dataesr/scientific-paragraphs-categorization huggingface dataset:
However, expression data strongly points to a major role of Btn2a2 in central tolerance. We show that, during steady-state conditions, the thymus is the major site of Btn2a2 expression. Thymic Btn2a2 expression was almost exclusive to TECs, which is compatible with the reticular staining pattern that was reported for Btn2a2 on thymic slices with anti-Btn2a2 antiserum  [5]. Btn2a2 expression was shown to be controlled by CIITA and RFX, the transcriptional machinery underlying MHC class II regulation  [9] , and CIITA-or RFX deficient APCs failed to upregulate Btn2a2 in response to IFNγ  [9] , a potent inducer of MHC class II genes  [44]. In accordance with that, we observed high Btn2a2 expression in MHC class II expressing mTEC hi , but low expression in MHC class II low mTEC lo. Concomitant with maturation and MHC class II upregulation, mTECs upregulated Btn2a2.
Acknowledgements We thank  Prof. Gerhard Krönke  for providing Nur77-GFP mice. We thank  Uwe Appelt  and  Markus Mroz  for cell sorting. We thank  Margarete Schimpf  and  Julia Harrer  for technical assistance. K. Sc. is supported by the  BMBF  (project  01KI2013 ). K. Sa. and M. F are supported by the  Else-Kröner-Fresenius Stiftung  (project  2019_A82 ) and the  Dr. Rolf Schwiete Stiftung. M. M. Z is funded by the  Deutsche Forschungsgemeinschaft (DFG)   FOR2886  project  A1  and  CRC1181  project  B07. We gratefully acknowledge the generous support by the  Manfred Roth-Stiftung , Fürth, Germany G. S. is funded by the  H2020 GA 810316 -4D - Nanoscope ERC Synergy Project , the  IMI funded projects RTCure and HIP-POCRATES , the  Emerging Fields Initiative MIRACLE of the FAU.
Plant -atmosphere exchange fluxes of CO 2 measured with the Eddy covariance method are used extensively for the assessment of ecosystem carbon budgets worldwide. The present paper describes eddy flux measurements for a managed upland grassland in Central France studied over two decades (2003-2021). We present the site meteorological data for this measurement period, and we describe the pre-processing and post-processing approaches used to overcome issues of data gaps, commonly associated with long-term EC datasets. Recent progress in eddy flux technology and machine learning now paves the way to produce robust long-term datasets, based on normalised data processing techniques, but such reference datasets remain rare for grasslands. Here, we combined two gap-filling techniques, Marginal Distribution Sampling (short gaps) and Random Forest (long gaps), to complete two reference flux datasets at the half-hour and daily-scales respectively.
Background & Summary Long-term carbon (C) flux measurements are critical to assess both the patterns and drivers of ecosystem function over space and time. Eddy covariance (EC) measurements are a direct and instantaneous way to measure carbon fluxes and energy between atmosphere and surface. In recent years, networks of flux towers (EC measurements) have played a pivotal role in improving understanding of broad-scale carbon budgets and responses to abiotic and biotic factors both across and within contrasting ecosystems  1. Although the installation of EC systems has increased worldwide (i. e. , NEON, Ameriflux, AsiaFlux, ICOS), generating more available and reliable datasets based on standardised data-processing pipelines, the availability of long-term grassland flux datasets lags behind that of woody systems  2.
Long-term grassland flux studies hold great potential for identifying and understanding effective approaches to mitigate and adapt to global changes, including the provision of ecosystem services at a global scale. Here, we describe 18-year datasets of greenhouse gas (GES) fluxes from an EC tower located in an upland permanent grassland site in the French Massif Central region, along with the methodology used for the pre-and post-processing of the data  3. The production of accurate long-term eddy flux datasets relies on a suite of software and statistical tools for data pre-and post-processing  4. Three general steps have a key effect on the quality of the final data in long-term eddy flux datasets: (i) raw-data pre-processing, (ii) time series discontinuity, that is, the number and length of gaps, and (iii) the gap-filling techniques (also called "imputation").
