dataesr/scientific-paragraphs-categorization huggingface dataset:
WRF and CHIMERE model configuration WRF version 3. 3 was used with lateral boundaries and initial meteorological conditions taken from the National Centers for Environmental Prediction (NCEP) final (FNL) global analyses that are available at 1 • × 1 • grid resolution every 6 h (http://rda. ucar. edu/datasets/ds083. 2/). The WRF Preprocessing System (WPS) of geographical input data (i. e. , land use, vegetation index, soil type, and albedo) was also obtained from the NCEP database. In total, 28 vertical levels were simulated, with the lowest level having the physical height of about 38 m. Analysis nudging was performed in the planetary boundary layer (PBL) and other layers for wind components (u and v), temperature (T ), and relative humidity (RH). Nudging coefficients were set for all parameters at 0.
, 2006) , which are available at the CHIMERE website. To assess the effects of the somewhat aged boundary conditions compared to the model year, a comparison between the monthly average concentrations at the boundaries between 2007 and 1998-2002 was made which showed that the difference among the datasets differed by 0. 98-1. 23 considering the ratios for aerosol and PM precursor gases (i. e. , BC, OC, NO 2 , CO, SO 2 , C 2 H 4 , CH 3 CHO, and NH 3 ) between the two datasets. This implies that basically the two datasets were almost similar. Thus, the impacts of the aged boundary conditions on the simulation are expected but with a small magnitude. Initial conditions of gases and aerosol concentrations in every grid were interpolated from the outputs of the global CTM of the LMDZ-INCA simulation.
Model evaluation The evaluation of WRF outputs was done using observed data from eight airport meteorological stations in five SEA countries that captured major subclimate zones (upper, near-Equator, and lower latitude) in the domain. Hourly observations from all these airport stations in 2007 were obtained from http://weather. uwyo. edu/surface/meteorogram/. The statistical evaluation of WRF outputs was done using the criteria provided by  Emery et al. (2001) , which include the mean bias error (MBE), mean absolute gross error (MAGE), and root mean square error (RMSE). The mod-D. A. Permadi et al. :  Modeling PM and BC distributions in Southeast Asia eled surface pressure and upper wind fields (at 850 hPa) were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) Interim data extracted from http://apps.
ecmwf. int/datasets/ for the same vertical levels. Simulated monthly accumulated precipitation fields were compared with the satellite-based observations provided by the Tropical Rainfall Measuring Mission (TRMM-3B43) (https://disc. sci. gsfc. nasa. gov/datasets/ TRMM_3B43_V7/summary?keywords=TRMM). Only limited air pollution data were available in SEA for the model performance evaluation. This study collected the observed concentrations of aerosol (BC, OC, PM 2. 5 , and PM 10 ) and related gases from various sources. For example, daily (24 h) concentrations of PM 10 , PM 2. 5 , BC, and OC in four SEA cities (i. e. , Manila, Hanoi, Bandung, and Bangkok) in 2007 were taken from the measurement data generated by the Improving Air Quality in Asian Developing Countries (AIRPET) project "Improving Air Quality in Asian Developing Countries"  (Kim Oanh et al.
gsfc. nasa. gov/) for the evaluation of the modeled AOD. The AERONET data were level 2 quality controlled and recorded at 10 AERONET stations (using a sun photometer) listed in Table  S2  in the Supplement. This AERONET dataset has already been pre-and post-field calibrated with cloud screening and quality assurance. The selected 10 AERONET stations had more complete datasets in 2007 and they represent all subclimate zones in the domain. The sun photometer measures AERONET AOD at six different wavelengths  (1020, 870, 675, 500, 440, and 380 nm). Therefore, to compare with the modeled AOD at 550 nm, the AERONET AOD at 500 nm was converted to that 550 nm using a logarithmic interpolation  (Chung et al. , 2012). For a qualitative evaluation of the spatial distributions we checked the consistency between the modeled AOD spatial distribution over the SEA domain and the monthly MODIS AOD (level 3 data measured at 550 nm wavelength downloaded from https://giovanni.
