dataesr/scientific-paragraphs-categorization huggingface dataset:
). Based on the seven levels of accessibility, examples of databases in the highest level of accessibility, Level 7, were the Medicare, Pharmaceutical Benefits Scheme (PBS), Centre for Health Record Linkage (CHeReL), HealthLinQ, Victorian Data Linkages (VDL), and SA-NT DataLink in Australia; and the Medical Data Vision EBM Provider, Japan Medical Data Centre (JMDC) Claims database, and Nihon-Chouzai Pharmacy Claim database in Japan. Examples of databases at Level 6 were the Hamamatsu Medical University Database in Japan. Examples at Level 5 were the National Health Insurance Research Database (NHIRD) in Taiwan and the Western Australia Data Linkage (WADL) in Australia. At Level 4 were the Medi-Trend database and Nihon University School of Medicine (NUSM) Clinical Data Warehouse in Japan, and the United Nations University-Casemix (UNU-Casemix) database in Malaysia.
L. A Beijing, China; PLAGH 0Chinese People's Liberation Army General Hospital database; Soochow 0Soochow University Affiliates Children's Hospital database; NRCMS 0New Rural Cooperative Medical Scheme database; CGRN0Convergence CT Global Research Network, Japan; Osaka 0Osaka University Database, Japan; COS 0Computer Ordering System Database Dokkyo Medical University, Japan; Kyoto 0Electronic Medical Record Retrieval System-Kyoto University Hospital; NHI0National Health Insurance Database; JMIRI 0JMIRI Pharmacy Claims Database; LTCI 0Long-Term Care Insurance; EHMIS 0Electronic.
L. A Beijing, China; PLAGH 0Chinese People's Liberation Army General Hospital database; Soochow 0Soochow University Affiliates Children's Hospital database; NRCMS 0New Rural Cooperative Medical Scheme database; CGRN0Convergence CT Global Research Network, Japan; Osaka 0Osaka University Database, Japan; COS 0Computer Ordering System Database Dokkyo Medical University, Japan; Kyoto 0Electronic Medical Record Retrieval System-Kyoto University Hospital; NHI0National Health Insurance Database; JMIRI 0JMIRI Pharmacy Claims Database; LTCI 0Long-Term Care Insurance; EHMIS 0Electronic Allows research No restriction on Collaboration and information research question/ Provision of Citizenship of with data No technical Level available open to privates raw data researchers involved custodian restrictions Examples of database 7 Access procedure No restriction on Raw data provided Any citizenship BUT Collaboration not Fee may be Australia: Medicare, PBS, CHeReL, HealthLinQ, VDL, made clear in research question/open BUT need a country need collaboration required required SA-NT DataLink public sphere to privates BUT no raw citizen as part of with data custodian Japan: MDV, JMDC, Nihon-Chouzai 6 data provided study Any citizenship/collaboration Japan: Hamamatsu required 5 Local citizen required Australia: WADL Taiwan: NHIRD 4 No raw data; data custodian provides results Japan: Medi-Trend, NUSM Malaysia: UNU-Casemix 3 Limited research/no privates/public interest questions only Japan: DPC South Korea: HIRA 2 Access unclear; no direct contact with data custodian could be established Australia: DVA China: Zhongnan, West China, Jinhua, CHIRA, Zhongshan, URBMI (Guangzhou), UEBMI (Guangzhou), UEBMI (Hebei), UEBMI (Tianjin), Minhang, GPH, PLA, PLAGH, Soochow, NRCMS Japan: CGRN, Osaka, COS, Kyoto, NHI, JMIRI, LTCI Malaysia: EHMIS Singapore: NEHR South Korea: NHIC Thailand: UCS, SSS, CSMBS, Ramathibodi, Buddhachinaraj, Sunpasitthiprasong, Nakhon Thai 1 No access Japan: JammNet Singapore: Medisave Medicare0Medicare Australia; PBS0Pharmaceutical Benefits Scheme; CHeReL 0Centre for Health Record Linkage; VDL 0Victorian Data Linkages; MDV 0Medical Data Vision EBM Provider; JMDC 0Japan Medical Data Centre Claims database; Nihon-Chouzai 0Nihon-Chouzai Pharmacy Claims database; Hamamatsu0Hamamatsu Medical University database; WADL 0Western Health Management Information System; NEHR 0National Electronics Health Records Database; NHIC 0National Health Insurance Corporation database, South Korea; UCS 0Universal Coverage Scheme Database; SSS0Social Security Scheme Database; CSMBS 0Civil Servants' Medical Benefit Scheme Database; Ramathibodi 0Ramathibodi Hospital Electronic Medical Record; Buddhachinaraj 0Buddhachinaraj Hospital database; Sunpasitthiprasong 0Sunpasitthiprasong Hospital Electronic Medical Record; Nakhon Thai 0Nakhon Thai Crown Prince Hospital Electronic Medical Record; JammNet0JammNet database, Japan; Medisave 0Medisave database, Singapore.
Polysome profile analysis, input and polysomal RNA purification Polysome extraction was performed as described previously  (23, 34)  with few modifications. Heparin was systematically omitted from the polysomal extraction buffer. Polysomal extracts (1. 2 g equivalent biomass) were resolved on a 15-60% sucrose gradient (27 ml) centrifuged for 3. 5 h at 30 000 rpm with rotor SW32 Ti. Polysome profile analyses were performed with an ISCO absorbance detector at 254 nm and sucrose gradient collected into 16 fractions of 2 ml each: fractions 1-4 containing free mRNPs, fractions 5-8 free ribosomes and 80S and fractions 9-16 polyribosomes. Values of absorbance at 254 nm were recorded with the Peak Track software, and polysome profiles represented as graphs with the Excel software. To determine the content in light polysome fractions relative to the total polysome content, the area covered beneath the peak(s) of interest was measured with Image J.
At 20 • C, XRN4 has globally no impact on the accumulation of these mRNAs in polysomes (Supplementary Figure  S3A ) but at 38 • C, their levels are globally higher in xrn4-5 than in wild-type (Supplementary Table  S1  and Figure  S3B ). All these results suggest that the heat-induced underaccumulation of transcripts in polysomes is, at least in part, dependent upon XRN4 function. In the input fraction from wild-type seedlings, almost 4500 mRNAs (precisely 4343 mRNAs) are downregulated by heat (Figure  1A , Supplementary Table  S1 ). Their fold ( av F) change ranging between 0. 5 and 0. 017 (mean of 0. 33), with 94% of them displaying an av F between 0. 5 and 0. 2. The extent and magnitude of this effect are both consistent with our previous study  (23)  and 58% of the present dataset significantly overlaps (P-value 5.
FUNDING l' Agence Nationale pour la Recherche  ( ANR-2010 BLANC 1707 01 ,  ANR Heat-Adapt   ANR -14-CE10-0015 );  Centre National pour la Recherche Scietifique (CNRS) ;  Université de Perpignan Via Domitia (UPVD)  through utilization of the confocal microscope and qPCR devices at the Technoviv platform;  National Science Foundation  [ MCB-1021636  to P. J. G. ]. Funding for open access charge:  French Agence Nationale pour la Recherche (ANR)  and  National Scientific Research Centre (CNRS).
Acknowledgements The authors thank the MIMA2 platform ( INRA-Jouy ) for access to confocal microscopy,  M. Vilotte  ( INRA-Jouy ) for help with animal dissection,  M. Moudjou  for help with immunoblotting ( INRA-Jouy ),  J. Bouchet  ( Pasteur Institute, Paris, France ) for the gift of the anti-Rab11 antibody,  S. Simon  ( CEA Saclay, France ) and  P. Clayette  ( Bertin Pharma, France ) for the kind gift of  Sha31 ,  T. M. Jessell ,  S. Brenner-Morton  and  O. D. Madsen  who kindly contributed to the DSHB, and  M. Wassef  ( IBENS, Paris ) for critical reading of the manuscript. This work was partially supported by the  ANR-09-BLAN-0015-01  and by the  ARC  (grant  SFI2011205489 ). S. M-L was supported by a fellowship from  Region Ile de France (DIM-Stem Pôle ).
Mouse embryos were prepared and transverse sections were analysed by immunofluorescence as previously described  14. Specific primary antibodies are described in Supplementary Table  S2. Sections were imaged using an inverted microscope with epifluorescence illumination (Zeiss Axio Observer. Z1, France) and a cooled CDD camera (CoolSNAP HQ2; Photometrics, Roper Scientific, Lisses, France). Images were captured using AxioVision (Zeiss-France). Z-series were acquired on a Zeiss LSM 700 confocal microscope. Images were analysed with ImageJ and when necessary with the Bio-Formats plugin. Real-Time PCR. Total RNA from embryos or 1C11 cells was isolated using Trizol (Invitrogen, Cergy Pontoise, France) according to the manufacturer's instructions. The corresponding cDNA was synthesized with oligo(dT) 17 primer using 200 units of Superscript III ™ reverse transcriptase  (Invitrogen)  according to the manufacturer's protocol.
Literature search and study selection The protocol of this systematic review was registered in ClinicalTrials. gov (NCT01998113), and endorses PRISMA. A comprehensive electronic search strategy was conducted in Medline, Cochrane Central Register of Controlled Trials, and Embase (see supplementary table  1 ). The last search update was performed on 20 May 2014. Bibliographies from relevant studies were reviewed, and authors of potentially eligible studies were contacted when necessary to request further information regarding study design or primary outcomes. Studies were eligible if they fulfilled all the following: (1) they were randomized controlled trials; (2) subjects were women with gestational diabetes requiring drug treatment; (3) they compared glibenclamide with insulin, metformin with insulin, or metformin with glibenclamide; (4) they provided information on one or more maternal or fetal outcome; and (5) they were published as a full paper.
Secondary fetal outcomes were fetal hyperinsulinism (measured as either cord C peptide or cord insulin), abnormal Apgar score at 1 and 5 minutes, obstetric trauma, severe neonatal hypoglycaemia (as defined by authors, or requiring intravenous glucose or admission to the neonatal intensive care unit), neonatal jaundice, significant respiratory distress (described as respiratory distress syndrome or requiring respiratory support), stillbirth, neonatal mortality, and admission to the neonatal intensive care unit. Two investigators independently screened abstracts, reviewed the full text, decided on eligibility, and collected data using a previously designed form. Discrepancies were solved after reviewing the source and by consensus with a third investigator. The risk of bias was assessed at the study level taking into account the randomization process, quality of allocation concealment, blinding, selective outcome reporting, sample size calculation, attrition rates, intention-to-treat analysis, and comparability of subjects' baseline characteristics (maternal age, pre-pregnancy body mass index, fasting plasma glucose concentration and two hours after oral glucose tolerance test, gestational age, and HbA 1c level at study entry).
Statistical analysis Outcome data from original studies were pooled into either relative risks for dichotomous outcomes or mean differences for continuous outcomes using Review Manager 5. 1 software. Heterogeneity was explored for all the meta-analyses. Analyses were undertaken using a fixed effects model; in case of substantial heterogeneity (I 2 ≥50%) a random effects model was used. Pooled analyses were also used to compare baseline characteristics. In the metformin versus glibenclamide comparison including two studies, we performed a qualitative assessment of the effects (direction, magnitude, and precision) in addition to the meta-analysis. In the metformin versus insulin comparison, the only baseline characteristic that presented significant and relevant differences between treatment groups was body mass index.
, 2011). Quantitative real-time PCR (qRT-PCR) was performed according to the manufacturer using SYBR Green super-mix (Quanta BioSciences) on an ABI7500 machine (Applied Biosystems). For microarray analysis, quality of total RNA from biological triplicates was assessed using Agilent Bioanalyser, and samples were processed according to the Illumina Whole-genome Gene Expression Direct Hybridization Assay Guide, using Illumina TotalPrep-96 RNA Amplification kit (Life Technologies). Quality control was performed on labeled cRNA, and 750 ng of labeled cRNA was hybridized to Mouse Ref-8v2 arrays and scanned by BeedStudio v. 3. 1. 3 (quantile normalization with background correction). Normalization among arrays and significant analysis were performed using GeneSpring X (Agilent). Further details of data processing, including gene clustering, are described in Supplemental Information.
ChIP-seq For Ascl1 ChIP-seq, NS5 Ascl1-ERT2 cells were fixed sequentially with 2 mM di(N-succimidyl) glutarate and 1% formaldehyde in PBS and then lysed, sonicated, and immunoprecipitated with anti-HA antibody, as previously described  (Castro et al. , 2011). DNA libraries were prepared from 10 ng of immunoprecipitated DNA according to the standard Illumina ChIP-seq proto-col and sequenced with Illumina GAIIx. Raw reads were mapped to the mouse genome (NCBI37/mm9) with Bowtie 0. 12. 7  (Langmead et al. , 2009). Uniquely mapped reads data ($11. 5 million for both Ascl1 ChIP and input chromatin samples) were then subsampled before peak calling with MACS 1. 4. 1  (Zhang et al. , 2008). For histone marks ChIP-seq, Ascl1-ERT2 cells were fixed in 1% formaldehyde in PBS, prepared as described above and immunoprecipitated with anti-H3K27ac and anti-H3K4me1 antibodies.
Libraries were sequenced with Illumina HiSeq 2000. Raw reads were mapped as above (25 million uniquely mapped reads for all samples) and data processed with MACS 2. 1. 0. For chromatin state characterization, we used a Hidden Markov modeling of chromatin enrichment software-ChromHMM  (Ernst and Kellis, 2012). Further details of data processing are described in Supplemental Information.
ACCESSION NUMBERS The ChIP-seq dataset for Ascl1-ERT2 (t=30') was submitted to the ArrayExpress database (www. ebi. ac. uk/arrayexpress) and is publicly available under the accession number E-MTAB-2384. The DNase-seq datasets were submitted to the ArrayExpress database (www. ebi. ac. uk/arrayexpress) and are publicly available under the accession number E-MTAB-2270. The ChIP-seq datasets for H3K4me1 and H3K27ac were submitted to the ArrayExpress database (www. ebi. ac. uk/arrayexpress) and are publicly available under the accession number E-MTAB-3104.
( PTDC/SAU-NEU/100208/2008  and  PTDC/NEU-NMC/0315/2012  to D. S. C. ),  Marie Curie CIG  ( 303644  to D. S. C. ),  Wellcome Trust  ( WT095908  and  WT098051  to P. F. ), the  European Community  ( FP7-223210  to P. F, L. E. and F. G. ), and an  FCT doctoral fellowship  to F. F. V. DSC is supported by the  FCT investigator program.
O B S E RVAT I O N S A N D DATA R E D U C T I O N Our Johnson-Cousins U, B, V and I images were taken during a seven night observing run at the National Optical Astronomical Observatory (NOAO) 2. 1-m telescope at Kitt Peak between 2011 March 11 and 17. Only nights 1, 4 and 6 were partially photometric and the results we present here derive from these photometric data only. During nights 2, 3, 5 and 7 and non-photometric parts of nights 1, 4 and 6 we carried out photometric monitoring of those targets that will be presented in a later paper. We used the optical camera T2KB, with 2048 × 2048 pixels yielding a field of view of 6. 5 arcmin × 10 arcmin on the sky after cropping (we used T2KB default sampling of 0. 3 arcsec pixel - 1  ). The pixel size is 24 µm with a typical readout noise of 4 electrons rms.
This model PSF for the image is then applied to all the detected stars in the image using PSF. The routine ALLSTAR is used to determine the actual instrumental magnitude of the stars. It uses the PSF modelled with PSF and scales it to each star detected with FIND. The two main parameters are the PSF radius (PS) which quantifies the spatial extent of the star on the image and the fitting radius (FI), which defines the region that will be used when scaling the model PSF to the field star. The PSF radius can be determined by the limit radius at which the PSF wings blend with the background. Experimentation with our data led us to use a PSF radius of four times the FWHM (full width at half-maximum). The fitting radius by definition smaller than the PSF radius is the portion that is considered with certainty being 'good data' in the observed PSF.
5σ detection for SkAc 1, a 2. 7σ detection for We 2-34 and a 1. 4σ detection for NGC 6781 (Table  11 , Fig. 5 ). Also, the J-band excess for EGB 6 -already detected in Paper Ihas been refined by including the newly measured magnitudes and the NIR photometry of Fulbright & Liebert (1993, see table 2 in Paper I), because their H -K colour are more consistent with those of a star than those in the 2MASS data base. As a result of the larger uncertainty on their J magnitude, the uncertainty on the detection is larger. None the less, we obtain a similar J-band detection to what obtained in Paper I. The spectral types of the companions for the detections in the I and J band agree within one spectral subtype. This excellent agreement adds confidence in our analysis.
The u filter is known to have a red leak however this would have had the opposite effect. On the other hand, the g filter can be affected by nebular [O III] λ5007 light, which would result in too high a reddening. The model colours have been obtained using the SYNPHOT package in IRAF by convolving CSPN model spectra from TheoSSA and TMAP with temperatures between 40 and 170 kK (see Paper I) with the SDSS filters. The SDSS colours of main-sequence stars were adopted from the synthetic photometry of  Covey et al. (2007). However, we did not use their approximate values for M J , but those determined in Paper I. Out of 15 objects in common between our sample (Paper I and this work) and the SDSS sample, we recover our detections with great consistency (A 31, EGB 6, SkAc 1, see Table  14 , Fig.
S U M M A RY A N D D I S C U S S I O N In this work, we have continued the search for I-and J-band flux excess to detect cool companions around CSPN. We measured U, B, V and I-band fluxes of newly observed targets using PSF-fitting photometry. The aim of this paper has been to extend the sample analysed in Paper I, refine the technique and investigate the use of archival data. To the Paper I sample of 25 objects (revised down from 27 because of the identification of two mimics) we add 9 new objects resulting in a sample of 34 objects in total. Of these 34 objects, 16 have also been observed in the J band and we analyse these data separately, as also done in Paper I. The detection rate in the combined samples in the I band is (32 ± 16) per cent, while in the J band it is (50 ± 24) per cent.
AC K N OW L E D G E M E N T S The authors thank the anonymous reviewer for his comments and suggestions. DD thanks  Quentin Parker  for his support during the writing of this paper and  Ivan Bojičić  for his help in using the Macquarie University GPN Database. OD acknowledges financial support from the  Australian Research Council  via the  Future Fellowship scheme  ( FT120100452 ). NOAO is operated by the  Association of Universities for Research in Astronomy (AURA)  under cooperative agreement with the  National Science Foundation. Funding for the SDSS and SDSS-II has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbukagakusho , the  Max Planck Society , and the  Higher Education Funding Council for England.
Changes in drivers To analyze past change in climate and the human population and land-water use, we compared the mid-19th century with the present time. In the mid-19th century, land use and the distribution of the human population should still largely reflect the time before the agrarian revolution. It is also a time when the collection of official statistics increased substantially following the establishment of Statistics Sweden in 1858. For example, the number of national weather stations measuring temperature increased from 4 to 31 in the early 1860s. Likewise, yearly reports on agriculture were published from 1865 onward, although the level of detail varied. To analyze past and present climate in NDB, we retrieved measured data on mean monthly temperature from all temperature stations operating in the region in 1861-1890 and 1981-2010 from the Swedish Meteorological and Hydrological Institute.
ensembles-eu. org/; van der  Velde et al. 2013). From these models we calculated the ensemble mean and range for monthly temperatures, as well as the ensemble mean for yearly temperatures. Ensemble means are more reliable than single model projections  (Jarsjö et al. 2012 , van der Velde et al. 2013). We retrieved data on the human population and land use from Statistics Sweden. Most early reports present statistics at the level of counties, but reports concerning 1865 include finer spatial scales. Hence, we used data from 1865 to represent the mid-19th century. Data on population size were then available for towns and judicial districts (Statistics Sweden 1867). Recent data  were available for densely built areas and municipalities (downloaded from Statistics Sweden http://www. scb. se/).
In 1865 the smallest town in NDB had 504 inhabitants. For consistency, we defined densely built areas as urban when they had more than 500 inhabitants. We calculated rural population density for each judicial district or municipality. To project the population in 2045, we fitted linear mixed-effect models to the 1990-2010 data on rural population density and urban population size, modeling municipality and urban area as random effects. The fitted models explained > 99% of the variation in the data, and we used the fitted equations for the random effects to project the population in 2045. The Stockholm region, i. e. , Stockholm city and nearby municipalities with transboundary urban areas, was an outlier because of its large population. Hence, we fitted and projected a separate linear model for this region.
The fitted model explained 96% of the variation in the data. We used data on cropland area as a proxy for land-water use intensity in each district or municipality to represent past and present land use. In contrast to other land-cover classes, such as the seminatural grasslands that were an important feature in the mid-19th century landscape, data on cropland area were reported in a consistent manner. We used published data from 1865 (Statistics Sweden 1868) and downloaded data from 2005 (from Statistics Sweden). We complemented this assesement with a brief literature review on changes in land-water use in NDB.
The data used in our analyses were available from national databases and publications, but they provided sparse spatial coverage or were reported for large areas only. Climate monitoring increased over time, but the spatial coverage of temperature stations remained sparse (Fig. 2a ). Hence, only regional climate change can be assessed. This limitation makes it difficult to investigate the effects of climate change on the spatial scales of main importance for sedentary species, because local temperature variation due to topography and land-water use can be substantial. The resolution in climate models is often coarse, and the local climate to which organisms respond can differ considerably from the mean over a larger area  (Dobrowski et al. 2009). In NDB, present climate models also underestimate current regional temperatures, although model projections might capture the relative degree of future change.
eea. europa. eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database), but historical land-use data are not available at similarly large geographical scales. Detailed historical maps are generally available only for local farming regions  (Cousins and Eriksson 2002) , similar to the situation in other parts of Europe  (Krauss et al. 2010). Scale mismatches in data also affect assessments of water-use change. Across regions, Ecology and Society 20(1): 23 http://www. ecologyandsociety. org/vol20/iss1/art23/ there are disparities between areas covered by hydrological monitoring and areas with substantial pressure from eutrophication and pollution  (Hannerz and Destouni 2006)  or climate change  (Bring and Destouni 2011). Substantial variation in land cover and water conditions makes it both essential and difficult to assess hydro-climatic changes at the scales relevant for water governance  (Koutsouris et al.