dataesr/scientific-paragraphs-categorization huggingface dataset:
this study, particularly Lilian Davies. N. E. C. was supported by the Irish EPA (2015-NC-MS-4). R. N. C. acknowledges support from the Alexander von Humboldt Foundation. F. B. , P. N. and P. M. A. were supported by Marine and Environmental Sciences Centre (UID/ MAR/04292/2019). S. J. B. acknowledges support from NERC, Environment Agency and South West Water (NE/N008391/1). E. B. is grateful for financial support from Alexander von Humboldt Sofja Kovalevskaja Award. K. D. and Z. Č. acknowledge funding received from Czech Science Foundation (19-05510S). N. F was supported by a post-doctoral fellowship (Xunta de Galicia Plan I2C 2017-2020, 09. 40. 561B. 444. 0) from the government of the autonomous community of Galicia, with thanks also to the University of Oklahoma. A. M. L. was supported by the Institute of Environmental Sciences Jagiellonian University (DS/WB/INOŚ/757/2018).
M. O. was supported by the Polish Minister of Science and Higher Education Program 'Regional Initiative of Excellence' 2019-2022 (008/RID/2018/19). Participation by E. M. P. and L. A. H. was supported by the Argentine Agencia Nacional de Promoción Científica y Tecnológica (PICT 2015-3513). B. W. acknowledges funding provided by the University of Szczecin. F. S. was supported by research grant PICT 2016-0631 from the Argentine Agencia Nacional de Promoción Científica y Tecnológica. We thank Manuel Lopes-Lima, Bill McDowell, Rob Britton and an anonymous reviewer for helpful comments. AUTH O R S ' CO NTR I B UTI O N S N. E. C. conceived and designed the study; all authors contributed to data collection; N. E. C. , E. M. C. , R. N. C. and P. W. S. J. performed the data analysis; all authors contributed to the interpretation of results, which was led by N.
Human Samples A thymic biopsy was obtained from a 23-month-old infant with MHCII deficiency upon informed consent in accordance with the Research Ethics Board at The Hospital for Sick Children in Toronto (Canada). Patient's data were compiled prospectively and retrospectively from medical records and entered into the Primary Immunodeficiency Registry and Tissue Bank (REB protocol no. 1000005598). The patient presented at 18 months of life with a history of recurrent respiratory tract infections, chronic diarrhea, and CMV hepatitis. She had a family history of MHCII deficiency and was found to be homozygous for a mutation in the CIITA gene. Immunological data are reported in Table  1. The patient received a matched related HSCT, but engraftment was poor. A human thymic sample from a healthy control used for comparison was analyzed retrospectively in compliance with Declaration of Helsinki and policies approved by Ethics Board of Spedali Civili in Brescia.
Bulk RNA-Seq The cDNA libraries of sorted cTEC and mTEC from WT and Ab 0/0 mice were constructed using the Takara SMART-Seq v4 Ultra low input RNA Kit. PCR library products were quantified using the Agilent DNA 1000 assay (Agilent#5067-1504) on an Agilent Technologies 2100 bioanalyzer. Pooled libraries were loaded on a Paired End Flow Cell using the cBot System (Illumina) and the HiSeq 3000 platform. At the end of the run, around 30 M of 50 bp paired-end reads per sample were generated. Transcript abundance was estimated with Kallisto (29) and differentially expressed (DE) genes were identified using DeSeq2 (30) R package and a FDR corrected p-value <0. 05. Gene set enrichment analysis (GSEA) was applied in order to identify gene sets and pathways that were significantly perturbed across conditions.
Sample Preparation and Proteomic Analyses Murine TEC for proteomic analyses were obtained from three pools of WT or Ab 0/0 mice, three mice per pool (5-6 weeks-old). TEC were isolated, enriched and processed through CD45 + celldepletion as described above. CD45 -cell-samples were pelleted and stored at -80°C until use. Detailed descriptions of proteomic analysis, data processing, and interaction network reconstructions are described in the Materials and Methods section in this article's Supplementary Materials.
