dataesr/scientific-paragraphs-categorization huggingface dataset:
ncbi. nlm. nih. gov/assembly/GCA_000689215. 1). Blast searches using the mRNA sequence as query against European sea bass WGS databases allowed to identify the linking group (i. e. LG14_196, Sequence ID: CBXY010003402. 1) containing the sghC1q gene sequence. GENESCAN program (http://argonaute. mit. edu/GENSCAN. html) was next used to locate and identify the flanking genes of sghC1q in European sea bass genome. Orthology assignment was performed by comparing these flanking genes with those of sghC1q genes in other species which genome is available on Ensembl genome browser databasis (https://www. ensembl. org/info/website/index. html). Special attention was focused on the zebrafish sghC1q genes listed by Carland and collaborators  [58]. Protein sequences of 25 cerebellin (cbln11) orthologs were obtained on the Genbank databasis (https://www.
ncbi. nlm. nih. gov/gene/100149559/ortholog/?scope=186625 Alignment of European sea bass CBLN11 protein sequence with orthologs from clupeocephala species was performed using MAFFT program in SnapGene® software (Version 4. 3). Phylogenetic reconstruction was performed by aligning CBLN11 protein sequences of clupeocephala species using MAFFT v7. 313 in automatic mode  [59]. Misaligned regions or with a large number of gaps were trimmed using Gblocks [60] (-t=p; -p=n; -b3=8; -b4=5; -b5=h) resulting in a total of 93 aligned amino acid sites. Through IQ-TREE [61], the maximum likelihood (ML) estimation of the MLST tree was performed assessing branch supports with a ultrafast bootstrap approximation (1,000 iterations) and a single branch tests (1,000 bootstrap replicates). The WAG model was identified as the best-fitting substitution model according to the BIC criterion using ModelFinder  [62].
Genbank databasis indicated twenty five orthologs of cbln11 gene limited to clupeocephala species. Alignments of the full protein sequences of European sea bass CBLN11 protein with these orthologs revealed that the C1q domains were conserved among species but otherwise there was a great degree of sequence divergence in size and amino acid composition (Figure  3 ). In some species (e. g. Cyprinodon variegatus, Poecilia formosa, Sinocyclocheilus anshuiensis, Denticeps clupeoides), CBLN11 sequences are short and do not have a signal peptide and/or coiled coil region. Our phylogenetic analysis based on the most conserved regions of CBLN11 sequences shows that European sea bass, two cyprinodondiforme and one beryciforme species clustered together and quite separate from other clusters that include different orders of clupeocephala (Figure  4 ).
This role would be in agreement with the known antibacterial and immuno-regulatory properties of several C1qDC proteins in marine organisms including fish species  [49] [50] [51] [52] 67]. Our alignment data indicated that the C1q domain known to be involved in target binding and formation or stabilization of the hydrophobic core of the gC1q structure is relatively well conserved among CBLN11 orthologs  [44, 54, 68]. This data suggests that this C1q domain might play important roles in CBLN11 activity. However, CBLN11 function may be divergent among the clupeocephala species given the differences of sequence (i. e. lack of signal peptide, heterogeneity of coiled coil region) observed among orthologs. There is very limited information related to cbln11 gene expression available in the literature and databases.
Legends Figure  1 : Nucleotide and deduced amino acid sequences of the full length coding region of cbln11 gene. The amino acids are shown as single-letter codes above the nucleotide sequence. The signal peptide, the coiled coil region and the globular C1q domain revealed by SMART analysis (http://smart. embl-heidelberg. de/) are boxed in white, grey and black, respectively. Primers used for RT-PCR cloning of the full length cDNA and qPCR analysis are underlined using (_) and ( ) respectively. Figure  2 : Identification of European sea bass cbln11 gene by microsynteny analysis. Flanking genes of cbln11 gene are partially conserved between European sea bass and zebrafish (Danio rerio). Genomic sequence including European sea bass cbln11 gene is originated from Genbank databasis (GenBank: CBXY010003402.
Acknowledgments We wish to thank  Dr Lucile Durand-Le Rolland  from  Laboratory of Microbiology of the Extreme Environments  ( UMR 6197 IFREMER/UBO/CNRS ) for microscopic analysis. This work was supported by  LabexMer  ( ANR-10LABX-0019 , OASYS  project), the  Ministry of Ecological and Solidarity Transition and the Foundation for Biodiversity Research (Ocean Acidification Program ,  PACIO  project) and the  Deutsche Forschungemeinschaft ,  PE 1157/8-1 ,  MA4271/3-1  (the  FITNESS  project).
