dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). For example, the predominant number of costs from Spain and France seem to be primarily related to management costs, whereas a higher amount of costs reported in Spanish corresponded to South America and seemed to be related to damage costs. Nuñez and Pauchard (2010)  found that the scarcity of scientific reports on invasive species in developing countries was associated with low funding for ecological research in comparison to other disciplines closely related to medicine, water shortage and food supply. This may explain the high proportion of reported costs related to agriculture in South American countries. Finally, the number of cost entries coming from invasive plant species reported in non-English languages also contributed significantly, and amounted to ~30% of the total money associated with plants when considering both English and non-English datasets.
Non-English sources on invasive species that are often overlooked mostly include the grey literature and unpublished reports from practitioners, resource managers, and researchers. Therefore, we demonstrated the importance of multilanguage collaborations in biological invasions, which are in essence an international issue. The non-English database now complements the original English database in an updated version of InvaCost (InvaCost_3. 0, Diagne et al. , 2020b) , and we hope that this study will encourage others that aim to bridge linguistic barriers. The benefits of these collaborations are clear: improving management efficiency, decreasing research effort, and adequately guiding policy. In that way, we have provided the Appendix S5, with abstracts and figure legends in several languages, as a proof of concept for promoting the overall message of this study.
Acknowledgements We want to acknowledge all environmental managers, national officials, practitioners, and researchers who kindly answered our request for information about the costs of invasive species. DA would also like to thank  Fawaz Azizieh  from  GUST  for the translation of the abstract into Arabic. Covid-19 lockdown provided a test of our capacities of doing science while dealing with all the problems for managing children, home and telework; we would like to specially thank our children (Juan, Pol and  Nuria Cerdá Angulo ; Laurine and  Cyrian Courchamp ;  Sorahya Van Geenen ; Ilaria and  Sophia Haubrock ; Tamayo and  Kiho Watari ;  Elaia, Antonin  and  Naellie Dobigny ; Coline and  Mathieu Renault; Margarida  and  Rodrigo Capinha ; and  Lana Lujayn Danish ) for their patience with their parents.
Available bioinformatics workflows for SARS-CoV-2 sequencing data were not directly suitable for wastewater data since they were designed for the analysis of individual patient data, where each sample is assigned to one single lineage. We therefore designed a dedicated bioinformatics workflow where we first generated PCR primer trimmed, SARS-CoV-2 aligned reads with the ARTIC pipeline. We subsequently counted the mutation frequency at each position of the virus with the iVar software and assigned lineages based on mutational signatures defined in the Pangolin Github repository  [13]  with a custom pipeline written in R (detailed in the methods section). The associated code is available at https://github. com/ucagenomix/cagablea. A global overview of the mutations found in the samples and the associated lineages is presented in Figure  3.
Clinical samples A total of 1409 nasopharyngeal swabs was collected by Biogroup (Mouans-Sartoux, France) during week 43 (2021) in the area of Nice (France). This represents the full collection of samples that was processed during this week in this area by Biogroup. Out of these 1409 samples, 172 were positive by RTqPCR. We were able to generate SARS-CoV-2 sequencing data for 81 of them. The use of patient samples complied with French legislation. During the health emergency SARS-CoV-2 related patient data and laboratory results in France are recorded in the SARS-Cov2 Screening Information System SI-DEP («Syst eme d'Informations de DEPistage», French decree No. 2020-551 of May 12, 2020,Article 11 of Law No. 2020-546 of May 11, 2020 extending the state of health emergency). In the context of this law, patients can not object to the use of their SARS-CoV-2 related data for health surveys and epidemiological monitoring.
Bioinformatics A dedicated bioinformatic pipeline was adapted from ARTIC, Pangolin and Ivar to identify the main circulating lineages. PCR primers were trimmed and reads were mapped using the ARTIC pipeline  [9]. The iVar tool [20] was used to generate for each sample a table with mutation frequency data and p-Values for altered positions of SARS-CoV-2 from the BAM files. iVar was run with a minimum base quality filter of 20 using the reference genome of SARS-CoV-2 (MN908947) and the feature file Sars_cov_2. ASM985889v3. 101. gff3 from NCBI. The tables generated by iVar were filtered for positions that were covered by at least 100 reads and for alterations with an adj p-value (Fisher's test) below 10 À8. A custom R-script was implemented to fix a known issue with the iVar output, the incorrect assignment of deletion frequencies and to code the mutations events in amino acid format.
A database with the mutations associated with known lineages was built from the publicly available data  [21]. Every mutational event in the sample was associated with mutations described in the database and matched to one or several lineages. A list of specific mutations for each lineage of interest is provided in Supplementary Figure  3. For each of these positions we calculated the ratio of the number of mutant reads to the total number of reads. The frequency of each lineage was then defined as the median of these lineage-related ratios. We usually noticed that the total percentage of the different lineages was close to 100%. Percentages below 100% were probably due to the presence of additional lineages that could not be properly identified (e. g. lineages that lack characteristic mutations that are not shared with other lineages).