Data gaps in EC time series may be related to technical failures and/or changes in analyser technology, often non-randomly located across the EC time series, as well as to data quality checks (i. e. , rejection of low-quality C fluxes  5, 6  ), which are typically randomly located in the time series  7. Further, data measured in periods of low turbulence, which occurs mainly at nighttime, are rejected, thus generating more gaps  7, 8. Standard gap-filling methods based on Marginal Distribution Sampling (MDS  9  ) are effective for short gaps  7  because the missing value is replaced by the average of the response variable under similar weather conditions in a small-time window. However, recent studies show that MDS has low accuracy and high uncertainty when dealing with long gaps  10, 11. To overcome problems of long gaps in EC datasets, a variety of machine learning (ML) techniques (i.
e. , Random Forest and artificial neural networks) have been used to reconstruct long-term EC time series  [10] [11] [12]. The application of ML techniques to flux data has the potential to provide robust gap-filling and requires few predictive variables to be measured continuously over long time periods  10, 12, 13. Moreover, ML considers the temporal dependence and structure of the time series (i. e. , trend and seasonality) and can deal with "noise" and complex interactions between variables  10. In the present work, we therefore combined different statistical techniques to gap-fill data gaps of different origin and length in our EC time series, i. e. , MDS and Random Forest techniques, generating two complete flux datasets (half-hourly and daily scale). Our grassland study site is managed with low intensity cattle grazing typical for the region  14, 15  , and the tower-based measurements include ecosystem-atmosphere turbulent fluxes of CO 2 and H 2 O.
, Lincoln, NE, USA). Data is recorded at 10 to 20 Hz and recorded on a computer and datalogger  14, 15. The site is equipped with a meteorological station that provides high frequency measure of atmospheric (Tair: air temperature, RH: relative humidity, PA: atmospheric pressure, P: total precipitation, ws: wind speed, wd: wind direction) and solar radiation (PPFD: photosynthesis active radiation, Rg: global radiation, Rn: net radiation). The frequency for atmospheric and solar radiation is 30 and 20 seconds, respectively. Flux data processing and post-processing. Raw-data (10 Hz until 2016 and 20 Hz onwards) from the EC tower and meteorological station were pre-processed with EddyPro ® software (Li-COR, version 7. 0. 9) following the processing steps and methods  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  presented in Supplementary Table  1  and Table  1.
Processed data was converted into half-hourly flux data and post-processing was performed following international recommendations of FLUXNET 2 using R Studio Software. In brief, post-processing steps included: (i) data filtering of low-quality values of NEE, (ii) filtering of values outside the footprint area 20 , (iii) filtering of values under low friction velocity (uStar), (iv) gap-filling of missing values using the MDS method  11  for half-hour data (shorter gaps), (v) partitioning net ecosystem exchange (NEE) into ecosystem respiration (R eco ) and gross primary productivity (GPP), based on the nighttime and daytime algorithms  9, 28  , (vi) gap-filling of missing values using RF algorithms for daily data (long gaps)  2, 10. Short gaps are random gaps often produced during data quality check that were distributed throughout the EC time.
On the other hand, long gaps are non-random gaps that are mainly related to instrumental failures or changes, and they located in specific points across the EC time series. For instance, in our EC time series we identified four long gaps (Fig. 3 ), the largest gap being a sequence of 26 months, from October 2014 to December 2016. Post-processing steps are described in detail below. Data quality check. The quality check procedure for the half-hour data was performed in six steps (Table  1 ) using the R packages "FreddyPro"(https://github. com/cran/FREddyPro) and "REddyProc" 8 : Fig. 2  Workflow of pre-and post-processing step for half-hour and diurnal-daily data. EddyPro software assigns two hard flags for each half-hourly data, where "0" represents "passed" and "1" represents "failed". Data with a hard flag of 1 for the spike test were rejected.
The quality check results related to all other statistical screening procedures (Supplementary Table  1 ) are presented in the dataset. • Standard deviation and outliers: We rejected data with values greater than 3 standard deviations from the mean positive and negative values of the complete EC time series (i. e. , outliers from the interquartile range with 75th and 25th percentiles). • Footprint: Data were filtered with respect to field margins to minimize the risk of fluxes from outside the field. We rejected values where the distance between the tower and the peak was greater than that of the fetch, so that only values in the target area remained. • uStar: Data were filtered for insufficient atmospheric turbulence (i. e. , mostly at night) using multiple uStar thresholds (0. 05, 0. 5, 0.
