dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Study locations. Extending over 2,500,000 km 2 , French Polynesia includes 118 islands spread across five archipelagos: the Society Islands, Tuamotus, Marquesas, Austral Islands and Gambiers. We collected data across four archipelagos, including six distinct islands: Mo' orea and Manuae (Society Islands), Hao and Mataiva (Tuamotus), Mangareva (Gambiers), and Nuku Hiva (Marquesas) (Fig. 2 ). All fishes were collected in the lagoon and/or reef slope, depending on the accessibility of the respective habitats. Sea surface temperatures (SST) substantially varies around these six islands distributed across French Polynesia (Table  1 ). Additional fishes from Mataiva were bought at the fish market in Tahiti. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
Permits. Sample collection was permitted by the French Polynesian government (authorization number: 681MCE/ENV). Research methods. Field/Laboratory. In the laboratory, total length (TL) was measured to the nearest millimeter, and fishes were weighed to the nearest 0. 1 grams. Then, pairs of sagittae (the largest otoliths of the inner ear) were extracted, cleaned with distilled water, dried, and stored in microtubes. For each species, otoliths were cut transversely, using a diamond disc saw (Presi Mecatome T210) to obtain a section of 500 µm. Sections were then fixed on a glass side with thermoplastic glue (Crystalbond TM). Small otoliths were directly embedded in the thermoplastic glue and polished to obtain a transversal section. Otoliths were Fig. 1  Illustration of the different steps that allowed the production of the dataset associated to this article.
sanded with abrasive discs of decreasing grain size (2,400 and 1,200 grains cm -2 ) and polished with a 0. 25 µm diamond suspension to reach the nucleus. All sections were photographed under a Leica DM750 light microscope with a Leica ICC50 HD microscope camera and LAS software (Leica Microsystems). When sections were too large for a single photograph, multiple photographs were taken and assembled with the software Photostitch (Canon). A standardized transect across the otoliths (from the nucleus to the edge) was chosen for each species. On this transect, fish age was estimated and distances between annual growth increments were measured using the software ImageJ (Supplementary File 1). The age estimation was performed twice by two independent researchers to prevent biases induced by a single observer.
Von bertalanffy growth curves. The Von Bertalanffy growth model (Eq. 4) is the most frequently used model to describe fish growth. This model is defined as: = - ∞ - - Lt L e (1 ) (4) K t t ( ) 0 where Lt is the average length at age i, L ∞ is the asymptotic average length, K is the growth rate coefficient, and t 0 is the age when the average length was zero. In order to validate the accuracy of our back-calculated size-at-age data, we compared growth curves fitted with raw data (total length at capture and estimated age at capture) to those fitted with back-calculated data. As back-calculated size-at-age data within individuals are highly auto-correlated, we designed a Bayesian hierarchical model that takes this auto-correlation into account by fitting individual growth curves as well as an average population-level growth curve.
The model was applied on back-calculated data with at least five individuals and for individuals with an age at capture that was greater than two years. We fitted models both for each species and for each species per location. In all models, we used informative priors for growth parameters extracted from FishBase (https://www. fishbase. se/search. php). We ran models with 2,000 iterations and a warmup of 1,000. When the R was above one, indicating non-convergence of the Markov Chains Monte Carlo (MCMC), we ran models again augmenting iterations to 4,000 with a warmup of 2,000. If despite that, model convergence was still not achieved, we use MCMC chain plots of the model parameters to remove the individual(s) responsible for non-convergence. As a comparison, we also ran a general non-linear Bayesian model on the raw data (i.
e. using size and age at capture only). Back-calculated data contains more points (multiple points for each individual) than raw data (one point by individual), so the comparison was limited to the species with a sufficient number of individuals (n > 10) and age range in the raw data. These models were run using the package brms  30. All analyses were done with the software R v. 3. 6. 3  31  and the packages rstan (2. 19. 3), tidyverse (1. 3. 0)  32 , plyr (1. 8. 6)  33  , rfishbase (3. 0. 4)  34  , and brms (2. 13. 0)  30.
