dataesr/scientific-paragraphs-categorization huggingface dataset:
| Annotation of the reference nrEVEome of A. aegypti While we were finishing our study, another annotation of the nrE-VEome of AaegL5 assembly was published  (Russo et al. , 2019). The number of nrEVEs and the phylogenetic classification in the two studies are roughly similar (252 vs. 277). However, around one-fifth of the nrEVEs annotated in our study (46) have not been annotated by  Russo et al. (2019). Conversely, 43 nrEVEs annotated by Russo are not present in our data set (Data S7). nrEVEs that have been missed by one of the two studies are dominated by unclassified nrEVEs. A wide variety of RNA viruses has been unearthed by metagenomics (C. -X. Li et al. , 2015; Zhang et al. , 2019) , many of which are awaiting classification. Hence, the differences in nrEVEs repertoires of the two studies probably reflect the diversity of viruses included in databases used to annotate nrEVEomes and it is likely that ongoing identification of new mosquito viruses will require regular updates of nrEVE annotation.
nrEVEs annotated in the AaegL5 assembly are flanked by TEs, often LTR retrotransposons (Figure1c), similar to what was observed in earlier, higher fragmented, genome assemblies (Palatini et al. , 2017; Whitfield et al. , 2017). Chuvi-nrEVEs are an exception as all 39 members are completely embedded within four different elements of the Bel/Pao family. Strikingly, all Chuvi-nrEVEs derive from viral glycoprotein sequences, which cluster together according to the Bel/Pao element in which they are embedded (Figure S5). LTR retrotransposons normally do not possess an envelope gene (env), but can acquire env-like genes from disparate viral sources and, through F I G U R E 1 Atlas of Aedes aegypti nrEVEs. (a) Violin plot showing nrEVEs identified in the A. aegypti genome (AaegL5 assembly). (b) Scatter plot representing the amino acid identity of each nrEVE and its best hit retrieved by blastx searches against the NR database grouped by viral family.
This research was funded by a  Human Frontier Science Program   Research grant  ( RGP0007/2017 ) to R. v. R. and M. B. ; by the  Italian Ministry of Education, University and Research FARE-MIUR  project  R1623HZAH5  to M. B. ; by a  European Research Council   Consolidator Grant  ( ERC-CoG)  under the European Union's  Horizon 2020 Programme  (Grant No. ERC-CoG 682394 ) to M. B. ; by a  European Research Council   Consolidator Grant (ECR-CoG ) under the European Union's  Seventh Framework Programme  ( ERC-CoG 615680 ) to R. v. R. ; by a  VICI grant  from the  Netherlands Organization for Scientific Research (NWO , grant no. 016. VICI. 170. 090 ) to R. v. R. ; and by the  Italian Ministry of Education, University and Research (MIUR) :  Dipartimenti Eccellenza Program  ( 2018-2022 ) to the  Department of Biology and Biotechnology "L.
Monitoring of ozone pollution International monitoring networks of air pollution in Asia are young and less developed compared to those in the U. S. and Europe. In East Asia, the Acid Deposition Monitoring Network (EANET: http://www. eanet. asia) started regular measurements of air pollution and ecological impacts in 2001. As of 2018, O 3 was measured at 25 sites in seven countries. In South Asia, the "Malé Declaration on Control and Prevention of Air Pollution and its likely Transboundary Effects for South Asia" was initiated in 1998, and the monitoring network was established in 2003 (http://www. rrcap. ait. asia/male). A total of 15 sites from 8 countries are operated for monitoring O 3 and other pollutants. National networks have also been built and operated in some countries. For example, the TOAR (Tropospheric O 3 Assessment Report) database accommodates O 3 monitoring data at 1260 sites in Japan, 312 sites in South Korea, but only 26 in China  (Schultz et al.
2017). For South Asia, 8 datasets were aggregated in the TOAR database. With these existing data, however, spatial coverage is clearly insufficient in Asia, as shown by the wide gaps of monthly mean O 3 concentrations over Asia on the 5° × 5° grids (Fig. 6  of  Schultz et al. (2017) ). However, the situation for China has been drastically improved since 2013, when the Ministry of Environmental Protection of China started publicizing hourly data of major air pollutants including O 3 at 1497 monitoring stations  (Li et al. 2018a). In India, a projectbased monitoring network for O 3 was started with a few sites in 2008, and the site number grew to 14 in 2015  (Lal et al. 2017). However, a nation-wide monitoring network of air pollution (https://app. cpcbccr. com/ccr/#/caaqmdashboard-all/caaqm-landing) has been developed in India since 2017.
