dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling NH 3 from the CHIMERE model The CHIMERE runs used in this study were obtained from the framework of the Copernicus Atmospheric Monitoring Service (CAMS, https://atmosphere. copernicus. eu/; last access: 10 January 2020) and its annual task devoted to the production of regional reanalysis over Europe. The hindcasts for the years 2014 and 2015 (raw simulation without data assimilation) were produced over Europe with a horizontal resolution of 0. 1 • per 0. 1 • and nine vertical levels stretched from the surface up to 500 hPa (∼ 5000 m). The data input to feed CHIMERE  (Menut et al. , 2013; Mailler et al. , 2017)  were the Integrated Forecasting System (IFS) meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the annual emission inventory provided by the Netherlands Organisation for Applied Scientific Research (TNO)  (Kuenen et al.
, 2014)  for 2011. These annual emissions are then distributed in hourly data to feed CHIMERE using seasonal, weekly, and hourly factors. Fire emissions come from the Global Fire Assimilation System (GFAS,  Kaiser et al. , 2012). The model computes hourly concentrations for more than 180 species, including regulated pollutants such as ozone, PM 10 , and NH 3. The processes influencing NH 3 concentrations taken into consideration in CHIMERE are dry deposition (following  Wesely et al. , 1989)  and wet deposition due to in-cloud processes and precipitation. The gas-particulate phase equilibrium is computed with the ISOROPPIA module  (Nenes et al. , 1998) , which is a thermodynamic equilibrium model for NH + 4 , NO - 3 , and SO 2- 4. It simulates the NH 4 NO 3 contribution to particulate matter, which is especially large during March-April pollution episodes  (Petit et al.
, 2017). These datasets were evaluated over Europe for several pollutants before being used for air quality studies (http: //policy. atmosphere. copernicus. eu/Reports. html; last access: 10 January 2020). The model NH 3 profiles were integrated vertically along the 9 km model layers to provide a column that can be compared to that of the satellite measurements. Concretely, this makes a reasonable assumption that all of the NH 3 is located within this 0-5 km layer (see e. g. Fig. 1  in  Whitburn et al. , 2016). To evaluate the capacity of the model to reproduce NH 3 variability in space and time at the regional scale and its impact on air quality at the local scale, comparisons were performed in 2014 and 2015 for the following reasons. At the regional scale (over the 400 km radius around Paris), NH 3 total columns derived from IASI in 2014 and 2015 are highly variable in spring, reaching 10 % higher in March and 50 % lower in May than the 10-year average.
Relative scales and coincidence criteria for dataset comparisons Direct quantitative comparisons of satellite NH 3 products are difficult because of the different overpass times and ground footprint sizes of the two space borne instruments, which are not compatible with the high variability in NH 3 in space and time. Therefore, the evaluation of satellite observations is often made with the use of in situ measurements performed at surface and onboard aircrafts  (Nowak et al. , 2012; Van Damme et al. , 2015b)  or with ground-based remote-sounding FTIR  (Dammers et al. , 2016 (Dammers et al. , , 2017)). The purpose of comparing CrIS and IASI here is to qualitatively assess the spatiotemporal patterns of the NH 3 sources derived from the two datasets and use these regional observations to evaluate the CHIMERE model in the domain of analysis at the local time for their respective overpasses, 09:30 and 13:30 LT.
Since the purpose of our study is not to quantitatively compare IASI and CrIS NH 3 data, but rather to use these independent datasets to assess NH 3 source patterns over the domain and qualitatively evaluate the CHIMERE model in terms of NH 3 concentrations and variabilities, a standardization procedure was applied to their retrieved absolute NH 3 columns. We computed "standardized columns" for each independent dataset (IASI, CrIS, and CHIMERE, separately) for 2014 and 2015 over the domain of study in such a way that the corresponding values have a standard deviation of 1 and a mean of 0, as in  Wilks (2011). The standardized columns have been computed following Eq. (1): X data stand = (X data -µ(X data )) S(X data ) , (1) where S X data = 1 N-1 N N =1 (X i -µ) 2 , X data = 1 N N i=1 X data i , X data corresponds to NH 3 columns derived from a dataset (IASI, CrIS, or CHIMERE), and X data stand is the corresponding standardized dataset.
