dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome annotation and genome comparison Genome annotation was obtain through the NCBI Prokaryotic Genome Annotation Pipeline  [8]. The genome sequence data were uploaded to the Type (Strain) Genome Server (TYGS), a free bioinformatics platform available under https://tygs. dsmz. de, for a whole genome-based taxonomic analysis  [9]. Determination of closest type strain genomes was performed in two complementary ways: first, all user genomes were compared against all type strain genomes available in the TYGS database via the MASH algorithm, a fast approximation of intergenomic relatedness  [10] , and, the ten type strains with the smallest MASH distances chosen per user genome. Second, an additional set of ten closely related type strains was determined via the 16S rDNA gene sequences. These were extracted from the user genomes using RNAMMER  [11]  and each sequence was subsequently BLASTED  [12]  against the 16S rDNA gene sequence of each of the currently 12 983 type strains available in the TYGS database.
This was used as a proxy to find the best 50 matching type strains (according to the bitscore) for each user genome and to subsequently calculate precise distances using the Genome BLAST Distance Phylogeny approach (GBDP) under the algorithm 'coverage' and distance formula d5  [13]. These distances were finally used to determine the ten closest type strain genomes for each of the user genomes. All pairwise comparisons among the set of genomes were conducted using GBDP and accurate intergenomic distances inferred under the algorithm 'trimming' and distance formula d5. One hundred distance replicates were calculated each. Digital DNA-DNA hybridization values and confidence intervals were calculated using the recommended settings of the GGDC2. Complementarily, the degree of genomic similarity of strain Marseille-Q2057 with closely related species was estimated using ORTHOANI software with default parameters  [14] , the nine closest species were determined on a DNA-DNA hybridization basis.
Antibiotic-resistance genes and presence of pathogenesis-related proteins was investigated using the ABRICATE TOOLS v1. 0. 1 against ARG-ANNOT  [15] , EcOH  [16] , NCBI Bacterial Antimicrobial Resistance reference Gene Database  [17] , PLASMIDFINDER  [18] , RESFINDER  [19] , CARD  [20]  and VFDB  [21]  using the Online Galaxy platform  [22].
Strain identification and classification Mixta mediterraneensis strain Marseille-Q2057 T was isolated from the hand skin swab of a 30 -year-old healthy woman. Mixta mediterraneensis strain Marseille-Q2057 T was not identified by our systematic MALDI-TOF MS screening, suggesting that the corresponding species was not in the database (https://www. mediterranee-infection. com/acces-ressources/base-de-donnees/urms-data-base/) (Fig. 1 ); it analysed within the closest members of Erwiniaceae on the IHU databases available spectra and did not belong to any known cluster (Fig. 2 ). Moreover, strain Marseille-Q2057 T exhibited 97. 66% 16S rRNA sequence similarity with Mixta gaviniae strain DSM 22758 (extracted from the genome accessible CP026377. 1), the phylogenetically closest bacterium with standing in nomenclature (Fig.
Deposit in culture collections and Sequences Database Mixta mediterraneensis strain Marseille-Q2057 T , was deposited in CSUR collections under accession CSUR-Q2057. The 16S rRNA and genome sequences are available under accession numbers MW177953 and JACFXY000000000. 1, respectively. relative to other closely related bacterial taxa. Trees were generated with FASTME 2. 1. 6. 1  [24]  from Genome BLAST Distance Phylogeny (GBDP) distances calculated from genome sequences our 16S sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values >60% from 100 replications, with an average branch support of 66. 6%. The tree was rooted at the midpoint  [25]. Information storage and processing.
Funding sources MB is PhD granted by the collaboration between  M&L Laboratories and Aix Marseille University  referenced PVM:2018-200. This study was supported by the  French State  managed by the  National Research Agency  under the Investissements d'avenir (Investments for the Future) programme under the reference  ANR-10-IAHU-03  ( Méditerranée Infection ) and by the  Région Provence-Alpes-Côte-d'Azur and the European funding FEDER PRIMI.
Data Availability Statement ice-core data for this research is available at the Arctic Data Center via https://doi. org/10. 18739/A2X-S5JJ1N with Creative Commons Attribution. GEOS-Chem is open software and available on https://doi. org/10. 5281/zenodo. 5047976. GEOS-Chem historical simulation output is archived in the University of Washington ResearchWorks repository via http://hdl. handle. net/1773/46969.