Both approaches have been successfully applied to different mosquito species and geographical contexts  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , resulting in a better understanding of their distribution  [5-8, 11, 12, 16]  and dynamics  [9, 10, 13, 17, 18]  and the assessment of different mosquito control strategies  [19, 20]. However, most case studies only develop one of the two approaches (either empirical  [5-8, 11, 12, 14, 16]  or process-based  [9, 10, 13, 15, 17]  depending on the availability of data and knowledge), and do not compare the capacity of the two approaches to predict mosquito population dynamics. Moreover, although many models have been developed, including spatially explicit simulation models (e. g. , 'Skeeter buster' software for Aedes aegypti  [15] ), there is a lack of operational tools that can be used by public health authorities and vector control services.
Different modelling studies of Ae. albopictus distribution and dynamics have been developed in recent years. Distribution maps have been derived from environmental and meteorological datasets using empirical models, at worldwide  [7] , regional  [8] , national  [11, 12]  and local scales  [6, 16]. In Reunion Island, theoretical process-based population dynamics models have been used to assess control strategies  [20, 25] , taking into account mosquito dispersal  [26, 27]  and the impact of human behavior  [28]. Nevertheless, none of these precedent research efforts have addressed both space and time dynamics of Ae. albopictus to produce mosquito density maps that can be used by the public health authority to target vector surveillance and control actions. Our objective was twofold. First, we developed and compared two types of models of Ae.
Meteorological data The meteorological service 'Meteo France' (https://publitheque. meteo. fr) provided the daily temperature (minimum and maximum) and rainfall records from 2011 to 2014 at 32 weather stations, including the five weather stations where entomological data were collected (Fig  1 ). Indeed, although entomological data were collected between 2012 and 2014 (see above and S1 File), the acquisition of meteorological data covering the year before the period of interest was necessary for both models. The data was used for the calculation of climatic variables to be tested in the empirical approach, and for the initialization of the process-based model.
A Support Vector Machine (SVM)  [36]  is a supervised learning technique that analyses data and identifies patterns used for classification or regression. SVM maps input vectors (here the weather variables) to a higher dimension feature space using kernel functions in order to find maximum separating hyperplanes. Here we used the SVM version called Support Vector Regression (SVR) able to perform regression with continuous variables (here the mean number of larvae per trap). The SVR method was used to perform the non-linear regression with a radial kernel, a cost coefficient set to 1 and the epsilon parameter set to 1. All of the statistical calculations were performed in R language  [37]  using the package "e1071"  [29]. For some input variables such as the ones related to rain accumulation, a logarithm was applied to obtain a Gaussianlike distribution which gave better results in terms of mean square error.
Development of the 'ALBORUN' tool Spatial dynamic models of Ae. albopictus populations were built within the 'Ocelet' language and simulation environment (www. ocelet. org). This programming language is dedicated to the modelling of spatially explicit systems and their dynamics and facilitates the handling of spatial information using interaction graphs  [43]. In the 'ALBORUN' model, the main elements of the models ('entities') are i) the 1,203 operational zones defined by the vector control service (polygon geometry), characterized by their respective values of standard fixed (κ Xfix ) and variable (κ Xvar ) environment carrying capacities, and ii) the 32 weather stations (point geometry) whose daily rainfall and temperature are imported as text file (csv format). Operational zone entities interact with the weather stations through a 'relation': the temperature and rainfall of each operational zone is defined as those of the closest weather station.
The Ocelet source code is available on Github (https://github. com/OceletTeam/ocelet) and the 'ALBORUN' files are available in a dataverse repository (doi:10. 18167/DVN1/XF2I3L). To provide an operational tool for the staff of the vector control service of the Regional Health Agency, a user-friendly interface was developed using Xthe JavaFx library (https://docs. oracle. com/javafx). The Ocelet simulation environment automatically translates the model written in Ocelet language into a program written in Java programming language (www. java. com). That program is saved in the form of a java archive (jar) file which can be embedded into the JavaFx based user interface.
