dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. All CMIP6 data are available on the Earth system grid. IPSL-CM6A-LR output is available at https://doi. org/10. 22033/ESGF/CMIP6. 5195 (Boucher et al. , 2018) , https://doi. org/10. 22033/ESGF/CMIP6. 5269 (Boucher  et al. , 2019a), and https://doi. org/10. 22033/ESGF/CMIP6. 5271 (Boucher et al. , 2019b). CESM2-WACCM data are available at https://doi. org/10. 22033/ESGF/CMIP6. 10071 (Danabasoglu,  2019a), https://doi. org/10. 22033/ESGF/CMIP6. 10114 (Danabasoglu, 2019b), and https://doi. org/10. 22033/ESGF/CMIP6. 10115 (Danabasoglu, 2019c). CanESM5 data are available at https://doi. org/10. 22033/ESGF/CMIP6. 3610 (Swart et al. ,  2019a), https://doi. org/10. 22033/ESGF/CMIP6. 3694 (Swart et  al. , 2019b), and https://doi. org/10. 22033/ESGF/CMIP6. 3696 (Swart et al. , 2019c).
UKESM data are available at https://doi. org/10. 22033/ESGF/CMIP6. 6113 (Tang  et  al. ,  2019), https://doi. org/10. 22033/ESGF/CMIP6. 6397 (Good et  al. , 2019a), and https://doi. org/10. 22033/ESGF/CMIP6. 6405 (Good et al. , 2019b). UVic-ESCM output is available at https://doi. org/10. 5281/ZENODO. 4663137 (Mathesius and Zickfeld, 2021). CNRM-ESM2-1 data are available at https://doi. org/10. 22033/ESGF/CMIP6. 4068 (Seferian, 2018) , https://doi. org/10. 22033/ESGF/CMIP6. 4221 (Voldoire, 2019a), and https://doi. org/10. 22033/ESGF/CMIP6. 4226 (Voldoire,  2019b). All analysis code is available as a Jupyter notebook at https://github. com/ckoven/longterm_carboncycle/blob/ master/longterm_carboncycle. ipynb (last access: 9 May 2022, https://doi. org/10. 5281/ZENODO. 5934643, Koven, 2022).
Results Description of the study population. Between January 1 st 2015 and May 15 th 2020, 429 patients were enrolled in the iNTD patient registry. Of these entries, 350 patients had a diagnosis of biogenic amine disorders. 75 patients who were transferred from the JAKE database on aromatic L-amino acid decarboxylase deficiency (http://www. biopku. org/home/jake. asp) were not analyzed in this study due to the high number of missing variables of interest. The remaining cohort of patients with disorders of biogenic amines consisted of 275 patients from 248 families (157 female (57%), from 42 countries: 196 patients born in Europe, 42 in North America, 34 in Asia, three in Central/South America and one in Africa). 109 patients had primary disorders of biogenic amine metabolism, 161 BH 4 deficiencies (BH 4 Ds) and five patients DNAJC12D (Tables  2  and 3 ).
Based on the fact that the inclusion criteria and requirements for patient enrollment into the iNTD registry were strictly defined and bound to complex ethical standards, the number of patients enrolled is not comparable with earlier retrospective registry studies (e. g. JAKE; http://www. biopku. org/home/jake. asp, BIODEF 57 ; http:// www. biopku. org). Our registry provides data depending on the participating centers and does not present the overall incidence of the diseases worldwide. In addition, the medical history module of the registry analyzed in our study is the only part, which gathers data summarized by treating physicians, parents, caregivers as well as patients. Therefore, some information was retrospective or missing. Following our observation of a high incidence of prematurity and SGA in some diseases, a revision of the exclusion criterion "BW < 1500 gram" will be proposed to the iNTD steering committee to avoid an underestimation of patients who are born preterm or with very low birth weight.