In contrast, monocultures exhibited no network and less specific endothelial markers. The transcriptomic analysis did not reveal a marked difference between the profiles of both culture conditions. Nevertheless, the analysis allowed us to highlight different upstream regulators in cocultures (SP1, EBF1, and GATA3) and monocultures (PML, MECP2, and NRF1). In cocultures, the multi-omics dataset after 14 days of maturation in biochips has shown the activation of signaling related to hepatic maturation, angiogenesis, and tissue repair. In this condition, inflammatory signaling was also found to be reduced when compared to monocultures as illustrated by the activation of NFKB and by the detection of several cytokines involved in tissue injury in the latter. Finally, the extracted biological processes were discussed regarding the future development of a new generation of human in vitro hepatic models.
G. Transcriptome analysis The comparison of the transcriptomic profiles of monoculture and coculture biochips after 14 days of maturation in biochips performed with iDEP (False discovery rate of 0. 1, fold change of 2) did not lead to significant discrimination between the two conditions in the DESQ2 analysis. However, using pathway analysis, a clear distinction between the two culture conditions could be made. The Gene Set Enrichment Analysis (GSEA) formalism led to the extraction of several GO_biological processes (xenobiotic metabolism, glutathione processing, cholesterol, and various lipids regulation), upregulated in coculture biochips. In monoculture biochips, adenosine triphosphate (ATP) and DNA processing was found to be upregulated [Fig. 5(a) , the complete dataset is given in supplementary material File 2].
H. Motif Activity Response Analysis The transcription factors (TFs) differentially expressed between monoculture and coculture biochips were extracted by ISMARA. From the analysis, TFs with the highest activity in monoculture (MECP2, AHR_ARNT2, PML, NRF1, ZFX, and TFDP1) and coculture biochips (SP1, EBF1, RHOXF1, and GATA3) were identified (Table  II ). A network bridging those TFs could be established by SPRING processing  [Fig. 5(b) ]. From the top target analysis in ISMARA, the regulatory networks between TFs, targeted molecules, and the related downstream pathways could be established  [Figs. 5(c)  and 5(d), complete dataset in supplementary material File 3]. In monoculture, the central mechanisms highlighted were glucose homeostasis, hypoxia-inducible factor (HIF) signaling, and processes related to the cell cycle and liver differentiation.
I. Proteome analysis Partial Least Squares Discriminant Analysis (PLS-DA) on the proteomic dataset allowed us to discriminate monoculture from coculture biochips (Fig. S5  in supplementary material File 1). However, as far as the value of the cross-validated predictive ability (Q 2 ) is concerned, the predictability of the model remained poor. Nonetheless, statistical analysis has allowed extracting 104 proteins differentially expressed between monoculture and coculture biochips (P value below 0. 05, Fig. 6 , the complete dataset is given in supplementary material File 4). In monoculture biochips, NFKB1, NKRF, MALT1 (NFKB pathway), ESAM (endothelial adhesion molecule), SHC1 (response to ROS, regulation of angiogenesis and sprouting, regulation of lipid metabolism), MILCA2 (response to VEGF), and ECM-related proteins (MILCA2, MYLK, ACTB, PKP3) were found to be over-expressed.
J. Metabolome analysis Analysis of the metabolome was performed in both monoculture and coculture biochips after 7 and 14 days of maturation. The Gas chromatography coupled to mass spectrometry (GC-MS) analysis led to the identification of 86 metabolites differentially expressed between the four conditions. A multivariate analysis on those allowed us to identify the specific signatures for each condition. Normalization of the datasets to a blank culture medium was performed to allow for comparison. While Principal Component Analysis (PCA) was not able to discriminate the different conditions, supervised PLS-DA showed a better separation  [Figs. 7(a)  and 7(b)] but with a low predictive ability as illustrated by the Q 2 values [Fig. 7(c) ]. Only three components (ribose, oleic, and palmitoleic acids) were found to be statistically different in monoculture and coculture biochips after 7 days of maturation, while only one component (triethanolamine) was found to be statistically different in monoculture and coculture biochips after 14 days of maturation (P value below 0.
