dataesr/scientific-paragraphs-categorization huggingface dataset:
Results will be displayed directly in a new window in the mitoXplorer platform. The network analysis function has been developed using the NetworkX Python library  (15). The cellular interactomes were created from the STRING database (v11) (16) using high-confidence physical protein-protein interactions and have been added to the MySQL database. The network neighbourhood of a selected gene is explored by using the 'environment search' algorithm described by the viPEr Cy-toscape app  (6). Once the analysis has been performed, the active subnetwork is drawn using the D3. js library  (10). IDconverter. In order to facilitate data upload, we added an IDconverter to allow usage of any gene identifier. This function converts input gene IDs (e. g. ENSEMBL, Entrez, RefSeq, Genecode, Flybase) on the fly to gene symbols, without any intervention from the user.
The conversion is performed using the latest release of BioMart  (11). BioMart data were downloaded and stored in text format. We retrieved them locally for reasons of speed. Upgrade to Python. In order to enable local deployments and future development of the platform, we upgraded the backend of the web tool from Python 2. 7 to Python 3. 6. The new mitoXplorer 2. 0 menu is detailed in Supplementary Figure  S2A-F ; details on how to use new mitoXplorer 2. 0 functions are shown in Supplementary Figures  S3  and S4  and online at the mitoXplorer 2. 0 tutorial pages (http://mitoxplorer2. ibdm. univ-mrs. fr/tutorials).
Data processing for use cases 1 and 2 Flight muscle data (use case 1). Temporal expression data of developing D. melanogaster IFM were taken from GSE107247  (17)  in the form of raw read counts. In order to obtain differential expression values for the different time points versus the mean over all time points, we created two pseudo-replicates of the mean by first calculating the mean over all time points using one replicate per time point each. Normalization of the raw read counts as well as pairwise differential expression analysis was performed using DE-Seq2  (18).
Ataxia data (use case 2). Data from the ataxia mouse model ATXN1 82Q Tg from cerebellum were taken from GSE122099  (19). We downloaded sequencing reads and mapped them to mouse genome version mm10 using STAR  (20)  with default parameters. FeatureCounts  (21)  was used to calculate read counts. Normalization and differential expression analysis were done using DESeq2 via RN-fuzzyApp  (22). We compared all conditions to each other: 5week wild type (WT) versus ATXN1 82Q Tg, 12-week WT versus ATXN1 82Q Tg, 5-versus 12-week WT, and finally 5-versus 12-week ATXN1 82Q Tg. We uploaded the resulting data to mitoXplorer 2. 0. For enrichment analysis of genes found in the active subnetworks extracted by mitoXplorer 2. 0, we first downloaded and parsed the respective JSON files to obtain gene lists. These were then submitted to EnrichR  (7) ; for further downstream analysis, we used the KEGG resource  (23).
We used RNA expression data from a mouse model of SCA1 from  (19) , which looked at cerebellum and inferior olive of 5-and 12-week-old transgenic ATXN1 82Q (ATXN1 82Q Tg) mice. We focused our analysis on the cerebellum and were mostly interested in the temporal differences of pathways possibly affecting mitochondrial Ca 2+ signalling and transport. We uploaded differential expression data of ATXN1 82Q Tg compared against WT at 5 and 12 weeks, as well as the temporal comparisons of WT (5 and 12 weeks) and ATXN1 82Q Tg (5 and 12 weeks), to mitoXplorer 2. 0. We used comparative plots to visualize differences in gene expression in 'Ca 2+ signalling and transport' in those four datasets. Among the genes most affected in this mito-process was Itpr1 (Figure  3A  and Supplementary Figure  S4A ), an intracellular receptor for inositol 1,4,5-trisphosphate that is located at the endoplasmic reticulum (ER).
DISCUSSION Here, we present an important upgrade of the mitoXplorer visual data mining platform. While mitoXplorer version 1. 0 (1) allowed to inspect mitochondrial datasets in multiple ways, mitoXplorer 2. 0's integrative analysis functions permit the user to go beyond simple data visualization. Our new integrative functions can be used to better understand the interaction between mitochondria and their cellular environment, as we have shown in two of our use cases. In the first use case, we showed how the new functions of mi-toXplorer 2. 0 (the mito-process enrichment, time-course visualization and TF-enrichment) can be used to identify key transcriptional regulators of important mitochondrial processes. In the second one, we employed network analysis, which, by integrating the mitochondrial interactome into the complete cellular interactome, allows the identification of important non-mitochondrial genes that are connected with and potentially regulate mitochondrial processes.