Autoantigen Array This analysis was performed at the Genomics & Microarray Core Facility at UT Southwestern Medical Center (USA) on human and murine serum or plasma samples. The Autoantigen array contained 95 autoantigens and eight internal control antigens. Profiling of both IgG and IgM autoantibodies was performed. The autoantibodies binding to the antigens on the array were detected with Cy3 labeled anti-IgG and Cy5 labeled anti-IgM and the arrays were scanned with GenePix ® 4400A Microarray Scanner. The images were analyzed using GenePix 7. 0 software to generate GPR files. The averaged net fluorescent intensity (NFI) of each autoantigen was normalized to internal controls (IgG or IgM).
Statistical Analyses All results are expressed as median and interquartile range if not stated otherwise. In the present study a descriptive statistical analysis has been performed. No formal inference was performed due to the small sample size. Statistical significance was assessed using a two-tailed Mann-Whitney test to compare continuous outcomes between groups. T test was used for splicing entropy result analysis. Levels of significance were defined as p ≤0. 05 (*), p <0. 01 (**), p <0. 001 (***), and p <0. 0001 (****). Statistical testing was performed using Prism GraphPad (Version 5. 0f, La Jolla, CA). Graphs were created using the same software.
Peripheral Tolerance Impairment in Patients With MHCII Deficiency To confirm the results obtained in the mouse model of MHCII deficiency, we set out to evaluate peripheral tolerance in MHCII-D patients. To this purpose, we collected PB samples from a cohort of patients with MHCII deficiency, whose main clinical features are summarized in Table  2. RFXANK gene mutations were identified in eight of them. Median age at sampling was 4. 7 years (range: 0. 4-24. 1 years). All samples were collected before HSCT, except in one patient for whom only post-transplant sample was available. They suffered from severe and recurrent infections and the majority of them also presented with chronic diarrhea. Autoimmune manifestations were described in three patients. Available data about patients' immune phenotype and serum immunoglobulin levels are reported in Table  3.
ACKNOWLEDGMENTS We thank  Emanuele Canonico ,  Daniela Boselli ,  Simona Di Terlizzi  and  Chiara Villa  of the  Flow cytometry Resource, Advanced Cytometry Technical Applications Laboratory (FRACTAL) of San Raffaele Scientific Institute  for their help in performing cell sorting. We thank  Stefano Zancan  and  Ambra Corti of Tiget Clinical Trial Office (TCTO)  for the biological samples collection protocols. We are grateful to all patients who have participated in this study and their families.
Methods In this population-based, observational, before-and-after study, we collected and validated nationwide cohorts of consecutive kidney, liver, lung, and heart transplants from 22 countries. Data were collected from Jan 1 to Dec 31, 2020, along with data from the same period in 2019. The analysis was done from the onset of the 100th cumulative COVID-19 case through to Dec 31, 2020. We assessed the effect of the pandemic on the worldwide organ transplantation rate and the disparity in transplant numbers within each country. We estimated the number of waitlisted patient life-years lost due to the negative effects of the pandemic. The study is registered with ClinicalTrials. gov, NCT04416256. Findings Transplant activity in all countries studied showed an overall decrease during the pandemic.
Study population This cohort study in 22 countries comprised all consecutive organ transplants, including kidney, liver, lung, and heart allografts, done during the COVID-19 outbreak and the year before the pandemic. Multiorgan transplants were counted separately. For example, one liver-kidney transplant was considered one liver transplant and one kidney transplant. Transplanted organs included both adult and paediatric transplants from deceased and living donors. Transplant data were collected from the beginning of the COVID-19 outbreak in 2020 (the study inclusion period was from Jan 1 to Dec 31, 2020), along with data from the same time period in 2019. 19 The 22 countries included 16 in Europe (Austria, Belgium, Croatia, Finland, France, Germany, Greece, Hungary, Italy, the Netherlands, Norway, Portugal, Slovenia, Spain, Switzerland, and the UK), two in North America (Canada and the USA), three in South America (Argentina, Brazil, and Chile), and Japan.
Organ transplant and COVID-19 data sources We obtained data related to the number of solid organ transplants from national transplant authorities and Eurotransplant (an international organ exchange organisation), which prospectively record these data (appendix p 2). Sources include centralised systems such as NHS Blood and Transplant in the UK, Eurotransplant in Europe, Agence de la Biomédecine in France, and the United Network for Organ Sharing in the USA. Data procurement systems used by the relevant competent authorities are described in more detail in the appendix (p 2). The data for national and regional COVID-19 cumulative prevalence and related deaths were obtained from the Center for Systems Science and Engineering at Johns Hopkins University. 20 Disparities in COVID-19related data collection and reporting have been reported and the definition of cases has been updated by each country since the onset of the pandemic, with discrepancies in testing accuracy and reporting.
