dataesr/scientific-paragraphs-categorization huggingface dataset:
Emission inventories The Regional Emission inventory in ASia (REAS) version 2. 1  (Kurokawa et al. , 2013 ) is a regional emission inventory for Asia, including monthly emissions of most major air pollutants, e. g. , black carbon (BC), organic carbon (OC), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and greenhouse gases between 2000 and 2008. The spatial resolution of REAS is 0. 25 × 0. 25 • , covering East, Southeast, South, and Central Asia and the Asian part of Russia (Russian Far East, eastern and western Siberia, and the Urals). The area coverage of REAS is from 60 to 160 • E in longitude and from 10 • S to 50 • N, which is smaller than our domain configuration. For this reason, we use the Emissions Database for Global Atmospheric Research (EDGAR) version 3. 2 (year 2000 emission;  Olivier et al.
4. Additional uncertainty analysis of modeled LVDs by using a method for dichotomous (with or without LVDs) cases is presented in Sect. S1 of the supplementary material. On the other hand, the model has overestimated the visibility range for many cases with observed visibility lower than 7 km. Such a result is likely due to the 36 km model resolution used in the study, which could be too coarse to resolve the typical size of air plumes containing high concentrations of fine particulate matters. The detailed discussion of potential uncertainty factors of modeled visibility, including meteorological datasets, fire emission inventories, and the model resolution can be found in  Lee et al. (2017). The observed CO and O 3 levels from the only WMO GAW station in the region, Bukit Kototabang, Indonesia (West Sumatra), are used to evaluate the model performance in simulating gas-phase chemistry.
Impact of missing components in the emission inventories on modeled results In this study, we have noticed that the simulated PM 2. 5 concentrations in Singapore are often lower than the observations of the National Environment Agency of Singapore (https://data. gov. sg/dataset/ air-pollutant-particulate-matter-pm2-5, last access: 27 April 2018) (6. 1 versus 20. 3 µg m -3 in annual mean during  [2002] [2003] [2004] [2005] [2006] [2007] [2008]. Owing to the lower simulated PM 2. 5 concentration in Singapore, the model could not capture many ± 2 % 3 ± 1 % 21 ± 3 % Unhealthy 101-200 2 ± 1 % 0 ± 0 % 11 ± 1 % Very unhealthy 201-300 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 301-400 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 401-500 0 ± 0 % 0 ± 0 % 0 ± 0 % Kuala Lumpur AQI (O3) FF BB FFBB Good 0-50 95 ± 2 % 100 ± 1 % 83 ± 6 % Moderate 51-100 5 ± 2 % 0 ± 1 % 15 ± 5 % Unhealthy 101-200 0 ± 0 % 0 ± 0 % 2 ± 1 % Very unhealthy 201-300 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 301-400 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 401-500 0 ± 0 % 0 ± 0 % 0 ± 0 % Singapore AQI (O3) FF BB FFBB Good 0-50 99 ± 1 % 100 ± 0 % 94 ± 3 % Moderate 51-100 1 ± 1 % 0 ± 0 % 5 ± 2 % Unhealthy 101-200 0 ± 0 % 0 ± 0 % 1 ± 1 % Very unhealthy 201-300 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 301-400 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 401-500 0 ± 0 % 0 ± 0 % 0 ± 0 % 50 ASEAN cities AQI (O3) FF BB FFBB Good 0-50 94 ± 1 % 99 ± 0 % 88 ± 2 % Moderate 51-100 6 ± 1 % 1 ± 0 % 10 ± 2 % Unhealthy 101-200 0 ± 0 % 0 ± 0 % 2 ± 0 % Very unhealthy 201-300 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 301-400 0 ± 0 % 0 ± 0 % 0 ± 0 % Hazardous 401-500 0 ± 0 % 0 ± 0 % 0 ± 0 % observed LVDs (Table  2 ) and consequently underestimated AQI levels resulting from PM 2.
Acknowledgements. This research was supported by the  National Research Foundation Singapore  through the  Singapore-MIT Alliance for Research and Technology, the interdisciplinary research program of Center for Environmental Sensing and Modeling. It was also supported by the  US National Science Foundation  ( AGS-1339264 ) and  US Department of Energy  ( DE-FG02-94ER61937 ). The authors thank  MIT Greater China Fund for Innovation 2015  for facilitating the collaboration between the  Chinese University of Hong Kong  and MIT research teams. The authors would like to acknowledge the  Ministry of Natural Resources and Environment, Department of Environment, Malaysia , for making the Malaysia Air Pollution Index data available; the  World Meteorology Organization (WMO) Global Atmosphere Watch (GAW) station Bukit Kototabang , SPARTAN, NCEP-FNL, and  NCAR  FINN working groups for releasing their data to the research communities; and the  NCAR WRF developing team  for providing the numerical model for this study.
Data availability. FINNv1. 5 emission data are publicly available from http://bai. acom. uar. edu/Data/fire/. REAS and EDGAR emission data can be downloaded from https://www. nies. go. jp/REAS/ and http://edgar. jrc. ec. europa. eu/overview. php?v=42, respectively. Malaysia API records can be obtained from http://apims. doe. gov. my/public_v2/home. html. The observational visibility from the GSOD can be downloaded from https://data. noaa. gov/dataset/ global-surface-summary-of-the-day-gsod. CO and O 3 in WHO GAW station can be obtained from http://ds. data. jma. go. jp/gmd/ wdcgg/. Fine particle data from SPARTAN are publicly available in http://spartan-network. weebly. com/. WRF-Chem simulated data are available upon request from Hsiang-He Lee (hsiang-he@smart. mit. edu). The Supplement related to this article is available online at https://doi.
► The CPRD includes continuous patients' records for about 7% of the UK population and is broadly representative of the characteristics of patients and general practitioner practices in the UK, with long follow-up and quality data. ► Sixty per cent of patients in the CPRD have data available in the Hospital Episode Statistics database and we used these linked data. ► A matched cohort of immunocompetent individuals was included, with 1:1 matching based on year of birth, gender and practice geographical location. ► The individuals with none of the selected immunocompromised conditions diagnosed are not necessarily immunocompetent. Open Access regimen. 3 4 6-9 IC patients with HZ are also more likely to develop recurrent HZ episodes. 10 hile there are few studies providing an estimate of the incidence of HZ in a set of selected IC conditions or patients with altered immune function,  3 4  many studies have assessed the risk of developing HZ in specific individual IC conditions, such as cancers,  5 11 12  rheumatoid arthritis (RA),  13  psoriasis (PSOR)  [14] [15] [16]  and inflammatory bowel diseases (IBD).
MethOds This was a retrospective observational cohort study using data from the UK Clinical Practice Research Datalink (CPRD). This is a primary care database of almost 12 million anonymised patient medical records including clinical events (medical diagnoses), referrals to specialists and secondary care, diagnostics tests, medical prescriptions, other routine medical services provided in general practices and some lifestyle factors (ie, smoking and alcohol consumption). 18 The information in CPRD is coded with Read codes, which contain hundreds of thousands of clinical terms, synonyms and abbreviations covering all aspects of patient care including signs and symptoms, treatment and therapies, investigations, occupations, diagnoses and drugs. 19 PRD currently contains records for about 4. 4 million individuals (7% of the UK population) registered with 674 general practitioner (GP) practices  20  and is considered to be broadly representative of the characteristics of patients and GP practices in the UK.
4 3] [34]  No previous study has reported on the frequency of vascular complications in IC individuals with HZ infection. We estimated an overall proportion with at least one vascular complication of around 0. 9% in the IC cohort of patients with HZ but this cannot be compared with the general population studies. Additional analyses were performed to evaluate the proportions of HZ-related complications, including PHN, occurring within 365 days of incident HZ (data not shown). However, no difference was observed in the proportion of HZ complications reported within 90 and 365 days of incident HZ. This confirms the findings of Yawn et al that the majority of HZ-related complications occur within a short time (3 months) of rash onset. 35 he proportion of patients with HZ experiencing HZ recurrence within 2 years of an HZ episode estimated in this study was lower than those reported in the literature, 10 but higher than those observed in the IC-free cohort or in an immunocompetent US population.
1] [42]  However, such data were not available in CPRD so these potential risk factors could not be assessed in this study. The IC cohort in this study represented 16. 8% of the study population; which was higher than their estimate in other studies. 43 This might be due to the large number of IC conditions that were followed up during the study The main strength of this study was the use of a large medical records database considered to be broadly representative of the UK population. 20 The linkage with HES data is only available for participants in England, but it has been reported that there are no substantial differences in participant characteristics between those that are linked to HES and those that are not. 44 Therefore, over a 12+ year study period, the use of CPRD with HES linkage allowed accurate and powerful estimation of HZ IRs.
In addition, the incidence of HZ in the IC-free cohort was slightly higher than the incidence of HZ observed in the UK general population (including both immunocompetent and IC people). 31 45 46 owever, due to the varying quality and completeness over patients and time of this primary care database,  20  a misclassification of individuals into the IC or IC-free cohorts may have occurred, as the classification was based on the coding practices and depended on the reliability of the GPs' data entry. 47 Further research should explore the temporal relationship between the time of HZ incident and the level of severity of immunosuppression, in order Open Access to develop appropriate interventions to improve the quality of life of IC individuals. In addition, patients with chronic medical conditions might attend the GP practices more frequently than IC-free population.
All authors had full access to the data and the corresponding author was responsible for the submission of the manuscript. Confirmed Competing interests VB, FH, EE and EAY are employees of the GSK group of companies. AEG, YEH and MG have nothing to disclose. GF was employed by the GSK group of companies between 2012 and February 2015, during which the study was designed and implemented. Later, as an employee of P-95 Epidemiology and Pharmacovigilance, GF provided contracted consultancy services to the GSK group of companies for this and other GSK-sponsored studies. P-95 provides contracted services to the GSK group of companies beyond the scope of this study. Patient consent Not required. ethics approval Approval was obtained from the Clinical Practice Research Datalink Independent Scientific Advisory Committee (14_222R).
Acknowledgements The authors thank  Emmanuel Aris ,  Lidia Oostvogels ,  Dominique Rosillon ,  Desmond Curran ,  Antonio Olivieri ,  Catherine Cohet ,  Corinne Willame ,  Sophie Caterina ,  Manjit Hunjan  and  Celine Jegou  for their contribution to the study. They also thank the  Business and Decision Life Sciences platform  for editorial assistance and coordination, on behalf of GSK. Gregory Collet coordinated the manuscript development and editorial support. Niels Neymark , independent scientific writer on behalf of GSK, provided writing support.
Crystallography Data were collected on an APEXII, Bruker-AXS diffractometer, for complexes 5 and 9, Mo Kα radiation (λ = 0. 71073 Å). The structures were solved by direct methods using the SIR97 program  [39] , and then refined with full-matrix least-square methods based on F 2 (SHELXL-97)  [40]  with the aid of the WINGX program  [41]. All non-hydrogen atoms were refined with anisotropic atomic displacement parameters. H-atoms were finally included in their calculated positions. Crystallographic data on X-ray data collection and structure refinements are given in Table  4. The X-ray crystallographic data for structural analysis reported in this article have been deposited in the Cambridge Crystallographic Data Center, under the deposition numbers CCDC 1834591-1834592. These data can be obtained free of charge from the Cambridge Crystallographic Data Center via http://www.
Data calibration and self-calibration We perform the data calibration in the IDL-based software MIR (Millimeter Interferometer Reduction) and the data reduction package MIRIAD 1. The calibration includes: an initial flagging of high system temperatures T sys and other wrong visibilities, a bandpass calibration, a correction of the cross-receiver delays, a gain and a flux calibration. The various calibrators observed for each of these steps and the list of antennae used for the observations are summarized in.
Analysis of the Stokes I visibilities Part of the extended dust continuum emission is lost during the reconstruction of the final image itself, since an interferometer can only sparsely sample Fourier components of different spatial frequencies of the incoming signal. To analyze the impact of the map reconstruction on the flux values derived, we can a Flux density estimated within the central 6000 au for SVS13, 3000 au for other sources. b We remind the reader that not all the sources have an intensity peak co-spatial with the polarized intensity peak. c Polarization fraction defined as the unweighted ratio between the median polarization over total flux. d The visibility data allows us to separate the two components of wide binaries. The fluxes reported here are those of the object located at the phase center, e.
We use the visibility data to separate the binary sources. As the sources cannot be modeled using a simple Gaussian fitting, we use the MIRIAD / imsub function to produce a sub-image containing L1448N-A, SVS13-A and IRAS16293-B from the cleaned image. We then use MIRIAD / uvmodel to subtract these sources from the visibility data and make an image of L1448N-B, SVS13-B and IRAS16293-A from the modified visibility datasets. We proceed the same way to independently map L1448N-A, SVS13-A and IRAS16293-B. The sepa-rated visibility profiles are shown in Fig. 3. The region mapped around NGC 1333 IRAS4B contained IRAS4A (outside the primary beam) and IRAS4B2 located about 10 east of IRAS4B. These two sources are also isolated and removed from the visibility data presented in Fig. 3. We use a Gaussian fitting method to fit the visibilities and extrapolate these models at 0 kλ to derive the integrated 0.
Acknowledgments We thank  Baobab Liu  for fruitful discussions on this project and  Bilal Ladjelate  for providing the internal luminosities from the  Herschel Gould Belt Survey dataset , for the sources belonging to the CALYPSO sample. This project has received funding from the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation programme (MagneticYSOs project , grant agreement N • 679937). JMG is supported by the  MINECO (Spain)   AYA2014-57369-C3  and  AYA2017-84390-C2  grants. SPL acknowledges support from the  Ministry of Science and Technology of Taiwan  with Grant  MOST 106-2119-M-007-021-MY3. This publication is based on data of the  Submillimeter Array. The SMA is a joint project between the  Smithsonian Astrophysical Observatory  and the  Academia Sinica Institute of Astronomy and Astrophysics , and is funded by the  Smithsonian Institution  and the  Academia Sinica.
Sequence Data Analysis All Illumina sequencing data were filtered and analyzed using the QIIME 1. 7. 0 software package (Quantitative Insights Into Microbial Ecology)  [28]. Firstly, the data were filtered to remove forward/reverse primers and barcode sequences considering quality phred ≥ Q20. Quality filtering on the data were performed under specific conditions to obtain the high-quality clean sequences  [29]. Chimera sequences were removed using the UCHIME Algorithm (http://www. drive5. com/usearch/manual/uchime_algo. html) [30]. Operational Taxonomic Units (OTUs) were assigned (97% similarity) using the pick_closed_reference_otus. py scripts with the UCLUST method and annotated in the Greengenes 13_8 database reference (http://greengenes. lbl. gov/cgi-bin/nphindex. cgi) [31]. The similarity level used by GreenGenes assignment was ≥ 97%.
Different indexes were selected to identify species richness (Chao1  [32]  and ACE  [33]  estimators) and species diversity (Shannon index  [34]  and Simpson index  [35] ). Rarefaction curves of the expected species richness (using Chao1) with 95% confidence intervals were generated based on the cumulative sampling using make_rarefaction_plots. py script from QIIME software. Finally, Good's coverage was used to estimate the sequencing depth. OTUs abundance information was normalized using a number of standard sequences corresponding to the biggest sample. Subsequent analysis of alpha and beta bacterial diversities were performed based on OTUs normalized data using QIIME software. To estimate the beta diversity (differences between samples), a principal coordinates analysis (PCoA-Weighted UniFrac distance analysis) was conducted with the software package of Fathom Toolbox for Matlab (http://www.
Moreover, shrimp midgut possesses an epithelial layer that is protected by a semi-permeable, non-permanent peritrophic matrix  [36, 37] , thereby representing a potential route of pathogen entry  [38]. The full dataset for this project has been deposited at NCBI Sequence Read Archive (SRA accession: SRS3345544, SRS3345545, SRS3345546, SRS3345547). A total of 275,561 raw sequences were generated using the Illumina Hi-Seq 2500 platform sequencing and, after quality trimming, 185,745 high-quality sequences were used for further analysis (Table  S2 ). From those high-quality sequences, 39,938 were generated from BFT, 47,030 from CWS, 50,836 from BFT. W and 47,941 from CWS. W. Sequences were clustered into 1299 Operational Taxonomic Units (OTUs) at 97% sequence similarity using the Greengenes 13_8 reference OTU collection database as reference.
W" and "CWS. W"). We focused on investigating the midgut portion from the shrimp gut because it lacks the chitinous layer of the cuticle normally found in the other portions of the intestine (foregut and hindgut). Moreover, shrimp midgut possesses an epithelial layer that is protected by a semi-permeable, non-permanent peritrophic matrix  [36, 37] , thereby representing a potential route of pathogen entry  [38]. The full dataset for this project has been deposited at NCBI Sequence Read Archive (SRA accession: SRS3345544, SRS3345545, SRS3345546, SRS3345547). A total of 275,561 raw sequences were generated using the Illumina Hi-Seq 2500 platform sequencing and, after quality trimming, 185,745 high-quality sequences were used for further analysis (Table  S2 ). From those high-quality sequences, 39,938 were generated from BFT, 47,030 from CWS, 50,836 from BFT.
W and 47,941 from CWS. W. Sequences were clustered into 1299 Operational Taxonomic Units (OTUs) at 97% sequence similarity using the Greengenes 13_8 reference OTU collection database as reference. Analysis of the rarefaction curves from the 16S rRNA gene sequences of all the samples reached a plateau (saturation), indicating that the sequencing depth was sufficient to cover the bacterial communities of the L. vannamei midgut (Figure  2A ), confirmed by the Good's coverage analysis (Table  1 ). Estimation of phylotype richness was calculated according to the bias-corrected Chao1 estimator and the abundance-based coverage estimator (ACE). Chao1 scores ranged from 487. 07 to 674. 05, while ACE predicted a range from 408. 01 to 672. 01 phylotypes (Table  1 ), indicating that shrimp samples from BFT exhibited a higher phylotype richness than the CWS samples.
