dataesr/scientific-paragraphs-categorization huggingface dataset:
As this study deals with demersal species, we tested if surface parameters show the same spatial distribution and temporal variation than seafloor parameters derived from MEDAR/ MEDATLAS datasets (S2 Fig). To derive the habitat and spatial predictor variables, we used publicly available raster data that we sourced from MARSPEC's SRTM30_PLUS high resolution bathymetry dataset (www. marspec. org). To accurately describe aspect, we used two variables, the eastness and the northness of the slope  [33]. Aspect represents the azimuthal direction of the steepest slope and was transformed into two derived variables: Eastness (values close to 1 represent an eastward aspect, while values close to -1 represent a westward aspect) and Northness (values close to 1 represent a northward aspect, while values close to -1 represent a southward aspect).
We developed our GDM using a downloadable toolkit package ('Gdm01') for R software. This package provides a matrix-regression tool that models compositional dissimilarity as a function of environmental dissimilarity and geographic distance. We selected the three I-spline basis function option  [7, 34]  and accounted for spatial autocorrelation by including the geographic distance between pairs of sites as a predictor variable. To evaluate the impact of each environmental variable, we ran multiple GDMs and removed one predictor at a time. We then compared the variance of the full GDM to each of the partial models to evaluate the importance of environmental variables on the change of communities.
Results Our dataset included information on a total of 692 sites and 174 species from 77 families. The most represented families in this dataset were Sparidae (21 species), Rajidae (9 species) and Gadidae (6 species). On average, species were prevalent at 30% of sites. The most prevalent species occurred at 58% of the studied sites, while the least prevalent species only occurred at only 1% of the studied sites.
The group contribution approach allows users to upload molecular information in the form of SMILES strings and UManSysProp will automatically extract the relevant information for calculations. Built using open source chemical informatics, and hosted at the University of Manchester, the facilities are provided via a browser and device-friendly web-interface, or can be accessed using the user's own code via a JSON API. In this paper we demonstrate its use with specific examples that can be simulated using the web-browser interface.
Chemo-informatics base of UManSysProp The discipline of chemo-informatics typically concerns the use of both software and with the site to perform specific calculations for a compound represented as a SMILES string. All calculations rely on a representation of individual compounds, be it inorganic ions or neutral organic molecules. Raw model or measurement molecular information needs to be converted into an appropriate format for use in property predictions. Common molecular file formats include Wiswesser Line Notation (WLN), ROSDAL and SYBL. In addition, IUPAC and NIST recently developed the IUPAC International Chemical Identifier (InChl). Another linear notation using short ASCII strings is the SMILES format (Simplified Molecular Input Line Entry System), a simplified chemical notation that allows a user to represent a two dimensional chemical structure in linear textual form.
For example, the SMILES notation for carbon dioxide is O=C=O, whereas cyclohexane is represented as C1CCCCC1. UMansSysProp uses SMILES for several reasons. The notation is commonly employed in commercial and public software for prediction of chemical properties. It can be imported by most molecule editors for conversion into 2-D/3-D models and has a wide base of software support and extensive theoretical backing (www. daylight. com). Common database searches for organic molecules include NIST (http://webbook. nist. gov/chemistry/) and the National Chemical Database service (http://cds. rsc. org/). From these, the SMILES representation of individual molecules can be found. In Table  1 , SMILES for common inorganic ions are provided along with a selection of organic compounds used by  O'Meara et al.
Appendix: Accessing predictions outside of a web-browser Here we provide brief details on how to call UManSysProp from your own code, without the need for a web-browser. For full details, please refer to our documentation on our ReadTheDocs. org webpage (https://umansysprop. readthedocs. org/). It is recommended that you use the local client installation UManSysProp from within the IPython shell. This is simply because the API is designed with documentation built in which can be queried from within the environment, and this is considerably easier from within the IPython shell. The client component of UManSysProp can be installed on any machine with Python available, provided you have Python 2. 7 of greater. On Ubuntu, the waveform PPA can be used for simple installation: $$ sudo add-apt-repository ppa:waveform/ppa $$ sudo apt-get update $$ sudo apt-get install python-umansysprop On other platforms, the package can be installed from PyPI.