Annual cycle Standardized monthly mean concentrations derived from IASI, CrIS, and CHIMERE for 2014 and 2015 are shown in Fig. 6. As can be seen from the plot, the three datasets exhibit similar patterns in terms of seasonality; all are enhanced in March-April and summer and show a decrease in May. However two major differences can be noted. First, CrIS standardized NH 3 columns are higher in winter (November, December, and January) compared to the other dataset, which can be also be seen in Fig. 3. This could be attributed to a higher number of outliers, given the larger standard deviation (shaded areas in Fig. 6 ), and no attempt to account for potential non-detections when concentrations fall below the instrument detection limits. For these months, NH 3 levels are low and undetectable by satellite observations (Figs.
2  and 3 ) so these high values could be interpreted as observational noise. The detection limit depends on the instrument characteristics and atmospheric state, with an IASI minimum detection limit of ∼ 2-3 ppbv (∼ 4-6 × 10 15 molecules cm -2 )  (Clarisse et al. , 2010 ) and a CrIS minimum detection limit of ∼ 0. 5-1. 0 ppbv (∼ 1-2×10 15 molecules cm -2 )  (Shephard and Cady-Pereira, 2015; Kharol et al. , 2018). Note that values below detection limits have not been filtered out from the IASI dataset whereas the quality flag used to discard CrIS's retrievals associated with DOFS ≥ 0. 1 (Sect. 2. 2. 2) favours larger observed columns. Consequently, the normalized seasonal cycle amplitude derived from CrIS data is weaker than the IASI-derived one. Second, CHIMERE standardized NH 3 columns are enhanced in September 2014, which is not supported by the observations.
It has been recently shown that CHIMERE overestimated NH 3 emissions in autumn over Europe  (Couvidat et al. , 2018). Generally, the amplitude of the modelled seasonal cycle exceeds the measured ones, which could be explained by higher concentrations measured in winter due to the observational noise and lower emissions. This is a different finding than in  Schiferl et al. (2016)  since they restricted IASI high relative errors when comparing it to the Over the whole period, the coefficient of determination (r 2 ) between the standardized monthly mean NH 3 columns derived from IASI (CrIS) observations and the CHIMERE model is 0. 58 (0. 18) for the annual cycles of 2014 and 2015, with low associated p values (p < 0. 1) reflecting the significance level of each fit (not shown here). If we only consider months with high NH 3 in the domain from March to August, the correlation between the observational datasets and the model results is weaker, with r 2 values between IASI (CrIS) and CHIMERE of 0.
5 surface concentrations, and five meteorological parameters (temperature at 2 m, boundary layer height, total precipitation, relative humidity, and wind field) derived from ECMWF ERA-5 data  (Dee et al. , 2011 , Copernicus Climate Change Service (C3S, 2017)  were analysed. To compute daily and monthly means, IASI NH 3 total columns have been averaged over IdF (black box in Fig. 1 ), PM 2. 5 concentrations measured between 09:00 and 11:00 LT have been averaged over the 14 stations (dark points in Fig. 1 ), and ECMWF data have been averaged over a 300 km region around Paris (the blue box in Fig. 1 ). Figure  12  shows all these parameters for spring 2014. We have flagged pollution episodes in both time series (PM 2. 5 and NH 3 ) by selecting data 1σ (standard deviation) above the mean of the datasets from 2013 to 2016.
5 forma-tion have been identified. The results show that relatively a low temperature and thin boundary layer, coupled with almost no precipitation and wind coming from the north-east, favour PM 2. 5 formation with the presence of atmospheric NH 3 in the IdF region. Based on a more observational approach over a large timescale, this work is in agreement with previous studies. This study highlights the need for a better representative NH 3 monitoring to improve the numerical simulation of spatial and temporal NH 3 variabilities, especially at fine scales. In order to compare IASI and CrIS data in absolute values (comparisons of the derived columns instead of the standardized ones), it would be recommended to derive both datasets using the same retrieval algorithm. Thus, by combining these datasets bi-daily NH 3 total columns in absolute values at the regional scale would be provided.
5 h over Europe) with an unprecedented spatial resolution (pixel size of 4 km). Code and data availability. The near-real-time IASI NH 3 (ANNI NH3-v2. 1R) data used in this study are freely available through the Aeris database https://iasi. aeris-data. fr/nh3r-i/ (Van Damme et al. , 2017) (last access: 3 December 2019). The CrIS data and CHIMERE outputs are currently available upon request. All MAT-LAB codes used to create any of the figures and/or to create the underlying data are available on request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-577-2020-supplement. Author contributions. CV wrote the paper with contributions from all coauthors. CV and CC designed the study. MV, LC, and SW performed IASI retrievals and ED, MWS, and KEC performed the CrIS retrievals.
2 and 3 have been obtained by averaging satellite NH 3 observations in 0. 25 • × 0. 25 • grid boxes. Both satellite datasets exhibit the same variability over the domain even if the time period is different (10 years versus 5 years) and the sampling hour differs (∼ 09:30 versus ∼ 13:30 LT). It should be noted that CrIS and IASI NH 3 columns present small differences in terms of NH 3 total columns in low concentration regimes in the domain of study. In Figs. 2 and 3, high NH 3 concentrations (up to 2 × 10 16 molecules cm -2 ) can be observed from March to August at different locations of the domain: the French Champagne-Ardennes region in March and April (box A in Figs. 2 and 3), the northern part of the domain corresponding to the Netherlands and northern Belgium from April to August (box B in Figs.
Acknowledgements. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). This work was supported by the  CNES. It is based on observations with IASI onboard Metop. The IASI Level 1C data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. The authors acknowledge the Aeris data infrastructure (http://iasi. aeris-data. fr/NH3/;last access:  10 January 2020 ) for providing access to the IASI level 1C data and level 2 NH 3 data used in this study. The French scientists are grateful to  CNES (TOSCA)  and the  Centre National de la Recherche Scientifique (CNRS)  for financial support. The research in Belgium is also funded by the  Belgian Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA   Prodex IASI Flow  project).
Adapter sequences were removed (at an alignment score above 7, allowing 2 mismatches), and bases with a quality score below 18 and below an average of 20 on a 5-bp window were trimmed using Trimmomatic v. 0. 39  (7). Reads smaller than 50 bases or with no pair (singletons) were discarded. Identification of contaminant reads was performed with Kraken2 v. 0. 8  (8) , with a confidence score of 0. 7 and using a custom database comprising UniVec and RefSeq genomes from Bacteria, Archaea, viruses, plants, and fungi (accessed December 2019). Based on this analysis, read pairs that were not classified as belonging to other taxa were used for de novo assembly, invoking error correction with BayesHammer as bundled with SPAdes 3. 13. 0 (9, 10), using k-mers 33, 55, 77, and 99, automatic computation of the coverage threshold, and an option to minimize the number of mismatches.
ACKNOWLEDGMENTS This work was supported by the  TASCMAR European project (Horizon 2020 , grant agreement no. 634674 ) and the  research infrastructure (RI) "Centre for the study and sustainable exploitation of Marine Biological Resources " (CMBR) at the Hellenic Centre for Marine Research (HCMR)  in Crete (principal investigator, Antonios Magoulas). CMBR is funded by the  Operational Program "Competitiveness, Entrepreneurship and Innovation" (MIS 5002670 ) and is cofinanced by Greece and the  European Union  ( European Regional Development Fund ). Computational resources were provided by the  High Performance Computing (HPC)  facility of the  Institute of Marine Biology, Biotechnology and Aquaculture of HCMR. The  HPC  facility has been funded by the  RIs MARBIGEN  ( EU Regpot ), LifeWatchGreece, and  CMBR.
2008; Meunier D et al. 2009; He X et al. 2013; Betzel RF et al. 2014; Damoiseaux JS 2017; Varangis E, Q Razlighi, et al. 2019; Yaple ZA et al. 2019; Luo N, J Sui, A Abrol, D Lin, et al. 2020 ). To our knowledge, there is currently no reference brain functional atlas derived from rs-fMRI data from older adults, and this may undermine neuroimaging efforts to characterize the brain functional connectome and its cognitive role in late adulthood. Critically, differences in the spatial composition of the RSNs derived from large samples of younger versus older adults have yet to be systematically investigated. In this context, the current study focused on the spatial definition of the five major RSNs (DMN, ECN, SAL, VIS and SMN) and their subdivisions in healthy adults aged 55 years and older. The specific aims were: (a) to test the reproducibility of RSNs in three large independent cohorts comprising a total of 563 older healthy adults (age range: 55-95 years); accordingly, we analyzed data from the Center for Ageing and Neuroscience (CamCAN) project  (Shafto MA et al.