, 2015)  and are consistent with in vivo regenerative patterns in the liver  (Rudnick and Davidson, 2012). In the present work, higher expression of FADS3 was found in the proteomic dataset of coculture biochips. This could be linked to a higher capability of desaturation of intracellular fatty acids which may illustrate better oxygenation of the tissue and which is consistent with the development of endothelial tissue. Staining in biochips revealed the presence of a dense collagen network  [Figs. 4(a)  and 4(b)]. In coculture, the staining appeared located in the center of the channels in proximity to the PECAM-1 tubular structures detected by immunostaining [Fig. 2(n) ]. Liver regeneration and angiogenesis have been frequently linked  (Poisson et al. , 2017; Gressner et al. , 2002; Elpek, 2015).
Moreover, VEGF, known as an important regulator of angiogenesis, is also known to be involved in fibrogenesis and in the release of angiocrine signals by LSECs which control liver regeneration and fibrogenesis signaling  (Poisson et al. , 2017). In the present dataset, higher levels of VEGF, ANGPT2, Endostatin, VEGFR2, VEGFR3, and TIE2 [Fig. 4(c) ] were found in coculture biochips and describe well the closely related signature of angiogenesis and liver regeneration observed in the culture. As signaling related to liver regeneration and angiogenesis are often linked with inflammatory signaling, a pro-inflammatory-like response could be expected in biochips. Initially, inflammation markers were found to be overexpressed in monocultures [TIMP1, TIMP2, MCP1, ENA78, Fig. 4(c) ; NFKB, MALT1 proteins in supplementary material file 4] as compared to coculture.
Additionally, the observed activations of NFKB, STAT3, and PI3K have been confirmed to be strongly linked to events observed in the case of liver regeneration events caused by hepatectomy  (Michalopoulos, 2010). Moreover, hepatic proliferation has been defined as the concerted action between MET and EGFR receptors with several cytokines including tumor necrosis factor  (Michalopoulos, 2010). Finally, the termination of liver regeneration has been associated with the interactions between HGF, TGFB1, and extracellular matrix proteins  (Apte et al. , 2009; Michalopoulos, 2010). In the present dataset, coculture biochips presented a high production of collagen (Fig. 4 ) which would be expectedly linked to a fibrotic-like response or a pro-inflammatory state. However, those events could not be linked so far as higher levels of ENA78, TMP1, and TMP2 were found in monoculture biochips and higher levels of anti-inflammatory markers such as IL-4 and MMP9 were found in coculture biochips.
Besides, the proteomic dataset also exhibited higher levels of the NFKB signaling protein in monocultures which further corroborates those theories. Finally, the metabolomic analysis did not reveal signatures related to cellular stress. Production of pyroglutamate and modulation of glutamate and methionine were shown to be involved in the response to oxidative stress and liver toxicity  (Liu et al. , 2014)  and are often linked to inflammation which suggests that no patterns specific of inflammation were observed in coculture biochips when compared to monoculture.
J. CAGE transcriptome profiling The generation and sequencing of the NanoCAGE libraries were performed as previously described  (Poulain et al. , 2017). To reduce the number of sequencing reads mapping to ribosomal DNA, pseudorandom primers were used  (Arnaud et al. , 2016). Detailed protocols for the total RNA extractions, library preparation steps, sequencing, and data processing are given in supplementary material File 1. Custom R scripts using the CAGEr package were used to produce the expression tables uploaded on the iDEP server (http://ge-lab. org:3838/ idep/) for differential gene expression and pathway analysis  (Haberle et al. , 2015; Ge 2017).
K. Proteomic analysis Digestion of protein samples was performed, and separation of the peptides was done into five fractions. Drying by speed-vacuum was made, and the fractions were solubilized in 10 lL of a 0. 1% TFA, 10% Ceric Ammonium Acid (CAN) solution. A U3000 RSLC nanoflow-high-performance liquid chromatography (HPLC) system coupled to an Orbitrap fusion MS analysis (Thermo Fisher Scientific) was used for liquid chromatography and mass spectrometry analyses. A concatenation of human sequences from the Uniprot-Swissprot database (Uniprot, release 2018-06) and an incremented list of contaminants were used as a database for this study. On both peptides and proteins, the false discovery rate (FDR) was kept below 1%. Unique and razor peptides were used for the Label-free protein quantification (LFQ) and at least 2 ratio counts were required for the latter.
