dataesr/scientific-paragraphs-categorization huggingface dataset:
However, a question already discussed  [6]  with respect to genetic data, is who would have access to information through DNA analysis? Would this information be available in a database? To answer these questions, proper regulations are urgently needed. Another debatable concern the necessity of storing already investigated/prosecuted phenotypic information into a database. Preliminarily, one could argue that there is no necessity in creating a database of physical characteristics of crime suspects. Furthermore, it may begin a dangerous debate of enabling research avenues aimed to associate specific phenotypes with criminal acts. Unfortunately, these researches are already in development, and we recently reported a meta-analysis of data demonstrating the lack of association among facial traits and aggressive behaviors in a large and composite database  [98].
Acknowledgments The authors would like to thanks  Dr. John Butler ,  Dr. Manfred Kayser , and  Dr. Susan Walsh  for providing assistance by email. C. C. S. de C. and R. G. -J. conceived and designed the study. V. R. , T. H. , L. S. -F. , and M. C. B. supervised the research. C. C. S. de C. and M. Q. -S. performed the figures and table. Critical revision of the manuscript was done by V. R. , T. H. , S. de A. , M. Q. -S. , C. P. , C. C. , M. G. , R. G. -J.
Acknowledgements  Ibrahim Bulut  and  Patricia Cha  ´vez contributed equally to this work. This work was supported by the  Interreg IV-A program  under  Rhin-Solar  project (nr C25) and the  French National Research Agency  ( ANR   ORION  project  ANR-13-PRGE-0001 ). Elsa Cece ,  Dr Thomas Bura  and  Nicolas Zimmermann  are acknowledged for the preparation of some of these compounds and devices. We thank the  Pohang Accelerator Laboratory (PAL)  for giving us the opportunity to perform the GIWAXS measurements,  MEST  and  POSTECH  for supporting these experiments,  Drs Tae Joo Shin  and  Hyungju Ahn  for adjustments and help, and other colleagues from the  9A  USAXS beamline for assistance. Dr P. Montgomery  is acknowledged for his careful reading of the manuscript and his suggestions for the English spelling.
324 Chlorogenic acid, rutin, kaempferol rutinoside and caffeoylputrescine concentrations in 325 adjacent tissues were used as marker molecules to assess the impacts of P. syringae and P. 326 corrugata inoculation on the defensive tomato compounds. One DAI, caffeoylputrescine 327 concentration increased markedly in the adjacent stem (2. 2-3. 4 fold, ***; Fig. 5a ) and leaves 328 (1. 6-2. 7 fold, **; Fig. 5b ). These increases were amplified 12 DAI both in stems (2-9. 5 fold, 329 ***; Fig. 5b ) and leaves (5. 6-48. 3 fold, ***; Fig. 5d ). P. syringae decreased the concentration 330 of chlorogenic acid in the leaves adjacent to the inoculation site 1 DAI (1. 1-1. 3 fold, *; Fig. 331  5c ) and 12 DAI (1. 6-1. 9 fold, ***; Fig. 5d ) but this was not the case in the adjacent stems 332 (data not shown).
Study design This was a qualitative study, using semi-structured interviews, observations, and document analysis to collect information on communication strategies used to promote childhood vaccination uptake in both states. Data collection: semi-structured interviews Data collection took place from January to June 2014. We purposively selected stakeholders who had been involved in the development or delivery of communication strategies for vaccination at national, state, and local government levels as well as through developmental partners (e. g. UNICEF and WHO, Table  1 ). We used a semi-structured interview guide to gain insight into the vaccination communication interventions used for both routine vaccination and campaigns. The interview team was comprised of an interviewer and a note taker.
Data analysis We commenced data analysis by going through each interview transcript and each document and extracting information about the communication interventions used for vaccination-related activities, the context in which they were used, the deliverer, and the purpose. After we had identified existing communication interventions, we used the COMMVAC taxonomy to categorise each intervention based on purpose (see below). We developed separate maps for Bauchi and Cross River states and then combined them to create a Nigerian map comprising all interventions in the two states organised by purpose and target audience. We presented the first draft of the map to some of the interviewees in both Cross River and Bauchi and asked them to add communication interventions that we had omitted and to clarify any unclear interventions.
