dataesr/scientific-paragraphs-categorization huggingface dataset:
We then selected the longest SMAG to represent a group of redundant SMAGs. This analysis provided a non-redundant genomic database of 713 SMAGs. Taxonomical inference of SMAGs. We manually determined the taxonomy of SMAGs using a combination of approaches: (1) taxonomical signal from the initial gene calling (Prodigal), (2) phylogenetic approaches using the RNA polymerase and METdb, (3) ANI within the SMAGs and between SMAGs and METdb, (4) local blasts using BUSCO gene markers, (5) and lastly the functional clustering of SMAGs to gain knowledge into very few SMAGs lacking gene markers and ANI signal. Protein coding genes. Protein coding genes for the SMAGs were characterized using three complementary approaches: protein alignments using reference databases, metatranscriptomic mapping from Tara Oceans and ab-initio gene predictions.
While the overall framework was highly similar for MAGs and SAGs, the methodology slightly differed to take the best advantage of those two databases when they were processed (see the two following sections). Protein-coding genes for the MAGs. Protein alignments: Since the alignment of a large protein database on all the MAG assemblies is time greedy, we first detected the potential proteins of Uniref90 + METdb that could be aligned to the assembly by using MetaEuk 91 with default parameters. This subset of proteins was aligned using BLAT with default parameters, which localized each protein on the MAG assembly. The exon/intron structure was refined using genewise  92  with default parameters to detect splice sites accurately. Each MAG's GeneWise alignments were converted into a standard GFF file and given as input to gmove.
3. 3). A second time, AUGUSTUS was launched on each MAG assembly using the dedicated calibration file, and output files were converted into standard GFF files and given as input to gmove. Each individual line of evidence was used as input for gmove (http://www. genoscope. cns. fr/externe/gmove/) with default parameters to generate the final protein-coding genes annotations. Protein coding genes for the SAGs. Protein alignments: The Uniref90 + METdb database of proteins was aligned using BLAT  95  with default parameters, which localized protein on each SAG assembly. The exon/intron structure was refined using GeneWise  92  and default parameters to detect splice sites accurately. The GeneWise alignments of each SAG were converted into a standard GFF file and given as input to gmove. Metatranscriptomic mapping from Tara Oceans: The 905 Tara Oceans metatranscriptomic individual fastq files were filtered with kfir (http://www.
Identifying the environmental niche of SMAGs. Seven physicochemical parameters were used to define environmental niches: sea surface temperature (SST), salinity (Sal), dissolved silica (Si), nitrate (NO3), phosphate (PO4), iron (Fe), and a seasonality index of nitrate (SI NO3). Except for Fe and SI NO3, these parameters were extracted from the gridded World Ocean Atlas 2013 (WOA13)  99. Climatological Fe fields were provided by the biogeochemical model PISCES-v2  100. The seasonality index of nitrate was defined as the range of nitrate concentration in one grid cell divided by the maximum range encountered in WOA13 at the Tara sampling stations. All parameters were co-located with the corresponding stations and extracted at the month corresponding to the Tara sampling. To compensate for missing physicochemical samples in the Tara in situ data set, climatological data (WOA) were favored.
More details are available in the supplemental material. Cosmopolitan score. Using metagenomes from the Station subset 1 (n=757), SMAGs were assigned a "cosmopolitan score" based on their detection across 119 stations (see the supplemental material for more details). A database of manually curated DNA-dependent RNA polymerase genes. A eukaryotic dataset  101  was used to build HMM profiles for the two largest subunits of the DNA-dependent RNA polymerase (RNAP-a and RNAP-b). These two HMM profiles were incorporated within the anvi'o framework to identify RNAP-a and RNAP-b genes (Prodigal 79 annotation) in the SMAGs and METdb transcriptomes. Alignments, phylogenetic trees and blast results were used to organize and manually curate those genes. Finally, we removed sequences shorter than 200 amino-acids, providing a final collection of DNA-dependent RNA polymerase genes for the SMAGs (n=2,150) and METdb (n=2,032) with no duplicates (see the supplemental material for more details).
