dataesr/scientific-paragraphs-categorization huggingface dataset:
Wavelength conversion from 675 and 532 nm has been conducted using Angström exponents  [47] , similarly to Bègue et al. (2017)  [24]. A sharp increase in the stratospheric aerosol loading was observed above la Reunion a few weeks after the Calbuco eruption, with SAOD values increasing by a factor of 2-3 in the observed records with respect to the beginning of 2014 and by a factor of ~4 in comparison with the end of 2016. Overall good agreement is observed between the various datasets in the occurrence of the maximum Calbuco signal above la Reunion and decay trend. In late 2016, the aerosol content seemed back to "background" conditions which is similarly captured by the various datasets. The significant short term (i. e. , daily) variability in the lidar observations and in the model outputs reflects a transient behaviour of the aerosol layers above la Reunion Island and inhomogeneous spatio-temporal distribution of the Calbuco plume controlled by the fluctuations of the subtropical dynamical barrier  [24].
This may partly explain the differences between model and in situ observations in Figure  7a. Some periods show some discrepancies in terms of SAOD absolute values with the model tending to overestimate SAOD with respect to observations around day 300, and between 400 and 700 days after the eruption. At the period of the first LOAC OPC observations (around day 414), the simulations and the other datasets show very variable SAOD reflecting the presence of transient layers (with possible filamentary structures) highlighted by  Bègue et al. (2017)   [24]. During the period before the Calbuco pulse, the aerosol content showed more variability and higher levels than in late 2016 possibly due to some remaining signature of the Kelud aerosols estimated to be of ~3. 10-3 from OMPS zonally averaged anomalies (Figures 2 and 3) for days 0-200, i.
). Comparisons of the model outputs with in situ observations show contrasting results and illustrate issues with the horizontal and vertical resolutions differing between the datasets. Good agreement is obtained with the aerosol content COBALD backscatter sondes (extinction) and the LPC (concentrations) in the layers impacted by the Kelud plume. For the Calbuco aerosols, strong differences are shown between the WACCM simulations and LOAC OPC measurements at an early stage of the plume propagation (~4 weeks after the initial injection) whereas good agreement is observed at the period when the plume has spread throughout the SH (~4 months after). This result is also reflected in the comparisons with lidar observations from la Reunion Island and illustrates the difficulty of the model to simulate transient aerosol structures with a more local scale.
B. , T. P. , S. G. -B. , G. P. , V. D. , N. M. ; balloon operations in la Réunion Island during the MORGANE campaign, J. -M. M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the VOLTAIRE project (ANR-10-LABX-100-01) funded by ANR through the PIA (Programme d'Investissement d'Avenir), in particular through the IANU sub-project, is gratefully acknowledged. The authors are grateful for the support of Agence Nationale de La Recherche under grant ANR-17-CE01-295 0015 (TTL-Xing). The KLASH campaign was funded by the NASA Rapid Response and Novel Research in Earth Science. The authors acknowledge the European Communities, the Région Réunion, CNRS and Université de la Réunion for their support and contributions in the construction phase of the research infrastructure OPAR (Observatoire de Physique de l'Atmosphère de La Réunion) and the Bureau of Meteorology.
Acknowledgments: The authors wish to thank the  CaSciModOT structure (Calcul Scientifique et Modélisation Orléans-Tours ), part of the  French national network of complex systems (RNSC-Réseau National des Systèmes Complexes ), thanks to which the simulations could be completed. KLASH was a collaborative effort between  NASA LaRC , the  University of Wyoming  and  ETH. Mariam Tidiga  would also like to thank the  NCAR/CESM  online discussion board for many helpful technical discussions that helped throughout this study.
Data Availability Statement: The aerosol extinction dataset from OMPS v2 are available at https://daac. gsfc. nasa. gov/ (accessed on 27 November 2021). IASI Level 1c data are provided by the AERIS/ICARE data center (https://en. aeris-data. fr/direct-access-icare/ (accessed on 27 November 2021)) and may be requested from Lieven Clarisse for updated versions. LOAC in situ aerosol data are available in open access at https://zenodo. org/record/5918581#. YfV2vudCdEY (accessed on 27 November 2021). LPC in situ aerosol data are available in open access at ftp://cat. uwyo. edu/pub/permanent/balloon/Aerosol_InSitu_Meas/AU_Darwin_13S_132E/ (accessed on 27 November 2021). Lidar data have to be requested from Nelson Bègue. The model and simulation outputs may be requested from the corresponding author.
G. W. ; processing and investigation of lidar data, data curation, N. B. , T. P. , S. G. -B. , G. P. , V. D. , N. M. ; balloon operations in la Réunion Island during the MORGANE campaign, J. -M. M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the VOLTAIRE project (ANR-10-LABX-100-01) funded by ANR through the PIA (Programme d'Investissement d'Avenir), in particular through the IANU sub-project, is gratefully acknowledged. The authors are grateful for the support of Agence Nationale de La Recherche under grant ANR-17-CE01-295 0015 (TTL-Xing). The KLASH campaign was funded by the NASA Rapid Response and Novel Research in Earth Science. The authors acknowledge the European Communities, the Région Réunion, CNRS and Université de la Réunion for their support and contributions in the construction phase of the research infrastructure OPAR (Observatoire de Physique de l'Atmosphère de La Réunion) and the Bureau of Meteorology.
1 nuclear genome (5 chromosomes), chloroplast  (Pltd)   (Chesnais et al. , 2021) ] and TuYV [NC_003743,  (Veidt et al. , 1988) ]. Note that the CaMV reference sequence was extended at the 3'-end by 74 nts from the 5'-terminus to account for its circular genome and allow for mapping reads containing the first and last nucleotides of the linear sequence. In the case of TuYV, some discrepancies with the reference sequence were detected, when the reads were mapped to the viral reference sequence. Therefore, the reads were used to generate a new consensus master genome in the viral quasispecies population. For both viruses, the consensus genome sequences (Supplementary Sequence information S1) were used for (re-)mapping and counting total viral reads as well as viral reads representing forward and reverse strands of the viral genomes (Supplementary Dataset S1).
Raw data processing and quality control for transcriptome profiling Processing was carried out on the Galaxy France platform (https://usegalaxy. fr/) (Afgan et al. , 2016). Raw reads quality were checked with  FastQC (v0. 11. 8 ) and the results were then aggregated with MultiQC (v1. 9). For Arabidopsis, between 58. 6 and 69. 4 million 75 nt paired-end reads were sequenced with a mean phred score >30 for all bases. For Camelina, between 56. 4 and 77. 6 million 75 nt paired-end reads were sequenced with a mean phred score >30 for all bases. In all samples, there were no overrepresented sequences and really few adapter (0. 15% of adapter for the last bases of reads). Reads are aligned on the reference genome with STAR (v2. 7. 6a) using default parameters and quality again checked with MultiQC.
mock-inoculated). GO enrichment analysis was performed with GOseq (v1. 36. 0) on the DEGs. To measure viral RNA loads in plants, the RNA-seq reads from each sample were mapped to the reference genome sequences of the host plant (Arabidopsis or Camelina) and the virus (CaMV or TuYV) with zero mismatches, and the mapped reads were sorted by polarity (forward, reverse and total) and counted. Viral read counts were then normalized per million of total (viral + non-viral) or plant reads (see Supplementary Dataset S1).
We used the PROBE 3. 5 software (EPG systems, www. epgsystems. eu) to acquire and analyze the EPG waveforms as described  (Tjallingii and Hogen Esch, 1993). Relevant EPG variables were calculated with EPG-Calc 6. 1 software  (Giordanengo, 2014). We chose variables based on five different EPG waveforms corresponding to: "probing duration", "stylet pathway phase", "phloem sap ingestion", "time to first phloem sap ingestion" and "salivation in phloem sap". For each aphid x plant x virus combination, we collected 8-hour recordings from 20 to 23 individuals.
Aphid fecundity To investigate the effects of TuYV and CaMV plant infections on the fecundity of M. persicae, we randomly selected synchronized wingless adults (8±1 day-old) and transferred them onto experimental host plants. For Arabidopsis experiments, we used one plant per aphid, and we covered the pots with vented bread bags. For Camelina experiments, to force aphids to settle on symptomatic leaves, we placed adults on detached leaves that were laid on 1 % agarose in a Petri dish. The number of nymphs produced per adult was recorded after 5 days. We discarded from the analysis the adult aphids that died before the end of the experiment. Data on both Arabidopsis and Camelina host-plants were collected in three repetitions, comprising altogether 27-33 aphids per aphid x plant x virus combination.