In order to compare the bacterial communities' diversity from each sample, Shannon and Simpson indices were calculated from the obtained OTUs. Shannon indexes were 6. 24 for BFT, 4. 13 for CWS, 5. 79 for BFT. W, and 4. 55 for CWS. W (Table  1 ). Simpson indexes of diversity were 0. 96 for both BFT and BFT. W, 0. 79 for CWS and 0. 89 for CWS. S. High Simpson indexes in BFT-reared animals indicate a higher bacterial communities' diversity in the midguts from shrimp reared in BFT than in CWS. Indeed, BFT represents a rich microbial environment that provides the establishment of many different bacterial communities in the shrimp gut  [22]  and, the composition of the L. vannamei midgut seems to be closely related to the rearing system. The Principal coordinate analysis (PCoA) plot data revealed the similarities and dissimilarities between bacterial communities present in the midgut of L.
3, and 0. 1 % of PAR(0 + ). CTD sensors were calibrated and data processed post-cruise using Sea-Bird software in 1 m bins. Conservative temperature, absolute salinity, and potential density were computed using  TEOS-10 (Mc-Dougall and Barker, 2011). Chlorophyll a (Chl a) in mg m -3 were measured with an Aqua Trak III fluorimeter (Chelsea Technologies Group Ltd. ). All samples were collected from 24 12 L Niskin bottles equipped with silicone rubber closures and tubing for measurements (see Sect. 2. 2, "Analytical chemical methods") for stock variables (dissolved oxygen; dissolved inorganic carbon, DIC; total alkalinity, TA; nutrients; Chl a; and particulate and dissolved organic C, N, and P) and fluxes (primary and bacterial production rates; N 2 fixation rates; and dissolved inorganic phosphate, DIP, turnover times, T DI P ; i.
The main species removed were copepods, crustaceans (ostracods, euphausiids, amphipods), and pteropods. Microphotographs of each sample were taken. After the swimmers were removed, the whole sample was then rinsed 3 times with ultrapure (Milli-Q) water in order to remove salt and then freeze-dried. Mass particle fluxes were obtained by weighing the freeze-dried sample 5 times. The accuracy of the weighing (and thus of the flux) was 1 % over the whole data series. In this study, swimmers were rinsed and freeze-dried and their dry weight was also determined. Settling particulate matter and swimmers were analyzed separately on an elemental analyzer coupled to an isotope ratio mass spectrometer EA-IRMS (Integra2, Sercon Ltd. ) to quantify total C and N. Total P was analyzed as described in Sect.
esrl. noaa. gov/psd/data/gridded/data. ncep. reanalysis. html, last access: 19 December 2017). 6 ). Inputs from below associated with vertical turbulent diffusion were calculated using the mean vertical eddy diffusivity, and slopes of nutriclines (Table  2 ) and DIC gradients calculated between 70 and 200 m using linear regressions (data not shown). The ocean-atmosphere CO 2 fluxes were detailed in the previous paragraph. The input of N by N 2 fixation was calculated for each area (Table  6 ) using depth profile sampling and ondeck 24 h 15 N 2 incubations (Sect. 2. 2). The C, N, and P particulate and dissolved organic export were estimated. The method of obtaining particulate export by settling material (Table  4 ) was described above. Output of dissolved and particulate organic matter by turbulent diffusion was calculated from the mean vertical eddy diffusivity (Table  1 ) and from gradients estimated with linear regressions (data not shown) between the surface and 70 m depth of DOC-POC (Fig.
Surface waters carbonate system climatology The climatological gridded values proposed in  Takahashi et al. (2014) , hereafter referred as NDP-094 climatology, were used to validate our estimated values for the carbonate system in the upper surface previous winter conditions (July 2014). The dataset is based on interpolated p OC CO 2 and calculated TA data (based on regional linear potential alkalinity-salinity relationships) on a 4 • latitude by 5 • longitude monthly grid in the reference year 2005. The variable DIC (among others) is calculated from p OC CO 2 and TA. Data were downloaded from http://cdiac. ess-dive. lbl. gov/ftp/ oceans/NDP_094/ (last access: 19 December 2017). Climatological July data centered on 20 • S were extracted along the cruise transect and 2, 3, and 3 pixels were averaged for comparison in the WMA, EMA, and WGY areas, respectively (Table  5 ).