dataesr/scientific-paragraphs-categorization huggingface dataset:
The satellite top-of-atmosphere (TOA) reflectances are corrected for the absorption by the atmospheric gas molecules using the absorption part of the Simplified Model for Atmospheric Correction (SMAC) method by  Rahman et al. (1994). The concentrations of the ozone, the oxygen and the water vapor are obtained from satellite data (ozone) and meteorological data (water vapor, pressure). 2. The detection of the clouds (and clouds' shadows) is based on the multi-temporal cloud detection method proposed by  Hagolle et al. (2010). 3. The estimation of the aerosol optical thickness (AOT) relies on a hybrid method merging the criteria of a multi-spectral method with the multi-temporal technique developed initially for the VENµS satellite mission by  Hagolle et al. (2010). The AOT is used along with the surface altitude, the viewing geometry and the wavelength in the parameterization of look-up tables for the conversion of TOA reflectances already corrected in step 1 into surface reflectances.
Relationship between SAR data and vegetation variables The polarization ratio and the interferometric coherence have been shown to be related to vegetation growth. In this section, the relationships between PR; ρ VV and ρ VH ; and vegetation variables, including AGB, VWC, H , the GLAI and the NDVI, are analyzed. Figure  11  displays the results at a 35. 2 • incidence angle, and Fig. A9  in Appendix A displays the results at 45. 6 •. H is used to illustrate the vegetation growth because its evolution is monotonic so that data corresponding to before and after maximum development can be easily separated. The determination coefficient R 2 and the Spearman rank correlation R s are superimposed on the subplots together with the fitting equations using the whole database. Overall, a good correlation has been found between SAR variables (PR, ρ VV and ρ VH ) and AGB, VWC, the GLAI and H.
Relationship between backscattering coefficient and SSM Figure  12  displays the relationships between σ 0 and SSM using the entire database at 45. 6 and 35. 2 • incidence angles. H is used as an indicator of vegetation growth. The correlation coefficient is computed separately for the entire database and for data corresponding to H lower than a threshold value (H tr ) corresponding to GLAI < 1. 5. This value of the GLAI corresponds to wheat not fully covering the soil  (Ouaadi et al. , 2020b). H tr is about 23. 5, 23. 5, 32. 9 and 26 cm for F1 and F2 during 2017/18, for F2 during 2016/17 and for F3. Overall, σ 0 VV is obviously better correlated to SSM than σ 0 VH , in line with the results of numerous studies  (Holah et al. , 2005; Li et al. , 2014; Ulaby and Batlivala, 1976). Likewise, metrics at 35.
Data availability This database is archived in DataSuds repository of the French National Research Institute for Sustainable Development (IRD). The database is accessible free of charge with a CC BY license at https://doi. org/10. 23708/8D6WQC (Ouaadi et al. , 2020a). It can be downloaded as xlsx files accompanied by a variable dictionary containing the variable names and units. The files are also accompanied by metadata including a description of the database, time coverage, keywords and other general information.
Conclusion This paper presents a 3-year database of C-band radar data and all necessary ancillary ground measurements to improve our understanding of the radar signal and to develop inversion methods for land surface parameter retrieval. The data are collected from three heavily monitored wheat fields under semi-arid conditions in the center of Morocco. The database offers a complete set of data for radar applications for wheat monitoring. The measured parameters include fresh and dry aboveground biomass, canopy height, the leaf area index, the cover fraction, surface soil moisture, root zone soil moisture, and surface roughness, in addition to the normalized difference vegetation index and SAR data (the backscattering coefficient and the interferometric coherence). The irrigation and meteorological data are also provided.
This database opens up the opportunity to use remote sensing together with measured parameters to understand and investigate the behavior of wheat crops and subsequently to retrieve soil moisture and vegetation variables. The database analysis presented in this paper demonstrates the potentialities of SAR data for wheat monitoring by addressing the well-known sensitivity of SAR to surface soil moisture and vegetation variables. The obtained relationships between SAR measurements including the backscattering coefficient, polarization ratio and interfer- ometric coherence can be used for the application of several backscattering models, the retrieval of biophysical variables and yield prediction in crop models. They can also be useful for land surface models relying on accurate estimation of vegetation height such as the energy balance models (i.