Base year emissions The obtained total national emission estimates of Indonesia, Thailand, and Cambodia for 2007 were compared with the existing regional EI databases of EDGAR for 2007 and CGRER for 2006. Table  2  shows a reasonable agreement in the ranges of the estimates between the emission databases for three countries: Cambodia, Indonesia, and Thailand. Detailed EI results for Indonesia were presented in  Permadi et al. (2017b). There are certain discrepancies between the databases that may be explained by several factors, including the uncertainty in activity data levels and emission factors used as well as the different coverage of the emission sources by different EI works. Specifically, for the emission sources of N 2 O, our EI for the three countries did not cover the direct emissions from cultivated soil (fertilized land) and the indirect N 2 O emissions from agriculture-related activities (microbial nitrification and denitrification), hence resulting in lower N 2 O emission estimates.
Similar reasoning may be used to explain our lower estimates for CH 4 as compared to EDGAR for all three countries. For Indonesia, our emission estimates were between those of CGRER and EDGAR for a number of species. The estimates for PM 10 , PM 2. 5 , and BC actually agreed well between these databases while OC of CGRER appeared to be higher. However, the SO 2 estimates differed a lot between the databases and our value was lower than others (mainly for on-road transportation and industry), which may be attributed to a more bottom-up approach used in our EI that relied on actual sulfur content used in the country and implementation of air pollution control devices  (Permadi et al. , 2017b). For example, our SO 2 estimate for the power plants of Indonesia was 300 Gg yr -1 , which was more comparable with the CGRER estimate (409 Gg yr -1 ) but much lower than the EDGAR estimate (1000 Gg yr -1 ).
The most striking difference was for the CO 2 emissions, which showed a much higher value by EDGAR; this could be clearly explained by the inclusion of two major sources in the EDGAR dataset: (i) the forest fire post-burn decay (698 000 Gg yr -1 ) and (ii) decay of drained peatland (504 000 Gg yr -1 ). If these two emission sources are excluded from the EDGAR results, the CO 2 estimates of all three databases are similar for Indonesia. The EI results for Thailand and Cambodia also showed reasonable agreements between the available datasets, except for CO 2 , which was estimated higher by EDGAR for Cambodia. The BC and OC emissions for these two countries were mostly comparable, i. e. , differing by a factor less than 2. 0 among the datasets. The emission shares by source category for the three countries are presented in Fig.
Synoptic-scale model evaluation Spatial distribution of surface pressure over the WRF domain is presented together with the ERA-Interim dataset in Fig. S4  for 3 selected days (1 January 2013, 8 October 2007, and 7 November 2007; 00:00 UTC). Both modeled and ERA data showed similar spatial distribution patterns of pressure but WRF appeared to produce slightly lower surface pressure over central Papua of Indonesia for all three cases presented. In fact, both datasets showed lower pressure zones over the high mountain areas of the Himalayas, eastern parts of China, and central Papua of Indonesia that indicated the effects of the topography. The simulated wind fields at 850 hPa (∼ 1500 m) are compared with the ERA-Interim upper wind fields in Fig. S5  that also showed a consistency of the two datasets and more in the center of the domain both for wind speeds and wind directions.
A large discrepancy was seen in the northwestern corner of the modeling domain and this may be attributed to the boundary conditions (taken from NCEP FNL in this study). The modeled monthly precipitation for 2 selected months (August and October 2007) was compared with the TRMM-3B43 dataset in Fig. 2 , which showed good agreement in the distribution patterns, but the model somehow underestimated the domain maximum monthly precipitation column that occurred, e. g. , over Myanmar in August 2007 and over the central part of Vietnam in October. The domain maximum hourly values of simulated PBL in different months of 2007 (Fig. S6 ) showed the PBL of 1800-3900 m. The maximum value of 3900 m occurred in March, which was lower than the model top level of 500 hPa (∼ 5500 m) mentioned above, while the lowest PBL was in August.
