dataesr/scientific-paragraphs-categorization huggingface dataset:
Phytolith-forming water values are predicted using equilibrium 18 α silica-water estimated from  Dodd and Sharp (2010)  and λ silica-water values of 0. 524  (Sharp et al. , 2016)  and 0. 522 (Table  S2 ). For comparison, values from the 2018 natural transect dataset  (Alexandre et al. , 2018)  and from the AMMA-CATCH grass stem phytoliths and rainwater (RW) data (Table  3 ) are plotted. natural transect  (Alexandre et al. , 2018)  and from -1. 9 ‰ to -4. 3 ‰ for the AMMA-CATCH site) to the δ 18 O of the irrigation water of the 2018 experiment (-5. 6 ‰). These data support the idea that, as for bulk leaf water, the isotope composition of the source water and the difference in composition between source water and the water vapor govern the starting point from which the triple oxygen isotope compositions of grass phytoliths evolve with decreasing RH.
Conclusions An inter-laboratory comparison (CEREGE-UNM) allowed us to standardize the phytolith triple oxygen isotope data relative to the VSMOW-SLAP scale. After this standardization, the apparent λ Phyto-LW values obtained so far vary between 0. 521 and 0. 523±0. 0004 (Alexandre et al. , 2018 (Alexandre et al. , , 2019)). Heterogeneous silicification mechanisms in the leaves may result in an apparent kinetic fractionation and explain at least partly that λ Phyto-LW values are lower than the 0. 524 value expected for equilibrium. When the forming water is well constrained as for phytoliths from non transpiring grass stems, the apparent λ silica-water value is 0. 523 ± 0. 0004, i. e. , close to the equilibrium value. The datasets presented for phytoliths, leaf waters and source waters show that the δ 18 O value of the source water governs the position of the evaporation lines shown in the δ 18 O vs.
Figure5. 17 O excess vs. δ 18 O of irrigation water (IW), final water vapor (V ), bulk leaf water (LW), phytolith (Phyto) and phytolith-forming water (FW) observed and predicted for the current and 2018 RH treatment where RH varies from 40 % to 60 % and 80 %. Phytolith-forming water values are predicted using equilibrium 18 α silica-water estimated from Dodd and Sharp (2010) and λ silica-water values of 0. 524 (Sharp et al. , 2016)  and 0. 522 (TableS2). For comparison, values from the 2018 natural transect dataset(Alexandre et al. , 2018)  and from the AMMA-CATCH grass stem phytoliths and rainwater (RW) data (Table3) are plotted.
METHODOLOGY The work adopted a mixed-method approach integrating a literature review with the opinion of experts and stakeholders from a wide range of organizations and European countries. Starting from the CRFS definition, a literature review was carried out to identify the most critical food system areas affected by the COVID-19 pandemic. The literature was systematized by a mixed method of research based on scientific papers and materials coming from gray literature. Concerning the peer review literature, the high number of documents acquired and revised have been collected through SCOPUS and Web Of Science. The gray literature review was carried out through Google Scholar, expert opinions, direct interviews, as well as daily press in various languages, and blogs. Collected information was clustered into five food system areas: (1) agriculture, fisheries, and production systems; (2) innovative business models for increased resilience and sustainability; and (3) evolving technologies; (4) consumers behavior changes and adaptations; and (5) schools and education.
ACKNOWLEDGMENTS The authors would like to thank the following contributors for their valuable inputs and feedbacks:  Ilaria Braschi ,  Margherita Del Prete , and  Francesca Monticone  from the  University of Bologna ;  Chiara Cirillo  from the  University of Naples Federico II ;  Luuk Graamans  from  Wageningen University and Research. The authors thank all partners in the  FoodE consortium  for their collaboration.
Infectivity Assays Infectivity assays were performed with isolates of DNA-A and DNA-B from both WCSV and SLCV. For the WCSV, the DNA-A component isolate LCM_52 (GenBank accession MW588390) and the DNA-B component isolate LCM_53 (GenBank accession MW588417) were used to generate two infectious clones. For the infectious clones of the SLCV, the DNA-A component isolate LCM_89_SP41 (GenBank accession MW588381) and the DNA-B isolate LCM_89_SP58 (GenBank accession MW588407) were used. These DNA-A and DNA-B components were recovered from cactus samples (Table  1 ). Specific primers were designed to amplify two copies of each cactus-derived begomoviruses component that were cloned in tandem to the binary vector pJL-89  [29] , with the 35S promoter excluded as previously described by  Ferro et al. [30] , using Gibson assembly  [31]  (New England Biolabs, Ipswich, MA, USA).
Sequence Analyses The full-length sequence of each component from the SLCV and WCSV together with available genomes from the same species (downloaded from GenBank on 16 December 2020) were used to generate a dataset for each virus component. For each dataset of SCLV and WCSV components, sequences were aligned using MAFFT v. 7  [32]  and subsequently used to detect recombination using the program RDP v. 5. 5  [33]. The methods RDP  [34] , GENECONV  [35] , BOOTSCAN  [36] , MAXCHI  [37] , CHIMERA  [38] , SISCAN  [39]  and 3SEQ  [40]  were used with default parameters for the recombination analysis. Only recombination events that were detected by more than three methods with a p-value <0. 05 were accepted. Alignments with recombinant regions removed were used for phylogenetic analysis. Following ModelFinder  [41]  analyses, the nucleotide substitution models used were TIM + F+G4, TIM3 + F+G4, TIM2 + F+G4 and HKY + F+G4 for WCSV DNA-A, WCSV DNA-B, SLCV DNA-A and SLCV DNA-B, respectively.
The Maximum-Likelihood (ML) phylogenetic tree for each dataset was inferred using IQ-TREE  [42]  with 1000 bootstrap replicates for branch support from which branches with <60% support were collapsed using TreeGraph2  [43]. With the exception of the SLCV DNA-A ML phylogenetic tree that was rooted with SLCV DNA-A sequences from Pakistan (MF504011 and MF504010); all other ML phylogenetic trees were rooted with other begomovirus sequences. The ML phylogenetic trees were visualized and edited using iTOL  [44]. All pairwise identities were determined using SDTv1. 2 [45].
5-98. 3% pairwise identity with other SLCV DNA-A isolates and 98. 6-100% identity amongst themselves (Supplementary Data S1). The SLCV DNA-B component sequences share 88. 7-95. 3% pairwise identity with other SLCV DNA-B isolates and 94. 5-100% identity amongst themselves (Supplementary Data S1). Recombination analysis of the SLCV DNA-A and DNA-B sequences from this study and others available in GenBank resulted in the identification of nine putative recombination events for DNA-A and 10 for DNA-B (Figure  1A, B ). In the DNA-A dataset (n = 162), eight sequences were found to be recombinants, one of which presented two recombination events (MG763920). The recombinantionally derived genomic fragments ranged in size from 138 nts to 1292 nts with the majority occurring in the virion sense genes.
For the SLCV DNA-B dataset (n = 44) eleven sequences were identified as recombinants with one sequence (MG763921) presenting four recombination events. Recombination event #6 was identified in five sequences and event #4 represents a recombination that occurred between sequences recovered from this study (USA). The transferred fragments ranged from 35 to 1000 nts. Phylogenetic analysis of the SLCV DNA-A and DNA-B sequences indicated a degree of geographical clustering (Figure  2 ). The SLCV DNA-A sequences identified in this study all cluster within the same clade (Figure  2A. Clade A IV ). In this clade all the cactus-derived sequences cluster together and are collectively most closely related to SLCV isolates from tomato plants (Figure  2A. Clade A IV ). There is a distinct clade containing SLCV DNA-A sequences from the Middle East (Figure  2A.
Phylogenetic analysis of the SLCV DNA-B sequences revealed three distinct clades (Figure  2B ). Clade B II contains SLCV DNA-B sequences from the Middle East and Clade B III contains all but one of the North American sequences, two sequences from Pakistan and one from Jordan. Lastly, one sequence from the USA identified infecting watermelon  [47]  sits outside those two clades (Clade B I ). This pattern suggests that DNA-B may have been introduced to the Middle-East at least twice. These apparently distinct DNA-A and DNA-B evolutionary histories highlight once again the importance of component reassortments for bipartite begomoviruses  [48, 49]. ummary of the recombination events detected in the datasets of (A). SLCV DNA-A, (B). SLCV DNA-B, (C). WCSV DNA-A and D. WCSV DNA-B by R ethods used to detect recombination are RDP (R) GENCONV (G), BOOTSCAN (B), MAXCHI (M), CHIMERA (C), SISCAN (S) and 3SEQ (T).
Watermelon Chlorotic Stunt Virus Three WCSV DNA-A isolates and three DNA-B isolates were recovered from six plants from the Cactaceae, Lamiaceae and Solanaceae families (Table  1 ). To our knowledge, with the exception of one other Solanaceae plant (Datura innoxia) [50], this is the first identification of WCSV from plants in the Cactaceae and Lamiaceae. The three WCSV DNA-A sequences share >97. 7% genome pairwise identity amongst themselves and 96. 5-99. 7% identity with other WCSV DNA-A sequences available in GenBank (Supplementary Data S2). The WCSV DNA-B sequences share 92-99% genome pairwise identity with other WCSV DNA-B sequences and 99. 9% sequence identity amongst them. Similar to what was observed for the SLCV components, the WCSV DNA-B sequences are more diverse than the WCSV DNA-A sequences.
A single putative recombinant region was identified in the WCSV DNA-A dataset (n = 144) (Figure  3 ). The recombinant sequence was isolated from a watermelon plant in Israel. The recombinationally derived genome fragment was 315 nucleotides long and includes a portion of the rep and ac4 coding regions. Meanwhile, three recombinant regions were identified in the WCSV DNA-B dataset (Figure  3 ). The recombinationally transferred genome fragments ranged in size from 343 nts to 1113 nts (~40% of the genome). No recombination was detected in any of the WCSV sequences recovered in this study. The ML phylogenetic tree of the WCSV DNA-A sequences has four clades displaying some degree of geographical clustering (Figure  3A ). In Clade A I , there are two sequences from Sudan while Clade A II includes sequences from Saudi Arabia and Yemen.
We first clarified the dose-response effects using single VPCs to create the background. A significant effort was paid to improve the control of stimulus intensity and to establish dose-response relationships. We evaluated the intrinsic activity for each VPC and determined their type of interaction with pheromone. We compared impact of VPC background at antennal and AL levels. Then, we prepared binary, ternary, and quaternary blends of VPCs to investigate interactions between VPCs and to determine whether properties of the blend could be deduced from the properties of single VPCs. Our data confirm that common VPCs interfere with the moth pheromone system in a dose-dependent manner. Activity varies among VPCs. The activity of a blend reproduces that of the most active component with only few interactions between components.
First, we evaluated the effects of single VPCs. Linalool, whose ability to activate the MGC-neurons of A. ipsilon had been already demonstrated  [29] , was used to establish the dose used for all VPCs of the panel. We first measured the activity of different dilutions of linalool on MGC-neurons, showing that a dilution of 1% triggered a clear firing activity. We adjusted dilutions of the other VPCs in function of their differences of volatility according to procedures proposed by Munch et al. [54]  based on data from  [55]  (Table  S1 ). The same dilutions were used for the sources of background in electrophysiological and Calcium imaging experiments. Then, the effects of binary blends of VPCs and their components alone were tested at 4 concentrations each, while the pheromone dose was kept constant.
Identification of individual glomeruli was done by superposing activity maps using Adobe Photoshop (Version CS2). We chose not to include isoprene in calcium imaging experiments due to the limited number of channels available in the odor stimulation device. Raw data analysis was done using custom-made software written in IDL (Research Systems Inc. , Boulder, CO, USA) and Visual Basic (Microsoft Excel). After noise filtering and bleaching correction, relative fluorescence changes (δF/F) were calculated as (F -F 0 )/F 0 (where F 0 = reference background). For each glomerulus, the time course of δF/F was calculated by averaging 25 pixels (5 × 5) at the center of each glomerulus.
We estimated the capacity of neurons to extract the pheromone signal from the background, the pheromone salience, by subtracting prepheromone activity level in TW3 from max TW4. For experiments evaluating the effects of single VPCs we used pairwise paired t tests on all possible pairs of background types to compare values of response to background, response to pheromone and pheromone salience in the different backgrounds. False discovery rate was controlled using the Benjamin-Hochsberg's procedure (FDR < 0. 05). For experiments comparing blends to their components we used a permutational MANOVA (PERMANOVA, function Adonis()) under R package vegan  [58, 59]  to evaluate how background composition and dose affected neuronal responses, the three measured variables taken together. The PERMANOVA used a Euclidian distance matrix calculated from response to background, response to pheromone, and pheromone salience.
Subtracting this simplified Hill's equation from 1 produces a curve that decreases with increasing C, which is empirically appropriate for fitting on the observed decreasing response to pheromone or pheromone salience as function of the background dose: R norm or S norm = 1 - C n C n + EC n 50 = EC n 50 C n + EC n 50 (5) We fitted Equation (  5 ) using the non-linear regression function nls2() (R package nls2, G. Grothendieck, 2013, CRAN deposit) to estimate EC 50 and n for the Z7-ORN recordings pooled together (n = 16), and for each background type separately, using total background concentration in arbitrary units as C. We compared the dose-responses observed under blended background and under single compound's background by checking whether the confidence intervals for the fitted parameters did or did not overlap.
Comparison with C. elegans germline transcriptomics reveals a poor conservation of B. malayi genes involved in oogenesis, especially in the proliferative zone In an attempt to better examine the genes involved in adult female oogenesis in B. malayi, we compared the germline transcriptome of B. malayi to that of C. elegans, whose germline has been extensively studied. C. elegans is hermaphrodite, the same gonad producing sperm prior to oocytes, due to a temporally-regulated germline genetic programme  [33]. Genetic manipulation in this experimental model has allowed the generation of mutants that only make either sperm or oocytes. We used the most recent transcriptomic dataset generated from spermatogenic and oogenic C. elegans gonads to assess the potential evolutionary conserved mechanisms in the making of a female germline between these species  [34].
Development and maintenance of the distal germline -PZ-rely on the highest number of filarial specific genes of the Onchocercidae family The majority of genes expressed in the B. malayi germline or body wall do not have C. elegans orthologs. By using a comparative genomic approach, we sought to assess the phylogenetic origin of the expressed genes in the ovary and BW samples to potentially detect orthologous genes present outside C. elegans and to determine which genes may be the result of evolutionary innovation or adaptation within filarial nematodes. To this end, we established an orthology map of the B. malayi genome, with a focus on genes specifically expressed in the ovary and the BW. The B. malayi genome contains 11002 proteincoding genes, of which 64 have no WormBase ID and 16 have a WormBase ID but do not exist in the WormBase Parasite Compara database (S4 Table ).
Orthology information was obtained for the remaining 10,922 B. malayi genes from the WormBase Parasite Compara database, through comparisons with 30 chosen species genomes (Fig  5 ). This orthology map of the B. malayi genome is divided in 10 different clade origin categories, from the Opisthokonta clade down to the species level "malayi". Of the 10,922 genes, 5,629 (51. 5%) are shared by the Opisthokonta, Metazoa and Bilateria; 2,171 (19. 9%) have a common origin within the Nematoda and Chromadorea; and 1,347 (12. 3%) share a common origin with the Spirurina and Spiruromorpha. Importantly, 816 B. malayi genes have orthologs within the Onchocercidae only, a family of filarial nematodes in which some members share a symbiosis with Wolbachia  [6]. At the genus level, 247 genes are specific to Brugia, and 712 genes appear unique to the B.
Comparative genomics In order to better define the genetic features of the B. malayi genome, ranging from those genes shared by all species within the Opisthokonta, to the evolutionary innovations unique to the Brugia species, we created an orthology map of the B. malayi genome using data hosted in the Wormbase Parasite Compara database. To obtain a thorough view of the B. malayi orthology we selected 24 nematode species genomes (Brugia pahangi, Brugia timori, Wuchereria bancrofti, Loa loa, Litomosoides sigmodontis, Onchocerca flexuosa, Onchocerca ochengi, Onchocerca volvulus, Dirofilaria immitis, Ascaris suum, Strongyloides ratti, Panagrellus redivivus, Bursaphelenchus xylophilus, Globodera pallida, Meloidogyne hapla, Meloidogyne incognita, Pristionchus pacificus, Haemonchus contortus, Heterorhabditis bacteriophora, Necator americanus, Caenorhabditis elegans, Romanomermis culicivorax, Trichinella spiralis and Trichuris muris) spanning the entire nematode phylogeny  [61] , the tapeworm Taenia saginata and five outgroup model organisms (Drosophila melanogaster, Homo sapiens, Mus musculus, Danio rerio and Saccharomyces cerevisiae).
We then collated protein homology values for each of the 10,922 B. malayi genes present in the Compara database in each of the 30 species described above. The detailed methodology and pipeline implemented to produce the Wormbase Parasite Compara database has been described previously (https://parasite. wormbase. org/info/Browsing/compara/index. html) and (http://www. ensembl. org/info/genome/compara/homology_method. html). We interrogated the Wormbase Parasite Compara database in June 2019 via the biomaRt library implemented in R  [62]. We then used the WormBase ParaSite REST API Endpoints to obtain the root clade of each gene tree phylogeny. Circular maps harboring all the B. malayi gene orthologies along with their clade root origin were made with Circos v0. 69-6  [63]. To classify genes using both their expression patterns and their phylogenetic origin, we selected genes specifically upregulated in each of the three tissues.