Novelty score for the DNA-dependent RNA polymerase genes. We compared both the RNA-Pol A and RNA-Pol B peptides sequences identified in SMAGs and MetDB to the nr database (retrieved on October 25, 2019) using blastp, as implemented in blast+ 102 v. 2. 10. 0 (e-value of 1e  -10  ). We kept the best hit and considered it as the closest sequence present in the public database. For each SMAG, we computed the average percent identity across RNA polymerase genes (up to six genes) and defined the novelty score by subtracting this number from 100. For example, with an average percent identity is 64%, the novelty score would be 36%. Phylogenetic analyses of SMAGs. The protein sequences included for the phylogenetic analyses (either the DNA-dependent RNA polymerase genes we recovered manually or the BUSCO set of 255 eukaryotic single-copy core gene markers we recovered automatically from the ~10 million protein coding genes) were aligned with MAFFT  103  v.
764 and the FFT-NS-i algorithm with default parameters. Sites with more than 50% of gaps were trimmed using Goalign v0. 3. 0-alpha5 (http://www. github. com/evolbioinfo/goalign). The phylogenetic trees were reconstructed with IQ-TREE 104 v1. 6. 12, and the model of evolution was estimated with the ModelFinder  105  Plus option: for the concatenated tree, the LG+F+R10 model was selected. Supports were computed from 1,000 replicates for the Shimodaira-Hasegawa (SH)-like approximation likelihood ratio (aLRT)  106  and ultrafast bootstrap approximation (UFBoot)  107. As per IQ-TREE manual, we deemed the supports good when SH-aLRT >= 80% and UFBoot >= 95%. Anvi'o v. 6. 1 was used to visualize and root the phylogenetic trees. EggNOG functional inference of SMAGs. We performed the functional annotation of protein-coding genes using the EggNog-mapper  53, 54  v2.
0. 0 and the EggNog5 database  52. We used Diamond 108 v0. 9. 25 to align proteins to the database. We refined the functional annotations by selecting the orthologous group within the lowest taxonomic level predicted by EggNog-mapper. Eukaryotic SMAGs integration in the AGNOSTOS-DB. We used the AGNOSTOS workflow to integrate the protein coding genes predicted from the SMAG into a variant of the AGNOSTOS-DB that contains 1,829 metagenomes from the marine and human microbiomes, 28,941 archaeal and bacterial genomes from the Genome Taxonomy Database (GTDB) and 3,243 nucleocytoplasmic large DNA viruses (NCLDV) metagenome assembled genomes (MAGs)  59. AGNOSTOS functional aggregation inference. AGNOSTOS partitioned protein coding genes from the SMAGs in groups connected by remote homologies, and categorized those groups as members of the known or unknown coding sequence space based on the workflow described in Vanni et al.
All statistics were generated in R 3. 5. 3. Functional clustering of SMAGs. We used anvi'o to cluster SMAGs as a function of their functional profile (Euclidean distance with ward's linkage), and the anvi'o interactive interface to visualize the hierarchical clustering in the context of complementary information. Data availability. All data our study generated are publicly available at http://www. genoscope. cns. fr/tara/. The link provides access to the 11 raw metagenomic co-assemblies, the FASTA files for 713 SMAGs, the ~10 million protein-coding sequences (nucleotides, amino acids and gff format), and the curated DNA-dependent RNA polymerase genes (SMAGs and METdb transcriptomes). This link also provides access to the supplemental figures and the supplemental material.
Contributions Damien D. Hinsinger, Morgan Gaia, Eric Pelletier, Patrick Wincker, Olivier Jaillon and Tom O. Delmont conducted the study. Tom O. Delmont and Morgan Gaia characterized the SMAGs and RNA polymerase genes, respectively. Damien D. Hinsinger (analysis of the ~10 million genes), Morgan Gaia (phylogenies), Paul Fremont (climate models and world map projections), Eric Pelletier (METdb database, mapping results) and Tom O. Delmont performed the primary analysis of the data. Artem Kourlaiev, Leo d'Agata, Quentin Clayssen and Jean-Marc Aury assembled and annotated the single cell genomes and helped processing metagenomic assemblies. Emilie Villar, Marc Wessner, Benjamin Noel, Corinne Da Silva, Damien D. Hinsinger, Olivier Jaillon and Jean-Marc Aury identified the eukaryotic genes in the MAG assemblies.
, 2017; Aydemir et al. , 2018; Fola et al. , 2018; Waltmann et al. , 2018). In population genetic analyses, sequence data are collected from parasites from multiple geographic locations. One approach to analyzing these data is to compare and cluster sequences based on the distribution of genetic variation across a geographic area: members of the same parasite population share similar patterns of genetic sequence variation because they have been interbreeding (e. g. ,  Pritchard et al. , 2000; Jombart et al. , 2010). An alternative approach is to explore the history of parasite movement by simulating the expected distribution of genetic variation under different parasite migration histories, and comparing test statistics derived from these simulated data to test statistics calculated using the observed genetic variation (e.
O. volvulus Population Genetic Structure O. volvulus population genetic structure is determined by the geographic range and movement of its hosts and vectors. The major blackfly vectors in Africa belong to a species complex (S. damnosum s. l. ) divided into a number of subspecies based on cytotaxonomy  (Boakye, 1993; Boakye and Meredith, 1993; Krueger and Hennings, 2006) , although these subspecies are not all distinguishable using DNA sequence data  (Tang et al. , 1995; Tang et al. , 1996). Variation in vector species distribution could influence O. volvulus population structure, because different species may influence transmission dynamics differently. Specific vector-parasite associations have been postulated  (Duke, 1981; Basáñez et al. , 2009). Blackflies from forest areas transmit more infective larvae of O.
However, the basic principles of algal growth and competition, and building blocks of the HAB cells and HAB-enabling environments, are being directly and indirectly altered by changes in the physical state (climate) and the social state (anthropogenic activities) of the Earth. With these foundational limits, the best approximation of the future can be discussed, projections established, and conclusions ascertained. A Scientific Symposium on Harmful Algae and Global Climate Change was held in May 2015 at the University of Gothenburg (pices. int/meetings/international symposia), where 59 scientists from 23 nations gathered to consider the future directions for HAB and climate change research. The objectives of the Symposium were to bring together plankton physiologists, ecologists, oceanographers, modelers and climate change specialists to develop a consensus on the priority research directions for future HAB and climate change funding, develop "proactive" research strategies build from rigorous, testable hypotheses to guide scientists, managers and the public on what environmental and HAB changes are projected, and consider realistic designs for observing infrastructures (combining research studies, detection schemes, observing systems) to capture the critical datasets needed to assess HAB patterns in relation to measurable indicators of climate change.
The hypothesis that increasing temperature mechanistically drives increasing HABs needs critical assessment at three levels. 1) Experimental studies should be designed to test whether HAB species have a demonstrated physiological advantage at higher temperatures over at least some regionally, or locally, abundant non-HAB species. These studies need to account for regionally relevant growth conditions during HAB development-e. g. , representative nutrient inputs and light conditions rather than standard "cookbook" culturing norms-to best inform model development. 2) There is a clear trend of increasing surface water temperatures in freshwater and marine systems over the past few decades, so longterm datasets within globally distributed HAB monitoring programs offer a chance to statistically test the HAB/temperature relationship, as well as the temperature/HAB relationship (i.
e. , is increasing temperature more often than not associated with increasing HABs). These studies should be restricted to those regions not suffering simultaneous increasing impacts from anthropogenic runoff. Although these observational data alone cannot inform on the underlying linkages, when combined with mechanistic-based laboratory studies these data would provide a more solid basis for testing the HAB/temperature hypothesis. The northward trend in increasing freshwater HABs provides a good platform for investigating temperature thresholds and forecasts for future HAB development in lakes and rivers, again independent of increasing anthropogenic nutrient loading. 3) Given that most HAB monitoring datasets either lack or have only limited records of oceanographic data, new HAB observational programs will be essential for testing the hypothesis that increasing temperature drives HAB development.
Fundamental questions remain surrounding the present-day factors influencing toxin production, substrate preferences, species behavior, benthic release and spreading, and life cycles (which are poorly known for most of these species); studying the effects of climate drivers may help accelerate the basic understanding of these processes. How climate drivers will shape future structures and compositions of benthic substrates are active areas of research in other fields but selecting several "standardized" substrates to study HAB species preferences would provide valuable datasets to support modeling experiments under different global benthic response scenarios. Similarly, experiments under controlled conditions on toxin transfer, accumulation, and transformation in fisheries-related species would enable preliminary parameterization of these complex processes in the rapidly evolving field of ecosystem modeling.
Fisheries, aquaculture, and HABs Although the term "harmful algal bloom" has evolved to include a broad array of ecological impacts, the major concern arguably remains that climate drivers may intensify threats to human health through magnifying HAB impacts on fisheries and aquaculture  (Barange et al. , 2018). New experimental strategies are needed to study not only how climate-scale processes could generate local stresses that stimulate HAB occurrences, but also how best to distinguish local-scale human influences from those driven by a shifting climate. Beyond the study of the potential for HAB expansion, what can be done to address concerns about future HAB effects on fisheries and aquaculture? The normal, constrained strategies of HAB monitoring to focus on HAB species and toxin measurements should expand to sample whole phytoplankton communities, and at least a suite of basic oceanographic parameters, to build datasets on HAB ecophysiologies that can better inform and improve management practices.
g. ,  McGillicuddy et al. , 2011). Although these added tasks will be beyond the realistic limitations of many monitoring programs, it should be possible to focus on a limited number of geographically-distributed sentinel sites covering wild and aquaculture harvesting regions. Some of these should be located in climate hotspots  (Hobday et al. , 2016). Extensive shellfish toxin monitoring programs with decades of historical data can be found in the Gulf of Maine  (Shumway et al. , 1988) , the Salish Sea  (Trainer, 2002) , along the Irish coastline  (McPartlin et al. , 2017, and references therein) , in the United Kingdom  (Hinder et al. , 2011) , in Japan  (Fukuyo et al. , 2002) , and many other locations worldwide  (Wright, 1995) , most of which have access to at least some regional oceanographic datasets.
Although many of these datasets likely been utilized for developing short-term (monthly, seasonal) forecasts of HAB development, they could help provide the data richness essential for resolving longer-term trends in past HAB occurrence (e. g. ,  Hallegraeff, 2010; McCabe et al. , 2016). Similarly, fish aquaculture sentinel sites should span wide geographic and latitudinal ranges, including net-penned fish aquaculture in South Korea, the Salish Sea in British Columbia, Canada, and the fjordic Chilean, Norwegian and Scottish coastlines. For those mixotrophic species specifically reliant upon certain prey  (Mitra et al. , 2016) -the specialist non-constitutive mixotroph HAB Dinophysis, being the most obvious example  (Hansen et al. , 2013) -monitoring prey for HABs also is important. What steps might be taken now to help reduce or minimize the potential economic, cultural, and human health effects from HAB-affected fisheries and aquaculture? One approach should be to explore the types of fish or shellfish for species that can be cultivated in higherrisk regions but are more resistant to, or accumulate less, HAB toxins.
, 2013; Leles et al. , 2019 Leles et al. , , 2017)). Developing successful ecological models necessitates identifying the minimum (functional) trait combination required, balanced against the need for useful discriminating traits and computational load. We should parameterize only the subset of drivers that are sufficient in number and relative importance to capture the response of the whole system. However, a major challenge is the identification of traits of consequence that define HAB species and, critically, those for co-occurring non-HAB species. HAB events only result when activity of competitors and predators allows their proliferation; i. e. , we can only model such conditions with knowledge and incorporation of all the major players. Herein lies a problem in HAB science; there are only scant data relating HAB species existence to other organisms they encounter in nature-data that are essential to formulate dynamic models.