Acknowledgments This study has been funded by  National Natural Science Foundation of China  (No. 41771034 ,  42061160479 ,  31950410547 ,  4190738 , and  M-0105 ),  Key Research Program of Frontier Sciences, CAS  ( QYZDB-SSW-DQC019 ),  CNR-CAS  bilateral agreement 2017-2019 (Ozone impacts on plant ecosystems in China and Italy), and  Chinese Academy of Sciences   President's International Fellowship Initiative (PIFI) for Senior Scientists  ( 2018VCA0026 ). AG and YJ were supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-12-LABXARBRE-01 ).
Funding This work was supported by  National Natural Science Foundation of China  [ 31950410547 , 41771034 , 4190738 , 42061160479 , M-0105 ]; the  Chinese Academy of Sciences  [QYZDB-SSW-DQC019];  French National Agency for Research (ANR)  [ ANR-12-LABXARBRE-01 ]; The  Startup Foundation for Introducing Talent of Nanjing University of Information Science & Technology  [ 002992 , 003080 ];  Chinese Academy of Sciences President's International Fellowship Initiative  [2018VCA0026]. Plant Physiology and Biochemistry 154:  675-688. doi:10. 1016/j. plaphy. 2020. 05. 037. Han, Z. ,  H. Ueda , and  K. Matsuda. 2005. " Model Study of the Impact of Biogenic Emission on Regional Ozone  and the  Effectiveness of Emission Reduction Scenarios over Eastern China. "Tellus B: Chemical and Physical Meteorology 57  (1):  12-27.
Foamability Optimization Foamability was investigated to define an appropriate stirring range allowing a sufficient foam overflowing flow rate during O-CC processes. Experiments performed at peripheral speeds of the tip of the Rushton turbine of 0. 40, 0. 80, 1. 00, and 1. 20 m•s -1 have achieved a constant value of the foam height corresponding, respectively, to a foamability K of 0. 15, 0. 30, 0. 48, and 0. 60. According to the authors  (Saint-Jalmes et al. , 2005) , K values lower than 0. 1 represent a low foamability. In our application, the goal is to promote foaming. Thus, it was decided to fix the minimum peripheral speed to 0. 80 m•s -1 , providing a minimal K value of 0. 30. An additional experiment conducted at 1. 60 m•s -1 (data not shown) did not allow the stabilization of the foam height in the experimental time.
CYPs are also associated with the metabolism or detoxification of xenobiotics such as plant natural products and pesticides and are key components for the insects' successful adaptation to their host plants  (Heidel-Fischer and Vogel, 2015). Carboxylesterase (Pfam PF00135 domain) is a gene family belonging to the α/β hydrolase protein superfamily (Pfam PF00561 domain)  (Oakeshott et al. , 2005). The CCE family comprises mostly catalytic proteins that hydrolyze a number of carboxylic esters but there are noncatalytic exceptions  (Oakeshott et al. , 2010 , Oakeshott et al. , 2005). CCEs are also involved in the modification of insect juvenile hormone, an endogenous sesquiterpene. Glutathione S-transferases  (EC 2. 5. 1. 18, GSTs) , and the uridine diphosphate-glycosyltransferases (EC 2. 4. 1.
Transcriptome data Transcriptome data from adult forms of D. vitifoliae that feed respectively on leaves (GA) and roots (RA) was published by  Rispe et al. 2016 (project accession: PRJNA294954). We mapped these data on the D. vitifoliae genome v3. 1 using OGS3. 2 annotations  (Rispe et al. , 2020). Hisat2 and transcript abundance was subsequently quantified using RSEM to obtain a "transcript per million" estimation. We screened these data with our detoxification gene annotation lists (Suppl data Table  2 ), and used the heatmap. 2 function from the R gplots package with the Euclidean distance and the complete hierarchical clustering methods to obtain our heatmap figures.
Gene annotations and phylogenetic analysis We used described sets of Hemiptera P450, CCE GSTs, UGTs and ABC transporter proteins to search the D. vitifoliae genome, Pcf7 strain v3. 1  (Rispe et al. , 2020). , by TBLASTN using Galaxy  (Giardine et al. , 2005). All gene models were manually validated or corrected in WebApollo based on homology with other Hemipteran sequences, and on alignment with RNAseq data, when available. Alternatively, a direct keyword query search was used against the AphidBASE website (http://bipaa. genouest. org/is/aphidbase/). The classification of deduced proteins and their integrity were verified using BlastP against the non-redundant (nr) GenBank database. When genes were suspected to split in different scaffolds, protein sequences were merged for further analyses.
