dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 4. 2  (Rambaut, 2014). For the final 102-tip matrix, the sampling of  Wurdack et al. (2005)  was reduced to 66 tips, the trnL-F alignment then collapsed empty columns, and the additional data were manually inserted under similarity criteria using Se-Al ver. 2. 0a11  (Rambaut, 1996 (Rambaut, -2002)). As noted in the study of  Wurdack et al. (2005) , the trnL-F alignment is complex with a mix of discrete indels and hypervariable regions of ambiguous alignment. For ITS and ETS, the MSAs were generated under the Q-INS-i refinement method of MAFFT ver. 7. 452  (Katoh & Standley, 2013)  and then adjusted by eye using Se-Al, similarity criteria, and limiting matrix fragmentation. The ITS and ETS MSAs are relatively compact with mostly 1-3 bp indels. For ITS, the MSA is relatively homogeneous within clades C1 and C2, but it was challenging to align clades with each other; for ETS, the MSA was relatively homogeneous as the taxon sampling included only two tips (Aleurites, Tapoides) outside of Ricinocarpeae s.
Results The elemental analysis to assess whether the first Weda Bay species might be a nickel hyperaccumulator showed no increased values (Fig. 2 ). The nickel present at low concentrations appeared to be evenly distributed, though less so above the major veins, which is consistent with the spread of this metal, as part of water-soluble salts, through the leaf due to evaporation (Antony van der Ent, pers. comm. ). However, the leaf had manganese accumulation, especially along the midrib and major veins (Fig. 2 ). These findings are corroborated by  Lopez et al. (2019b Lopez et al. ( , 2019c) )  for the second species (referred to as "Macaranga sp. " in their papers), which accumulates some nickel but mainly manganese. Search results of the matK and trnL-F sequences of the Weda Bay material against GenBank and/or BOLD using BLAST showed best matches with members of tribe Ricinocarpeae.
Although our clade C2 taxon sampling is the most comprehensive to date (48 genera, including 10 new to GenBank, but missing Benoistia H. Perrier & Leandri, Loerzingia Airy Shaw [=Deutzianthus Gagnep. fide Webster, 2014], Oligoceras Gagnep. , and Parapantadenia Capuron [=Pantadenia fide Webster.
Acknowledgements The authors thank the mining company PT  Weda Bay Nickel , and especially  Steven Dickinson ,  Gavin Lee  and  Edi Permana , for inviting some of them to organize the botanical inventory and for providing logistical help during the collection, and they also thank all members of the botanical collecting team Fig. 8. Weda lutea  Welzen. A, Habit  with hairy, slightly peltate leaves (Gushilman et al. 178, photo IG 820). B, Young shoot with exstipulate developing leaves, stellate (brown) hairs and long white simple hairs (Bangun et al. 199, photo TF 2517). C, Cymose inflorescence with basal young fruit and terminal staminate flowers and buds (Bangun et al. 199, photo TF 2520). D, Staminate flower with yellow petals (Bangun et al. 199, photo TF 2510). E, Pistillate flower (Bangun et al.
| Location data Over three consecutive breeding seasons  (2015/16, 2016/17, and 2017/18) , GPS data loggers (CatLog-S GPS loggers, Perthold Engineering LLC USA, 50 × 22 × 8 mm, 34 g) were deployed on four albatross species on Marion Island (46°55ʹS; 37°40ʹE)during the incubation period : wandering (WA), grey-headed (GHA), sooty (SA), and light-mantled (LMSA) albatrosses (Table  S1 ). Loggers recorded locations at one-hour interval and were retrieved after one foraging trip. All deployments were within colonies on the south-east coast of Marion Island and within GHA colonies along an inland ridge on the south side of the island. Following the removal of erroneous locations using a McConnel speed filter (trip package;  Sumner, 2016)  based on a conservative movement speed threshold of 135 km/hr  (Phillips et al.
| Environmental data To investigate the association between the foraging behavior of albatrosses and oceanographic features, five environmental variables were considered: four satellite-derived measures of altimetry: sea surface height (SSH), sea-level anomaly (SLA), eddy kinetic energy (EKE), and wind speed, and one static variable: ocean floor slope (Table  S2 ). Sea surface temperature was not used as it is highly correlated to SSH. Environmental variables were extracted for each location identified as foraging or transiting by the DCRWS. A Spearman's correlation matrix revealed that there was no collinearity (coefficient < 0. 7) between any of the extracted environmental variables  (Dormann et al. , 2013). Within the Southern Ocean, fronts are identifiable by the SSH values (Table  S3 ;  Swart, Speich, Ansorge, & Lutjeharms, 2010).