95 quantiles) during the year to account for seasonality in vegetation and climate classes (air temperature and precipitation). The uStar thresholds were estimated using the bootstrapping method  31  (n = 1000 resamples). The percentage of missing values before and after data cleaning by day and diel period is given in the XLSX file "FR_Lq2_EXTENSIF_Li_7500_CR3000_2003_2021_gaps. xlsx". Gap-filling of short gap periods and C flux partitioning. Following data quality checks, short gaps in NEE were imputed using Marginal distribution sampling-MDS 8 as recommended by FLUXNET 2 , using the R package "REddyProc"  8. The MDS combines two gap-filling techniques: the "look-up table" and the "mean diurnal course". In essence, the MDS technique creates look-up tables which seek similar meteorological conditions (global radiation Rg, air temperature Tair, and vapor pressure deficit VPD) under different window sizes that are physically and temporally similar to the missing data and imputes them using the average values.
Use of RF is robust in the presence of noise and in detecting complex relationships between variables, but its performance depends on the tuning of its hyperparameters, the number of features, and the dataset size. Typically, the more the training data are increased, the greater the model accuracy becomes, reducing overfitting. For time series, a complete sequence of data should be large enough to detect patterns such as trend and seasonality. Given that RF requires high computation performance and that C fluxes have different patterns with respect to time-of-day, we downscaled our data into diel observations per day (daytime/night-time). Daytime was defined by using the R function "solartime::computeIsDayByLocation"  41. Detailed description of the variables for RF training is described in Table  2.
Model training. • The EC time series after MDS gap-filling was 100% complete between 2003 and 2008 for all response variables. Therefore, we used this sequence to generate the training and testing datasets. The time sequence from 2003 to 2007, corresponding to 70% of the data, was used to train the RF models and predict NEE, R eco , and GPP in 2008. The testing dataset (2008), corresponding to 30% of the data, was subsequently used to validate the RF models. • RF models were built using the R functions "recipe", "bake", and "juice" from the "recipe"  36  package. During RF model building, we insert all the aforementioned predictors, as well as the time series signatures using the R function "timetk::step_timeseries_signature"  44. Time series signatures use the "Date" column to generate a set of time-based features (i.
Model validation. • The validation of the models was carried out by predicting the entire year of 2008 and comparing it with the testing dataset. The models with the highest (R 2 ) between the predicted and observed values were chosen for gap-filling of missing values in NEE, GPP, and R eco. • To ensure high predictive capacity and lower uncertainty, each model was run 50 times. The average of the predicted values was used both in validation and in imputation, as well as to calculate the standard deviation (SD) of the coefficient of determination (R 2 ), root mean squared prediction error (RMSE), and mean absolute error (MAE). Sensibility of RF models to gap length and timing. • We evaluated the sensitivity of the RF models to gap length and location by generating testing datasets based on 2008; the complete dataset was altered to generate varying degrees of missing values (4, 14, 28, 41, 55, 69, 82, and 100%) starting from the 1 st day of the year.
Data Records The long-term datasets (2003-2021) are distributed in files (CSV format, UTF-8 comma delimited) separated by temporal aggregation, e. g. , half-hourly (HH suffixes) and daily split (daytime/night-time period, DD-DN suffixes). Each file is accompanied by its respective metadata in XLSX format, containing the full list of variables, the measurement units, and the variable description. The half-hour dataset is a complete dataset generated by the pre-and post-processing in EddyPro and REddyProc, respectively. This dataset contains 258 variables, including the original (_original suffixes) and gapfilled (_f suffixes) values for Rg, VPD, Tair, NEE, R eco , and GPP using the MDS technique. The daily dataset contains 31 variables aggregated from the half-hour dataset (_RF suffixes for gapfilled data and _original suffixes for non-gapfilled data) into daytime and nighttime period.
Uncertainty is higher in the colder months of the year (December-February) and during nighttime, possibly associated with the greater flux magnitude. Changes in NEE related to pre-processing and data filtering (i. e. , missing values allowance, uStar, footprint) were assessed with respect to the choices made in a previous work using a subset of the same EC raw-data  16. The pre-processing of the current dataset generated similar patterns of C flux over time to those generated by the raw-data pre-processing in a previous study  16. However, our outputs were significantly higher at several moments along the EC time series between 2003-2011 (Fig. 7 ). Although raw data from Klumpp et al. (2011) was pre-processed using the EdiRe (no longer available) to estimate C flux, and here pre-processed with EddyPro, a previous work has shown that there is an agreement between both software when the pre-processing steps are similar  38.