Protocol deviations are assessed by on-site inspections and database controls. In February 2016, more than 94,000 participants yielding around 30 million readings from physical exams, had been covered by our quality program. Results: Participating centres accepted to revise their practices in accordance with the study research specifications. Distributors of medical devices were asked to comply with international guidelines and Constances requirements. Close monitoring enhanced the quality of measurements and recordings of the physical exams. Regarding lung function testing, spirometry acceptability rates per operator doubled in some sites within a few months and global repeatability reached 96. 7 % for 29,772 acceptable maneuvers. Conclusions: Despite Constances volunteers being followed in multiple sites with heterogeneous materials, the investment of significant resources to set up and maintain a continuous quality management process has proved effective in preventing drifts and improving accuracy of collected data.
Background Developing large-scale prospective cohorts is of utmost importance to address epidemiological, clinical or pathophysiological research questions. In such cohorts, quality control is a major issue to ensure that collected data are sufficiently reliable and robust. Constances is a large population-based general-purpose cohort intended to serve as an open epidemiologic research infrastructure accessible to the research community and to provide public health information  [1]. At term, it will comprise a sample of 200,000 participants aged 18-69 years at inclusion who are representative of the French adult population. The survey was launched towards the end of 2012 and has already included more than 94,000 volunteers (February 2016). New participants are continuously being included and the cohort will be fully constituted by the end of 2018.
At inclusion, volunteers are asked to complete a questionnaire and attend a medical exam in one of the 20 participating recruitment sites located throughout France. These sites are Health Screening Centres (HSC) managed by the national social security fund (CNAMTS) to provide free health check-ups for the French population  [2]. As well as the medical examination, laboratory tests and questionnaires, data related to the participants are extracted from national health and social databases on a regular basis. The volunteers are followed up by annual questionnaires and invited to attend a new medical exam at a participating site every 5 years (Fig. 1 ). During the physical exam, biometry (weight, height, waist and hip circumference), blood pressure, electrocardiogram, vision, hearing and lung function are measured.
Blood and urine samples are collected for laboratory tests. Cognitive and physical functioning assessments are conducted for volunteers 45 years and older. All the data collected in the HSCs as well as from the other data sources (questionnaires, administrative databases) are transferred to the central Constances site where the data are consolidated in a single centralized database. Numerous procedures and controls have been set up for the Constances study in order to ensure that the data is of high quality and reproducible and to allow appropriate assessment of potential measurement biases. The present paper focuses on quality issues encountered in the cohort, describes how these issues were addressed and the results of corrective measures with special interest in spirometry as an illustrative example.
Material The medical devices with their specific software and the middleware are purchased by the HSCs at a local level. Consequently, not all the participating sites have the same equipment. As precision and accuracy could differ depending on the material used, the manufacturer's instructions for each device used for the measurements were initially reviewed  [6]  and are updated throughout the study to take into account any changes in material or versions of middleware. If the technology of a device undergoes significant modifications, the measurements for a series of volunteers are doubled within a short period (first with the new device and second with the previous one). Quality controls performed during the pilot study revealed that various middleware used by the study nurses did not transfer the exact value of the medical device measurements to the local database.
Case report form and data transfer All the medical records of the participating sites are electronic and data are stored in a local database, the structure of which is common to all sites. Most of the data collected during the physical examinations are then extracted from the local database and exported to the Constances data centre with cryptography procedures. This is to ensure standardization of a part of the data collection and to reduce potential data capture errors by the study nurse who enters source document information in the study CRF  [8]. For research purposes, the experts requested to increase the precision of some data and to collect some additional specific items. To satisfy their expectations a CRF was specifically designed to complete the data collection.
Validation plan Two validation plans run permanently in the database. The first consists in a crosscheck of identical data collected from different sources, the data entered in the CRF being compared with the data extracted from the electronic medical records of the sites to check for consistency. This is feasible when the study procedures are requesting, for a specific item, a higher precision than the value usually entered in the medical records of the sites. The second validation plan tracks missing data, discrepancies between different questionnaires, out of range values or any warning requiring consistency check as predefined by the researchers. Data flagged by the validation plans generate queries processed directly by the data managers or forwarded to the monitors to be answered during the on-site monitoring visits.
