharge spectra were deconvoluted using the BioMass program implemented in the Bioworks 3. 1 package provided by the manufacturer. In these experiments, proteins were incubated in the presence of the two compounds for 24 h at room temperature in 1:10 protein to metal ratio, 20 mM ammonium acetate pH 6. 8. Surface Plasmon Resonance (SPR) experiments were performed at 25 • C on a Biacore 3000 instrument (GE Healthcare, Buckinghamshire, UK). HEWL was immobilized at 1095 RU, on a CM5 Biacore sensor chip in 10 mM sodium acetate pH 5. 5, by using the EDC/NHS chemistry, with a flow rate of 5 µL × min -1 and an injection time of 7 min.
Acknowledgments: The X-ray single-crystal diffraction studies were carried out at the  Biological and Chemical Research Centre, University of Warsaw , established within the project co-financed by  European Union  from the  European Regional Development Fund  under the  Operational Programme 'Innovative Economy ', 2007-2013. This study was also supported by the  National Science Centre Poland MAESTRO  grant -DEC-2012/04/A/ST5/00609  (D. T. and K. W. ), which enabled the X-ray structural analysis to be performed. RC thanks the  European Research Council (ERC)  for support in the framework of the  MSCA RISE  Project no. 645628.
Patients underwent baseline 24-h cough recording with the Leicester Cough Monitor (LCM) prior to starting pirfenidone, and after 4 and 12 weeks of treatment. The LCM is a validated ambulatory cough monitoring system  [7]. Recordings were centrally analysed with automated cough software as previously described  [7]. On the days of cough recording, participants completed the Leicester Cough Questionnaire (LCQ, a cough-specific health status questionnaire), the VAS cough severity, VAS urge-to-cough, King's Brief Interstitial Lung Disease health status questionnaire (K-BILD), the Hospital Anxiety and Depression Scale (HADS) and the seven-item Generalised Anxiety Disorder screener (GAD-7)  [8, 9]. Analyses were performed on the intention-to-treat population. Objective cough frequency data were log transformed.
To assess the effect of pirfenidone on cough over time we used a linear mixed model, which accounts for missing data  [10]. Sensitivity analyses were performed on the per protocol population with a linear mixed model and paired t-tests. SPSS Statistics version 21. 0 and R version 3. 2. 2 software packages were used for statistical analysis. The study was approved by the ethics committees of all participating centres (clinical trials. gov, NCT02009293). All patients provided written informed consent. Of the 46 patients screened for the study, 43 patients were included. Reasons for exclusion were low TLCOc and/or VAS scores. 38 patients completed 4 weeks of the study, and 31 completed 12 weeks. Reasons for dropout included: cessation of pirfenidone because of adverse events (8), logistic reasons (2), change of proton pomp inhibitor (1) and death (1).
Acknowledgements: The authors thank  Marion Durand  ( Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France ),  Marjolein Gerrits  ( Erasmus University Medical Center, Rotterdam, The Netherlands ) and  Claire Wood  ( King's College London, London, UK ) for clinical research assistance, and  Daan Nieboer  ( Erasmus University Medical Center ) for statistical support. Author contributions:  S. S. Birring ,  C. Vancheri ,  V. Cottin  and  M. S. Wijsenbeek  designed the study. M. J. G. van Manen ,  C. Vancheri ,  V. Vindigni ,  E. Renzoni ,  A. Russell ,  V. Cottin  and  M. S. Wijsenbeek  were involved in data collection. M. J. G. van Manen ,  S. S. Birring ,  M. Wapenaar  and  M. S. Wijsenbeek  performed data analyses. M. J. G. van Manen  and  M. S. Wijsenbeek  drafted the manuscript. All authors interpreted the data and worked collaboratively to review and prepare the final article.
Pressure-volume (PV) loop investigation was performed using the Powerlab Equipments and software, adapted for large animals (ADInstruments, Dunedin, New Zealand). After completing the baseline investigations, bare metal stents (9 mm of diameter, 20 mm of length, Cordis S. M. A. R. T. CONTROL, Cordis, Fremont, California) were implanted in the descending aorta immediately under the arcus aortae (Figs. 1  and 2 ) in 7 pigs (HYpertrophic PIgs, Group HYPI), while sham procedures were performed in 5 pigs (Group Control). Pressures of the ascending and descending aortae pre-and post-stent were measured via the pigtail catheter. After the final MRI investigation at the 5-month follow-up, during continuous deep anaesthesia (1. 5-2. 5 vol% isofluran, 1. 6-1. 8 vol% O 2 and 0. 5 vol% N 2 O), the pigs were administered an additional dose of intravenous heparin (10,000 U).
The mean of two technical replicates was used for each individual sample. A standard curve was prepared and run for each gene for assessing PCR efficiency. Expression quantities are expressed relative to beta-Actin. Sequences of pig miRNAs were derived from the miR-Base database  [12]. For miRNA quantification small RNAs were transcribed to cDNA using the Qiagen miScript II RT Kit according to the manufacturer's instructions and qPCR was performed using the Qiagen qPCR Sybr Green Kit with the Universal Primer and an miRNA specific primer (mir-1: GCAGTCTGGAATGTAAAG AAG, mir-29a: CGGACCTAGCACCATCTGAA, let7a: GCAGTGAGGTAGTAGGTTGT, miR-26: GCACGG TTCAAGTAATCCAGG, miR-27: GCTAGTTCACAGT GGCTAAG, Qiagen miScript primer assay for RNU6B). The mean of two technical replicates was used for each individual sample.