2014; Taylor JR et al. 2017) , the Southwest University Adult Lifespan Dataset (SALD)  (Wei D et al. 2018 ) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database  (Jack CR, Jr. et al. 2008; Petersen RC et al. 2010) ; (b) to identify differences in the anatomical constitution of RSNs defined in the above cohorts compared to those in younger adults (age range 18-35); and (c) generate a reliable consensual RSN atlas to enhance the reproducibility of template-defined RSNs in older adults.
Cohorts Older-Adult Cohorts: We used data from the CamCAN  (Shafto MA et al. 2014; Taylor JR et al. 2017) , the SALD  (Wei D et al. 2018 ) and the ADNI datasets  (Jack CR, Jr. et al. 2008; Petersen RC et al. 2010). In each cohort, we selected healthy individuals aged 55 years and above for whom both rs-fMRI and structural MRI data were available. This selection resulted in a sample of 250 individuals for CamCAN (referred to as "CamCAN55+"), 190 for SALD and 134 for ADNI (details in supplementary material). Following quality control of the imaging data (described below), 11 participants across cohorts were removed for excessive head motion. The total analysis sample for the older individuals comprised 563 individuals, described in Table  1  and Supplementary Figure  S1. Younger-Adult Cohort: A subset of the CamCAN cohort, referred to as "CamCAN35-", was used to test for differences between younger (18 to 35 years-old) and older (55 years and older) adults in RSN spatial composition.
This age range was chosen to match the typical age range of healthy participants used in currently available functional atlases  (Doucet GE et al. 2019). The CamCAN35-included 128 individuals. Following the same quality control process, we excluded two individuals from CamCan35-(Table  1 ). More information on the MRI sequences can be found in  Taylor JR et al. (2017). In the SALD cohort, rs-fMRI data were acquired while participants rest with their eyes closed on a 3T Siemens Trio scanner, using the following acquisition parameters  (Wei D et al. 2018) : TR/TE= 2000/30 msec, 32 axial slices, flip angle=90 degrees; FOV=220 mm×220 mm; voxelsize =3. 4 mm×3. 4 mm×4 mm, acquisition time=8min, number of volumes: 242. More information on the MRI sequences can be found in  Wei D et al. (2018). In the ADNI cohort, rs-fMRI data were acquired while participants kept their eyes open.
Acquisition tool place in 43 sites using 3T scanners [General Electric (GE), Philips or Siemens] and variable acquisition protocols (details in http://adni. loni. usc. edu/methods/documents/) that commonly used a TR=3000 sec, with 140 to 200 volumes. When multiple resting-state fMRI datasets were available for a participant, the first one was selected for analyses. Regardless of the cohort, the rs-fMRI data were preprocessed in an identical fashion, using SPM12 and the DPABI Toolbox  (Yan CG et al. 2016)  stereotaxic standard space; spatial smoothing within the functional mask with a 6-mm at fullwidth at half-maximum Gaussian kernel; wavelet despiking  (Patel AX et al. 2014) ; linear detrending; and regression of motion parameters and their derivatives (24-parameter model)  (Friston KJ et al.
Resting-state networks identification in the older-adult cohorts We used a process validated by  Naveau M et al. (2012)  to identify reliable brain networks. First, for each individual within each cohort, single-subject independent component analyses (ICAs) were conducted 20 times with random initialization using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) software, version 3. 15, included in the FMRIB Software Library (FSL) v6. 0. 3 (http://www. fmrib. ox. ac. uk/fsl)  (Smith SM et al. 2004 ). The number of independent components (ICs) was estimated by Laplace approximation  (Minka T 2000). A symmetric approach of the FastICA algorithm  (Hyvarinen A 1999)  was used to compute the ICAs. Second, for each repetition, we used the multi-scale clustering of individual component algorithm (MICCA) to classify ICs into N groups  (Naveau M et al.
