dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome annotation A C C E P T E D M A N U S C R I P T The Vector NTI Suite software (Vector NTI Advance® 11. 5. 2, Invitrogen, Life technology) was used to manually analyse the contigs, to assemble the smaller contigs, analyse ORFs with coding capacity for proteins larger than 10kDa, detect specific badnaviral motifs (tRNA Met , RT and RNase H) and to confirm their badnaviral origin. The full genome sequences have been deposited at DDBJ/EMBL/GenBank under the accession MF642716-MF642736.
Phylogenetic studies Seaview version 4. 0 software was used to analyze the DNA sequences and these were aligned using the MUSCLE multiple alignment algorithm  (Edgar, 2004)  and treated by Gblocks  (Castresana, 2000). Phylogenetic relationships between CSSV sequences were estimated with PhyML (maximum likelihood method,  Guindon and Gascuel, 2003)  with SH-aLRT (approximate likelihood ratio test,  Anisimova et al. , 2011)  branch supports and phylogenetic trees were visualized with the Darwin 5 program  (Perrier and Jacquemoud-Collet. , 2006).
For many sample sequences impacted by distortions between the two reconstructed phylogenies in ORF3A and RT/RNase H regions, a mixed infection was confirmed by two distant species, one amplified by one primer pair, the other amplified by the second. The possibility of a recombination event between two divergent viral isolates has therefore not been confirmed for any new isolate sequenced in this study. Additionally, recombination analysis on complete viral genome alignment A C C E P T E D M A N U S C R I P T has been conducted using RDP4 software  (Martin et al. , 2005)  but without any conclusive results (data not shown).
Experimental validation The resonance frequencies and mode shapes of a circular microplate are determined with the Micro System Analyzer (MSA-500) of Polytec industry. This vibrometer is mounted with scanning mirrors that can help to measure the displacement/velocity at a grid of points on the moving surface. Also it is equipped with a decoder with different precisions (can go up to 1 pm for the displacement and 10 m/s for the velocity) and wide frequency range (up to 24 MHz). The data acquisition is done with a MSA processing unit equipped with software to display frequency-domain and time-domain data and simplifies the transient response analysis. As we can see in Figure  4 , we placed our wafer into a vacuum chamber in order to eliminate the fluid interaction with the microsystem. Two probes are placed inside the chamber to insure the electric connections.
Flashlet intensity was selected to saturate the fluorescence rise within around 30 of 40 flashlets  (Laney 2003; Laney and Letelier 2008). For each FRRf induction curve, data were exported from the FluorWin data capture software to fit a model with four parameters: minimal fluorescence, F 0 ; maximal fluorescence, F M ; effective absorption cross section for PSII photochemistry, σ PSII ; coefficient of excitonic connectivity ρ; and the rapid and slow kinetic phase lifetimes for PSII reopening, τ 1 and τ 2 , respectively  (Kolber et al. 1998 ) using the PSIWORX-R package (A. Barnett, sourceforge. net) (Murphy et al. 2016; Ni et al. 2016). For each measurement we applied an FRRf induction before and then again after a 1 s period of darkness to allow PSII to reopen after illumination. We thus determined in the dark F 0 , F M , σ PSII , ρ; under actinic light F S , F M ʹ, σ PSII ʹ ρʹ; and following 1 s of darkness after actinic light F 0 ʹ1s, F M ʹ1s, ρʹ1s, σ PSII ʹ1s.
This mechanism was retained whilst samples remained under high light exposure, but was lost when samples were returned to growth irradiance during the final RLC step. Notably, τ 1 lifetimes measured after only 1 s of darkness (Fig. 7b ) remained stable at ~ 500 µs throughout the duration of non-sequential light response treatments and thus dynamism in τ 1 appeared strictly dependent upon exposure to high illumination. Under induction/recovery curves (Fig. 8 ), again τ 1 generally decreased in a timedependent manner following exposure to induction phase irradiances, with H. silbo remaining an exception under high light, while stable τ 1 lifetimes were apparent at ~ 500 µs when measured after 1 s darkness. Overall, examination of the relationship between Y(PSII) and τ 1 across all datasets revealed a significant negative relationship (R 2 = 0.