| D ISCUSS I ON We have investigated the response in foraging behavior of breeding sympatric albatross species to environmental variability using concurrently collected tracking data over three consecutive years. Results from WA provided strong support for our "nomad hypothesis" as individuals exploited an extensive area and demonstrated large variance in interindividual foraging habitat use. By comparison, the smaller albatrosses provided partial evidence toward two of our hypotheses. All three smaller albatrosses showed consistency in spatial distribution, supporting the "faithful hypothesis". However, the interannual differential use of foraging habitat by GHA and LMSA also provided partial support for our "flexible hypothesis". Furthermore, the high variance in habitat use by SA indicated that this species was not faithful to a particular foraging habitat thus providing partial evidence toward our "nomad hypothesis".
, 2000)  remains unknown. Using large-scale screening of domestic horse breeds alongside the Przewalski horse, we aimed to evaluate the robustness of STRUCTURE and ΔK with emphasis on the effects of the numbers of MCMC iterations and replicates. Results are compared with alternative clustering methods based on different model assumptions. STRUCTURE assumes linkage equilibrium, LE, and Hardy-Weinberg equilibrium, HWE  (Pritchard et al. , 2000). In contrast, the multilocus maximum likelihood approach  (Paetkau, Calvert, Stirling, & Strobeck, 1995)  as implemented in FLOCK (Pierre  Duchesne & Turgeon, 2012)  iteratively allocates genotypes into genetic clusters without assuming HWE and LE. Discriminant analysis of principal components, DAPC, as implemented in the software ADEGEnET  (Jombart, 2008; Jombart, Devillard, & Balloux, 2010 ) is a model-free multivariate method.
The information content was also evaluated using the clustering program FLOCK  (Duchesne, Méthot, & Turgeon, 2013; Duchesne & Turgeon, 2012)  which designates an "undecided stopping condition" when an optimal number of population clusters explaining the data cannot be identified. Two underpinning scenarios are possible: lack of "true" genetic structure or insufficient genetic information content. Discriminating these scenarios is part of the interpretation of the FLOCK results described further below in the context of population clustering.
We evaluated the convergence of the two estimates over r by first partitioning all R replicates into B subsequent blocks of 5 replicates each. P(K), K opt  [Evanno]  , and K opt  [Pritchard]  were then calculated for both, each block separately and accumulated over all preceding blocks. In order to evaluate the impact of R in smaller data sets on the optimal K selection, a subset of 295 individuals from five Arab breeds, Algerian, Moroccan and Tunisian Barb, Akhal Teke, Caspian, Turkoman and the Przewalski horse was analyzed with R = 40. A similar selection of breeds from the same data base was used previously with R = 10 and 120,000 MCMCs and 200,000 MCMCs, respectively (A. Khanshour, Conant, et al. , 2013). Alongside, data on 1,361 Anacapa deer mice  (Ozer, Gellerman, & Ashley, 2011a; Ozer, Gellerman, & Ashley, 2011b)  were also re-evaluated with the originally applied STRUCTURE parameters, but with R = 100.
| DAPC analysis The DAPC analysis utilized the ADEGEnET 2. 0. 1 module for the R environment  (Jombart et al. , 2010; Jombart et al. , 2016). We used R 3. 3. 1 (R Foundation for Statistical  Computing, 2016). The first step is to transform all genotypes into noncorrelated variables using a principle component analysis, PCA. For downstream analysis, all principle components, PCs, were retained. After PCA, discriminant analysis, DA, is applied. It partitions the variance within and between a priori defined clusters, K, such that the separation between clusters is maximized. We evaluated K for 2 to 94, the total number of breeds. To prevent overfitting by DAPC, 50 PCs, approximately one-third of the total number of PCs identified by the PCA model was used  (Jombart & Collins, 2016). The optimal K, denoted K opt[DAPC] henceforth, was chosen by visually and statistically identifying sharp changes in the Bayesian information criterion, BIC, over increasing K  (Jombart et al.
