dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015)  from the SNP locations corresponding to the 10 genetic maps. As noted elsewhere, 46 Mb of assembled sequences were not anchored. In addition, 24. 5 Mb of the anchored sequences were oriented arbitrarily. Annotation method and estimation of centromere positions. Transcript assemblies were made from ~1. 5 B pairs of 2 9 100 paired-end Illumina RNA-seq reads  (Yao et al. , 2016; Santos et al. , 2018)  using PERTRAN (Shu, personal communication); 89 300 transcript assemblies were constructed using PASA  (Haas et al. , 2003)  from EST-derived UNIGENE sequences  (Muchero et al. , 2009; P12_UNIGENES. fa; harvest. ucr. edu ) and these RNAseq transcript assemblies. Loci were determined by transcript assembly alignments and/or EXONERATE alignments of proteins from Arabidopsis, common bean, soybean, Medicago, poplar, rice, grape and Swiss-Prot proteomes to repeat-soft-masked cowpea genome using RepeatMasker  (Smit et al.
PASA-improved gene model proteins were subject to protein homology analysis to the proteomes mentioned above to obtain Cscore and protein coverage. Cscore is a protein BLASTP score ratio to MBH (mutual best hit) BLASTP score, and protein coverage is the highest percentage of protein aligned to the best homolog. PASA-improved transcripts were selected based on Cscore, protein coverage, EST coverage and its CDS overlapping with repeats. A transcript was selected if the Cscore and protein coverage were at least 0. 5, or if it had EST coverage while its CDS overlap with repeats was less than 20%. For gene models whose CDS overlap with repeats was more than 20%, its Cscore had to be at least 0. 9 and homology coverage at least 70% to be selected. The selected gene models were subjected to Pfam analysis, and gene models whose protein was more than 30% in Pfam TE domains were removed.
Identification of genetic variation Nearly 1M SNPs with strong support were discovered previously by aligning WGS data from 36 diverse accessions to a draft assembly of IT97K-499-35 (Muñoz-Amatria ın et al. , 2017). To position those SNPs on the cowpea reference genome, the 121-base sequences comprised of the SNP position and 60 bases on each side were BLASTed against the cowpea genome assembly with an e-score cutoff of e À50. Only the top hit for each query was kept. The exact SNP position was then calculated. SNPs previously identified as organellar were excluded, together with those hitting multiple locations in the reference genome sequence. For detection of insertions and deletions, WGS data from 36 diverse accessions (Muñoz-Amatria ın et al. , 2017) were used. Reads from each cowpea accession were mapped to the genome assembly using BWA-MEM version 0.
7. 5a  (Li, 2013). Variant calling was carried on each resulting alignment using BreakDancer version 1. 4. 5  (Chen et al. , 2009) , with a minimum mapping quality score of 30 and 10 as the minimum number of pair-end reads to establish a connection. The maximum structural variation size to be called by BreakDancer was set to 70 kb. A deletion was considered validated when at least 75% of the SNPs contained in the deletion region were 'No Call'. Among the 5095 putative deletions that spanned SNPs represented in the iSelect array, data were available to validate only 1558 (30. 6%) by this method, leaving the false-positive rate uncertain. To validate the inversion, the sequence assembly of the reference genome was compared with that of a cowpea accession typical of California breeding lines via MUMmer  (Kurtz et al.
Gene families The legume-focused gene families from the NSF Legume Federation project (NSF DBI#1444806) were used to compare annotated genes in cowpea with those from other legume proteomes. This is 18 543 gene families, monophyletic for the legume family, including proteomes from cowpea (this study), 13 other crop and model legumes, and five non-legume species for phylogenetic rooting and evolutionary context (Table  S11 ). Gene families were generated as follows (summarizing method details from https://github. c om/LegumeFederation/legfed_gene_families). All-by-all comparisons of protein sequences were calculated using BLAST  (Camacho et al. , 2009) , with post-processing filters of 50% query coverage and 60% identity. The top two matches were used to generate alignments of coding sequences, which were then used to calculate synonymous (Ks) counts per gene pair.
For each species pair, histograms of Ks frequencies were the basis for choosing per-species Ks cutoffs for that species pair. A list of all-by-all matches, filtered to remove pairs with Ks values greater than the per-speciespair Ks cutoff, was used for Markov clustering implemented in the MCL program  (Enright et al. , 2002)  with inflation parameter 1. 2 and relative score values (transformed from Ks values) indicated with the -abc flag. Sequence alignments were then generated for all families using muscle  (Edgar, 2004) , and hidden Markov models (HMMs) were calculated using hmmer  (Mistry et al. , 2013). Family membership was evaluated relative to median HMM bitscores for each family, with sequences scoring less than 40% of the median HMM bitscore for the family being removed. The HMMs were then recalculated from families (without low-scoring outliers), and used as targets for HMM search of all sequences in the proteome sets, including those omitted during the initial Ks filtering.
ACKNOWLEDGEMENTS The authors thank  Panruo Wu ,  Laxmi Buyhan ,  Zizhong Chen  and  Tamar Shinar  ( University of California Riverside, CA, USA ) for allowing access to their HPC server;  Suresh Iyer ,  Amy Mraz ,  Jon Wittendorp  and  Robert Bogden  ( Amplicon Express, Pullman, WA, USA ) for DNA extraction and library prep;  Derek Pouchnik  and  Mark Wildung  ( Washington State University, Pullman, WA, USA ) for PacBio sequencing and library preparation;  Thiru Ramaraj  ( National Center for Genome Resources ),  Matthew Seetin  and  Christopher Dunn  ( Pacific Biosciences of California, Inc. , Menlo Park, CA, USA ),  Brian Walenz  ( University of Maryland, College Park, MD, USA ),  John Urban  ( Brown University, Providence, Rhode Island, USA ) and  Xingtan 'Tanger' Zhang  for helpful discussion on assembly tools, trouble shooting, usage and parameter selection;  Haibao Tang  ( JVCI ) for help with ALLMAPS;  Suk-Ha Lee  ( Seoul National University, South Korea ) for allowing access to their most recent mung bean and adzuki bean genome assemblies;  David Goodstein  ( Joint Genome Institute, Walnut Creek, CA, USA ) for assistance coordinating genome annotation;  Yi-Ning Guo  and  Savannah St Clair  ( UC Riverside, CA, USA ) for technical assistance with DNA preparation; and  Ira Herniter  ( UC Riverside, CA, USA ) for helpful discussion.
DATA AVAILABILITY The genome assembly of cowpea IT97K-499-35 is available for browsing and is downloadable through Phytozome (phy tozome. jgi. doe. gov), the Legume Information System Data Store (https://legumeinfo. org/data/public/), and NCBI SRA BioSample accession SAMN06674009 (also ASM 411807v1). Raw PacBio reads for cowpea accession IT97K-499-35 are available at NCBI SRA sample SRS3721827 (study SRP159026). As stated in Muñoz-Amatria ın et al. (2017), raw Illumina reads from 37 diverse cowpea accessions are available under SRA accession SRP077082. RNA-Seq raw reads are available as NCBI SRA biosample accessions SAMN071606186 through SAMN071606198, SAMN07 194302 through SAMN07194309 and SAMN07194882 through SAMN07194909, and were described in  Yao et al. (2016)  and  Santos et al. (2018).
Bioinformatic processing Raw signal level data from the MinION were basecalled using Albacore version 2. 0. 2 (Oxford Nanopore Technologies, Oxford, UK) and demultiplexed using Porechop version 0. 2. 3_seqan2. 1. 1 (github. com/rrwick/Porechop). Primer binding sites were trimmed from reads using custom scripts, and trimmed reads were mapped to Zika reference strain H/PF/2013 (GenBank Accession KJ776791) using BWA v0. 7. 17  [29]. We used Nanopolish version 0. 9. 0 (github. com/jts/nanopolish) to determine single nucleotide variants from the event-level data, and used custom scripts to extract consensus genomes given the variant calls and the reference sequence. Coverage depth of at least 20x was required to call a SNP; sites with insufficient coverage were masked with N, denoting that the exact nucleotide at that site is unknown.
Dataset curation All publicly available Asian lineage ZIKV genomes and their associated metadata were downloaded from ViPR  [30]  and NCBI GenBank. The full download contained both published and unpublished sequences; we sought written permission from submitting authors to include sequences that had not previously been published on. Any sequences for which we did not receive approval were removed. Additionally, we excluded sequences from the analysis if any of the following conditions were met: the sequence had ambiguous base calls at half or more sites in the alignment, the sequence was from a cultured clone for which a sequence from the original isolate was available, the sequence was sampled from countries outside the Americas or Oceania, or geographical sampling information was unknown.