Usage Notes The dataset is provided as a csv file, which can be directly used by most statistical software. It contains eighteen variables, as described in Table  2. Additional growth parameters can be obtained by fitting other growth models (e. g. Gompertz model) using the variables 'Age i ' and 'Li_sp_m' (species across all locations) or 'Li_sploc_m' (species by location). Back-calculated data are highly auto-correlated, so we recommend using a hierarchical structure to fit growth models. Within the dataset, 'NA' indicates a missing value. Missing values are present for the variables 'R i ' (n = 387), 'R 0p ' (n = 2,811), 'Li_sp_m' (n = 410), 'Li_sp_sd' (n = 410), 'Li_sploc_m' (n = 757), 'Lp_sploc_sd' (n = 757), and 'Weight' (n = 603). For the variable 'R i ' , missing values correspond to individuals for which it was impossible to estimate the radius at hatching from photographs.
acknowledgements We thank the  French Polynesian Urban Planning Department  for providing the GIS shape files for the Polynesian coastline. The project was supported by the  BNP Paribas Foundation  (REEF  SERVICES  project), the  Agence Nationale de la Recherche  ( ANR-17-CE32-006 ), the  Fondation de France , a  Make Our Planet Great Again Postdoctoral Grant  (mopga-pdf-0000000144), and " Direction des ressources marines " (convention number  09419 ).
Data records The dataset is publicly accessible in the permanent figshare repository (https://doi. org/10. 6084/m9. figshare. 12156159. v5)  35. This dataset consists of: 1. 855 individuals from 51 fish species in 15 families collected across six locations in French Polynesia, 2. Fish total length and weight (when measured) for each individual, 3. Age estimations and back-calculated size-at-age for each individual, by species (45 species and 710 individuals) and by species across multiple locations (44 species, and 669 individuals).
Matching criteria were: age (< 30; 30-50; > 50 years old), gender, household income (< 1110; 1110-1999; 2000-2699; ≥ 2700 € per month and per consumption unit) and household composition (single adult with no child; single adult with at least 1 child; > 1 adult with no child; > 1 adult with at least 1 child). Data were collected at baseline (t0) and 12 months later (t1). Household fruit and vegetables supply was the main outcome of the study. A total sample size of 160 participants (80 gardeners and 80 non gardeners) was previously evaluated to detect an increase of one portion of fruit and vegetables per day and per person in the gardeners group with 80% power at the 0. 05 level of significance and a planned attrition rate of 30%  [33]. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the French Institute for Health and Medical Research and the Commission Nationale Informatique et Libertés (IRB00003888); verbal informed consent was obtained following recommendation of the Research Ethics Committee.
Quantitative evaluation Data collection and lifestyles sustainability assessment Details about collection and assessment of all outcome variables of the JArDinS study have been summarized in a previous methodological paper  [33]. To investigate the social/health, environmental and economic dimensions of sustainability, we instructed participants to 1) complete a 1-month food supply diary and collect food receipts, 2) wear a hip-worn triaxial accelerometer (wGT3X-BT or wActiSleep-BT, Actigraph, Pensacola, FL, USA) for 9 consecutive days and 3) fill in an online questionnaire including self-reported body height and weight, and validated questionnaires on mental wellbeing, social health, sensitivity to food waste and connection with nature. The social/health dimension was approached by measuring: 1) the healthiness of household's food supply based on i) fruit and vegetables household supply (including fruit and vegetables from the garden) in gram consumed per day and per person living in the household and being concerned by the food supplies (see Additional file 1); ii) two indicators of nutritional quality: the mean adequacy ratio (MAR) which assesses the percentage of adequacy in relation to 20 essential nutrients  [34] , and the mean excess ratio (MER), which assesses the excess intake of sodium, free sugars and saturated fatty acids  [35] ; iii) the Healthy Purchase Index (HPI) estimating the healthiness of household food purchases, based on food expenditure only  [36] ; 2) participant's physical activity energy expenditure (PAEE) and time spent during daytime in inactivity (< 1.
Statistical analyses Sociodemographic characteristics and key outcome variables of participants at baseline were compared between the two groups (gardeners vs non-gardeners) using paired t-test for continuous variables and McNemar test for categorical variables. The changes in the lifestyles sustainability components between the two groups across time (pre-to post-test) was investigated only for remaining gardener-control pairs at t1. We used linear mixed-effect models (SAS PROC MIXED) for continuous variables and logistic mixed-effect models (SAS PROC GLIMMIX) for dichotomous variables. Group, time and the time*group interaction were treated as a fixed effect. We modelled within-person variation by using a compound symmetry covariance matrix. Significant time*group interaction indicated a difference in outcome over time between the two groups.
Different adjustments were used depending on the outcome variable. Models on physical activity were adjusted for baseline education level and BMI (Model A). Models on food supply and models on self-report data were further adjusted for percentage of meals consumed outside of the home (Model B), and social desirability scale (Model C), respectively. Model on BMI was adjusted for baseline education level, percentage of meals consumed outside of the home and social desirability scale (Model D). Indicators with skewed distribution were logtransformed to improve heteroscedasticity and improve normality of the residuals. Post-hoc analyses were performed only on active gardeners (who visited the garden at least once a month throughout the year) and on those who did not drop out the garden during the year.
Participant eligibility and sample size In total 296 potential participants were approached between March and November 2018 (152 gardeners starting gardening in a community garden and 144 matched non-gardeners). Fifty-nine were excluded, as they did not meet the inclusion criteria and 85 declined to participate or did not answer resulting in a final sample of 155 participants at baseline (response rate: 61. 0% for gardeners and 70. 2% for controls) (Fig. 1 ). Only 14 participants were lost to follow-up between t0 and t1 (conducted from March to November 2019), leading to 66 remaining matched pairs for the analysis. Gardeners came from 19 different community gardens and held either a collective (68. 2%) or individual (31. 8%) plot. We saw no difference with mixed models on gardeners using garden type as fixed effect (collective versus individual plots), therefore the data on garden type were pooled together.
Acknowledgements We would like to thank the Montpellier department of lanscape and biodiversity, local garden networks ( Semeurs de Jardins, APIEU ) and the associations in charge of organizing and managing community gardens in Montpellier who facilitated the recruitment process, as well as the study participants for their time and involvement in the study. We thank the team of the  Surfood-Foodscape  project, especially  Pascale Scheromm ,  Christophe Soulard ,  Nicolas Bricas ,  Simon Vonthron  for their comments and suggestions. We also would like to thank  Maël Garnotel  for assisting in the use of the accelerometers.
Funding This research was part of the  Surfood-Foodscape  project. This work was publicly funded through  ANR  (the  French National Research Agency)  under the " Investissements d'avenir" programme , reference  ANR-10-LABX-001-01   Labex Agro , and coordinated by the  Agropolis Foundation. It was also supported by the  Olga Triballat Institute. The funders had no role in the design of the study or the collection, analysis and interpretation of data, or in writing the manuscript.
Genome Analysis Complete and draft genomes were annotated using the RAST server to identify RNAs and protein-coding genes  (Overbeek et al. 2014). Based on the quality metrics, all the genomes included in this study were sufficient for the assessment of virulence-associated gene content and the comparison between strains (supplementary table S1, Supplementary Material online)  (Chain et al. 2009 ). The genomes were screened for genes encoding virulence factors acting by various mechanisms (toxins, enzymes, secretion system components, and flagellin) and well characterized in Aeromonas spp. (table 1) by using reference protein sequences and either translated sequences of the validated subset of UniProt (SwissProt) or annotated genes of the previously sequenced Aeromonas spp. in the TrEMBL database.
Sequence comparisons with reference protein sequences were performed with SEED viewer, which uses bidirectional protein-protein BLAST (BlastP) sequence comparison of translated open reading frames. Proteins with amino acid sequence similarities !65% and E-values 10 À10 were considered homologs  (Altschul and Lipman 1990). All the results of BlastP analysis were manually verified. Sequences homologous to virulenceassociated genes were checked to identify coding sequences (CDSs) that harbor an open reading frame without nonsense mutations. The HMMSCAN program (HMMER website, EMBL-EBI,  Potter et al. 2018 ) was used to evaluate the putative impact of nucleotide polymorphisms between homologs on protein functions. Amino acids in aerolysin that are critical for oligomerization (H in 155) and heptamerization (K in 374 and E in 390) were examined in the protein homologs  (Degiacomi et al.
Genome-Based Phylogenetic Relationships All genome assemblies used in this study were adequate to reconstruct a single-nucleotide polymorphism (SNP)-based phylogenomic tree using the k-mer method. The aeromonad phylogeny was inferred using the kSNP 3. 1 software package in which SNPs were based on k-mer analysis  (Gardner et al. 2015). The maximum-likelihood (ML) tree was reconstructed on the basis of 27,856 SNPs identified in whole-genome sequences (WGS) for at least 75% of all strains (k-mer ¼ 19). The aligned SNP sequences were used for decomposition analyses with the neighbor-net algorithm available in SplitsTree 4. 0 software  (Huson and Bryant 2006).
Phylogenetic Analysis of Virulence-Associated Genes Phylogeny was inferred from the sequences of 13 virulenceassociated genes (aer/act, alt, ast, colAh, rtxA, exoA, ascF, ascG, ascV, aexT, aexU, lafA, and ser). In the case of T3SS genes (ascF, ascG, and ascV), the three genes were concatenated. Nucleotide sequences were aligned using the Clustal x2 program in the Seaview 4 package  (Gouy et al. 2010). ML phylogenetic trees were reconstructed for each gene or concatenated genes using the best-fit model of evolution determined by the Akaike criterion (http://iqtree. cibiv. univie. ac. at/). ML bootstrap supports were calculated after 100 reiterations. To study the phylogenetic inference of the exoA gene, we added the nucleotide sequence of the eta gene (GenBank locus PA1148) coding for exotoxin A (SwissProt PA11439) produced by Pseudomonas aeruginosa PA01.
Variable Hypothetical protein mesophilic species in the phylogroup Salmonicida, that is, A. salmonicida subsp. pectinolytica (fig. 1 ). None of the virulence-associated genes were present in any of the genomes. The HMMSCAN program detected a peptide signal in each CDS homologous to the aerolysin, collagenase, Ast, exotoxin A, and serine protease genes. In addition, all aerolysin homolog genes were predicted to encode APT and aerolysin domains. The amino acid positions important for oligomerization (H in 155) and heptamerization (K in 374 and E in 390) were conserved with the exception of a change of unknown consequence with N instead of K at position 374 in the aerolysin gene of A. tecta CECT 7082T. The N-terminus of a bacterial virulence factor lipase and a peptidase M9 domain were detected in all the alt and colAh genes, respectively.
2013). Cumulative data on the three non-T3SS-related toxins that we evaluated (ast, rtxA, and exoA) showed that they were likely acquired by HGT from other environmental/aquatic bacteria, such as Pseudomonadaceae, Enterobacteriaceae, or vibrios. These toxins are particularly associated with the Hydrophila phylogroup, A. hydrophila species for the gene ast, A. dhakensis species for exoA, and both species for rtxA. The exoA gene detected in A. hydrophila genomes codes for a homolog of P. aeruginosa exotoxin A, a major virulence factor for this bacterium. Mutagenesis experiments showed that exoA is associated with host tissue destruction, which allows invasive deep infections, such as necrotizing fasciitis, in a murine model  (Ponnusamy et al. 2016). We found that this gene was particularly frequent in the highly virulent species A.
Acknowledgments This work was supported by the  Association des Biologistes de l'Ouest (ABO)  and the  Association pour la recherche et le d eveloppement en microbiologie et pharmacie (ADEREMPHA)  to E. T. -R. Part of this research was supported by  U. S. Department of Agriculture (USDA) / Agricultural Research Service (ARS)  agreement  58-1930-4-002  and  USDA/ARS CRIS  Project  1930-32000-005-00  to J. G.
Statistical analysis Statistical analyses of foaming properties and biological responses were performed using GraphPad InStat software. Different variables are expressed as means ± standard deviation. For foaming properties, experimental data was studied using one-way analysis of variance (ANOVA). Tukey's test was used for the multiple comparison procedure and the significance was accepted at the P<0. 05 level. For biological responses, non-parametric Dunn and Kruskal-Wallis tests were used for comparisons as most of the continuous values measured had a non-Gaussian distribution. A value of P<0. 05 was taken as significant.
Foaming properties Foaming behavior of the three synthesized anionic sugar-based surfactants and the two anionic reference surfactants was studied using gas-sparging method. Fig. 3  displays the foam volume and liquid volume as a function of time for surfactant solutions and Table  2  presents the summary of experimental data on foaming properties. In order to compare foaming properties of different surfactant systems, it is more appropriate to consider these systems in the same thermodynamic state. Below CMC, surfactants are under a monomer form. Thus the flux of surfactant molecules to the interface and the foaming behavior of solution are not influenced by kinetic of micellization and break-time of micelles  [39]. Moreover, the results of our previous study showed that low concentration conditions are favorable for the discrimination of foaming behavior of different surfactants  [35].
Outcrop-scale observations and sampling Sedimentary deposits and textural variations are described with internal structures of the different lithostratigraphic units (Table  1 ). Use of the Shape Preferred Orientation software (SPO,  Launeau and Robin, 1996)  provided a semi-quantitative description of breccia deposits, allowing us to estimate the a/b ratio (largest axis/minor axis), clast axial distributions and clast imbrications. Clast/matrix ratios in matrix deposits are calculated by binary image analysis using the software ImageJ  (Bernard, 2015). Forty-one non-cohesive samples of matrix were collected at the surface of the rock avalanche-debris flow deposits in the following structural units (Fig. 1A ,  Guthrie et al. , 2012) : thirteen samples from the debris barrier along the ridges at the Capricorn-Meager confluence (MB in Fig.
Laboratory analysis Complementary methods are used to characterize the grain-size distributions: 1. sieving of the coated clasts compared to dry sieving of the clasts and matrix at a one-Φ interval in the >--5 to <5 Φ size; 2. Laser particle-size analysis (Malvern Master Size) of the finer particles (<32 μm, CNRS UMR 6296 in Clermont, France); 3. and the Fourier shape analysis using the ImageJ Plugin "Gold morph" to compute the maximum Feret diameter  (Blott and Pye, 2008; Crawford and Mortensen, 2009). The cumulative curves of the grain-size distributions were compared to distinguish the matrix lithofacies in each structural unit. Grain-size distributions were processed using the Gradistat v8 software in Excel to compute the following statistical parameters: median diameter (Md), sorting index (σ), skewness (SKG) and kurtosis (K,  Folk and Ward, 1957).
Micro-scale analysis alt-text: Fig. 1 Semi-quantitative microtextural analyses of thin sections contributed to differentiating the mineralogical assemblage of the inner and outer rims of clasts from the surrounding matrix. We examined the coating matrix of clasts in each structural unit using a binocular microscope on 108 clasts of thin sections ranging in size from 2 to 8 mm. The matrix percent and clast/matrix ratio in the coating matrix are calculated using the software ImageJ and SPO analysis  (Launeau and Robin, 1996; Bernard, 2015). A Fourier shape analysis was applied to 430 clasts of thin sections to calculate the maximum Feret diameter, the axial ratio, the perimeter roughness, and the Riley circularity indices  (Blott and Pye, 2008; Crawford and Mortensen, 2009; Bernard, 2015). We estimated qualitatively the relative abundance of minerals as a function of microtextural variations between the inner and outer rim of clasts.
1B ) by a cataclastic, grey rhyodacite lithofacies (2) and a matrix-rich lithofacies (3, Fig. 1B ) showing sub-rounded rhyodacite blocks. A semi-quantitative description of deposits is established using a SPO analysis of the 1157 clasts and the software ImageJ (see Supplemental File 1). The clast/matrix values are between 0. 54 in MB and 1. 09 in DC with a mean value around ~0. 93. The mixed debris in MB is associated with a polymodal clast-size distribution, with a/b = 1. 35 and ellipse = 3. 87 (SPO from 51 clasts, Table  1 ). We differentiate the cataclastic, grey rhyodacite lithofacies (2) in downstream of the Capricorn-Meager confluence (Fig. 2A ). The flow breccias are extruded and tilted northward. Cataclastic lava breccias are tilted in semi-horsts and matrix mixture in graben in downstream of frontal thrust (Fig.
Clast shape parameters of coated clasts Binocular and microscopic observations are used to establish the textural and structural variations between the inner and outer rims of clasts in each structural unit ( Fig. 6A ). We calculate the evolution of the granular ratios and the clast shape parameters with the software ImageJ and the SPO analysis. The axial a/b ratios are between 1. 1-and 1. 4 for faceted clasts with a shiny surface and 1. 35 to 1. 65 for the coated clasts with ellipse values between 1 and 4. The ellipse/a/b values are between 4. 05 and 1. 02. Along the runout distance, the axial ratios increase (+0. 29) in MB related to the coating matrix, and decrease downstream (--0. 3 in PL) with the perimeter roughness (--0. 78). alt-text: Fig. 5 alt-text: Fig. 6  Fig. 6. Fig. 6  Microscopic observations show a microtextural gradient inside clastic coating (Fig.