FUNDING Agence Nationale de la Recherche  [ ANR-18-CE45-0016-01  to B. H. H. ];  Fondation pour la Recherche Médicale  [ MND202003011460  to B. H. H. ];  Centre National de la Recherche Scientifique ;  Aix-Marseille University. Funding for open access charge:  Agence Nationale de la Recherche  [ ANR-18-CE45-0016-01 ];  Fondation pour la Recherche Médicale  [MND202003011460].
DATA AVAILABILITY mitoXplorer 2. 0 is available as a web server at http:// mitoxplorer2. ibdm. univ-mrs. fr. The source code together with installation instructions is available in our GitLab repository (https://gitlab. com/habermann lab/MitoX2). Public gene expression data used for this study were obtained from the Gene Expression Omnibus repository at NCBI  (42). Drosophila developmental IFM time-course data have been downloaded from GSE107247  (17). Mouse ATXN1 raw sequencing data have been downloaded from GSE122099  (19).
(2006)  that we modified by using alternative allometric parameterization, allowing to measure abundance changes of resident species over time in absence/presence of the invader with few empirical data inputs. We computed impact metrics supporting the classification of population abundance modifications and extinctions. Specifically, we distinguished positive or negative impacts on abundances (i. e. , increases or decreases) and we characterized the ecological interactions involved in these modifications (i. e. , predation, competition, trophic cascades). We applied this approach to assess the ecological consequences of the recent invasion (∼10 years) of the European catfish (Silurus glanis) in large peri-Alpine lakes that are representative of successful water quality restauration plans and biodiversity management which both could be threatened by new pressures originating from modifications of resident species abundances following invasive species introductions.
In order to provide global analyses and results valuable for large peri-Alpine lakes, we applied our approach to a dataset including species co-occurring in Lake Bourget, Lake Geneva and Lake Annecy. By combining food web theory and a population dynamic model, we aimed to answer three major questions regarding the impacts that S. glanis could exert on a resident community typical of large French peri-Alpine lakes. First, we asked which S. glanis body size could cause the highest changes on species abundances. We hypothesized that large S. glanis (>100 cm) could induce the greatest magnitude in species abundance changes due to its highest position in the food web. We then focused at identifying whether direct or indirect negative ecological interactions (i. e. , predation, competition and trophic cascade decreasing species abundances) could be balanced by indirect positive interactions (i.
, 2010). Interactions not corresponding to these three ecological interactions were classified as "Others. " All statistical and graphical displays were performed using R. 3. 5. 1 (R Core Team, 2018) with the packages ade4  (Dray and Dufour, 2007) , deSolve  (Soetaert et al. , 2010) , cheddar  (Hudson et al. , 2013) , igraph  (Csárdi, 2019) , NetIndices  (Kones et al. , 2009) , foodweb  (Perdomo, 2015) , ggplot2  (Wickham, 2016). R Markdown files with data and R codes summarizing the conducted analyses are available at https: //github. com/chloevagnon/aNM-and-population-dynamics to provide reproducible examples.
Acknowledgments We are especially grateful for the time spent at the  Department of Biology of the University of Fribourg (Switzerland)  and the associated fruitful discussions with members of the labs. We thank both two reviewers for their advised comments that improved early draft of the manuscript. We also wanted to thank  Vincent Miele  ( LBBE, Laboratoire de Biométrie et Biologie Evolutive ) for discussions during the manuscript elaboration and the  Observatory of LAkes OLA  for providing full access to lake monitoring data from monitoring surveys financially supported by the  CISALB (Comité Intercommunautaire pour l'Assainissement du Lac du Bourget) ,  SILA  ( Syndicat Mixte du Lac d'Annecy ) and  CIPEL (Commission internationale pour la protection des eaux du Léman).