Statistical analyses of aphid behavior and fecundity Data on aphid feeding behavior were analyzed using generalized linear models (GLMs) with a likelihood ratio and the chi-square (χ²) test. Since duration parameters (i. e. probing duration, stylet pathway phase, phloem sap ingestion and salivation) were not normally distributed, we carried out GLMs using a gamma (link = "inverse") distribution. For the "time to first phloem phase", we used the cox proportional hazards model and we treated cases where the given event did not occur as censored. The assumption of validity of proportional hazards was checked using the functions "coxph" and "cox. zph", respectively (R package "survival"). For aphid fecundity, count data were not normally distributed. Accordingly, we carried out a GLM using a Poisson distribution, a quasilikelihood function was used to correct for overdispersion, and Log was specified as the link function in the model.
When a significant effect of one of the main factors was detected or when an interaction between factors was significant, a pairwise comparison using estimated marginal means (R package "emmeans") (p value adjustment with Tukey method) at the 0. 05 significance level was used to test for differences between treatments. The fit of all GLMs was controlled by inspecting residuals and QQ plots. All statistical analyses were performed using R software v. 4. 0. 4 (www. r-project. org/).
Quality of RNA and sequence alignment data Roughly 29-35 million reads were obtained for Arabidopsis mRNA-seq datasets, of which >80 % could be aligned for mock-inoculated and TuYV-infected samples and 80 % for CaMV-infected samples (Supplementary Table  S2 ). For Camelina, 28-38million reads were obtained and 61-71 % of the reads could be aligned (Supplementary Table  S2 ). Principal component analysis of RNA-seq libraries (Figure  4a, b ) indicated for both plant species that the three biological replicates per condition clustered well together and that the different conditions (mock-inoculated or infected with either virus) were well separated. Thus, the reads were of excellent quality and suited for a transcriptome analysis. For 8 selected Arabidopsis genes, the trends of gene deregulations detected in the transcriptome data could be reproduced by an alternative analysis method, RT-qPCR (Supplementary Figure  S1  RT-qPCR).
All 8 genes followed the same trend using either method for CaMV-infected Arabidopsis, and all except two (At_AOS and At_EDS5) for TuYV-infected Arabidopsis. The discrepancies were probably due to the rather weak expression changes, which are sometimes difficult to detect by RT-qPCR due to its intrinsic exponential amplification kinetics. Quantification of viral RNA loads by counting viral reads normalized per million of total plant reads in each sample revealed that CaMV pregenomic (pg)RNA and TuYV genomic (g)RNA (both represented by forward reads; Supplementary Dataset S1) accumulated to comparable levels in each of the three biological replicates, with the exception of one of the three TuYV-Arabidopsis replicates which showed a lower number of normalized viral reads. The data confirmed further that the mockinoculated plants were not cross-contaminated.
Note that, because TuYV gRNA is not polyadenylated (unlike CaMV pgRNA) the poly(A) enrichment step of Illumina library preparation protocol should have led to its depletion. This might explain the greater variation in relative abundance of TuYV reads between biological replicates, compared to CaMV reads. Despite this high variability, a lower virus load was observed in TuYV-infected Arabidopsis compared to TuYV-infected Camelina plants (Supplementary Dataset S1). Notably, CaMV loads in Arabidopsis were also lower than those in Camelina (ca. 1. 5 times; Supplementary Dataset S1). This indicates that despite drastic differences in disease symptoms between CaMV (severe symptoms) and TuYV (mild symptoms) in both Arabidopsis and Camelina, Camelina appears to be more conductive for replication of both viruses than Arabidopsis.
5 %). This is in line with the relative loads of viral RNA (Supplementary Dataset S1), indicating that Camelina is more susceptible to TuYV infection than Arabidopsis (3 times more TuYV RNA accumulation in Camelina compared to Arabidopsis), while CaMV accumulates in both hosts at comparable levels (only 1. 5-fold difference in average viral RNA loads between Arabidopsis and Camelina). 956 DEGs, corresponding to 3. 4 % of the genome, were common for both viruses in Arabidopsis. The proportion of common DEGs rose to 7. 5 % (~6,700 genes) in infected Camelina. Since CaMV and TuYV are viruses with entirely different replication mechanisms, mediated by respectively viral reverse transcriptase and viral RNA-dependent RNA polymerase, these common host genes might be involved in general stress responses and/or are constituents of the core defense mechanisms.