This choice was also based on the fact that, to the best of our knowledge, there are currently no validated and reproducible RSNs from rs-fMRI data of a large sample of older healthy individuals that we could use as normative templates. In each cohort, we computed the Dice coefficient  (Dice LR 1945)  to quantify the degree of spatial overlap between each group-level component and each of the five aforementioned CAREN RSNs. Components were then assigned to the RSN with which they had the largest overlap (Supplementary Table  S1 ). We applied this process to each cohort separately to create spatial maps of the major RSNs. We were thus able to construct a spatial map for each RSN. We used the Dice coefficient to test the pairwise similarity between the same-labeled RSNs across cohorts. Lastly, to ensure that the results did not depend on the template used (i.
Spatial comparison of the major RSNs between young and older adults Prior to testing for differences in the spatial constitution of RSNs between older and younger adults we constructed the major RSNs in the CamCAN35-by applying the same procedure as for the older cohorts (Supplementary Figure  S12 ). We then compared their respective RSNs to those derived from the other two independent cohorts (ADNI and SALD). For this, we used Dice's coefficient to quantify the spatial similarity of the RSNs in CamCAN35-, and in CamCAN55+ respectively, to those of SALD and ADNI, and compared these pairwise coefficients (i. e. , we compared the coefficients resulting from the RSN comparison CamCAN55+ vs. SALD and ADNI to those resulting from the mRSN comparison CamCAN35-vs. SALD and ADNI). This choice of approach was based on two major reasons: (1) among the three cohorts, CamCAN was the largest and also provided more reliable data (having the largest number of rs-fMRI volumes  (Elliott ML et al.
Generation of Atlas55+ The major RSNs identified in each of the three older-adult cohorts were used as input data to create a consensual atlas, named Atlas55+. For this, we used the function consensus_similarity. m from the Network Community Toolbox (http://commdetect. weebly. com), which constructs a representative partition. To test the spatial reproducibility of the major RSNs in Atlas55+, we conducted supplementary analyses: (1) we assigned each brain voxel to a network only if at least two of the three olderadult cohorts assigned this voxel to the same network. We then computed a spatial correlation between the two versions of Atlas55+ using Pearson's correlation analyses. (2) We created a voxel-wise confidence map to quantify the consistency of the network assignment between the RSNs of the atlas and those from each of the three older-adult cohorts.
Discussion This study investigated (a) the spatial definition of the major RSNs in healthy individuals in late adulthood using rs-fMRI data from three large independent cohorts of participants aged 55 years and above and; (b) differences in the spatial definition of RSNs in late adulthood to those derived from healthy individuals in mid adulthood. We found a high reproducibility of the spatial definition of the RSNs across the cohorts of older adults, but also significant differences when compared to the RSNs of younger adults. In response to these findings, we constructed Atlas55+, a robust brain functional atlas derived from rs-fMRI of 563 healthy adults between the ages of 55 and 95 years, in order to promote RSN reproducibility in future studies focusing on older populations. The age cut-off of 55 years may be considered arbitrary but there is currently no clear consensus on a specific age that distinguishes middle to late adulthood.
2015). Collectively, the findings reported in the current study underscore the importance of age-adapted brain functional atlases Moving forward, we propose Atlas55+ that has several advantages to study brain activity in late adulthood. To the best of our knowledge, Atlas55+ (a) is the only atlas based on rs-fMRI datasets from three independent cohorts including a total of 563 healthy participants above the age of 55 years; (b) includes functional partitions of the brain at the level of the large RSNs and their subdivisions; (c) accommodates differences in neuroimaging acquisition parameters (i. e. , different sites, MRI scanners and acquisition sequences); and (d) is independent of sample composition. We identified 15 reproducible RSN subdivisions (Figure  4 ), which is in a typical range for subdivisions reported in brain functional atlases based on younger adults' rs-fMRI data  (Yeo BT et al.
e. , for every decade past the 55 th year) is challenging at present as high-quality rs-fMRI datasets are scarce in older participants, particularly in advanced old age. The Human Connectome Project-Aging dataset may help achieve this goal when it will be fully released (https://www. humanconnectome. org/study/hcp-lifespan-aging). Third, we constructed Atlas55+ using a volumetric-based (ICA) rather than a surface-based approach. We previously demonstrated that MICCA provided reliable networks  (Naveau M et al. 2012) , and the analytic approach does not significantly alter the reproducibility of the RSNs as long as they are derived from a sample of at least 100 individuals  (Doucet GE et al. 2019) , which is the case for each cohort analyzed in the current study. Therefore, it is unlikely that this choice strongly influenced the spatial definition of the RSNs.