Further characterisation of these isolates revealed that nine out of 109 strains grew on N-free medium, harboured the nifH gene, and showed high nitrogenase activity (Fig. 4b ). We then cross-referenced the V5-V7 region of the 16 S rRNA gene of the 109 isolated strains against the highly abundant OTUs (> 0. 01%) in VE. We found that these isolated strains shared sequence identity with 14 of the 25 core OTUs in VE, which accounted for 81. 33% of the total bacterial abundance in VE (Fig. 4c , Supplementary Fig. 7  and Supplementary Data 7). Further, two of the 14 strains displayed remarkably high nitrogenase activity (Klebsiella variicola MNAZ1050 and Citrobacter sp. MNAZ1397, their combination hereafter referred to KC), and harboured the entire nif gene cluster in their genomes (Fig. 4d ). To explore whether the non-N-fixers in the core VE microbiota can assist in biological N-fixation, we mixed 12 non-N-fixers with diazotrophs and determined the total nitrogenase activity of each combination.
Three treatments, i. e. , no fertiliser (Control), chemical fertiliser N, P, and K (NPK), and organic manure plus chemical fertiliser (NPKM), have been applied in triplicate plots in each field for 29 years or more. Climate data corresponding to the sampling site coordinates were obtained from the China Meteorological Data Network (http://data. cma. cn/). Further details of experimental sites are provided in Supplementary Data 1. Sample collection. Sampling was performed during the silking-maturity period of maize in 2019 and 2020, with the exact date of sampling depending on the developmental stage of plants at each location (Supplementary Data 2). In the FSA and RSA soils, three individual maize plants were selected from each subplot (a total of 27 maize plants per site). From each maize plant, we collected the following compartments in the field: mixed leaves, xylem sap, stem, roots, bulk soil.
9. 1  39. After barcodes and primers, lowquality reads were filtered and non-redundant reads were identified using VSEARCH 2. 12. 0  40. Unique reads with ≥ 97% similarity were assigned to the same OTU. Representative sequences were selected using UPARSE  41  and the classify. seqs command in mothur  42  was used to taxonomically classify each OTU with reference to the SILVA 138 database  43. The OTUs classified as host plastids, cyanobacteria, and others not present in our samples were removed from the dataset. Statistical analysis. Alpha and beta-diversity analyses were with R script as described in previous studies  44  and on the QIIME2 39 platform. PerMA-NOVA analyses were performed using the 'Adonis' function implemented in the vegan package  45  of R. We used the microbial source-tracking method FEAST  46  to determine the potential origin of the microbiota inhabiting the various compartments of maize plants.
Bacterial OTUs were assigned into multiple functional groups using FAPROTAX v. 1. 2. 1  47. All analyses were conducted in the R Environment  48  except for beta-diversity analyses. All plots were generated with ggplot2  49  and GraphPad Prism 8. 0. 0 (GraphPad Software, San Diego, CA, USA, www. graphpad. com). Distance-decay relationship analyses. We conducted distance-decay relationship analyses to assess the relationship between the similarity of communities in individual plant compartments and spatial distance, edaphic distance, and climatic distance. We used the Geosphere package to calculate the geographic distances in km from the latitude and longitude coordinates, and calculated edaphic and climatic distances separately as the Euclidian distance. We used the 'vegdist' function in the vegan package to calculate the Bray-Curtis similarity of microbial communities.
Here, the variation in the slope of distance decay reflects the degree to which the similarity of microbial communities in plant compartments varies with environmental distances. The relationships between the Bray-Curtis similarity of each compartment and specific soil or climatic factors were determined by calculating Pearson's correlation (r) values. The significance of r values was assessed with the Mantel test implemented in the vegan package. Differential abundance testing. A negative binomial generalised linear model was implemented with the edgeR package  50  to detect differences in OTU abundance among samples. We compared individual plant compartments (RS, RE, VE, SE, LE, and P) against bulk soil (BS) and conducted pairwise comparisons among fertilisation treatments. For each comparison, after constructing the DGEList object and filtering out low counts, the calcNormFactors function was used to obtain normalisation factors and the estimateDisp function was used to estimate tagwise, common, and trended dispersions.
We then used the glmFit function to test the differential OTU abundance. The corresponding P values were corrected for multiple tests using FDR with α = 0. 05. Core taxa selection. We used the UpSetR package  51  to visualise the OTUs that overlapped among all plant compartments and soils. The overlapping OTUs were defined as those detected in at least one sample from each compartment. We further identified the union of overlapped OTUs and enriched OTUs in the xylem using the EVenn online tool  52. Importantly, abundance-occupancy analyses were conducted to identify the core OTUs across environmental gradients. We calculated occupancy with the most conservative approach, which restricted the core to only those OTUs that were detected in all xylem sap samples (i. e. , occupancy=1). Triple-qPCR to verify FAPROTAX results.
