dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, all the hypomorphic or amorphic variants were predicted to be deleterious and are therefore true positives (Fig. 8 A ). We also tested the performance of other software for predicting the deleteriousness of mutations (Condel, PPH2, Sift, FATHMM, Mutation Assessor, and Provean;  Adzhubei et al. , 2010; Choi and Chan, 2015; González-Pérez and López-Bigas, 2011; Reva et al. , 2011; Rogers et al. , 2018; Vaser et al. , 2016). For all six programs, there was no correlation between the predicted deleteriousness score and the activity of the STAT3 variants (Fig. 8 B ). Thus, predictions of Figure  5. Allele activity and the mechanism of dominance for all reported STAT3 variants in AD-STAT3-HIES. Luciferase assay on STAT3 -/-A4 cells (A) or HEK293T cells (B) transfected with mock, EV, WT, or the reported STAT3 variants, together with the pGL4.
17; CHD7, -2. 11; and KMT2D, -2. 75; Table  S7 ). This finding is consistent with the AD inheritance of STAT3-HIES and the low frequency of missense variants in the general population, as shown by our analysis of the gnomAD database. Moreover, there is only one nonsense variant in gnomAD (Q247*) pLOF, but this variant is not DN (Fig. 7, B-D ). In our analysis of 150 STAT3 variants, 143 (95%) were predicted to be DN, causing HIES (Table  S3 ). Thus, the population genetics of STAT3 provides no evidence for HI associated with AD disease in STAT3.
742. Misprediction for intronic variants is a well-known source of FNs  (Mather et al. , 2016). Conversely, five variants from HIES patients predicted to be deleterious actually encoded STAT3 proteins with normal activity, resulting in a FP rate of 3. 3%. The remaining two have a lower CADD score. With the benefit of hindsight, the patients carrying the seven isomorphic variants did not have HIES or had not been studied for other genetic etiologies. By contrast, only one of the coding variants from the gnomAD database selected for experimental testing on the basis of their rarity and high CADD score (n = 14), a nonsense variant (pLOF), was experimentally LOF, and none of these variants were DN (including the LOF variant). We did not thoroughly investigate all other 143 nonsynonymous coding variants found in the general population, but we can assume that most public variants are isomorphic, and at least not DN, because they are more common or have lower CADD scores than those tested.
Moreover, the gno-mAD database includes very few patients with developmental or infectious diseases, the two major characteristic of individuals with HIES. Nevertheless, most of the nonsynonymous coding variants found in the general population were predicted to be deleterious on the basis of CADD, with a FP rate of 98% that questions the usefulness of this score to diagnose HIES patients (141/143; Fig. 8 A ). Further experimental studies are required to determine whether these predictions are correct or false. Meanwhile, computational prediction of the functional impact of coding variants of STAT3 remains challenging due to the high FP rate. We therefore suggest that any new variant found in a patient with HIES should be tested functionally for an accurate molecular diagnosis. The mechanism of dominance underlying AD conditions should, ideally, be reliably established, to improve both our understanding of the pathogenesis of the condition and its clinical management.
Each experiment (B, C, D, and F) was independently performed twice. Error bars represent the means with SEM. trapping and/or deep RNA-seq  (Boisson et al. , 2019). In terms of population genetics, STAT3 is under strong purifying selection  (Rapaport et al. , 2021) , consistent with the reported AD mode of inheritance. Interestingly, in the absence of a pLOF allele in public databases (with one exception), STAT3 clusters with other genes (Table  S7 ) for which AD phenotypes have been shown to be due to HI  (Lek et al. , 2016). Our work suggests an alternative hypothesis: pLOF variants are absent from public databases because these alleles are expressed and have a DN effect on the WT allele. Our finding that the only nonsense variant (pLOF) in gnomAD (assuming that the person does not have a HIES phenotype) was actually amorphic, but not DN, is consistent with this alternative hypothesis.
Genetic analysis Genomic DNA (gDNA) was prepared from blood samples from patients and controls by the standard phenol-chloroform extraction method. WES was performed with an individual institutional protocol. We performed gDNA extraction, WES data collection, and analysis as previously described  (Béziat et al. , 2020; Khourieh et al. , 2019). Exome capture was performed with the SureSelect Human All Exon 71 Mb kit (Agilent Technologies). Paired-end sequencing was performed on a HiSeq 2500 sequencing system (Illumina) generating 100-base reads. We aligned the sequences with the GRCh37 reference build of the human genome with the Burrows-Wheeler aligner  (Li and Durbin, 2010). Downstream processing and variant calling were performed with the Genome Analysis Toolkit  (McKenna et al. , 2010)  and SAMtools  (Li et al.
