dataesr/scientific-paragraphs-categorization huggingface dataset:
Bootstrap values were 100. Pairwise transcriptomic comparisons were made by using Sleuth version 0. 30. 0  55. We defined transcripts as differentially expressed if they had a false discovery rate (q-value) < 0. 05, p-value < 0. 01, and β-values < -0. 4 or >0. 4. GO-enrichment analysis for the A. fumigatus genome was carried by using the Gene ID. GO enrichment was performed using FungiDB  56. Potentially regulated biochemical pathways were identified using the Search Pathway feature in KEGG Mapper  57. The search mode was set to "Afm," to specify A. fumigatus as our reference organisms. DEGs for the 30 and 120 mpi timepoints were entered as search objects. A list of objects was returned (Supplementary Data 8). Experiments with C. glabrata. C. glabrata was grown overnight on yeast extract-peptone-dextrose medium (1% yeast extract, 2% peptone, 2% dextrose), supplemented with uridine (80 μg ml -1 ) on an orbital shaker at 200 r.
No differences between experiments were observed. Prediction of proteins involved in LCO synthesis and LysM-containing proteins in fungi. The predicted number of genes encoding chitin synthases, chitin deacetylases, N-acyltransferases, and LysM-containing proteins was reported according to the respective references  [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70]. Statistical analyses. Statistical analyses were performed using RStudio (version 1. 2. 1335, RStudio Team 2015, Boston, MA) and GraphPad Prism software version 8. 3. 0 (GraphPad, San Diego, CA). One-way analysis of variance differences were considered significant when p < 0. 05. For the A. fumigatus experiments, the Tukey's single-step multiple comparison test was used to compare the different concentrations of C16:0 sLCOs and control, and the Dunnett's pairwise test was used to compare the treatments (LCOs and COs) to the control.
Acknowledgements We thank  Dr. Hugues Driguez  for providing some synthetic lipochitin standards for mass spectrometry. RNA-Seq data were based upon work supported by the  National Science Foundation  under Award Numbers  DBI-0735191 ,  DBI-1265383 , and  DBI-1743442  (URL: http://www. cyverse. org). Support for mass spectrometry analyses was provided by the  ICT-Mass spectrometry and MetaToul-MetaboHUB Facilities , and by the  MetaboHUB-ANR-11-INBS-0010 network. Support for synthesis and characterization of COs and LCOs was provided by Labex Arcane and CBH-EUR-GS  ( ANR-17-EURE-0003 ),  CDP   Glyco@alps  ( ANR-15-IDEX-02 ),  PolyNat Carnot Institut  ( 16-CARN-0025-01 ), and  ICMG  ( FR 2607 ) mass spectrometry platform. We thank  Dr. Gregory Bonito  for the cultures of Mortierella elongata, M.
bicolor, C. geophilum, G. stellatum, and L. palutris; and  Dr. Anne Pringle  for cultures and samples of Amanita muscaria and A. thiersii. We also thank  Marie Rodriguez ,  Juliette Teyssandier ,  Clément Bertrand , and  Sandrine Paute  for technical assistance, and  Drs. Jacob Golan  and  Katie Morey Gold  for statistical advice. This work was supported by the  French Agence Nationale de la Recherche  under contract  ANR-14-CE18-0008-01 , the research project  Engineering Nitrogen Symbiosis for Africa (ENSA) , which is funded through a grant to the  University of Cambridge  by the  Bill & Melinda Gates Foundation  ( OPP11772165 ), the  Laboratoire d'Excellence entitled TULIP  ( ANR-10-LABX-41 ), the  Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program , the  Office of Biological and Environmental Research in the US Department of Energy (DOE) Office of Science (Oak Ridge National Laboratory  is managed by  UT-Battelle, LLC , the  DOE  (contract  DE-AC05-00OR22725 ), the  United States Department of Agriculture  ( WIS01695 ), and by the  National Science Foundation  ( DGE-1256259 ,  PGRP -1546742 , and  IOS-1331098 ).