Thus, we assume that observed differences between the C fluxes are likely due to the parametrization choices made during pre-and post-data processing (Supplementary Table  1  and Table 1 ). Some steps of data processing may have been critical in this difference. For instance, during the raw-data pre-processing, we applied a planar fit for tilt correction, while Klumpp et al. (2011) used double rotation. Likewise, algorithms used in spectral analyses, dropouts in the registration of raw data in 20 Hz compared to initial 10 Hz, as well as performances in low and high path filtering have been improved since the EdiRe software, providing slightly modified C flux estimations  39. Finally, unlike Klumpp et al. (2011) who applied an annually fixed uStar thresholds (u* ~ 0. 8) to filter the data under low friction velocity, we applied seasonal uStar thresholds that was estimated using nighttime NEE measurements and bootstrap procedure.
Indeed, we found that sliding thresholds minimized the risk of excluding realistic and high-quality data which could lead to C-flux underestimation. of CO 2 by the analyzer can be reduced under low friction velocity, resulting in underestimated fluxes. Likewise, by influencing the performance of autotrophic organisms, mainly of plants, meteorological variables can alter the balance between respiration and photosynthesis, mainly under high climatic amplitude. On the other hand, the effect of LE and H on C fluxes seems to be mediated by their effect on water fluxes (evapotranspiration) and consequently stomatal closure of the plants. This physiological change can also alter the balance between respiration and photosynthesis in the ecosystem  45. Validation of the RF models using alternative training and testing datasets (either "training: 2004-2008, testing: 2003" or "training: 2003-2007, testing: 2008") indicated that the two models resulted in very similar C flux output (Fig.
The results of each model are presented in the Supplementary Table  2 ". All models presented low uncertainty and the gap-filled values of C fluxes were obtained by averaging their outputs. Finally, visual screening was used to check whether the RF models were able to detect and reproduce the temporal component of the C fluxes (NEE, daytime GPP, and nighttime R eco ) across the long-term time series. The imputed databases presented similar seasonality along the years, that is, with the highest C sequestration and respiration in the summer and spring (Fig. 11 ).
Usage Notes Our datasets have been produced using best-practice processing and quality check procedures as recommended in the literature  2, 8. The dataset 3 can be used stand-alone to address climate-flux relationships at both fine-scale (half-hour) and coarser (daily) temporal resolutions for this model ecosystem; it is of particular value for improved understanding of the mechanisms underlying variation in grassland production and C sequestration, as well as exploring the proximal and distal climatic drivers of single anomalous events  46. The data can also be used to explore as part of a larger database to answer broader questions related to interactive effects of management and climate on grassland functions across pedoclimatic gradients, analyses of trade-offs and/or synergies between a wider range of ecosystem services and energy fluxes in the food-web  42  , or cross-ecosystem comparisons.
Further, the RF pipeline for gap-filling described here can be transposed to other flux datasets, independent of temporal resolution, and used to facilitate the compilation of older datasets. The half-hour dataset presents important variables, i. e. , time stamp (YYYYMMDDHHMM), quality flags, and statistical analysis (hard flags), which will be useful for final users in filtering and aggregating the dataset according to their objectives. We also present the original NEE, R eco , and GPP ("_original") values, as well as those ones gapfilled using the different uStar thresholds ("_U05", "_U50" and "_U95"). More detailed information about the use of EC data at different temporal resolutions can be found in numerous scientific publications, as well as on FLUXNET website (https://fluxnet. org).
Missing values in half-hour dataset are indicated with NA, and column name descriptions are provided in the associated metadata file. This long-term EC time series fills an important information gap for grassland systems. It is of particular value for improved understanding of the mechanisms underlying variation in grassland production and C sequestration, as well as exploring the proximal and distal climatic drivers of single anomalous events  46. Finally, we emphasise that the use of long-term C-flux measurements helps to understand possible adaptation of grassland ecosystems to future climate changes. By using different statistical models, such as path analysis  47  , that explore the causal relationship among the variables, and machine learning algorithms  12, 48  to forecast C-fluxes for future periods, we can contribute to the development of management strategies to meet high-C sequestration and climate mitigation goals.
Post-processing steps Klumpp et al. (2011) This dataset a Remove values beyond the physical boundaries -50 to 50 for CO 2 , -250 to 1000 for LE, -250 to 1100 for H FREddyPro::cleanVar ‡ (-50 to 50 for CO 2 , -250 to 1000 for LE, -250 to 1100 for H) Despiking Removed at pre-processing (see Supplementary Table 1) Removed all values flagged as 1 (not passed) during statistical test performed during the pre-processing in EddyPro (Mauder and Foken, 2004) Remove Spectra and co-spectra Removed all values flagged as 2 FREddyPro::qcClean (removed all values flagged as 2) Remove Values based on standard deviation of means See Supplementary Table 1 for detailed conditions FREddyPro::sdClean (removed all values higher than 3 standard deviation) Remove values based on outliers See Supplementary Table 1 for detailed conditions FREddyPro::removeOutliers (removed all values below the 25th percentile or above the 75th percentile) Remove values lower than a given u* threshold Fix u* = 0.
This result suggests that individuals can deal with different numbers of rDNA units to face contrasting environmental conditions and that copy number variation can also distinguish individuals living in the same environmental conditions. The p-value is lower than 5%, so we can reject the null hypothesis of the normality of the dataset. (C) 18S copy number variation in At66. Each bar of the bar plot represents one individual of At66, and the color code indicates the PO site of origin. Individuals are ordered along the altitudinal gradient from sea level to altitude, and the six localities are labeled below the x axis. The horizontal red dashed line indicates the number of copies already known for Col-0 (~800).
Genomic DNA was randomly fragmented to a size of 350 bp by shearing. DNA fragments were end-polished, A-tailed, ligated with NEBNext adapter for Illumina sequencing and further PCR-enriched by P5 and indexed P7 oligos. The PCR products were purified with AMPure XP system and the resulting libraries were analyzed for size distribution by an Agilent 2100 Bioanalyzer and quantified using real-time PCR. Qualified libraries were fed into unspecified Illumina sequencers. The total output of raw data received on 29 September 2019 for 173 individuals of At66 was 585. 0 GB (99. 65% clean reads), with an average error rate of 0. 03%, a QPHRED20 of 97% and 37% GC content. Raw reads were submitted to the European Nucleotide Archive (ENA) with the BioProject accession number PRJEB48282 and the SRA study accession number ERP132629.
FASTAQ raw reads were trimmed to remove adapters and poor-quality sequences using Trimmomatic V0. 36 paired-end mode and the following trimming steps: ILLUMINA-CLIP, LEADING, TRAILING AND MINLEN  [60]. Duplicated reads were removed using a homemade script (available upon request) in order to improve the yield of recovered sequences compared to SAMtools V0. 1. 9 RMDUP. Cleaned reads were aligned both to the TAIR-10 Col-0 reference genome and a 45S rDNA reference using Bowtie2 V0. 2. 3. 4  [61]. SAMtools V0. 1. 9 SORT was used to map the reads by position on the chromosomes  [62, 63]. The 45S rDNA reference includes the most abundant 5 and 3 ETS variants in Col-0 (VARA and VAR1), and the annotation of 18S and 25S rRNA genes coordinates, respectively, are 1841-3644 and 4264-7639 (Figure  S8 ).
We retrieved per-base read depth for each individual using BEDtools coverage  [64]  for both 18S and 25S rRNA genes and for the first 10 MB of chromosome 3 that was free of 45S rDNA copies. Copy number variation was estimated by dividing the average coverage along both 18S and 25S rRNA genes by the average coverage along the first 10Mb of chromosome 3  [11]. Using recently developed bioinformatic scripts  [11] , we retrieved the sum of frequencies of all alternative alleles for each position along our ~10. 5 kb 45S rDNA reference for each of 173 among 320 individuals of At66. Then, we plotted the proportion of individuals of At66 with an alternative allele for each position along this rDNA. The alternative variant must be present in at least 5% of the individual's total 45S rRNA genes (>0.