The Benjamini-Hochberg adjustment was applied to correct type 1 error, when applicable. No a-priori hypotheses were tested and therefore all p-values had descriptive character. Classification and regression trees (CART) were used to identify constellations of symptoms that might have an impact on age at diagnosis or diagnostic delay (= age at diagnosis -age at initial symptom) for different diseases. Standard deviation scores (SDSs) for anthropometric variables at birth were computed according Fenton et al. 62  network. All participating iNTD members approved this study. Source data for all the figures, tables 2-4andsupplementary table 1 are provided with the paper. Data transferred from the JAKE database (http://www. biopku. org/home/jake. asp) was not analyzed in this study. Source data are provided with this paper.
56. Boycott, K. M. , Vanstone, M. R. , Bulman, D. E. & MacKenzie, A. E. Raredisease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14, 681-691 (2013). 57. Blau, N. , Barnes, I. & Dhondt, J. L. International database of tetrahydrobiopterin deficiencies. J. Inherit. Metab. Dis. 19, 8-14 (1996). 58. Chabra, S. Clearing the confusion about completed weeks of gestation. J. Obstet. Gynecol. Neonatal. Nurs. 43, 269 (2014). 59. Spong, C. Y. Defining "term" pregnancy: Recommendations from the defining "term" pregnancy workgroup. JAMA 309, 2445-2446 (2013). 60. Leviton, A. , Holmes, L. B. , Allred, E. N. & Vargas, J. Methodologic issues in epidemiologic studies of congenital microcephaly. Early Hum. Dev. 69, 91-105 (2002). 61. Sharma, D. , Shastri, S. & Sharma, P.
Acknowledgements We thank all patients and their families for their contributions to this study and for their trust. T. H. and J. K. were supported the grant from the  Ministry of Health of the Czech Republic   RVO-VFN 64165 GJIH-0599-00-7-846  and  ProgresQ26/LF1. A. G. C. and N. J. P. are supported by  FIS P118/00111  " Instituto de Salud Carlos III (ISCIII)" and "Fondo Europeo de desarrollo regional (FEDER) ". T. O. , K. J. , G. F. H. and O. K. H. were supported in parts by the  Dietmar Hopp Foundation ,  St. Leon-Rot , Germany. M. A. K. is funded by an  NIHR Professorship , the  Sir Jules Thorn Award for Biomedical Research  and the Rosetrees trust. M. V. is supported by  Stichting Stofwisselkracht  Grant. D. H. acknowledges funding by the  Molecular Diagnostics Program of the National Center for Tumor Diseases (NCT) Heidelberg.
In France, the analysis by INERIS (Institut national de l'environnement industriel et des risques) compared air pollution forecast data (calculated without incorporating changes in emissions due to lockdown measures) with adjusted simulations performed a posteriori by assimilating observation data influenced by the lockdown measures. They estimated that NO 2 concentrations were on average approximately 50 % lower than expected in France's largest cities  (INERIS, 2020). Regarding Italy, maps of NO 2 surface concentrations estimated from satellite data (e. g. Sentinel-5p) were published by several websites and media showing large reductions in NO 2 concentrations over northern Italy in March 2020 as compared to the previous months and to March 2019 (e. g. Copernicus Atmosphere Monitoring Service headlines published on 17 and 26 of March 2020; CAMS, 2020a, b).
Material and methods Model and observation air pollution data from four sites located in Lombardy covering the time periods 17 February-24 May 2019 and 2020 were collected and analysed. We selected three sites located in the Milan conurbation as representative of the Milan urban background and the site of https://doi. org/10. 5194/acp-21-7597-2021 Atmos. Chem. Phys. , 21, 7597-7609, 2021 Ispra as representative of the regional background of the upper Po Valley (Fig. 1 ). Ground level concentrations of NO, NO 2 , SO 2 , O 3 and PM 10 as measured in situ at the monitoring stations and as calculated by the ENSEMBLE model forecast were considered. Particle number size distribution and aerosol light absorption Ångström exponent data from Ispra for the 2017-2020 period were also utilised.
