dataesr/scientific-paragraphs-categorization huggingface dataset:
This problem was solved using the modelling language AMPL and the solver ipopt. Code is available upon request. Tables  4  and 5  describe the results of the calibration. Table  4  refers to the model's variables at steady-state (both the variables which have an associated differential equation and the intermediary variables). Table  5  refers to the model's parameters. For each variable and parameter, the lower and upper bounds are given, as well as the data source used for defining this range of admissible values (if no bounds are given, it means that a value was directly imposed based on the data source). Then, the value obtained after calibration is presented. Finally, a brief description of the variable or parameter is given, 29  as well as its unit if it is not dimensionless. Note that the reduced form model was built such that practically all variables and parameters are dimensionless.
Acknowledgements This work has benefited from discussions and comments of Elise Dupont,  Pierre-Yves Longaretti ,  Jean-Yves Courtonne ,  Victor Court ,  Aymeric Lamorlette  and  Paul Valcke  whom we thank for their support. We would also like to thank  Stanislas Augier  for the use of his R package sysde in the code of our model. Louis Delannoy acknowledges the financial support of the  French National Institute for Research in Digital Science and Technology (INRIA) , and  Baptiste Andrieu  thanks The Shift Project for their funding. Finally, we are grateful to the two anonymous reviewers for their insightful comments and suggestions. All authors have read and agreed to the published version of the manuscript.
More fundamentally, representing tissues as networks is advantageous as it is possible to draw from a vast literature in statistical physics, mathematics and computer science to analyze the structure and quantify the relationships between the cells composing the tissue  [12]. Here we describe Griottes, an open-source tool to construct a network representation from imaging data. The routine creates a "network twin" of the imaged sample by extracting individual cell information from segmented images and building a network representation of the tissue. This new object makes it easier to address quantitative questions on complex tissues with single-cell precision. Below we begin by describing the code and its functions, followed by a few examples on how biologically relevant information can be obtained from typical images.
Program architecture The Griottes analysis pipeline contains two main steps: individual cell attribute measurements step and then the network construction step. These processes and the program inputs and outputs, described in Fig. 1 , are elaborated immediately below. Input Griottes takes as input the output of standard cell segmentation codes. This can be in either as a labeled image (2D or 3D) or as a data table. The labeled image has at least one channel composed of a zeropadded matrix in which the area occupied by each cell is filled with an integer label that is unique to this cell. A data table containing single cell information can also be used as an input (for supplementary details on the input formatting we refer the reader to the progr am docum entat ion).
Building the network Once the information on the cell properties is extracted from the image, Griottes constructs the connectivity graph binding neighboring cells together (Fig. 1 ). Several options are implemented by the program for determining the connectivity between two cells. The first method uses geometric graph construc-A geometric graph is generated by connecting cells separated by less than a given distance [10]. This connection method can be particularly relevant when cell-cell communication is mediated by chemical cues  [16] , in which case the distance between cells determines the time necessary for diffusion to take place. However, this construction rule The second method is a Delaunay-triangulation based connection condition (Fig. 2b ). This is a robust state-ofthe-art heuristic widely used to build biologically relevant networks in many different contexts  [16] [17] [18].
Building a network from a data table Image processing software can often yield cell information in a table format containing the cell positions as well as other attributes such as the cell type. The data table can contain the positions of the cells in the x-y plane, as well as further information about the cells, such as their cell type. Below we show how such data can be integrated and analyzed using Griottes. In the current example a region of interest of the zebrafish telencephalon was segmented and analyzed [20], attributing a cell type to each of the visible cells in the sample. Understanding cellular organization and cell-cell relationships of this tissue will be critical to better understand how this stem cell population coordinates its behavior to sustain large-scale and long term homeostasis.
Conclusion Griottes is part of a growing ecosystem of imageanalysis tools enabling the spatial analysis of biological images  [3, 17, 28, 29]. The library is built in Python and uses state of the art open-source libraries, allowing investigators to seamlessly integrate Griottes in their image and data analysis workflows. A Napari plugin is also available, thus providing a graphical user interface. We expect to continue developing Griottes, with the long-term goal to make the step from pixel-based to node-based analysis as seamless as possible. Fig. 4  a Reconstruction of the zebrafish telencephalon from a data table. Colors represent the different cell types entered in the table by the user. b Network construction with Griottes from a point-cloud using the Delaunay construction rule. c Degree distribution of cells composing the zebrafish telencephalon after the network construction using the Delaunay rule.
