dataesr/scientific-paragraphs-categorization huggingface dataset:
To run the simulations, we defined domains of five equally spaced values for the initial FEC parameters (r, p) and specificity parameter a. We used a constant value of p, equals to the mean value estimated on the dataset from  25. We selected five values of r such that the corresponding means of the initial FEC distributions cover the domain of the observed mean in the dataset. We used p = 32. 55e -5 , r = (0. 099646, 0. 66386, 1. 2281, 1. 7923, 2. 3565) and a = (0. 0010. 02575; 0. 0505; 0. 07525; 0. 1). For each of the parameter combination, we ran 1000 simulations. There is thus 5 * 5 = 25 parameter combinations and 125*1000 simulations were run in total. A simulated trajectory thus includes: • An individual initial FEC value, drawn from a negative binomial with parameter (r, p) and mean * - r p p  (1 ).
See Fig. 7  for a pseudo code. Simulation scheme S2. We used a simplified scheme for the other experiments by choosing specificity parameter randomly for all goats g ≥ 1. In this case, there are 5*1000 simulations. We used Matlab to run the simulation and all of the codes are available in the supporting information All mathematical notations are summarised in Supplementary Table  S2. Ethical approval and informed consent. The animals were already present at the farm and because the drone flew at an altitude of 50 m, the animals' behavior and welfare were not impacted. Our experiment did not require any intervention of any type on the animals. The animals were manage under normal condition at the PTEA farm, respecting French and European laws.
e. with >150 counts (blue), Supplementary Fig. 5 ), with an overlaid normal error curve (mean = 0. 02, standard deviation = 0. 2 log2 units, equivalent to σ = 1. 15 fold). (b) Formula used to calculate pause strength for an XY(Z) motif, with the amino acid in the ribosomal A-site in brackets. (c) Plot of pause strength against log2(fold change) of all possible 3-aa motif frequencies relative to the NNS 15 library. Yellow points correspond to intrinsic 3-aa pause motifs with a pause strength ≥ 0. 25. All other motifs are shown as gray dots. (d, e) Plot of pause strengths calculated in this study against pause scores calculated from ribosome profiling data obtained from E. coli cells lacking EF-P, for (d) PP(X) and (e) XP(P) motifs  10. The scores obtained by both methods are strongly correlated, as indicated by R 2 values of 0.
For the systems studied here, the correlation between in vitro and in vivo data is generally good, but the small discrepancies observed could be due to the NNS 15 library's intrinsic focus on the early cycles of translation. Finally, our method may also be used as a general tool to study sequence-dependent translational events. For example, inverse toeprinting may be modified to study the sequencedependent impact of various protein factors on the speed of translation (e. g. signal recognition particle, SecYEG translocon, translation factors like EF-4/LepA), providing a complementary approach to ribosome profiling. The concepts presented here may further be extended to operate in vivo or in eukaryotes, paving the way for numerous applications.
Data analysis Unless it is indicated otherwise, data analysis was carried out using a series of custom scripts written in-house in Python, which relied upon the use of the Biopython package  33. Read assembly and trimming. Read pairs were assembled using PEAR v0. 9. 10 34 on a Mac Book Pro with a 2. 7 GHz Intel Core i7 processor and 16 GB 1600 MHz DDR3 memory, with the maximal proportion of uncalled bases in a read set to 0 (-u option) and the upper bound for the resulting quality score set to 126 (-c option). Regions immediately upstream of the start codon and downstream of the point of cleavage by RNase R were removed using a modified version of the adaptor_trim. py script written by Brad Chapman (https://github. com/chapmanb/bcbb/blob/master/align/adaptor_trim. py). The 5' flanking region was defined as GTATAAGGAGGAAAAAAT, while the 3' flanking region was GCGATCTCGGTGTGATG for the NNS 15 and ErmBL libraries, and GGTATCTCGGTGTGACTG for all other samples.
