dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression constructs Full-length PfMyoA heavy chain (PlasmoDB ID PF3D7_1342600/ GenBank accession number XM_001350111. 1), with Sf9 cell preferred codons, was cloned into the baculovirus transfer vector pFastBac (pFB) (Thermo Fisher). A 13 amino acid linker separates the C-terminus of the PfMyoA heavy chain from an 88 amino acid segment of the Escherichia coli biotin carboxyl carrier protein {Cronan, 1990 #1}, which gets biotinylated during expression in Sf9 cells, followed by a C-terminal FLAG tag for purification via affinity chromatography. Heavy chain mutants (point mutants E6R, T586F, S691G; triple mutants R707A/E711A/Y714A and R707L/E711R/Y714A) were generated on this backbone using site directed mutagenesis. An N-terminal truncation of MTIP (MTIP-Δn, residues 1-E60 deleted and an N-terminal HIS tag), and an N-terminal truncation of the heavy chain (ΔN-terminal HC extension (ΔNter), residues 1-S20 deleted) were also cloned for use in constructs that were crystallized.
X-ray diffraction data were collected at the SOLEIL synchrotron, on PX1 beamline (λ = 0. 906019 Å for type A, λ = 0. 978570 Å for type B, λ = 0. 978570 Å for type C), at 100 K. Diffraction data were processed using the XDS package  35  and AutoPROC  36. Crystals type A and C belong to the P212121 space group, crystals type B belong to the P21212 space group, with one molecule per asymmetric unit for type A and C and two molecules per asymmetric unit for type B. The data collection and refinement statistics for these crystals are presented in (Supplementary Table  1 ).
Structure determination and refinement Molecular replacement was performed with the PfMyoA motor domain coordinates (21-768) (PDB code 6I7E for the PPS; PDB code 6I7D chain A for the PR  9  ) with Phaser  37. The structure in the PR state (PDB code 6I7D, chain A) without ligand and water was used as a target model for type A and C crystals. PfMyoA motor domain in the PPS state (PDB code was used as a target model for type B crystals. Manual model building was achieved using Coot  38  , the structure of MTIP complexed with the cognate IQ motif peptide (PDB code 4OAM  13  was used to rebuild MTIP. Refinement was performed using Buster  39. The statistics for most favored, allowed and outlier in Ramachandran angles are for each crystal type respectively (in %): 96. 40, 3. 41, 0. 19 for PfMyoA•FL-PR; 91.
ml -1 curve was used for further analysis because of the higher signal/noise ratio. The theoretical SAXS curves were calculated with CRYSOL and compared based on the quality of their fits against the different experimental curves. We computed the SAXS envelopes of the two samples with GASBOR, two programs from the ATSAS suite  40. The analysis and the fit of the computed SAXS envelopes and the X-ray structures were performed with FoXS  41. Furthermore, to analyze if we have a mixed population of PR-Closed and PR-open conformations of PfMyoA-PR in solution we used Oligomer  40. The model of PR-open conformation used in this study was generated by Swiss model  42  after manual positioning of the lever arm, as found in the PPS structure in which the pliant region is not kinked.
Molecular dynamics Molecular dynamics experiments were performed with a procedure close to  22. All the systems (WT + mutants) were built with the CHARM-GUI  43, 44  , with the Solution Builder module. The entire proteins were relaxed in a box containing explicit water (TIP3P) and salt (150 mM KCl) at 310. 15 K in the CHARMM36m force field  45. The duration of the simulations was 320 ns in GROMACS (version 2018. 3)  46.
Growth analysis by FACS Tightly synchronised ring-stage parasites were diluted to 0. 1% parasitemia (in triplicate) for growth curves and transferred into 96-well plates containing fresh red blood cells at 0. 1% haematocrit. Parasites were allowed to proceed to the next ring-stage cycle, were stained with SYBR Green (1:10,000, Sigma) for 10 min and washed three times with PBS prior to collection by flow cytometry using a BD LSRFortessa TM. A 100,000 cells were counted and FCS vs SSC was used to gate for red blood cells, SSC-A vs SSC-W for singlets and FSC-H vs SYBR-A was applied to gate for infected red blood cells. Flow cytometry data was analysed in FlowJo. Biological replicates are indicated in the figures. 2  shows values from additional experiments. (c) ADP release rates from acto-PfMyoA.
