dataesr/scientific-paragraphs-categorization huggingface dataset:
the project in which the data were collected dataset The name of the dataset to which the data belongs to studysite The name of the study site in which the data were collected taxonidentifier The unique identifier of the taxon ("Climate" for climatic events) taxon The scientific name of taxon (climatic group for climatic events) eventtype The type of the event year The year dayofyear The date of the observation as the number of days since January 1 st in the focal year quality An indicator variable of any quality issues identified with the data.
Acknowledgements The field work was conducted as part of the monitoring program of nature reserves,  Chronicles of Nature. The work was funded by  Academy of Finland , grants  250243 ,  284601 ,  309581  (OO); the  European Research Council ,  ERC Starting  Grant  205905  (OO);  Nordic Environment Finance Corporation Grant (OO) ;  Jane and Aatos Erkko Foundation  Grant (OO and TR);  University of Helsinki   HiLIFE Fellow Grant   2017-2020  (OO); the  Kone Foundation   44-6977  (MD);  Spanish Ramon y Cajal  grant  RYC-2014-16263  (MD); the  Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences   220-2017-0003 ,  0220-2017-0005  (LV, SS and JK); the  Russian Foundation for Basic Research  Grant  16-08-00510  (LK), and the  Ministry of Education and Science of the Russian Federation   0017-2019-0009  ( Keldysh Institute of Applied Mathematics, Russian Academy of Sciences ) ( NI, MSh).
Modelling of mineral dust is often done using one single mean species. But for biogeochemical studies, it could be useful to access to a more detailed information on differentiated mineral species and the associated chemical composition. Differentiating between mineral species would also induce different optical properties and densities and then different radiative impact, transport and deposition. In this study, the mineralogical differentiation is implemented in the CHIMERE regional chemistry-transport model, by using global databases. The results show that this implementation does not change the results much in terms of aerosol optical depth, surface concentrations and deposition fluxes. But the information on mineralogy, with a high spatial (a few kilometres) and temporal (1 h) resolution, is now available and is ready to be used for future biogeochemical studies.
Our computation includes the explicit chemical composition and solubility of each mineral. In addition, the concentrations and deposition fluxes of the following chemical elements: magnesium, iron, phosphorus, aluminium, calcium, silicon, manganese et potassium are modelled. This implementation is done using the existing datasets of  Journet et al. (2014) , that until now, has only been used at the global scale. A simulation is performed for the whole year 2012 over a large domain encompassing Africa and Europe. This geographical domain allows us to have the most complete aerosol source estimation as possible and to reproduce correctly all possible transport pathways from the source areas to the Mediterranean Sea. Results are presented through a comparison to surface measurements in terms of atmospheric concentrations and deposition fluxes.
Concentrations and deposition with EMEP data For the surface aerosol concentrations evaluation, we use the EMEP network providing measurements of PM 2. 5 and PM 10 (aerosol with a mean mass median diameter less than 2. 5 and 10 µm, respectively), gaseous species such as NO 2 and O 3 , and aerosols including nitrates, ammonium and sulfates. The data are stored in the EBAS database, and information about these measurements is available at https://ebas. nilu. no (last access: 29 November 2019). In addition, and to evaluate the realism of the development of the mineralogical speciation, the non-sea-salt calcium concentrations, nssCa 2+ , are compared as well as their wet deposition. The list of the selected stations is provided in Table  A2  as well as a map showing their locations in Fig. A2.
The aerosol size distribution is represented using 10 bins, from 40 nm to 40 µm, in mean mass median diameter as described in  Menut et al. (2016)  and updated in  Mailler et al. (2017). The photolysis rates are explicitly calculated using the FastJX radiation module (version 7. 0b)  (Wild et al. , 2000; Bian et al. , 2002). The modelled AOD is calculated by FastJX for the several wavelengths over the whole atmospheric column. As limit conditions, climatologies from global model simulations are used at the boundaries of the domain. In this study, outputs from LMDz-INCA  (Hauglustaine et al. , 2014)  provided all gaseous and aerosol species except for mineral dust, for which the simulations from the GOCART model are used  (Ginoux et al. , 2001). Anthropogenic emissions are prescribed from the Hemispheric Transport of Air Pollution (HTAP) global database  (Janssens-Maenhout et al.
