dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein-protein interaction networks We next sought evidence that proteins encoded by genes at common variant loci interact physically with known myocardial repolarization proteins. We have constructed a proteinprotein interaction network from the InWeb database (Supplementary Note)  26. Using the DAPPLE algorithm  27  we seeded the network with the first 12 known Mendelian LQTS genes and seven loci harboring previously-identified common QT variants (but not known Mendelian genes)  3, 4. Consistent with the known relationships among several of the Mendelian genes, significant interconnectivity was observed (P = 0. 0006 for direct connections, P = 0. 008 for indirect connections, Supplementary Note). We thus identified 606 proteins interacting directly with the seed proteins and investigated whether these protein-protein interactions could help identify candidate genes within any of the 22 novel loci identified in the current study.
Cardiac enhancer analyses Enhancer annotations were generated by integrating combinations of histone modifications obtained from the Roadmap Epigenomics project using ChromHMM  23, 50. We identified SNPs in LD (r 2 > 0. 8) with each of the 68 QT interval-associated loci using genotype data from the 1000 Genomes Project (CEU population) and computed overlap with ChromHMM-annotated enhancer elements in the left ventricle tissue sample (BC Left Ventricle N41) in the NIH Roadmap Epigenomics Program 22 using the intersect BED command in BED Tools (v2. 12. 0). To assess significance of the overlap, we compared the set of SNPs at 68 QT interval-associated loci against 100,000 sets of randomly sampled control SNPs. Control SNPs were chosen from the Affymetrix 660W genotyping array and were matched for size of the LD block (+/-5 SNPs), MAF of the lead SNP (+/-0.
Protein-protein interaction in silico analyses We used a public database of protein-protein interactions  26. This database contains 428,430 interactions, 169,810 of which are high-confidence interactions across 12,793 proteins. All human interactions were pooled and interactions in orthologous protein pairs passing a strict threshold for orthology were included. Each interaction was assigned a probabilistic score based on the neighborhood of the interaction, the scale of the experiment in which the interaction was reported and the number of different publications in which the interaction had been sited. We used a published algorithm called DAPPLE (Disease Association Protein-Protein Link Evaluator) to build and analyze a network of seed genes  27. We seeded the network with 12 known Mendelian LQTS proteins (KCNQ1, KCNH2, SCN5A, KCNE1, KCNE2, CAV3, SNTA1, KCNJ2, CACNA1C, ANK2, AKAP9, SCN4B) as well as genes from 7 previously associated common variant QT loci  3, 4.
We considered direct connections among the seed proteins as well as indirect connections through other proteins, filtering on connections between proteins from different loci. DAPPLE evaluates the significance of the network and individual proteins within it by comparing it to 10,000 random, matched networks that are generated using a within-degree node-label permutation  27. We considered the ability of protein-protein interactions to identify proteins newly associated in the QT-IGC meta-analysis. We translated the novel loci into genes, identifying 124 genes in total, 85 of which were in the In Web database.
Supplementary Material Refer to Web version on PubMed Central for supplementary material. Genome-wide association results for GWAS meta, annotated with gene names. Shown are association results from meta-analysis of QT interval GWAS in 76,198 individuals of European ancestry across 22 autosomes. Loci meeting P < 5×10 -8 upon meta-analysis with replication data are annotated for novel (large font) and previously reported (small font) loci. Nearest genes are used for annotation but the causal gene at any given locus is unknown. Common genetic variants at loci associated with QT interval (P < 5×10  -8  ) on meta-analysis of GWAS+replication results (Supplementary Table  6 ). N is the effective number of samples contributing to the signal. For a given SNP, the effective sample size is the sum of the product of the cohort-specific sample size and imputation quality (ranging from 0 to 1).
M. Bruneck: Data analysis, interpretation, writing: S. K. DNA preparation: F. K. , C. L. ECG measurement, database: M. Knoflauch. Supervision, funding, administration, PI: J. W. Carla: Study concept, design: K. H. G. , K. W. Genotyping: H. M. z. S. Supervision: K. W. Study design, analysis: A. K. Study concept, supervision, PI: J. H. Cyprus: Study concept, funding, supervision, analysis: A. N. Data acquisition, analysis, interpretation: M. G. Genetic, biochemical data acquisition, statistical analysis: A. G. P. Czech Post-MONICA: Data collection, submission: J. A. H. , V. A. Galicia: Cohort collection: M. B. Study design: M. B. Genotyping platform management: A. C. Genotyping: M. T. Analysis: M. T. Interpretation: M. B. , A. C. Financial support: A. C. Intergene: Genotyping, data analysis, epidemiology expertise: F.
D. E. A. and S. L. P. independently performed quality control and meta-analysis of GWAS and replication association results. P. I. W. d. B. , A. P. and C. N. -C. supervised the analyses. Non-QT trait lookups. CARe-COGENT: Meta-analysis, lookup: J. G. S. HRGEN: Metaanalysis, lookup: M. d. H. Overall study supervision: R. J. F. L. QRS GWAS: Study supervision: N. S. Meta-analysis: D. E. A. , P. I. W. d. B. Results lookup: S. L. P. eQTL Analyses. Dataset acquisition: A. S. P. , V. E. Analysis, interpretation: A. D. J. Cell-type enrichment tests: S. R. Left ventricle eSNP analyses. Overall supervision: T. P. C. Recruitment, sample collection: K. M. , C. M. Sample processing, expression analysis: J. B. Statistical analysis: M. M. Left ventricle enhancer analyses. Analysis: X. W. Overall supervision: L.
Since 1991 and 1994, positioning observations have been collected from two global positioning system (GPS) receivers and one very long baseline interferometry antenna, respectively. Tide gauge data are also available since 1976 and from 1998 to 2012, eight campaigns of absolute gravity (AG) measurements were performed  (1998, 2000, 2001, 2002, 2004, 2007, 2010, 2012). Geodetic measurements handled at Ny-Ålesund contain both the secular contributions from the past and PDIMCs. However, up to now, no studies have satisfactorily explained the decadal rates observed either in gravity variations  (Mémin et al. 2011)  or ground vertical motion measurements  (Hagedoorn & Wolf 2003; Kierulf et al. 2009b; Omang & Kierulf 2011) , or both simultaneously  (Sato et al. 2006b; Omang & Kierulf 2011; Mémin et al.
42 µGal hPa -1  (Sato et al. 2006a). Using different absolute gravimetres can induce an uncertainty on the gravity value up to 2 µGal  (Robertsson et al. 2001; Francis et al. 2005). Considering that three instruments were used at Ny-Ålesund, we add 2 µGal in quadrature to all the formal errors  (Sato et al. 2006b; Mémin et al. 2011). The AG measurements (Fig. 2a ) have not been made at the same period of the year  (Mémin et al. 2011). To minimize the effects due to hydrology loading we must estimate the rate of gravity variations by taking the seasonal signal into account  (van Camp et al. 2013). To do so, we make use of the SG time-series. We analyse Ny-Ålesund SG data between 2000 January and 2012 June. We apply corrections for the three geophysical phenomena cited above. Contrary to the AG measurements, the change of gravity due to atmospheric pressure is corrected using the ERA-interim model from the European Centre for Medium-Range Weather Forecasts (ECMWF,  Dee et al.
03 and 0. 01 µGal yr -1 on average over a 10 and 15 yr time span (see Table  2 therein ). We fit a degree 2 polynomial, solar annual and semi-annual signals from the SG data using the least squares method. We remove the estimated degree 2 polynomial from the SG time-series. The obtained residuals contain high-frequency and seasonal signals. To attenuate the high-frequency signals and estimate the seasonal signal, we run a monthly average on the residual time-series (Fig. 2b ). The rate of gravity variations is estimated after correcting AG measurements for the seasonal signal extracted from the SG data. We obtain ġag/sg = -1. 39 ± 0. 11 µGal yr -1 , in agreement, within the uncertainties, with the trend estimated by  Mémin et al. (2011)  but lower than the trend determined by  Sato et al. (2006b)  and  Omang & Kierulf (2011; Table 2).