Code and data availability • Griottes is an open-source Python package available in the following GitHub repository: https:// github. com/ Barou dLab/ Griot tes. • All data are available in the accompanying GitHub release https:// github. com/ Barou dLab/ Griot tes/ relea ses/ tag/ v1. 0-alpha. Zebrafish data are drawn from Dray et al. [20].
Funding The UtechS Photonic BioImaging (Imagopole), Center for technological ressources and research (C2RT), Institut Pasteur, supported by the French National Research Agency (France BioImaging; ANR-10-INBS-04; Investments for the Future). We thank the support of the Inception program (Investissement d' Avenir grant ANR-16-CONV-0005). The DiSPIM microscope system benefited from financial support provided by the Region Ile de France via the DIM Elicit program. We also acknowledge the cytometry and biomarkers platform (CB-Utechs) for use of the cell sorters. GR. was supported by the Direction générale de l' Armement.
, 2013) , including in melanoma where it is downregulated  (Levati et al. , 2009; Wang et al. , 2020). The methylation status of these eight genes (MYH1, RAC3, SOCS3, HOXB2, PCDHB15, PCDHB16, BCL2L10, and MIR155HG) was further validated in WM115 vs WM266-4 cell lines by DNA pyrosequencing after bisulfite conversion and PCR amplification on 100 bp regions containing the CpGs identified in step 2 (Figure  1C ). Seven genes pass the threshold of validation, 20% DNA methylation difference between the cell lines (Figure  3 -figure supplement 1). methylation levels increased by 20% in the aggressive cell line were retained (hypermethylated genes). (B) Step 2: two strategies for data analysis were used: the oriented strategy is based on a statistical analysis of the distribution of the hypermethylated genes across the genome, and the non-oriented strategy uses Ingenuity Pathway Analysis software to identify potential links to described networks and functions.
1 year was chosen because, at the time of the sampling and the beginning of the study, it was the average OS of diagnosticated cutaneous melanoma. In addition, the delay of 1 year well distinguished the two group of LS and SS. The DNA methylation profile in primary tumours with SS similar to that observed in metastatic patients (top, in red, Figure  4 -figure supplement 1). The analysis showed that MYH1 was globally hypomethylated in SS patients, whereas PCDHB16, PCDHB15, and BCL2L10 were hypermethylated. Data from 29 additional primary tumours samples were then analysed. The extended cohort of patient with primary melanoma primary (n = 49) confirmed that these four differentially methylated genes represent specific markers of more aggressive SS primary and metastatic melanoma tumours (Figure  4A ).
, 2013). More recently,  Guo et al. , 2019  identified four DNA methylation biomarkers by analysing all melanoma types in the The Cancer Genome Atlas (TCGA) database but without validating this on another patient cohort. Importantly, we have discovered five CpG sites that are key to metastatic melanoma formation and are grouped in genomic clusters. This signature is unique, as is the integrated approach and the baseline assumption used to identify it. Importantly, survival based on the methylation score calculated for the five CpGs reported in panel B. The methylation score = 2 corresponds to at least two CpGs that showed a methylation difference >15% when compared to the DNA methylation median of the primary metastasis. The online version of this article includes the following figure supplement(s) for figure  4 :   patients samples were from different regions and continents (France, Italy, Portugal, and USA).
1; Illumina Inc)  for the extraction of DNA methylation signals from scanned arrays (methylation module version 1. 9. 0, Illumina Inc). Methylation data were extracted as raw signals with no background subtraction or data normalization. The obtained 'ß' values, i. e. , the methylation scores for each CpG range from 0 (unmethylated, U) to 1 (fully methylated, M) on a continuous scale, were calculated from the intensity of the M and U alleles as the ratio of fluorescent signals (ß = [Max(M,0)]/[Max(M,0)+Max(U,0)+100]). All pre-processing, correction, and normalization steps were performed using an improved version of the in-house developed pipeline using subset quantile normalization based on the relation to sequence annotation provided by Illumina  (Touleimat and Tost, 2012). Probes were considered as differentially methylated if the absolute value of the difference between robust median ß-values in samples of each phenotypes was higher than 0.