RNA-seq EBV-B cells from healthy controls (n = 2), D427ins17 patients (n = 1) who were previously known to have the AD-STAT3-HIEScausing variant with alternative transcript, and AD-STAT3-HIES patients (P3: R518*; n = 1) who we studied herein were analyzed. RNA was extracted with the RNeasy plus micro kit (Qiagen). RNA quality was assessed with the Agilent 2200 TapeStation nucleic acids system. RNA-seq was performed as previously described  (Hernandez et al. , 2018). In brief, samples were mapped onto the human genome sequence (hg38 assembly) with STAR  (Dobin et al. , 2013) , and counts for the aligned reads for each gene were calculated with HTSeq-count  (Anders et al. , 2015). RNA-seq datasets for these EBV-B cells have been deposited to the NCBI GEO database under accession numbers GSE153886 (GSM4658112, GSM4658118, and GSM4658124) and GSE149602 (GSM4505723).
Prediction software We used seven software suites to predict the deleteriousness of STAT3 variants (CADD: https://cadd. gs. washington. edu/score, Condel: http://bbglab. irbbarcelona. org/fannsdb/ signin?next=%2Ffannsdb%2Fquery%2Fcondel, Polyphen2 (PPH2): http://genetics. bwh. harvard. edu/pph2/, Sift: http://sift. jcvi. org/, Mutation Assessor: http://mutationassessor. org/r3/, FATHMM: http://fathmm. biocompute. org. uk/fathmmMKL. htm, and Provean: http://provean. jcvi. org/genome_submit_ 2. php?species=human). Online supplemental material Fig. S1  shows the schematic representation of the exon trapping results for STAT3 nonsense or frameshift variants. Fig. S2  shows STAT3 transcript analysis by RNA-seq in patients' cells carrying heterozygous STAT3 nonsense variants. Fig. S3  shows protein expression in STAT3 -/-A4 cells and STAT3 activity for nonsense or frameshift STAT3 variants.
Fig. S4  shows schematic summary of 150 reported variants, including 8 new STAT3 variants in HIES. Fig. S5  shows deeper analysis for 20 STAT3 variants identified as not DN. Table  S1  shows a summary of the infectious and clinical phenotypes of patients with STAT3 nonsense or frameshift variants in our cohort. Table  S2  lists canonical and alternative transcripts of STAT3 nonsense or frameshift variants in HIES. Table  S3  lists the summary of 150 reported and new STAT3 variants. Table  S4  shows the correlation between variants consequences and their localization by domain. Table  S5  lists the summary of allele activity of both canonical and alternative transcripts for 15 STAT3 missense variants. Table  S6  lists the summary of 14 selected STAT3 variants from gnomAD database. Table  S7  lists the summary of parameters correlated with negative selection.
Acknowledgments We would like to thank the patients and their families for participating in this study. We thank  James E. Darnell  and  Claudia Mertens  (The  Rockefeller University, New York, NY ) for providing the STAT3-deficient A4 cell lines. We thank members of the  St. Giles Laboratory of Human Genetics of Infectious Diseases Laboratory ,  Masato Ogishi ,  Paul Bastard ,  Peng Zhang , and  Yuval Itan  for highly pertinent intellectual discussions and suggestions and  Yelena Nemirovskaya ,  Dana Liu ,  Dominick Papandrea ,  Mark Woollett ,  Cécile Patissier , and  Tatiana Kochetkov  for administrative and technical assistance. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Job Research Foundation , the  National Center for Research Resources , and the  National Center for Advancing Sciences of the National Institutes of Health Clinical and Translational Science Award program  ( UL1TR001866 ); the  National Institute of Allergy and Infectious Diseases  ( P01AI061093 ); the  French National Research Agency (ANR)  under the  "Investments for the Future" ANR program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the " PNEU-MOPID" project  ( ANR 14-CE15-0009-01 ), and the " ProLegio" project  ( ANR-15-CE17-0014 ); the  French Foundation for Medical Research  ( EQU201903007798 ); the  Howard Hughes Medical Institute ; The  Rockefeller University ; the  St.