Acknowledgements. The database is collected within the framework of the  Joint International Laboratory TREMA  (https://www. lmi-trema. ma/,last access:  19 July 2021 ). Omar Rafi , the owner of the private farm in which the three fields are located, is acknowledged. We would like to thank the following projects:  Rise-H2020-ACCWA  (grant agreement no. 823965 ),  ERANETMED03-62 CHAAMS, PHC TBK/18/61  and the  MISTRALS-SICMED2 program. We also thank the Moroccan  CNRST  for awarding a PhD scholarship to  Nadia Ouaadi. Finally ,  ESA  and Theia are acknowledged for providing free products of Sentinel-1 and Sentinel-2 (corrected for atmospheric effects), respectively. Financial support. This research has been supported by the  Joint International Laboratory TREMA, TENSIFT Observatory, CNRST SAGESSE, Rise-H2020-ACCWA  (grant no.
Finally, cells were fixed with a 4% paraformaldehyde solution and stored in the dark at 4°C until acquisition. All phenotyping data were acquired on a BD FACS LSR II (BD Biosciences) or on a LSR Fortessa (BD Immunocytometry Systems). The data were further analyzed using FlowJo 10. 4. 2 software (FlowJo, LLC, Ashland, OR, USA). T-SNE was performed using FlowJo isuali FlowJo, LLC, Ashland, OR, USA). The antibodies used are listed below: anti-CD45 (clone D058-1283, BD bioscience); anti-CD3 (clone SP34-2, BD bioscience); anti-CD4 (clone L200, BD bioscience); anti-CD32 (cloneFLI8. 26)(BD bioscience); anti-CD20 (clone 2H7, (BD bioscience); anti-CD14 (clone TÜK4,M iltenyi Biotec); anti-CD28 (clone CD28. 2, BD bioscience); anti-CD95 (clone DX2, BD bioscience), anti-CXCR3 (clone 1C6/CXCR3, BD bioscience); anti-CXCR5 (clone MU5UBEE, Ebioscience); anti-PD-1 (clone EH12.
Bioinformatic Analysis of the Genome-Wide Sequence Data Bioinformatic analyses were performed using the RNA-seq pipeline from Sequana  (44). Reads were cleaned of adapter sequences, and low-quality sequences were removed using cutadapt version 1. 11. Only sequences ≥ 25 nucleotides (nt) in length were considered for further analysis. STAR version 2. 5. 0a, with default parameters, was used for alignment on the reference genome (Chlorocebus sabaeus, from Ensembl release 90). Genes were counted using featureCounts version 1. 4. 6-p3 (45) from Subreads package (parameters: -t gene, -g ID and -s 1). Data were analyzed using R version 3. 4. 3 and the Bioconductor package DESeq2 version 1. 18. 1. Normalization and dispersion estimation were performed with DESeq2, using the default parameters, and statistical tests for differential expression were performed by applying the independent filtering algorithm.
A generalized linear model, including the monkey identifier as a blocking factor, was used to test for the differential expression between the biological conditions. For each pairwise comparison, raw p values were adjusted for multiple testing according to the Benjamini and Hochberg (BH) procedure  (46). Genes with an adjusted p value <0. 05 were considered differentially expressed. Analyses and visualization of GO terms associated with differentially expressed genes were performed using ClueGO (47). Both groups of genes (up-and downregulated, p value < 0. 05) were used as dual input for GO and pathway annotation networks of the expressed genes and proteins pathway enrichment analysis. Each list was used to query the Kyoto Encyclopedia of Genes and Genomes (KEGG), GO-biological function database and Wiki pathways.
Statistical Analyses Continuous variables were compared between groups throughout using non-parametric tests. Where three groups were compared, a Kruskal-Wallis test was used; pairwise comparisons were performed on all combinations of groups only if the overall test p-value was <0. 05. To compare differences between two independent groups, the Mann-Whitney U test was used. Correlative analyses were performed using Spearman's rank correlation. Correlation analyses were performed according to the Spearman coefficient of correlation. Analyses were performed using GraphPad Prism (GraphPad Software, La Jolla, CA, USA) version 7. 0 or R version 3. 2. 2.
A dendrogram, in which the pattern of branch length reflects the comparative difference in gene expression profiles between samples is shown. A p-value adjustment was performed to account for multiple comparisons and control the false positive rate to a chosen level. Transcriptome similarity between clusters of spleen sample was evaluated by the Euclidean distance and visualized via heatmap. Each row represents a variable gene among clusters, and each column represents one subset per monkey. Deep sequencing results were deposited in the Gene Expression Omnibus database; the accession number is GSE169736.