Acknowledgements The authors want to express their gratitude to all other partners of the Fibro-Targets consortium:  Catherine Closel  and  Marie-Alix Fauvel ,  Institut National de la Santé et de la Recherche Médicale-Transfert SA, France ;  Javier Díez  and  Arantxa González ,  Fundación para la Investigación Médica Aplicada, Spain ;  Kenneth Mac Donald ,  University College Dublin, National University of Ireland, Ireland ;  Stephane Heymans  and  Anna Papageorgiou ,  Universiteit Maastricht, The Netherlands; Quoc-Tuan Do, Greenpharma S. A. S. , France ;  Hueseyin Firat  and  Kaidre Bendjama , Firalis S. A. S. , France;  Isbaal Ramos  and  Clarisa Salado ,  Innovative Technologies in Biological Systems, Spain; Natalia López-Andrés, Fundación Pública Miguel Servet, Spain.
Global Reference Image Validation Method This activity consists in validating the completeness (coverage) and geolocation performance of the Global Reference Image that will be used in the ground processing to refine the geometric model of images by image matching and spatiotriangulation techniques. Note that at the time of writing, the validation of North American block is on-going. GRI being a collection of L1B products, the nominal method for assessing its geolocation performance is exactly the same as the one used for geolocation validation on L1B products. The only difference is that the geometric model of the GRI has been refined by IGN. This assessment is based on detection of Ground Control Points (GCP) by correlation with a database of accurately localised images spread over the world.
The estimation of performance is then directly in metres and accounts for all contributing factors. The most important constraint of this method in view of a global validation is that it requires a lot of GCP as reference images worldwide. Those GCP should be independent from those used for the GRI construction by IGN. Pleiades High Resolution DataBase (BDPHR) including more than 500 images with accuracy less than 5 m is used by CNES for the GRI geolocation performance validation. BDPHR accuracy is estimated at 5m CE95. This performance is measured with the help of around 15 sites over the World (Figure  4 ) with 25 GCP for each site very-well geolocalised: planimetric precision between 0,1m and 3m. Few of these points are also used for GRI generation but GRI generation uses a lot of other GCP.
e. not projected to ground), which means that the DSM can only be generated from L1B products (sensor geometry). Those products can be generated by CNES in the frame of its cooperation with ESA on the Sentinel-2 mission. Also, since the triangulation process is very sensitive to attitude angular error, sensor modelling of those images should be refined using the GRI dataset. Each L1B product is pre-processed so as to stitch all sub-swath (detectors) into a single image and to compensate residual oscillations. The corrected, stitched perfect sensor products can then be fed into the DSM generation tool.
Data selection and coverage assessment In order to analyse coverage opportunities and automatically select qualified data for a given area, we developed a small tool to parse the entire Sentinel-2 archive and return the product set given an area to produce, the required period of the year, the expiration time of data and the maximum cloud cover. As shown in figure  21(a-b ), if we consider the entire European area, this tool allows us to determine for a given date what fraction of the area we can actually produce given the current available archive. For instance, for a DSM in august with one month expiration time and cloud coverage lower than 10 %, we can see that around half of the theoretical area can be produced with available data. For the same area in spring, this fraction falls down to only 20 %.
In the frame of this work, we focused on a 4 squared degrees area over Sweden, for which reference data were available and fraction of DSM that can be produced given current archive is almost 100 %. Figure  21(c-d ) shows the coverage available for both dates in this area. For the summer date, the tool selected 7 products, whereas only 6 products where selected for the spring date. Each set of products comes from 4 different orbits. Figure  21. Area that can actually be produced given current archive for two dates (one in summer and one in spring), with a time frame of one month and cloud cover less than 10 %, over the whole GRI area, and the Sweden test site. Blue areas represent overlap between adjacent orbits, while green (resp. red) areas represent overlap between 1 orbit (resp. 2 orbits).
DSM generation DSM generation has been carried out with the open-source tool S2P developed by  CMLA and CNES (De Franchis, 2014 and Michel, 2015). This tool uses local stereo-rectification to increase matching performance and is built with a modular architecture that allows to change the matching (or disparity estimation) algorithm. In this work, matching has been carried out by the in-house correlation tool Medicis  (Cournet, 2016) , which seems the most appropriate given the observed landscape and allows efficiently filtering bad matches over shadows, cloud or watering areas. Some modifications were made by CNES to the S2P software. The major one concerns the use of the viewing directions intersection method  (Delvit, 2006) , instead of the height maps fusion method. Other modifications were made to accommodate some corner cases and the specificity of this work.
The first step occurs right after disparity has been estimated. Validity flags from the Medicis tool are used to filter out bad matches corresponding to shadows, clouds or water. Left-right consistency is also used to this end. The second post-processing step occurs once point clouds have been generated. A statistical filter is applied to filter outliers based on the mean distance to the K nearest neighbours of each point. The PCL open-source software has been used to this end  (Radu Bogdan Rusu, 2011). For each image used as reference image, a set of points clouds is generated. Those 3D points clouds can then be fused to increase completeness of the final DSM. Because of clouds, shadows or water areas, it might be interesting to use several images from the same orbit for this purpose. Fusion is done at point cloud level, i.