dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We would like to thank  Ramesh Sharma  for his helpful comments and productive collaboration with the new nighttime mechanism implementation. We would also like to thank  Daniel Marsh  for providing WACCM atmospheric inputs for our non-LTE model. The work by  P. A. Panka  was supported by the  NASA  grant  NNX14AN71G  and the  NESSF Fellowship. The work by  A. A. Kutepov  was supported by the  NSF  grant  1301762  and the  NASA  grant  NNX15AN08G. The contributions of K. S. Kalogerakis were supported by the  NSF  Grant  1441896. The work of A. G. Feofilov was supported by the project " Towards a better interpretation of atmospheric phenomena " of the  French National Program LEFE/INSU. Edited by:  Franz-Josef Lübken  Reviewed by: three anonymous referees.
The data from three distinct group of BMDM cultures were combined and analyzed for different gene expression levels using RNAseq (the raw data are accessible from Gene Expression Omnibus repository, upon request). Consistent with previous reports  24  , the RNA-seq analysis revealed numerous differentially expressed genes (1383) in the LPS-stimulated BMDMs among which 439 and 397 genes increased and decreased in expression ≥ 1. 5 log2-fold, respectively (Fig. 5c  Supplementary Table  S2 ). The inflammation-and immune response-related genes exhibited the most dramatic induction levels following the LPS challenge (Supplementary Table  S2 ). However, the use of a high-stringency ABX464 treatment only modulated the expression of a limited number of genes in un-stimulated (18 genes; Fig. 5c  and Supplementary Table  S3 ) and LPS-stimulated BMDMs (21 genes, Supplementary Table  S4 ), among which 6 genes were in common (Supplementary Table  S4 ).
Mice received 40 mg/kg ABX464 suspended in MC or MC only via gavage when indicated. For antagonizing IL-22 in vivo, mice received either blocking IL-22 antibody (Ebioscience; clone IL-22JOP) or isotype control (Ebioscience; clone eBR2a). Histology, immunohistochemistry and immunofluorescence microscopy. Mouse colons were fixed in formalin solution for 16 h. A histological examination was performed on paraffin-embedded sections counterstained with hematoxylin and eosin. Lesion size was determined using NDP viewer software. Immunohistochemistry was performed on paraffin-embedded tissues cut into 4-μm sections. After blocking with 10% goat serum, samples were incubated with primary antibody overnight at 4 °C using F4/80 (clone BM8, Hycult biotech) or galactin-3 (clone M3/38, ebioscience) for the detection of macrophages or anti-IL-22 (Abcam ab18564).
Secondary antibodies were either HRP-coupled anti-rat or anti-rabbit IgGs, visualized with 3,3'-diaminobenzidine (DAB). Histologic grading of DSS-induced lesions was determined with blinded genotype by a pathologist. Scoring included crypt distorsion and loss, immune cell infiltration thickening of muscularis mucose and serosa as well as loss of goblet cells (Supplementary Table  1 ). For immunofluorescence analysis primary antibodies were incubated with fluorescent-labeled secondary anti-mouse/rabbit antibodies (Vector Laboratories). DNA was stained with 20 µg/ml 4' ,6'-diamidino-2-phenylindole (DAPI). Fluorescent images were acquired on a brightfield microscope (Leica) using Metamorph software, or inverted Confocal SP5 (Leica) using the Leica LAS AF software. Images were processed with ImageJ.
Statistical analyses. Mann-Whitney U tests followed by Bonferroni post hoc tests and area under the curve (AUC) analyses were performed using GraphPad Prism V. 4. 00 (GraphPad Software, San Diego, California, USA). Data are shown as the mean ± SEM. A value of p < 0. 05 was considered significant; p < 0. 01 was considered very significant; and p < 0. 0001 was considered extremely significant. Cell and tissue culture and cytokine measurement. Cell isolates from the femurs of C57BL6 mice were cultured for 9 days in the presence of GM-CSF (50 ng/ml; Peprotech). Cell cultures were treated for the last 3 days with either ABX464 (5 µM) suspended in DMSO or DMSO alone. After additional stimulation for 6 h with LPS (4 µg/ml; Invivogen), the supernatants were analyzed for cytokine production using a cytometric bead array technology (CBA; Becton Dickinson).