This task was automated with our find-exact-match. py script. Translation into amino acid sequences. Processed reads were translated using the translatereads. py script. For samples that had undergone inverse toeprinting, the ribosome-protected region downstream of the A-site codon was removed prior to translation. Unique peptidic sequences were identified and the frequency of occurrence of these sequences within each sample was calculated. Calculation of 3-aa motif frequencies. All possible 3-aa motifs centered on the P-site of the stalled ribosomes were identified and counted within the translated sequences, using the process-kmers. py script with a word size of 3. In each case, the frequency of occurrence ( !"" ) of the 3-aa motif was 𝐹 !"" = 𝑛 !!! 𝑛 !"!#$ where  !!! is the number of occurrences of this 3-aa motif at the C-terminus of the translated sequences and  !"!#$ is the total number of processed reads in the sample.
Acknowledgments. We thank  A. Malhotra  for providing RNase R,  N. Vazquez-Laslop  and  A. S. Mankin  for providing the pERMZα reporter plasmid, E. coli TB1 cells and oleandomycin, and  A. C. Seefeldt  for purifying fully modified EF-P. We thank  A. Buskirk  for providing the pause scores for PPcontaining motifs calculated from ribosome profiling data. C. A. I. , B. S. and G. S. received funding for this project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 724040 ). C. A. I. is an  EMBO YIP  and the recipient of a  Marie Curie career integration grant  ( PCIG14-GA-2013-631479 ). D. D. is funded by  Inserm. B. S. and C. A. I. received funding from the  Fondation pour la Recherche Médicale  ( AJE201133 ), the  Région Aquitaine  ( 2012-13-01-009 ) and a  Chaire d'Installation from the excellence initiative (IdEx)  of the  University of Bordeaux  awarded to C.
This data article presents the compilation of mechanical properties for 370 high entropy alloys (HEAs) and complex concentrated alloys (CCAs) reported in the period from 2004 to 2016. The data sheet includes alloy composition, type of microstructures, density, hardness, type of tests to measure the room temperature mechanical properties, yield strength, elongation, ultimate strength and Young's modulus. For 27 refractory HEAs (RHEAs), the yield stress and elongation are given as a function of the testing temperature. The data are stored in a database provided in Supplementary materials, and for practical use they are tabulated in the present paper.
Value of the data The database covers the main mechanical properties of HEAs and CCAs tested under uniaxial loading from published reports since 2004 until end of 2016. The database can be used to assess the potential of HEAs and CCAs as possible structural materials. The database can be used to represent various property spaces and calculate performance indices. The database can enable data mining to extract insights and uncover patterns to guide and accelerate the development of HEAs and CCAs.
Experimental design, materials and methods The database has a tree-like classification (Fig. 1 ) which includes four different families: 3d transition metal (3d TM), refractory metal (RHEAs and RCCAs), light metal family, and bronzes and brasses HEAs/ CCAs. Each family is expanded in classes (a class is a unique combination of principal elements), and each class contains members having variations in principal element concentrations. Each member is characterized by a set of attributes which includes: alloy composition, phase content, density, hardness (Vickers), type of mechanical test (tension or compression), yield strength, ultimate strength, elongation, and Young's modulus. A listing of these entries makes up a material record. The database was used by Gorsse et al. [2]  with Cambridge Education Software (CES) enabling users to (i) browse the materials data, (ii) search and filter to narrow down the set of materials using given parameters (e.
The missing genes were not randomly distributed; i. e. , eight genes were missing in two or more genomes (KOG0062, KOG0261, KOG0434, KOG0969, KOG1123, KOG1185, KOG2311, KOG3232). Two genes, KOG1185 (thiamine pyrophosphate-requiring enzyme) and KOG2311 (NAD/ FAD-utilizing protein possibly involved in translation) were missing in all five genome assemblies, indicating that these genes either do not exist in Magnaporthales or less likely, all reside in difficult-to-assemble DNA regions. To facilitate dissemination of the Magnaporthales genome and transcriptome data, we set up an FTP site (ftp://172. 21. 7. 71/pub/Magnaporthales/) that includes the assembled genomes and transcriptomes of the five species sequenced for this project, transcriptomes from an additional 16 Magnaporthales species  9  , and the genomes and transcriptomes of eight species that were downloaded from public databases and used in our work.