Black carbon For model evaluation purposes, we used available measurements in the previous projects for SEA. The 24 h BC measured by the optical method was available at several SEA sites under the AIRPET project  (Kim Oanh et al. , 2014). The hourly-based EC (elemental carbon, measured by a Sunset analyzer) measurements, available from the APN project  (Kondo et al. , 2009)  for the AIT site (suburban), were used to calculate 24 h BC levels. EC was measured using thermal optical method while BC was measured using light absorption method (continuous soot monitoring system or COSMOS). The model performance evaluation was done using 24 h BC data of both APN and AIRPET projects. The APN hourly EC dataset for the AIT site was available for both dry and wet seasons, from March to December 2007. The hourly EC and hourly BC measured simultaneously by the APN project at AIT were found to have a strong linear correlation  (Kim Oanh et al.
4. The model was also not able to capture AOD hotspots over mainland Southern China in this month. The results for August and November both showed some significant underestimation of AOD as compared to the MODIS-observed values. There are several reasons for these discrepancies, including the temporal and spatial inconsistency in the observed and modeled values used for comparison. For example, the Terra MODIS satellite daily passed a region for a particular time (i. e. , 13:30), thus giving only a snapshot of the value, while the model provided the hourly average for 13:00-14:00. Thus there is certainly inconsistency in the monthly averages derived from these two datasets. The discrepancy may come from the fact that in the simulation AOD was covering up to 500 hPa and could not include aerosol in the upper layers as mentioned above.
Summary and conclusions This study developed and evaluated the EI databases for Indonesia, Thailand, and Cambodia for 2007. The results were compiled with the existing CGRER and EDGAR emission datasets to generate the emission input data of the entire SEA domain for regional WRF-CHIMERE modeling. Our EI results for the three countries were comparable to other existing databases and the differences are explained mainly by the differences in the sources covered by different EI works. The BC emissions were mainly from residential and commercial combustion in Indonesia (71 %) and Cambodia (70 %) but were dominated by biomass OB emissions in Thailand (31 %). The model performance for 2007 was evaluated using the hourly and daily observed data in the SEA domain. The WRF model outputs were in good agreement with the observed data at eight international airport stations in Indonesia, Thailand, Vietnam, Cambodia, and the Philippines.
(Olivier et al. , 2001))r in comparison with the existing regional EI datasets (Gg yr -1 ). EDGAR b CGRER c This study a EDGAR b CGRER c This study a EDGAR b CGRER c countries d part of China domain EI conducted for base year of 2007 using the ABC EIM framework(Shrestha et al. , 2013). Detailed methodology and results were presented inPermadi et al. (2017a). bEDGAR for base year of 2007(Olivier et al. , 2001). Retrieved from http://edgar. jrc. ec. europa. eu/overview. php?v=42FT2012. The CO 2 emissions, excluding forest fire post burn decay and decay of drained peatland, are given in brackets for comparison with our estimates.
Acknowledgements. We warmly thank the  OHP  staff for their support on the observations. We thank the anonymous referee for their careful reading and valuable comments which helped to improve the manuscript. This work was supported by the  Programme National de Planétologie (PNP)  of  CNRS/INSU , co-funded by  CNE. X. De. , X. B. , I. B. and T. F. received funding from the  French Programme National de Physique Stellaire (PNPS)  and the  Programme National de Planétologie (PNP)  of  CNRS (INSU). X. B. acknowledges funding from the  European Research Council  under the  ERC Grant  Agreement n. 337591-ExTrA. This work has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). This work is also supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ.
Grenoble-Alpes. V. B. acknowledges support by the  Swiss National Science Foundation (SNSF)  in the frame of the  National Centre for Competence in Research PlanetS , and has received funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (project  Four Aces ; grant agreement No  724427 ). This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalização by these grants:  UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953  and  PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113. N. A-D. acknowledges support from  FONDECYT  # 3180063. N. H. acknowledges the financial support of the  National Centre for Competence in Research PlanetS of the Swiss National Science Foundation (SNSF).