Rate of vertical displacements To determine the rate of vertical displacements at Ny-Ålesund, we use the GPS time-series solutions for stations NYAL and NYA1 from the Jet Propulsion Laboratory (JPL) from 2000 January to 2012 December (Fig. 3 ). They are publicly available from http://sideshow. jpl. nasa. gov/post/series. html. The GPS analysis is undertaken using the GPS Inferred Positioning System/Orbit Analysis and Simulation Software (GIPSY/OASIS) package in a precise point positioning mode  (Zumberge et al. 1997). Standard solid Earth tide, ocean tide loading and pole tide loading corrections are applied according to the IERS2010 standards  (Petit & Luzum 2010). In addition to the standard corrections, we correct for atmospheric pressure loading using the ERA-interim surface pressure fields assuming that the ocean responds to changes in the atmosphere as an inverted barometre (e.
2010). However, the orbit of most of the radar altimetres do not span high latitude areas because of their inclination, which is, for instance, 66 • for Topex/Poseidon and its successors. We have to rely on an ocean general circulation model to predict the rate of absolute SLCs; we choose the GLORYS2v1 (GLobal Ocean ReanalYsis and Simulation) model  (Ferry et al. 2012). This model is an eddy-resolving ocean model with spatial resolution of about 25 km with 75 layers, assimilating sea-surface temperature and elevation in addition to temperature and salinity profiles. A sea-ice module is also included in GLORYS. At the location of the tide gauge, we obtain ṅ = 2. 78 ± 0. 26 mm yr -1 , leading to utg = ṅṡ = 8. 29 ± 1. 60 mm yr -1 , therefore confirming the uplift rate estimated from the GPS time-series.
Hydrology models We compare the seasonal gravity variations and vertical displacements, observed by the SG and GPS, to global hydrology models. We use the classical high resolution (0. 25 • and 3 hr) GLDAS/Noah  [Global Land Data Assimilation System, Rodell et al. (2004) ] and the hydrology component of ERA-interim  (Dee et al. 2011) , with about 0. 7 • and 6 hr spatial and temporal samplings. Both models include 4-layer soil moisture estimates (0-2 m for GLDAS/Noah and 0-2. 55 m for ERA-interim) and a snow layer. In addition, GLDAS/Noah also includes the water content stored in the canopy; this contribution is almost negligible compared to the soil-moisture and snow contributions. The forcings of the two models are different: ERA-interim hydrology is coupled to the atmospheric model, whereas the forcing data set of GLDAS/Noah combines various products.
Seasonal variations of gravity The gravity seasonal signal is extracted from the SG data (black curve, Fig. 2b ) as explained in Section 2. 1. The phase of this signal is in very good agreement with that from the ERA-interim model (Fig. 2b ). Specifically, the SG and ERA-interim seasonal signals have an annual period with amplitudes, from peak to trough, of up to 10-12 µGal. There are few discrepancies of 2-4 µGal in winter  2001, 2004, 2005, 2011 and 2012. These anomalies may be the result of episodic fluctuations in precipitations that are not adequately represented by the hydrology model. They may also be the result of small offsets that remain in the SG time-series because of our processing strategy. The seasonal signal from GLDAS/Noah has lower amplitudes, less than 1 µGal, than the SG data.
In eq. (  2 ) above, the first term accounts for the direct effect of ice loading, while the second represents the contribution of the meltwater load. The relative SLC S has been evaluated solving the so-called 'sea level equation; (SLE, see  Farrell & Clark 1976 ) by means of an improved version of the open source code SELEN that includes a module for the elastic deformations that can use usersupplied compressible Love numbers  (Spada & Stocchi 2007; Spada et al. 2012a). Hence, S is not assumed to be uniform throughout the oceans. In all our computations, the mantle is assumed to be elastic and compressible (the load-deformation coefficients pertain to the Preliminary Reference Earth Model (PREM) of  Dziewonski & Anderson 1981) , the rotational feedback on sea-level is taken into account following the theory of  Milne & Mitrovica (1998) , the fixed-shorelines approximation is used and the geodetic variations are expressed in the reference frame of the center of mass of the whole Earth.
C O N C L U D I N G R E M A R K S We estimated a new rate for the gravity variations in Ny-Ålesund using repeated absolute and continuous relative gravity measurements. This rate is -1. 39 ± 0. 11 µGal yr -1. It is obtained by removing the significant seasonal contribution as suggested by  van Camp et al. (2013). We also updated the rate of vertical displacements from the NYAL and NYA1 GPS time-series provided by the JPL. The average vertical velocity is estimated by removing the seasonal solar and draconitic signals. It is 7. 94 ± 0. 21 mm yr -1 , in agreement with the vertical uplift rate estimated using the rate of SLC from the Ny-Ålesund tide gauge data and the rate of absolute sea level variations computed using the ocean general circulation model GLORYS. We extracted the seasonal signals from the gravity variations and vertical displacements time-series and compared them to the GLDAS/Noah and ERA-interim hydrology models.
GS is partly funded by a DiSBeF (Dipartimento di Scienze di Base e Fondamenti, Urbino University) Research grant. The authors thank O. Omang for providing the SG time-series. Financial and logistical support from the French Polar Institute (IPEV) to perform AG measurements at Ny-Ålesund (program GRAVITE 337) is gratefully acknowledged. O. Omang and another anonymous reviewer are acknowledged for their comments. Downloaded from https://academic. oup. com/gji/article/198/1/285/610419 by KIM Hohenheim user on 20 April 2022.
Background: Since microscopic slides can now be automatically digitized and integrated in the clinical workflow, quality assessment of Whole Slide Images (WSI) has become a crucial issue. We present a no-reference quality assessment method that has been thoroughly tested since 2010 and is under implementation in multiple sites, both public university-hospitals and private entities. It is part of the FlexMIm R&D project which aims to improve the global workflow of digital pathology. For these uses, we have developed two programming libraries, in Java and Python, which can be integrated in various types of WSI acquisition systems, viewers and image analysis tools. Methods: Development and testing have been carried out on a MacBook Pro i7 and on a bi-Xeon 2. 7GHz server. Libraries implementing the blur assessment method have been developed in Java, Python, PHP5 and MySQL5.
For web applications, JavaScript, Ajax, JSON and Sockets were also used, as well as the Google Maps API. Aperio SVS files were converted into the Google Maps format using VIPS and Openslide libraries. Results: We designed the Java library as a Service Provider Interface (SPI), extendable by third parties. Analysis is computed in real-time (3 billion pixels per minute). Tests were made on 5000 single images, 200 NDPI WSI, 100 Aperio SVS WSI converted to the Google Maps format. Conclusions: Applications based on our method and libraries can be used upstream, as calibration and quality control tool for the WSI acquisition systems, or as tools to reacquire tiles while the WSI is being scanned. They can also be used downstream to reacquire the complete slides that are below the quality threshold for surgical pathology analysis.
The human assessment was distributed among three possible answers: Poor; Fair; Good. The computer assessment represented the computed highest acceptable magnification for a WSI, higher magnifications being therefore considered by the computer as of insufficient quality for diagnosis. The libraries implementing the blur assessment method we designed have been developed in Java, Python, PHP5 and MySQL5 using Eclipse IDE, Apache HTTP Server. For web usage, JavaScript, Ajax, JSON and/or Sockets were used for multithreaded interactions between the web application hosted on one server, the java or Python services hosted on the same server, or a different (decentralized) one and the files stored on the same server or on a decentralized storage server. We also used the Google Maps API, as demonstrated in the NYUVM (NYU's virtual microscope, developed by NYU school of medicine)  [14].
The WSI is thus considered as of insufficient quality in terms of blurriness, for all its magnification levels being under their respective blur assessment thresholds. thereby in real-time  [17]. Implementation is shown in Figure  5. Programs 1. , 3. and 4. also have Python implementations. Our Python implementations were 3-times slower in average than our Java implementations as we haven't yet used Python's multithreading capabilities. We are also currently developing multithreaded Python and Open MPI C++ implementation. Tests were made on 5000 single images, 200 WSI in Hamamatsu formats, 100 WSI in Aperio SVS format converted to the Google Maps format. It is currently being implemented in the French national project FlexMIm and additional results should be provided in the last quarter of 2014.
Acknowledgements The authors are grateful for financial support from the  FlexMIm  project, the  SATT idfinnov , and the  French Agence Nationale de la Recherche (ANR). We also like to thank our colleagues in the  Pathology Department of the Saint-Louis Hospital , the  Hematology University Institute , the  SATT idfinnov , the  FlexMIm  project and the  University Paris Diderot's Laboratory of Algorithmic Informatics (LIAFA)  who made all these experiments easier, and  Julie Auger-Kantor  for her immense help and support throughout this work. Publication of this supplement has been funded by 12th  European Congress on Digital Pathology. This article has been published as part of Diagnostic Pathology Volume 9 Supplement 1, 2014: Selected articles from the 12th European Congress on Digital Pathology.
© 2014 Ameisen et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
To facilitate these new endeavours, we introduce the COMPADRE Plant Matrix Database (COMPADRE, for short), an open-access online repository of plant (MPM-based) population dynamics. COMPADRE (version 3. 0) contains MPMs from 468 studies with 598 plant species, as well as ancillary information that allows for in-depth interpretation of the species' demography, such as geographic location of the study populations, ecoregion, study periods, treatments and plant growth form descriptors (see 'What is in the COMPADRE portal?' below). COMPADRE is the result of efforts initiated over 25 years ago  (Franco & Silvertown 1990 ) and later continued in parallel by several research groups, before being recently integrated into a single repository. The information in the database has been standardized and error-checked to facilitate user analyses and made publicly available at www.
compadre-db. org. The goal of this publication is to introduce and describe this resource. We first offer a historical description of its origins and development; next, we explain how the database is managed and organized, and detail its current content. Finally, we briefly highlight its research potential and suggest future directions towards improving our understanding of plant population dynamics world-wide. A historical perspective: from Leslie to COMPADRE 3. 0 Introduced by  Bernardelli (1941)  and  Leslie (1945 Leslie ( , 1948)) , MPMs were largely neglected by ecologists, evolutionary biologists and demographers for two decades. The pioneering work of  Lefkovitch (1965)  and  Keyfitz (1964)  (Fig. 1 ) indicated the potential of MPMs to examine how individuals contribute to a population's dynamics as a function of attributes such as age, ontogeny, size, spatial location and causes of death.
2010 ) and an assessment of utility for recovery of endangered species  (Zeigler, Che-Castaldo & Neel 2013). The current database, COMPADRE 3. 0, has been hosted by the Max Planck Institute for Demographic Research since 2011. There, the priorities have been to (i) minimize redundant efforts in data digitization by integrating independent research groups around the world, (ii) continue archiving plant demographic data, adding important ancillary information, and (iii) build an online portal to share the information on an open-access basis. The efforts have entailed incorporating parallel MPM databases into a single repository and significantly scaling up the digitization (See Internal Organization below) of both published MPMs and MPMs obtained directly from the authors or calculated by us (Table  1 ).
The third priority, building an online portal, is described in more detail in the section What is in the COMPAD- RE portal? (below). The COMPADRE Plant Matrix Database I N T E R N A L O R G A N I Z A T I O N The continuous increase in published MPMs in the recent decades (Fig. 1 ) requires a coordinated effort to search, digitize, error-check and release the information (Fig. 2 ). To facilitate this, COMPADRE is supported by two committees and a digitization team. The core committee (Appendix S1 in Supporting Information) is responsible for creating and updating protocols for data search, digitization, error-check and release, and for the overall infrastructure of the database and the internal organization of COMPADRE. The science committee, composed of a group of expert demographers located world-wide, provides external advice to the core committee on future directions, helps expand the geographic reach of COMPADRE and supplies the COMPADRE digitization team with grey literature.
Both committees work together to secure funding for COMPADRE in the long term. The COMPADRE digitization team primarily digitizes published information containing plant MPMs and ancillary information. The team is composed of students and postdoctoral fellows primarily based at the MPIDR (but see Appendix S1). The team has been trained in population ecology, MPMs and database archiving by the project leaders. In addition to entering information in a standardized format (Table  1 ; Fig. 3 ; Appendix S2), they contact authors to request information that is not included in the publication, or for help interpreting demographic information and carrying out error-checks. 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005    Once the publications containing MPMs have been acquired, the matrices and metadata are digitized into COMPAD- RE under a strict data entry protocol (Appendix S2) before being published online via the COMPADRE portal.