dataesr/scientific-paragraphs-categorization huggingface dataset:
EXTERNAL DATASETS AND CLUSTER CONFIRMATION To confirm the SZ candidates as galaxy clusters we make use of targeted optical and near-infrared follow-up observations, data drawn from the wide area Dark Energy Survey  (Flaugher et al. 2015) , the Pan-STARRS1 survey  (Chambers et al. 2016) , the all sky Wide-field Infrared Survey Explorer (WISE) dataset  (Wright et al. 2010) , and the literature. In this section we describe each dataset and how it is used to confirm and/or characterize the SZ-selected clusters. Overall, we focus our targeted follow-up efforts on ensuring nearly complete imaging of high-significance (ξ > 5) cluster candidates to depths sufficient to confirm clusters to z ∼ 0. 8 -1. 0. For lower-significance targets we rely significantly more on the availability of wide-area imaging datasets.
01 for clusters z < 0. 9. There are 2 different RM catalogs: a "flux-limited" sample that includes significant numbers of high-redshift clusters for which the optical richness estimates must be extrapolated and a "volume-limited" sample for which the DES data is sufficiently uniform and deep that the richnesses can be well measured; DES cluster cosmology constraints are derived using the volume-limited sample  (Rykoff et al. 2016; McClintock et al. 2019). In this work, we explore characteristics of the joint SPT-RM cluster sample using the volume-limited catalog. 7 In total there are 53,610 (21,092) RM clusters at λ > 20 in the full (volume-limited) DES sample, with ∼ 36, 000 (∼ 16, 000) and ∼ 14, 000 (∼ 6, 000) of these clusters within the total SPT and SPT-ECS survey area, respectively.
Observations We use the Parallel Imager for Southern Cosmology Observations (PISCO;  Stalder et al. 2014 )-a new imager with a 9 field-of-view installed on the 6. 5 m Magellan/Clay telescope at Las Campanas Observatory in Chile-to obtain approximately uniform depth griz' imaging data for over 500 SPT-selected clusters and cluster candidates, including 173 candidates at ξ ≥ 4. 5 in SPT-ECS. These data were obtained as part of an ongoing effort to characterize the strong lensing and bright galaxy populations of the SPT cluster sample. To analyze the PISCO data, we have constructed a reduction pipeline that includes standard corrections (i. e. , overscan, debiasing, flat-fielding, illumination, though we note defringing is not required) as well as specialized routines that correct the data for non-linearities and artifacts caused by bright stars.
Spitzer/IRAC and Magellan/Fourstar Based on initial PISCO imaging, we were able to identify a small number of high-redshift cluster candidates worthy of additional follow-up observations. Two systems were imaged as part of a Spitzer Cycle 11 program and 5 additional SPT-ECS cluster candidates were part of a Cycle 14 program (ID: 11096,14096; PI:Bleem) 8. The Cycle 11 (14) candidates were observed with Spitzer/IRAC  (Fazio et al. 2004 ) for 360 (180) s on source time integration time in both the 3. 6 and 4. 5 µm bands. These data were reduced as in  Ashby et al. (2009) , and are of sufficient depth for cluster confirmation to z ∼ 1. 5. We have additionally obtained ground-based nearinfrared J-band imaging for 19 candidates with the Fourstar imager  (Persson et al. 2013 ) installed on the Magellan/Baade telescope.
For each candidate, a large number of short exposures were taken using predefined dither macros provided in the instrument control software with the candidate centered on one of the four Fourstar detectors. These images were flatfielded using IRAF routines  (Tody 1993) , astrometrically registered and relatively calibrated, and coadded using the PHOT-PIPE pipeline. Coaddition was performed with SWarp and source identification with SExtractor. Absolute calibration is tied to the J-band flux from stars in the 2MASS point source catalog. While conditions varied between the Fourstar observations these data are typically sufficient to confirm clusters to z ∼ 1. 2 or better.
Literature Search We additionally search the literature for known clusters in the vicinity of the SZ-selected candidates. Using the SIMBAD 11  database we search for systems within a 5 radius of the candidate locations. When such a system is found we consider it to be a match if it is at z < 0. 3; we reduce the matching radius to 2 for clusters at higher redshifts (except for systems in the Planck catalog, see Section 5. 3 below). When available, we adopt. Distribution of the telescope/surveys used to confirm and provide redshifts for the SPT clusters presented in this catalog. While some clusters may have redshifts from multiple sources (for example there is significant overlap between PISCO and RM), we only represent each cluster once in this figure, highlighting the sources of the redshifts reported in Tables  8  and 9.
The DES column corresponds to clusters with redshifts from DES data but not from RM (see Section 4. 2. 2). Generally, data from Pan-STARRS is deep enough to confirm clusters to z ∼ 0. 6, DES and PISCO to z ∼ 0. 8 -1. 0, FourStar to z ∼ 1. 2 and Spitzer to z ∼ 1. 5. reported spectroscopic redshifts as the SPT cluster redshifts for previously identified systems. We also make use of reported photometric redshifts for a small number of systems not in DES, Pan-STARRS1, or directly targeted in our follow-up imaging. When possible we use the other external datasets to identify spurious associations with previously reported systems, finding several in this distance-based match.
Cluster confirmation from other imaging datasets Here we describe the techniques used for confirming cluster candidates not confirmed via the RM algorithm or literature search. We obtained imaging for ∼ 100 candidates outside of the volume searched by RM as well as some imaging redundant to the DES imaging (in terms of confirmation) as part of our strong lensing search program that we use here for redshift comparisons. In total, 173 candidates were imaged with Magellan/PISCO (about 2/3 in common with RM, see Figure Figure  4 ), 19 with Magellan/Fourstar, and 7 with Spitzer (note the NIR imaging overlaps areas with optical imaging). 10 candidates are located in the DES footprint but are either at high redshift or are missing photometry in filters required for RM, and 22 candidates only fall in the Pan-STARRS footprint.
To conduct our targeted search for red-sequence galaxies in these areas, we first calibrate our synthetic model for the colors and magnitudes of red-sequence galaxies, generated with the GALAXEV package  (Bruzual & Charlot 2003)  by assuming a passively evolving stellar population with single formation burst at z = 3, to match the relevant survey photometry using samples of known clusters with spectroscopic redshifts. For PISCO, the dataset that we most use to confirm clusters outside of DES, we use 58 SPT-SZ galaxy clusters with spectroscopic redshifts that were imaged as part of our broader SPT characterization program. In Figure  4  we plot in red the measured PISCO redshifts versus those from the training sample as well as additional SPT-ECS clusters with spectroscopic redshifts reported in the literature.
2019). A weak-lensing calibration of an X-ray selected cluster sample yields constraints on the richness-mass relation that are centered on the results from McClintock et al. (  2019 ), but with large uncertainties  (Mantz et al. 2016). 15 We use the Colossus package https://bitbucket. org/bdiemer/ colossus 16 Strictly speaking, we compare the slopes of the λ-mass and the mass-λ relations. We checked that the conversion of our relation to mass-λ mostly shifts the amplitude of the relation while leaving the slope almost unchanged. Figure  11. The mass-λ relation evaluated at our pivot redshift z = 0. 6 determined from SPT cluster number counts assuming our fiducial ΛCDM cosmology. We convert our λ -M500c relation to M200m -λ for ease of comparison with the literature. The relations calibrated from DES Y1 shear or CMB lensing (the latter driven by an informative prior) favor steeper slopes, but there is good agreement at their pivot richness λ0 = 40  (McClintock et al.
Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework  Program (FP7/2007 -2013)  including ERC grant agreements 240672, 291329, and 306478.