A total of 10 μL of PCR product was rendered single-stranded as previously described, and 4 pmol of the respective sequencing primers were used for analysis. Quantitative DNA methylation analysis was carried out on a PSQ 96MD system with the PyroGold SQA Reagent Kit (Qiagen) and results were analysed using the PyroMark software (V. 1. 0, Qiagen). Percentages of methylation (% CpG) were measured for each individual CpG present in the regions analysed by pyrosequencing. The regions chosen were around the CpGs identified by the Illumina methylation 450 K analysis and include other CpGs. DNA methylation heatmaps were obtained using Prism8 software. The heatmaps in Figure  3  refer to the median of the median of the methylation percentages of the n CpG analysed by pyrosequencing (median [CpG1:%,…CpGn:%]).
Signature score and survival information were reported in Supplementary file 3. This score was evaluated for each gene to assess potential correlation between individual gene scores. A random simulation using R software and based on a Chi-square test indicate that the methylation of these genes was independent from one another p<0. 01. Kaplan-Meier plots were created using GraphPad Prims8 software. The survival in months was indicated depending whether the score was under vs equal or superior than 2. Survival analysis was performed using a log-rank and the Gehan-Breslow-Wilcoxon test with a p-value<0. 001 for each considered significant. Hazard ratio was estimated on R software using survival, survminer, and ggplot2 packages. Methylation cluster identification through statistical analysis of the distribution of the hypermethylated genes Identified methylation clusters highlight regions where the methylation distribution on the chromosome is not random.
Maps of the 229 hypermethylated genes were visualized on the Ensembl website using the tool view on karyotype (http://www. ensembl. org). Sex chromosomes were excluded from the analysis. The clusters were defined as a group of at least two methylated genes in close proximity separated by non-methylated genes. The statistical relevance of the number of observed clusters on each chromosome was addressed using bootstrapping. For each simulation, methylated and non-methylated genes were randomly repositioned (shuffled) along each chromosome before recomputing the number of clusters. 1000 simulations were performed to estimate the probability of obtaining the number of observed clusters. All analyses were performed using custom-written scripts implemented in the statistical programming language R (http://cran.
Functional annotation and pathway analysis The list of hypermethylated genes was imported into QIAGEN's IPA (QIAGEN Redwood City, https:// digitalinsights. qiagen. com/). In IPA, hypermethylated genes were mapped to molecular and cellular functions and to networks available in the ingenuity database and then ranked by score or p-value (p<0. 05). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Below the euphotic zone, Syndiniales sequences (40%) dominated the dataset followed by Radiolaria (31%), Dinophyceae (14%) and other heterotrophic groups like Marine Stramenopiles and ciliates (1-1. 5%). Among Radiolaria, several unidentified ASVs assigned to Spumellaria were most abundant, but showed significantly different distributions between STW and SAW highlighting the need to further investigate the taxonomy and ecology of this group. The present study represents a significant step forward towards characterizing protistan communities composition in relation to major physical oceanographic features in the southwest Pacific providing new insights about the biogeography and ecological preferences of different planktonic protist taxa from class to species and genotypic level.
Specifically, we want to know how (dis-)similar are protist communities in the biogeochemically contrasting STW and SAW? What are the main environmental factors responsible for these differences? Which are the main taxonomic groups associated with each water mass and their environmental preferences? To do so we have applied DNA metabarcoding analysis (18S rRNA) to >450 samples collected during 12 oceanographic voyages conducted over several years  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  and different seasons across STW and SAW east of New Zealand. This sequence data together with core physico-chemical and biological parameters (e. g. temperature, salinity, mixed-layer depth (MLD), macronutrients, total and size-fractionated chlorophyll a provides the most comprehensive dataset of protistan plankton diversity in STW and SAW in the SW Pacific and contributes significantly towards building a robust baseline against which future changes in the region can be evaluated.