Finally, we also excluded viruses whose estimated clock rate was more than 4 times the interquartile distance of clock rates across all sequences in the analysis. Sequences that deviate this greatly from the average evolutionary rate either have far too many or too few mutations than expected given the date they were sampled. This deviation usually occurs if the given sampling date is incorrect, or if the sequence has been affected by contamination, lab adaptation, or sequencing error. After curation, the final dataset consisted of 360 sequences; 352 publicly available ZIKV full genomes from the the Americas (including Colombia) and Oceania, and the 8 Colombian genomes from the present study.
Phylogeographic analysis Data were cleaned and canonicalized using Nextstrain Fauna (github. com/nextstrain/fauna), a databasing tool that enforces a schema for organizing sequence data and sample metadata, thereby creating datasets compatible with the Nextstrain Augur analytic pipeline (github. com/nextstrain/augur) and the Nextstrain Auspice visualization platform (github. com/nextstrain/auspice). A full description of the Nextstrain pipelines can be found in Hadfield et al. [31]. Briefly, Nextstrain Augur performs a multiple sequence alignment with MAFFT  [32] , which is then trimmed to the reference sequence. A maximum likelihood phylogeny is inferred using IQ-TREE  [33]. Augur then uses TreeTime  [34]  to estimate a molecular clock; rates inferred by Tree-Time are comparable to BEAST  [34] , a program that infers temporally-resolved phylogenies in a Bayesian framework  [35].
Rarefaction analysis We generated a series of datasets in which ZIKV genomes from either Colombia or Mexico were subsampled. For each value from 1 to n, where n is the total number of available sequences for the country of interest, we generated 30 subsampled datasets in which n sequences were randomly sampled without replacement. Each subsampled set was then combined with all ZIKV genomes from other countries included in the main phylogeographic analysis described above, and the phylogeographic inference was rerun. For the Colombian rarefaction analysis, we have n = 20 high quality whole genomes, so we inferred 20 × 30 = 600 phylogeographically labelled trees. For the Mexican rarefaction analysis, we have n = 51 high quality whole genomes in total, and inferred 51×30 = 1530 phylogeographically labelled trees.
ZIKV dispersal out of Colombia appears widespread, with movement to bordering countries (Panama, Venezuela, Ecuador, and Peru) as well as more distal countries in the Caribbean. While it may be tempting to read the inferred phylogeographic migration history as a complete record of transmission between countries, we caution against doing this for analyses of ZIKV. In contrast to other large outbreaks, such as the Ebola epidemic in West Africa, genomic sampling of the American ZIKV epidemic is sparse. Many ZIKV-affected countries have minimal genomic sampling; others have none at all. Thus while the phylogeographic model will correctly infer the geographic location of internal nodes given the dataset at hand, adding sequences from previously unsampled countries may alter migration histories such that apparent direct transmission from country A to country C instead becomes transmission from country A to country B to country C.
Acknowledgements We thank  R. Shabman ,  B. Pickett ,  P. Rahal ,  L. Karan ,  R. Delgado ,  A. Enfissi ,  N. Grubaugh , and  R. Lanciotti  for giving us permission to include unpublished genomes available on GenBank in our analysis. We also thank  Maria Fernanda Torres  for facilitating this collaboration and  Adam Geballe  for generously loaning space in his laboratory at the  Fred Hutch.
Availability of data and materials All priming schemes, kit specifications, laboratory protocols, bioinformatic pipelines, and sequence validation information are openly available at github. com/blab/zika-seq. Nextstrain analytic and visualization code is available at github. com/nextstrain. Code and builds specific to the analysis presented here, as well as all genomes sequenced for this study, are openly available at github. com/blab/zika-colombia. Consensus sequences are also available on NCBI GenBank (accessions MK049245 through MK049252). The phylogeny and all inferences reported here can be interactively explored at nextstrain. org/community/blab/zika-colombia.
RNA-seq data analysis RNA-seq data were processed using Kallisto  (Bray et al. 2016). Reads were mapped to the Amborella genome v1. 0 (Amborella Genome Project 2013), obtained from Ensembl Plants  (Kersey et al. 2016). Gene expression values were determined using Transcripts Per Million (TPM)  (Conesa et al. 2016 ) and clustered using Python Seaborn software package with "Euclidean" distance and "complete" method, which was also used to generate the heat map shown in Fig. 3c. A comprehensive overview on normalized expression data in Amborella egg apparatus cells, leaves, roots and tepals is shown in Supplementary Table  1. RNA-seq data will be publicly available in the open source web server CoNekT  (Proost and Mutwil 2018).
RNA-seq analysis of isolated embryo sac cells After RNA extraction, library preparation and Illumina sequencing, we obtained a total of 57. 58 million (M) reads for the three groups of egg apparatus cells that mapped to Amborella genome v1 (Amborella Genome 2013). We also generated RNA-seq data from sporophytic control tissues (three biological replicates of leaves, roots and tepals, respectively) and obtained a total number of 22. 62 M mapped reads for leaves, 19. 07 M for roots and 50. 38 M for tepals. Supplementary Table  1  provides Amborella gene symbols and normalized transcripts per million (TPM) values obtained for the three egg apparatus cell samples and the sporophytic control tissues. A principal component analysis (PCA) performed on the expression data revealed that the three egg apparatus samples group closely together and are clearly separated from the clusters formed by the biological replicates of the three control tissues (Supplementary Figure  2 ).
Among the egg apparatus cell size categories, L-and M-size cell types grouped together more closely. We employed a threshold level of ≥ 1 TPM to estimate the number of expressed genes in the three groups of egg apparatus cells. Thereby, we detected 11,841 expressed genes in the category of large (L) cells, 11,808 expressed genes in the intermediate-sized cell category (M) and 12,501 expressed genes in the group of small (S) egg apparatus cells (Fig. 3b ). Overall, 15,409 genes were expressed in the Amborella egg apparatus cells. Considering a total number of 27,313 coding genes in the genome of A. trichopoda (Ensembl Plants database release 41; September 2018), the number of egg apparatus-expressed genes comprises 56. 42% of all Amborella genes. When comparing gene expression patterns in the three cell size categories, we detected 8737 genes to be conjointly expressed in large, medium and small egg apparatus cells (Fig.
Egg apparatus-enriched genes encode CRPs, novel proteins and proteins with homologs in eudicots and/or monocots We next analyzed the strongest expressed genes that were specifically enriched in the respective size category of egg apparatus cells, but were not expressed in the sporophytic control tissues. Therefore, we excluded all genes with a TPM ≥ 3 in roots, leaves and tepals. In Table  2 , we summarize the ten strongest expressed Amborella genes enriched in each cell size category and their best match in sequence similarity searches. Table 2 also provides information on three more plant species annotated for subsequent BLASTP hits. The division of the best BLAST matches to Amborella sequences, approximately equally between eudicots and monocots, is in agreement with Amborella's phylogenetic position as a basal angiosperm, equally distantly related to these two groups (Amborella Genome 2013).
Amborella egg cells express EGG CELL 1 and other DUF784/DUF1278 gene family members Importantly, we also identified four genes encoding CRPs with a prolamin-like domain (PF05617) to be specifically expressed in Amborella egg apparatus cells (Supplementary Table  3 ). These include two of strongest expressed genes in egg cells (L-group: AMTR_s00067p00156690 and AMTR_s00032p00113070), the strongest expressed gene in the M-group (AMTR_s00017p00240840) and one gene in the S-group (AMTR_s00067p00155100). A fifth CRP with prolamin-like characteristics was detected when we manually inspected the predicted protein sequence encoded by AMTR_s00058p00172040, which is the strongest expressed genes in egg cells (L-group) (Table  2 ).
Acknowledgements We are grateful to  Maximilian Weigend ,  Cornelia Löhne  and  Bernhard Reinken  ( Botanical Garden of the University of Bonn, Germany ) for providing Amborella plant material. We thank  Maria Lindemeier  for her support in single-cell collection. Illumina deep sequencing was carried out at a genomics core facility:  Center of Excellence for Fluorescent Bioanalytics (KFB, University of Regensburg, Germany ). This work was supported by the  ERA-CAPS  Grant  EVOREPRO (DR 334/12-1 ) to SS and TD, funded by the  Deutsche Forschungsgemeinschaft (DFG).
