dataesr/scientific-paragraphs-categorization huggingface dataset:
CONDITIONS OF SUCCESS AND IMPACT The development of a European programme of clinical investigator training and certification aims at further increasing the quality of clinical trials in Europe, in order to support the faster discovery and development of better medicines for patients and to enhance Europe's competitiveness, which are the goals of the IMI. At the end of the day the impact of investigator training on the quality of clinical research will need to be evaluated using qualitative and quantitative indicators, such as a decrease in the number of observations during audits and inspections. Success depends on the full cooperation and involvement of investigators, sponsors, ethics committees, medical associations and regulatory authorities. In particular the pharmaceutical sponsors should view clinical investigator training as a new opportunity to enhance quality and efficiency, with the ultimate aim of accruing more patients data in European countries and facilitating EU regulatory filings.
The Global Emissions InitiAtive, GEIA (http://www. geiacenter. org/), is a center for emissions information exchange and competence building created in 1990 in response to the need for high quality global emissions data  (Graedel et al. , 1993). While the past two decades have seen considerable progress in developing, improving and assessing emission estimates, emissions continue to be a major contributor to overall uncertainty in atmospheric model simulations. Moving forward, GEIA aims to help build emissions knowledge in a rapidly evolving society by: 1) enhancing understanding, quantification, and analysis of emissions processes; 2) improving access to emissions information; and 3) strengthening the community of emissions groups involved in research, assessment, operations, regulation and policy.
geiacenter. org/analysis/working-groups). GEIA also liaises with other scientific initiatives addressing emissions, including the IGAC/iLEAPS/WMO Interdisciplinary Biomass Burning Initiative (http://mpic. de/projekte/ibbi), and with teams developing historic emissions datasets for the Coupled Model Intercomparison Project and the Intergovernmental Panel on Climate Change Assessment Reports. More groups and partnerships will form through dialogs identifying emissions challenges (e. g. , changes in energy sources) and establishing paths for translating these challenges into improved emissions databases. Access. GEIA seeks to advance accessibility to, and improved transparency of, emissions data and observations by improving how databases interact with each other and building on evolving interoperability efforts  (Husar et al.
, 2008). In collaboration with the GEO Air Quality Community of Practice (http://wiki. esipfed. org/index. php/GEO_AQ_CoP) and the Federation of Earth Science Information Partners (http://www. esipfed. org/), GEIA is implementing the infrastructure and insights demonstrated by the Community Initiative for Emissions Research and Applications (http:// www. geiacenter. org/access/interoperability). These and other groups are working to develop data standards, conventions, and metadata for Earth science datasets. Data interoperability is enabling the Earth science community to develop on-line visualization and evaluation tools, with the goal of creating a virtual workspace for emissions analysis and forecasting applications. GEIA will continue to provide and expand the datasets, algorithms, and documentation available through its Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) portal (http://pole-ether.
fr/eccad), with an emphasis on distributing consistent emission inventories and models for research and assessment efforts (e. g. , TF HTAP, CCMI, and observational field studies) and ancillary datasets that inform inventory development. GEIA is also building a searchable bibliography for journal articles and reports on emissions. Community. GEIA works to broaden and strengthen interactions between producers and users of emissions information by q Something to say? Comments on this article, or suggestions for other topics, are welcome. Please contact: new. directions@ uea. ac. uk, or go to atmos_env@uea. ac. uk http://www. elsevier. com/wps/find/journaldescription. cws_home/246/ authorinstructions for further details. introducing innovative communication strategies, including its highly interactive new web site with forums for discussing emission information and challenges, and by organizing international events, such as the upcoming GEIA 2014 Conference (http://www.
If these recordings are made available to the research community, such recordings can be used as reference points for researchers to compare each others work. There are now several real-world noise databases, for example the AURORA-2 corpus  [5] , the CHiME background noise data  [6] , and the NOISEX-92 database  [7]. Unfortunately, these databases provide only a very limited variety of environments, are limited to at most 2 channels and, with the exception of CHiME, are not free. In current research projects, we are investigating the use of source separation algorithms and beamforming techniques for signal enhancement and acoustic noise suppression/removal where the signal is captured using a multi-microphone array. Since the above mentioned databases do not provide more than two channels, we decided to create our own set of recordings and make these available under a Creative Commons Attribution-ShareAlike 3.