03) over-represented among the fatal cases comparing with the survivors (56% vs 27%)  [11]. More recently, Campbell et al reported that 7 out of 15 IMD cases (of whom 5 died) presented predominantly with a history of acute gastrointestinal symptoms in the 24 hours before attending hospital. These cases were all provoked by the currently expanding hypervirulent capsular serogroup W strain (the South American UK strain and its sublineages)  [12]. The link of these NmW isolates with abdominal presentations of IMD remains to be evaluated in large cohorts of patients. We therefore aimed in this work to review the database of the French National Reference Centre to describe a large cohort of patients with abdominal presentation of IMD in terms of clinical, biological and bacterial parameters. We also aimed to analyze the association of different genomic types of meningococci with abdominal presentations of IMD.
Ethics statement Samples and clinical data were received at the NRCM for routine typing and epidemiological surveillance that are institutional missions of the NRCM. Clinical information format was provided by the NRCM. Procedure of collecting samples and informations was submitted and approved by the CNIL N°1475242/2011 (Commission Nationale de l'Informatique et des Libertés). The patients were informed on the secondary use of samples/data and gave their consent for this use.
Isolates and database This is a retrospective study using the database of the French National Reference Centre for Meningococci (NRCM) at the Institut Pasteur, Paris, France between 1991 and 2016. Isolates and samples corresponded to biological confirmed cases of IMD that were defined as a positive culture for N. meningitidis and/or positive testing PCR (used since 1998) in sterile sites. IMD cases were screened using the following key words for the clinical presentations: "abdominal pain" or "diarrhea" or "vomiting" or "peritonitis" or "appendicitis" or "appendicular syndrome" or "gastro-enteritis". Cases were also screened for the site of sampling "peritoneal fluid".
Bacterial whole genome sequencing PCR-confirmed cases were typed by MLST analysis and all cultured isolates were typed by WGS. Typing data were expressed as a genetic formula (g :P1. PorA-VR1,PorA-VR2:FetA :cc) that define the group (g), the two variable regions (VR1 and VR2) of the outer membrane protein PorA, and of the VR of the protein FetA, as well as the clonal complex (cc). In this manuscript, group "g" will be used to refer to both serogroup and genogroup to characterise cultured-confirmed and PCR-confirmed cases respectively. WGS was performed by Illumina HiSeq 2000 sequencer (Illumina, San Diego, CA, USA) and assembled as previously described  [13]. Sequences are available through the PubMLST database which runs on the Bacterial Isolate Genome Sequence Database (BIGSdb) platform  [14].
WGS data were analyzed using a "gene-by-gene" approach available through the PubMLST Genome Comparator tool using N. meningitidis core genome v1. 0 that includes 1605 core loci  [14]. SplitsTree4 (version 4. 13. 1) was used to visualize the resulting distance matrices as Neighbour-net networks  [15]. Further genomic analysis were performed for the period 2010-2016 to compare the NmW isolates of cc11 with abdominal presentations to all the NmW isolates belonging to the cc11 in France  [16]. The IDs of all culture isolates are given in the supplementary Table  1  to allow retrieving of WGS sequence in FASTA formats. Loci with alleles specific to isolates of serogroup W belonging related to the South American UK strains were characterized according to the KEGG Orthology (KO) groupings of the KEGG database (www.
Clinical data of IMD patients with abdominal presentations One hundred fourteen patients with abdominal presentation of IMD were initially selected between 1991 and 2016, according to criteria of inclusion. Among these patients, 9 were excluded for the following reasons: (i) 3 patients had abdominal pain with constipation, (ii) 2 patients had diarrhea 24h after the diagnosis of IMD and after starting antibiotic treatment (iii) 1 patient had salpingitis with no link to the IMD, (iv) 2 patients had cirrhosis and (v) 1 patient had an abdominal trauma. The final study population consisted of 105 patients with abdominal presentations of IMD. These cases corresponded to 92 cultureconfirmed cases and 13 PCR-confirmed cases (Fig. 1 ). The clinical data and characteristics of the patients were depicted in Table  1.
Acknowledgments This publication made use of the Neisseria Multi Locus Sequence Typing website (https://pubmlst. org/neisseria/)developed by  Keith Jolley  and sited at the  University of Oxford  [14]. The  Wellcome Trust  and  European Union  have funded the development of this site. We also acknowledge the  PIBNET-P2M  platform at the  Institut Pasteur. We would like to thank all biologists and clinicians who sent the samples, the isolates and the medical reports of hospitalization of the cases. The National Reference center for meningococci is funded by the  Institut Pasteur and Public Health France.
Online tool for Enzyme Cost Minimization We implemented ECM in the Network-Enabled Optimization System (NEOS), an internetbased client-server application that provides access to a library of optimization solvers. The NEOS Server is available free of charge and offers a variety of interfaces for accessing the solvers, which run on distributed high-performance machines enabled by the HTCondor software. The NEOS Guide website (https://neos-guide. org) showcases optimization case studies, presents optimization information and resources, and provides background information on the NEOS Server. Using our online service, users can run EFCM for their own models, using different rate laws. With our E. coli model, the optimization for one flux distribution takes a few seconds, and for the complete set of all EFMs several minutes on a shared Dell PowerEdge R430 server with 32 intel xeon cores.
This work was funded by the  ZonMW Zenith  project nr. 40-41009-98-10038  (to MTW), by the  Swiss Initiative in Systems Biology (SystemsX. ch) TPdF fellowship  ( 2014-230 ) (to EN), and by the  German Research Foundation  ( Ll1676/2-1 ) (to WL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Experimental procedure The computerized experiment was run in Jena, Germany, in June 2012, involving 210 subjects distributed over 8 experimental sessions. Recruitment was done with ORSEE  [21]  from the database of experimental subjects of the Max Planck Institute of Economics in Jena. Subjects sign an informed consent agreement when entering the database; they then voluntarily sign up for experiments, and are randomly allocated to sessions. Most of the subjects were students at the university of Jena (12% economists). Average age was 23. 5 years (st. dev: 3. 43, min: 18; max: 37), and 63% of the sample was composed by women. Though the sample was not representative of the general population at large, it is fairly representative of the population of Jena, a very young university town. Seventy-four subjects took part in three baseline Tree sessions.
, 2016)  or artificial rainfall  (Meshesha et al. , 2016). The DSD also has an impact on the spread of crop disease  (Huber and Gillespie, 1992) , notably through the dispersal of pathogens through splashing  (Walklate, 1989; Walklate et al. , 1989). Given the numerous potential applications of DSD data, the Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (HM&Co-ENPC) believes it is important to make the data available from a 2-month measurement campaign involving three collocated optical disdrometers from two manufacturers. Devices, data processing, and the campaign period are presented in Sect. 2. The corresponding database and available tools are presented in Sect. 3.
However it does not affect the retrieved matrices, meaning that it is up to the user to decide whether to use it or not. It should be mentioned that for the PWS100, data are measured only 9/10 of the time; hence, to have comparable values, one should multiply the number of drops in each class for this device by 10/9. The underlying assumption behind this approach to compensate for the gap in data is homogeneity of the process at this temporal scale. It is a strong assumption that is not necessarily valid. All outputs presented in this paper and in the database take this correction into account. However it is not implemented in the raw data (basically the matrices) that have been made available. From this matrix, it is then possible to compute the rain rate (in mm h -1 ) for each time step as R = π 6 t i,j n i,j D 3 i S eff (D i ) , (1) where S eff (D i ) is the sampling area of the device (expressed in mm 2 ) and t is in h.
Python_scripts/ This section contains some Python scripts that can be used to carry out some initial analysis and data treatment with the database. The tools are located in the script "Tools_data_base_use_v3. py". The main functions are given as follows (only a short description is given here -more details, including a precise description of the inputs and outputs of the functions, are provided as comments in each script): -Quicklook_and_R_series_generation_Carnot_1: generating a quicklook image and the corresponding 30 s and 5 min rain rate time series for a given rainfall event for the Carnot_1 campaign; -extracting_one_event_Carnot_1: reading daily. npy files and generating three lists (one for each disdrometer) containing all the data that can be analysed for the Carnot_1 campaign; -exporting_full_matrix: reading daily.