Statistical analysis All transplant data were smoothed in two steps: first, by calculating a 14-day moving average-14-day moving average was selected instead of 7-day because of the sparsity of data for some countries-and second, by applying locally estimated scatterplot smoothing to the 14-day moving average. 26 Additionally, we compared the trends and diminutions in the number of transplants in 2019 and 2020 by country, continent, and across all 22 countries. Starting from the date of the 100th recorded COVID-19 case for each country until the end of followup, the LOESS smoothed 14-day moving average of transplants for each country was included in the analysis. The correlation between COVID-19-related deaths and worldwide organ transplant activity is represented by bubble charts, which include the following information: x-axis, COVID-19 deaths per million inhabitants calculated from data retrieved from the Johns Hopkins COVID-19 Data Repository and the World Bank (population size per country in 2019); y-axis, percent diminution of transplant rate in 2020 from the 100th cumu lative COVID-19 case to Dec 31, 2020, compared with the same period of time in 2019; bubble size, number of transplants in 2019 according to the See Online for appendix GODT; and label, country name.
The total number of transplants includes solid organs (kidney, liver, lung, and heart). We counted each organ in a multiorgan transplant separately to ascertain the total number of transplants for each organ type. We estimated the number of patient life-years lost during the COVID-19 pandemic compared with the same time period in 2019 due to the reduced number of transplants. We first calculated the reduction in transplants in 2020 for each organ compared with 2019. For each organ lost, the total life-years lost for a patient was assigned based on the methodology applied by Rana and colleagues  27  by calculating the gain of life-years for patients transplanted versus patients who were placed on the waiting list but did not undergo a transplantation. We developed a web-based dashboard that provides three data visualisations: line graphs displaying total COVID-19 cases and total transplants over time (2019, 2020, or ratio of 2020 to 2019); bubble charts (three-dimensional scatter plots) displaying the percent diminution in transplants between 2020 and the same time period in 2019 versus the total COVID-19-related deaths per million inhabitants with bubble size determined by the total number of transplants in 2019; and geographical heatmaps dis playing transplant totals in 2019 and 2020, as well as total COVID-19 cases by region for each country with available data.
Results We assessed overall transplant activity by organ, including kidney, liver, lung, and heart. We compared transplant activities in 2020 from the date of the 100th reported cumulative COVID-19 case to Dec 31, 2020 (or the end of follow-up, whichever was earlier) to the same period of time in 2019. We observed an overall decrease of 11 253 (-15•92%) organ transplants across all 22 countries. This decrease can be stratified to -8560 (-  When we assessed deceased donor transplant activity using the same timeline as above, we observed an overall decrease of 6169 (-11%) organs transplanted across all countries, excluding Canada because the data could not be stratified by donor type. Deceased donor transplant activity was decreased with -3823 (-11•91%), -1370 (-9•33%), -697 (-16•64%), and -279 (-5•46%) for kidney, liver, lung, and heart, respectively.
Based on the reduced number of transplants during the COVID-19 pandemic compared with 2019, we calculated the number of patient life-years lost by comparing patients remaining waitlisted with those transplanted for each organ. The estimated numbers of life-years lost were 37 664 years for patients waitlisted for a kidney, 7370 years for patients waitlisted for a liver, 1799 years for patients waitlisted for a lung, and 1406 for patients waitlisted for a heart, corresponding to a total of 48 239 life-years lost (figure  3 ). To facilitate understanding of the temporal trends and consequences of the pandemic on worldwide, national, and regional solid organ transplant activities for researchers, clinicians, and public health authorities, we created an open-access dashboard that presents data interactively for solid organ transplant activities and COVID-19 cases (appendix p 7).
Discussion Solid organ transplantation often provides remarkable improvements in survival and quality of life for patients with end-stage organ disease. However, transplant procedures involve increased susceptibility to infection and substantial investment of health-care system resources, posing major challenges during a pandemic. To our knowledge, this is the first study to analyse correlations beyond a single country and not limited to mortality related to the COVID-19 pandemic. 28, 29 This study leveraged international data from 22 countries with comprehensive ascertainment of transplant events, revealing major variation in the response of transplant programmes to the COVID-19 pandemic. Kidney transplantation showed the largest reduction across nearly all countries, probably due to the nonimmediate life-saving nature of this surgery and the possibility to postpone procedures.
Nonetheless, we used the best available data for this purpose. We are aware that selecting the study period is non-trivial regarding the COVID-19 pandemic. Although there is no standard rule to define an index date and timeline for COVID-19 studies, we established the 100th COVID-19 case as the starting point for our analysis. 19 Finally, we acknowledge the absence of many countries, including all of Africa, for which we were unable to obtain relevant data; the inclusion of nations in Africa and elsewhere should be a priority for future studies. However, we observed consistent results among the included countries from four continents, suggesting that our findings can be generalised to promote awareness of pandemic preparedness and associated challenges in organ transplantation. The primary observations from our study included changes in solid organ transplantation during and before the pandemic, as well as estimated life-years lost.
Abstract. Since early 2020, the COVID-19 pandemic has led to lockdowns at national scales. These lockdowns resulted in large cuts of atmospheric pollutant emissions, notably related to the vehicular traffic source, especially during spring 2020. As a result, air quality changed in manners that are still currently under investigation. The robust quantitative assessment of the impact of lockdown measures on ambient concentrations is however hindered by weather variability. In order to circumvent this difficulty, an innovative methodology has been developed. The Analog Application for Air Quality (A 3 Q) method is based on the comparison of each day of lockdown to a group of analog days having similar meteorological conditions. The A 3 Q method has been successfully evaluated and applied to a comprehensive in situ dataset of primary and secondary pollutants obtained at the SIRTA observatory, a suburban background site of the megacity of Paris (France).
, 2020; Grange et al. , 2021). But weather-corrected studies of PM chemistry are still scarce, especially in western Europe. The present study aims at reconciling a robust and innovative methodology with a quasi-comprehensive in situ dataset, acquired within the Paris region (France). The 12 million inhabitants of the region, representing around 20 % of the total French population, were placed under lockdown from 17 March 2020 to 10 May 2020, further designated as LP2020. 2 In situ characterization of the atmospheric composition.
Instrumentation In situ measurement datasets used in this study have been primarily obtained at the SIRTA atmospheric observatory (2. 15   et al. , 2011) , constituting the longest ACSM dataset worldwide. Measurement principles of the ACSM are extensively described elsewhere  (Budisulistiorini et al. , 2014; Zhang et al. , 2019; Poulain et al. , 2020). Here, 30 min concentrations of submicron organic aerosols (OAs), nitrate (NO 3 ), sulfate (SO 4 ), ammonium (NH 4 ) and chloride (Cl) were corrected with a time-dependent collection efficiency  (Middlebrook et al. , 2012) (CE). The ACSM at SIRTA was regularly calibrated using 300 nm ammonium nitrate and ammonium sulfate particles to derive ionization efficiencies (IEs) and showed satisfactory performances during ACTRIS intercomparison exercises  (Crenn et al.
, 2015; Freney et al. , 2019). The black carbon dataset consists in aethalometer measurements  (Drinovec et al. , 2015). It is composed of subsequent and harmonized datasets obtained from AE31 (January 2011-March 2013) and AE33 (March 2013-June 2020) devices and applying a common validation procedure  (Petit et al. , 2017a). Briefly, for each elementary measurement data point (5 and 1 min time base for AE31 and AE33, respectively), BC λ concentrations were set as invalid when below LoD (limit of detection; 100 ng/m 3 ); for BC 950 nm ≥ 200 ng/m 3 , the spectral dependence was calculated from the linear regression of ln(λ) versus ln(B atn ). Measurements were considered valid for a r 2 (of this linear regression) higher than 0. 9 and aerosol Ångström exponent (AAE) comprised between 0. 8 and 3.
In this respect, urban NO x concentrations refer here to the average of all urban background stations measuring NO x. As the ozone instrument from SIRTA experienced a major breakdown in 2020, daily ozone (O 3 ) concentrations were obtained between 2012 and 2020 from a peri-urban station in Les Ulis (2. 165 • E, 48. 68 • N), operated by Airparif. This station is located around 10 km away from SIRTA, and O 3 from Les Ulis has shown a good comparability with O 3 from SIRTA during the period of common measurements. Meteorological variables at SIRTA (wind speed and direction, temperature, relative humidity and pressure) were provided from the ReObs database  (Chiriaco et al. , 2018).