MICROPHONES, AMPLIFIER, AND A/D CONVERTER The array used 16 Sony ECM-C10 omnidirectional electret condenser microphones. They were connected to an Inrevium / Tokyo Electron Device TD-BD-16ADUSB USB soundcard, which internally used Asahi Kasei AK4563A 16-bit A/D converters with internal preamps. The soundcard was connected to laptops running either Microsoft Windows or the Linux operating system. The choice of operating system did not affect the recordings.
DATABASE DESIGN The database of recordings was designed to consist of six broad categories, with three environments being recorded within each category. Four of these categories consisted of enclosed This emphasis on indoor environments reflected the fact that the microphone array used for the recordings was not well suited to outdoor use, especially in less than ideal weather conditions (the array had no protection against rain or wind). Descriptions of the categories and the recordings within each category are given in Table  1. All recordings were made in Rennes (France) and its immediate vicinity, in the period between May and August 2012. Three environments were omitted from the initial release of the database: DLIVINGR, NRIVER, and SCAFE. These will be added to the database when conditions are suitable to perform more field recordings.
In this study, we report the characterization of the TssK protein encoded by the sci-1 T6SS gene cluster in enteroaggregative Escherichia coli (accession numbers: EC042_4526, YP_006098803. 1). We found that TssK1 interacts with the cytoplasmic domain of the two inner membrane proteins TssL1 and TssM1. We also identified direct contacts between TssK1 and the T6SS tail components TssC1 and Hcp1. Further biochemical and structural characterization of the TssK1 purified protein using size exclusion chromatography, electron microscopy and small angle x-ray scattering showed that TssK1 is trimeric in solution and forms three arm-shaped particles.
0, NaCl 150 mM. Size exclusion chromatography (SEC) was performed on an Alliance 2695 HPLC system (Waters) using KW803 and KW804 columns (Shodex) run in Tris-HCl 20 mM, pH 8. 0, NaCl 150 mM at 0. 5 ml/min. MALS, UV spectrophotometry, QELS and RI were achieved with MiniDawn Treos (Wyatt Technology), a Photo Diode Array 2996 (Waters), a DynaPro (Wyatt Technology) and an Optilab rEX (Wyatt Technology), respectively, as described  (39). Mass and hydrodynamic radius calculation was done with ASTRA software (Wyatt Technology) using a dn/dc value of 0. 185 ml/g. Surface Plasmon Resonance Analysis-Steady state of the interaction between TssK1 and Hcp1 was performed using a BIAcore T200 at 25 °C. All the buffers were filtered on 0. 2 m membranes before use. The HC200m sensor chip (Xantech) was coated with Hcp1 immobilized by amine coupling (⌬RU ϭ 4300).
A control flow-cell was coated with thioredoxin immobilized by amine coupling at the same concentration as Hcp1 (⌬RU ϭ 4100). Binding traces resulting from passage of purified TssK1 (Five concentrations ranging from 3. 125 to 100 M) in HBS-EP buffer (Hepes 10 mM, NaCl 150 mM, EDTA 3 mM, Polysorbate 20 0. 005%) in the Hcp1 and control flow cells were recorded in duplicate. The signal from the control flow cell and the buffer response was subtracted from all measurements. The K D values were obtained using the fitting tool of the BIAevaluation software (BIAcore).
Characterization of the T6SS TssK Component Transmission Electron Microscopy, Single Particle Analysis, and Image Reconstruction-The purified TssK1 protein (50 g/ml) was immobilized on a glow-discharged carbon grid by incubation for 1 min, and then stained with 2% uranyl acetate. Then, the grids were air-dried and 2k ϫ 2k images were collected using a FEI Tecnai Spirit microscope operated at 120 kV at 60,000 magnification with a 3. 5 Å/pixel size. Approximately 5000 particles were selected from ten images using the Boxer program from the EMAN2 package  (40) , extracted into boxes of 72 ϫ 72 pixels, and combined into a single dataset. This dataset was pre-treated using the SPIDER package  (41)  and submitted to maximum likelihood (ML) classification and alignment (42) using the Xmipp package  (43).