Measurements At ECAtmO in Ispra, online in situ air pollution measurements are performed from appropriate inlets located at 6. 5 and 9 m above ground level for gaseous and particulate pollutants, respectively. The inlet for reactive gas is made of PTFE (inner diameter: 2. 7 cm). The sample residence time in the inlet tube is ca. 2 s. Each analyser samples from the main inlet through a Nafion dryer. In 2019-2020, the measurement programme included CO, NO, NO x , NO 2 , SO 2 , O 3 , non-methane hydrocarbons (until 6 March 2020) and NH 3 (since 28 January 2020) as gaseous pollutants. The NO x (i. e. NO and NO 2 ), SO 2 and O 3 data reported in this work were obtained with trace level instruments based on infrared (IR) (1200 nm) chemiluminescence and a Mo converter (Thermo Fisher 42iTL), ultraviolet (UV) (214 nm) fluorescence (Thermo Fisher 43i TLE), and UV (254 nm) absorption (Thermo Fisher 49C), respectively.
These instruments are calibrated every 3 months using zero air and certified gas cylinders (NO and SO 2 ) or a primary standard ozone generator (O 3 ). In 2019, annual average concentrations of NO, NO 2 , SO 2 , O 3 and PM 10 were 4, 16, 0. 4, 38 and 21 µg m -3 , respectively. Particulate matter is sampled through metal-made inlets characterised by negligible losses. Each instrument samples isokinetically from the main aerosol inlet through Nafion dryers. In 2019-2020, the aerosol online in situ measurement programme included PM 10 mass concentration, particle number concentration and number size distribution, particle light extinction, absorption, scattering, and backscattering at several wavelengths. The PM 10 mass, particle number concentration and light absorption data reported in this work were obtained with a TEOM-FDMS (Thermo Fisher 1405-DF), a differential mobility particle sizer (home-made Vienna-type differential mobility analyser + TSI 3772 condensation particle counter) covering the particle mobility diameter range 10-800 nm and a seven-wavelength aethalometer (Magee AE31), respectively.
The TEOM has been calibrated using a standard filter provided by the manufacturer, while the differential mobility particle sizer (DMPS) and the aethalometer are operated, maintained and controlled according to ACTRIS guidelines (https://www. actris. eu, last access: 9 October 2020). They were both calibrated at the specific ACTRIS central facility (https://www. actris-ecac. eu, last access: 9 October 2020) on 3-7 June 2019. Near-real-time data are available from the JRC data catalogue at http://data. jrc. ec. europa. eu/collection/ abcis (last access: 9 October 2020). The three stations in the Milan conurbation are part of the ARPA Lombardia air quality network, compliant with Directive 2008/50/EC requirements in terms of measurement methods, macro and micro localisation, and data coverage.
Inlets are located at 2. 5 m above ground level for all pollutants. The measurement programmes comprise NO, NO x , NO 2 , SO 2 and O 3 at all three sites. Additional measurements include benzene, toluene, xylenes, PM 10 , PM 2. 5 , benzo[a]pyrene and NH 3 in Milan and CO and PM 10 in Limito. Each gas analyser samples from the main inlet through a Nafion dryer. The NO x data reported in this work for the Milan conurbation were obtained with trace level instruments based on IR (1200 nm) chemiluminescence and a Mo converter (Teledyne API 201E, Thermo Fisher 42i and Thermo Fisher 42c in Milan Pascal, Limito and Cormano, respectively), and O 3 was measured by UV (254 nm) fluorescence (Thermo Fisher 49i at all three sites). All measurements are performed according to a specific quality assurance and quality control programme.
CAMS regional ensemble forecast description The Copernicus Atmospheric Monitoring Service (CAMS) provides 4 d ahead air quality forecasts daily for Europe from currently nine different regional air quality models (CHIMERE, DEHM, EMEP, EURAD-IM, GEM-AQ, LOTOS-EUROS, MATCH, MOCAGE, SILAM). Hourly pollutant concentrations are calculated for altitudes ranging from the 40 m thick surface layer to 5 km. The outputs of the different individual models are interpolated on a common regular 0. 1 • × 0. 1 • latitude × longitude grid (about 10 km × 10 km) over the European domain (30-72 • N, 25 • W-45 • E). Forecasts are performed independently by all the individual regional air quality systems: each air quality model is based on different chemical (gas and aerosols) and physical parameterisations but uses the same meteorological drivers as input (the ECMWF Integrated Forecasting System, IFS) and the same anthropogenic emissions data  (Kuenen et al.
S1-S3 in Supplement), CAMS 2020 was corrected for this seasonality. Thus, 2020 daily expected pollutant concentrations (Exp) were calculated as follows: Exp 2020 = CAMS 2020 CAMS 2019 Obs 2019. (1) The comparison of observations with these expected concentrations for 2020 has the great advantage of being insensitive to the fact that the emissions inventories used to calculate ENSEMBLE forecast data for 2019 and 2020 were https://doi. org/10. 5194/acp-21-7597-2021 Atmos. Chem. Phys. , 21, 7597-7609, 2021 different. The disadvantage of this approach is that Obs and Exp cannot be compared to each other on a daily basis since Exp values are affected by random variations in the Obs 2019 / CAMS 2019 ratio. Therefore Obs 2020 and Exp 2020 data were compared statistically for the three periods A, D and P.
Since changes in pollutant emission rates are expected to result in changes in pollutant concentrations in terms of percentages or ratios, statistical analyses were performed on Obs 2020 / Exp 2020 daily ratios. We calculated occurrence frequency distributions of the Obs 2020 / Exp 2020 ratio using eight class bins ranging from <0. 25 to >2, all equally wide on a logarithmic scale (except the last one when specifically indicated). Cumulative frequencies of occurrence were also plotted to facilitate comparisons (Fig. 4 ). To detect possible specific impacts of lockdown measures on the highest concentrations, specific occurrence frequency distributions were also calculated by selecting the 28 d on which ENSEMBLE forecast data were greater than the median during the lockdown period. These days are different for each pollutant and each site.
Conclusions Northern Italy has been an air pollution hotspot for decades. Northern Italy also hosted the very first clusters of the COVID-19 epidemic in Europe, and from February 2020, containment measures were gradually implemented, culminating in strict lockdown measures in force between 9 March and 4 May 2020. We isolated specific impacts of the lockdown measures on air pollution by comparing observed with expected data at one regional background site (Ispra) and three urban background sites (in the Milan conurbation) across the period 17 February-24 May 2020. All four stations were in the COVID-19 "red zone". Expected pollutant concentrations were derived from ENSEMBLE forecasts, which are based on actual meteorological conditions and historical emissions estimates that ignored the COVID-19 epidemic and related lockdown measures.
Acknowledgements. The authors thank the  Copernicus Atmosphere Monitoring Service Information , in particular the  Regional Production Service. Jean-Philippe Putaud ,  Luca Pozzoli ,  Enrico Pisoni ,  Sebastiao Martins Dos Santos  and Friedrich Lagler thank their colleagues from  JRC  for fruitful tele-discussions during the whole lockdown period and for helpful comments on the manuscript. Financial support. This research has been supported by the  European Commission ,  H2020 Research Infrastructures (ACTRIS-2  (grant no. 654109 ) and  ACTRIS IMP  (grant no. 871115 )). Review statement. This paper was edited by  Rolf Müller  and reviewed by  S. Fadnavis  and one anonymous referee.