Auditory Background Noise The background noise was taped from a working area in the university. This noise consisted of people speaking, typing and other natural background noises. This noise varied in intensity, similar to natural changes in real-life background noise intensity changes. Using SpectraLab software (http://www. spectraplus. com/), processing the entire audio file (size 16 bits, Hanning smoothing window, with an exponential scale), we observed that, indeed like typical background noise, our recording covered the audio spectrum resulting in broad-band noise (0-20,000 Hz). This background-noise also fluctuated in intensity (30-110 dBV). A peak in intensity was noticed at 20 Hz, 40 Hz and a slight rise in intensity between 400 Hz to 600 Hz (see Supplementary Fig. S1 ).
For each participant we calculated the average success rate in all the six noise blocks, across tasks (color, shape and conjunction) and stages. Using a ρ correlation coefficient test for non-normal data we found a high correlation between the self-reported disturbance level and the average success rate in the noise blocks (ρ = -0. 84, p = 0. 006) (see Fig. 6 ). Crucially, after performing a normal approximation to the binomial for each participant with Bonferroni correction for multiple comparisons, we found that all eight participants' success rate in noise blocks was still significantly higher than chance level (all p-values < 0. 000001).
Acknowledgements This work was supported by a  European Research Council   Starting-Grant  ( 310809 ) to A. A. , a  James S. McDonnel Foundation scholar award  (no. 652 220020284 ) to A. A. ,  European Research Council   Consolidator-Grant  ( 773121 ) to A. A. , and two post-doctoral fellowships from the  Edmond and Lily Safra Center for Brain Sciences (ELSC)  and from the  Jerusalem Brain Community (JBC)  to B. H. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The features of the auditory sounds used in the experiment were described with the help of  Wojciech Bedzinski  from the  Institute of Physiology and Pathology of Hearing in Warsaw.
52°N/0. 21°E). Geostrophic velocities (current speed, m s -1 , and direction, degrees) derived from satellite altimetry (orange dots in the two lower panels, respectively) obtained for the three campaigns conducted in 2007 (Table  1 ) are also validated by using in situ measurements (green lines) collected from the surface Tarragona buoy (40. 68°N/1. 47°E). The wind and current rose diagrams (m s -1 ) are derived from the two aforementioned buoy datasets, respectively. Notice that in situ wind direction indicates the origin of the local wind whilst surface current direction denotes where it is going. product resembles the in situ wind measurements with origins ranging mainly between 150 and 190 degrees. As a result, we obtain an RMSE between both datasets of 1. 76 m s -1 for the wind speed and 44 degrees for the wind direction.
However, both datasets appear to have the same temporal behaviour at monthly scale with an RMSE of 0. 07 m s -1. The in situ total current direction (lowermost panel and current rose in the lower right side of Fig. 3 ) also has large temporal variability. It ranges roughly between 150 and 250 degrees so that surface currents flow mainly towards the south south-west. The surface geostrophic currents derived from altimetry resemble this general pattern, although they have much lower temporal variability with a mean direction of nearly 170 degrees (towards the south) until 20 August, and then shifts towards the southwest (current direction between 200 and 250 degrees) until the end of the analysis period. These discrepancies give an RMSE of 48 degrees between the two datasets.
However, the criteria used here to identify the rafting behaviour of shearwaters differs from that followed by  5  (i. e. , we use a speed threshold of 0. 5 m s -1 whilst those authors used 2. 5 m s -1 ) so the percentage of trip time that individuals spend resting on the sea surface cannot be compared. Moreover, the seabird tracks reported by  5  were recorded every minute, whilst here we have used 5-min records. As a result, the dataset used in 5 allowed for a finer analysis. Longer rafting periods per trip provide more complete datasets for the purposes of mesoscale current pattern detection; nevertheless, the tracks analysed here were long enough to properly compare with local sea surface velocity mesoscale patterns and wind fields in order to assess the driving forces of the seabird trajectories in the Balearic Sea.
Birds were recaptured within two weeks after deployment in order to remove the loggers and retrieve the data. All birds showed no sign of damage after release, and any birds that were not caught would lose the device within a few weeks due to moulting. The loggers were programmed to record a fix every 5 minutes. These protocols agree with standard guidelines of bird handling and tagging, and were approved by the competent wildlife authorities (Servei de Protecció d'Espècies of the Balearic Government and Reserva Natural de les illes Columbretes of the Valencian Government). Some birds were either not recovered, returned without the GPS, or did not record data properly. However, for most birds (n = 75; 71%) the tracking data was safely recovered. The quantity and quality of the data collected was sufficient for an investigation of the foraging trips of tracked individuals in the study area (see tracks in right column in Fig.