Evaluation of the models The predictions of both the empirical and process-based approaches were consistent with the observed Ae. albopictus larvae abundance in the five collection sites (Fig  4 ). The predicted and observed abundances were highly correlated in the sites with higher larval densities and marked seasonal variations (St-Paul, Possession, Ste-Marie) (Table  4 ). In the eastern site (St-Benoit), where the observed Ae. albopictus abundances are low (<20 larvae/trap) with few seasonal variations, the correlation coefficients were lower and not significant. The predictions of the process-based model for egg dynamics were significantly correlated with observed egg abundances (see S3 File). The prediction accuracy of the empirical model deteriorated slightly when the 5 fold method is used or when the calibration is performed over the full dataset leaving out the data from the site in question.
'ALBORUN' simulation tool With a user-friendly interface ('ALBORUN' tool, see S4 File), the simulation model displays several input parameters that are used to control the simulations from the user interface. The users' choices include the time interval and the geographical area (operational sectors) to run the simulations, as well as the input folders (the folders where input data are stored as shapefiles for weather stations and operational zones, and csv text files for daily weather data), and the output folder (the folder where outputs are saved as a shapefile for visualization with a Geographic Information System and a log file).
Spatial model outputs Using daily precipitation and temperature data collected from a network of weather stations and the 'ALBORUN' tool, empirical and process-based models are used to simulate the spatial � For the empirical model, Root Mean Square Errors are reported for each site when the model is calibrated using the full data set, a 5 fold method, and when the model is calibrated using all data except the site in consideration. https://doi. org/10. 1371/journal. pone. 0227407. t004 dynamics of Ae. albopictus population over Reunion Island, at a spatial scale adapted to vector control interventions  (Fig 5). For each operational zone defined by the vector control services, the 'ALBORUN' tool predicts the abundances of Ae. albopictus mosquitoes per stage at a weekly frequency (this frequency was defined in consultation with the vector control service).
These conditions correspond to the coastal, more densely populated areas, where a good knowledge of the population dynamics of Ae. albopictus is needed for the organization of vector control measures by public health authorities. In these areas, the strong agreement of the two models strengthens the confidence in models' predictions (S3 Fig). In other areas, the process-based approach should be preferred, as it is more reliable than the empirical approach to predict mosquito population dynamics in areas where the meteorological conditions differ from the observed dataset.
Transfer of research results to stakeholders and decision-makers The 'ALBORUN' tool developed in this study was successfully transferred to the vector control service of the Regional Health Agency as a mapping tool for the surveillance and control of vector populations. To the best of our knowledge, this tool is the first population dynamics model for Ae. albopictus populations in a tropical environment that simulates in time and space mosquito densities using daily meteorological data and environmental characteristics. It allows one to compare different areas and/or periods of time, and helps decision-makers target areas for surveillance and control. Whereas all recent modelling studies of Ae. albopictus distribution  [6-8, 11, 12, 16]  use environmental data such as the land cover or satellite-derived indices as proxies of the suitable areas for Ae.
albopictus, the 'ALBORUN' tool relies on field data on the number of potential oviposition sites. From this large and exhaustive dataset (all operational zones are monitored by the Regional Health Agency services), the inputs of the 'ALBORUN' tool are as close to reality as possible, and indeed the model outputs reflect the high spatial heterogeneity of Ae. albopictus populations that is observed on Reunion Island  [24].
According to our expertise, the values are realistic for Reunion, but additional local experimental studies may improve the parameter and function estimates, and consequently the model outputs. In addition, it should be noted that the evaluation of the models was based on one-year entomological field data for four out of the five study sites. New entomological collections in varied eco-climatic zones of Reunion Island would be needed to assess the validity of the 'ALBORUN' tool at a regional scale. In particular, standard mark-release-recapture experiments  [56]  would allow one to evaluate the capacity of the process-based model to predict the absolute values of mosquito densities. Moreover, such experiments would allow evaluating the impact of mosquito dispersal on the population dynamics.
Moreover, the model of mosquito population dynamics could be combined with an epidemiological model of transmission to predict the areas at risk of transmission for different pathogens, such as dengue, chikungunya, or Zika viruses. Another perspective would be to apply the 'ALBORUN' model in other tropical areas and test its genericity. Due to the way it was constructed, the process-based model of Ae. albopictus developed for Reunion Island can be used in other areas, with a geographic breakdown adapted to the needs of health and vector control policy makers. However, its application may be limited by the availability of environmental input data. Indeed, in Reunion the network of weather stations is very dense (Fig  1 ), and accurate data on the spatial distribution of oviposition sites are collected on a regular basis by the vector control service of the Regional Health Agency.