Genes were also searched by TBLASTN against the whole genome assembly using CCE, CYP, UGT and GST proteins sequences from N. vitripennis, R. prolixus, A. pisum and M. persicae collected from the latest NCBI release. ABC transporters genes from D. vitifolia genome were searched by TBLASTN using ABC transporter proteins sequences from D. melanogaster and A. mellifera. Amino acid sequences were aligned using MAFFT (using L-INS-i option)  (Katoh et al. , 2019)  with different gene families members from Myzus persicae, Rhodnius prolixus and Acyrthosiphon pisum  (Ramsey et al. , 2010 , Schama et al. , 2016). Phylogenetic trees were constructed using PhyML  (Guindon et al. , 2010)  based on the LG substitution model (see Suppl data Table  4 ) as determined by the SMS server  (Lefort et al. , 2017)  using Nearest Neighbour Interchange (NNI).
Acknowledgements Funding for Daktulosphaira vitifoliae clone Pcf genomic sequencing was provided by  Inra  ( AIP Bioressources ) and  BGI Biotech  in the frame of i5k initiative. Parts of the transcriptomic resources were obtained within the  1KITE projects (Bernhard Misof, Bonn, Germany). We are grateful to  Dr David Nelson  for naming CYP. We would like to thank  Dr C Rispe  and  F Legeai  for giving access to their GA and RA transcriptomic data. We also thank  Dr C Coustau  for constructive discussions on the manuscript. The authors would like to thank  Dr Abby Cuttriss  ( UCA-Office of International Scientific Visibility ).
First, EST datasets from the migratory locust revealed a repression of anabolic, biosynthetic and muscle-specific gene expression in the hindleg, midgut and head tissues of gregarious versus solitary locusts  [53]. A strong upregulation of a subset of juvenile hormone binding proteins was also observed in the head of gregarious locusts, thus providing a link between transcriptional changes and hormonal signalling of crowding. A comparison in the desert locust of central nervous system (CNS) transcriptome during the two phases revealed the upregulation of heat-shock proteins (HSP) and immunity genes in gregarious locusts  [54] , suggesting that population density increase might induce a general physiological stress state. A reduction of energy metabolism and biosynthesis was also observed in gregarious locusts in another transcriptomic study in the migratory locust  [55] , together with the overexpression of gene coding for synaptic transport components, neuromodulators receptors and neurotransmitter synthetases, confirming the neuronal modulation of phase transition.
Data Collection Procedure Before visiting the sampled cooperatives, the individual questionnaire was tested on six farmers and amended. Data were collected using a paper questionnaire in French or English. The questionnaire was split into three parts: 1. a basic characterization of the cocoa plantation through 5 variables (active area in 2018 in hectares, production in 2018 in kilograms, age of the plantation, cocoa varieties, presence or absence of a certificate); 2. an estimate of revenues from the sale of cocoa beans in CFA francs in 2019, distinguishing between the different quality grades assigned to cocoa beans; 3. an assessment of the annual costs associated with operating this plantation in CFA francs in 2019, based on a preestablished list of all the usual cost items for a cocoa field.
The amount of unpaid family labor and subsidies received were also recorded. The average time for completing an individual questionnaire was around one and a half hours. This set of data was enough to calculate the value added and the net profit of each plantation. The surveys were conducted between March and May 2019, using the 2018-19 production season as a reference. With the exception of the South West region, where access was dangerous at the time of the survey, all interviews took place at or around the cooperatives' headquarters, and within a few kilometers of the respondents' plantations. The meetings with cocoa producers took place in or around six cities: Yaounde, Ntui and Bokito for the Center region, Sangmelima and Ebolowa for the South region, Douala for the Littoral and South West regions (Figure  1 ).
Data Analysis Procedure We have adopted the usual terminology and approach for compiling the operating accounts of cocoa producers. In this framework, the (gross) value added is the difference between the value of the output and the value of intermediate inputs, or the sum of wages, interest charges, taxes and gross profits. Similarly, the net profit is the balance when depreciation (equipment) is subtracted from the gross profit. Three steps were followed to establish the average operating accounts for the four archetypes of cocoa producers in Cameroon. First, we collected data from each producer on plantation size, 2018-2019 yield and the values of the income and cost categories. Second, we estimated the average size and cocoa yield for each of the four archetypes. Third, we extrapolated the cost and revenue data for each individual plantation to calculate the average plantation by archetype in order to obtain the average operating accounts for each type of producer.