Briefly, 1 μg of RNA was used to generate 20 μl of complementary DNA. Next, the complementary DNA samples were diluted and then subjected to 45 cycles of PCR at 95 °C for 4 s, 65 °C for 10 s and 72 °C for 15 s. The internal controls used were β-glucuronidase (GUS), hypoxanthine guanine phosphoribosyl transferase 1 (HPRT 1) and TFIID TATA box binding protein (TBP). The reactions were performed using a LightCycler 480 (Roche) machine. The data are shown as relative units where control bands were given a value of 1, and experimental values represented the fold change over the control following normalization with the three housekeeping genes. All of the experiments were performed in duplicate at least three times. The list of primers used in qRT-PCR are indicated in Supplemental Table  S6.
Acknowledgements We are grateful to the Montpellier-RIO imaging platform (Montpellier, France), the histology and animal experimentation platforms RHEM and RAM of Montpellier, as well as the IGMM mouse facility. We thank  Florence Bernex Joële Simony  for critical analysis of the histological data. This work was supported by the collaborative laboratory  ABIVAX, OSEO-ISI CaReNA grant. J. T. is a senior member of the  Institut Universitaire de France. L. P. was supported by a post-doc fellowship from the  CNRS.
Methods Data from the Initiatives BPCO French cohort of smokers and ex-smokers (. 10 pack-years), aged. 40 years with spirometry-confirmed COPD (n=1,128 when data were extracted), were analyzed. As previously described,  12  patients from this cohort are recruited by respiratory physicians from tertiary care university hospital centers. A current main diagnosis of asthma is an exclusion criterion, but a past history of asthma in childhood or early adulthood is allowed. Patients were divided into Eos+ and Eos-groups based on blood Eos level, using several thresholds ($ and ,2%, 3%, and 4%). Differences in mortality rates and clinical characteristics were assessed using chi-square tests or Fisher's exact tests, as appropriate, for discrete variables, and Wilcoxon or Kruskal-Wallis tests for quantitative variables.
The following data collected at inclusion were compared between groups: medical and smoking history, occupational exposures, dyspnea, cough and sputum production, exacerbations in the previous year, history of allergy and asthma, nasal symptoms, body mass index, St George Respiratory Questionnaire (SGRQ) total score, post-bronchodilator spirometry, physician-diagnosed comorbidities, and medications. Vital status was available for all patients. Overall survival was assessed using the Kaplan-Meier method.
spheric processes, and the environment is still debated in the scientific community. The extent of their influence remains to be determined and particularly requires a better understanding of the variability of their distribution. In this work, we studied the variability of these aerosols in western Africa using different types of satellite observations. Sea-WiFS (Sea-Viewing Wide Field-of-View Sensor) and OMI (Ozone Monitoring Instrument) data have been used to characterize the spatial distribution of mineral aerosols from their optical and physical properties over the period  2005-2010. In particular, we focused on the variability of the transition between continental western African and the eastern Atlantic Ocean. Data provided by the lidar scrolling CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) onboard the satellite CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) for the period 2007-2013 were then used to assess the seasonal variability of the vertical distribution of desert aerosols.
AErosol RObotic NETwork (AERONET) We first used data of AOD from AERONET between January 2005 and December 2010. AERONET is a global network of in situ observations developed by the NASA Earth Observing System (NASA EOS)  (Dubovik et al. , 2000). AERONET consists of the CIMEL solar photometers providing measures of AOD every 15 min, refractive index and also allows inversions such as particle size distribution of aerosols and SSA at 440, 670, 870, and 1020 nm wavelengths  (Holben et al. , 1998)  with an accuracy of ±0. 01  (Eck et al. , 1999; Dubovik et al. , 2000; Holben et al. , 2001). This uncertainty is inherent in the algorithm inversion used to retrieve aerosol characteristics. Some approximations are used in the numerical inversion algorithm which produce errors named relative errors having a standard deviation of 0.
01  (Dubovik et al. , 2000). AERONET's SSAs are computed for favorable atmospheric conditions (AOD 440 nm > 0. 4 and solar zenith angle > 45 • ) using an algorithm which performs almucantar inversions  (Jethva et al. , 2014). These data are used to validate remotely sensed AOD and SSA measurements. AERONET is available in three different products: Level 1. 0, 1. 5, and 2. 0. In this study, we use Level 1. 5 product for the Cabo Verde station, due to a lack of sufficient Level 2 data, and Level 2. 0 for the other stations. Concerning the temporal resolution of AERONET observations, we compute a "daily" mean based upon data collected between 10:00 and 15:00 in order to use observations collected during the same time window as satellite overpass. We then use this 10:00-15:00 daily averages to compute monthly 10:00-15:00 AOD.