Study area and sample collection Seawater samples and data were collected during 12 research cruises conducted in SW Pacific waters east of Aotearoa New Zealand between 2009 and 2017 (Fig. 1 ). Six of these cruises were conducted as part of the Biophysical Moorings long-term monitoring project (BiophysMoorings)  (Nodder et al. , 2016)  that revisited the same sites in STW (Bio-STM), STF (Bio-STF) and SAW (Bio-SAM)  (Nodder et al. , 2016)  through time. The rest of the cruises providing data to the present study represent single cruises to the specific region, typically focusing on a process of interest (Table  1 ). The dataset covers 100 stations distributed between 54. 3 and 33. 4 • S with seawater samples (𝑛 = 482) collected over the 0-3100 m depth range and covering spring, summer and autumn periods (Table  1 ).
482 samples were sequenced generating a total of 9208994 reads, with a median sequencing depth across all samples of 18906 reads per sample (range = 6539-36551). Obtained sequences were processed using the DADA2 pipeline  (Callahan et al. , 2017)  following the procedure described by  Trefault et al. (2021). The default option of DADA2 (dada(. , pool=FALSE)) was used and therefore amplicon sequence variants (ASVs) only supported by more than a single read in each sample were considered. Additional thresholds to remove low abundance ASVs inferred by DADA2 were not applied. Taxonomic assignation was conducted using the assignTaxonomy(. ) function of DADA2 that implements the RDP naive Bayesian classifier method  (Wang et al. , 2007)  against PR2 database version 4. 12 (https://pr2-database. org/).
We adopted the taxonomic structure of PR2 database which includes 8 levels (Kingdom/ Supergroup/Division/Class /Order/Family/Genus/Species). The classifier method does not use sequence distance, instead the assignation is done by comparing the kmer profile of the sequences to be classified and the kmer profile generated from the reference sequence database. A bootstrapping approach is then followed to provide a measure of the confidence in the assignation for each taxonomic level including species level. In addition, we ran a BLAST against the nr database and a table with the top 20 hits for each ASVs can be found in https://github. com/ agutierrez2001/Catalyst_Biogeography. For the most abundant ASVs with a low (<90) bootstrap support for species level we provided the % similarity to the closest formally identified species, and the taxonomic assignation provided by PR2 was changed if required.
, 2019)  in its default configuration that uses the natural log. Similarity analysis was undertaken using principal component analysis (PCA), redundancy analysis (RDA) and PERMANOVA using phyloseq (McMurdie and Holmes, 2013), microviz  (Barnett et al. , 2021)  and vegan  (Oksanen et al. , 2019 ) R packages. Differences in species abundance across waters masses and regions was analysed using negative binomial generalized linear models coded in the DESeq2 package  (Love et al. , 2014). For analysis of higher taxonomic rank (division, class), distribution and their relation to environmental variables, the tax_glom function in phyloseq was used to agglomerate the previously standardized ASV table into the chosen taxonomic level. While all protist classes were included in the analysis we focused on phytoplankton and heterotrophic and mixoplankton groups such as ciliates and radiolaria that are known to prey on phytoplankton, rather than parasitic groups like Syndiniales.
Data accessibility Physico-chemical, biological and geographic data including measurements of temperature, salinity, oxygen and transmissivity obtained with a Seabird CTD 9plus; inorganic nutrients; total and size-fractionated chl a; and estimated mixed-layer and euphotic zone depth can be found in PANGAEA repository archive data sets (https://doi. pangaea. de/10. 1594/PANGAEA. 937633). Raw sequence data are available on NCBI under accession number PRJNA756172. The quality filtered ASV table together with the taxonomy and metadata table used to build the phyloseq object are provided as csv tables. R scripts for data processing and figures can be found in https://github. com/agutierrez2001/ Catalyst_Biogeography.