These two limitations put the reliability of the analysis into perspective and mitigate against the lessons that can be learned from it. But in the absence of detailed national databases that allow for a more comprehensive and rigorous analysis, it is difficult to estimate the potential influence of certification on producers' net incomes without relying on such methodological proxies. These methodological biases being noted, the success of private certification may be due to its positive correlation with the profitability of smallholders. A comparison of the financial performance of archetypes 2-3 and archetype 1 shows profit rates that are two to three times higher for the former. The additional profit associated to certification may not only be due to the higher price per kilo of beans, but also to lower production costs and better farming practices, which increase yield per hectare.
ACKNOWLEDGMENTS The authors would like to thank all the cocoa producers who took the time to answer our questions, as well as the Cameroonian stakeholders who helped us to better understand the governance of this sector. We would also like to thank  Bienvenue Belinga  who conducted ten interviews with producers. This article is based on discussions held with  Elsa Sanial ,  François Ruf ,  Raphaël Tsanga ,  Laurence Boutinot ,  Pietro Goglio , and  Aurélie Carimentrand. Finally ,  Isis Olivier  proofread and amended the English language of this article.
Blastn and blastx searches were performed against GenBank. Five contigs presenting similarities with sobemoviruses were used as templates for iterative mapping of reads to contigs with Geneious 8. 1. 9 (Biomatters, New Zealand). To confirm sequences of low coverage areas, RT-PCR was performed with primers SMAMV-1360-F+SMAMV-1860-R (5'-GTTCTTGTGGAGGTTCCTCA-3' + 5'-GCCCTCATAAGCGGACCA-3' ; positions 1356-1866) and SMAMV-10-F+SMAMV-880-R (5'-AAGCCGACTAAGAATAGTTAGC-3' + 5'-CCACAGCCATATCACTATCGC-3' ; positions 7-885) ; amplified fragments were sent for Sanger sequencing to GenoScreen (Lille, France). No contig showed similarity to satellites associated with some sobemoviruses  [1, 9]. To confirm the sequences of the 5' and 3' ends, RNA was extracted from purified virus  [5]  with TRI-reagent®. Extracted RNA was self-ligated (method 1) or ligated with a nonviral primer (method 2), using T4 RNA ligase (NEB, Ipswich, USA) before RT-PCR with specific primers SMAMV-140-R (5'-CAACTCAATCGATAATCTAATGGG-3') and SMAMV-4120-F (5'-GACTATTCAAGAATTGTCCGT-3'), 100 to 150 nt from the expected 5' and 3' ends respectively (method 1), and SMAMV-4120-F and a primer complementary to the nonviral primer (method 2).
Amplified fragments were purified from agarose gel and sent to Sanger sequencing. The sequence obtained was 4225 nt long (GenBank accession MT989351), similar to known sobemoviruses (  [1, 9]  and Table  1 ). Sequence identity with sobemoviruses was below 50% (Table  1 ). The 5' end sequence was ACAAAA, the same as that for several sobemoviruses  [9]. As in other sobemoviruses, an ACAAA motif was present at position 3382 in the genome and may constitute the start of a subgenomic RNA allowing the expression of the coat protein  [9]. The 5' untranslated region was 120 nt long (44 to 99 nt for other sobemoviruses), and the 3' noncoding region was 85 nt long (44 to 283 nt for other sobemoviruses). Secondary structure prediction with RNAfold  [8]  did not reveal any specific structure for the noncoding regions (data not shown).
ORF3 corresponds to the putative coat protein (251 aa, 27. 8 kDa), probably expressed through a subgenomic RNA initiated at the internal motif ACAAA. The RdRp region (ORF2b) showed the highest aa identity with other sobemoviruses (Table  1 ), but that identity did not exceed 50%. Multiple nt sequence alignments were performed with MAFFT alignment software  [4]. Evidence for recombination explored with RDP, GeneConv, Chimaera, BootScan and SiScan included in RDP4  [7]  did not reveal recombination for SMAMV. The best substitution method, estimated with MEGA6  [11]  for the aligned nt and aa sequences, was applied for maximum-likelihood tree reconstruction (Figure  1  and data not shown). In the complete nt sequence or in aa sequences, SMAMV did not appear particularly related to any other sobemovirus.
Phenological traits distribution is then compared to an empirical dataset of flowering plants from Sweden. (2) We then study the impact of a change of the environmental window on traits and on local diversity. We link the loss of phenological trait diversity with the ability of species to evolve for both date and spread. We expect greater losses of biodiversity when evolutionary potential are decreased. (3) Finally, we undertake metacommunity simulations along a continuum of latitudes and investigate whether dispersal or phenology evolution better explain evolutionary rescue events. For a given phenotype ( ! ,  ! ), population density ni follows a simple Lotka-Volterra system.
Empirical analysis of flowering phenological spread We test whether the pattern of more spread phenologies at extreme dates (figure  3b ) is relevant for species constrained by the same environmental window. We investigate how the flowering interval of 63 plant species in Sweden depends on their flowering time. For this dataset, the seasonality of the conditions favorable to the development of flowers (light, pollinators. ) is very marked and reduced in time. 30 specimens per species were collected in the herbarium of the Swedish Natural History Museum  (Weinbach 2015). The number of flowers per specimen was reported for each observation. We fit a Gaussian on the counting of flowering observations through the year of each species. It is assumed that the sampling effort was the same for all species over time.
mexicana) (Table  1 ). The test set was used to test the predictive power of the image processing algorithms (images from these populations were not used during the algorithm training step), while the species test set was used to test the predictive power of the algorithms on images of a different but closely related species that never seen by any parts of our process 2. 2 Manual landmarks and observed metrics Landmarks were placed thanks to Fiji software  (Schindelin et al. , 2012)  and the Landmarks package  (Longair and Jefferis, 2006). Metrics used for this study were chosen to make image processing easier because they are metrics on objects easy to spot on the image's scene. In short, two metrics were tested in this study, the "basal knobs width" (BKW) and the "basal knobs to head length" (BKTH) (Fig.
To process a CNN, the size of the "training set" must be enlarged. This was obtained by extraction of colour channels to create new images as described in  Le et al. (2020). The final number of images considered was 14 490 for training and validation (with a ratio of 80% for training and 20% for validation). A total of 30 initial images were kept for the testing step; these images were not yet seen by the network before this step. Implementation of the network used the Lasagne framework  (Dieleman et al. , 2015)  and can be found at https://github. com/linhlevandlu/EBNet_nematodes. We trained the network from scratch using K-fold cross-validation, i. e. training on 9 out of 10 of the populations and predictions on the individuals of the remaining population. Hence, each population was predicted at only one timepoint (not trained upon) and we used these predictions to extract BKW and BKTH as Euclidean distances between the corresponding predicted landmarks.
mexicana and G. pallida for BKW was not observed using CNN predictions. For both metrics, CNN predicted measurements that were statistically similar for G. mexicana and G. pallida, and significantly lower measurements for G. rostochiensis. With CCVA, measurements were extracted for 75% of the individuals. Surprisingly, predictions by CCVA (Fig. 4 C ) yielded a significantly smaller BKW for G. mexicana than for G. pallida, and a similar BKW for G. mexicana and G. rostochiensis. For BKTH, prediction by CCVA was similar to observed metrics and CNN predictions, meaning similar prediction for G. pallida and G. mexicana, and lower prediction for G. rostochiensis. Including G. mexicana in our dataset showed that the predictions could yield a different ranking depending on the metric and method used.
We have shown that we are able, mostly with data predicted by CCVA, to predict the species class of an individual and more accurately of a population. Nonetheless, the procedure used to classify individuals in this work will necessarily yield an answer between proposed choices (i. e in the species test set, between G. pallida, G. rostochiensis and G. mexicana). The classifier builds edges between each class with given data, with the aim of choosing between them, but does not build a high boundary of class. This means that we are not able to detect other species. With the aim of species detection, it would be essential to build the boundaries of each species to allow us to determine whether an unknown specimen belongs to one of the studied classes or to none of them.
Acknowledgments We wish to thank all the colleagues who helped in material multiplication, and would like to thank the  INRAE Plant Health Department and ANSES Plant Health Laboratory  for their support of the NemAlliance partnership agreement which helped to make this work possible. This work is part of the  PhD  project of  Romain Thevenoux , who is funded by  ANSES  and the  Conseil Régional Bretagne (Brittany Regional Council).