Acknowledgements This publication benefited from funding from the  European Commission  under the Community's  Seventh Framework Programme BIOVADIA  (Contract No. FP7-PEOPLE-2010-IRSES-269294 ,  Biodiversity and Valorisation of Blue Diatoms ), and from the  Horizon 2020 Research and Innovation Programme GHaNA  (The Genus Haslea, New marine resources for blue biotechnology and Aquaculture) under Grant Agreement No  734708/GHANA/ H2020-MSCA-RISE-2016. Equipment used in the study was funded by the  New Brunswick Innovation Foundation , the  Canada Foundation for Innovation , and the  Natural Sciences and Engineering Research Council of Canada. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4.
In these conditions, the conjugated p electron systems of these rigid macrocycles have persistent shapes of cyclic twisted ribbons. A rst evidence of the conjugation between the three helicenes composing the cyclo-tris-[5]helicenes 2 and 3 has been obtained from their optical absorption. The cyclo-bis- [5]  helicene 1 was expected not to exhibit any conjugation between its two parts due to a lack of coplanarity at the junctions between helicenes. This was conrmed by the absorption spectra, where this cyclic dimer of ester-substituted [5]helicenes behaved approximately like two independent ester-substituted [5]helicenes  21  (Fig. 2 ). In contrast, trimeric cyclo-tris- [5]  helicenes 2 and 3 show a signicantly modied absorption prole compared to the single [5]helicene. For a closer inspection of the topology of 2 and 3, we used our program ANEWWRITHM  22  and determined the topological parameters L k , T w and W r.
The helicities of the three helicene units Fig. 3  Topologies and aromaticity parameters of cyclo-tris- [5]helicenes 2 and 3. Geometries are optimized at the B3LYP/6-31G* level of density functional theory. The ester substituents are omitted for clarity. The topological parameters L k , W r and T w are calculated with the program ANEWWRITHM. 22 The looped double headed arrows define that both objects are topologically equivalent (homeomorphic). (a) top and (b) top: cyclo-tris-helicene 2 and 3 in topological stretched, writhe-free (W r ¼ 0) representations. In these forms the linking numbers L k equal the twists T w. (a) bottom and (b) bottom: 3D structures of 2 and 3. In these "relaxed" structures a considerable part of the twist T w is projected into writhe W r. The topological parameters are given for the inner (red) and the outer conjugation path (blue).
The program implements a three-step pipeline to efficiently identify a suitable search model in a database of known structures. The first step performs a lattice-parameter search against the entire Protein Data Bank (PDB), rapidly determining whether or not a homologue exists in the same crystal form. The second step is designed to screen the target data for the presence of a crystallized contaminant, a not uncommon occurrence in macromolecular crystallography. Solving structures with MR in such cases can remain problematic for many years, since the search models, which are assumed to be similar to the structure of interest, are not necessarily related to the structures that have actually crystallized. To cater for this eventuality, SIMBAD rapidly screens the data against a database of known contaminant structures.
(2008)  and can be exploited using the AMPLE application  (Bibby et al. , 2012). Other approaches make use of idealized fragments or regularly occurring fragments and motifs from known structures as search models in MR. ARCIMBOLDO  (Rodrı ´guez et al. , 2009)  and Fragon  (Jenkins, 2018)  are two developments exploiting this approach. All of these applications mainly rely on small but highly accurate fragments being placed correctly in the unit cell of the target. In the most extreme cases, where data are available to 1 A ˚resolution or better, it has been shown that it is possible to use single atoms as a successful search model  (McCoy et al. , 2017). For the more traditional sequence-based approach, much effort has been put into developing software pipelines that will attempt to find a solution from a large set of carefully crafted search models from potentially suitable homologues.
4 A ˚;  Thorn & Sheldrick, 2013)  and ACORN ($1. 7 A ˚;  Foadi et al. , 2000; Yao et al. , 2005) , respectively, when improving upon the phases given by the initial placement of the fragment by Phaser. Some developments have sought to overcome these problems by attempting to unearth suitable search models through a bruteforce search of the PDB  (Stokes-Rees & Sliz, 2010; Hatti et al. , 2016). ContaMiner  (Hungler et al. , 2016)  is another approach specifically aimed at finding contaminants by testing a library of known contaminants in MR. Here, we present a new pipeline, SIMBAD (Sequence-Independent Molecular replacement Based on Available Databases), which can be used for both contaminant and brute-force approaches. Its ability to detect contaminant crystal structures is relevant to cases such as  Keegan et al.