Genome assemblies, annotations, and associated raw sequence reads from this study were submitted to GenBank (BioProject #: PRJNA438993). Using the genome data from the 13 Sordariomycetes species, a total of 14,835 gene families were generated using OrthoFinder that are present in at least two different species. Using a phylogenomic approach, we generated phylogenetic trees for all orthologous gene families and singleton genes. The orthologous gene families and the phylogenetic trees (with ≥5 sequences) are available for download from the FTP site and were used for downstream analysis. Estimating a timeframe for Magnaporthales diversification. We generated a Bayesian maximum credibility tree with mean node age inferred using BEAST, based on 1000 randomly selected single copy orthologs. This phylogeny is shown in Fig.
2C ). Although these two transferred proteins share limited sequence identity (<30%), it is likely that a similar function was independently acquired in these two instances. The remaining two HGTs represent an antibiotic biosynthesis monooxygenase and a hypothetical protein (Fig. S1 ). Taken together, the small number of HGTs found that are specific to Magnaporthales is consistent with the overall paucity of cross-phylum gene transfers in this lineage  19. Given our focus on a conservative HGT dataset and extensive intra-phylum gene exchanges that may compromise HGT detection  19  , these HGTs likely represent a subset of genes that were transferred into Magnaporthales. The existence of clear evidence of bacterium-derived genes suggests that HGT might have played an important role in Magnaporthales evolution and function.
2 g KH 2 PO 4 , 0. 25 g MgSO 4 •7H 2 O, 0. 15 g NaCl, and 15 g agar in 1 liter distilled water) under 12/12 hours light-dark cycle, MM in constant darkness. A cellophane membrane was placed on top of the media. After 14 days of incubation, the mycelium was harvested and total RNA extracted from each sample using the Plant RNeasy Mini Kit (Qiagen, CA). A TruSeq mRNA library was constructed for each sample following manufacturer's instructions (Illumina). The libraries were sequenced on MiSeq (Illumina) to 2 × 158 bp. Adapter sequences were trimmed from sequence reads of genomic DNA using Trimmomatic  38. Trimmed reads were corrected using the correction module in SOAPdenovo2 r239 package  39  with k-mer size 21. Corrected reads were assembled using SOAPdenovo2. A range of k-mer sizes were tested and optimal k-mer sizes were choosen for individual genomes.
Scaffolds and singleton contigs less than 500 bp in length were filtered out from final assemblies. To test the completeness of assembled genomes, CEGMA (Core Eukaryotic Genes Mapping Approach)  40, 41  analysis was used to look for the 248 conserved eukaryotic genes in assembled genomes. The assembled genomes were annotated using the MAKER2 package  42  with three gene prediction tools used: AUGUSTUS  43, 44  , SNAP  45  and the ab initio tool GeneMark-ES version 2  46. AUGUSTUS was trained with M. oryzae, and SNAP was initially trained with CEGMA results. MAKER2 was run for four iterations for each genome. Gene models from the previous run were used for re-training in the following run. Adapter-trimmed transcriptome sequences were used to assemble EST sequences with TRINITY 47 using the assembled genomes as guide.