Simultaneous analysis of all samples was performed with the "match between runs" option, with a match time window of 0. 7 min and an alignment time window of 20 min. The protocol used in this study is fully detailed in supplementary material File 1. Multivariate statistical analysis was performed (PLS-DA, partial least squares-discriminate analysis), and Student's t-test (XLSTAT. 2016, Addinsoft) was performed to analyze the proteomic dataset. Differences with a P-value of 0. 05 or less were highlighted and considered significant. MetaboAnalyst 4. 0  (Chong et al. , 2018)  was used to highlight the top 50 differentially expressed proteins and to summarize them into a heatmap.
L. Metabolomic analysis Gas chromatography (Agilent 7890B) coupled to mass spectrometry (Agilent 5977A, GC-MS) was used to perform the metabolomic analysis on the culture medium from the different culture conditions. Rxi-5SilMS columns (30 m with a 10 m Integra-Guard column, 13623-127, Restek) were used. Preparation of samples and extraction of metabolites  (Jellali et al. , 2018) , GC-MS injections, and analysis steps  (Fiehn et al. , 2008)  were performed as previously described. The AMDIS software (http://chemdata. nist. gov/mass-spc/amdis/) was used for the analysis. Masshunter Quantitative Analysis (Agilent) was used to determine peak areas, and normalization was done to ribitol. Further details on the protocols for metabolite extraction and sample injection are given supplementary material File 1.
The XLSTAT. 2016 software (Addinsoft) and MetaboAnalyst 4. 0  (Chong et al. , 2018)  were used to perform the metabolomic multivariate data analysis. Significant variations between the groups were highlighted by unsupervised PCA and supervised PLS-DA. Differentially detected metabolites were identified according to their variable importance for the projection (VIP > 1) and P-value (Student's t-test, P < 0. 05). MetaboAnalyst 4. 0  (Chong et al. , 2018)  was used to perform the metabolic pathway analysis.
ACKNOWLEDGMENTS This work was supported by  French Investissements d'Avenir  in the framework of the iLite project (No. ANR-16-RHUS-0005 ). The molds used for the fabrication of the PDMS biochips were made by the  LAAS  in the frame of RENATECH support. St ephane Poulain was supported by the  JSPS   Grant-in-aid for Scientific Research  (S)  16H06328  and  Charles Plessy  by a grant to  RIKEN CLST (DGT)  from the  MEXT, Japan. The Orbitrap Fusion mass spectrometer was acquired with funds from the  FEDER  through the  «Operational Programme for Competitiveness Factors  and employment 2007-2013», and from the  «Canceropole Ile de France». The authors declare no conflict of interest.
Materials and Methods This study was exempt from local institutional review board approval. PubMed bibliographic data to the usual bibliographic fields: publication date, first and last name of all authors, affiliation addresses, article type. This dataset was exported to Excel, and the following variables were recorded for each entry: manuscript title, publication year, first and last name of the first and last authors, article type, journal impact factor (IF) according to the JCR 2019, and country of provenance of first and last authors. The Gender-API software (Gender-API. com) was used to determine the gender of the first and last authors. Performance metrics of this software can be found elsewhere  (13). Date of censoring for the year 2020 was February 24, 2021. Preprints of the year 2020 were excluded.
Best available data supporting this hypothesis come from observations of small numbers of bumblebees foraging on potted plants  [19, 20]  or artificial flowers (in effect mimicking inflorescences or plants)  [18, 21]  in large flight tents. In these experimental foraging conditions with limited numbers of bees and feeding sites, foragers tend to avoid spatial overlaps as a consequence of competition by exploitation (when bees visited empty flowers) and interference (when bees interacted on flowers)  [21]. Computational modelling is a powerful approach to further explore the mechanisms by which such partitioning might emerge from spatial learning and competitive interactions. At the individual level, trapline formation has been modelled using an iterative improvement algorithm where a bee compares the net length of the route it has just travelled (sum of the lengths of all transitions between two flowers, or the nest and a flower, comprising the flower visitation sequence) to the length of the shortest route experienced so far  [22].
Simulations with one forager We first tested the ability of our models to replicate trapline formation by real bees, by comparing simulations of a single forager to published experimental data in which individual bumblebees were observed developing traplines between five equally rewarding artificial flowers in a large open field  [13, 22]. Lihoreau et al. [22]  tested seven bumblebees in a regular pentagonal array (S1A Fig), which we judged enough to run quantitative comparisons with model simulations. While Woodgate et al. [13]  tested six bees in a narrow pentagonal array (S1B Fig), only three of them presented enough successive foraging bouts in a single day to allow statistical comparisons with our model. Thus, for this dataset only qualitative comparisons were made with the model simulations.
Simulations with two foragers Having tested our models with one forager, we next explored conditions for the emergence of resource partitioning within pairs of foragers. Here experimental data are not available for   [22] ). See details of models in Fig 1. For each dataset, we show the estimated average trends across foraging bouts (coloured curves), along the standard error (grey areas) of the mean. For the sake of eye comparison, in the simulation plots the standard error of the mean is estimated from a sample of 7 simulations (N = 7 bees in  [22] ). Average trends were estimated over 500 simulation runs, using GLMM Binomial model with bee identity as random effect (bee identity nested in simulation identity for simulated data). 2. Exploitation and interference competition. We first analysed exploitation competition by quantifying the frequency of visits to non-rewarding flowers by each bee during each foraging bout.
Environments Simulations with one forager. Our first goal was to test the ability of our models to replicate observations of real bees. To do so, we ran simulations in environments replicating published experimental studies in which individual bumblebees (Bombus terrestris) were observed developing traplines between five equally rewarding artificial flowers in a large open field  [13, 22]. To our knowledge, these studies provide the most detailed available datasets on trapline formation by bees. Lihoreau et al. [22]  used a regular pentagonal array of flowers (S1A Fig) in which they tracked seven bumblebees. We judged this sample size enough to run quantitative comparisons with model simulations (raw data are available in the supporting information of  [22] ). Woodgate et al. [13]  used a narrow pentagonal array of flowers (S1B Fig).
Here, however, the small sample size of the original dataset (three bumblebees, data shared by J. Woodgate) only enabled a qualitative comparison with the model simulations (S3 Text). Simulations with two foragers. We then explored conditions leading to resource partitioning by running model simulations with two foragers. Here we simulated environments containing 10 flowers, in which each bee had to visit five rewarding flowers to fill its crop to capacity. The simulated flowers should thus be considered as feeding sites such as plants or inflorescences, which are more likely to contain such large amounts of resources (20% of the bee's crop). To test whether model predictions were robust to variations in spatial distributions of resources we simulated three types of environments characterized by different levels of resource patchiness: (i) a patch of 10 flowers, (ii) two patches of five flowers each, and (iii) three patches of five, three and two flowers respectively (see examples in S2 Fig).
QL is standardized between 0 and 1 by the quality of the optimal route in each array QL opt (shortest possible route to visit all 5 flowers). ii. a similarity index SI ab between flower visitation sequences experienced during two consecutive foraging bouts a and b as follows: SI ab ¼ s ab 2l ab ð3Þ Where s ab represents the number of flowers in transitions found in both sequences, and l ab the length of the longest flower visitation sequence between i and j multiplied by 2 to make sure that SI ab = 1 occurs only when two consecutive sequences sharing the same transitions also have the same length (more details and examples in S5 Text). We applied generalized linear mixed effect models (GLMM) with binomial error, using the glmer function in 'lme4' package  [71] , to assess whether the estimated trends across foraging bouts for QL and SI ab obtained from model simulations with one forager differed from trends obtained from experimental data.
QL optimal was calculated in each environment by computing all possible combinations of two routes visiting five flowers each and extracting the combination with the highest quality. To assess whether the trends across foraging bouts obtained from simulations with two bees differed between models (Fig 1 ) and types of environments (S2 Fig) , we applied GLMMs for each of the following response variables: (i) frequency of competition types (Poisson error distribution), (ii) SI ab (Binomial error distribution), (iii) Q norm (Binomial error distribution) and (iv) QL group (Binomial error distribution). In each model, we used a random structure to account for bee identity nested in flower arrays (i. e. 100 simulations of each spatial array for each model). To statistically compare the trends across foraging bouts, we estimated the marginal trends of the models, as well as the 95% confidence intervals of the mean using the emtrends function in 'emmeans' package  [76].