FUNDING For sequencing and analysis support, we thank the Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Geńomique (ANR-10-INBS-09-09) and IBISA. NH was supported by the Fondation Jacquelin Beytout and Institut Pasteur. PR was recipient of a PhD fellowship from the University Paris Diderot, Sorbonne Paris Citéand also supported by the NIH (R01AI143457). CP was the recipient of a PhD fellowship from the ANRS. HM received core grants from the Institut Pasteur, the INSERM and the Milieu Inteŕieur Program (ANR-10-LABX-69-01). We would like to acknowledge funding support from ANRS, MSDAvenir and Institut Pasteur to MM-T and AS-C. We gratefully acknowledge the support to IDMIT from the French government: Investments for the Future program for infrastructures (PIA) through the ANR-11-INBS-0008 grant as well as from the PIA grant ANR-10-EQPX-02-01 to the FlowCyTech facility at IDMIT.
g. , Sulfadimethoxine)  [16]. The 3-dimensional structure of some aptamer-antibiotic complexes by crystallographic analysis is available on a public database named Protein Data Bank (https://www. rcsb. org/ (25 March 2021)). Some aptamers were specific for one antimicrobial (i. e. , Chloramphenicol (CAP)) while others were selective for the detection of several antimicrobials of the same family. For instance, an aptamer anti-penicillin could also bind to other beta-lactams such as ampicillin and amoxicillin. Kwon et al. developed a colorimetric aptasensor for the detection of the tetracyclines' family  [17]. The same aptamer was able to detect tetracycline, oxytetracycline, doxycycline and chlortetracycline. This characteristic was of utmost importance to develop selective aptasensors, with a wide spectrum of detection.
To confirm high-throughput screening compatibility, we treated Mpro-sensing cells seeded in 96-well and 384-well plates with varying concentrations of GC-376 and obtained similar nanoLuc induction levels in both culture formats (Supplementary Fig. 9 ). Furthermore, we calculated the Z′ factor as a measure of assay robustness  40. We treated Mpro-TAGS, PLpro-TAGS, or FlipGFP-PLpro transgenic cells growing in 384well plates either with DMSO (negative control) or 10 µM of the corresponding inhibitor GC-376/GRL-0617 (positive controls). The calculated Z′ factors for Mpro-TAGS and PLpro-TAGS (0. 6   7. Source data for this figure is provided in a Source Data file. and 0. 66) confirmed the robustness of both assays in a highthroughput setting and, in addition, showed that the FlipGFP-PLpro assay was not robust (Supplementary Fig.
Fluorescence imaging and fluorescence intensity measurement. Single-timepoint fluorescence microscopy was performed with a fluorescence microscope (Nikon Eclipse Ti) equipped with a fiber illuminator (Nikon Intensilight C-HGFI), optimized optical filter sets (Semrock), and a digital camera system (Hamamatsu, ORCA Flash 4). The filter set contains a combination of an excitation bandpass filter, emission bandpass filter, and dichroic filter. We measured YPet with 200 ms exposure time and a GFP filter set: HC 470/40, HC 520/35, and BS 495. Images were processed and analyzed using ImageJ software. GFP and mCherry fluorescence intensity of transfected cells was measured in a black 96-well plate (655090, Greiner Bio-One) using a Tecan Infinite M1000 multiplate reader (Tecan AG). Reporting Summary.
Acknowledgements This work was supported by the  Swiss National Science Foundation National Center of Competence in Research  ( NCCR ) for Molecular Systems Engineering. The authors would like to thank the  FAST Lab team  for external collaboration from  Novartis AG ,  Ulrich Hassiepen ,  Laurent Tenaillon  and  Sandra Lopez Romero  for generous advice on cell-based assays and  Haijie Zhao  for helping with cell encapsulation.
Data availability All the data is available in the main text or in the supplementary information materials. Sequence data of plasmids encoding PLpro-TAGS and Mpro-TAGS have been deposited in GenBank under accession codes OK425851, OK425852, and OK425853. Original plasmids are available upon request. All vector information is provided in Supplementary Table  4. Detailed statistical analysis is provided in Supplementary Table  7. Source data is provided in the Source data file. Source data are provided with this paper.
Modifications of tRNAs act as checkpoints for tRNA integrity, regulate protein translation, modulate the structural stability and ensure the correct folding of the linear tRNA molecule into its three-dimensional shape. They are also involved in the structural fine-tuning of local elements and instigate a rapid adaptation of tRNAs to environmental changes, such as stress and temperature  [6, 7]. In addition, tRNA modification can contribute to regulation of tissue and cell development by influencing the translation efficiency  [8, 9]. Nucleotide modifications may affect reverse transcription (RT), a crucial step during the preparation of tRNAs for high-throughput sequencing, and thus become visible in RNAseq data sets in different ways. While several modifications do not cause any changes in the cDNA sequencing pattern, others result in a position-specific increase of sequencing errors.