All three primer sets were amplified in a three-step qPCR  57  run at 95 °C for 15 s, 55 °C for 30 s and 72 °C for 40 s for 40 cycles followed by a melting curve analysis. The amplification efficiency varied from 83 to 117%. Isolation of bacteria from xylem sap. To isolate strains, four gradient dilutions (10 -3 , 10 -4 , 10 -5 , and 10 -6 ) of xylem sap were incubated on TSB, R2A, and Ashby's Nitrogen-Free Agar media for 5-7 days at 30 °C (Supplementary Data 9). After incubation, colonies were selected based on their character and colony morphology and were purified by triple serial colony isolation. The isolates were subjected to Sanger sequencing and identified on the basis of PCR analyses with 27 F and 1492 R primers, and alignment against reference. 16S rRNA gene sequences using the BLAST algorithm.
After incubation at 30 °C for 12 h, the gas phase was analysed with a gas chromatograph (Agilent Technologies 6890 N). Data are presented as mean values from five replicate cultures. To test the hypothesis that the core non-N-fixers might assist N-fixation by modifying the oxygen concentration, the nitrogenase activity was measured as described above except that the headspace atmosphere in the sealed vial was not adjusted to 1% O 2 by flushing with argon gas so that the initial oxygen concentration was that found in ambient air. Draft whole-genome sequencing of cross-referenced core strains. Isolated genomic DNA was extracted with a TIANamp Bacteria DNA Kit (Tiangen Biotech, Beijing, China). The purified genomic DNA was used to construct a sequencing library, which was generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, Beverly, MA, USA) following the manufacturer's recommendations.
Pooled libraries were sequenced on the NovaSeq-PE 150 platform. After trimming low-quality reads by fastq  64  , the clean reads were assembled into draft genomes (excluding contigs of < 300 bp) by SPAdes 3. 13. 1  65. Gene prediction and annotation were performed by NCBI PGAP  66  , and the putative genes were further annotated by searching against the eggNOG database 67 by emaper. The functional mapping and analysis pipeline (FMAP 0. 15)  68  was used to align the filtered reads using BLAST against a KEGG Filtered UniProt  69  reference cluster (e < 1e-5, identity > 70%) and to calculate the number of reads mapping to each KEGG Orthologous group (KO). Other data manipulation was performed using perl scripts developed in-house. Potted plant experiment. Two potted plant experiments were performed to (1) reproduce the endophytic behaviour of isolates from xylem sap; and (2) verify their N-fixation potential in maize.
Acknowledgements We thank  Peng Xu  for helpful advice regarding assembly, annotation and interpretation of the genome of core strains, and  Yongliang Yan, PhD , and  Xiaoxia Zhang, PhD , for assistance in laboratory work. We also thank  Jennifer Smith, PhD , from  Liwen Bianji (Edanz)  (www. liwenbianji. cn/),for editing the English text of a draft of this manuscript. This work was financially supported by the  Young Elite Scientists Sponsorship Program  by  CAST  ( 2017QNRC001 ).
Data availability Raw 16 S rRNA gene amplicon sequence data have been deposited in the Sequence Read Archive under BioProject PRJNA795590. The Whole Genome Shotgun data has been deposited at DDBJ/ENA/GenBank under the accession PRJNA798255. The SILVA database is available at https://www. arb-silva. de. The eggNOG database is available at http://eggnog5. embl. de/download/eggnog_5. 0/. KEGG Filtered UniProt reference cluster is available at https://ftp. uniprot. org/pub/databases/uniprot/uniref/uniref90/. Source data are provided with this paper.
The IPCC report AR6 indicates a general consensus that anthropogenic climate change is modifying frequency and intensity of class of extreme events such as cold spells, heatwaves, storms or floods. A different point of view is to investigate whether a specific extreme event would have been possible in the absence of climate change, or whether climate change may have affected its specific characteristics. Here, we address this question by performing an attribution of some major extreme events that occurred in 2021 over Europe and North America: the winter storm Filomena, the French Spring cold spell, the Westphalia Floods, the Mediterranean summer heatwave, the hurricane Ida, the Po Valley tornadoes outbreak, the medicane Apollo and the late autumn Scandinavian cold-spell. We focus on the role of the atmospheric circulation associated with the events and its likelihood in present (factual world) and past climate conditions (counterfactual world) -defined using the ERA5 dataset 1950 to present.
For all cases, we have tested that our method is qualitatively insensitive to modest changes in the domain size. Four our analysis, we split the ERA5 dataset into two periods:  1950-1979 and 1992-2021. We take the first period to represent a counterfactual world with a weaker anthropogenic influence on climate than in the second period, which represents our factual world affected by anthropogenic climate change. For each period, we scan scan all the daily average slp maps and select the best 33 analogs, namely the maps minimizing the euclidean distance with respect to the map of the event itself. The number of 33 corresponds approximately to the smallest 3‰ of euclidean distances in each subset of our data. We have tested extracting between 25 and 50 analogue maps, without finding any qualitatively large differences in our results.
Attribution of Medicane Apollo to climate change We now use the ERA5 dataset to perform the attribution of the cyclonic circulation associated with Apollo in the past and present climate. First of all, let us note that we will select analogs independently on the extratropical or tropical nature of the depression that have produced them. Figure  10  shows the results for Apollo. We already observe that the analogs average slp for both the periods (b,c) do not reach slp minima comparable to that of Apollo (a) hinting to the uniqueness of this event. The ∆slp (d) do not display any interesting structure. Instead, we observe that temperatures (e-g) are significantly warmer (h) the factual world especially on the island of Sicily and on the southern Mediterranean basin. This warming is associated with a significant increase of precipitation in the factual period due to the larger availability of heat and humidity from the sea (i-l).
Acknowledgements. The author wishes to thank  MC Alvarez-Castro ,  J Riboldi ,  M Galfi ,  M Vrac ,  A Hisi ,  E Coppola  and  R Vautard  for the discussions. The authors acknowledge the support of the  INSU-CNRS-LEFE-MANU  grant (project  DINCLIC ), as well as the gran  ANR-19-ERC7-0003  ( BOREAS ). This work has received support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101003469  (XAIDA), by the  European Research Council (ERC)  grant agreement No. 948309  (CENAE), and the  Marie Sklodowska-Curie  grant agreement No. 956396  (EDIPI).
Code availability. The code to compute the dynamical indicators of predictability D and persistence θ is available at https://fr. mathworks. c om/matlabcentral/fileexchange/95768-attractor-local-dimension-and-local-persistence-computation Data availability. ERA5 is the latest climate reanalysis being produced by ECMWF as part of implementing the EU-funded Copernicus Climate Change Service (C3S), providing hourly data on atmospheric, land-surface and sea-state parameters together with estimates of uncertainty from 1979 to present day. ERA5 data are available on the C3S Climate Data Store on regular latitude-longitude grids at 0. 25°x 0. 25°resolution at https://cds. climate. copernicus. eu/#!/home, accessed on 2022-01-26.
It is the product of a multidisciplinary collaboration amongst heritage, digital humanities and ICTs experts, and combines data and methodologies from a range of approaches to humanities together with the customisation of effective digital tools. It has been designed for deployment also in cloud systems compliant with the Infrastructure-as-a-Service paradigm. All data is Findable, Accessible, Interoperable, Reusable (FAIR data). It hosts and integrates different tools, making the data gathered with/for local stakeholders usable and making the same data re-usable within the tools' functions, generating integrated heritage knowledge. It comprises data on 19 rural pilot territories, where the tools and their integration have been developed and tested, while 62 more are partially included as additional territories which participate in certain activities within the project.
ruritage-ecosystem. eu which provides an entry for interested parties to get involved and/or get further information. Data from several tangible and intangible CNH sources as well as different socio-economic analyses and threats are included for 19 rural territories (principle "role models" and "replicators")  [18]  , although 62 more rural territories are partially included during the project runtime, reaching a total of 81 rural territories  [19]. RRE also integrates third party software and datasets, such as CORINE land cover data  [20]  , and demographic data from EUROSTAT  [21]. As illustrated in Fig. 1  , RRE tools support these local stakeholders in different ways.