dataesr/scientific-paragraphs-categorization huggingface dataset:
Members of each putative subfamily were identified using Net-workX  (78). SSNs were visualized with Cytoscape (79) using the yFiles organic layout. To simplify the display of large SSNs, nodes representing highly similar sequences (E value of 10 Ϫ85 ) were merged into metanodes using the depth-first search algorithm  (80). The bioinformatics workflow used here has been packaged into a graphical user interface-based program, SSNpipe, which is freely available on GitHub (https://github. com/ahvdk/SSNpipe). 5.
Subfamily assessment/validation using hidden Markov models Each SSN, defined by its clustering threshold (BLASTP E value), can be considered as a set of N assignments (p 3 s), where each of the 22,946 proteins, p, is assigned to its subfamily, s, among S total subfamilies. HMMs for each subfamily in each SSN were used to measure precision and recall rates to assess SSN utility and validate the choice of an optimal threshold value for GH16, as follows. A library of Sϩ1 HMMs was assembled, corresponding to one HMM for each subfamily s and an additional HMM for the remaining GH16 members. Each HMM was generated using the hmmbuild command in HMMER3. 2 with default parameters  (81). Sequence sets were first reduced in redundancy (75%) using UCLUST  (55) , the resulting sequences were aligned with MAFFT using the G-INS-i strategy (iterative refinement, using weighted sum-of-pairs and consistency scores, of pairwise Needleman-Wunsch global alignments)  (82) , and each alignment was inspected in Jalview  (83)  to manually define the boundaries of the GH16 module.
The hmmscan command in HMMER3. 2 was then used to search the 22,946 GH16 modules against the collection of S ϩ 1 HHMs. A protein pЈ was considered to belong definitively to a subfamily HMM, sЈ, only if (i) the best-matching HMM E value was below 10 Ϫ30 and (ii) the second best-matching HMM had an E value at least 10 Ϫ10 fold greater (i. e. less significant). The resulting set of P predictions (pЈ 3 sЈ) was compared with the N reference assignments (p 3 s) from the SSN. Identities between predictions and assignments were counted as true positives (TP). Predictions (pЈ 3 sЈ) for a protein pЈ not initially assigned to the same subfamily or to any subfamily (GH16 members unclassified in a subfamily by the SSN) were counted as false positives (FP). The assignments (p 3 s) for a protein p not.
Molecular phylogeny For each subfamily, 30 random sequences (or all sequences in subfamilies with less than 30 members) were aligned with MAFFT using the G-INS-i (iterative refinement, using weighted sum-of-pairs and consistency scores, of pairwise Needleman-Wunsch global alignments) strategy  (82). Three GH7 sequences (GenBank TM accessions CAA37878. 1, ABY56790. 1, and AAM54070. 1) were included as an out-group. The quality of the alignment was ensured by manual inspection in Jalview  (83)  and corrected according to available structural information if necessary. A maximum-likelihood phylogenetic tree was estimated with RAxML (84) (100 bootstrap replicates) and visualized with iTOL  (85).
Structural comparison The crystal structure coordinates for 42 GH16 members were downloaded from the Protein Data Bank (PDB) and pairwise-superimposed starting from one of the shortest sequences (PDB code 1GBG) using the SSM algorithm  (86)  in Coot  (87). One representative member was selected for those subfamilies where multiple structures are available (Table  1 ). For each subfamily, at least 10 randomly chosen sequences, in addition to that of the structural representative, were aligned with Multalin (88) and visualized adding the secondary structure elements using Espript  (89). For each subfamily the superimposed coordinates were visually inspected for conserved and divergent residues around the active site groove, the central Ϫ1 and ϩ1 binding subsites, and conserved and characteristic features were highlighted in structural icons using PyMOL (PyMOL Molecular Graphics System, version 2.
This work was supported in part by funding for the project SYNBIOMICS:  Functional Genomics and Technoeconomic Models for Advanced Biopolymer Synthesis from Genome Canada  (Project no. 10405 ), with additional support from  Ontario Genomics ,  Genome Quebec , and  Genome British Columbia  (to H. B. ). This work was also supported in part by computational resources provided by  WestGrid and Compute Canada  (to H. B. ), by Grants  ANR-14-CE06-0017  and  ANR-17-CE20-0032  from the  Agence Nationale de la Recherche  (to B. H. ), and by the  Novozymes Prize  from the  Novo-Nordisk Foundation  (to B. H. ). Finally, this work was supported in part by the  Agence Nationale de la Recherche  via the investment expenditure program  IDEALG  Grant  ANR-10-BTBR-04  (to M. C. and G. M. ) and by  European Union   Horizon 2020 Program Project 727892, GenialG-GENetic diversity exploitation for Innovative Macro-ALGal biorefinery  (to G.