Yet, it produces RT signals at certain nucleotide modifications  [19, 49, 50]. This allows for an easier classification of tRNA modification patterns from analysis of RT-based RNA-seq data, especially methylations  [49]. Table  S1  summarizes our improved collection of common tRNA modifications visible in untreated RNA-seq data and describes how these modifications are detectable. Since some RT signatures fit to several modification types, an unambiguous assignment without prior knowledge is challenging. Therefore, we incorporated information compiled in the tRNAmodviz database  [3]  to classify specific modification patterns by incorporating the knowledge about chemical modifications of homologous tRNA positions in other species. Yet, analysis of the RT signatures alone cannot exclude the possibility of misclassifications or false positive hits resulting from, e.
For example, G10 and G26 modifications could probably be m 2 G or m 2 2 G modifications. It is also indistinguishable whether modified adenosines (A37) refer to N 6 -threonylcarbamoyladenosine (t 6 A), N 6 -isopentenyladenosine (i 6 A), 2-methylthio-N 6isopentenyladenosine (ms 2 i 6 A), or N 6 -methyl-N 6 -threonylcarbamoyladenosine (m 6 t 6 A). In addition, we profiled modifications at positions G7, G20, A21, C24, G24, G42, G45, and A64, where no modifications are known in other species of the tRNAmodviz database. Depending on the RT signature, different types of tRNA modifications may be applicable, as listed in Table  S1. We also processed our data with HAMR (Table  S3 ), which accounts for nucleotide misincorporation but does not evaluate RTa site enrichment. Considering the time point 0h after starvation as example, the mismatch-based HAMR methods recovers 22% of the modified tRNA positions detected by our method applying a stringent parameter setting.
In particular, it can be decided quickly whether modification patterns are consistent with knowledge from related species, or whether there are unusual modified or unmodified positions that may warrant an in-depth characterization. A major advantage is that our methodology can also be applied to fortuitously available datasets that were generated with other biological questions in mind. Recent work in human cells suggested that differences in tRNA expression are not primarily reflected in the abundance of mature tRNAs, but rather lead to changes in the abundance of immature tRNAs and tRNA fragments  [67]. A combination of LOTTE-seq and small-RNA-seq could quickly clarify whether this is true in general. Finally, it remains an interesting question for future research whether the enzymes responsible for chemical modifications in tRNAs are actively involved in regulating the composition of the pool of mature tRNAs.
tRNA Read Mapping Demultiplexing of the sequenced reads were performed using bcf2fastq (v. 2. 2, https: //support. illumina. com/downloads/bcl2fastq-conversion-software-v2-20. html, accessed on 9 March 2019), allowing 0 mismatches in the barcode sequences. Adapters of raw reads were trimmed applying Cutadapt (v1. 16)  [68]. Only reads which surpass a quality cutoff of 25, a maximum allowed error rate of 0. 15 and a read length from 8 to 95 nts were retained. Standard pre-and post-trimming quality control was computed using FASTQC (v0. 11. 4)  [69]. Annotation of pre-and mature tRNAs as well as the mapping of the high-throughput data were prepared on the basis of the best practice workflow for accurate mapping of tRNA reads, as previously described in  [25]. In brief, tRNAs were annotated with tRNAscan-SE  [70]  and masked in the reference genome.
The detected modification sites were classified according to our collection of modification-specific TGIRT enzyme signals for the relevant modifications in D. discoideum, see Table  S1. In addition, we compared the putative modifications to the modification information for all species provided by the tRNAmodviz database  [3] , since specific tRNA modifications for D. discoideum are not available. We also applied HAMR  [15]  as an alternative approach for the identification of tRNA modifications with the following recommended parameter settings: minimum base calling quality score: 25; minimum read coverage of a genomic position: 10; expected percentage of mismatches based solely on sequencing error: 0. 05; hypothesis to be tested: H4; maximum pvalue cutoff: 0. 01; maximum FDR cutoff: 0. 05; minimum fraction of reads that do not differ from the reference sequence at a potentially modified position: 0.
Differences regarding the number of modified tRNAs. The following abbreviations are used in this manuscript: cAMP cyclic adenosine monophosphate Cm 2'-O-methylcytidine D dihydrouridine ESA enhanced suffix array Gm 2'-O-methylguanosine HAMR high-throughput annotation of modified ribonucleotides I inosine i 6 A N 6 -isopentenyladenosine LOTTE-seq long hairpin oligonucleotide based tRNA high-throughput sequencing miRNA microRNA mt-tRNA mitochondrial tRNA ms 2 i 6 A 2-methylthio-N 6 -isopentenyladenosine m 1 A 1-methyladenosine m 1 G 1-methylguanosine m 2 G N 2 -methylguanosine m 2 2 G N 2 , N 2 -dimethylguanosine m 3 C 3-methylcytidine m 5 C 5-methylcytidine m 7 G 7-methylguanosine m 6 t 6 A N 6 -methyl-N 6 -threonylcarbamoyladenosine NCBI Database resources of the National Center for Biotechnology Information PCA polymerase chain reaction pre-tRNAs precursor tRNAs Q queuine rmRNA ribo-minus RNA-seq RNA-seq RNA sequencing RT reverse transcription RTa reverse transcription arrest t 6 A N 6 -threonylcarbamoyladenosine snoRNA small nucleolar RNA tRNA transfer RNA Ψ pseudouridine.
Instrumentation Two intensive sampling campaigns were carried out in July 2019 (1-31 July) and in January-February 2020 (16 January-10 February) at NAOK. During the campaigns, several physical and chemical atmospheric aerosol properties were measured together with complete meteorological data collected from a professional meteorological station  (WMO station 11628). The size-resolved NR-PM 1 chemical composition (the sum of organics, sulfate, nitrate, ammonium, and chloride) was measured by a compact time-of-flight aerosol mass spectrometer (C-ToF-AMS, Aerodyne, USA,  Drewnick et al. , 2005)  with a 5 min temporal resolution. The instrument was connected to an inlet consisting of a PM 2. 5 sampling head (16. 7 L min -1 ) and a Nafion dryer (Perma Pure MD-110-24P-4). Isokinetic subsampling was used to split the flow into the AMS (0.
Data analysis The standard data processing procedure of AMS data (i. e. m/z calibration, baseline subtraction, and air beam correction) was carried out by running the Squirrel v1. 62 programme in Igor Pro data analysis software (WaveMetrics, Inc. ). The statistical data treatment was performed using R version 3. 6. 1 (R Core Team, 2019) with the ggplot2  (Wickham, 2016)  and Openair  (Carslaw and Ropkins, 2012)  packages.
Trajectory analysis For both campaigns, 96 h backwards trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model  (Rolph et al. , 2017)  with a 500 m a. g. l. (above ground level) starting position and Global Data Assimilation System (GDAS) Archive Information at a resolution of 1 • × 1 • as input data. The calculations were initialized every 6 h for the cluster analysis. For the episodes of high mass concentrations (Sect. 2. 2. 4), the trajectory ensemble option with calculation initialized every hour and a total duration of 72 h was utilized. The trajectories were further clustered using Hysplit4 software based on the total spatial variance. From HYSPLIT, the planetary boundary layer height data were extracted using the vmixing programme (https: //www.
6 ). In both seasons, the correlation of the linear fit between Org size and f 44 was significant (p value < 0. 001). However, the data presented here do not allow us to extend this size range due to both instrumental (C-ToF-AMS particle size range is approximately from 50-800 nm) and data characterization reasons, as we did not observe a major mode of organics at sizes below 200 nm. In general, however, Fig. 6  suggests that the larger the particles with the organic contribution, the more oxidized they are due to its longer residence time in the atmosphere. The milder slope of the line for the summer dataset (Fig. 6 ) indicates that oxidation is still occurring on the particles but appears to be approaching an oxidation limit with growing particle size. In the case of winter, the steeper slope of the line and lower f 44 values for smaller particles suggest that the change of oxidation state with particle size is relatively more intense than in summer (Fig.
Acknowledgements. We would like to thank  Daniel Vondrák  for the graphical editing. We also thank  American Journal Experts  and  Laurence Windell  for the English proofreading of the manuscript. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www. ready. noaa. gov,last access:  10 June 2020 ) used in this publication. We greatly thank the two anonymous reviewers for their effort to critically review the manuscript and for providing constructive comments. Financial support. This work was supported by the  GACR  under grant  P209/19/06110Y  and by the  MEYS of the Czech Republic  under grant  ACTRIS-CZ LM2018122  and  ACTRIS-CZ RI (CZ. 02. 1. 01/0. 0/0. 0/16_013/0001315 ) as well as by  COST Action   CA16109  COLOSSAL within STSM.