To serve that purpose, this study addresses the following two questions: -What are the observed aerosol trends over the last 2 decades in the different regions of the world (Sect. 4. 1)? -Can the climate models reproduce these observed trends (Sect. 4. 2)? Then, having developed an understanding of the models' skills in reproducing the observed aerosol trends, the last section of this study aims to answer the following question: -What are the global aerosol trends derived from the model data (Sect. 4. 3)? The CAMS reanalysis dataset and output from six Aero-Com models and four CMIP6 models (both model groups performed historical experiments) are evaluated in this work. CMIP6 (Coupled Model Intercomparison Project, Phase 6) is an intercomparison project organized by the WCRP (World Climate Research Programme).
Participating models will contribute to the assessment of climate change in the upcoming 2024 IPCC (Intergovernmental Panel on Climate Change) report. Figure  1  presents the time series of modeled global AOD (aerosol optical depth) between 1850 and 2014. All of the cli-mate models appear to exhibit a large increase in AOD, especially between 1950 and 1990  (Tegen et al. , 2000) , followed by more stable conditions up to the present. While the models show some diversity in absolute values, the trends (focus of this paper) seem to be consistent among the different models at a global scale. Long-term monitoring of many optical and chemical parameters was initiated in the late 1990s (e. g. ,  Holben et al. , 2001; Laj et al. , 2020) , providing a highquality dataset for the investigation of aerosol trends over the last 2 decades  (e.
Particulate matter concentrations The particulate matter (PM) measurements are from EMEP (covering Europe) and IMPROVE (for North America). The PM data have been made available either via the EBAS database infrastructure (http://ebas. nilu. no, last access: 26 October 2020) or in the original IMPROVE data to be found in the VIEWS database (http://views. cira. colostate. edu, last access: 26 October 2020). Both PM 10 and PM 2. 5 (in µg m -3 ) are used in this study. Note that the PM 10 size fraction of particles below 10 µm encompasses the PM 2. 5 aerosol mass below 2. 5 µm. The first PM measurements in EMEP started in 1996, and the number of sites increased steadily in the following decade  (Tørseth et al. , 2012). Most of the sites use the gravimetric method for both size fractions, though some have used automated monitors, i.
Sulfate aerosol concentration The sulfate aerosol (SO 4 ) concentration dataset is a subset of the global data presented in  Aas et al. (2019)  and is based on  measurements obtained in different regional networks as described in Table  1. The sulfate aerosol concentrations are obtained from analysis of aerosol filters. In the EMEP, CAST-NET, CAPMoN and EANET networks, these are sampled with either a PM 10 inlet or a total aerosol inlet, with no specific-size cutoff effective, using a filter pack sampler. In the IMPROVE network, sulfate measurements are carried out using a filter pack sampler with a PM 2. 5 inlet. The filters are typically analyzed by ion chromatography after water extraction of the aerosol filter. The data have been screened to be of satisfactory quality. Urban sites are not included and nor are sites where the surroundings have changed considerably in the period in question.
Scattering and absorption coefficients For the surface in situ PM measurements, the scattering and absorption coefficient measurements were accessed through EBAS database infrastructure. The level 2 data (quality controlled, hourly averaged, reported at standard temperature and pressure -STP -conditions) were used. Detailed information on the quality assurance and quality control procedures for GAW aerosol in situ data is available in  Laj et al. (2020). The difference in measurement conditions (i. e. , observations being made at STP versus models simulating at ambient conditions) was not expected to impact the calculated trends, so no adjustment was made to account for this. Scattering and absorption coefficients are measured by different instruments: -Scattering coefficients (σ sp ; in Mm -1 ) were measured by integrating nephelometers.
CAMS reanalysis The CAMS reanalysis, which is the successor to the MACC (Monitoring Atmospheric Composition and Climate) reanalysis, is the latest global reanalysis dataset of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service  (Inness et al. , 2019). It is produced using 4D-Var data assimilation in the CY42R1 model cycle of the ECMWF (European Centre for Medium-Range Weather Forecasts) Integrated Forecast System (IFS), with 60 hybrid sigma-pressure vertical levels. The model used in the CAMS reanalysis includes several updates to the aerosol and chemistry modules on top of the standard CY42R1 release. The IFS model assimilates several satellite products, from aerosols (AOD) to greenhouse gases (CO 2 , CH 4 ;  Inness et al. , 2019) , whereof most relevant for aerosol trends are data from both MODIS sensors and AATSR and ATSR-2.