On-site verification Each month samples of the data exported from the site and imported in the Constances database are extracted from the database for quality controls. First, for each category of data the monitor identifies the source document (where the data was initially recorded). For data entered directly in the local electronic medical record, the site prints out the results. The study monitor then checks the consistency between the site data and the Constances database extraction. For any discrepant data, the study monitor identifies the origin of the discrepancy. In the case of data entry error, the correction is transmitted to the Constances data centre. If the discrepancy does not originate from the data entry process, then one of the electronic data transfers may be involved (medical device to middleware, middleware to • Volunteer in underwear, motionless.
2 % (135 of 384) to 91. 5 % (108 of 118). The site with the lowest performance had conducted 384 spirograms, involving 13 different operators who had conducted more than ten exams for the study; the percentage of acceptable spirograms per operator varied from 11 to 65 %. However, as non-acceptable maneuvers were not sent to the Constances data centre, the weakness of some operators did not impact the global quality of FVC and FEV 1 values collected. The repeatability of the acceptable maneuvers (according to the 2005 criteria) is also an important indicator of quality. This indicator was high-grade: 93. 4 % of the acceptable maneuvers were declared repeatable (4081 of 4369) ranging from 82. 1 to 100 % according to the site. The repeatability of each maneuver was checked by calculation: although no more than 1.
Acknowledgments The authors wish to thank the  INSERM-Versailles Saint Quentin en Yvelines University  " Population-based Epidemiological Cohorts Unit " (Cohortes épidémiologiques en population) which designed and manages the Constances Cohort Study. They also thank the  National Health Insurance Fund  (" Caisse nationale d'assurance maladie des travailleurs salaries", CNAMTS) and its Health Screening Centres ("Centres d'examens de santé "), responsible for collecting a large part of the data, as well as the  National Retirement Fund (Caisse nationale d'assurance vieillesse), ClinSearch, Asqualab and Eurocell , responsible for the data quality control.
Funding The Constances Cohort Study was funded by the  National Health Insurance Fund (Caisse nationale d'assurance maladie des travailleurs salaries, CNAMTS) , the  Ministry of Health , the  Council of the Ile de France Region , and by the  Cohorts TGIR IReSP-ISP INSERM (Ministère de la santé et des sports, Ministère délégué à la recherche, Institut national de la santé et de la recherche médicale, Institut national du cancer et Caisse nationale de solidarité pour l'autonomie). The  Constances Cohort Study is an « Infrastructure Nationale en Biologie et Santé » and benefits from a grant from ANR  ( ANR-11-INBS-0002 ).
, 2014). This would yield only three subsets of DCs: conventional type 1 DCs (cDC1s), conventional type 2 DC (cDC2s), and pDCs. However, due to a lack of consensus regarding how to define DC subsets experimentally, such classification remains of limited practical use  (Guilliams and van de Laar, 2015). Recent progress in the unsupervised analysis of high-dimensional flow cytometry datasets has rendered the identification process of cell subsets more objective and more reproducible  (Saeys et al. , 2016). However, a limitation of those approaches is that they give an equal weight to all the surface markers, not necessarily yielding the most biologically meaningful clusters. For instance, both Langerhans cells (LCs) and cDC1s express CD207, CD24, MHCII, and CD11c, but they have completely different localization, ontogeny, lifespan, and functional specialization  (Malissen et al.
Unsupervised Analysis Aligns cDC1 and cDC2 across Mouse Tissues The flow cytometry dataset from distinct tissues was subjected to the unsupervised identification method FlowSOM (Van  Gassen et al. , 2015). FlowSOM uses a self-organizing map (SOM) to cluster cells in different nodes based on the expression of the distinct markers used in a given flow cytometry dataset and subsequently structures the nodes in a minimal spanning tree. We first concatenated live CD45 + cells from all tissues and used FlowSOM to generate a single FlowSOM tree. To identify the node(s) corresponding to the cDC1s and cDC2s, we defined them ab initio as XCR1 hi CD24 hi CD26 hi CD11c hi MHCII hi CD11b lo CD172a lo F4/80 lo CD64 lo Lin lo FSC lo SSC lo and CD11b hi CD172a hi CD26 hi CD11c hi MHCII hi XCR1 lo F4/80 lo CD64 lo Lin lo FSC lo SSC lo cells, respectively (Figure  3 ).