Data availability. Observation data from Ispra are available at https://data. jrc. ec. europa. eu/collection/abcis (European Commission, 2021) and https://actris. nilu. no/ (NILU, Norsk Institutt for Luftforskning, 2021). Observation data from Milan are available at https://www. arpalombardia. it/Pages/Aria/Richiesta-Dati. aspx (ARPA Lombardia, 2020b). Model forecast data for all sites are available at https://ads. atmosphere. copernicus. eu/cdsapp#!/dataset/ cams-europe-air-quality-forecasts?tab=form (CAMS, 2020d). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-7597-2021-supplement. Author contributions. JPP, EP and LP contributed to conception and design. SMDS, FL and UDS contributed to acquisition of data. JPP, EP, LP, GL and AC contributed to analysis and interpretation of data.
| Parentage analysis and accuracy tests All collected juveniles were screened against the total pool of adult samples to reveal parent-offspring relationships, which were identified using a maximum likelihood approach implemented in the software program FAMOZ  (Gerber et al. , 2003; Marshall et al. , 1998). The resulting probability of assigning a juvenile to a parent that was not its true parent, knowing that the true parent was not sampled, was 2. 0% (false positive-type I error). Conversely, the probability of a true parent-offspring pair not being identified knowing that the true parent was sampled was less than 12. 3% (false negative-type II error). Despite the high number of loci, low polymorphism in microsatellite loci precluded higher accuracy in the parentage analysis. The high rate of type II errors indicates that other true parent-offspring are likely to exist in our data; however, low polymorphism precludes our ability to identify them confidently.
| Fitting dispersal kernels to genetic parentage data Larval dispersal kernels were fit to the parentage assignment dataset following the methods outlined in  Bode et al. (2018). In brief, larval dispersal was assumed to follow a distance-based relationship, where s ij is the number of larvae that settle in nursery habitat patch j that were spawned on reef i, calculated by: where N i is the abundance of adults on reef i, f is the constant per capita fecundity of the species, d ij is the shortest in-water distance between locations i and j and (d) is a probability density function that represents a dispersal kernel. In our case, we estimated the abundance of adults on each reef using the area of adult habitat identified for each reef, multiplied by the average density observed in each zone from underwater visual census (UVC) surveys undertaken in 2012  (Hamilton et al.
, 2016 ; see Supporting Information; Appendix S4). Larval output from each reef was therefore proportional to local abundance. To estimate this abundance, we used the specific abundance estimate for sampled reefs, and we estimated the density on unsampled reefs to be the average density on the sampled reefs. Uncertainty in these estimates was incorporated in our bootstrap resampling procedure described below. Our candidate kernels were generalised Gaussians with integer exponents β (i. e. the Laplacian function when = 1, the standard Gaussian when = 2 and the Ribbens function when = 3): Any proposed larval dispersal kernel (or even a biophysical larval dispersal model;  Bode et al. , 2019)  can be used to create a multinomial vector of predicted settlement proportions q j at reef j, where each element of the vector i is defined as: Of course, the parentage assignment dataset only offers unambiguous information about the minority of juveniles (generally between 1% and 30%) that could be assigned to an adult.
To construct the probability that a sampled juvenile on reef j was assigned to an adult on reef i, we modify Equation 3 by i , the proportion of adults on reef i that was genotyped: and the probability that a sampled juvenile on reef j could not be assigned to any adults in the genotyped set is: Using these probabilities, we can calculate the kernel function and parameterisation that maximises the (log) likelihood of observing the parentage assignment dataset for B. muricatum. We searched for the best-fit scale parameter 1 across values between 10 -6 and 10 3. The parameters in this estimator contain substantial uncertainty, and we propagated this uncertainty through to our estimates of larval dispersal. First, we fit the kernel to 250 bootstrap resamples of our dataset (with replacement, at the reef level), to include the uncertainty introduced by our set of parentage sample sites.