Impact of CaMV and TuYV infection on plant hosts: Heatmap analyses To better characterize the impact of viral infection on aphid-infested plants, we established the lists (Supplementary Dataset S2) and corresponding heatmaps (Figures 6-10) of DEGs for selected categories. Note that if not otherwise indicated, information on gene function is from the TAIR site (https://www. arabidopsis. org/). For mapping of Arabidopsis and Camelina genes in the heatmaps, we used the syntelog matrix  (Kagale et al. , 2014). This matrix assorts each individual Arabidopsis gene to the corresponding triplet of Camelina homologues. 62,277 Camelina genes out of 89,418 are syntenically orthologues (syntelogs) to Arabidopsis genes, among which some are considered 'fractionated' (if one or two of the homologues were lost).
Aphid performance changes were more pronounced on CaMV-infected hosts, whatever the plant species, compared to those caused by TuYV infection. In spite of more DEGs in TuYV-infected Camelina than in TuYV-infected Arabidopsis, aphid behavior was slightly more impacted on TuYV-infected Arabidopsis (Figure  2 ). This likely indicates modification of plant metabolites that cannot be identified by transcriptome profiling. A metabolomic analysis of virus-infected leaves or phloem sap should provide complementary data on the aphid-plant-virus interactions. In this study, we did not compare the contribution of aphid infestation alone on the plant transcriptome. However, recent work  (Annacondia et al. , 2021)  on the transcriptome changes of healthy Arabidopsis plants infested or not with M. persicae for 72 h, identified a limited number of DEGs (265) suggesting that the contribution of aphid infestation to the transcriptome in healthy and probably also in virus-infected and infested plants is minor.
Acknowledgments We thank  Claire Villeroy  for aphid rearing and the experimental unit of  INRAE Grand Est -Colmar  ( UEAV ) for help with plant production. This work was supported by a public grant overseen by the  French National Research Agency (ANR)  (reference:  ROME ANR-18-CE20-0017-01 ). Dr. Quentin Chesnais  was supported by  Région Grand Est  (Soutien aux jeunes chercheurs, reference:  18_GE5_013 ). The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. The authors declare no conflict of interest.
Fixed-sample imaging and 3D segmentation Optically cleared organoids were transferred to an ethyl-cinnamate-resistant 96-well plate (Ibidi, #89621) for high-throughput imaging. The imaging was performed with an automated spinning-disc microscope (Yokogawa, Cell Voyager 7000S) equipped with a CSU-W1 spinning disc and a complementary metaloxide-semiconductor camera (1,280 pixels × 1,080 pixels). To cover an entire well from a 96-well plate, four fields of view were acquired with a ×4 objective lens. These overview images were processed on the fly via a custom ImageJ macro executed by the SearchFirst module in the Wako Software Suite 2. 0. 20 that identified the coordinates of individual organoids. These coordinates were subsequently revisited with a ×20 air (NA = 0. 75) Olympus objective. For each position, 300 planes with 0.
Surface construction and morphological analysis The marching cubes algorithm was used to extract triangulated meshes from the segmented 3D images that represent the organoid outer boundary and apical surfaces. To reduce the mesh complexity, a combination of PyMesh functions were used to collapse the short edges (tolerance = 2 × the minimum voxel length of the input image), remove duplicated vertices, remove duplicated faces and remove degenerated/ obtuse triangles. For each closed surface, we calculated volume V, surface area A, integral mean curvature M and Euler characteristic χ using the functions implemented in PyMesh and our custom code (Supplementary Note). These quantities were used to calculate the reduced volume v = 3√4 V/A 3/2 , reduced curvature m = M/√4A , and topological genus g = 1 - χ/2 of epithelial lobules.
Epithelial lobules with lumen volume V < 100 μm  3  were excluded from the analysis. Organoid-level quantities were defined as total quantities g = ∑ i g i or as weighted averages 〈v〉 = ∑ i v i V i /∑ i V i and 〈m〉 = ∑ i m i V i /∑ i V i , where index i enumerates all the N epithelial lobules of an organoid. All the computational analyses and visualizations in this study were performed using Python 3. 7 with the libraries NumPy 51 , SciPy 52 , Pandas 53 , Matplotlib  54  , seaborn  55  , scikit-image  56  , PyMesh 57 , PyVista 58 and Polyscope  59.
For ligation, custom adaptors were used (Adaptor-Oligo 1: 5'-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT-3'; Adaptor-Oligo 2: 5'-P-GAT CGG AAG AGC ACA CGT CTG AAC TCC AGT CAC-3'). The libraries were sequenced as 75 bp single end on an Illumina NextSeq500 system. RNAseq reads were trimmed using Trim Galore v0. 5. 0, filtered to remove abundant sequences using Bowtie 2 v2. 3. 4. 1, aligned to the GRCm38 genome  Extended Data Fig. 2  | Framework for morphological analysis of organoids. First, we acquire 3D images of a tissue sample, here, an organoid. Second, we perform image segmentation and surface construction of triangulated meshes to represent 3D tissue morphology as a set of apical surfaces and organoid outer boundary. Next, we use individual apical surfaces to define epithelial lobules and characterize their morphology.
Acknowledgements We thank the members of the groups led by J. B. , E. M. T. and F. J. for discussions. We thank  M. Shahbazi  for providing the mouse ES cell lines and discussions. We thank the  Light Microscopy Facility and Technology Development Studio at MPI-CBG , particularly  R. Barsacchi  and  M. Bickle , for assistance with the automated confocal imaging. We thank  DRESDEN-concept Genome Center at TU Dresden and MPI-CBG  for assistance with the RNAseq analysis. We thank the  BioOptics and Bioinformatics  facilities at the  Vienna BioCenter  for assistance with imaging and RNAseq analysis. K. I. was supported by the  ELBE Fellowship  awarded by the  Center for Systems Biology Dresden. A. M. acknowledges support from the  Joachim Herz Foundation  via the Add-on fellowship. This research was funded by the  Austrian Science Fund (FWF)  grant  SFB-F78  and core funding from the  Research Institute of Molecular Pathology  and the  Max Planck Society.
The lter radiometer is composed of two sensors measuring the downward and upward uxes located above and below the aircra's fuselage, respectively. The J(NO 2 ) lter radiometer was calibrated at Jülich following the standard procedure as described in Bohn et al. 75  During the SAFMED aircra campaign, upward J(NO 2 ) data was not available. To deduce the total value of J(NO 2 ) which is the sum of upward and downward J(NO 2 ), the photolysis frequencies from the/SAFMED ights performed during summer 2014 (ref. 46) above the sea has been used. A relationship between upward and downward J(NO 2 ) has been quantied (Fig. S1  in the ESI †). It was found that the maximum value of upward J(NO 2 ) equals 10% of the value of downward J(NO 2 ) (Fig. S1  of the ESI †). As a consequence, the following relationship was used for the 2013 SAFMED dataset to calculate the total value of J(NO 2 ): J(NO 2 ) total ¼ 1.
The uncertainty s is calculated following eqn (5). We also investigated one of the reasons for those positive and negative deviations by looking at rapid changes in the mixing ratios of the four terms: J(NO 2 ), NO 2 , NO and O 3 (see Section 3. 2. 1). Here, a rapid change is dened when the difference between two consecutive data is greater than 20% as suggested by Griffin et al. 58  Several studies have shown that the deviation from unity can be affected by rapid changes in the photooxidant concentrations and NO 2 photolysis rates  24, 58, 85, 92  The rapid change in J(NO 2 ) can be explained by the presence of clouds (e. g. in SWA) or the heterogeneity of the cloud cover (e. g. in Paris). PSS during DACCIWA-SWA. During DACCIWA (3 ights), the F values range between 0. 1 and 10. 5 inside the urban plumes.
Seventeen to 45% of the F values are within the PSS range while most of the data are higher than 1 + s (Table  S4  †). As for MEGAPOLI and SAFMED, frequent and intense (several tens percent) rapid changes in J(NO 2 ), NO, NO 2 and O 3 are responsible for positive and negative deviations of the Leighton ratio from unity. Fig. 8  shows the average values of the Leighton ratios and the associated standard deviations found in the eleven investigated plumes compared to previous studies. Table  S5  † shows the aggregated F value from those previous studies. On average and despite the heterogeneous distribution of the Leighton ratio discussed above, the PSS is either reached within its lower and upper limits in half of the explored ights or overpassed during the eastern MEGAPOLI ights and the DACCIWA ights.
Acknowledgements Airborne data were obtained using the ATR-42 aircra managed by Sare, the  French facility  for airborne research, an infrastructure of the  French National Center for Scientic Research (CNRS) ,  Météo-France  and the  French National Center for Space Studies (CNES). Part of the research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  FP/2007-2011 within the project  MEGAPOLI , grant agreement  212520. The authors also acknowledge for their nancial support both the  ANR  through the  MEGAPOLI   PARIS  project and the  INSU/LEFE program  through the  MEGAPOLI France  project. This study also received nancial support from MISTRALS by  ADEME ,  CEA ,  INSU , and  Meteo-France  and from  ANR  through the  SAFMED  project (grant number  ANR-12-BS06-0013-2502 ).