OBSERVATIONS We have been monitoring transits in the WASP-4 system for approximately a decade using the Danish 1. 54 m telescope and DFOSC imager at ESO La Silla, as a side-project of the MiND-STEp Consortium  (Dominik et al. 2010). Four transits observed in the 2008 season were presented in  Southworth et al. (2009b) , and we also observed one transit in 2009, two in 2011, three in 2015, three in 2016, two in 2017 and three in 2018. All observations were obtained through a Cousins R filter with the CCD unbinned and the telescope significantly defocussed to enhance the photometric precision  (Southworth et al. 2009a). The scatters of the datasets versus transit fits (see below) range from 0. 54 to 1. 03 mmag with cadences of typically 140 s. We also observed one transit in the WASP-4 system using the NTT and EFOSC2 imager  (Buzzoni et al.
We did not perform bias or flat-field calibrations because they have a negligible effect on DFOSC data except for an increase in the noise level of the light curve  (Southworth et al. 2014). All new data are shown in Fig. 1. The timestamps were converted to the BJD(TDB) timescale using routines from  Eastman et al. (2010). Manual time checks were performed for images in most observing sequences, and no discrepancies were noticed 1. We have previously found a good agreement between the timestamps on the Danish and MPG 2. 2m telescopes  (Southworth et al. 2015) , supporting the reliability of both. Southworth et al. (2009b)  used an early version of DEFOT so the 2008 data were re-reduced with the current version. This allowed the use of image motion tracking, additional comparison stars, and timestamps on the BJD(TDB) timescale.
MEASUREMENTS OF THE TRANSIT TIMES Each transit was fitted in isolation using the JKTEBOP 2 code  (Southworth 2013, and references therein). The fitted parameters were the transit midpoint T0, the fractional radii of the two components (rA = R A a and r b = R b a where RA is the radius of the star, R b is the radius of the planet, and a is the semimajor axis of the relative orbit) expressed as their sum and ratio, the orbital inclination, and the coefficients of the polynomial versus time. We modelled limb darkening using the quadratic law, fitted for the linear coefficient, and fixed the quadratic coefficient at an appropriate value. Uncertainties on the transit times were obtained using Monte Carlo simulations and multiplied by χ 2 ν where χ 2 ν is the reduced  χ 2 of the fit. This final step is necessary because the aperture photometry routine used in our pipeline tends to underestimate the observational uncertainties.
To investigate this we rejected the six timings clearly affected by starspot crossing events (Table  1 ) and recalculated the ephemerides. We find that the χ 2 ν drops to 1. 30 for the quadratic and 1. 70 for the linear ephemeris, the quadratic term is not significantly changed, and its detection significance becomes 8. 2σ. We conclude that spot activity increases the scatter of the transit timings but that TTVs are still detected to high confidence. Visual inspection of Fig. 2  suggested the possibility of a sinusoidal variation superimposed on the quadratic term. We therefore calculated a periodogram of the residuals of the best fit to the quadratic ephemeris, using the PERIOD04 package  (Lenz & Breger 2004). The periodogram contained no significant peaks, with the highest having a signal-to-noise ratio of 2.
SUMMARY We have presented 22 times of mid-transit for the WASP-4 transiting planetary system that confirm the early arrival of transits noticed in TESS data (B19) and are consistent with a quadratic ephemeris. A constant-orbital-period model is rejected at the 8. 4σ significance level, making WASP-4 b only the third hot Jupiter with TTVs detected to high significance. We have explored possible explanations for the observed TTVs. Instrumental timing effects cannot be blamed because there is good agreement between different telescopes in the same observing seasons, and they are also not apparent in any of the tests we have performed. Biases due to spot crossing events in the light curves have a negligible effect on our results so can be ruled out as the source of the TTVs. The Applegate mechanism gives TTVs a factor of six smaller than observed.
RV measurements of the planet host star would give stricter limits on the change in systemic velocity of the system. WASP-4 and WASP-12 stand out as the only hot Jupiters for which unexplained TTVs have been detected: as this phenomenon is uncommon it is reasonable to propose unusual or unlikely scenarios for such systems. http://www. astro. keele. ac. uk/jkt/. We are grateful to Luke Bouma, Adrian Barker and an anonymous referee for very useful comments that helped to significantly improve our paper. This project was partially funded by the MINEDUC-UA ESR project ANT 1795. The following internet-based resources were used in research for this paper: the ESO Digitized Sky Survey; the NASA Astrophysics Data System; the SIMBAD database operated at CDS, Strasbourg, France; and the arχiv scientific paper preprint service operated by Cornell University.
ACKNOWLEDGEMENTS This paper uses data from the  South African Astronomical Observatory (SAAO)  under programmes Southworth- 2016-05-1. 0m  and  Southworth-2019-01-40-inch-228 , and from  ESO  under programme  ID 088. C-0204  (P. I. Tregloan-Reed). The reduced light curves presented in this work will be made available at the CDS (http://cdsweb. u-strasbg. fr/) and at.
Initial crack propagation code The initial FM-SGBEM code and its performance are well detailed in  [24, 25]. This Fortran code inherits a number of innovative algorithms from the BE community such as (i) the singular integration schemes by Andrä and Schnack  [2, 8] , (ii) the index of severity  [17] , (iii) the nested Flexible GMRES which makes use of the nearinteraction matrix  [4] ; and (iv) the extension of the BIEs to multizone configurations  [10]. Subroutines of matrix-vector operations are taken from BLAS library. Flexible GMRES and GMRES scripts are downloaded from www. cerfacs. fr. Figure  5  resumes the main phases of the code. In a nutshell, the aim is to solve a linear system (K. X = b) with an iterative solver: the Flexible GMRES. The matrix K which corresponds to (2) is composed of double surface integrals B tt , B tu and B u u presented in (3).
Parallel implementation with OpenMP Parallelization is a technique for dividing a large problem into small problems that can be solved simultaneously. The aim is to solve the initial problem in the smallest possible time. A multiprocessing parallelization is achieved in this work by Q6 using OpenMP  [5]. OpenMP is an Application Program Interface (API) for parallel computing on shared memory architecture. It simplifies writing multi-threaded applications by using compiler directives and library routines. The goal here is to speed up the existing code by avoiding big changes. A simple observation of Trinh's  [24]  results (see Table  2 ) shows that the solving phase is time-consuming. To reduce this duration, it is necessary to reduce the number of iterations or the duration of one iteration.
Numerical examples This section presents the results of all the optimizations presented in this paper. The calculation times are measured on an Intel Xeon (20 cores, 2. 2 GHz) computer with 128 Go of RAM. T tot is the total time including pre-processing (input reading, octree construction, etc. ) and post-processing (results writing in files). This duration is compared with Trinh's code  [24]  duration noted T old tot. Table  5  shows computational data for static analyses. Table  6  shows computational data for propagation analyses. For the cube with 8 cracks (model C8), the evolution of the total time according to the cycle number is presented in Fig. 14. Other optimizations will be investigated in future, for example the use of hierachical matrix representation and the coupling of the FM-SGBEM code with the FEM.
The planar structures of 58, 60 and 61, were then elucidated by comparison of their spectroscopic data with that of 59. 51  In a later study using the cyanobacterium S. salina LEGE 06099, closely related to the bartolosides B-D (59-61) producer (S. salina LEGE 06155), Afonso et al. 52  reported new analogues of metabolite 58, bartolosides E-K (62-68) by following cytotoxic activity towards cancer cell lines. Most of the structural differences of these new analogues related to the length of the alkyl chain and the presence or absence of a chlorine substituent in the 5-alkyl chain. A notable exception is compound 66 that features a rare, mid-chain gem-dichloro moiety. 52  Interestingly, this same strain produced bartoloside A in elevated amounts (reaching up to 0. 6% of its dry weight), suggesting a potential structural role.
Diversity of alkylresorcinol pathways in cyanobacterial genomes Genomic data for the phylum Cyanobacteria have been increasing over the past years, mostly as a result of a few major sequencing efforts,  [71] [72] [73]  but most genera have either none or a single assembly available publicly. As of August 2018, over a thousand cyanobacterial genomes were publicly available in the GenBank database, more than half of which corresponding to the smaller-genome genera Prochlorococcus and Synechococcus picocyanobacteria. This wealth of genomic data can be used to obtain a predictive overview of MAR and DAR diversity (according to currently understood biosynthesis) in the phylum Cyanobacteria. In fact, Nakamura et al. 8  have done this exercise for CylC/CylK containing cyanobacterial genomes and found a series of different orphan BGC archetypes that likely correspond to unprecedented natural product classes.
Likewise, Costa et al. 19  who looked into the distribution of hid-like BGCs in cyanobacteria observed that these are widely distributed in picocyanobacteria. Within the scope of this review, we carried out a distribution analysis of MAR-and DAR-encoding pathways among currently available cyanobacterial amino acid sequence data (mostly from genome-sequencing studies) available in the GenBank. We performed phylogenetic analysis of type III PKSs (Fig. 9 ) and DarB homologues (Fig. 10 ) which overall suggest that most scaffolds of MAR and DAR types are still to be uncovered in this phylum. ‡ Type III PKSs in cyanobacteria are distributed among eight well-supported clades (Fig. 9 ). The majority of these enzymes are HidC homologs and are found among picocyanobacteria (hidABC clade) and other Synechococcales (hidCA/hidCBA clade).
Acknowledgements This work was funded by the  BLUEandGREEN  project ( H2020   TWINNING , Grant Agreement no. 692419 ) and was further supported by an  European Research Council (ERC)   Starting Grant  ( 759840 ) to PNL and  National Science Foundation (NSF)  through grant  CHE-1454007  to EPB. We acknowledge funding by  Fundação para a Ciência e a Tecnologia (FCT)  through projects  PTDC/BIA-BQM/29710/2017  (co-nanced by  COMPETE 2020, Portugal 2020, ERDF )  UID/Multi/04423/2013 , as well as through scholarship  SFRH/BD116009/2016  to SF and grant  IF/ 01358/2014  to PNL.
Analyses presented in this paper are the first to estimate the distribution of dFAD beaching events from the largest tropical tuna purse seine fishery in the world. dFAD beaching events and their corresponding deployment locations were analysed using data from 22,000 dFADs deployed in the WCPO in 2016-2017, as well as Lagrangian simulations of over 1. 5 million virtual dFADs. Using the trajectories of both real beached dFADs and simulated particles from Lagrangian simulation experiments, the connectivity between these observed beaching areas and dFAD deployment source locations was quantified. Methods dfAD tracking data. In order to quantify and manage the number of dFADs deployed in and drifting through the EEZs of PNA members (Federated States of Micronesia, Kiribati, Republic of the Marshall Islands, Nauru, Palau, Papua New Guinea, Solomon Islands and Tuvalu), a dFAD-tracking programme was initiated in January 2016.
This programme requires fishing companies to report data from satellite buoys deployed on dFADs to the PNA via the satellite service provider. These data consist of a location and time stamp recorded periodically by the dFAD buoy. Here, these data are used to investigate beaching events and the associated deployments and drift tracks between 1 January 2016 and 31 December 2017. Transmission frequency (usually every hour) may vary over time due to fishers setting different transmission 'modes'. For example lower frequencies are typically used when dFADs drift away from main fishing areas or during the WCPO dFAD closure period. This annual dFAD closure corresponds to a three to four month period (July to September, extending to October for some vessels), where all dFAD-related activities (deployment, setting and servicing) are prohibited  25.
However, there is no obligation to remove dFADs from the water and beaching can still occur. Transmissions start when the buoy is activated, which can be a few hours to several days before deployment, and continue until deactivation (e. g. dFAD lost, retrieved, beached or outside the productive area that each vessel operates in). The trajectories of 26,921 buoys with 13. 6 million transmissions were assessed. This provides location data for when dFADs were drifting at sea, but may also include some data from when the dFAD is onboard a vessel either before deployment or when dFADs were recovered at sea. An initial data cleaning process was undertaken to remove buoys activated for only short periods, buoys with transmissions from only single positions, double transmissions or consecutive transmissions corresponding to unrealistic drift speeds.
Results Observed dFAD beaching and deployment hotspots. The cleaned dataset consisted of 22,620 observed dFAD trajectories (84% of the original dataset) split into 32,665 at-sea drift trajectories separated by periods on-board a vessel (15,455 dFADs had a single, continuous drift track) (see Escalle et al. 16  for a detailed overview of the dFADs tracking dataset). Of these, we estimated that 5. 8% of all dFAD trajectories (1,320) ultimately beached. The largest number of beaching events were in the EEZs of Papua New Guinea (483), Solomon Islands (379), Kiribati Gilbert Islands (155) and Tuvalu (117) (Fig. 1 ). High seasonal variability in beaching was detected. In the 1 st quarter, Nauru had the highest beaching density per 1° grid cell (10 beaching events). During the 2 nd quarter, a relatively high number of beaching events occurred in Papua New Guinea (142), as well as during the 4 th quarter in Solomon Islands (110) and Tuvalu (49).
Acknowledgements The authors are grateful to the members of the Parties to the  Nauru Agreement  for access to their dFAD tracking data. Virtual FAD simulations were run on resources and services from the  National Computational Infrastructure (NCI) , which is supported by the  Australian Government. Research was funded by  The Pew Charitable Trusts. J. S. P. and A. S. G. were also supported by  Australian Research Council Linkage Project  Grant  LP140100327. We thank two anonymous Reviewers for their constructive comments on an earlier version of the manuscript.
The performance of this method was assessed on 1843 isolates prospectively collected by the French National Surveillance System and analysed in parallel using phenotypic reference methods, leading to nearly complete (1814; 98. 4 %) agreement at species and infra-specific (biotype and serotype) levels. For 29 isolates, incorrect phenotypic assignments resulted from atypical biochemical characteristics or lack of phenotypic resolution. To provide an identification tool, a database of cgMLST profiles and reference taxonomic information has been made publicly accessible (https:// bigsdb. pasteur. fr/ yersinia). Genomic sequencing-based identification and subtyping of any Yersinia is a powerful and reliable novel approach to define the pathogenic potential of isolates of this medically important genus.
Next, a genus-wide cgMLST scheme was developed to record nucleotide variation at 500 shared gene loci. Finally, an automated process for strain identification and characterization was set up, validated and implemented in the daily surveillance of Yersinia isolates in France. To disseminate the method, a database of cgMLST profiles and their corresponding identification was made publicly accessible using the web-based tool BIGSdb (Bacterial Isolate Genome Sequence Database)  [29, 30] , allowing external users to identify, type and subtype their Yersinia isolates using their own genomic sequences.
Genomic sequencing A total of 802 isolates from the YNRL were sequenced using an Illumina NextSeq 500 instrument. DNA extraction was performed using a PureLink genomic DNA mini kit (Invitrogen) following the manufacturer's instructions, except that DNA was eluted in 150 µl H 2 O (Ambion). Sequencing libraries were prepared using a Nextera XT DNA library preparation kit (Illumina). Paired-end reads of 150 nt were obtained using the Mid Output or High Output kits (Illumina). Trimming and clipping were performed using AlienTrimmer v0. 4. 0  [33]. Redundant or over-represented reads were reduced using the khmer software package v1. 3  [34]. Finally, sequencing errors were corrected using Musket v1. 1  [35]. A de novo assembly was performed for each strain using SPAdes v3. 12. 0  [36]  with the pre-processed reads.
Constitution of a genome dataset A total of 544 publicly available assembled genomes were downloaded in February 2016 (Tables  1  and S1 , available with the online version of this article) from the GenBank repository, representing all Yersinia species according to the recognized taxonomy at that time, except Y. entomophaga. In addition, the genome sequences of 802 Yersinia isolates received at the YNRL (Tables  1  and S1 ), and previously characterized phenotypically, were determined and used in this work. Together, this constituted a reference dataset of 1346 assembled genomes (Tables  1  and S1 ). Finally, the cgMLST scheme was validated on 1843 additional isolates received at the YNRL between 2016 and mid-2017 that were sequenced and phenotyped in parallel.
Design of theYersinia cgmLST scheme From the 1346 reference genomes, 200 genomes representative of genus Yersinia diversity were selected in the following way. Genome assemblies made up of more than 500 contigs with N50 values below 10 000 nt were discarded. From the remaining genomes, we calculated pairwise genome distances using the program andi  [37]. From the resulting matrix, we defined 200 clusters based on an UPGMA (unweighted pair group method with arithmetic mean) hierarchical clustering. For each cluster, one representative with the largest N50 value was selected, leading to a subset of 200 phylogenetically representative genomes (Table  S1 ). A total of 1672 genes present in more than 95 % of these 200 representative genomes was defined as the core genes. For each of these core genes, a file containing all alleles (typically between 50 and 100) was generated.