I G U R E 1 The study area within the Solomon Islands. Coral reef habitats are colour coded into multiple regions: Kia fishing grounds (Region A), Western Province (Region B), Choiseul Province (Region C), Russell Islands (Region D), Shortland Islands (Region E) and eastern Santa Isabel Island (Region F). Reefs outside of these six regions are indicated as 'other' and were not considered in the study The program computes log of the odds ratio (LOD) scores for assigning individuals to candidate parents based on the observed allelic frequencies at each locus. Simulated datasets with known parentoffspring relationships were used to determine a critical LOD score for assigning parentage and to assess Type I and Type II errors following the procedure described in Harrison et al. (2014). The minimum LOD score thresholds for single-parent assignments were identified from simulations as 8.
F I G U R E 1 The study area within the Solomon Islands. Coral reef habitats are colour coded into multiple regions: Kia fishing grounds (Region A), Western Province (Region B), Choiseul Province (Region C), Russell Islands (Region D), Shortland Islands (Region E) and eastern Santa Isabel Island (Region F). Reefs outside of these six regions are indicated as 'other' and were not considered in the study The program computes log of the odds ratio (LOD) scores for assigning individuals to candidate parents based on the observed allelic frequencies at each locus. Simulated datasets with known parentoffspring relationships were used to determine a critical LOD score for assigning parentage and to assess Type I and Type II errors following the procedure described in Harrison et al. (2014). The minimum LOD score thresholds for single-parent assignments were identified from simulations as 8.
LAM-HTGTS LAM-HTGTS was performed as previously described  [23]  using activated splenocytes or Peyer's patches cells (enriched with the STEMCELL "B-cell isolation kit"). Jκ5-biotin (5'-TGT GTA AGA CAC AGG TTT TCA TGT) and Jκ5-nested (5'-CAG AAA ATC TTG AGA AAA TGG AGA-3') primers and Sμ primers  [24]  were used to generate libraries. Data analysis of MiSeq sequencing reads was performed as previously described  [25]. Graphical representation were performed as previously described  [26]. All sequence alignments were done with the mouse mm10 genome (or its variant sequence corrected for our KIKS changes to the Igκ locus (Figs  1  and S1 ). Sequences are deposited under the reference PRJEB34781.
Measure of wings Adult flies were kept in 70 % ethanol for 48 h and transferred into PBS glycerol (1:1 v/v). Wings were dissected and mounted on glass slides, dorsal side up, in Hoyer's medium. Slides were scanned with a Hamamatsu Nanozoomer Digital Slide scanner, running the Nanozoomer software with a 20 x objective and an 8 bit camera. Wing pictures were separately exported into tif format using NDP. view and the 5 x lens. Measurements of wing length were performed as described in  (27).
72). Reads were then aligned against the D. melanogaster genome (dm6 genome assembly, release 6. 30) using STAR (Galaxy Version 2. 6. 0b-2). Reads were counted using featurecount (Galaxy Version 1. 6. 0. 6). Differential analysis was performed using DeSeq2 version 1. 32. 0. Gene ontology was analysed with DAVID (https://david. abcc. ncifcrf. gov/home. jsp). Motif discovery was performed using MEME  (31)  and motif comparison using TomTom  (32)  [MEME Suite, version 5. 3. 3, (33) ]. The RNA-Seq gene expression data and raw fastq files are available at the GEO repository (https://www. ncbi. nlm. nih. gov/geo/info/seq. html) under accession number GSE181926. RT-qPCR were performed on wing imaginal disc cDNAs as described in  (27). Expression levels were quantified with the Pfaffl method (34) and normalized to the geometric mean of two reference genes, GAPDH and Spt6, the expression of which did not vary in the mutants.
for details). This motif significantly overlaps the transcription factors Mad (idmmpmm2009 database, p-value 4. 9e-07) and Med (dmmpmm2009 database, p-value 1. 22e-04) motifs. D -Snapshots showing the mapping of reads on CG13516, Hsp67Bc and GstE6 in the reference line w 1118 , corto 420 /corto L1 , uL11 K3A and uL11 K3Y wing imaginal discs.