Within STW for instance, species richness in the euphotic zone was higher in oligotrophic waters and decreased with increasing Chl a, being generally higher in warmer waters (Figure  S6 ). Samples from the STF presented lower species richness in the euphotic zone compared to SAW and STW across the entire range of Chl a and nitrate concentrations (Figure  S6 ). Seasonal variability analysed at the Biophysical Moorings sites (Bio-STM, Bio-STF and Bio-SAM, 𝑛 = 56) showed an increase in species richness from spring through summer and peaking in autumn in both STW and SAW (Fig. 3C ). Only two samples per season were available for the euphotic zone at the Bio-STF site which yielded higher richness in spring than autumn with year-round minimum and maximum values estimated during summer. Diversity patterns in the aphotic zone were investigated on the Biophysical Moorings dataset only (𝑛 = 137 aphotic samples).
Division and class level taxonomic composition in the euphotic zone Dinoflagellata reads dominated the sequencing datasets from samples taken in the euphotic zone (51% of protist reads -sequencing depth-normalized), with relatively even partitioning between Dinophyceae (24%) and Syndiniales classes (27%) (Fig. 5 ). Chlorophyta accounted for 20% of reads followed by Ochrophyta (9%), which comprised mainly by Bacillariophyta (5%) and Pelagophyceae (2%)). Haptophytes belonging to Prymnesiophyceae class (5%) were the other most important phytoplankton division, while Stramenopiles_X (mainly through Marine Stramenopiles, MASTs, 4%), Radiolaria (3%), and Ciliophora (3%) contributed most among the heterotrophic protists. Although such groups were consistently dominant, their relative contributions and particularly, their composition at class (Fig.
2%) and RAD-A (1. 4%) and RAD-B (1%) radiolarian groups contributed most to the STW metabarcoding data (Fig. 5  and Fig. 6 ). The SAW sequencing dataset was clearly dominated by Syndiniales (35% total reads) and Dinophyceae (25% total reads) followed by Prymnesiophyceae (8%) and Chloropicophyceae (6%) (Fig. 5 ). Unlike STW, Chloropicophyceae dominated over Mamiellophyceae (4%) which accounted for a smaller proportion of Green algae reads. Among Ochrophyta, Bacillariophyta and Pelagophyceae phytoplankton classes showed a more even partitioning than observed in STW (4% each) (Fig. 5 ). The heterotrophic component in the SAW metabarcoding data was dominated by MASTs (3%) followed by Spirotrichea ciliates (2%) while the relative contribution of Radiolaria in the euphotic zone was minor (<1%) and mainly attributed to Polycystinea (0.
5%) rather than RAD-A and -B classes (0. 2%) (Fig. 5  and Fig. 6 ). The STF protist metabarcoding data presented intermediate characteristics between STW and SAW (Fig. 5 ). As in SAW, Dinophyceae (30%) and Syndiniales (27%) co-dominated the metabarcoding dataset. The contribution of Chlorophyta (17%) was, however, higher and closer to levels found in the STW metabarcode dataset. Accordingly, Mamiellophyceae was the most abundant class of green algae (12%), with Chloropicophyceae (4%), and Pyramimonadophyceae (1. 5%) having lower but still substantial relative abundances. The division Ochrophyta (10%) accounted for a similar fraction of phytoplankton reads as in STW and SAW, but in STF the phytoplankton community was clearly dominated by Bacillariophyta (8%) with minor contributions from Pelagophyceae (1%) and Dictyochophyceae (0.
3%) classes. The heterotrophic component was dominated by MASTs and Spirotrichea ciliates (3% each) while the contribution of Radiolaria (<0. 2%) was on average lower than in STW and SAW datasets and dominated by RAD-A and RAD-B lineages (Fig. 5  and Fig. 6 ). Temporal variability analysed from the Biophysical moorings dataset showed an increase in the relative contribution of Mamiellophyceae during spring at both Bio-STM and Bio-SAM sites. At the Bio-STM site, diatoms accounted for a substantial proportion of protist reads in the surface mixed-layer during the TAN1113 spring voyage, while they peaked in subsurface waters during the TAN0902 summer voyages (Fig. 7 ). Diatom contributions at the Bio-SAM site was also higher in spring and summer compared to autumn, although their overall contribution remained lower than in the Bio-STM site.