(Henderson et al. 2016)ths (e. g. ,LSST, LSST Science Collaboration et al. 2009, Euclid, Euclid Collaboration et al. 2019, WFIRST,  Spergel et al. 2015), as well as SZ surveys by AdvACT(Henderson et al. 2016)and Simons Observatory (Simons Observatory Collaboration et al. 2019), with all of the SZ surveys ultimately setting the stage for the next-generation CMB-S4 survey (Abazajian et al. 2016; CMB-S4 Collaboration et al. 2019). CH acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation, in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research, in addition to support from the European Research Council under grant number 647112. SJ acknowledges support from the Beecroft Trust and ERC 693024. TS acknowledges support from the German Federal Ministry of Economics and Technology (BMWi) provided through DLR under projects 50 OR 1610 and 50 OR 1803, as well as support from the Deutsche Forschungsgemeinschaft, DFG, under project SCHR 1400/3-1.
ACKNOWLEDGEMENTS Facilities: Blanco (DECAM),  Hubble Space Telescope  ( WFC3 ),  Magellan:Baade  (FourStar), Magellan:Clay (PISCO,  LDSS3C ),  NSF/US Department of Energy 10m South Pole Telescope (SPTpol) , Spitzer (IRAC), AAT(2dF+AAOmega) This work was performed in the context of the  South-Pole Telescope scientific program. SPT is supported by the  National Science Foundation  through grant  PLR-1248097. Partial support is also provided by the  NSF Physics Frontier Center  grant  PHY-0114422  to the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Kavli Foundation  and the  Gordon and Betty Moore Foundation  grant  GBMF 947  to the  University of Chicago. This work is also supported by the  U. S. Department of Energy. PISCO observations are supported by  NSF   AST-1814719.
The Stanford/SLAC group acknowledges support from the  U. S. Department of Energy  under contract number  DE-AC02-76SF00515. AS is supported by the  ERC-StG  ' ClustersXCosmo ' grant agreement  716762 , and by the  FARE-MIUR  grant ' ClustersXEuclid '  R165SBKTMA. Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No.
Barcoded, large-insert (10 kb) libraries were constructed using 150 ng of pure high-molecularweight DNA. Sequencing was performed in an SMRT cell using P6 polymerase with C4 chemistry on the PacBio RSII instrument at the Uppsala NGI Platform (Uppsala University, Sweden). The data were filtered to obtain high-quality reads (reads with quality < 0. 75 and length < 500 bp were removed). The sequences were assembled using the CANU assembler with default parameters  (Koren et al. 2017). Subsequently, the chloroplast contigs were extracted in Geneious by mapping the resulting contigs to the Passiflora complete cp genomes obtained in this study. A final assembled cp genome of P. costaricensis was obtained in Geneious by joining the contigs using the "de novo Assembly" function (supplementary table  S2 , Supplementary Material online).
Chloroplast Genome Annotation, Identification of Repeated Elements, and Comparative Analysis The annotation of cp genomes was carried out in the GeSeq (Organellar Genome Annotation) online program  (Tillich et al. 2017)  with default settings to identify protein-coding gene sequences, rRNAs, and tRNAs based on the chloroplast reference sequences and BLAST homology searches, followed by manual corrections for start and stop codons and intron positions in GenomeView software. All tRNA genes were further confirmed using the tRNAscan-SE and ARAGORN online search server  (Laslett and Canback 2004; Lowe and Chan 2016). Pseudogenes were classified based on nucleotide losses in sequences or the presence of internal stop codons. OGDRAW software was used for constructing the circular cp genome map  (Greiner et al.
2019). REPuter software  (Kurtz et al. 2001 ) was used to identify direct and palindromic repeat elements based on the following criteria: minimum repeat size !30 bp and sequence identities !90% (Hamming distance equal to 3). Two different multiple sequence alignments were run in Progressive Mauve v. 2. 4. 0  (Darling et al. 2010 ) to identify possible rearrangements in the cpDNA molecules. The first contained 11 cp genomes to represent the diversity in size and structure in each Passiflora subgenus, in addition to the cp genomes of D. retusa and M. brevifilis (Passifloraceae), with Populus trichocarpa (Salicaceae) as a reference. The second alignment contained all 20 cp genomes studied herein. Subsequently, to detect possible expansions or contractions in the IR regions, the IR boundaries of both LSC and SSC regions with full annotations for the adjacent genes were analyzed across the cp molecules used in the first multiple sequence alignment.
In addition to reconstructing the evolutionary history of Passiflora chloroplast genomes, we analyzed the plastome structures run in the first multiple sequence alignment against the available GenBank cp genomes of Passiflora foetida, Passiflora tetrandra, Passiflora obovata, Passiflora auriculate, and Passiflora biflora, studied by  Shrestha et al. (2019)  (supplementary table  S4 , Supplementary Material online).
Phylogenomic Studies We performed a phylogenomic study based on all the available cp genomes of passionflowers (49 species in total, 20 generated in this study and 29 species whose cp DNA sequences were imported from the GenBank database, supplementary table  S4 , Supplementary Material online). A set of 68 passionflower chloroplast protein-coding genes was used in a phylogenetic analysis, and the species Po. trichocarpa (Salicaceae) used as the outgroup, with the aim of obtaining a rooted tree (supplementary table S5, Supplementary Material online). A local python pipeline was used to extract each gene in the data set (consisting of 50 taxa), align them individually at nucleotide level in MUSCLE and make an interleaved Nexus matrix consisting of all individual alignments. The resulting matrix was then analyzed in ModelFinder  (Kalyaanamoorthy et al.
The content ratio of SnAu (1:1) was obtained from XRD measurements. This was also confirmed from the obtained capacity which matched the theoretical capacity for lithiation of SnAu. For long life cycle studies, the cells were initially pre-cycled in the same potential range (0. 02-1. 5 V vs. Li/Li + ) for two cycles at 0. 1 C (0. 1 mA cm -2 ) before ramping the rate. The surface morphology of the electrodes was examined by scanning electron microscopy (SEM) on a Hitachi S-4800 field emission electron microscope. The crystallographic structures were analyzed by grazing incidence X-ray diffraction (XRD) measurements on a Bruker D8 Advanced X-ray diffractometer with Cu K α radiation (1. 54184 Å), operating at 40 kV and 40 mA. DFT-based Vienna ab initio simulation package (VASP) was employed to perform calculations, using Perdew, Burke, and Ernzerhof (PBE) method for treating exchange and correlation.
Acknowledgements This work was supported by  LAAS-CNRS  technology platform, a member of Renatech network. D. P. acknowledges the support from the  European Research Council  ( ERC , Consolidator Grant, ERC-2017-CoG, Project  771793 3D-CAP ). D. G. acknowledges the support of the  Natural Science and Engineering Research Council (NSERC)  of  Canada. This work was granted access to the high-performance computing resources of CALMIP supercomputing center.
Data sources and searches A review protocol was drafted, but central registration was not completed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement was used to guide the conduct and reporting of the study  [40]. A Biomedical Information specialist (Wichor M. Bramer) performed a search for studies in Medline, Embase, Cochrane Library, Web of Science, Google Scholar ProQuest and Cinahl from 2000 up to 6 February 2019. This starting point was used since multiple reviews are available that already cover the period up to the year 2000 (Table  1 ). Search terms included phalangeal, metacarpal and carpal injuries, anamnestic assessment, provocative test(s), diagnostic test(s) and imaging tests. The full electronic search strategy for the Embase database is presented in Table  2  (the others are available upon request).
Data extraction and methodological quality assessment Two reviewers (PK, JR) independently extracted the data. Data were extracted describing the study design, characteristics of the study population, test characteristics, study population setting (hospital care or noninstitutionalized general practitioner care) and diagnostic parameters. Methodological quality was assessed by two independent reviewers (PK, JR), using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist  [41]. Disagreements were resolved by discussion.
OR 'diagnostic error'/exp. OR 'diagnostic accuracy'/exp. OR 'diagnostic value'/exp. OR 'differential diagnosis'/ exp. OR 'delayed diagnosis'/exp. OR 'sensitivity and specificity'/exp. OR (((diagnos* OR detect* OR differen* OR strength* OR motion*) NEAR/3 (test* OR accura* OR error* OR false OR fail* OR value* OR impact* OR effective* OR earl* OR missed OR correct* OR incorrect* OR delay* OR difficult* Search terms for the other databases are available upon request Fig. 1 Flow chart study selection.