CMIP6 The upcoming 2024 IPCC sixth assessment report (AR6) will feature new state-of-the-art CMIP6 models with model runs in higher resolution and with new physical processes. An overview of the experimental design and organization can be found in  Eyring et al. (2016). In this study, we use a preliminary extract of the data of four CMIP6 models from the historical experiment, as available on ESGF (Earth System Grid Federation) nodes (https://esgf. llnl. gov, last access: 26 October 2020), which provided output from 1850 to 2014. The last year of the study period of the analysis presented here was selected as 2014. 3 Methods.
, 2018; Schutgens et al. , 2017; Schutgens, 2019). The analysis here is dedicated to characterizing the representativity of the observation networks specifically for the purpose of computing the trends. These two perspectives on representativity might give different results, since a station associated with a bias could still have a representative tendency in time. In order to evaluate the effect of the partial space and time sampling of the observations for the evaluation of the trends, two sensitivity studies, focusing on the time sampling and the space sampling, have been conducted using NorESM2 model data subsets. For each of these studies, the trends are computed for one reference (Ref) and one experiment (Exp) dataset and compared with each other. The reference dataset corresponds to the model data co-located with the available observations, while the experiment dataset uses all model points.
-Time representativity study. -For Ref time , model data are co-located in space and time with available observations. -For Exp time , model data are co-located in space with available observations using the complete model time series. -Space representativity study. -For Ref space , model data are co-located in space with available observations using the complete model time series (= Exp time ). -For Exp space , all of the model grid points in the region use the complete model time series. The difference between the relative trends is computed for each parameter and region. In order to summarize the representativity, those differences are then converted into a score (%) by using a mapping function which has been defined based on a normal distribution. The choice of the parameters describing this function leads to a representativity score of 100 % when there is no difference in the trends computed for a reference and an experiment dataset, while a difference of 0.
5 % yr -1 obtained with these two datasets would indicate a representativity score of 50 %. Finally, the total score is computed as the mean of the time and the space representativities. An example of the calculation is presented in Fig. 4  for AOD in Europe and North America. In both regions, the Ref time dataset, corresponding to the available observations, reveals strong seasonal cycles when considering the number of points used to compute the regional time series. These cycles are observed with most of the sun photometer datasets since the instruments only operate during daytime and cloudfree conditions and the amount of daylight and cloud varies with the season. Together with this seasonal cycle, one observes an increase in the number of points with time, which reflects the increasing number of stations over these two regions.
5 from 2005 through 2016 was -2. 6 % yr -1 , while it was -3. 9 % yr -1 for the reconstructed fine mass, correcting for the possible bias in the measurements. SO 4 decreases by about 3 % yr -1 , which is twice as large as the decrease observed in Europe, where the reference value is however larger than in North America. The sulfate trend is similar to the trend reported by  Aas et al. (2019)  in this region (-3. 15 % yr -1 ). The regional time series extend farther back in time for σ sp and σ ap in North America than in Europe. However, no significant trends are found for these datasets. This is in contrast to Collaud  Coen et al. (2020) , who found a large decrease for σ sp (-2. 66 % yr -1 ). Our study used regionally averaged time series to calculate the trend rather than regionally averaged trends as used by Collaud  Coen et al.
Evaluation of the models trends against observations In order to evaluate the trends from the models, the regional time series have been computed with the model output colocated in space and time with the available observations at the station level. The model trends are computed in a similar manner to the trends for the observation datasets. The results, shown in Fig. 6 , reveal (a) the differing abilities of the various models to reproduce the observed trends and (b) that the model performance depends on the parameters and the regions. -For AOD, the models show trends with the same sign as the observed trends over all the regions except in Asia, where the associated uncertainties are usually larger than the trend values. Some differences among the three model groups are observed when investigating the different regions: -Europe.
A few of the models simulate positive trends, but these are associated with large uncertainties. -North Africa. All the models capture the observed decreasing AOD tendency. With a trend of -3. 0 % yr -1 , CAMS-Rean is the closest to the observed trend (-2. 8 % yr -1 ). AP3 and CMIP6 multimodel trend averages are both equal to -2. 1 % yr -1. -Asia. A large intermodel variability is found in this region where the uncertainty is also important. The means of the trends in each group are close to 0 % yr -1. -For AOD f , usually, the same patterns are found as for AOD. The models that underestimated the AOD underestimate AOD f and vice versa. For AOD f and the following parameters, only NorESM2 provides data for the CMIP6 group. -Europe. The underestimation of the decrease in AOD f captured by the models is larger than the underestimation of AOD.
1 % yr -1 to some extent (from -0. 6 % yr -1 to -2. 7 % yr -1 ). The fact that some models with a fixed SST (e. g ECHAM-HAM) reproduce this decrease does not support the hypothesis of the SST changes impacting dust emissions  (Fan et al. , 2004; Bauer and Koch, 2005; Bauer et al. , 2007; Neubauer et al. , 2019). -Asia. CAMS-Rean captures a similar negative trend to that computed with the observation dataset. As with AOD f , no certain trend can be identified in this region with the NorESM2 CMIP6 model. -For the AE, the trends are usually smaller than for AOD in the respective regions. This can mean that the number of particles is more subject to variations than the size (type) of these particles but could also illustrate that the AE is less sensitive to the change in a relative sense.
Positive trends are found for all the models in the AP3 (except GEOS) and CMIP6 groups. As for PM 2. 5 , NorESM2 has better performance in North America. CAMS-Rean produces a trend that is twice as high as the observed trends over both Europe and North America. -For SO 4 , the AP3 and CMIP6 models perform quite well for the SO 4 surface concentration. The magnitude of the model trends is however higher than the observed trends in all the regions except North Africa. -For σ sp and σ ap , as mentioned in the previous section, the observation trends have been computed for these two parameters using data up to 2018. The two models providing output for these parameters are NorESM2 and Oslo CTM3. NorESM2 provides data until 2014, so the NorESM2 trends correspond to the period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) , while Oslo CTM3 provides data until 2017 and the respective trends correspond to  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017).
Global trends As discussed previously, the regional trends found are probably not always representative of the trends in the extended regions and over the whole study period. The reason for this is the partial spatial and temporal coverage of the groundbased observations. Moreover, the observation stations are obviously located on land. This does not allow for a depiction of global aerosol trends and is unfortunate as sea salt particles are among the most predominant aerosols on Earth  (Schulz et al. , 2004). Unlike observations, models provide data at a global scale and for the entire study period. The completeness of these model datasets offers the opportunity to derive global aerosol trends. In order to provide an assessment of the aerosol trends at a global scale, we present, in this section, the trends computed with NorESM2 (CMIP6 group), which provides data for all of the nine parameters considered in this study.
-The models tend to capture observed AOD, AE, SO 4 and PM trends but show larger discrepancies regarding AOD c. The smaller number of data available for establishing σ sp and σ ap trends makes the validation of the modeled trends more uncertain. -The rather good agreement of the trends across different aerosol parameters between models and observations, when co-locating them in time and space, implies that global model trends, including those in poorly monitored regions, are likely correct. -The global trends computed with NorESM2 (CMIP6 group) model data give a different picture than the trends obtained when using only ground-based observations. Global positive trends are found for all of the parameters related to aerosol loading. The trends in AOD are dominated by the increase in the fine particles both in the column and at the surface.
List of observations and model datasets used in this study (see text for explanation). Aerosol Robotic Network. 2 European Monitoring and Evaluation Programme. 3InteragencyMonitoring of Protected Visual Environments. 4CleanAirStatusandTrendsNetwork. 5CanadianAir and Precipitation Monitoring Network. 6AcidDepositionNetwork in East Asia. 7GlobalAtmosphereWatch -World Data Centre for Aerosols. 8National Oceanic and Atmospheric Administration Federated Aerosol Network. 9 Aerosol, Clouds, and Trace Gases Research Infrastructure.