Acknowledgements + Fundings PB's group is supported by  FRM  ( DEQ20180339158 ). The present work was possible thanks to the support of the National Infrastructure France G enomique ( Commissariat aux Grands Investissements ,  ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ), Conseil D epartemental  06  ( 2019-390 ), and the  3IA Côte d'Azur Investments  in the  Future  project managed by the  National Research Agency (ANR)  ( ANR-19-P3IA-0002 ). BM is supported by Plan Cancer 2018 « ARN non-codants en canc erologie: du fondamental au translationnel » (Inserm number  18CN045 ) and  Canc eropole PACA. We are particularly grateful to  Delphine Zapalski ,  Hugues Decobecq ,  Jean Lafay  and  Jean-Marc Campeggio , at the  Wastewater Department at Nice M etropole Côte d'Azur  for their excellent support throughout this project.
Genome-wide expression profiling has been a common end point for these studies for analyzing pluripotency, differentiation and the function of genetic variants associated with monogenic diseases. A growing body of transcriptomic datasets for various hPSC lines and their specific differentiated counterparts has been deposited in public data repositories  (Assou et al. , 2007; Au and Sebastiano, 2014; Ghosh and Som, 2020; Godoy et al. , 2018; Mair et al. , 2019; Mallon et al. , 2013; Streeter et al. , 2017). Despite this wealth of data, exploring gene expression profiles from different experiments is not trivial. Different challenges still remain such as the integration and interpretation of massive amount of available data, the heterogeneity in original data processing as well as the quality data sets  (Pinto et al.
, 2018). To overcome these challenges, we provide here a large well-characterized collection of human pluripotent stem cell lines and their derivatives for which transcriptomic analysis has been generated using a single experimental pipeline. Moreover, we have developed a user-friendly search engine to facilitate comparisons of gene expression data between these different biological samples. This open resource presented in SISTEMA Web portal is designed for the community to examine individual or multiple genes for expression under different states from pluripotency to differentiated conditions and under normal or pathological conditions, with 8 different monogenic diseases already represented in this growing database.
These small molecules were evaluated in the context of different pathologies such as lithium for the autism spectrum disorder caused by SHANK3 haploinsufficiency, MG132 for Hutchinson-Gilford progeria syndrome, thiostrepton and bortezomib for limbgirdle muscular dystrophy type 2D, VPA for Wolfram syndrome or Kenpaullone for spinal muscular atrophy (SMA)  (Darville et al. , 2016; Harhouri et al. , 2017; Hoch et al. , 2019). All the transcriptomic data were generated in comparison to DMSO-treated controls. Of interest, some of the data provided by the database have not been published yet all the scientific community to have access to a larger spectrum of data. All the cell lines used for the creation of this database as well as the metadata files are also available upon request and following the French legal policy.
Then, the users can define the gene(s) of interest (Figure  2A ). As an example of querying for a single gene, the expression profile of NANOG, one of the key pluripotent markers  (Chambers et al. , 2007; Silva et al. , 2009) , has been analyzed in different samples of undifferentiated hESC samples or hiPSC-derived keratinocytes. Once the queried data are processed, detailed information regarding the chromosomic localization, the gene structure, and its interacting partners is first displayed as SISTEMA interface is linked to other databases such as Genecards, NCBI, MIM, HGNC, Ensembl, Vega, and STRINGDB (Figure  2B ). For better visualization, the results are presented as a histogram and a downloadable table in which data are expressed as the normalized counts in logarithmic scale observed in each sample.
Example applications To illustrate the utility and the potential applications of SISTEMA, we first sought to evaluate the assignments of the samples from clustering the entire database in function of the expression of selected markers. For that purpose, the expression of a set of 10 genes of pluripotency, extracted from the database KEGG pathway, was analyzed across all the biological samples. Hierarchical clustering allowed the correct distinction between pluripotent stem cells and differentiated cells (Figure  4A ). Conversely, the expression profile of a set of 10 genes associated with the GO term ''spinal cord motoneuron differentiation'' led to the (D) Visualization of the expression table gathering the different information concerning each cellular sample. This table is downloadable as an excel file.
Figures C-D represent the results obtained with 9 different hESC lines. See also Table  S1. distinction between pluripotent stem cells and their motoneuronal derivatives, confirming the relevance of the SISTEMA database (Figure  4B ). We also evaluated the possibility to define the temporal expression profile of 10 genes known to be associated with the development of the melanocyte lineage (from GO) at different time points of the   hsa04550 ) on all the biological samples included in SISTEMA database. These genes were selected on the basis of the different quality controls performed in the laboratory to validate the pluripotency of the different hiPSC and hES lines. Other genes have been selected based on the literature such as the example of ID1 gene that belongs to a family called ''inhibitors of the differentiation'' and previously described to be specific of human pluripotent stem cell lines  (Aloia et al.