The mineral dust flux calculation In the original version of the model, one unique vertical flux of emissions is estimated for a mean averaged species representing all dust species and elements. Emissions are calculated using the Alfaro and Gomes (2001) scheme, optimised following  Menut et al. (2005)  and using the soil and surface databases presented in  Menut et al. (2013b). Since this latter article was published, several changes have been implemented in the emissions scheme. For the erodibility, the original scheme takes into account United States Geological Survey (USGS) land use only. In this model version, we added the database proposed by  Beegum et al. (2016)  to calculate a new erodibility factor, more related to arid areas. For all model cells regarded as "desert", the MODIS erodibility is used, while for all other cells, a constant erodibility factor is applied depending on the USGS land use, as in  Menut et al.
Mineralogical species information In order to split the emission flux into several mineral and chemical elements, additional information is required: soil databases describing the relative part of each mineral and each chemical element in each model grid cell the relative part of clay and silt in each grid cell for each mineral, its density and refractive index the solubility of each chemical element as a function of each mineralogical species. We describe below how we use information from already known and published databases to gather these data.
Silt-clay partition and density In order to have information on the dust mineralogical composition, the global databases presented in  Journet et al. (2014)  are used. These databases are delivered with a horizontal resolution of 0. 5 • × 0. 5 • and are then interpolated on the model grid used in this study. Data are provided for 12 different species, listed in Table  1. For each mineral, we indicate if it can be found in the clay or silt fraction of the soils. The density of each is also provided: this information comes from several references, including  Perlwitz et al. (2015). These values for densities have a non-negligible but not quantified uncertainty. For each mineral, the reference (peer-reviewed publication or internet database) is specified in the table. Note that for the Mica density, being a group of numerous minerals, the density of muscovite is used.
2  as a function of the solar radiation wavelength (micrometre). In addition to the individual minerals, the model species DUST is added. This "mean" species corresponds to what is usually used in models having only one lump species for the mineral dust. All values are reported in Table  2. The refractive indices of the mineralogical species modelled here are presented in Table  2. Data are shown for five wavelengths, which are the ones chosen to run the FastJX radiative transfer model, implemented online in CHIMERE. For each species and each wavelength, the real and imaginary parts of the refractive index are displayed.
Boundary conditions Boundary conditions for mineral dust are calculated using a climatology calculated with the GOCART model  (Ginoux et al. , 2001). This climatology was provided by Mian Chin and Paul Ginoux for the CHIMERE validation and distribution to users. The data are freely available on the CHIMERE download website. The data represent a monthly global climatology simulation of mineral dust with a horizontal resolution of 2. 5 • × 2 •  (Ginoux et al. , 2001). The monthly mineral dust concentration fields are an average of the  years 1987, 1988, 1989, 1990 and 1997  and divided into seven size bins, later reprojected in the CHIMERE aerosol bins. The use of this climatology in the case of this study has a major weakness: knowing that the data are for the usual mean mineral dust species.
Conclusions The present study consists of the implementation of the mineralogical speciation of dust in the CHIMERE chemistrytransport model. Several databases were implemented and 12 minerals are explicitly treated in ways of emission, transport and deposition in contrast to a single one with a classic approach. A new and simple function is also introduced to  correct the effect of wet sieving and partition accurately the relative part between the silt and the clay fractions. Several motivations justify the need to have dust mineralogical speciation: to better follow the emissions depending on soil type, to better capture the aerosol radiative effect, to better inform biogeochemical models and to improve the comparison of deposition fluxes to the available measurements. We infer that surface concentrations of particulate matter, considered here as PM 2.
Acknowledgements. We acknowledge  Jan P. Perlwitz  for his very important help to find all information about the refractive index and densities of the mineralogical species. The EBAS database has largely been funded by the  UN-ECE CLRTAP (EMEP) ,  AMAP  and through NILU internal resources. Specific developments have been possible due to projects like  EUSAAR  ( EU-FP5 ) (EBAS web interface),  EBAS-Online (Norwegian Research Council INFRA)  (upgrading of database platform) and  HTAP (European Commission DG-ENV)  (import and export routines to build a secondary repository in support of http://www. htap. org,last access: 29 November 2019). Many specific projects have supported development of data and metadata reporting schemes in dialogue with data providers (EU) (CREATE, ACTRIS and others). Review statement.
Since then, a number of NGVS-motivated spectroscopic programs have been undertaken (see, e. g. ,  Zhang et al. 2015 Zhang et al. , 2018;; Toloba et al. 2016; Spengler et al. 2017; Longobardi et al. 2018). We have collected radial velocities from these and other previous works  (Binggeli et al. 1985; Hanes et al. 2001; Côté et al. 2003; Brinchmann et al. 2008; Strader et al. 2011 Strader et al. , 2012;; Pota et al. 2013; Blom et al. 2014; Norris et al. 2014; Li et al. 2015; Pota et al. 2015; Forbes et al. 2017; Ko et al. 2017; Toloba et al. 2018) , as well as from the NASA/IPAC Extragalactic Database (NED) 1  , the SIMBAD Astronomical Database 2   (Wenger et al. 2000)  and the Sloan Digital Sky Survey (SDSS,  Abolfathi et al. 2018). In all, we have a total of 31,346 velocity measurements for objects in the NGVS footprint brighter than g 0 = 21.
Size Measurements Size is the defining parameter of UCDs 3. As shown by  Liu et al. (2015a) , the excellent image quality of the NGVS allows us to measure reliable sizes for compact objects in Virgo (mainly GCs, UCDs, and galactic nuclei) larger than ∼10 pc (see their Section 2. 3). We measure half-light radii using the KINGPHOT package   (7 vs 15 pixels) , with data taken from  Liu et al. (2015a). Right panel: Comparison of r h measured using different data (blue circles, ACSVCS data,  Haşegan et al. 2005)  or different methods (orange squares, ISHAPE software,  Ko et al. 2017). (Jordán et al. 2005) , focusing on the g and i bands because of the former's depth and the latter's exquisite seeing. Comparisons of the two sets of r h measurements show that they are consistent with each other (see  Figure 3 in Liu et al.
Otherwise, the two sets of r h measurements are statistically equivalent, so we therefore adopt the KINGPHOT measurements made with r fit = 15 pixels. The right panel of this figure shows a comparison between the r h measurements from this work and those from previous studies. The blue circles are taken from  Haşegan et al. (2005) , who measured r h using KINGPHOT and HST data (ACSVCS,  Côté et al. 2004 ). The orange squares denote the r h measurements from  Ko et al. (2017) , who used ISHAPE software and NGVS data. From this figure, we can see that our r h measurements are consistent with the measurements from previous studies, even though they used different methodologies and/or data sets.
With the addition of K-band data though, we can reduce the number of BGs that pass our u * giK-based selection from 14 to 1, which compares very favourably with the results of our u * giK s -based selection. As for the other contaminants, we note that all three of our selection methods pass two objects from our training set that are labelled as foreground stars. In fact, these two objects are really UCDs that have negative radial velocities through their association with the M86 subgroup. This shows that the combination of optical and near-infrared photometry removes most foreground and background objects. Where this photometric combination falls short is in rejecting dE,Ns, but we can easily accomplish this through visual inspection since nuclei are surrounded by obvious envelopes. To summarize, our tests in the central 4 deg 2 of sub-cluster A show that a u * gizK-based selection of UCDs can be a reliable alternative to that based on u * giK s.