The aligned dataset was then classified by principal component analysis (PCA) using SPIDER into classes with Ͼ 10 members/class. The initial model was built to form a shapeless sphere of equivalent size to the class averages and first refined by three-dimensional ML refinement using the class averages and then the entire dataset with a sampling rate of 5°imposing C3 symmetry to obtain the final model at ϳ26 Å resolution as estimated by Fourier Shell Correlation (FSC) and 1 ⁄ 2-bit threshold criterion  (44). The EAEC TssK1 electron microscopy map has been deposited to the Electron Microscopy Databank (EMDB) under accession code EMD-5739. Small-Angle X-ray Scattering Analysis and ab initio Threedimensional Shape Reconstruction-Small-Angle X-ray Scattering (SAXS) analyses were performed at the ID29 beamline (European Synchrotron Radiation Facility, Grenoble, France) at a working energy of 12.
5 keV ( ϭ 0. 931 Å). Thirty microliters of protein solution at 1. 1, 2. 8, 5. 3, 7. 9, 11, and 15. 7 mg/ml in Tris-HCl 20 mM, pH 8. 0, NaCl 150 mM were loaded by a robotic system into a 2-mm quartz capillary mounted in a vacuum and ten independent 10-s exposures were collected on a Pilatus 6 M-F detector placed at a distance of 2. 85 m for each protein concentration. Individual frames were processed automatically and independently at the beamline by the data collection software (BsxCUBE), yielding radially averaged normalized intensities as a function of the momentum transfer q, with q ϭ 4sin()/, where 2 is the total scattering angle and is the x-ray wavelength. Data were collected in the range q ϭ 0. 04 -6 nm Ϫ1. The ten frames were combined to give the average scattering curve for each measurement.
Data points affected by aggregation, possibly induced by radiation damage, were excluded. Scattering from the buffer alone was also measured prior to and after each sample analysis, and the average of these two buffer measures was used for background subtraction using the program PRIMUS (45) from the ATSAS package  (46). PRIMUS was also used to perform Guinier analysis (47) of the low q data, which provides an estimate of the radius of gyration (R g ). Regularized indirect transforms of the scattering data were carried out with the program GNOM  (48)  to obtain P(r) functions of interatomic distances. The P(r) function has a maximum at the most probable intermolecular distance and goes to zero at D max , the maximum intramolecular distance. The values of D max were chosen to fit with the experimental data and to have a positive P(r) function.
Tridimensional (3D) bead models that fit with the scattering data were built with the program DAMMIF  (49). Ten independent DAMMIF runs were performed using the scattering profile of TssK1, with data extending up to 0. 35 nm Ϫ1 , using slow mode settings, assuming P3 symmetry and allowing for a maximum 500 steps to grant convergence. The models resulting from independent runs were superimposed using the DAMAVER suite  (50). This yielded an initial alignment of structures based on their axes of inertia followed by minimization of the normalized spatial discrepancy (NSD)  (51). The NSD was therefore computed between a set of ten independent reconstructions, with a range of NSD from 0. 678 to 0. 815. The aligned structures were then averaged, giving an effective occupancy to each voxel in the model, and filtered at half-maximal occupancy to produce models of the appropriate volume that were used for all subsequent analyses.
S1 ). TssK1 Displays a Three-armed Structure-Attempts to crystallize the TssK1 protein were unsuccesful. To get further insights into the TssK1 structure, the purified protein was analyzed by transmission electron microscopy. Approximately 5000 particles were selected and processed using single-particle analysis techniques. Particles and class averages (Fig. 7, A  and B ) show clear 3-fold symmetry features. Hence, we applied a 3-fold symmetry throughout data processing. The tridimensional reconstruction of the TssK1 particle at ϳ26 Å resolution (Fig. 7C ) shows that the trimeric protein appears as three "arms" entangled in a pyramidal shape (Fig. 7D ). The length of the triangle side is 95 Å whereas the height of the particle is 70 Å (Fig. 7D ). The trimer organization of the protein and its overall fold at low resolution were confirmed in solution by SAXS analysis (Fig.
Bacterial two-hybrid and co-immunoprecipitation assays revealed that TssK1 forms oligomers. Further gel filtration experiments showed that the TssK1 protein elutes with an apparent molecular weight corresponding to that of a trimer. The 3-fold organization of TssK1 was validated by visualization of the purified protein using electron microscopy. Image reconstruction of TssK1 particles showed that it forms a three-armed structure with an overall pyramidal shape. In electron microscopy, the three arms are bent under the structure while are in an open conformation in solution as shown by SAXS analysis. Although a conformational flexibility of the protein might exist, our data cannot rule out that (i) the length of the arms has been underestimated due to the average between "bent" and "elongated" conformations on EM grids or (ii) the TssK1 conformation on EM grids is induced by preparation of the specimen (fixation or staining procedures).