Source apportionment of carbonaceous aerosols Fossil fuel (BC ff ) and biomass burning (BC wb ) fractions were estimated from aethalometer measurements  (Sandradewi et al. , 2008). Since the choice of α ff and α wb is critical, and given the size of our dataset, their determination was based on the statistical hourly distribution of the AAE (Fig. S1 ). The value of 1. 85 was chosen for α wb , corresponding to a maximum frequency during the night. This value is close to the values used previously at SIRTA  (Zhang et al. , 2019)  and to the recommended value of 1. 72  (Zotter et al. , 2017). PM ff and PM wb were estimated from BC ff and BC wb concentrations, respectively, following the conversion factors of 2 and 10. 3 found for SIRTA during the same season  (Petit et al. , 2014). A source apportionment study of OA was carried out by positive matrix factorization (PMF;  Paatero and Tapper, 1994)  from January to May 2020.
Back-trajectory calculation With the PC-based version of HYSPLIT  (Stein et al. , 2015)  using 1 • × 1 • Global Data Assimilation System (GDAS) files, 120 h back-trajectories ending at SIRTA (49. 15 • E, 2. 19 • N) at 500 m a. g. l. were calculated every 6 h from 2012 to 2020. Calculations using HYSPLIT executables were automatically controlled by ZeFir  (Petit et al. , 2017b) , a userfriendly interface based on Igor Pro 6. 3 (Wavemetrics © ). The cluster analysis presented in Sect. 3. 1 was also applied from HYSPLIT executables, controlled by ZeFir. Five clusters were used (Fig. S3a ), in accordance with the total spatial variance (TSV). The two oceanic clusters were summed as one. 3 Methodology to estimate the impact of lockdown measures: an attempt to compare apples to apples.
Choice of the comparison reference period The assessment of lockdown impact on air quality lies on the use of a reference period, which is assumed to be representative of business-as-usual conditions during LP2020, following percentage change = 100 • LP 2020 -ref ref. (1) In the current literature, different reference periods are used, from a pre-lockdown period (pLP2020,  Toscano and Murena, 2020; Dantas et al. , 2020; Otmani et al. , 2020) , to the weeks corresponding to LP of previous years (e. g. , 17 March to 11 May during 2017-2019 is LP2017-2019). Nevertheless, in the case of SIRTA, applying these methodologies unquestioningly, without verifying the inherent hypothesis that data are comparable, can lead to significant variability and counterintuitive results. Figure  1  presents concentration relative changes for the SIRTA dataset, using pLP2020, LP2019, LP2017-2019, LP2015-2019 and LP2012-2019 as references.
Description Analogs of atmospheric circulation  (Yiou et al. , 2013; Lorenz, 1969; Van Den Dool, 1994; Zorita and Storch, 1999; Cattiaux et al. , 2012)  have been widely used for different climatological purposes, notably for atmospheric reconstructions and in the characterization of the role of synoptic circulation in extreme meteorological events  (Yiou et al. , 2013; Vautard et al. , 2018). Circulation analogs are generally computed from daily pressure spatial distributions. Here, we built the analogy based on three successive layers: Synoptic. Circulation analogs are computed similarly to previous studies. We used daily sea-level pressure (SLP) data to better characterize near-surface atmospheric circulation as our study covers near-surface pollutants. The SLP data are extracted from NCEP/NCAR reanalysis data  (Kalnay et al.
Data preparation Daily averages have been computed for each variable. A valid average is considered if at least 75 % of the day is covered. Because the dataset consists in multi-year observations, long-term trends may impact the estimated concentration change due to lockdown. To that end, a seasonal Mann-Kendall (MK) test was performed on each variable for the January 2012-February 2020 period. The Mann-Kendall R package was used  (Bigi and Vogt, 2020; Collaud Coen et al. , 2020) , which includes three pre-whitening approaches in order to reduce the weight of autocorrelation. Results are summarized in Table  1. From this analysis, NO x , OA, HOA, OOA, O 3 and BC ff concentrations (2012-2020, including lockdown) were linearly corrected on a daily basis. In order to limit the unwanted weight of positive outliers which have poor statistical representativity, the 1 % highest daily concentrations of each variable were removed.