tSNE Automatically Identifies cDCs across Tissues and Species We next used tSNE to perform an unsupervised analysis of the flow cytometry dataset generated from different tissues of mouse, human, and macaque (Figure  5 ). For each species (Figures 5A, 5E, and 5I), CD45 + Lin À MHCII hi cells of the distinct tissues were exported, concatenated, and displayed in a single tSNE contour plot (tSNE_dim1-tSNE_dim2). Heatmap representations of the expression of various markers that define Macs, cDCs, and pDCs defined clusters of cells corresponding to the different cell subsets (Figures  5B, 5F,  and 5J ). Cells falling in the cDC1 (blue), cDC2 (green), and (for human and macaque) pDC (pink) tSNE cell clusters were overlaid on classical contour plots, indicating that the DC subsets automatically delineated on tSNE fit the criteria used to define them by manual gating (Figures  5C, 5G,  and 5K ).
CADM1 hi CD172a lo cells were further analyzed for the expression of XCR1 (mouse), CD26 (human), and IRF8 (macaque) to validate the correct cDC1 identification. For each organ, the IRF8-IRF4 profiles of cDC1 (blue) and cDC2 (green) are shown. Data are representative of at least three independent experiments using cells from different individuals. Please see Figure  S4  for the gating strategies. and human tissues and total DC events were analyzed using One-SENSE (one-dimensional soli-expression by nonlinear stochastic embedding)  (Cheng et al. , 2015). Here, manually chosen lineage-imprinted markers define the first dimension, and the other markers define the second dimension (Figure  6 ). The ''lineage dimension'' was defined using two positive markers per DC subset. This resulted in the generation of multiple clusters of CADM1 hi CD26 hi cDC1 (blue), CD172a hi CD11b hi (mouse), or CD172a hi CD1c hi (human) cDC2s (green) and SiglecH hi B220 hi (mouse) or CD123 hi CD303 hi (human) pDCs (pink) (Figures  6A  and 6D ).
DISCUSSION Recent developments in computational methods permit more robust analysis of flow cytometry and CyTOF data. Relying on objective mathematical principles to define cellular clusters, automated analyses increase the reproducibility of flow analysis by circumventing manual gating  (Saeys et al. , 2016). This constitutes a major improvement, since manual gating is one of the largest variables in the analysis of flow cytometry experiments  (Mair et al. , 2015). Furthermore, automated analyses assess the expression of all markers simultaneously and are not influenced by the order in which cells are gated, as in manual sequential pairwise comparisons. Finally, these techniques simplify the visualization of the multidimensional datasets, which is particularly important when analyzing such data with more than 30 markers.
CADM1 hi CD172a lo cells were further analyzed for the expression of XCR1 (mouse), CD26 (human), and IRF8 (macaque) to validate the correct cDC1 identification. For each organ, the IRF8-IRF4 profiles of cDC1 (blue) and cDC2 (green) are shown. Data are representative of at least three independent experiments using cells from different individuals. Please see FigureS4for the gating strategies.
ACKNOWLEDGMENTS We thank  Gert Van Isterdael  and  Charlene Foong Shu Fen  from the  Flow Cytometry Core Platform  of the IRC and SingHealth, respectively. This work was supported by core grants of the  Singapore Immunology Network  to F. G. and E. W. N. and by the  National Research Foundation   Singapore under its cooperative basic research grant new investigator grant  ( NMRC/BNIG/ 2026/2014 ) and administered by the  Singapore Ministry of Health's National Medical Research Council  to C. -A. D. C. L. S. is supported by a  Marie Curie Intra-European Fellowship  (IEF) as part of Horizon 2020. M. G. is supported by a  Marie Curie Reintegration  grant, an Odysseus grant, and  FWO  grants of the  Flemish Government. B. M. is supported by the  CNRS, INSERM, PHENOMIN , and  European Research Council  ( FP7/2007-2013  grant n  322465 ).