Acknowledgments-We thank the members of the Cascales, Cambillau, Lloubès, Bouveret, and Sturgis research groups for discussion,  Tâm Mignot  for fluorescence microscopy,  Charlotte Gaviard ,  Valentin Tutagata ,  Annick Brun ,  Isabelle Bringer , and  Olivier Uderso  for technical assistance and  Mylène Micautond  for encouragement.
* This work was supported in part by the  Centre National de la Recherche Scientifique (CNRS) , the  Aix-Marseille University , and a grant from the  Agence Nationale de la Recherche  ( ANR-10-JCJC-1303-03 ) (to E. C. ). This work was also supported by the  CNRS , the  Aix-Marseille University , and by grants from the  Marseille-Nice Genopole ,  IBiSA , and the  Fondation de la Recherche Médicale  ( FRM DEQ2011-0421282 ) (to C. C. ). □ S This article contains supplemental Tables  S1-S2  and Fig. S1. 1 Supported by doctoral fellowships from the  French Ministère de la Recherche. 2Supported by a post-doctoral fellowship from the  Fondation pour la Recherche Médicale  ( SPF20101221116 ).
The ectodermal neural cortex (ENC) gene family, whose members are implicated in neurogenesis, is part of the kelch repeat superfamily. To date, ENC genes have been identified only in osteichthyans, although other kelch repeat-containing genes are prevalent throughout bilaterians. The lack of elaborate molecular phylogenetic analysis with exhaustive taxon sampling has obscured the possible link of the establishment of this gene family with vertebrate novelties. In this study, we identified ENC homologs in diverse vertebrates by means of database mining and polymerase chain reaction screens. Our analysis revealed that the ENC3 ortholog was lost in the basal eutherian lineage through single-gene deletion and that the triplication between ENC1, -2, and -3 occurred early in vertebrate evolution.
The expression patterns of chicken ENC1 in the developing telencephalon were characterized in great detail and resemble the dynamic pattern in mouse  (Garcia-Calero and Puelles 2009). Expression patterns of the full set of ENC genes (ENC1, -2, and -3) have been investigated only in one species, the amphibian Xenopus laevis  (Haigo et al. 2003). The only expression data of ENC genes outside tetrapods are reports of enc3 in developing zebrafish  (Kudoh et al. 2001; Thisse B and Thisse C 2004; Thisse C and Thisse B 2005; Bradford et al. 2011 ; available on the ZFIN database: http://zfin. org/, last accessed July 24, 2013;  Qian et al. 2013). In this study, our exhaustive gene and taxon sampling revealed the diversification pattern of the ENC gene family in a higher resolution. Conserved synteny between genomic regions containing ENC1, -2, and -3 suggested the triplication through 2R-WGDs early in vertebrate evolution.
The 5 0 -ends of S. canicula ENC1 and ENC3 transcripts were obtained using the GeneRacer Kit (Invitrogen). These cDNA fragments were used as templates for riboprobes used in in situ hybridizations. In addition, the entire 3 0 -untranslated region (UTR) plus substantial parts of the coding regions of zebrafish enc1, -2, -3, and egr2b (krox20) cDNAs were cloned to prepare riboprobes. Gene-specific primers for these PCRs were designed based on publicly available sequences (ENSDART00000062855 for egr2b, see supplementary table S1, Supplementary Material online, for zebrafish accession IDs). A 249-base pair fragment of E. burgeri ENC-A was identified by performing a TBlastN search in a hagfish EST archive (http://transcriptome. cdb. riken. go. jp/vtcap/, last accessed July 24, 2013;  Takechi et al.
2011)  using human ENC1 peptide sequence as query. Based on this sequence, gene-specific primers were designed, and the 5 0part of the coding region plus 5 0 -UTR of E. burgeri ENC-A was obtained using the GeneRacer Kit (Invitrogen). Assembled fulllength S. canicula ENC1 and ENC3 cDNA sequences and the obtained fragments of E. burgeri ENC-A, H. francisci ENC1 and ENC3, N. brevirostris ENC3, and L. platyrhincus ENC2 are deposited in EMBL under accession numbers HE981756, HE981757, HE981759, HE981760, and HE981762-HE981764. Because the chicken ENC3 gene sequence was incomplete with a stretch of "N"s in the open reading frame (ORF) of ENSGALG00000024263 (Ensembl genome database: http:// www. ensembl. org, last accessed July 24, 2013; release 64;  Hubbard et al. 2009) , we performed a reverse transcriptase (RT)-PCR with gene-specific primers and sequenced the missing part.
By aligning the overlapping regions of the deduced protein sequences of the newly obtained fragment and the incomplete sequence in Ensembl, we detected an amino acid substitution. The comparison with other vertebrate ENC proteins clearly showed that this is a highly conserved residue (asparagine). Therefore, we assume that the lysine residue of the Ensembl chicken ENC3 protein was caused by a sequencing error, which is also plausible with respect to the stretch of "N"s. The curated cDNA fragment is deposited in EMBL under accession number HE981758.
Retrieval of Sequences from Public Databases Sequences of ENC homologs were retrieved from the Ensembl genome database and National Center for Biotechnology Information (NCBI) Protein database, by performing BlastP searches  (Altschul et al. 1997 ) using human ENC1 as query. An optimal multiple alignment of the retrieved ENC amino acid sequences including the query sequence was constructed (fig. 1B ) using the alignment editor XCED in which the MAFFT program is implemented  (Katoh et al. 2005). Similarly, a second alignment including human, zebrafish, Drosophila.
Molecular Phylogenetic Analysis In phylogenetic analyses, we employed PhyML 3. 0  (Guindon et al. 2010)  for maximum-likelihood (ML) tree inference and MrBayes 3. 1  (Huelsenbeck and Ronquist 2001)  for Bayesian method. For the ML analyses including large data sets (fig. 2  and supplementary fig. S1 , Supplementary Material online), we used RAxML  (Stamatakis 2006) , because this software tends to outperform PhyML under these conditions  (Guindon et al. 2010). Optimal amino acid substitution models were determined by ProtTest  (Abascal et al. 2005). To identify invertebrate orthologs of ENC genes and to investigate the phylogenetic relationships within the ENC gene family, a data set that contained relevant representatives of each major vertebrate class for each ENC subtype was created (fig.
2001; Manousaki et al. 2011). In double in situ staining, enc1 transcripts were detected using nitro blue tetrazolium/ 5-bromo-4-chloro-3-indolyl-phosphate (BCIP) and egrb2 transcripts by a p-Iodonitrotetrazolium/BCIP-based detection. Stained embryos were examined with a Zeiss Axiophot microscope. Immunohistochemistry on whole-mount S. canicula embryos was performed as described previously  (Kuratani and Eichele 1993)  with minor modifications. Monoclonal anti-acetylated tubulin antibody (Sigma T7451) was used to detect developing axons. As secondary antibody, AlexaFluor 568 goat anti-mouse IgG (H + L, Invitrogen A-11004) was applied, and the signal was detected using fluorescence microscopy (Leica). Images were processed with Zeiss Axiovision and Adobe Photoshop software.
Identification of ENC Genes in Diverse Nontetrapod Species By means of RT-PCR, the full-length cDNA of S. canicula ENC1 and ENC3, including 5 0 -and 3 0 -UTRs, and fragments of E. burgeri ENC-A were sequenced. PCRs using gDNA identified fragments of H. francisci ENC1 and ENC3, N. brevirostris ENC3, and L. platyrhincus ENC2. The inclusion of these genes into the ENC gene family was suggested in BlastX searches in the NCBI nonredundant protein sequence database (nr). These BlastX searches failed to identify any ENC3 orthologs in all available eutherians. An alignment of the deduced amino acid sequences with proteins downloaded from public databases was constructed. The amino acid sequence alignment revealed a high level of conservation especially in the diagnostic residues described previously (fig.
Phylogenetic Relationships within Vertebrate ENC Our sequence data set included selected gnathostome ENC genes and deduced amino acid sequences of the three newly isolated cyclostome ENC genes. Unexpectedly, a protein of a plant, Ipomoea trifida (EU366607 in GenBank), was placed inside the group of teleost ENC1 genes and was found to cluster with stickleback ENC1 (bootstrap support in the ML analysis, 79; data not shown). This placement is in stark contrast to the generally accepted species phylogeny, and therefore we conclude that a contamination of a teleost sequence is the most likely explanation. On the basis of our molecular phylogenetic analysis, we suggest the new gene names enc3 for the formerly called enc1l gene in zebrafish, and Xenc-1 and Xenc-3 for the Xenopus genes previously referred to as Xenc-3 and Xenc-1, respectively (fig.