For the whole horse data set, the graphs for the 1,500,000 MCMC /-LP scenario, the 1,500,000 MCMC /-LP scenario, and for two iterations of the 3,5000,000 MCMC /-LP scenario are shown. For Arab horses, the results for the 750,000 MCMC /-LP scenario and for the Anapaca deer mouse dataset the results for the 110,000 MCMC / -LP set (i. e. the STRUCTURE settings used in the original analysis by  Ozer et al. , 2011a)  are shown. In each case, 40 replicates were conducted except for the the 1,500,000 MCMC /-LP scenario with 100 replicates F I G U R E 4 Membership coefficients for horse breeds from STRUCTURE at various K and ±LP models, posterior probabilities for DAPC cluster assignments at the optimal K opt[DAPC] =27, and summary of FLOCK breed cluster assignments. Panels for STRUCTURE are specified by the ±LP models, visualize membership coefficients within each genetic cluster as indicated by different colours.
The membership coefficients shown were averaged over all horses within each breed. With the exception for K =45, the membership coefficients for each horse are averages for all replicates found in the major modes as estimated by CLUMPAK. CLUMPAK analysed 160 replicates stemming from 40 replicates from each MCMC/-LP scenario and 120 replicates stemming from 40 replicates from each MCMC/+LP scenario. The percentages of replicates within the major clusters are presented in brackets. All these presented panels correspond to K without ghost clusters and with only majority modes (Fig. 2 ). The exception K=45 refers to the estimated optimum, K opt  [Pritchard]  =45, which was inferred by the visual Pritchard method and includes ghost clusters. Major mode could not be calculated as the data volume for K=45 could not be processed by CLUMPAK.
Thus, knowledge that two separate but similar populations are indeed different is critical for proper resource use. However, this is inherently difficult  (Evanno et al. , 2005; Pritchard et al. , 2010). Our large empiric data set produced reliable mean P(K) estimates even with relatively low numbers of MCMC iterations and replicates. However, despite that our number of MCMCs (up to three million) and replicates (up to 100) is among the highest in the literature the ∆K estimator failed to converge, powerfully demonstrating the problems the ∆K statistics may face. How often this problem might have occurred in published studies remains unknown as studies have, in general, not evaluated the issue. The large observed variance between MCMC iterations within each evaluated K for both locprior models is the proximate reason for the poor performance of the ∆K method.
Results: To provide new features on the lignocellulose degradation in isopod holobionts, shotgun sequencing of 36 metagenomes of digestive and non-digestive tissues was performed from several populations of four aquatic and terrestrial isopod species. Combined to the 15 metagenomes of an additional species from our previous study, as well as the host transcriptomes, this large dataset allowed us to identify the CAZymes in both the host and the associated microbial communities. Analyses revealed the dominance of Bacteroidetes and Proteobacteria in the five species, covering 36% and 56% of the total bacterial community, respectively. The identification of CAZymes and new enzymatic systems for lignocellulose degradation, such as PULs, cellulosomes and LPMOs, highlights the richness of the strategies used by the isopods and their associated microbiota.
To this end, shotgun sequencing of 36 metagenomes was performed from digestive and nondigestive tissues of one freshwater and three terrestrial isopod species from several populations. In addition, we used 15 shotgun metagenomes of our previous study on the pill bug Armadillidium vulgare  [25]. Combined with the host transcriptomic data, this large dataset enabled us to identify the CAZome of both host and microbiota, and microbial taxa associated with lignocellulose degradation. The comparison of different CAZomes, as well as the identification of CAZymes, PULs ("Polysaccharide Utilization Loci") and cellulosomes in isopods, highlighted the diversity of strategies for this process in isopods. Altogether, these results show that isopod holobionts are promising models to study lignocellulose degradation and to potentially discover new lignocellulose-degrading CAZymes.
Quality of metagenome and transcriptome assemblies To build the CAZomes of the isopod holobionts, 51 metagenomic samples from digestive and non-digestive tissues of five isopod species were processed from 36 new datasets, along with 15 datasets from our previous study  [25]. Samples from different origins enabled us to compare CAZomes of different populations for a single species. These samples represented a total of 5. 7 billion reads, assembled into 25 million contigs including 4. 7 million contigs > 1 kb length (Additional file 1). Meanwhile, host transcriptomes were obtained from whole individuals from several populations (for more details, see  [18] ). Transcriptome assemblies resulted in 40,916 from 143,383 transcripts depending on the species. Assemblies showed an N50 from 1096 bp to 1523 bp depending on the transcriptome (Additional file 2).
CAZyme identification in isopod holobionts Both contigs and transcripts from metagenomes and host transcriptomes were subjected to the CAZy database (http://www. cazy. org) to identify CAZomes in isopod holobionts. The use of dbCAN2 for the identification of CAZymes resulted in more stringent criteria than in our previous studies  [18, 25]  for the identification of CAZymes. In total, 15,834 CAZymes were identified distributed among 201 CAZy families of which 27 were specific to the hosts, 136 were found only in microbiota, and 36 were present in both (Additional files 3, 4, 5). Of these CAZymes, 12,916 belonged to the metagenomes (distributed among 174 families) and 2918 belonged to the host transcriptomes (distributed among 63 families). Eightyfour GH families were identified in the CAZomes of isopod holobionts; they represented the most abundant and diversified families of those CAZomes.