Giles Foundation ;  Institut National de la Santé et de la Recherche Médicale ; and the  Université de Paris. A. N. Spaan was supported by the  European Commission  ( Horizon 2020 Marie Skłodowska-Curie Individual Fellowship   789645 ), the  Dutch Research Council  ( Rubicon Grant   019. 171LW. 015 ),and the  European Molecular Biology Organization (Long-Term Fellowship   ALTF 84-2017 , nonstipendiary). V. Béziat was supported by the  ANR  (grant  NKIRP-ANR-13-PDOC-0025-01 ). Author contributions:  T. Asano ,  J. -L. Casanova , and  B. Boisson  designed the study and wrote the manuscript. F. Rapaport,  V. Béziat ,  Q. Zhang ,  A. Puel , and  S. Boisson-Dupuis  discussed the results and the manuscript. T. Asano ,  J. Khourieh ,  P. Zhang ,  F. Rapaport ,  S. J. Pelham ,  J. E. Han ,  A. Guérin ,  J.
Ammonia (NH 3 ) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH 3 and its spatial and temporal variability. In this study, long-term measurements of NH 3 derived from multiscale datasets are examined. These NH 3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH 3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH 3 over Toronto: 3.
34 ± 0. 89 %/yr from 2002 to 2018 in the FTIR columns, 8. 88 ± 5. 08 %/yr from 2013 to 2017 in the surface in situ data, and 8. 38 ± 1. 54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH 3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤ 25 km and ≤ 20 min, with r = 0. 73 and a slope of 1. 14 ± 0. 06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r = 0. 72 and r = 0. 75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r = 0. 39. The GEOS-Chem model, run at 2 • × 2.
, 2014a; Warner et al. , 2016; Shephard et al. , 2020 ), regional (Van Damme et al. , 2014b; Warner et al. , 2017; Viatte et al. , 2020) , and point-source scales  (Van Damme et al. , 2018; Clarisse et al. , 2019a; Dammers et al. , 2019). In this study, NH 3 variability over Toronto is investigated using ground-based FTIR data, in situ measurements, and satellite-based observations from the Infrared Atmospheric Sounding Interferometer (IASI). This study is a part of the AmmonAQ project, which investigates the role of NH 3 in air quality in urban areas. Trends in the NH 3 time series and their statistical significance are determined, and the GEOS-Chem model is also used to supplement and compare to observations. Additionally, correlations between FTIR and IASI, FTIR and in situ, in situ and IASI, FTIR and model data, and IASI and model data on a regional scale are analyzed to assess the representativeness of the FTIR NH 3 measurements.
In situ measurements To complement the FTIR total column NH 3 measurements, the publicly available in situ data obtained by Environment and Climate Change Canada (ECCC) as a part of the National Air Pollution Surveillance Program (NAPS) were used (http://maps-cartes. ec. gc. ca/rnspa-naps/data. aspx, last access: 1 November 2020). The data span December 2013 to April 2017, with a sampling frequency of 1 in 3 d. The sampling interval is 24 h, from 00:00 to 24:00 local time, and samples were collected with a Met One SuperSASS-Plus Sequential Speciation Sampler. The detection limit is 0. 6 ppb  (Yao and Zhang, 2013). The integrated samples were brought back to the lab for analysis  (Yao and Zhang, 2016). While errors are not reported in the dataset, the uncertainty is 10 % when the NH 3 VMR is between 3 to 20 ppb  (Hu et al.
GEOS-Chem The GEOS-Chem (v11-01) global chemical transport model (CTM) (http://geos-chem. org, last access: 1 November 2020) was used in this study to supplement and compare to observational data. The model was run at 2 • × 2. 5 • resolution (latitude × longitude) using MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) meteorological fields  (Molod et al. , 2015)  and the EDGAR v4. 2 emissions database  (Janssens-Maenhout et al. , 2019)  for anthropogenic emissions. For NH 3 , EDGAR v4. 2 and GEIA were used as global inventories, with GEIA providing the natural source of NH 3  (Bouwman et al. , 1997; Olivier et al. , 1998; Croft et al. , 2016). The global inventories were replaced with the US EPA National Emission Inventory for 2011 (NEI11; https://www. epa.
For comparison, analysis of TAO FTIR NH 3 total columns during the same time period resulted in trends of 9. 31 ± 5. 73 %/yr and 7. 42 ± 4. 48 %/yr, with and without outliers, respectively. The in situ NH 3 VMRs were compared to the TAO FTIR columns by standardizing both measurements, following Eq. (1) of  Viatte et al. (2020) : X i standardized = X i -µ X σ X , ( 1 ) where X is the dataset, indexed by i, µ is the mean, and σ is the standard deviation of the dataset. The standardized dataset is centered around zero and normalized by the standard deviation of the measurements. As the standardized dataset is unitless, it allows for comparison between different measurements in different units. In this study, the TAO FTIR NH 3 total columns were used because the DOFS for the retrieval was around 1 (mean DOFS of the entire time series was 1.
10), meaning there is only about one piece of vertical information in these measurements. Standardized FTIR and NAPS NH 3 are plotted in Fig. 4a. Monthly averages and monthly standard deviations are shown in Fig. 4b. The two measurements show similar seasonal cycles, with a maximum in May and a minimum in December and January. There is a smaller secondary peak in November for both measurements. This may be due to lateseason fertilizer application and cover crop growth. The Min-istry of Agriculture, Food and Rural Affairs of Ontario recommends applying fertilizer in spring and fall  (Munroe et al. , 2018). Correlation between the two datasets can be seen in Fig. 5a , where each NAPS measurement is plotted against the average of FTIR measurements on that day (if any measurements are available).
IASI measurements The time series of IASI NH 3 total columns (2008 to 2018) within 50 km of TAO is shown in Fig. 6. The trend of these IASI measurements is 8. 38 ± 1. 43 %/yr, where the error indicates the 2σ confidence interval obtained by bootstrap resampling analysis. The  Weatherhead et al. (1998)  method for finding the statistical significance of this trend was not utilized here, as the analysis requires calculating the autocorrelation of data, which is not possible given the spatially scattered dataset. For comparison, the TAO FTIR trend over the same period is 4. 02 ± 1. 42 %/yr. The correlations between IASI and TAO FTIR NH 3 columns for the various coincidence criteria listed in Sect. 2. 3 are shown in Table  3 , along with the slopes, mean relative difference (MRDs), and total number of data points.
7a , 7b, 7c, and 7d, respectively. It should be noted that the slope was calculated through a simple linear least-squares regression. For comparison, an additional analysis was done propagating measurement uncertainty using the unified least squares procedure outlined by  York et al. (2004)  and yielded similar results, with a smaller slope for all cases due to the larger relative uncertainty on IASI measurements (∼ 68 % for IASI compared to ∼ 19 % for TAO FTIR). IASI column and NAPS surface NH 3 were also compared in this study by converting to standardized data (see Eq. 1). Comparing the monthly means resulted in r = 0. 79 and slope = 0. 79 when looking at IASI measurements made within 50 km of NAPS and r = 0. 74 and slope = 0. 74 for 30 km. Without temporal averaging, no significant correlation was found (r ≤ 0.
27) for any spatial coincidence criteria. This is in line with findings from  Van Damme et al. (2015a) , where significant correlation was found when comparing monthly averaged surface and IASI measurements. Van Damme et al. (2015a)  report r = 0. 28 when comparing IASI with an ensemble of surface observations over Europe and r as high as 0. 81 and 0. 71 for measurements made at Fyodorovskoye, Russia, and Monte Bondone, Italy, respectively. It should be noted that the comparisons in  Van Damme et al. (2015a)  were done by converting IASI NH 3 columns to surface concentration by using the same model used in the retrieval process, as opposed to the standardized dataset approach used in this study.
Comparison with GEOS-Chem The NH 3 total column from the GEOS-Chem CTM model grid cell containing Toronto (grid center at 44 • N, 80 • W) is shown in Fig. 8a , along with TAO FTIR data. The correlation was obtained by comparing the hourly model data for each FTIR observation. Comparison with the FTIR was done with and without smoothing the model data with the FTIR averaging kernel and a priori profile  (Rodgers and Connor, 2003). As smoothing the model data only resulted in differences of less than 1 %, the discussion here will focus on the unsmoothed dataset to be consistent with the comparison with IASI. While GEOS-Chem is able to capture the seasonal cycle seen with the FTIR, the correlation is not strong, with r = 0. 51 and the coefficient of determination, r 2 , at 0. 26 (see Fig. 9a ).
The spatial coincidence was calculated by binning the IASI data into the grids of GEOS-Chem, and temporal coincidence was determined by calculating the mean overpass time in the domain and averaging the model data between 1 h before and 1 h after the mean overpass time. The time series (both GEOS-Chem and IASI were averaged over the domain), and correlation plots are shown in Figs. 8b  and 9b , respectively. The correlation of GEOS-Chem against IASI is higher than GEOS-Chem against TAO FTIR, with r 2 = 0. 33 (see Fig. 9b ). Removing enhancement events from observational data led to poorer correlation with GEOS-Chem, with r 2 = 0. 22 and r 2 = 0. 29 for the FTIR and IASI, respectively (see Fig. 9c  and 9d ). This is comparable to findings by  Schiferl et al. (2016) , who observed IASI and GEOS-Chem correlations (r) of 0.
Comparing different coincidence criteria showed that, at least in Toronto, distance criteria can be larger than the 25 km used by  Dammers et al. (2016) , but temporal criteria may need to be stricter, at around ∼ 45 min (instead of 90 min). The highest correlation (r = 0. 73) was seen with coincidence criteria of 25 km and 20 min. TAO FTIR and IASI NH 3 columns were also compared with GEOS-Chem model data. The model did not show a very high correlation with the TAO FTIR for a single grid cell containing Toronto, with r 2 = 0. 26 and r 2 = 0. 28 when the model data were smoothed with the FTIR averaging kernel. The model comparison with IASI showed slightly better agreement on a domain spanning 35 to 53 • N and 93. 75 to 63. 75 • W, with r 2 = 0. 33. These results suggest that TAO FTIR, representative of NH 3 at a city-size scale (∼ 50 km), requires higher-resolution model runs for comparison.
Acknowledgements. This project was made possible thanks to the 2018  Centre National de la Recherche Scientifique -University of Toronto Call for Joint Research Proposals , which provided the initial support for the  AmmonAQ  project. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The authors acknowledge the Aeris data infrastructure (http://iasi. aeris-data. fr/NH3/,last access:  25 January 2021 ) for providing access to the IASI data as well as  Environment and Climate Change Canada  for the NAPS in situ data. ULB has been supported by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement IASI. FLOW). Lieven Clarisse  and  Martin Van Damme  are, respectively, research associate and postdoctoral researcher with the  Belgian F.
R. S-FNRS. Financial support. This research has been supported by the  University of Toronto -Centre National de la Recherche Scientifique , the  Natural Sciences and Engineering Research Council of Canada (NSERC , grant no. RGPIN-2019-06979 ), the  NSERC CREATE Training Program in Technologies for Exo-Planetary Science  (grant no. CREATE-482699-2016 ), and  Environment and Climate Change Canada  (grant no. GCXE19S039 ). Review statement. This paper was edited by  Hendrik Fuchs  and reviewed by two anonymous referees.
Code availability. The SFIT4 retrieval code can be accessed at https://wiki. ucar. edu/display/sfit4/ (described in text)  (Hannigan et al. , 2020). The latest model information can be found at https://doi. org/10. 5281/zenodo. 3959279 (The International GEOS-Chem User Community, 2020). Instructions for downloading and running the models can be found at http://wiki. geos-chem. org/ (last access: 1 November 2020). The GEOS-Chem model (v11-01) is freely available to the public. Data availability. The TAO FTIR data used in this study are publicly available from the NDACC data repository hosted by NOAA at https://www. ndaccdemo. org/stations/toronto-canada (NDACC, 2021). The IASI Level-1C data are distributed in near real time by Eumetsat through the EumetCast system distribution (https://www.
eumetsat. int/eumetcast) (EUMETSAT, 2021). The IASI ANNI-NH3-v3 data used in this study are available from the Aeris data infrastructure: http://iasi. aeris-data. fr/NH3 (Van Damme et al. , 2021). The in situ NH 3 data measured by the Canadian National Air Pollution Surveillance (NAPS) are available at https://open. canada. ca/ data/en/dataset/1b36a356-defd-4813-acea-47bc3abd859b (ECCC, 2019), provided by Environment and Climate Change Canada.
Mya, million years ago. K-Pg, Cretaceous-Paleogene. The dotted vertical line representing the K-Pg mass extinction event at 66 mya is included for context only. population divergence) and nucleotide diversity (π). These analyses used 32 genomic datasets, including 26 publicly available datasets in GenBank for P. jirovecii, P. carinii and P. murina and six datasets generated in this study for other four Pneumocystis species (Supplementary Note 1; Supplementary Table  2 ). We used a trained version LAST  22  to account for interspecies divergence during read mapping and ANGSD  23  to derive genotype likelihoods instead of genotypes. Since ANGSD's F ST requires outgroups, we analyzed interspecies divergence between P. jirovecii, P. macacae, and P. oryctolagi populations using a sliding window approach of 5-kb and P.