Acknowledgements. Data providers from all the regional and global networks are greatly acknowledged for sharing and submitting their data to be used. The ECHAM-HAMMOZ model is developed by a consortium composed of  ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford , the  Finnish Meteorological Institute  and the  Leibniz Institute for Tropospheric Research  and is managed by the  Center for Climate Systems Modeling  ( C2SM ) at  ETH Zurich. Computing and data storage resources, including the Cheyenne supercomputer (https://doi. org/10. 5065/D6RX99HX),were provided by the  Computational and Information Systems Laboratory  (CISL) at  NCAR. All simulations were carried out on the Cheyenne high-performance computing platform https:// www2. cisl. ucar.
edu/user-support/acknowledging-ncarcisl(last access: 20 November 2020), and are available to the community via the Earth System Grid. High-performance computing and storage resources were provided by  MET  and the  Norwegian infrastructure for computational science  (through projects  NN2345K ,  NN9252K ,  NS2345K  and  NS9252K ). Financial support. This research has been supported by the  European Commission, H2020 Research Infrastructures (FORCeS  (grant no. 821205 ),  CRESCENDO (Coordinated Research in Earth Systems and Climate: Experiments, Knowledge, Dissemination and Outreach ; grant no. 641816 )), the  National Science Foundation (NSF ; grant no. 1852977 ), the  Research Council of Norway  (grant no. 295046 ) and the  Copernicus Atmospheric Monitoring Service  (grant no. CAMS84 ). Review statement.
Code availability. The observation and model data were read and co-located with the pyaerocom Python library, developed by MET Norway (https://doi. org/10. 5281/zenodo. 4139879, version 0. 9. 1) (Gliß et al. , 2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-13355-2020-supplement.
Numerical Modeling The residence time within all the flow microreactors were kept at about 12 hrs. A reputed and proven commercial CFD software CFD-ACE+ was used. [6, 7] CFD-GEOM was used for geometry and grid generation. It worked for both structured and unstructured grid as shown in our previous work. [6, 7] Structured grid was used in this work for all geometries. Detailed mesh independency test was carried out presented in a later section. CFD-ACE was used for CFD simulations and the flow, heat and scalar modules available in it were used for the same. CFD-VIEW was used for post-processing of all the simulations. Five chemical species whose generation terms were represented by Equation (A1), (A2), (A9) -(A11) were modeled as passive scalars, denoted as Scalar1-5 respectively. The spatial distribution of velocity and all the five scalars were taken to be 3rd order so as to improve the numerical accuracy and to reduce the numerical diffusion in the simulations.
Patients We included participants from countries in the CO-HERE and EuroSIDA studies where at least 50 people presented for care during the study period. Regions were defined as in ECDC surveillance reports  [15]. EuroSIDA holds the largest number of participants from Central (Poland) and Eastern European countries (Belarus, Estonia, Russia, Ukraine). Participants from these two regions were included from EuroSIDA and excluded from COHERE, to avoid duplicates. All people aged ≥ 16 years, who presented for care (defined as earliest date of HIVdiagnosis, first clinic visit, or enrolment into the participating cohort) for the first time after 1st January 2010, were eligible for inclusion. The last merger of COHERE data was in 2015 and therefore patients from COHERE were included to 31/12/2014.
Sensitivity analyses In sensitivity analyses, we assessed the generalisability of our estimates of LP and AIDS-events/deaths to all diagnosed HIV-positive in each country. Data from The European Surveillance System (TESSy)  [25] , provided by the ECDC, were used to weight cohort data to reproduce key demographic characteristics of all diagnosed HIVpositive in the study period. In brief, we compared key characteristics (age, sex and HIV transmission category) in our study population with TESSy data and assigned weights to either increase or decrease different demographic groups in our study  [26]. For example, if 15% of participants in the UK were male, infected via injecting drug use and above 50 years, but this proportion using TESSy data was 20%, this group of individuals was under-represented in COHERE/EuroSIDA, and thus the number of LP or AIDS-events/deaths in this group was weighted upwards by 33% (0.