Taxonomic origin of the CAZymes from microbiota To identify the microbial communities associated to lignocellulose degradation, similarity searches of the predicted CAZymes from the metagenomes were performed against the NCBI Non-Redundant Protein database. Proteobacteria and Bacteroidetes were the most represented bacterial phyla, accounting for 56% and 36% of the identified CAZymes, respectively (Fig. 3a ). The microbial communities were rich and highly diversified in all host species. Indeed, 41 bacterial orders were found in all microbiota (Fig. 3b ). The taxonomic origin of the microbial CAZomes was highly different from one host to another (Figs. 3, 4 ), without apparent sex effect except for P. pruinosus (Fig. 4a ). Moreover, the microbial CAZomes were shaped by the environment, as for a given species these communities differed according to the host origin (field or laboratory) (Fig.
Biological samples Metagenomic data were generated from laboratory lineages of the freshwater isopod Asellus aquaticus, and the terrestrial isopods Porcellionides pruinosus, Porcellio dilatatus dilatatus and Porcellio dilatatus petiti. Field populations of Asellus aquaticus from the Pinail nature reserve (France, 46° 42′ 2. 698″ N, 0° 31′ 13. 378″ E) and Porcellionides pruinosus from Nouaillé-Maupertuis (France, 46 30′ 34″ N, 0° 24′ 54″ E) were also collected in July 2017 (Additional file 1). Those individuals were kept until dissection (within 2 days after collection) in plastic boxes with water or soil from their respective sampling sites. Armadillidium vulgare data were issued from our previous metagenomic study  [25].
Metagenomic shotgun assembly Read quality was checked with FastQC (version 0. 11. 2; http://www. bioin forma tics. babra ham. ac. uk/proje cts/ fastq c) and removal of sequencing adaptors and low quality bases was performed with Trimmomatic (version 0. 32;  [54] ). Trimmed reads shorter than 50 bp were discarded. To identify and filter rRNA reads, SortMeRNA was used with an E value cut-off of 1e-20 (version 2. 1;  [55] ). To discard host reads, remaining reads were mapped using BOWTIE2 (version 2. 3. 4. 3;  [56] ) against a custom isopod database comprising all isopod sequences from the Nucleotide NCBI database and unpublished isopod sequences from our laboratory. Non-mapped reads were then assembled with the MEGAHIT software (version 1. 0. 3; [57] ) using the following parameters: -min-count 2 -k-min 21 -k-max 127 -k-steps 1.
Assembly qualities were checked using Blobtools (version 1. 0;  [58] ). To ensure that all host sequences were filtered, two control steps were performed. (1) ORFs from contigs were predicted using Prodigal (version 2. 60;  [59] ) with "meta" parameter, and they were compared against the Non-Redundant Protein database (December 1, 2018) using BLASTX  [60]  with an E value cut-off of 0. 0001. The BLAST outputs were then imported into MEGAN6 software (version 6. 15;  [61] ) for taxonomic assignment based on the lowest common ancestor (LCA) algorithm using the NCBI taxonomy database. All contigs associated to an ORF assigned to eukaryotes were discarded. (2) Then, the remaining contigs were run through the CAT-BAT pipeline (version v4. 6;  [62] ) for taxonomic classification and those that were assigned to eukaryotes were discarded.
Host transcriptomes Host transcriptomes of whole individuals of P. dilatatus dilatatus, P. dilatatus petiti, P. pruinosus, A. aquaticus and A. vulgare were the same as those used in Bredon et al. [18]. In brief, reads were trimmed using Trimmomatic (version 0. 32;  [54] ), transcriptome assemblies were performed with IDBA-TRAN  [63]  with default parameters, then transcripts were clustered with ≥ 95% identity using CD-HIT-EST (version 4. 6;  [64] ) and ORFs were predicted using Transdecoder (version 3. 0. 1; https :// trans decod er. githu b. io/). The completeness of the resulting assemblies was assessed with BUSCO (version 3. 0. 1; [65] ) referring to core arthropod genes.
Carbohydrate-Active enZyme annotation CAZymes were identified using the Carbohydrate Active enZymes (CAZy) database  [13]. dbCAN2  [66]  was used to identify CAZy families (i. e. Glycoside Hydrolases (GHs), Glycosyl Transferases (GTs), Polysaccharide Lyases (PLs), Carbohydrate Esterases (CEs), Auxiliary Activities (AAs) and Carbohydrate-Binding Modules (CBMs)) from the previously filtered ORFs from the metagenomes and transcriptomes. The software integrates three tools for CAZymes annotation: (1) HMMER (version 3. 2. 1; [67] ) that uses the dbCAN CAZyme domain HMM database  [68]  for domain predictions, (2) DIAMOND (version 0. 9. 24;  [69] ) for sequence comparisons against a custom pre-annotated CAZyme sequence database, and (3) HOTPEP  [70]  that performs searches against a conserved CAZyme short peptide database.
dbCAN2 was run with the following parameter: -dia_eval 1e-50. In a conservative manner, only CAZymes that were predicted by the three tools were kept for the following analyses. As recommended by dbCAN2 authors, CAZyme counts (note that "CAZyme" refers to functional modules or domains, not genes) resulting from dbCAN assignment using HMMER tool were considered for the following analyses. For comparative analyses, CAZyme counts were normalized to even out the heterogeneity arising for differential library sizes. For each sample, CAZyme counts were divided by the number of ORFs in the metagenome or transcriptome of interest to calculate the relative abundance for each CAZyme family. Then, the normalized count of each family in each metagenome or transcriptome was calculated by multiplying the relative abundance by the lowest number of ORFs identified in corresponding datasets: 202,349 (i.
e. male tissues of P. dilatatus dilatatus) for the newly sequenced metagenomes, 22,641 (i. e. female tissues of A. vulgare from the field) for A. vulgare metagenomes of our previous study and 19,473 (i. e. A. aquaticus transcriptome) for host transcriptomes. All CAZy families known to potentially contribute to lignocellulose degradation were then selected for further analysis. However, a single CAZy family can bring together enzymes involved in a large variety of carbohydrate-modifying activities, including lignocellulose degradation. For that reason, the enzymatic activities of the CAZymes belonging to those families were predicted using Hotpep  [70]  in order to confirm their implication in lignocellulose degradation. When Hotpep could not predict the function of the CAZymes, we considered their most common activity reported in the CAZy database  [13].
Community profiling To identify microbial communities encoding CAZymes, ORFs annotated as CAZymes were compared with the Non-Redundant Protein database (December 1, 2018) using BLASTP  [60]. An E value cut-off of 0. 0001 was used and the top five hits were kept. MEGAN6 software (version 6. 15;  [61] ) was then used for taxonomic assignation of ORFs using the NCBI taxonomy database, and to construct principal component analysis (PCA) and phylogenic tree. Results were visualized using the Phyloseq R package  [71].
PUL and cellulosome identification PULs consist of co-localized and co-regulated genes organized around an SusC-SusD gene pair and encoding proteins that degrade complex carbohydrates. They might play an important role in the breakdown of lignocellulose in Bacteroidetes species  [49]. To identify potential PULs in microbiota, ORFs of metagenomes were first compared to the Pfam database (version 32. 0;  [72] ) using hmm-search (version 3. 2. 1; [67] ) with an E value cut-off of 0. 0001 to identify conserved domains. Then, contigs encoding PUL gene markers [SusD like proteins (PF07980) and TonB-dependent receptor/SusC like proteins (PF00593)] were extracted and those encoding one sequential pair of susC and susD were kept for the following analyses. These sequences were subjected to CAT-BAT for taxonomic assignation, dbCAN2 for CAZyme annotation and Prokka (version 1.
9;  [73] ) for gene annotation. Cellulosomes are multi-enzyme lignocellulosic systems organized around a scaffolding and attached to bacterial cells  [44]. Enzymes bind to the scaffolding thanks to interactions among cohesin and dockerin modules. To identify cellulosome systems in microbiota, we searched for cohesin Pfam domain (PF00963) and dockerin Pfam domain (PF00963) in the conserved domains predicted above, as well as for S-layer homology domain (SLH; PF00395). Then, contigs encoding those modules were subjected to CAT-BAT for taxonomic assignation and dbCAN2 for CAZyme annotation.
Funding This work was funded by the 2015-2020 State-Region Planning Contracts (CPER),  European Regional Development Fund (FEDER)  ( BiodivUP  project, coordinator DB), and intramural funds from the  Centre National de la Recherche Scientifique  and the  University of Poitiers. The work of MB was supported by a Ph. D. grant from the  French Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation.