dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification of mCherry+ cells in PG The mCherry+ cells in the adult PG were counted from confocal images using the ImageJ cell counter plugin. We used stacks of 5 mm from frontal sections containing the PGl. The total number of cells was determined with DAPI nuclear labeling, and proportion of mCherry+ cells was calculated. The cell count was performed in the brains induced at 4 different time points: 24 hpf, 48 hpf, 2 wpf, and 3 wpf, and the average from two specimens was presented as data for each time point (Figure  8 -figure supplement 2).
The rather high number of manual and threshold-based segmentation iterations is due to the auxiliary signal, which is also located in close proximity to the pattern of interest. Local thresholds for the (negative) segmentation of the auxiliary signal were adapted with respect to the voxel values of the surrounding region to a lower boundary value of 150-180 (higher boundary always was 255). The resulting regions of exceptionally bright voxels were dilated in all three dimensions by two pixels for removing the auxiliary signal in their entirety from the region of interest. For the final visualization we multiplied the original grey value dataset with the binary 3D mask of the segmentation. Since in this mask the region of interest is encoded as one (1) and the background as zero (0) the multiplication of the image with its mask will result in the separation of its original grey values within the mask from the background signal because the latter is multiplied by zero.
This isolated signal was visualized using the orange-yellow colormap (look-up-table, LUT) volrenRed. col with a truncated dynamic range (0-200) for better contrast and visibility of the signal of interest in a Volren-module of Amira. For the context of the signal of interest, we visualized in parallel the entire dataset in a separate Volren-module using a grey colormap with slightly truncated dynamic range (10-255) as a means of global contrast enhancement.
Acknowledgements The authors thank members of  Kawakami 's team for their contribution for generating Tg (gSAGFF279A) and Tg(UAS:GFP) zebrafish transgenic lines. Many thanks to the members of TEFOR, notably  Elodie de Job ,  Laurie Rivie  `re,  Elodie Machado , and  Isabelle Robineau  for their technical help. We also thank  Matthieu Simion  for his help and discussion, and  Catherine Pasqualini  for critical reading. We also thank members of the animal facility (especially  Krystel Saroul ) for taking care of zebrafish. Finally, we thank members of  Laure Bally-Cuif's, Jean-Stephane Joly 's, and  Philippe Vernier 's team, for their support including technical, administrative, and financial help.
, (2019). Briefly, the ARMS were dissembled within water obtained from the sampling site and the plates submerged in 0. For each sequencing run sequences were automatically demultiplexed on the MiSeq and raw reads were quality checked using FastQC  (Andrews, 2010). Primers were removed from the sequences using cutadapt  (Martin, 2011)  with a single mismatch allowed (parameters: -e 0. 05 --discarduntrimmed). The reads were subsequently processed using the DADA2 package (Callahan et al. ,  Team, 2018). Reads were truncated (165 and 160 bp for forward and reverse reads respectively) and filtered with a maximum allowable number of "expected errors" (maxEE) 2016) within R (R of four (forward reads) and six (reverse reads) with the exception of the Red Sea where sequence quality allowed a more stringent criteria of two and four for the forward and reverse reads respectively.
A parametric error matrix was constructed using the first 10 8 bp of the sequences from each sequencing run. Sequences were dereplicated and sequence variants inferred based on this error matrix with singletons being discarded. The remaining paired-end reads were merged with a minimum overlap of 10 bp and a maximum mismatch of 0 bp. At this point, all sequencing runs were combined and chimeric sequences were removed using the removeBimeraDenovo script within DADA2. Pseudogenes were detected and removed using the methods described in  Leray and Knowlton (2015). Sequences were translated and aligned against a subset of the MIDORI database  (Machida, Leray, Ho, & Knowlton, 2017)  using Multiple Alignment of Coding Sequences (MACSE;  (Ranwez, Harispe, Delsuc, & Douzert, 2011) ). The translations were first made using the invertebrate code and then the vertebrate code.
Accepted Article This article is protected by copyright. All rights reserved Reference sequences of the ASVs were taxonomically classified using the method implemented by the Ribosomal Database Project (rdp) as described in  (Wang, Garrity, Tiedje, & Cole, 2007)  against a redundant BOLD database  (Ratnasingham & Hebert, 2007)  supplemented with COI sequences from NCBI. The threshold for taxonomic assignment was 0. 51 and ASVs that were assigned at the kingdom level to eukaryotes kept. For comparisons between samples and fractions all samples were subsampled to an even depth of 10000 sequences (meaning that a total ARMS sample comprised 30000 sequences over the three fractions). For bioinformatics analysis, the fractions of the ARMS (sessile; mobile: 106 -500 μm and 500 -2000 μm) were combined in two different ways.
Firstly, the total ARMS was considered and this comprised all three fractions being merged together. If a fraction did not meet the threshold of subsampling then this whole ARMS was removed from the analysis. A second grouping of fractions separated the sessile from the mobile assemblages. The mobile organisms (106 -500 μm and 500 -2000 μm fractions) were merged to comprise a single mobile fraction (combined mobile samples comprised 20000 sequences per ARMS). Similarly, fractions with lower values were not considered further. Alpha diversity statistics were calculated within phyloseq  (McMurdie & Holmes, 2013)  and tested for significant differences (One way Analysis of Variance (ANOVA) on sqrt transformed data) for the factor region. Tests for normality (shapiro. test) and homogeneity of variance (leveneTest; package car  (Fox & Weisberg, 2019) ) were undertaken prior to running the ANOVA.
Post-hoc tests were undertaken using the Tukey HSD method. The ASV richness was correlated against environmental variables (see below for how they were selected) using Spearman's Rank correlations. To assess the taxonomic composition of the communities in each region, ASVs were combined at the phylum level using phyloseq in R. The composition was depicted via donut plots with ggplot (Wickham, 2016) and on bathymetric maps produced with marmap  (Pante & Simon-Bouhet, 2013). PCoA plots were produced using square root transformed community data with distances matrices constructed using both Bray Curtis and Jaccard dissimilarity functions and Euclidean distances for the environmental variables. The PCoA ordination was undertaken in phyloseq and plotted in ggplot. Statistical differences were tested using PERMANOVA undertaken in the adonis function of the R package vegan  (Oksanen et al.
For the biotic similarity matrix, we calculated pairwise community similarities using the Jaccard dissimilarity index across sites  (Legendre & Legendre, 2012)  with ASV presence/absence data at each station. Environmental distance was computed pair-wise among replicates, sites and seas based on Euclidean distance using five environmental variables obtained from the Bio-oracle database  (Assis et al. , 2018; Tyberghein et al. , 2012)   Available Radiation (PAR) (Supplementary Table  3 ). Environmental variables selected represent limiting factors, resources, or disturbances (natural or anthropogenic) causally linked to the marine benthic species and its habitat  (Reiss et al. , 2015) ; salinity provides information on freshwater sources and it is known to be low in the Black Sea and Baltic Sea compared to other European seas; chlorophyll-a concentration characterize productive seas and anthropogenic eutrophic areas; sea temperature is one of the main influencing factor to marine species; sea temperature range is a proxy to characterize seas with thermal extremes; diffusion attenuation coefficient is a proxy of water transparency which limits primary producers; and PAR is used to measure the spectral range of light that is available in the water column for use by algae for photosynthesis.
Accepted Article This article is protected by copyright. All rights reserved matrix was calculated as the shortest oceanic path between two sampling sites (km), avoiding land. To do so, we used the marmap  (Pante & Simon-Bouhet, 2013)  package in R. Prior to the analysis relating biotic and environmental variables, we tested for collinearity between explanatory variables by calculating variance inflation factors (VIF) with the ade package in R  (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). We excluded any variable that had a VIF > 5, and then recalculated VIF for the remaining variables. We iterated this process until all variables had a VIF < 5. Mantel and partial Mantel correlations  (Legendre & Legendre, 2012)  were calculated between ASV similarity and both environmental and geographic distances to analyze the relative contribution of niche and space descriptors to benthos community structure, respectively.
Accepted Article This article is protected by copyright. All rights reserved No clear latitudinal or longitudinal gradient was apparent from our dataset in terms of ASV richness. However, a negative Spearman's rank relationship with the range of SST (Spearman's Rank rho = -1, p = 0. 003) and Chl-a (Spearman's Rank rho = -0. 886, p = 0. 033), and a positive relationship with SSS (Spearman's Rank rho = 0. 886, p = 0. 033) were observed. For Shannon diversity we observed a negative correlation with the range of SST (Spearman's Rank rho = -0. 942, p = 0. 016) and Chl-a (Spearman's Rank rho = -0. 942, p = 0. 016). The two regions with the lowest number of observed ASVs also had the lowest local contribution to beta diversity (Figure  2A ). There was a general positive correlation (Linear regression: Total: R 2 = 0.
Accepted Article This article is protected by copyright. All rights reserved predominantly found in the Baltic and Black Seas in the current dataset. The Black Sea shared considerably fewer ASVs with the Adriatic and Western Mediterranean than it did with the Baltic. The Black Sea is connected to the Mediterranean through the Bosphorus strait. Prior to the Bosphorus connection being formed approximately 7000 years ago, there is evidence of long-term isolation of fauna dating from the Pleistocene  (Nikula & Väinölä, 2003; Papadopoulos, Peijnenburg, & Luttikhuizen, 2005). About 7000 years ago, connection to the Mediterranean Sea, through the Bosphorus, led to an increase in the salinity of the Black Sea, allowing for the introduction of species from the Mediterranean  (Zaitsev & Mamaev, 1997) ; however, the relatively lower salinity still present, as well as the colder winter temperatures may act as an ecological barrier that differentiates the communities within the two regions.
The highest number of ASVs were shared between the Western Mediterranean and the Adriatic   Bonhomme, 2000; Borsa et al. , 1997; Debes, Zachos, & Hanel, 2008). A complementary explanation could come from the environmental similarity between Western Mediterranean Sea and Bay of Biscay, since the Adriatic presents different environmental conditions (Supplementary Table  3 ) such as higher diffuse attenuation values, chlorophyll a concentration and sea temperature range, with hotter summers and colder winters, according to our results and described elsewhere  (Borja et al. , 2004; Cahill et al. , 2018). The fact that only a single ASV (belonging to the genus Oscarella (Porifera)) was found in all three regions in this dataset indicated that a combination of the variables described above was limiting the number of species found in these locations that shared a similar latitudinal position.
This can be due to differences in the production of eDNA  (Klymus, Richter, Chapman, & Paukert, 2015) , the production of co-purifying contaminants (e. g. mucopolysaccharides) by taxa  (Pereira, Chaves, Bastos, Leitão, & Guedes-Pinto, 2011) , and also through primer biases that skew these estimates  (Elbrecht & Leese, 2015). Indeed, this could explain the relative lack of molluscs in the current dataset. This low abundance was also found by  Cahill et al. , (2018)  in metabarcoding results which contrasted with morphological results undertaken at the same sites as this study but using ASUs instead. Furthermore, a large proportion of the ASVs could not be given reliably phylogenetic assignments even at phylum level. Improvements in taxonomic assignments would enhance the analysis of the biological groups and could allow the analysis to be undertaken at lower phylogenetic levels (e.
Conclusions By using a standardized sampling and analysis protocol, we show that oceanic distance among sampling sites are slightly more important than niche descriptors in driving ASV distribution. However, high-shared covariation between environmental distance and oceanic distance is observed, indicating that both descriptors might contribute to structuring the rocky bottom mobile and sessile communities, and making it difficult to estimate their relative importance. While understanding spatial patterns in biodiversity is essential to fully discern anthropogenic impacts on ecosystems, temporal patterns via long-term datasets, are also critical  (Magurran et al. , 2010). However, to avoid bias in assessments over time, which often involve changing monitoring equipment, quantifications and characterizations of biodiversity should be standardized.
Here, we have presented a study across multiple institutions spanning different regional seas using standardized ARMS and a metabarcoding approach. We believe that this framework has the potential to improve the monitoring of rocky areas at global scale. For instance, long term insights are crucial to understanding ecological processes  (Seddon et al. , 2014)  and the retrieval and replacement of ARMS in the exact same spot has the potential to create comparable long-term datasets. With ARMS units deployed temporally at local, regional and global scales investigations into the changes in the presence and absence of species across time may be feasible, which has the potential to show shifts in the species with global pressures such as climate change. This approach will provide managers and other stakeholders valuable information about how the cryptobiomecomprised of small and highly diverse, yet often neglected organisms -respond to a variety of natural and anthropogenic impacts.
Samples were normalized and pooled before samples were diluted to 4 nM and denatured as per the Illumina 16S metagenomics library prep manual. A 10-20% PhiX spike was added to the library. Sequencing was undertaken on an Illumina MiSeq at the King Abdullah University of Science and Technology (KAUST) Bioscience Core Laboratory, Centre d'Innovation Génome Québec, McGill University, LGC Genomics GmbH (Berlin, Germany) and SGiker Genotyping and Sequencing Unit (EHU/UPV, ERDF and ESF). Sequences used for this study are deposited in the NCBI short read archive under accession PRJNA557002.
Acknowledgements This manuscript is a result of the  DEVOTES (DEVelopment Of Innovative Tools for understanding marine biodiversity and assessing good Environmental Status ) project, funded by the  European Union  under the 7 th  Framework Programme, "The Ocean of Tomorrow" Theme  (grant agreement no. 308392 ), www. devotes-project. eu. S Carvalho  and  JK Pearman  were funded through the  Saudi Aramco-KAUST Center for Marine Environmental Observations (SAKMEO).
Accepted Article This article is protected by copyright. All rights reserved Tittensor, D. P. , Mora, C. , Jetz, W. , Lotze, H. K. , Ricard, D. , Berghe, E. V. , & Worm, B. (2010). Global patterns and predictors of marine biodiversity across taxa. Nature,  466 (  7310 ),  1098-1101. doi: 10. 1038/nature09329 Tyberghein, L. , Verbruggen, H. , Pauly, K. , Troupin, C. , Mineur, F. ,  & De Clerck , O. (2012). Bio-ORACLE: a global environmental dataset for marine species distribution modelling: Bio-ORACLE marine environmental data rasters. Global Ecology and Biogeography, 21(2),  272-281. doi:  10. 1111/j. 1466-8238. 2011. 00656. xVillarino, E. , Watson, J. R. , Jönsson, B. , Gasol, J. M. , Salazar, G. , Acinas, S. G. , … Chust, G. (2018). Large-scale ocean connectivity and planktonic body size.
PLOS ONE. doi: https://doi. org/10. 1371/journal. pone. 0022594 Ratnasingham, S. , & Hebert, P. D. N. (  2007 ). BOLD: The Barcode of Life Data System (www. barcodinglife. org). Molecular Ecology Notes, 7, 355-364. doi: 10. 1111Notes, 7, 355-364. doi: 10. /j. 1471Notes, 7, 355-364. doi: 10. -8286. 2006. 01678. 01678. x.
Results Genetic data were generated from 2,000 Antarctic fur seals sampled from eight populations covering the circumpolar distribution of the species (see Methods for details). The 39 microsatellite loci carried between 2 and 26 alleles (mean = 11. 44) and none deviated significantly from Hardy-Weinberg equilibrium in more than two out of eight sampling localities after correction for multiple tests (see Supplementary methods and results). The genotyping error rate determined by re-genotyping 96 samples was low (0. 003% per allele or 0. 005% per reaction). population structure. Bayesian analyses of the microsatellite dataset using STRUCTURE produced consistent results across different clustering solutions (Supplementary Fig. S1 ). ∆k, which provides an indication of the uppermost hierarchical level of structure, peaked at k = four, while the average log likelihood of the data also gradually increased until k = four, after which a plateau was reached (Supplementary Fig.
ABC was able to distinguish between the two models, with 87% of the simulations being classified correctly under the bottleneck model and 94% of the simulations being classified correctly under the non-bottleneck model (Supplementary Fig. S6 ). Posterior probabilities supported the bottleneck model in all five populations and the goodness of fit to the empirical data was consistently better for the bottleneck model (Supplementary Table  S3 ). As another indicator of model quality, posterior predictive checks  23, 52  showed that the bottleneck model was able to reproduce the relevant observed summary statistics across all populations (Supplementary Fig. S7 ). As a further evaluation step, we used cross-validation to estimate prediction errors for all model parameters. Cross-validation in ABC uses randomly selected simulations and treats them as pseudo-observed datasets.
gazella dataset for individuals with a high proportion of A. tropicalis ancestry (see Supplementary methods and results). We found that 49 of the Antarctic fur seal individuals were inadvertently pure A. tropicalis, while five of the A. tropicalis reference individuals were hybrids (see Figs. S11  and S12 ). A further 15 Antarctic fur seal individuals were also identified as hybrids. After having removed all pure A. tropicalis individuals as well as the hybrids and any additional individuals that failed to genotype at more than four loci from the dataset, we were left with a total of 2,000 Antarctic fur seal individuals, comprising 197 from the South Shetland Islands, 1,042 from South Georgia, 396 from Bouvetøya, 166 from Marion Island, 18 from Crozet Islands, 51 from Kerguelen Islands, 22 from Heard Island and 108 from Macquarie Island.
population structure and genetic diversity. For the full dataset of 2,000 Antarctic fur seals, we evaluated whether population structure could be detected without prior knowledge of sampling locations using STRUCTURE version 2. 3. 3  76. This program distributes individual genotypes into k clusters by subdividing the dataset in a way that maximizes Hardy-Weinberg and linkage equilibrium within the resulting clusters. The membership of each individual to a given cluster is then estimated as q, which varies between 0 and 1, with the latter indicating full cluster membership. STRUCTURE was run using ParallelStructure  77  with the following parameters: 500,000 burn-in length, 1,000,000 MCMC replications, 1-10 assumed clusters (k) and 10 iterations for each k. The most likely number of clusters was evaluated using the maximal average value of Ln P(D) as well as the Evanno method  78.
Individual admixture proportions were integrated over all simulations using pophelper  79. For comparison, we also implemented principle component analysis (PCA) using the R-package adegenet  80. Genetic diversity was quantified as allelic richness (A r ) within hierfstat  81. Observed heterozygosity (H o ) was calculated with adegenet  80  , the M-ratio was calculated with StrataG 82 , the inbreeding coefficient F is was calculated with diveRsity  83  and the number of private alleles was calculated with poppr  84. Pairwise F st values together with their 95% confidence intervals were obtained using hierfstat  81. Bottleneck analyses. We used two coalescent-based approaches to infer recent demographic histories. First, we quantified the proportion of loci in heterozygosity-excess using the program BOTTLENECK v.
The microsatellite mutation rate (µ) was drawn from a uniform prior with µ ~ U[10 -5 , 10 Genetic data were simulated under each demographic model for 181 individuals, reflecting the smallest genetic population (as determined by STRUCTURE) in the empirical dataset, and 39 microsatellites using fast-simcoal2  17  within the R-package strataG  82. This resulted in a total of 2 × 10 7 datasets. We then calculated three summary statistics (as means across all loci) for the simulated data: (1) allelic richness; (2) the proportion of low frequency (<5%) alleles; and (3) the M-ratio  88  , and retained the 5,000 simulations with summary statistics closest to the empirical data using a tolerance threshold of 5 × 10 -4. To evaluate model specification and fit, we implemented leave-one-out cross-validation using the cv4postpr function of the abc R-package  89.
Next, we tested the accuracy of the ABC parameter estimates using leave-one-out cross validation in the cv4abc function. For a randomly selected pseudo-observed dataset, parameters were estimated using all remaining simulations via ABC using the rejection method. This procedure was repeated 1,000 times and the mean prediction error was calculated. The smaller the prediction error, the better the fit to the true parameter value. After performing the four checks, we constructed posterior distributions of the parameters using a simple rejection algorithm with the abc function of the abc package. To provide a measure of the fit of the preferred models to the empirical data, we conducted posterior predictive checks  23, 52. After generating posterior distributions for each parameter, 1,000 multivariate parameters were sampled from their respective posterior distributions and used to simulate summary statistics a posteriori based on the preferred model.
We then compared the resulting allelic richness to the allelic richness generated in equivalent models with constant effective population sizes of 1,000, 10,000 and 50,000 respectively to estimate the proportional loss of alleles due to the bottleneck. To account for uncertainty in building coalescent trees, we simulated 100,000 datasets under both the bottleneck and constant size model and treated the difference in mean allelic richness between the distributions as the average loss of alleles. To further explore the effects of varying bottleneck strength and duration, we conducted a final round of simulations based on the scenario that provided the best fit to the empirical data (N e hist = 10,000, see Results). Specifically, we quantified the expected loss of alleles for N e bot ranging 25 to 600 in steps of 25 and for bottleneck duration ranging from one to 20 generations in steps of one.