(2016) , where the structure remained unsolved for 14 years. It ensures acceptably low run times by testing only the nonredundant PDB entries as defined in the MoRDa database and shortcutting the process by testing first for crystals with a familiar unit cell or containing known contaminants. MoRDa is a conventional MR pipeline built upon the MOLREP program. Its database contains chains from a redundancyremoved version of the PDB database and definitions of how to construct domains, oligomers, complexes and ensembles from the individual chains. In its current implementation, SIMBAD uses only the domain definitions to create search models. In total, SIMBAD contains three steps: a latticeparameter search, a contaminant search and the nonredundant PDB MoRDa database search (henceforth referred to as the MoRDa DB search).
SIMBAD performs the rotation search $90 000 times when screening the full MoRDa DB and, as such, speed and efficiency are very important. In light of this, the AMoRe rotation function was selected, as the modular nature of the program allowed us to pre-calculate a spherical harmonic coefficient database from the 90 000 models, a prerequisite for the rotation search. Ultimately this approach was not adopted, but it was the initial motivation behind the selection of AMoRe. However, the speed of the AMoRe rotation function (of the order of seconds) made the processing of such large numbers of search models tractable on a modest cluster. In all cases the best matches are tested by MR and refinement to ascertain whether or not they give a solution. SIMBAD can make use of multi-core clusters to speed up its processing of search models, enabling its combined three-step functionality to be run, for example, in the space of a few hours on a 100-core machine (2.
Strategy A flowchart of the SIMBAD pipeline is presented in Fig. 1. Within the three-step procedure of SIMBAD, two different methods are used to identify unknown crystals independently of sequence. The first method searches for structures in the PDB with similar lattice parameters to the unknown structure. Similar lattice parameters often indicate that a different, previously characterized protein has been crystallized by mistake  (Niedzialkowska et al. , 2016). The second method exploits the AMoRe  (Navaza, 1994)  rotation search to screen a database of candidate search models. This is split into two steps. The first step consists of screening a small database of structures that have been identified to commonly contaminate crystals. The second step consists of screening the full MoRDa DB.
Lattice-parameter search The SIMBAD lattice-parameter search employs a similar strategy to that used by the Nearest-cell server  (Ramraj et al. , 2012)  and the SAUC server  (McGill et al. , 2014). A database was created from the PDB containing the Niggli reduced cell, a reduced P1 cell  (Andrews & Bernstein, 2014) , for each structure using the explore_metric_symmetry routine in cctbx (Computational Crystallography Toolbox; https:// github. com/cctbx/cctbx_project). The Niggli reduced cell for the unknown data set is generated in the same way and compared with the Niggli reduced cells in the database. The comparison takes place in two steps. Firstly, the Niggli reduced-cell database is searched for cells where each lattice parameter is within AE5% of the respective lattice parameter in the experimental data.
Secondly, a penalty score is generated for each Niggli reduced cell using penalty ¼ jða e À a d Þj þ jðb e À b d Þj þ jðc e À c d Þj þ jð e À d Þj þ jð e À d Þj þ jð e À d Þj; ð1Þ where a, b and c represent the lengths of the cell edges and , and represent the angles between them. A subscript e signifies experimentally derived lattice parameters and a subscript d is used for Niggli reduced-cell database-derived lattice parameters. To test the intuition that a lower penalty score would be more likely to lead to a solution, a test set of 125 data sets were randomly selected from the PDB (Supplementary Table  S1 ).
Rotational search SIMBAD uses the AMoRe fast rotation function to screen databases for suitable MR candidates. By skipping models estimated to be unable to fit into the unit cell (by requiring a solvent content above 30%) and by exploiting coarse-grained parallelization across a multi-CPU cluster, the time required for the rotation function is minimized. SIMBAD uses the correlation coefficient between the observed amplitudes for the crystal and the calculated amplitudes for the model (CC_F) to score the results from AMoRe. A large peak in the CC_F score for the top-ranked solution is indicative of a correctly orientated structure. Therefore, in order to compare the solutions for each template structure used, AMoRe was modified to return a Z-score of the CC_F scores. The AMoRe ROTNDO subroutine was modified to output Z-scores derived from CC_F and the correlation map.
The CC_F-based Z-score estimates the mean and variance for the template using 200 random orientations. 2. 3. 1. Contaminant search. A set of 349 structures representing the different homologues and space groups of 60 proteins known to commonly occur as contaminants has been compiled. This set consists of contaminants identified in the course of developing SIMBAD and common contaminants listed by other sources  (Niedzialkowska et al. , 2016; Hungler et al. , 2016). In addition, corresponding domains from the MoRDa DB which may form subcomponents of the contaminants augment the original database. The complete list is processed in the AMoRe rotation search and the models are ranked by Z-score. The top 20 are passed on to MOLREP and REFMAC5 for full MR and refinement. was able to catch some nontrivial cases.
When SIMBAD is run locally this can be performed using Coot  (Emsley et al. , 2010). When executed online, the moleculargraphics tool UglyMol (https://github. com/uglymol) is used instead. The Z-scores from the AMoRe rotation search for the contaminant and MoRDa DB stages are also made available. Supplementary Fig. S1  shows the report page for a run of SIMBAD.
These structures were removed from our database to determine whether the contaminant search would be successful in identifying homologues in other space groups as suitable candidates for MR search models. The ten cases represented a broad range of space groups, resolutions and structure types. SIMBAD was successful in nine out of the ten test cases (Supplementary Table  S3 ). Analysis of the failed case (PDB entry 3fwe, an apo D138L CAP mutant) showed that the homologues for this structure had significantly larger conformational differences than for the nine successful cases. The conformational differences were measured using the pairwise structural alignment feature in GESAMT  (Krissinel, 2012). The best search models were compared with the targets in terms of a C r. m. s. d. and a Q-score.
For the nine cases that succeeded the average C r. m. s. d. and Q-score were 0. 51 and 0. 89, respectively, and for the one case that failed the closest match in the contaminant database (PDB entry 3hif) only gave a C r. m. s. d. and Q-score of 1. 56 and 0. 75, respectively. This model was ranked 172nd, with a Z-score of 3. 2. It has been shown that apo wild-type CAP (PDB entry 3hif) undergoes large conformational changes in order to bind DNA  (Sharma et al. , 2009). Such conformational changes would explain the intramolecular differences seen between the apo D138L CAP mutant (PDB entry 3fwe) and apo wild-type CAP (PDB entry 3hif) (Fig. 3 ). In conclusion, SIMBAD is able to identify contaminants that are crystallized in a similar unit cell to existing structures using the lattice-parameter search but is also able to identify contaminants crystallized in novel ways when a sufficiently similar (C r.
m. s. d. < 1 A ˚) structure is contained within our contaminant database. 3. 1. 2. Testing for novel structure solution. To simulate cases where the sequence is potentially unknown for a given target, we tested the SIMBAD combined search (latticeparameter, contaminant and MoRDa DB searches) against a set of 25 recently released structures in the PDB. These cases were all released in February or March 2017. The SIMBAD lattice database and the version of the MoRDa DB used at the time of testing did not contain any entries with information derived from this set of PDB structures or any subsequently released PDB entries. Other than this criterion, no particular constraints were placed on the PDB entries chosen. The set contained a wide range of resolution limits, numbers of copies in the asymmetric unit, space groups, monomer sizes and secondary-structure types (Supplementary Table  S4 ).
Firstly, we looked at the sequence identity. The mean sequence identity of a successful search model to the target was 98% in the lattice-parameter search and 83% in the MoRDa DB search. The lowest sequence identity between a successful search model and the target was 44% [PDB entry 5grh using search model 3blvA_1 (MoRDa DB format: PDB code 3blv, chain A, domain 1)]. We then examined the coverage of the target structure by the search model. The search model with the smallest relative size to the target was 3jwnH_2, making up approximately 14% of the overall content of the asymmetric unit of PDB entry 5jqi (eight chains, 1157 residues in total). This model ranked top in the MoRDa DB search and had 100% sequence identity to the part of the target matched. On average, a successful search model made up 44% of the content of the asymmetric unit of the target.
Finally, by utilizing the pairwise structural alignment feature in GESAMT, we compared the search models with the targets in terms of a C r. m. s. d. and a Q-score (a measure of structural similarity, where 1 is identical and 0 is structurally unrelated). Results for successful solutions showed an average C r. m. s. d. s and Q-scores of 0. 63 and 0. 93, respectively, for the lattice-parameter search and 0. 61 and 0. 46, respectively, for the MoRDa DB search. The highest C r. m. s. d. between the model and the target for a success was 0. 88 A ˚(PDB entry 5mg1) in the lattice-parameter search and 1. 08 A ˚(PDB entry 5grh) in the MoRDa DB search. The MoRDa DB search ranked this model 35th, with a Z-score of 5. 6. In conclusion, within our test set SIMBAD was capable of producing MR solutions using search models that are significantly different from the target in terms of sequence identity (!44%), model coverage (!14%) and C r.
07 A ˚C r. m. s. d. of the target structure (assuming a minimum alignment to 30% of the target). Of the four cases that did not produce a solution, three (PDB entries 5lnl, 5jfm and 5ayl) were multi-chain or multi-domain targets of at least seven domains. The MoRDa models most closely matching these targets provided too small a signal for them to be found in the AMoRe rotation-search step. The remaining case (PDB entry 5hsm) had a single chain of 131 residues and the best MoRDa model (3fm5A_1, C r. m. s. d. = 0. 97 A ˚) failed to produce a solution in SIMBAD. This model provided a weak signal in the rotation search (Z = 4) and was relegated to a low overall ranking by many similar, but higher scoring search models containing longer -helices. In the cases where a successful solution was found using the MoRDa DB search, the resulting best search model was ranked top on three occasions.
The lowest AMoRe ranking for a successful search model was 170. With this step trialling more than 90 000 search models, it demonstrates the sensitivity of the Z-scoring added to AMoRe but also the value of taking at least the top 200 ranked hits to the full MR and refinement stage. The Z-score values for successful solutions ranged from 5. 5 (PDB entry 5uqf) to 14. 0 (PDB entry 5uca) with a mean of 8. 9. Finally, we looked at the run times for the various test cases. The average run time for success during the lattice-parameter step was 0. 7 h on a maximum of 20 cores (2. 8 GHz, AMD Opteron 4184). Completion of the combined search required an average of 11. 6 h on 40 cores, regardless of success or failure.
User cases In this section, we present three cases in which SIMBAD has been used to determine a difficult-to-solve case owing to the unwitting crystallization of a contaminant. Although the targets were ultimately of low importance for the structural insights that they provided, their solution prevented further misdirected effort on the part of the researchers involved. All cases involve the crystallization of a known contaminant. Examples involving the use of SIMBAD for novel structure solution are available elsewhere, such as PDB entries 6byq, 6c87 and 5wol. Cases illustrating the use of SIMBAD for targets that had not been previously sequenced are not shown owing to the publications being in progress at the time of writing. Solutions for mislabelled crystals are also not shown. These cases were of little interest to the researcher once the mistake had been realized, and no further effort was devoted to structure completion.
62, b = 133. 97, c = 139. 11 A ˚, = = = 90 and a presumed six molecules of caspase 1 in the asymmetric unit. Diffraction data were measured at 100 K using a PILATUS3 6M detector (Dectris) on the 23ID-D beamline of GMCA@APS at the Advanced Photon Source, Argonne National Laboratory, USA. The data were indexed, integrated and reduced with XDS  (Kabsch, 2010). The SIMBAD MoRDa DB search led to success with the structure of a 167-residue protein identified as a DNAprotecting protein during starvation from E. coli (PDB entry 1f30), which is characterized as a ferritin-like protein in the SCOP database  (Murzin et al. , 1995). After refinement, it became clear that this was the protein that had crystallized instead of caspase 1. The structure of DPS was refined with REFMAC5 in the CCP4 suite to 1.
5 A ˚resolution, resulting in R and R free values of 17. 64% and 20. 77%, respectively. Manual model inspection and modifications were performed with Coot. In the crystal, 12 molecules of the protein form a hollow sphere closely reminiscent of that formed in crystals of ferritin (Fig. 4 ). The coordinates and structure factors have been deposited in the Protein Data Bank with accession code 6b0d and the raw data have been deposited in SBGrid  (Morin et al. , 2013). Caspase 1 had previously been successfully purified and crystallized, and its structure had been solved using MR (R. Wu, unpublished results). While there were telltale signs of possible contamination of the new protein preparation, they were not clear enough or had plausible alternative explanations. For example, the crystals from the current protein sample looked different from those used in the structure solution of caspase 1 and had very different unit-cell parameters.