The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an HPC. Procedures have been developed to use the national healthcare databases to allow identification and validation of diseases over the follow-up. The biological collection (serum, lithium heparinized plasma, EDTA plasma, urine and buffy coat) began gradually in June 2018. At the end of the inclusions, specimens from 83,000 donors will have been collected. Specimens are collected according to a standardized protocol, identical in all recruitment centers. All operations relating to bio-banking have been entrusted by Inserm to the Integrated Biobank of Luxembourg (IBBL). A quality management system has been put in place. Particular attention has been paid to the traceability of all operations. The nature of the biological samples stored has been deliberately limited due to the economic and organizational constraints of the inclusion centers.
Some research works may require specific collection conditions, and can be developed on request for a limited number of subjects and in specially trained centers. The biological specimens that are collected will allow for a large spectrum of biomarkers studies and genetic and epigenetic markers through candidate or agnostic approaches. By linking the extensive data on personal, lifestyle, environmental, occupational and social factors with the biomarker data, the CONSTANCES cohort offers the opportunity to study the interplays between these factors using an integrative approach and state-of-the-art methods.
DESIGN AND DESCRIPTION OF THE CONSTANCES COHORT CONSTANCES is a population-based general-purpose cohort designed as a randomly selected sample of the French adults aged  18 -69 years at study inception. The source population of CONSTANCES was restricted to salaried workers, professionally active or retired and their family (more than 85% of the French population, i. e. , ∼56 million people). Overall, in February 2020, 202,045 subjects have been included. The sampling was done within the database of the National Pension Insurance Fund (CNAV: Caisse Nationale d'Assurance Vieillesse). The participation rate at the enrolment in the cohort was 7. 3%. During the follow-up, the return rate of annual questionnaire varied slightly depending on the year and was of 75% on average. Regarding loss to follow-up, almost none of the people included in CONSTANCES will be permanently lost to follow-up, since the participants will be followed passively through the national administrative databases.
For participants aged 45 years and older, a specific work-up of functional, physical, and cognitive capacities is also performed. An active follow-up includes a yearly postal or web-based selfadministered questionnaire, and a complete 4-year follow-up to an HPC including an health examination. Moreover, data are regularly extracted from the French administrative and health national databases, including hospital discharge summaries, visits to health professionals, medication and other prescriptions, severe chronic diseases, sick leaves, handicaps, disabilities and injuries, cause of death, as well as social and demographic characteristics, socioeconomic and employment status. Extensive procedures have been developed to use the national healthcare databases to allow identification and validation of diseases over the follow-up (Table  1 ).
The projects are then examined by the CONSTANCES International Scientific Committee and, where applicable, by its Ethics Review Board; authorizations are issued by the Institutional Steering Committee comprised of CONSTANCES's partner organizations. Applications must be submitted to the French legal authority for personal data processing, before the required data are sent to the researcher in charge of the project. To ensure that data confidentiality and security are maintained, the CONSTANCES team generates a specific study number for each cohort participant included in a research project. Moreover, a log of variable requests submitted for a given project is kept to ensure that no-one has access, even after submitting multiple variable requests, to the entire CONSTANCES database.
M. , • Centrifugation of serum and lithium heparin plasma tubes within 30 and 45 min maximum after sampling, • Storage of the tubes at +2/+8 • C until pickup by the carrier. The collection protocol followed for urine samples is the same as above, except the centrifugation step. The data related to blood and urine collections is captured in the GeT application. GeT (Greiner eHealth Technologies) is a Greiner BioOne (GBO France, Les Ullis, F 91 941 Courtaboeuf CEDEX) application developed for registration of collection information, adapted to the specific needs of the CONSTANCES Biobank to include collection, primary processing, temporary storage and shipment information. Data from GeT are transferred to IBBL reception application on a daily basis.