Concordant with our observations, this gene has been previously shown to be involved in the regulation of pigmentation rather than differentiation  (Kusters-Vandevelde et al. , 2010; Van Raamsdonk et al. , 2004). Altogether, these analyses validated the relevance of the SISTEMA database both to evaluate the expression profiles of different gene sets across a large battery of biological samples and to visualize the expression profiles of gene sets along lineage specification. Since their first isolation, human pluripotent stem cells have sparked widespread enthusiasm for the development of new models of human disease and have enhanced platforms for drug discovery. To evaluate the potential of SISTEMA database for these applications, we focused on the expression profile of the survival motor neuron 1 (SMN1) gene, the homozygous deletions/mutations of which causes SMA, a severe genetic neuromuscular disease  (Lefebvre et al.
, 1995). As expected, SMN1 expression was significantly decreased only in the biological samples derived from SMA patients (Figure  5A ). Next, we sought to explore SISTEMA database to identify a potential small molecule capable to increase the level of SMN1, which therefore would present a therapeutic interest. Thanks to an unbiased analysis including all the pharmacologically treated biological samples present in SISTEMA database, we identified three proteasome inhibitors, bortezomib, thiostrepton, and MG132, that lead to an increased expression of SMN1 compared to mocktreated controls. The results were concordant with previous studies that showed at least for two of these proteasome inhibitors, bortezomib and MG132, a beneficial effect on SMN1 expression both in vitro and in vivo  (Kwon et al.
DISCUSSION Here, we present the data access and presentation services that SISTEMA database provides, which enable the community to both discover and to reuse the cell lines and data included in this project. SISTEMA further complements and outspreads the two gene expression databases dedicated to pluripotent stem cells, StemCellDB for human embryonic stem cells  (Mallon et al. , 2013)  and HipSci for human induced pluripotent stem cells  (Streeter et al. , 2017). These online resources are based on either microarrays or whole transcriptome RNA sequencing technologies, whereas the main strength of SISTEMA lies on the use of a unique pipeline to generate transcriptomic analysis providing a collection of quality-controlled curated data. Most of the other data resources from public repository, i.
e. EBI expression atlas or NCBI short read archive, are also useful tools to explore gene expression data set but cover a large set of biological field from microbiology to stem cells biology which makes difficult to easily and rapidly analyze a specific research. One of the objectives of SISTEMA is to provide a rapid overview of the gene expressing by providing a comprehensive and limited number of hESC and hIPSCs cells and their derivatives. Additionally, SISTEMA relies on the use of a recent technology, named AmpliSeq, designed for targeted amplification of over 20,000 distinct human RNA targets simultaneously using a highly multiplexed amplification method  (Li et al. , 2015). Because of this targeted nature and small amplicon size, the amount of raw data required for accurate gene expression quantification is much smaller with the AmpliSeq technology than classical RNA sequencing.
This clearly represents an advantage since for large scale of RNA sequencing experiments involving hundreds or even thousands of samples the bioinformatics analysis as well as the data storage needs can rapidly become strenuous  (Schmidt and Hildebrandt, 2017). All the data included in SISTEMA have thus been generated through this unique experimental workflow and on the same platform, giving rise to standardized operative procedures and the production of homogeneous samples in term of quality and coverage depth. Since the derivation of the first human embryonic stem cell line more than two decades ago  (Thomson et al. , 1998) , human pluripotent stem cells have ushered in an exciting new era for the fields of stem cell biology and biomedical research. Nowadays, human pluripotent stem cells are widely used for disease modeling, drug discovery, and cell therapy development.
Data collection Except for the RNA reference sample, all the samples have been generated at ISTEM. Human embryonic cell (hESC) lines were used following the recommendation of the French Law of Bioethics and declared at the French Agency of Biomedicine (Number SASB1020178S). For Human induced pluripotent stem cells (hiPSC), tissue samples were collected under specific participant consents. The SISTEMA portal provides all information regarding the generation, the culture conditions of human pluripotent stem cells (hPSC) as well as their differentiation. Such information is also provided in Table  S1. A unique standardized name for each sample was generated by combining ''cell type''-''Phenotype''.
Amplicon annotation The quality control of the sequencing data was evaluated using FastQC. The reads were trimmed using Prinseq (v0. 20. 4) (-trim-right 20) and filtered by average quality score (-trim-qual 20). Reads were mapped to the Amplicons sequence reference using bowtie2 local (v2. 3. 4. 1). Reads below a mapping score of 10 or multi-mapped were filtered using samtools (v0. 1. 19). The level of gene expression was quantified with the specific annotation described above. Each sample was normalized by RPM (reads per Million) and the expression values were converted in logarithm scale (log10(x+1)) using R (v3. 6. 0).