Specify the client option to pull in all dependencies required by the client component: $$ sudo pip install "umansysprop[client]" The first step in using the UManSysProp system is creating a UManSysProp instance, as demonstrated in the Python code snippet given below. By default this requires the URL of the UManSysProp server. Currently this is http://umansysprop. seaes. manchester. ac. uk. >>>[m for m in dir(client) if not m. startswith('_')] >>>  ['absorptive_partitioning', 'sub_cooled_density', 'test', 'vapour_pressure']  Once you have selected a method to call you can discover what parameters it takes and what it expects in those parameters by querying the method's documentation. Within the IPython shell this can be viewed simply by appending ? to the method name. Alternatively, the help() function can be used in a regular Python shell: >>>help(client.
Acknowledgements. The authors would like to acknowledge  NERC  grants  NE/H002588/1 ,  NE/J009202/1  and  NE/J02175X/1  for enabling  Mark Barley  to perform SMARTS library constructions and property prediction comparisons. Topping was similarly funded through the  National Centre for Atmospheric Science (NCAS). The authors would also like to thank  Andreas Zuend , of  McGill University , for his discussions on automating functional group selections for the AIOMFAC method. The authors would also like to thank  David Hughes , of  Waveform Computing Ltd Manchester UK , for constructing the UManSysProp web framework.
This paper presents the evaluation of the magnetic fields and mutual inductance between circular coils arbitrarily positioned in space. Firstly, based on an advanced and relevant model available in the literature, MATLAB code is implemented to evaluate the mutual inductance between circular coils arbitrarily positioned with respect to each other. The computed results are compared with the numerical results previously published in the literature and a detailed clarification regarding the huge computational errors made are presented. In the second part, a complex and relevant model available in the literature for evaluating the magnetic fields due to a circular coil is presented. Based on the useful information, the model for computing the magnetic fields between two circular coils is formulated.
In the first part of this paper, MATLAB coding is implemented to compute the mutual inductance between two circular coils arbitrarily positioned with respect to each other. This computation is achieved based on the advanced and relevant model given in  [4]. The computed results are presented graphically and numerically and then, compared with the ones presented in  [4, 5]. Also, based on a more efficient and relevant model given in  [7] , the formula for computing the magnetic fields between two air-cored circular coils with and without misalignment is presented. Lastly, experimental measurements are used to validate the computed results.
Presentation and Discussion of Results for Mutual Inductance Computation The results obtained for the mutual inductance between filamentary circular coils with arbitrary lateral ρ and angular θ misalignment are shown in Fig. 2  and Table  1. Based on the parameters given in example 12 (i. e. , R P = 16 cm, R S = 10 cm, x 1 = 20 cm and x 2 = 5 cm), MATLAB software is used to achieve the coding and computation of Eq. (  1 ). Although the authors of  [4]  studied only for the case with θ = 60 • and ρ = 0. 1803 m, the authors of this paper also studied for the cases with θ = 30 • , ρ = 0. 1587 m and θ = 45 • , ρ = 0. 1684 m. The results obtained in this paper are graphically presented (see Fig. 1 ) and also numerically tabulated (see Table  1 ). In the case with θ = 60 • and ρ = 0. 1803 m, Fig.
Conclusion The evaluation of the magnetic fields and mutual inductance between circular coils arbitrarily positioned in space is presented in this paper. In the first part of the paper, MATLAB coding is implemented to compute the mutual inductance between two circular coils arbitrarily positioned with respect to each other. This computation is achieved based on the advanced and relevant model given in  [4]. The computed results are presented graphically and numerically and then, compared with the ones presented in  [4, 5]. Also, the formula for computing the magnetic fields between two air-cored filamentary circular coils with and without misalignment is presented and validation of the results shows that the developed model (see Eq. 3) and the experimental measurements conducted are accurate and effective.