Liu et al. Programme Blanc VIRAGE (ANR10-BLANC-0506-01), and by the Canadian Advanced Network for Astronomical Research (CANFAR) which has been made possible by funding from CANARIE under the Network-Enabled Platforms program. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This publication has made use of data products from the Sloan Digital Sky Survey (SDSS). Funding for 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. This work is based in part on data obtained as part of the UKIRT Infrared Deep Sky Survey. This research uses data obtained through the Telescope Access Program (TAP), which has been funded by the National Astronomical Observatories, Chinese Academy of Sciences, and the Special Fund for Astronomy from the Ministry of Finance. Observations reported here were obtained at the MMT Observatory, a joint facility of the University of Arizona and the Smithsonian Institution. Facility: CFHT -Canada-France-Hawaii Telescope.
name; (2-3) Absolute B and V band magnitude, calculated using the transformation equation B = u * -0. 8116(u * -g) + 0. 1313, V = g -0. 2906(u * -g) + 0. 0885 (Lupton 2005) and a distance modulus of 31. 1 mag (Mei et al. 2007; Blakeslee et al. 2009); (4) Weighted mean half-light radius in the g and i bands, r h = r h,g if an i-band measurement is not available; (5) Stellar mass; (6) Heliocentric radial velocity (or weighted mean value if multiple measurements are available); (7) The source of our adopted vr measurement, SDSS: Abolfathi et al. (2018); NED: NASA/IPAC Extragalactic Database; SIMBAD: SIMBAD Astronomical Database (Wenger et al. 2000); VCC: Binggeli et al. (1985); NTT17: NTT 2017 program; AAT12: AAT 2012 program; MMT09: MMT 2009 program; Toloba2018: Toloba et al. (2018); Ko2017: Ko et al.
ACKNOWLEDGMENTS The NGVS team owes a debt of gratitude to the director and the staff of the  Canada France Hawaii Telescope  who helped make the survey a reality. This work is based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada France Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at Terapix available at the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS. C. L. acknowledges support from the  National Natural Science Foundation of China (NSFC , Grant No.
Analysis of the B. pertussis genome sequence identified eight genes encoding putative PBPs (Table  1 ). One of these was the cell division transpeptidase FtsI, and one was MrdA in the mre/mrd locus described above. Interestingly, of the other putative dd-transpeptidases, only that encoded by BP0102 was essential, and then only in the Bvg-phase. Another DacC homologue, encoded by BP1545, was identified. It displays little homology to BP0102 DacC at the amino acid level, although it contains strong homology to the DacC conserved domain (COG1686) with a blastp score of 1. 49e -72. This suggests that BP0102-encoded DacC has a function that is distinct from those of the other putative DD transpeptidases, and that is essential for viability in the Bvg-phase. BP1061 (MltE), BP3214 (MltD) and BP3268 (MltA) all encode putative murein lytic transglycosylases.
In the Maldives, chikungunya was first reported in 2006 with a total of 11,879 suspected and confirmed cases. This outbreak eventually affected more than 60% of the archipelago inhabitants by April 2007  [5]. In subsequent years, and until 2019, CHIKV circulation was rarely documented in the Maldives. Only five imported chikungunya cases were reported to EuroTravNet/GeoSentinel networks over the period 2009-2018. In 2019, the Maldives' local health authorities registered a total of 1736 confirmed cases, of which 277 cases were recorded in just one month (December 2019). Tourists to the Maldives at that time would have been at higher risk of CHIKV infection  [6]. The French GeoSentinel Marseille site reported a CHIKV confirmed infection acquired in Maldives in December 2019  [7] ; five additional cases imported to Denmark, Germany, and Spain in 2019 were retrospectively identified in the EuroTravNet/GeoSentinel Surveillance Network's database.
Discussion According to the data from the Ministry of Health and the Maldivian's Health Protection Agency [9,10] and the European Centre for Disease Prevention and Control (ECDC)  [11, 12] , no autochthonous case of CHIKV was registered in the Maldives from 2013 to 2019 (Fig. 1 ). From 2009 to December 2018, only three cases acquired by visitors to the Maldives were reported in the literature or in the ProMED International Society of Infectious Diseases database, one from France  [13]  and two from Germany  [14] , all in 2009. Since 2019, concomitantly with a dengue outbreak, the Maldives health authorities have reported an epidemic of CHIKV, with 1736 cases reported as of the 31st December 2019  [6]. As experienced in the United States Virgin Islands in 2014, current and possibly recurrent chikungunya outbreaks in the Maldives could have major human and economic impact  [15].
Study population The Whitehall II study is an ongoing population-based cohort started in 1985 among 10308 British civil servants (6895 men and 3413 women), aged 35 to 55 years. Data collection involves questionnaires on health behaviors, physical and mental wellbeing every 2 to 3 years, and clinical examinations including anthropometric measures and health-related parameters every 4 to 5 years until the most recent examination in 2015 to 2016. Participants' written, informed consent and research ethics approval are renewed at each contact; the most recent approval was from the National Health Service London-Harrow Research Ethics Committee, reference number 85/0938.
Incidence of dementia Dementia was ascertained by linkage to 3 electronic health records: Hospital Episode Statistics, Mental Health Services Data Set, and mortality register up to the March 31, 2019, using a unique National Health Service identifier for each individual. Dementia cases were identified based on ICD-10 codes F00, F01, F02, F03, F05. 1, G30, G31. 0, G31. 1, and G31. 8 (Table  S1 , http://links. lww. com/PAIN/B185); the date of diagnosis was set at the first record of disease in these databases. The sensitivity and specificity of dementia diagnosis in Hospital Episode Statistics data has been reported to be 78. 0% and 92. 0%. 26 dditional use of Mental Health Services Data Set in our study is likely to improve sensitivity of dementia diagnosis. 38.
Retrospective analysis of 27-year pain trajectories This analysis aimed at comparing individual pain trajectories in dementia cases with noncases among the study population. We used mixed linear models with continuous pain variables as the dependent variables and a backward timescale, such that index date (date of dementia diagnosis for cases and the earliest of March 31, 2019, or date of death for noncases) was year 0, and pain measures were modeled from year 0 to year 227. Similar analyses were undertaken for dichotomized pain measures ("moderate to severe" vs "mild or no" pain) using mixed logistic models. Mixed models have the advantage to use all available data over the follow-up, handle differences in length of follow-up, and account for the correlation of the repeated measures on the same individual.
Discussion This longitudinal study with repeated pain measures collected over 27 years suggests steady progression in self-reported pain before dementia diagnosis. Time-to-event analysis, where data on pain measures over the course of the study were used to assess the impact of the length of follow-up on the association between pain and incident dementia, showed the association to be evident only when the mean follow-up was 10 years or lower. The use of backward pain trajectories describes how small differences in pain scores between dementia cases and noncases become increasingly pronounced in the decade before diagnosis of dementia. Taken together, these results highlight the time-dependent association between pain and dementia; given the long preclinical phase of dementia, there is no strong evidence from our results that pain is a causal risk factor for dementia.
Noncase; case. Estimated from linear mixed models adjusted for age at year 0, sex, education, ethnicity, marital status, dementia status, slope terms (time, time 2 , and time 3 ), interaction of these covariates with slope terms, birth cohort, pain medication use, multimorbidity index, and depression assessed at the time of pain measurement. Estimates came from Margins command in STATA. Higher scores correspond to higher pain. Limitations of this study include lack of data on the characteristics, type, and location of pain. The specific chronic conditions that contributed to the association between pain and dementia could not be examined in sufficient detail. Our multimorbidity index included a range of conditions to reflect overall disease burden; it is possible that some of these play a more important role than others.