Identification of the Calcium-Binding Domains and Calcium-Signaling Components Calcium-binding domains were identified by mining the literature and helped by taking advantage of keywords in the SUPERFAMILY, Gene Ontology  (Ashburner et al. 2000)  and Prosite  (Sigrist et al. 2010)  databases. They were manually curated to produce a collection constituting the 31 superfamilies presented in table 1. To produce the functional labeling of full length protein domain architectures, the following procedure was followed. First calcium-signaling proteins were identified from: 1) KEGG  (Ogata et al. 1999 ) pathways which include a calcium ion as a compound, the most important being the "Calcium-signaling pathway" (map04020); 2) a review by  Kudla et al. (2010)  describing calcium-signaling proteins; and 3) reviews by  Shemarova and Nesterov (2005a , 2005b , 2007).
The proteins identified were then grouped by component within the calcium-signaling toolkit. Using their UniProt  (Apweiler et al. 2004 ) identifiers, the domain architectures of the proteins were extracted from the SUPERFAMILY database (see below). For components involving the ability to bind calcium, the architectures were restricted to those that include at least one of the 31 calcium-binding domains from table 1. All the functional component labels identified for the full length protein were inferred onto the participating calcium-binding architecture.
Domain and Architecture Assignments in Genomes The domain definitions and superfamily classification were taken from the SCOP database  (Murzin et al. 1995). The protein sequences of the genomes used in this analysis, and the domain annotation of those proteins were taken from version 1. 75 of the SUPERFAMILY database  (de Lima Morais et al. 2011). The SUPERFAMILY domain annotation of genomes follows a well-established method, using a curated library of hidden Markov models incorporating an assignment procedure to generate protein domain architectures, described as a string of domains and their superfamily classifications. At the time this work was carried out the SUPERFAMILY database included the 1,558 distinct species used in this study, including 114 Archaea, 1,061 bacteria, and 383 eukayotes.
Species Tree and Ancestral Genome Reconstruction To carry out this work a species tree of all the completely sequenced genomes was required. The reference tree was downloaded from the SUPERFAMILY resource, where it was first described in  Wilson et al. (2009)  but the procedure has subsequently improved  (Fang et al. 2013 ). In addition to the domain content of extant genomes (see above), the SUPERFAMILY resource also provides the molecular character content of ancestral genomes; this is limited to eukaryote lineages because of the prevalence of horizontal gene transfer in bacteria. The ancestral genome content from SUPERFAMILY allowed us to track the evolution of domain architectures in the calcium-signaling toolkit throughout eukaryote phylogeny. To facilitate the visualization of the ancestral genome contents and the evolutionary changes, we present these data phylogenetically.
Limitations of the Analysis The analysis presented here is dependent on our current knowledge of proteins, principally defined by: the domains of proteins in the PDB (Protein Data Bank) which have had their structures experimentally determined, the genomes that have been completely sequenced, and our ability to transfer the knowledge of one to the other via sequence homology. We only include such knowledge as exists and is encoded in the SUPERFAMILY resource, and thus our picture of the evolution of the calcium-based intracellular signaling system will not be complete. For example, the Arabidopsis thaliana vacuolar exchanger CAX proteins do not exhibit any SCOP domain annotation and are therefore uidentifiable in the genomes. However, roughly 70% of eukaryote proteins are annotated, and thus we expect that our fundamental findings will not be overturned by subsequent additions to the body of structural/ genomic data.
7 mM -1. s -  vs r1H2O = 43. 7 mM -1. s -  at 20 MHz, 25°C). In order to gain a better insight into the origin of the very high relaxivity measured for 1, the microscopic parameters characterizing rotational dynamics have been assessed by fitting the experimental field-and temperature-dependent relaxivity data to the Solomon-Bloembergen-Morgen (SBM) theory. To take into account internal dynamics, we incorporated in the fit the Lipari-Szabo treatment, often used to describe rotational motion of slowly (ns) tumbling species. 82 In this approach, the dipolar interactions between the Gd(III) electron spin and the density of relaxivity = r 1 n Gd M w x 1000 (1) water proton that generate the relaxation are influenced by both fast local rotational motion of the Gd(III)-water proton vector, characterized by the correlation time tRl, and a slower, global motion (characterized by tRg) that reflects the global motion of the system.