Acknowledgments We thank the  French National Weather Service Meteo-France  for providing the needed meteorological data. The authors thank Helène Thebault,  Olivier Reilhes ,  Boris Dumas ,  Manuel Rodicq ,  Brian Kurtkowiak Dafreville , and all of the technicians and engineers of the vector control service of the  Regional Health Agency  for fruitful discussions during the project and entomological data collections.
This study was partially funded by the  Regional Health Agency in Reunion Island  (https:// www. ocean-indien. ars. sante. fr/),and  FEDER INTERREG   TROI  project ( 2015-2017 ) under the platform in partnership One Health Indian Ocean (www. onehealth-oi. org). MM received a grant from  European Research Council  (https://erc. europa. eu): ERC   Consolidator grant   n˚682387   / REVOLINC  project. JSD ( Regional Health Agency, Reunion Island ) provided part of the entomological dataset and approved the final manuscript.
All datasets analyzed during the current study are available, with the exception of the meteorological data (available from MeteoFrance National Weather Service: https://publitheque. meteo. fr. ). The authors did not receive special access privileges to the data that others would not have. Entomological data are provided in Supplementary Information files (S1 Table and S1 File). ALBORUN source codes will be held in a public repository (dataverse. cirad. fr doi:10. 18167/DVN1/XF2I3L) after acceptance of the manuscript. Ocelet software is available from http://www. ocelet. fr/.
The Hand2 (F) and Gdnf (G) genes were quantified by qPCR using primer pairs, as indicated in Figure  S7B. Shown is the mean of 3 experiments (+ SEM). Student's test was performed to compare the H3K27me3 chromatin immunoprecipitation (ChIP) signal with the background IgG signal. (H) Inhibition of H3K27me3 by the PRC2 inhibitor UNC1999 compared with vehicle or the negative control UNC2400. (I) Unsupervised hierarchical clustering based on the 1,000 most variant features in RRBS data from cells treated with UNC1999, UNC2400, or vehicle. (J) Smooth scatterplot comparing methylation levels identified by RRBS in Sdhb À/À cells treated with UNC1999 or vehicle. (K) Collective migration was followed using a wound scratch assay after treatment with GSK343. Migration is represented as the closure percentage of the wound.
2eÀ16, Fisher's exact test). Molecular Signatures Database (MSigDB) enrichment analysis showed strong enrichment in PRC2 targets among the genes hypermethylated at their TSS 500 in Tet1+Tet2 KD cells (Figure  S7A ), as in Sdhb À/À cells. Accordingly, PRC2 targets that are both downregulated and DNA hypermethylated in Sdhb À/À cells were also repressed in Tet1+Tet2 KD cells. In contrast, the few targets downregulated but not DNA hypermethylated were only repressed and marked by H3K27me3 in Sdhb À/À cells but not in Tet1+Tet2 KD cells (Figures  4E  and S7B ). Altogether, these data reinforce the demonstration that, in Sdhb À/À cells, DNA methylation of most PRC2 targets is the result of TET enzyme inhibition rather than increased PRC2 activity and recruitment of DNA methyltransferase. They are in accordance with other studies in ESCs showing that TET1 and PRC2 have redundant targets  (Williams et al.
, 2013). Similarly, Sdhb KO in imCCs results in TET-mediated DNA hypermethylation even under normoxic conditions. The hypoxic conditions we used in this study were neither (C) Closure index of the wound scratch assay after 15-h migration of the indicated cell lines pre-cultured for 72 h at 21% or 2% O 2. (D) Actin staining was performed using fluorescent phalloidin toxin in the indicated cell lines cultivated for 72 h at 21% or 2% O 2. (E) qRT-PCR measurement of known drivers of neuro-endocrine-to-mesenchymal transition  (Loriot et al. , 2015)  in the indicated cell lines incubated for 72 h at 21% or 2% O 2. All data are the mean of 3 experiments. (F) Evaluation of in vivo metastatic behavior of Tet1+Tet2 KD imCCs (Cl74 and Cl12) precultured for 72 h at 21% or 2% O 2. After preculture in normoxia or hypoxia, 10 6 cells were injected in PBS in the tail vein of NMRI nude mice.
41 days later, the mice were sacrificed, and the lungs were snap frozen in liquid nitrogen. genomic DNA (gDNA) extraction was performed on entire lungs, and metastatic charge was quantified by qPCR using primers specific for the loxP site present in imCCs. The data in (A), (B), (C) and (E) are the mean of 3 experiments (+ SEM). Student's test was performed to compare normoxic and hypoxic conditions (*p < 0. 05, **p < 0. 01, ***p < 0. 001). necessary nor sufficient to inhibit TET activity in our cells. Therefore, the effect of hypoxia we observed on cell behavior is not mediated by increased DNA methylation but, rather, by HIF2a-mediated gene transcription. Thienpont et al. (2016)  showed that acute hypoxia is necessary to repress TET activity because the Michaelis Constant (Km) for TET1 and TET2 were, respectively, 0.
Duplicated reads were removed from the datasets using Picard Tools (http://picard. sourceforge. net) and total number of reads were down-sized to the minimum available read count for each type of samples (MeDIP 48. 10 6 reads and INPUT 74. 10 6 reads for human samples, and MeDIP 62. 10 6 reads and INPUT 73. 10 6 reads for mouse samples) using SAMtools  (Li et al. , 2009). Peaks were identified using MACS  (Zhang et al. , 2008)  (-s 50 -p 10 À5 -m 10,30) and associated to closest TSS using bedtools  (Quinlan and Hall, 2010). We used GAT  (Heger et al. , 2013)  to perform genomic association analysis of our peaks with gene annotations and CpG island, shore, and shelf annotation. We used the Bioconductor DiffBind package  (Stark and Brown, 2011)  to define a common set of peaks between all samples (peaks common to at least two samples are considered).
We used the bedmap tool from the BEDOPS suite  (Neph et al. , 2012)  to collect read counts for all MeDIP and INPUT samples within the common set of peaks. We created a normalized binding matrix with, for each peak and MeDIP sample, a read count normalized with peak size. The log2 normalized binding matrix from the 1,000 most variant peaks (based on standard deviation of the normalized read counts) was used to classify the samples according to their binding patterns using hierarchical clustering (with Euclidian distance and Ward method). We used the Bioconductor DESeq package  (Anders and Huber, 2010)  to test for differential read counts between samples, without correcting for size factors as libraries had been previously down-sized. d KEY RESOURCES.
RNA-seq analysis Quality of reads was assessed for each sample using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Fastq files were aligned to the reference Human genome hg19/GRCh37 or to the reference Mouse genome mm10 with tophat2 (-p 16 -r 150 -g 2-library-type fr-firststrand)  (Kim et al. , 2013). We removed reads mapping to multiple locations. We used HTSeq  (Anders et al. , 2015)  to obtain the number of reads associated to each gene in the Gencode vM9 (Mouse) database, restricting to proteincoding genes, antisense and lncRNAs. We used the Bioconductor DESeq package  (Anders and Huber, 2010)  to import raw HTSeq counts for each sample into R statistical software and extract the count matrix. After normalizing for library size, we normalized the count matrix by the coding length of genes to compute FPKM scores (number of fragments per kilobase of exon model and millions of mapped reads).
We used the Bioconductor DESeq package to obtain size factors and dispersion estimates, and perform differential expression analysis. oxRRBS and RRBS Analyses RRBS was performed by Integragen SA (Evry, France), as previously described  (Letouze et al. ). This analysis does not distinguish 5mC from 5hmC, and considering the low levels of 5hmC compared to 5mC, it represents a good estimation of 5mC levels. oxRRBS analysis is required for 5hmC quantification. An additional oxidative step converts 5hmC to 5fC which is sensitive to bisulfite treatment, unlike 5mC and 5hmC. Each sample is thus run for both RRBS and oxRRBS procedures in parallel. 5hmC levels are calculated by subtracting RRBS -oxRRBS signals. oxRRBS was performed by Integragen SA (Evry, France), as described in  Letouzeet al. (2013)  for the MspI Reduced Representation part, and following the TrueMethyl 24 kit manufacturer protocol user guide version 3.