Clubroot provokes the formation of galls on the infected roots that can restrict the flow of water and nutrients within the plant throughout the growth cycle. In order to get new insights into the impact of single or combined constraints on seed qualities, metabolic profiling assays were run for a collection of 330 seed samples (including developing, mature and imbibed seeds) harvested from plants of two B. napus cultivars ("Express" and "Montego") that were grown under either drought conditions, the presence of P. brassicae , or a combination of both stresses. Metabolites were identified and quantified by UPLC or GC. In addition, monitoring germination traits was conducted for 60 mature seed lots under in vitro conditions using an automated phenotyping platform. The present dataset contains the raw contents for 42 metabolites (nmol.
mg -  of seed dry weight) filtered and analyzed with statistical tests as well as germination speed and percentages. This dataset is available under accession at Data IN-RAE. These data will contribute to a better understanding of the crosstalk between the plant responses to water deprivation and/or pathogen attack and how it compromises seed quality. A better understanding of the molecular and physiological responses of the seed to (a)biotic stress on a molecular and physiological will be a first step to meet scientific and technological challenges of adapting seeds to their environment. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Value of the Data • The present study describes a metabolic dataset from developing, mature and imbibing seeds of B. napus harvested from plants grown under four environmental conditions (standard watering/drought with or without inoculation by P. brassicae ) and its relationship with germination behavior. • These data are useful for the scientific community working on the impact of single or combined abiotic and biotic constraints on seed quality. • These data provide clues to better understand the metabolic changes that occur in seeds when produced under various environmental conditions. They point out some metabolites that can be considered as potential biomarkers of stress response in seeds of oilseed rape. In addition, they also highlight correlations between the metabolic composition of the seeds and their germination behavior.
Data Description This paper describes a metabolic profiling dataset from seeds of two cultivars of Brassica napus , "Express" and "Montego", that were cultivated under single or combination of drought stress and/or Plasmodiophora brassicae infection. Drought during seed filling has been shown to have a significant impact on oilseed rape productivity  [2]  and is one of the prime abiotic constraints under current and future climate scenarios. On the other hand, clubroot, caused by the obligate telluric biotroph protist Plasmodiophora brassicae , is a disease that leads to severe yield losses for the Brassica crops worldwide  [3]. Clubroot leads to the formation of galls on the roots of infected plants that can restrict the flow of water and nutrients within the plant. Above-ground symptoms including wilting, stunting, yellowing and premature senescence have been observed that can eventually lead to the plant death.
Germination parameters Germination performances were monitored under in vitro conditions at 20 °C in darkness (except during LED flash lightening for imaging), using the automated high-throughput phenotyping facilities for seed quality analysis developed by the French National Seed Testing Station of the Variety and Seed Study Control Group (PHENOTIC platform, GEVES, Beaucouzé, France). Detailed information about the phenotyping system is given by  [1]. Seed germination assays were carried out using 4 replicates of 25 seeds per seed lot. Numerous germination parameters were acquired by image analysis of each single seed over 96 h of imbibition at 20 °C as no further increase in germination was observed beyond this time point. The data were then aggregated to provide accurate germination time progress curves for each seed set.
Acknowledgments We thank all our colleagues from  IGEPP ,  INRAE Le Rheu (France) , especially  J. P. Constantin ,  L. Charlon , and  P. Rolland  for supplying excellent greenhouse services,  P. Glory  and  C. Lariagon  for assistance with P. brassicae infection, and  E. Alix ,  F. Leprince ,  B. Moulin  and  S. Rolland  for technical support in the laboratory. We are grateful to the  BrACySol biological resource center (INRAE Ploudaniel, France)  for kindly providing us with seeds of Express and Montego. The PHENOTIC ( INRAE-IRHS, Angers University, AgroCampus Ouest, GEVES, France ) and  P2M2  ( IGEPP, Le Rheu, France ) platforms are acknowledged for access to their facilities. This research was funded through an  ANR grant  ( ANR-14-JFAC-007-01 ) in the framework of the  SYBRACLIM  project ( FACCE-JPI-ERA-NET + CLIMATE SMART AGRICULTURE  project call).