Data implementation and web interface SISTEMA database was implemented using MySQL (www. mysql. com) and organized on three main tables. Briefly the first table combines all the information extracted from NCBI such as the chromosome coordinate, the gene type and the gene name (present in the seven main genetic databases: NCBI, EnsEMBL, HGNC IMGT/GENE-DB, MIM et Vega  (Ashurst et al. , 2005; Yates et al. , 2017). The second table contains all the data curated for the sample sequence on the platform such as the library and sequencing number, the sequencing barcode, NGS user, sequencing date, FASTQ_name, sample_name, replicate, cell line, cell matrix, the team and the project). The last table corresponds to the expression value counting table transform in logarithm scale. For presentation and interpretation of the data, we chose to provide only reads per million for each gene analyzed rather than a presentation based on fold change analysis.
The web interface was developed using the programming language Php and JavaScript. Highcharts (ª 2020 www. highcharts. com) and StringDB  (Jeanquartier et al. , 2015)  APIs (application programming interface) were used. For NCBI viewer, a direct URL link in an embedded window is used. To facilitate the comprehension of the Figures 2C, 3B, 3C, and 4 on the manuscript we used color coding to label the cell type using R. The Figure  5B  was generated using Graphpad Prism 5.
QUANTIFICATION AND STATISTICAL ANALYSIS For RNAseq quantification, reads were mapped to the Amplicons sequence reference using bowtie2 local (v2. 3. 4. 1). Reads below a mapping score of 10 or multi-mapped were filtered using samtools (v0. 1. 19). The level of gene expression was quantified with the specific annotation described above. Each sample was normalized by RPM (reads per Million) and the expression values were converted in logarithm scale (log10(x+1)) using R (v3. 6. 0). For Figure  5 , data have been extracted from SISTEMA and are represented as the mean of normalized count G SEM and were statistically analyzed with DESeq2 R package using Negative binomial generalized linear model and Wald's test; *p < 0. 05, **p < 0. 01.
Figure4. Pairwise dissimilarities of markers identified from specific differentiation status (''complete'' method and Euclidiean distance) (A) Hierarchical clustering for 10 genes extracted from (A) KEEG ''Signaling pathways regulating pluripotency of stem cells'' (hsa04550) on all the biological samples included in SISTEMA database. These genes were selected on the basis of the different quality controls performed in the laboratory to validate the pluripotency of the different hiPSC and hES lines. Other genes have been selected based on the literature such as the example of ID1 gene that belongs to a family called ''inhibitors of the differentiation'' and previously described to be specific of human pluripotent stem cell lines (Aloia et al. , 2015; Hong et al. , 2011). (B) Level of expression of 10 genes selected from GO term ''Spinal Cord Motor Neuron differentiation (GO 0021522) in hPS cells and hPS-derived spinal motoneurons.
Stem is part of the Biotherapies Institute for Rare Diseases (BIRD) supported by the Association Francaise contre les Myopathies (AFM-Telethon). This project was supported by grants from Laboratoire d'Excellence Revive (Investissement d'Avenir; ANR-10-LABX-73) and the program ''investissement d'avenir'' INGESTEM. We thank the groups of Anselme Perrier, Laetitia Aubry, Christine Baldeschi, Xavier Nissan, Alexandra Benchoua, Christelle Monville, Christian Pinset, for providing biological resources and Lina El-Kassar for karyotyping the cell lines. We gratefully acknowledge support from the PSMN (Pole Scientifique de Modelisation Numerique) of the Figure 6. Large spectrum of applications offered by SISTEMA.
Data and code availability Some data sets used in this work are available from publicly available sources and have been deposited at GEO (GSE104091 and GSE119841). Accession numbers are listed in the key resources table. RNAseq data are publicity available on a user friendly interface and freely available at http://sistema. ens-lyon. fr All original code in this paper is available from the lead contact upon request. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
org/10. 1016/j. isci. 2021. 102767. ACKNOWLEDGMENTS Qiagen Ion PIä Hi-Qä Sequencing 200 Kit Thermo Fisher Scientific Ion AmpliSeqä Transcriptome Human Gene Expression Kit Thermo Fisher Scientific Ion PIä Hi-Qä OT2 200 Kit Thermo Fisher Scientific Ion PIä Chip Kit v3 Thermo Fisher Scientific Deposited data I-RNeasy Plus Mini Kit Analyzed data This paper Cat. No. / ID:74134 A26433 A26325 A26434 A26771 http://sistema. ens-lyon. fr/index. php Raw and analyzed data Galvan et al. , 2018 https://doi. org/10. GEO: GSE104091 1093/brain/awy057. (Continued on next page).
In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecule's calculated mass. These neutral nodes provide researchers with several advantages.
Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest.