dataesr/scientific-paragraphs-categorization huggingface dataset:
Evidence of independent domestication of O. glaberrima. To investigate the introgression of Asian rice into African rice, resequencing reads from 14 O. sativa and 20 O. glaberrima accessions (10-30× coverage) were mapped to the O. glaberrima RefSeq data using BWA V0. 5. 9. All reads with a mapping quality score of less than 20 were discarded. The alignment files of the O. sativa and O. glaberrima accessions were then input into POPBAM V0. 3 to detect evidence of introgression from O. sativa into O. glaberrima using the recently developed G min statistic  42. O. sativa domestication genes were selected according to criteria described in Meyer and Purugganan  48. Orthologous genes of the O. sativa domestication genes in O. glaberrima were identified by nucleotide and amino acid BLAST searches.
sativa ssp. japonica and O. glaberrima genomes. Two different insert libraries (500 bp and 10 kb) were constructed, sequenced and assembled using a Pacific Biosciences RS II instrument and HGAP software according to the manufacturer's recommendations. Both BACs were assembled into single contigs (139,241 bp for OG_BBa0035M03 and 176,785 bp for OB_ABa0085D15) without further finishing efforts. Sequence comparisons were performed using Dotter and Nucmer, and orthologs were identified using Nucmer and BLAST. To identify the Sh4 orthologous region, Sh4 gene sequences from O. sativa ssp. japonica were mapped to the O. glaberrima genome sequences. One BAC clone encompassing.
RepeatMasker, http://www. repeatmasker. org/; SNPs used for O. barthii population structure analysis and for investigating the genetic relationship of O. glaberrima and O. barthii can be downloaded at http://de. iplantcollaborative. org/dl/d/11EA9662-D292-4C64-8862-69466C087936/Oglaberrima_Obarthii_population_variation. vcf; SNPs used for the study of genetic variation of O. glaberrima and O. barthii can be downloaded at http://ensembl. gramene. org/Oryza_ glaberrima/Info/Index. Accession codes. The assembly and annotation of the O. glaberrima genome are available at http://ensembl. gramene. org/Oryza_glaberrima/Info/Index and have been deposited under GenBank accession number ADWL00000000. 1. The assembly of the Oryza punctata genome has been deposited under GenBank accession number AVCL00000000.
1. The BAC sequence assemblies have been deposited under GenBank accession numbers KF284071, KF284072 and KJ650506. The resequencing reads of O. glaberrima, O. barthii, O. sativa ssp. japonica and O. sativa ssp. METHODS Methods and any associated references are available in the online version of the paper. indica have been deposited under GenBank accession numbers SRX502298- SRX502317, SRX502162-SRX502255, SRX621487-SRX621493 and SRX621496-SRX621502, respectively. The RNA-Seq reads of O. sativa ssp. japonica leaf, panicle and root have been deposited under GenBank accession numbers SRX477950, SRX477951 and SRX477952, respectively. The RNA-Seq reads of O. glaberrima leaf, panicle and root have been deposited under GenBank accession numbers SRX474528, SRX474529 and SRX474530, respectively. The RNA-Seq reads of leaf and panicle of O.
Again, conservation of synteny in Lepidoptera genome organisation allowed the intron/exon boundaries in H. bolina genes to be inferred using the B. mori genome. Through tBLASTx analysis of the B. mori coding sequence of the targeted gene against the B. mori WGS (Whole Genome Shotgun contigs) database in NCBI, exonic regions were identified (as only these regions will align). The translated orthologous H. bolina contig and the corresponding B. mori amino acid sequence were aligned using ClustalW  [21]  and the position of the intron/exon boundaries subsequently located. Once intron/exon boundaries were identified in B. mori genes, and extrapolated to the H. bolina orthologous sequences, primers were designed for H. bolina that spanned introns of size 500-1000bp (Bombyx size approximation). This size range was chosen to enable successful amplification of the intronic region during PCR.
This allele was then removed (it was an allele increasing in frequency in each case), and the data retested to ascertain if the population samples were then homogeneous, or whether there was evidence for a second allele that changed in frequency (a second allele was identified in three cases). We additionally used Fst standardized population genetic differentiation to quantify the magnitude of change between allelic frequency distributions between the two samples. In each case, the rare individuals where sequence could not be obtained for particular alleles, or not inferred accurately, were coded as missing information. Nine unlinked markers, each situated on a different chromosome, were also sequenced for the 2001 and 2006 population samples to investigate the degree to which changes were observed in the wider genome and as a control for demographic effects.
Statistical analysis Linear mixed-effects models [LMMs  (Gałecki and Burzykowski, 2013) ] were used to examine the relationship between response variables (pulp citrate and malate concentration) and explanatory variables (fruit age, cultivar, treatment), and interactions. We used quadratic and cubic terms of fruit age when the curve passed through a maximum and had an asymmetrical shape. We used the lme function in the 'nlme' library  (Pinheiro et al. , 2013)  in the statistical program R 2. 14. 0. "Banana plant" was treated as a random effect because banana plants were assumed to contain unobserved heterogeneity which is impossible to model. A temporal correlation structure was used to account for temporal pseudo-replication. Model selection was made using the top-down strategy  (Zuur et al.
D involving genomes having undergone a whole genome duplication event and (ii) keep the trace of small rearrangements that may be responsible for small overlaps or inclusions between synteny blocks. SynChro is a simple algorithm that is not meant to bring new theoretical advances over existing and more sophisticated tools in the field of synteny block identification. The interests of SynChro lie in the all in one package with few parameters, rapid execution time and several useful visualization tools that are more flexible than that of other existing methods.
SynChro Input, Output and Parameter SynChro is a set of awk and python scripts with graphical outputs supplied using gnuplot. It can be applied to two or more genomes to realize all possible pairwise comparisons. The minimum input information that must be provided to SynChro is a list of protein-coding genes, ordered along the chromosomes (or scaffolds) and their associated amino-acid sequences. Their coordinates along chromosomes, centromere positions, and other genomic features are useful information but not compulsory for synteny block reconstruction. The indication of the coding strand is also a useful but optional information that is used to orient synteny relationships between genes in the synteny map (if they are not specified, genes are assumed to be all on the same strand). Formats of the input files are detailed in the README file (http://www.
lcqb. upmc. fr/CHROnicle/SynChro. html). Allowed formats include EMBL, GenBank and Fasta files and the scripts that convert these files into the expected input format are provided within the package. For each pairwise comparison, four different outputs are provided (see orange frames in Fig. 1 ): 1. a detailed synteny map allowing to visualize synteny blocks with all individual homology relationships (including their relative orientation in the two compared genomes) and the breakpoint regions including the protein-coding genes they encompass as well as other genetic features such as tRNA, pseudogene, LTR (Long Terminal Repeats), etc. This synteny map is interactive, the names of the different genetic features pop-up on the screen when the mouse points to their symbols. This map is a vectorial image, therefore it is possible to zoom in and out as necessary.
This detailed synteny map represents a true improvement compared to other tools where graphical outputs are often poor, being reduced to dotplots  [1, 17, 18]  or chromosomal painting  [19, 20]. 2. text files containing homology relationships (RBH and non-RBH) and synteny blocks description 3. a chromosomal painting representation 4. a genome-wide dotplot of syntenic homologs. Moreover, for several pairwise comparisons, SynChro provides scripts to compute, correlate and plot relevant information such as the proportion of genes/genome that is conserved in synteny, the average percentage of amino-acid similarity between orthologs, the number of synteny blocks, the average length (in nucleotides or in number of genes) of the breakpoint regions (i. e. regions between two contiguous synteny blocks), the average number of genes per synteny block or the proportion of consecutive synteny blocks whose homologous blocks map also on the same chromosome in the other species (see the README file for the complete list).
Benchmarking SynChro on Vertebrate Genomes To evaluate the performance of our algorithm, we compared the synteny block reconstruction achieved by SynChro to the synteny blocks reconstructed by two other commonly used tools that also reconstruct synteny blocks from annotated genome/ genes: MCScanX  [14]  and i-ADHoRe  [15]. These tools are regularly updated since their first publication  [9, 21]. The three tools were run on the same dataset composed of three pairwise comparisons of genomes corresponding to three different levels of synteny conservation: Human/Mouse (Homo sapiens/Mus musculus), Human/Zebra finch (Homo sapiens/Taeniopygia guttata) and Human/Zebrafish (Homo sapiens/Danio rerio). SynChro appears to be between 2 and 3 time faster than the two other tools to reconstruct synteny blocks between the three pairwise comparisons (SynChro takes, on a desk computer, on the order of 40 minutes to reconstruct synteny blocks between two vertebrate genomes, Table  2 ).
SynChro The RBH identification is achieved with OPSCAN (http:// wwwabi. snv. jussieu. fr/public/opscan/), which is based on the FASTA algorithm  [12] : 1. For each protein sequence from the query genome, OPSCAN scan the database with a simple version of the fastp algorithm where no gap is allowed and where the alignment is achieved through shifting sequences to maximize the number of matches between the two compared sequences. This step leads to the identification of a set of K most similar genes with K = 6 (default value). Other parameters are used with their default values (kuple: 2, fastp diag integ: 0 and fastp lower threshold: 5). 2. For each query gene OPSCAN refines the alignment with its K most similar target genes by performing a dynamic programming alignment (with zero cost end gaps).
The parameters used are BestFit (local) and BLOSUM60 scoring matrix. 3. RBH are defined from these refined alignments when the most similar gene to the query gene Gi amongst its K most similar database genes is Gj, and the most similar gene to the database gene Gj is the query gene Gi. The parameters used are Bestfit score threshold for ''homologs'': 40 (in the 0-100 range) and length ratio threshold (longest sequence divided by the shortest): 1. 3. The reason why we chose to use OPSCAN rather than blast is because this algorithm was optimized for RBH identification. The fastp part permits OPSCAN to quickly scan the database genome (by simply shifting the compared sequences), as a pre-filter for possible RBH, and then, the BestFit algorithm is run only between query genes and their K = 6 most similar homologs.
SynChro uses several other parameters (% of similarity between homologs, length of the alignments, minimal number of anchors per block (n~2, this value has no relationship whatsoever with the value) that have fixed values. These values were shown to be well suited to perform efficient synteny block reconstruction between a large range of organisms sharing various phylogenetic relationships (successfully applied to 18 yeast and 13 vertebrate genomes  [21] ). Nevertheless, the user can easily change the values of these parameters in the source code (SynChro:py) where they are clearly commented at the top of the file.
This work was supported by the  Agence Nationale de la Recherche  (' GB-3G ',  ANR-10-BLAN-1606-01 , http://www. agence-nationale-recherche. fr/) and by an  ATIP grant  from the  CNRS  (http://www. cnrs. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments The authors thank  Jean-Louis Smock  ( IRD ) for field assistance. This study was supported by grants from the  Fond Ecosystèmes Tropicaux, MEDD  ( 2002-2005 ): Evaluation multi-échelles de la diversité spécifique, structurale et fonctionnelle des arbres en forêt guyanaise: prise en compte du substrat géologique, des sols et de la dynamique.
The jumbo squid Dosidicus gigas plays an important role in marine food webs both as predator and prey. We investigated the ontogenetic and spatiotemporal variability of the diet composition of jumbo squid in the northern Humboldt Current system. For that purpose we applied several statistical methods to an extensive dataset of 3,618 jumbo squid non empty stomachs collected off Peru from 2004 to 2011. A total of 55 prey taxa was identified that we aggregated into eleven groups. Our results evidenced a large variability in prey composition as already observed in other systems. However, our data do not support the hypothesis that jumbo squids select the most abundant or energetic taxon in a prey assemblage, neglecting the other available prey. Indeed, multinomial model predictions showed that stomach fullness increased with the number of prey taxa, while most stomachs with low contents contained one or two prey taxa only.
Previous studies showed that the feeding ecology of jumbo squid is highly variable in time and space  [20, 21]. The feeding ecology of jumbo squid was investigated in the eastern Pacific from stomach content  [22] [23] [24] [25]  and stable isotopes  [26] [27] [28] [29]. By investigating stable isotope signatures along gladius,  [28]  showed that jumbo squids living in the same environment at a given time can have different historical backgrounds. These differences in life history strategies, illustrating a high plasticity, were confirmed by  [29]  who analysed carbon and nitrogen stable isotopes of individuals collected during 2008-2010. Here, we used an extensive dataset of more than 4000 stomachs sampled between 2004 and 2011 in the northern Humboldt Current to provide new insight on the size-related and spatiotemporal variability of feeding habits of D.
However, this fishing tactic and the squid voracity artificially increase the proportion of cannibalized jumbo squids in the stomach contents  [31, 32]. To remove this unnatural feeding, the easily identifiable fresh jumbo squid portions were systematically eliminated from the stomach contents. Even after this procedure, jumbo squid was still by far the dominant prey by wet weight and reached 75%, indicating that fishery-induced cannibalism was not fully eliminated. This high rate was mainly due to 859 stomachs containing D. gigas only. We were therefore not able to precisely estimate the importance of natural cannibalism with our dataset that was still blurred by artificially induced cannibalized conspecifics. We thus removed these 859 stomachs and worked with the remaining 4461 (83. 9%), from which 3618 were not empty (68% of the total number of stomachs) (Table  1 ).
Data Analyses A clear relationship exists between squid size and maturity stages (Fig. S1 ) indicating that size is, to some extent, a proxy for ontogenetic processes. Therefore we used size to investigate life cycle effect on jumbo squid diet. Jumbo squid diet did not significantly vary with sex (results not shown). This factor was thus not taken into account in further analyses. Jumbo squid were generally captured by jigging after dusk and therefore night samples (62%) dominated the dataset. Preliminary analyses were performed on night data and on the whole data set. Results were similar and we therefore reported results with the complete set of data only. In order to analyse the potential effects of explanatory variables on the number of taxa per stomach, a proportional-odds model for ordinal response  [41]  was fitted to the vector of prey richness, i.
This preliminary approach allowed us to perform an initial inspection of the dataset. Length, distance to the shelf break and SSTA were then each divided in four ordered categories, according to their ecological interpretation (the number of stomachs is given for each category); for mantle length: less than 40 cm (559), 41-60 cm (1553), 61-80 cm (934), over 80 cm (572); for distance to the shelf: less than 50 km (840), 51-75 km (682), 76-130 km (829), over , winter (997) and spring (863). However this approach did not account for dependence and interactions between explanatory variables, and then did not elucidate the complex relationships between the type of prey and the environmental factors. In addition the sampling scheme was very unbalanced in space and time. To cope with these issues, we ran a classification and regression tree (CART) analysis proposed by  [42]  and adapted to diet data by  [43].
Classification tree was used here as a tool to identify the relationships between explanatory variables and the distribution of prey groupings. This non-parametric method gives a clear picture of the structure of the data, and allows an intuitive interpretation of the interactions between variables. The classification tree uses a partitioning algorithm to estimate a series of binary decision rules that divide the data into smaller homogeneous subgroups in an optimal way. The whole dataset is represented by a single node at the top of the tree. Then the tree is built by repeatedly splitting the data. Each split is defined by a simple rule based on a single explanatory variable. Splits are chosen to maximize the homogeneity of the resulting two nodes. We followed the approach of  [43]  and transformed the diet data as follows: each row represents a unique predator-prey combination, where the proportion by wet weight of one of the eleven prey taxa potentially present in the stomach of a predator is used as a case weight for the classification tree.
As the splitting procedure grows an overlarge tree, we applied a prune back procedure to keep the tree reasonably small to focus on the first most informative splits. Each terminal node (or leaf) of the final tree is characterized by a predicted prey distribution (percentage by weight of 11 groups), given three explanatory continuous variables (stomach fullness index, distance to the shelf break and SSTA) and two categorical variables (season: summer, fall, winter and spring; and individual size (cm) divided into four ordered categories). Year effect was also tested but this factor had no significant effect on the pruned tree and was removed from the final model (Table  S2  for detailed data per year). Analyses were conducted using the statistical open source R software (R Core Team 2013), with the MASS package for the proportional odds-model  [44]  and the rpart package for the classification tree.
Prey Richness Using the detailed (55 taxa) or aggregated (eleven taxa) databases, prey richness in stomachs was similar with an average of 1. 8 taxa per stomach. This unexpected result has several consequences. It first empirically validates the eleven aggregated taxonomic groups (Table  S1 ). Second, it shows that when jumbo squid foraged on one prey among the 55 taxa, it did not feed on extra prey belonging to the same assemblage among the eleven aggregated taxa. A spatial segregation of prey of jumbo squids may explain this observation. If a taxon from one group of the eleven aggregated taxa occurred in a location where jumbo squids seek their prey, the probability of the presence of an extra taxon belonging to the same group may have been low. On the contrary, jumbo squid could select the most abundant or energetic taxon of a group, neglecting the other available prey belonging to the same group.
However, scrutiny on Fitzpatrick et al. [20]  data reveals that the gonadosomatic index (testes mass/body mass × 100) of pinnipeds is very close to the gonadosomatic index of ungulates from our dataset (mean ± SD gonadosomatic index [range]: 0. 06 ± 0. 03 [0. 02-0. 13] in pinnipeds (N = 14) and 0. 10 ± 0. 19 [0. 02-1. 23] in ungulates (N = 45); Mann-Whitney-Wilcoxon test: W = 330; p = 0. 80). Therefore, the absence of a negative association between weapon length and relative testes mass in ungulates is unlikely to come from a lower postcopulatory sexual selection in this group, which is not surprising since evidence of female multiple mating in ungulates are now compelling (e. g. [4, 33] ). Our results further show evidence of a negative association between relative weapon length and sperm length across species of ungulates.
Dataset Data were collected on adult males of Bovidae and Cervidae species, the two main families of weaponed Artiodactyla. We first conducted two separate literature surveys: one on weapon length (horn length for Bovidae and antler length for Cervidae). Once the maximum number of data on weapon length was collected, we focused our literature survey on testes mass and sperm length, as well as sperm head length, sperm midpiece length and volume, and sperm tail length, since sperm is a complex cell divided in three main structures. Additionally, following the classification proposed by Bro-Jørgensen  [23]  and later used by Plard et al. [29] , each species was characterized as having either a female defence mating tactic when they guard a female during their receptive period (level 0 in statistical analysis) or a territory defence mating tactic (level 1 in statistical analysis) depending on the males main mating tactic.
For territory defence category, both studies  [23, 29]  merge territorial species (where individuals defend large territories with food resources) and lekking species (where individuals defend small territories with no food resources but females) since lekking could be an alternative mating tactic of territorial species. Moreover, no intra-specific differences in weapon size have been found between territorial and lekking populations and thus combining both groups is justified  [23, 29]. The dataset was then supplemented using information from more specific sources. All data used were checked from the original source and are provided in the Additional file 4: Table  S4. The final dataset contains information on weapon length, body mass, paired testes mass, sperm dimensions and mating tactics for 58 Artiodactyla species including 40 species of Bovidae and 18 species of Cervidae.
Comparative method Species may share characteristics as a result of a common ancestry. This could create dependency among the data, which potentially compromises statistical tests  [46]. On Artiodactyla, no consensus phylogeny has been reached yet and we thus used two different phylogenies with topology and branch length (Additional file 5: Figure  S1 ). The first phylogenetic tree was derived from the phylogenetic supertree of mammals of Bininda-Emonds et al. [31]  and is commonly used in inter-specific studies focused on Artiodactyla. However, this tree displays an important number of polytomies and we thus repeated our analyses with another phylogenetic tree derived from Agnarsson and May-Collado  [32]. This second phylogenetic tree presents no polytomies. Unfortunately, five species from our dataset were absent from this tree: sunda sambar (Cervus timorensis), blue wildebeest (Connochaetes taurinus), lechwe (Kobus leche), red brocket (Mazama americana), and common duiker (Sylvicapra grimmia).