The OMAERUV algorithm assigns a flag to each pixel which carries information on the quality of the retrieval  (Jethva et al. , 2014). The SSA represents the ratio (ranging between 0 and 1) of scattering coefficient to extinction coefficient and provides information about the absorbing properties of the aerosols. SSA of 0. 9 indicates that 90 % of the total extinction of solar light is caused by scattering and 10 % by absorption effects  (Jethva et al. , 2014). This parameter depends on the wavelength, size, and the complex refractive index of particles  (Léon et al. , 2009). The closer this value is to one the more desert aerosols dominate  (Johnson et al. , 2008; Léon et al. , 2009; Ialongo et al. , 2010; Malavelle, 2011). OMI data were interpolated on the grid of SeaWiFS data to superimpose the products (AOD and SSA).
Data were gridded with a near-uniform horizontal resolution of 0. 5 • × 0. 5 • and a vertical resolution of 30 m for 290 vertical levels between 0. 5 and 8. 2 km above sea level. The CALIOP lidar on CALIPSO (also in the A-Train) has a 90 m instantaneous footprint which is smeared to 333 m in the along track direction by orbital motion over the lidar pulse duration. All satellites of the A-Train constellation, such as CALIPSO, fly in a sun-synchronous orbit with a 16-day coverage cycle consisting of 233 orbits separated by 1. 54 • longitude or about 172 km at the Equator. Each satellite completes 14. 55 orbits per day with a separation of 24. 7 • longitude between each successive orbit at the Equator. These CALIPSO orbits are controlled to cover the same ground with cross-track errors of less than ±10 km  (Winker et al.
OMI SSA retrievals are taken between 10:00 and 15:00, which covers AERONET measurements. As emphasized by  Jethva et al. (2014) , this comparison is done at the original wavelengths of each independent measurement (388 nm for OMI and 440 nm for AERONET) in order to avoid uncertainties induced by the interpolation at other wavelengths. Good correlations are retrieved between the two data sets at the different ground stations in western Africa for the period 2005-2010 within root mean square difference (RMSD) of 0. 03 in the selected region (Fig. 2 ). Globally, the OMAERUV SSA is well correlated with ground measurements. The correlation at all selected sites for this study is significant. The agreement between the two inversions is better over the continent (Banizoumbou station, r = 0. 47 and Agoufou station: r = 0.
Acknowledgements. We would like to thank the  IRD-BMBF   AWA  project and the international joint laboratory  ECLAIRS  for supporting and promoting our research activities. We thank the  Institute of Research for Development  for funding this PhD study. We also thank  ICARE  for the online availability of the CALIPSO aerosol products at http://www. icare. univ-lille1. fr/archive. We are very grateful to  Béatrice Marticorena  and  Isabelle Chiapello  for very fruitful discussions. We are finally very grateful to the two anonymous referees for very informative comments which have greatly improved the quality of this study. Edited by:  Paola Formenti Reviewed  by: two anonymous referees.
Data availability. All data links are provided below. Radiosounding data were taken from http://weather. uwyo. edu/ upperair/sounding. html. Two radiosoundings per day are launched at Dakar Airport (14. 73 • N, 17. 5 • W), providing weather data which are distributed by the Department of Atmospheric Science at the University of Wyoming. The CALIPSO aerosol products are available at http://www. icare. univ-lille1. fr/archive. The CALIPSO data provide global coverage of the vertical distribution of the properties of clouds and aerosols. NCEP Reanalysis data were found at http://www. esrl. noaa. gov/ psd/data/gridded/data. ncep. reanalysis. pressure. html. AERONET data sets are taken from http://aeronet. gsfc. nasa. gov/. OMI data were obtained from NASA's Giovanni: http://disc. gsfc. nasa. gov/gesNews/giovanni_3_end_of_service? instance_id=omil2g&selectedMap=Blue%2520Marble&.
Introduction Convincing astrophysical and cosmological observations for the existence of dark matter (DM) provide us one of the strong motivations to consider physics beyond the standard model (SM). The search for DM is thus one of the main pillars of the LHC physics program. As the nature of DM is known so little, a so-called simplified-model approach  [1]  has been widely adopted, and concrete simplified DM models have recently been proposed by the LHC DM working group to conduct the systematic DM searches at the LHC Run-II  [2]. Following the proposal, the Run-I data as well as the early Run-II data have already been analysed to constrain simplified DM models with s-channel spin-1 and spin-0 mediators, see e. g. [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. On the other hand, the model with a spin-2 mediator  [13, 14]  has not been fully explored for the LHC yet-it is one of the next-generation simplified DM models  [15].
Constraints from current LHC data 4. 1 Searches with missing energy The ATLAS and CMS experiments have been searching for new physics in a large variety of final states. As mentioned above, in the context of DM searches, the monojet signature is regarded as particularly interesting. In practice, at 13 TeV, the monojet analyses require one hard jet recoiling against / E T , but allow for additional jets from QCD radiation. Therefore one can expect that multijet+ / E T searches are also relevant  [41, 42]. To work out the current constraints on the spin-2 mediator DM model from these searches, we consider the following early Run-II analyses: -ATLAS monojet with 3. 2 fb -1  [5] , -ATLAS 2-6 jets + / E T with 3. 2 fb -1  [43]. In the monojet analysis  [5] , a simplified DM model with an s-channel spin-1 mediator is considered.
Resonance searches Direct resonance searches can also be used to explore schannel mediator DM models, see e. g. [65, 66]  for the spin-1 and spin-0 mediator models, respectively. Results from Run-II data are already available for a large variety of final states (dijet, dilepton, diphoton, W W , ZZ, b b, t t, hh) from ATLAS  [52-55, 57, 59, 60]  and CMS  [9, 56, 58, [67] [68] [69] , and give powerful constraints for mediator masses of a few hundred GeV up to several TeV. Lower masses are partly covered by Run-I results. 7 able  2  lists the current resonance search results which we use to constrain our spin-2 simplified model. The RS massive graviton is considered in the analyses for pairs of electroweak gauge or Higgs bosons  [54, 57, 58, 60, 63, 64]  decay mode reference limit Tab. /Fig. Fig.
The other analyses do not provide explicit values, and hence we have to extract these data from the exclusion plots 'by hand', e. g. using WebPlotDigitizer [73], a public software. To avoid that other people have to redo this exercise, our digitised data files are available at [72] and on the new PhenoData database  [74]. We encourage the experimental collaborations to provide digitised data together with their plots, in order to make it easier to use their results. Finally, we notice a caveat regarding the re-interpretation of the low-mass resonance search in dijet plus ISR final states  [53]. We found that final state radiation (FSR) may be also important and give rise to a non-trivial structure in the dijet invariant mass spectrum. Technically, simulated event shapes can differ by including FSR or not in the matrix elements, which may affect the parameter fitting procedure for a bump search.
Acknowledgements We would like to thank  G. Das ,  C. Degrande ,  V. Hirschi  and  H-S. Shao  for help with the NLO calculations, and  M-H. Genest ,  F. Maltoni ,  V. Sanz  and  M. Zaro  for valuable discussions. We are also thankful to  C. Doglioni  and  K. Krizka  for discussions on  ATLAS-CONF-2016-070. This work was supported in part by the  French ANR , project  DMAstro-LHC   ANR-12-BS05-0006. U. L. is sup-.
Environmental Data To model the abundance of Ae. albopictus females, we selected several meteorological and LULC variables according to the literature  [10, 12, 15]. We obtained precipitation data from meteorological stations located close to the sampling locations (Table  1 ). The total annual precipitation, precipitation of the second quarter (i. e. , April-May-June), and precipitation of the third quarter (i. e. , July-August-September) for 2014 and 2015 were calculated from the dataset freely available at the ARPA Veneto (Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto) and Meteotrentino websites The adult mosquitoes were killed by freezing at -20 • C and then identified using taxonomic keys  [26, 33]. Over the 2-year survey, the beginning and the end of the sampling could vary among locations, resulting in differences in the total number of trap nights.
Therefore, we estimated Ae. albopictus abundance per sampling location per year with the sum of females only collected during the eight trap nights between June and September, corresponding to the maximum activity period for mosquitoes in the study area. Five samples with less than eight trap nights during this period (i. e. , missing data because of field problems) were not included in the statistical analysis. The sum of females collected in the summer can be considered an estimator of the Ae. albopictus infestation level in a sampling location, and the number of Ae. albopictus females trapped by BG-sentinel traps may be related to the number of females biting humans  [34].
Environmental Data To model the abundance of Ae. albopictus females, we selected several meteorological and LULC variables according to the literature  [10, 12, 15]. We obtained precipitation data from meteorological stations located close to the sampling locations (Table  1 ). The total annual precipitation, precipitation of the second quarter (i. e. , April-May-June), and precipitation of the third quarter (i. e. , July-August-September) for 2014 and 2015 were calculated from the dataset freely available at the ARPA Veneto (Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto) and Meteotrentino websites (http://www. arpa. veneto. it/; http://www. meteotrentino. it/). Rainfall is essential to provide water-filled containers for egg deposition and larval development; an annual precipitation of less than 500 mm is considered a limiting factor for Ae.
albopictus  [35]. We used two sets of Land Surface Temperature (LST) data: a 2-year dataset (2014-2015) based on the years of sampling to carry out the abundance model using mosquito collections from 2014-2015, and a wider dataset based on a 13-year long-term average  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  to map the simulated abundance of Ae. albopictus females in the study area (because the long-term averages better characterized the temperatures of the region than the 2-year averages, which only considered 2014 and 2015). The temperatures for the period 2003-2012 gathered from the EuroLST bioclim dataset (spatial resolution to 250 m) are freely available at the EuroLST website (http://www. geodati. fmach. it/ eurolst. html). The temperatures for 2013, 2014, and 2015 were obtained from the MODIS version 5 LST products MOD11A1 and MYD11A1 with four records per day and a spatial resolution of 1000 m (Table  1 ); we applied outlier filtering and gap filling procedures to these data, and enhanced the spatial resolution to 250 m  [36].
LST data were extracted from the 250 m pixels corresponding to the trap locations. We computed the mean temperature of the coldest month (i. e. , January) and the mean temperature of the warmest quarter (i. e. , July-August-September) in 2014, 2015, and for the period 2003-2015. According to the literature, an average winter temperature higher than 0 • C enables the overwintering of diapausing eggs of Ae. albopictus, and a mean summer temperature ranging from 25 to 30 • C is optimal for the establishment of its populations  [15, 37]. The LULC data were obtained from the dataset freely available at the open data Trentino and Veneto websites (http://dati. trentino. it/; http://dati. veneto. it/) (Table  1 ). The LULC datasets from Trentino and Veneto were elaborated in 2003 and 2013, respectively, at a scale of 1:10000.
Statistical Analysis We investigated the association between Ae. albopictus female abundance (i. e. , the sum of females collected during eight trap nights between June and September) and a range of meteorological and LULC variables based on the collection carried out during 2014-2015 using R version 3. 2. 2 [42]. Collinearity among environmental variables was tested using Pearson's correlation test (See Figure  S2  in supplementary materials). We excluded variables with an absolute value of Pearson's coefficient >0. 7, and the selected variables were standardized by subtracting their mean values and dividing by their standard deviations. Afterwards, we performed negative binomial mixed models using the R package 'glmmADMB'  [43]. The explanatory variables included in the full model were the total precipitation of the second quarter, the mean temperature of the coldest month, the mean temperature of the warmest quarter, and the percentages of artificial areas, forests, and semi-natural areas.
The trap location was included as a random effect to take into account the variability among sampling locations  [44]. We carried out multi-model inference to compare all possible models using the R package 'MuMIn'  [45] , as described in Rosà et al. [46]. The models were ranked using AIC, and differences in AIC (∆AIC) between consecutively ranked models were used to calculate the weights and relative evidence ratios for each variable. The best models were selected using a threshold of ∆AIC ≤2, and the most parsimonious model was used to map the simulated abundance of Ae. albopictus females. Model assumptions, for the most parsimonious selected model, were evaluated by checking the model's normalized residuals for any pattern or dependency following the protocol as in Zuur et al. [47]. The model's performance was assessed using in-sample errors by computing the root mean squared error (RMSE), which represents the sample standard deviation of the differences between predicted and observed values, and could be interpreted as an estimation of the standard deviation of the unexplained variance.