Long term observatories Developing compelling evidence/documentation of climate change effects on the distribution, frequency and intensity of HABs in freshwater and marine environments ultimately depends upon rigorous longterm datasets. Current efforts to develop a global assessment of changes in HABs in the recent past  (Hallegraeff et al. , Global HAB Status Report, in prep. ) is an essential start, but the majority of past HAB records lack the necessary environmental data to test hypotheses. More comprehensive HAB/environment time series data are needed over the next decade to improve the skill of HAB models and lead the way to more reliable "weather-style" HAB trend forecasts. In most cases, augmenting existing observatories offers the most realistic and cost efficient mechanism. First, as noted above, direct or indirect linkages should be established between food safety monitoring programs and local or regional shore-based or coastal observation networks.
Acknowledgements The Symposium would not have been possible without the  Swedish Research Council   (FORMAS) , the  Global Ecology and Oceanography of Harmful Algal Blooms (GEOHAB) , the  North Pacific Marine Science Organization (PICES) , who provided financial support for the Symposium, along with the local sponsors and hosts, the  Swedish Meteorological and Hydrological Institute (SMHI)  and the  University of Gothenburg. Special thanks also go to the graduate student Rapporteurs:  Marie Johansen ,  Maria Karlberg ,  Johannes Johansson , and  Cathy Winterton. KD was supported by the  BBSRC /NERC sustainable aquaculture programme  grant  BB/M025934/1  and the  NWE Europe Interreg  project  PRIMROSE ). SS was supported by the  Academy of Finland  (grant  259357 ). EB's attendance was funded by  Spanish MINECO  through the  OstreoRisk  ( CTM2014-53818-R ) project.
VA's attendance to the workshop was funded in the framework of  M3-HABs  project ( ENPI CBC Mediterranean Sea Basin Programme , www. m3habs. net). Funding was provided by the  Commonwealth Scientific and Industrial Research Organisation of Australia  to allow PT to attend. We thank the anonymous reviewers for their thought provoking critical comments that helped improve this paper. In closing, we offer our heartfelt appreciation of two good friends and respected colleagues;  Professor Anna Godhe , who's contributions, collaborations, and passion for HAB science inspired so many of us, and  Professor Theodore J. Smayda , whose life long dedication reminds us to remain curious about the mysteries of HABs. We dedicate this contribution to them. [CG].
For a filtering fraction of 5% (left column) the high-pass filtered image (top row) results in a too smooth halo whereas from a filtering fraction of 15% on, it shows sharper structures due to the capture of speckles. For a filtering fraction of 5% the low-pass filtered image (bottom row) still shows a slight elongation along the wind speed direction at short separation, which completely disappears from 15% on. When the filtering fraction is too high, we can visualize very faint structures such as the grid of microlenses from the IFS design. After testing different data set, a good trade-off based on visualization of the images (the low-pass filtered image shows a very smooth structure while its high-pass version shows no further directional elongation), is to use 15% of the low spatial frequency content.
By fitting the slope of the WDH direction as a function of the parallactic angle variation, using a robust affine fit (to avoid taking into account outlier data points for which the WDH is too low to extract accurately its direction), we find that the slope is not perfectly equal to one but rather 1. 3. This slight discrepancy is expected if the jet stream layer wind direction changes over the timescale of the observing sequence, as shown in Fig. 6  (bottom). The strength is varying erratically, from 60% to 88%, which corresponds to what we can observe by visualizing the data cube 10. The asymmetry factor is decreasing slowly from 17% to almost no asymmetry, without link to the strength or the airmass variation (which, at first order, increases and decreases around the meridian crossing). For ADI-based algorithm, the important aspect is that the starlight residuals are spatially stable in time to be efficiently removed.
2012; Amara & Quanz 2012) , the intensity of the WDH residuals depends on the number of principal components kept to build the model. As shown in Fig. 19  (right), a more aggressive PCA removes low spatial frequencies but also considerably reduce the signal sought, mostly for extended features, and still leaves high residuals at close separation to the star. We additionally processed the F. Cantalloube et al. :  The wind-driven halo in high-contrast images I: analysis from the focal plane images of SPHERE Fig. 18. Temporal variation of the three WDH parameters in the case of the 51 Eri IFS data taken with SPHERE on September 25 th , 2015. Left: Direction as a function of time, compared to the variation of the parallactic angle (blue dashed line). Middle: Strength as a function of time. Right: Asymmetry factor as a function of time.