Background: The Pacific oyster, Crassostrea gigas, is one of the most important aquaculture shellfish resources worldwide. Important efforts have been undertaken towards a better knowledge of its genome and transcriptome, which makes now C. gigas becoming a model organism among lophotrochozoans, the under-described sister clade of ecdysozoans within protostomes. These massive sequencing efforts offer the opportunity to assemble gene expression data and make such resource accessible and exploitable for the scientific community. Therefore, we undertook this assembly into an up-to-date publicly available transcriptome database: the GigaTON (Gigas TranscriptOme pipeliNe) database. Description: We assembled 2204 million sequences obtained from 114 publicly available RNA-seq libraries that were realized using all embryo-larval development stages, adult organs, different environmental stressors including heavy metals, temperature, salinity and exposure to air, which were mostly performed as part of the Crassostrea gigas genome project.
This data was analyzed in silico and resulted into 56621 newly assembled contigs that were deposited into a publicly available database, the GigaTON database. This database also provides powerful and user-friendly request tools to browse and retrieve information about annotation, expression level, UTRs, splice and polymorphism, and gene ontology associated to all the contigs into each, and between all libraries. Conclusions: The GigaTON database provides a convenient, potent and versatile interface to browse, retrieve, confront and compare massive transcriptomic information in an extensive range of conditions, tissues and developmental stages in Crassostrea gigas. To our knowledge, the GigaTON database constitutes the most extensive transcriptomic database to date in marine invertebrates, thereby a new reference transcriptome in the oyster, a highly valuable resource to physiologists and evolutionary biologists.
Background The Pacific Oyster, Crassostrea gigas, is a bivalve mollusk that belongs to lophotrochozoans, an under-described phylogenetic group among protostomes despite being the sister clade of ecdysozoans, which encompass insects and nematodes. C. gigas is an intertidal bivalve with a pelagic larval phase followed by a benthic adult life. Pacific oysters are farmed in intertidal or nearshore areas that undergo highly variable environmental conditions as well as an important anthropization, making C. gigas considered a sentinel species. Besides, C. gigas constitutes one of the most important shellfish aquaculture resources worldwide with an estimated market value reaching 1. 3 billion dollars in 2013 (FAO). In this context, knowledge about the physiology of C. gigas is of fundamental, ecological and applied interests, leading to the generation of an important amount of transcriptomic data, as illustrated by the Gigasdatabase expressed-sequenced tags (EST) database  [1].
However, there is to date, to the best of our awareness, no convenient publicly accessible resource (i) where all the transcriptomic data is assembled and gathered and (ii) that is able to answer the need for a convenient way to browse into such an important amount of data. Therefore, we further exploited the C. gigas transcriptomic data generated in parallel to the genome characterization by Zhang et al  [13] , by persample assembling, then meta-assembling of the obtained contigs, the 2204 million reads from the 114 RNA-seq libraries. We provide these results including sequence of complete open reading frames (ORFs) (thereby protein), presence of transcript variants, knowledge of 5' and 3'-untranslated regions, polymorphism and expression levels between physiological stages and conditions into an extensive, searchable, highly potent and user-friendly online database based on RNABrowse  [14] , the GigaTON database.
Sequence/data cleansing The reads were first cleaned from remaining sequencing adapters using trim_galore (http:// www. bioinformatics. babraham. ac. uk/projects/ trim_galore/). Then the over-represented reads were filtered out using the normalize_by_kmer_coverage. pl script from the Trinity software package  [16]. The next step aimed at discarding the invalid base calls by extracting the longest sub-sequence without unidentified nucleotides (Ns) from each read. If the length of the longest sub-sequence did not exceed half of the sequence length, the read and his mate, if present, were removed. This last step was performed using an in-house script (available upon request).
Contig assembly The assembly was performed in two steps described hereafter. A per sample assembly was first performed, allowing us to build a contig set for each sample. Then, all sample contig sets were merged, then duplicates were removed and references with very low read counts were filtered out. For each sample, nine Oases  [17]  assemblies using different k-mers  (25, 31, 37, 43, 49, 55, 61 , 65 and 69) were performed on cleaned reads. Each assembly produced a transcripts. fa contig file, which header includes an assembly locus. Only the longest and most covered (read depth) loci were kept using the OasesV0. 2. 04OutputToCsvDataBase. py script developed by a Brown University team (https:// code. google. com/p/oases-to-csv/). After that, all files were merged. Finally, anti-sense chimeras (accidentally produced by the assembly step) were split or removed with an in-house script (available upon request).
Because similar collections of contigs were produced by different k-mers, a cd-hit-est  [18]  step was used to remove redundant contigs based on their sequence similarities (parameters -M 0 -d 0 -c 0. 98 -T 8). Because different kmers sometimes constructed different transcript parts, TGICL  [19]  was used to assemble contigs having significant overlaps (parameters -c 4 -l 60 -p 96 -s 100000). The contigs were then filtered on a minimum length of 200 base pairs. Input reads were mapped back to the contigs using bwa aln  [20]. The resulting alignment files were used to correct the contig sequences from spurious insertions and deletions using an in-house script (available upon request) and to filter out those with very low coverage. The filter excluded contigs with less than two mapped reads per million.
Variant calling The alignment files were processed with Samtools mpile-up. The information about the contigs can be searched, browsed and retrieved online using the Biomart portal (www. biomart. org) [6]. Briefly, the GigaTON contigs or subgroup of contigs can be filtered out according to various criteria and sub-criteria (name, length, annotation number in a wide range of repository databases, Gene Ontology, keyword, expression), and retrieved with their corresponding attributes selected by the user among an extensive list of 71, including: filter criteria, best annotation identifier, hit start/stop position, number of variants, DNA sequence, expression pattern in tissues, developmental stages and various physiological conditions (Fig. 3 ). The retrieved contigs can be browsed further for additional information (sequence, counterparts in other databases, expression between libraries) in convenient and user-friendly point-and-click interface tabs (Fig.
Gain of knowledge regarding existing databases In the present work, we generated 56,621 high quality contigs. This assembly brings an important amount of additional information when compared to existing databases (+52. 3 % in contig number compared to the EST library GigasDatabase v9  [1] ). This number is also greater than the 28067 consensus genes that are predicted in the oyster genome  [13]. This important increase in the transcript sequence information does not only lie in the number of contigs (Fig. 5a ) but also brings the 5' and 3' UTR information (Fig. 5b ). Indeed, even though 454 sequencing was performed on cDNA from various oyster samples  [13] , none of the corresponding assembled full-length transcript sequence is, to our knowledge, publicly available to date. Therefore, we believe that the GigaTON database constitutes the new reference trancriptome in the Pacific oyster, a work of significance for the community.
Some of its most relevant features are developed below. First, the novelty and gains of GigaTON are novel contigs. Among the 56621 contigs in GigaTON, 53,515 contigs (94,5 %) align to the Ensembl Oyter genome, with a vast majority displaying 95 % identity and 90 % coverage. A contig can be considered as 'novel' when its sequence is unknown or present in databases but not annotated, therefore including those which do not align with the genome, in addition to those that align with unannotated areas of the genome. The GigaTON database contains 38,316 contigs (67. 7 %) that display an overlap with one of the 28636 genes contained in the oyster Ensembl GTF file. The remaining 18305 'novel' contigs have been aligned to the RefSeq protein database, (http://www. ncbi. nlm. nih. gov/genome/annotation_euk/ Crassostrea_gigas/100/) which contains more transcripts (45,394 gene models).
Out of the 18,305 'novel' contigs, 7750 produce significant alignments with a RefSeq protein. Among these contigs, 2241 have no hit on the Ensembl oyster genome and 1779 match oyster proteins in the RefSeq database, whereas the remaining match known proteins in other organisms. More drastically, 11,817 'novel' contigs within GigaTON did not match any annotated region of the genome nor any oyster RefSeq protein, whereas 1078 have a RefSeq annotation in other organisms, mainly Lottia gigantea, Saccoglossus kowalevskii, Hydra vulgaris, Nematostella vectensis, Branchiostoma floridae and Strongylocentrus purpuratus (139, 83, 76, 72 62 and 52 hits, respectively). The distribution statistics of the length of the 'known' vs. 'novel' contigs concerning the coding regions are : minimum, 200 vs. 202; 1 st quartile, 821 vs.
Such examples illustrate the gain brought by GigaTON database, which, in addition to bringing new transcriptomic information, might help improving gene prediction. Second, the GigaTON contigs provide insights into the 5' and 3' untranslated regions (UTRs) of the oyster transcribed sequences (Fig. 3  and Fig. 5b ). Importantly, the 5' information is a significant asset towards the determination of a consensus promoter sequence in a bivalve The polymorphisms have been annotated using snpEff v4. 15  [22]  using the transdecoder open reading frame annotations made on the contigs. The complete annotated vcf files for SNPs can be downloaded from the GigaTON website (ALL_SNP. oysterAssembly1. 0_annot1. vcf) mollusk, which may help unlock functional genomics studies in lophotrochozoans. Besides, there is a widely admitted approximation that considers the 1 kb sequence upstream translation starts as being proximal promoter sequences, which is commonly used at present for the interpretation of NGS data  [24] [25] [26].
However, the mean 5'-additional information we provide in the GigaTON database is around 600 pb (Fig. 5b ), indicating a mean 600 pb length for 5'UTRs in the Pacific oyster. Therefore the aforementioned approximation presents a potential severe bias since up to 60 % of the sequence information might be mis-considered as promoter sequence instead of UTR, and thereby should be cautiously considered, as illustrated for some Hox genes orthologues  [27]. This contradicts the general assumption that oysters, like other lophotrochozoans, have very short 5'-UTRs. However, because the length of the predicted proteins corresponding to the GigaTON contigs also displays a great increase (Fig. 5d ), the additional 5' sequence information can also correspond to coding DNA, thereby minimizing the flaw in proximal promoter approximation previously stated.
The same rationale can be proposed regarding the additional 3' sequence information provided within the GigaTON contigs as being 3'UTRs and/or coding regions. This point is important for the understanding of the mi/piRNA pathway in oysters, which mostly affects 3'-UTRs in vertebrates  [28, 29]  but could also highlight alternative polyadenylation signals in mollusks. Therefore, additional work, including functional analyses dedicated to the determination of consensus promoter and/or polyadenylation signals, are required to decipher such potentially important issues for our understanding of gene expression regulation in the Pacific oyster, and should take great advantage of the present database. Second, the GigaTON pipeline led to the identification of a high number of assembly variants, since 30 % of the contigs display at least one variant (Fig.
This indicates a noticeable difference in protein sets between the Pacific oyster and other invertebrates. Such a discrepancy between invertebrates seems surprising, except if one considers the growing number of documented lophotrochozoan features, such as DNA methylation patterns  [24, 30] , angiotensin-converting enzyme characteristics  [31, 32]  or BMP cDNA sequences  [33] , that display a closer resemblance with vertebrates than with ecdysozoans. Besides, in a similar fashion than mapping long 454 reads helped assembling genomic scaffolds  [13] , the GigaTON database provides even longer contigs that we think could importantly help improve the current assembly of the oyster genome. These three features might be of functional significance for our understanding of the oyster physiology and support the importance of the GigaTON database, which encompasses a high diversity of transcriptomes, in the context of post-genomic studies in lophotrochozoans.
Easiness, potency and versatility of the tools The GigaTON database provides, to our knowledge, the most complete and integrated transcriptomic resource to date in a marine mollusk. A remarkable strength of this database is the possibility to browse data quickly and easily to get a global picture of the user's question using Venn diagrams or DDD, which can easily be further examined in deep details. This is noticeably allowed by the use of the BioMart portal within GigaTON, which links extremely massive sequence and expression data in various physiological conditions with a user-friendly, (See figure on previous page. ) Fig. 4  An overview of a contig search results. a: Caption from the 'General Info' tab. This screen enables the user to display and retrieve general information about the searched contig, including related keywords and functional annotation such as Gene Ontology.