experiments, 2 protein preparations); E6R (3 experiments, 3 protein preparations); T586F (3 experiments, 2 protein preparations); S691G (4 experiments, 3 protein preparations). Values, mean ± SD. (d) Correlation of ADP release rates and in vitro motility speed. (e) Actin-activated ATPase activity. WT data are from  9. Data from at least 2 protein preparations and 2 experiments for each construct were fitted to the Michaelis-Menten equation. Error, SE of the fit. (f) Ensemble force measurements using a utrophin-based loaded in vitro motility assay. A myosin that produces more force requires higher utrophin concentrations to slow motion: E6R, 4. 02 ± 0. 31 nM; T586F, 2. 38 ± 0. 18 nM; S691G, 1. 99 ± 0. 19 nM; WT, 1. 40 ± 0. 08 nM. WT data are from  9. Error, SE of the fit. Data from 2 protein preparations and 3 experiments for each mutant construct.
(f) Ensemble force measurements using a utrophin-based loaded in vitro motility assay. A myosin that produces more force requires higher utrophin concentrations to slow motion: E6R, 4. 02 ± 0. 31 nM; T586F, 2. 38 ± 0. 18 nM; S691G, 1. 99 ± 0. 19 nM; WT, 1. 40 ± 0. 08 nM. WT data are from 9. Error, SE of the fit. Data from 2 protein preparations and 3 experiments for each mutant construct. Supplementary Fig. 8dshows additional data with an expanded x-axis. Skeletal actin was used for all experiments. Temperature, 30°C. See also Supplementary Table2for values.
Data analysis The data were submitted to ANOVA analysis of variance, using STATISTICA 8. 0 software (Statsoft Inc. , Tulsa, USA), in order to investigate the significance of in the effects of biochars on the crop growth and the soil attributes. Significant differences were compared by the Tukey test at 5% probability. Regression curves were determined for each variable in order to determine the optimal biochar doses with the maximal efficiency.
Acknowledgements We thank  FACEPE  for the financial support (Process  APQ-0419-5. 01/15 ) and  CNPq  for the productivity scholarship granted to the corresponding author ( 306401/2015-0 ). "This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES 88887. 736369/2017-00 ). Finally, we thank the anonymous reviewers for the help and comments that have contributed to the improvement of the manuscript.
Acknowledgements Authors are grateful to the staff of the  Centre of Electron Microscopy of the Papanin's Institute for Biology of Inland Waters, RAS , for technical assistance. Samples were collected during expeditions organized and permitted by the  Joint Russian-Vietnam Tropical Centre , Ecolan 3. 2 theme. Publication is based on research carried out with financial support by  Russian Science Foundation  ( 19-14-00320 ) for LM, SEM, sample investigation and by framework of the state assignment (theme AAAA- A19-119041190086-6 ) for finishing the manuscript.
Study area New Caledonia is an archipelago located in southwest Pacific (20-23°S, 164-167°E). The study area is a 20 × 20 km landscape located in a plain surrounded by low-elevation mountains (500-1000 m), on the west coast of the main island (Kone-Pouembout plain). It receives ~1000-1400 mm yr -1 rainfall and the mean annual temperature is ~23-24°C (WorldClim2 database,  Fick and Hijmans 2017). The vegetation is a mosaic of evergreen dry forest patches ('tropical dry forest', Holdridge 1947) surrounded by secondary thickets and farmlands. All patches considered in this study were located on volcano-sedimentary substrate below 300 m a. s. l. The whole area was probably mainly covered by forest before anthropogenic landscape modification  (Jaffré et al. 1998). Then, the current forest cover results from clearing and fires dating from both pre-European (i.
Sampling design and landscape metrics We first characterized the present-day landscape structure by digitizing all forest patches at a scale of 1:3000 scale (i. e. 1 cm on the map represents 30 m), using recent georeferenced aerial photographs (2012). We also digitized past georeferenced aerial photographs  (1954)  to assess past landscape structure. Both recent and past aerial photographs were available online (< www. georep. nc >). We used Qgis software (QGIS Development Team) for digitization. Then, we designed a random sampling scheme stratified by patch area. Based on a present landscape map, we selected 36 accessible patches spanning a wide range of area (0. 5-272. 2 ha, median 23. 6). We excluded areas recently colonized by the forest (i. e. areas currently covered by forest, but which were open in 1954).
2004 ). Leaf area represents the light-capturing and transpiration surface and is thus related to water-use efficiency  (Moles 2018). We measured the wood density of one wood sample per individual and five individuals per species. Wood density is a key trait of the wood economic spectrum, from water use efficiency and high growth rate to lower growth rate and drought resistance  (Chave et al. 2009). Furthermore, we measured bark thickness. Thick bark provides stem protection from heat and fire  (Pausas 2015) , as well as from other damages  (Rosell 2016). For bark thickness, we calculated the mean of two bark measures on individual trees, and sampled at least 15 individuals per species (mean = 17. 8). For wood density and bark thickness of 38 species, data were complemented from the New Caledonian plant inventory and permanent plot network (NC-PIPPN) database, in which trait measurements were carried out using identical protocols  (Ibanez et al.
We performed crossvalidation to evaluate the reliability of parameter estimation, and to choose the best tolerance level for ABC (a tolerance of 0. 05 was chosen, see R package abc,  Csilléry et al. 2012). In order to evaluate the predictive accuracy of the assembly model, we designed posterior predictive checks  (Csilléry et al. 2010). First, we used the same model with 100 sets of parameter values randomly sampled from their posterior distribution to generate 100 simulated communities for each observed community. Then, we obtained the posterior predictive distributions of each summary statistics from the simulated communities. We used graphical checks to compare the obtained posterior predictive distributions with the observed values of summary statistics  (Csilléry et al. 2010).
Acknowledgements -We address special thanks to  Hervé Vandrot ,  Vanessa Hequet  and  Jérémy Girardi  for help on the field and species identification, and to the team of the  Nouméa herbarium (NOU)  for technical support. We thank the conservatory of natural spaces of New Caledonia (CEN), landowners and tribes of the Kone-Pouembout plain for providing access to forest patches. We also thank  Thomas Ibanez  for his comments on the manuscript. Funding -We thank the  Inst. Agronomique néo-Calédonien (IAC)  for co-funding GB's PhD and for logistical support. This study was funded by the  Agency for Economic and Environmental Development (DDEE)  of the North Province of New Caledonia. Author contributions -GB, FM and PB conceived the study. GB planned the field campaign and collected the data.
Since its inception in 1973, the slightly deleterious model of molecular evolution, also known as the nearly neutral theory of molecular evolution, remains a central model to explain the main patterns of DNA polymorphism in natural populations. This is not to say that the quantitative fit to data are perfect. A recent study used polymorphism data from Drosophila melanogaster to test whether, as predicted by the nearly neutral theory, the proportion of effectively neutral mutations depends on the effective population size (N e ). It showed that a nearly neutral model simply scaling with N e variation across the genome could not alone explain the data, but that consideration of linked positive selection improves the fit between observations and predictions. In the present article, we extended the work in two main directions.
(2017) ]. In later analyses that required unfolded SFS, we retained 11 species with high-quality genomic data sets and with an available outgroup. These 11 species are given in Table  1. They include both animal and plant species with contrasting levels of nucleotide polymorphism and mating systems. For each of the 11 species, we aligned short reads to the genome using BWA-mem  (Li and Durbin 2010)  and sorted the alignment using SAMtools. PCR duplicates were removed and insertions/deletions (indels) were realigned using the GATK toolkit  (McKenna et al. 2010). HaplotypeCaller was used for individual genotype identification and joint SNP calling was performed across all samples using GenotypeGVCFs. Variant and invariant sites were kept only if genotypes of all individuals were successfully identified  (Carson et al.
A grid of filtering criteria (see details in Supplemental Material, Table  S2 ) was also implemented on each species based on sequence similarity against the Swiss-Prot database (e-value, bit-score, and query coverage) and sequencing quality (sites with low read depth or ambiguous variants). We selected the filtering criteria to maximize the adjusted R 2 in the log-log regression of p 0 /p 4 on p 4. By doing so, we aimed to reduce the error introduced by annotation and quality difference between model and nonmodel organisms. Also, to evaluate the variance introduced by random sampling and grouping of SNPs, we performed 1000-iteration bootstraps to get the bootstrap bias-corrected mean and 95% C. I. s for l calculations.
The weight given to the estimate from model k is w k ¼ e 21=2DAIC k , where DAIC m ¼ AIC m 2 AIC min , with AIC being the Akaike information criterion and AIC min the minimum AIC among the four models  (Posada and Buckley 2004). All calculations were performed using the software polyDFE and the associated R script  (Tataru et al. 2017). A goodness-offit test was carried out by comparing observed SFS to expected SFS under g DFE or the full DFE model for each SNP category, respectively. P-values were calculated under a x 2 distribution with the number of d. f. equal to n (total number of SNP categories) -2 (synonynmous and nonsynonymous sites).
The effects of quality control and full DFE model The variation in D may come from two sources. First, it can be due to the estimation quality of b and b. Tests have shown that quality control on sequencing and SNP calling can have a dramatic influence on b calculations, and ignoring beneficial mutations in the DFE model could also distort the estimates of b  (Tataru et al. 2017). Second, the variation in D can be caused by departures from the assumptions underlying the simple version of the nearly neutral theory, for instance, a larger role of direct or linked positive selection than assumed by the theory. To assess the relative importance of these two sources we selected 11 species with genomic data of high quality and performed a series of stringent quality controls (see details in the Materials and Methods) before reestimating b.
A. Computational details Within all of our TD simulations, the dynamics is 32 fs long, and the incident electric field features a gaussian dependence in time and a linear polarization along the x-axis. We assign different time steps depending on the modelled situation. In most cases and unless stated otherwise, such an electric field is written as E(t) = E 0 e -t 2 2σ 2 (28) where the intensity I = 1 2 0 c|E 0 | 2 = 10 6 W/cm 2. This electric field shape is close to a delta pulse, relative to the propagation time scale, and thus it can excite a broad band at optical wavelengths. and 2 x 300 GB HD SAS (10 krpm). Both TD BEM and Freq BEM fortran 90 codes have been compiled with the ifort compiler and optimized with the compilation FLAGS: -O3 -ftz -align all -heap-arrays 1024. We employed the software gmsh to produce 2D meshes of the NPs surfaces that generates triangular and quadrangular finite elements.
Charges propagation have been performed through TDPlas, a homemade code. The fitting of the function f (ω) obtained by the experimental dielectric function of the metals considered here (gold  [30] , silver  [31]  and rhodium  [32] ) to a sum of DL like terms plus the high-frequency, purely real DL term discussed previously has been performed by an homemade Matlab script. In the first place, we have to fix the number of DL terms in the fitting function. There is a clear trade-off between optimal fit quality and computational time. Increasing the number of parameters, which in our scheme means to increase the number of DL terms in equation 7, can accomplish systematically better regressions, but it also implies considering more (coupled) EOM to integrate (see equations 14, 16 and 17), slowing down the real-time simulations.
Yet, such ample spectral regions are usually not of interest (and would require additional fitting of the dielectric function), so we do not push the comparison to what is technically possible with TD BEM and stay rather with what is useful for calculations of the absorption spectra. The double logarithmic plot shows that both TD BEM and Freq BEM computational times stay on straight lines (i. e. , they have polynomial scaling as expected). The slope of the straight line (i. e. , the exponent of the scaling fitting the computational time) in TD BEM calculations is close to 2 while the data collected from frequency domain calculation lies on a line with slope around 2. 7 (instead of 3). These numerical results substantially confirm the theoretical expectations. In practice, employing a relatively small number of tesserae, the algorithm developed in frequency domain is more convenient than that in time domain in terms of computational cost.
SUPPLEMENTARY MATERIAL We report in supplementary material: plot and data for fitting of gold f (ω) function with four and five DL terms (Fig. S1 , Tab. S1 and S2); plot for fitting of silver f (ω) function with three, four and five DL terms (Fig. S2 ) and data for fitting of silver f (ω) function with three, four, five and six DL terms (Tab. S3, S4, S5 and S6); plot for fitting of rhodium f (ω) function with three, four and five DL terms (Fig. S3 ) and data for fitting of silver f (ω) function with three, four, five and six DL terms (Tab. S7, S8, S9 and S10). We provide an analysis of the role of the time-step in the EOM numerical propagation of TD BEM (fig.
ACKNOWLEDGMENTS We thank the members of the 'Nanostructures and (bio)molecules modeling' group from the  Department of Chemistry  at the  University of Padova , and the  CNR Institute for Nanosciences at Modena , for stimulating discussions. We acknowledge financial support from  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , through the project  TAME-Plasmons  (Grant agreement No. 681285 ). Computational work has been partially carried out on the  C3P  ( Computational Chemistry Community in Padua) HPC  facility of the  Department of Chemical Sciences of the University of Padua. Additionally, G. G. acknowledges support from the  Abdus Salam International Centre for Theoretical Physics (ICTP)  through the grant  NT09-OEA.
However, if the BOLD database is used as reference library and is continuously enriched with new barcodes, then there is also an increase in mis-labelled sequences due to misidentification, which is a serious concern  (Vilgalys 2003; Wardetal. 2009). By comparing sequences of a marine fish family from a reference library to those available in Genbank, an important source of barcodes for  BOLD, Durand et al. (2017)  demonstrated that misidentified species of a fish family were more frequent in barcoding studies than other types of studies like molecular phylogeny and phylogeography. This may be due to the difficulties of developing reliable systematic expertise for a wide range of fish taxa, for studies that do not target specific families. As a consequence, while barcoding studies are still necessary to fill taxonomical knowledge gaps and solve some taxonomical issues, it is also necessary to review the DNA barcodes in BOLD, to better define species boundaries and track species misidentification.
2007 ;T r a ne ta l. 2016;H ae ta l. 2018; Huang et al. 2018)  and missing for the Epinephelidae family. In this context, this study aimed to create a reference barcoding library for Vietnam and test whether this approach can be used to update the species checklist and flag cryptic or undescribed species. Our ambition with this region-based barcoding study was also to review all BINs in BOLD for species present in Vietnam and determine significance of the presence of several BINs associated with a species. The last goal requires combining all available data to determine the congruency between species name associated with a BIN and some species' sequence considered as a reference (see hereafter), and investigate the phylogeographic structure of species associated with more than one BIN. Finally, this study represents a good opportunity to review the BOLD library and tracked misidentified specimens that can limit accuracy of species identification.
Furthermore, the species classification of the Epinephelinae (sensu  Smith and Craig 2007)  was revised according to their molecular phylogeny  (Ma et al. 2016 ). In the current study, the taxonomy proposed by  Smith and Craig (2007)  has been adopted but we have conserved the "traditional" genus naming of species of the Epinephelinae, since this has not yet been adopted by Eschemeyer's Catalog of Fishes  (Fricke et al. 2019)  and thus not recognized by BOLD or GenBank. However, in Table  1 , the second column has the revised taxonomic nomenclature of  Ma and Craig (2018) , involving the genus replacement of Aethaloperca by Cephalopholis, Anyperodon,a n dCromileptes by Epinephelus; Triso by Hyporthodus;a n dEpinephelus by Mycteroperca for the following species: E. epistictus, E. heniochus, E.
PCRproductswerevisualizedon1-2% agarose gels and the most intense products were selected for sequencing by Macrogen (https://dna. macrogen. com/). Electropherograms were visually checked using 4Peaks by A. Griekspoor and Tom Groothuis, nucleobytes. com,a n d sequences aligned using MUSCLE implemented in MEGA7  (Kumar et al. 2016). DNA barcodes and specimens' data are available in the project file DS-SERV: Epinephelidae of Vietnam on BOLD systems site dx. doi. org/10. 5883/DS-SERV). Each DNA barcode was assigned to a BIN by the BOLD system and deposited in GenBank under accession number MN708800 to MN708957 (Table  S1 ). Sequence divergences (Table  S2 ) were calculated using the Kimura two parameters (K2P) distance model  (Kimura 1980). Neighborjoining (NJ) trees of K2P distances were drawn to highlight pattern of divergence between species (Saitou and Nei 1987).
Species identification and species boundaries The accuracy of the OTU delineation provided by BOLD using the RESL algorithm was first evaluated by comparing BINs to OTUs identified by the Automatic Barcode Gap Delineation (ABGD) algorithm  (Puillandre et al. 2012). These two methods used DNA alignments as inputs, consisting of ABGD, of all DNA barcodes (with a core segment of a 502 bp) constituting BINs associated with species name listed in Table  1 , plus all Vietnamese barcodes generated in this study (Table  S3 ). The final fasta file contained 1648 sequences and was uploaded to the website that hosted the ABGD algorithm to run the analyses (https://bioinfo. mnhn. fr/abi/public/abgd/abgdweb. html). The first phylogenetic analyse was performed on a dataset composed exclusively of DNA barcodes obtained in this study, i.