The results were used as EST evidences in MAKER2 runs. Proteome sequences of G. tritici strain R3-111a-1, M. oryzae strain 70-15_8 and M. poae strain ATCC64411, and the manually annotated Swiss-Prot 48 protein sequences were used as protein evidences. The flanking regions were set to 50 bp to mitigate gene model fusion which could occur in compact fungal genomes. After four iterations, the optimal gene models chosen by MAKER2 from the three gene prediction tools were used for sownstream analysis. Sequence availability. Genome assembly, transcriptome and proteome sequences of eight species were downloaded from public databases. Those of C. graminicola strain M1. 001, C. higginsianum strain IMI 349063, F. graminearum strain PH-1, G. tritici strain R3-111a-1, M. oryzae strain 70-15_8 and M. poae strain ATCC64411 were downloaded from Broad Institute (http://www.
Construction of orthologous gene families. We collected proteome data from the 13 Sordariomycetes species described above. After removing short sequences (<50 amino acids), a total of 171,033 sequences were used to construct orthologous gene families using OrthoFinder version 0. 4  49  under the default setting (BLASTp e-value cut-off = 1e-5; MCL inflation I = 1. 5). A total of 14,835 gene families were generated that are available at our FTP site (ftp://172. 21. 7. 71/pub/Magnaporthales/).
Timing of Magnaporthales diversification. Divergence time estimates were based on a subset of 1000 orthologs randomly selected (using python package random) from the full set of 3,060 single copy orthologs. The 3,060 groups were selected by parsing the ortholog groups (OGs) derived above, and keeping only single-copy genes (i. e. , had no evidence of duplication) in all 13 taxa. Sequences were aligned using MACSE  50  , with default parameters. MACSE alignments were processed to remove all codons including sites with a gap in at least one of the samples, and then concatenated. Node ages were estimated using a Bayesian molecular clock model implemented in the program BEAST v1. 8. 0  51, 52. The analysis used a General Time Reversible model (GTR) with Gamma distribution for modeling site heterogeneity and base frequencies estimated from the data.
The program Tracer v1. 5  52  was used to evaluate all chains, determine appropriate burn-in length (10% of sampled trees), and obtain the effective sample sizes for all parameters. Mean node age and 95% highest posterior density were mapped on the maximum clade credibility tree. Screen for HGT candidates. For each Sordariomycetes genome, all predicted proteins were queried against NCBI's non-redundant protein database (November 10, 2015) using ublast with database seq masking enabled, dbaccel parameter of 100, accel parameter of 1, and e-value cutoff of 0. 001 (http://drive5. com/usearch)  54. A modified alien index approach  16  was used to screen each genome for genes with significantly better ublast hits to prokaryotes than eukaryotes and thus identify trans-domain HGT candidates. The AI score for this analysis is given by the formula: =      -      AI bbhO maxB bbhE maxB , where bbhO is the bitscore of the best blast hit to a species outside of the eukaryotic lineage, bbhE is the bitscore of the best blast hit to a eukaryote, and maxB is the maximum bitscore possible for the query sequence (i.
e. , the bitscore of the query aligned to itself). To identify horizontally transferred genes shared between multiple species of Sordariomycetes (e. g. , HGT that occurred in a common ancestor), all hits to Sordariomycetes were skipped and did not contribute to bbhE. AI can range from 1 to -1, and AI >0 if the gene has a better blast hit to a species outside of the eukaryotic lineage and is suggestive of either HGT or contamination. Contamination was suspected in cases when the majority of genes on an assembly contig had an AI ≥0; in contrast, HGT was inferred when the transferred gene was located within a well assembled contig, and the majority of genes on that contig had an AI <0  55. The perl script for calculating AI scores is freely available from the figshare repository, https://doi.
Phylogenomic analysis. To understand the evolutionary history of each Sordariomycetes gene, we carried out phylogenomic analysis in a similar way to our previous study  19. Briefly, a local database was established containing Pezizomycotina sequences from NCBI RefSeq database (version 69) and the sequences for the remaining species from RefSeq database (version 55)  19. For each Sordariomycetes orthologous gene family and singlet, we used the corresponding protein sequence(s) as query to search the above-mentioned local database using BLASTp (e-value cut-off = 1e-5). For each singlet query, the top 1,200 significant hits (with query-hit identity ≥30%) were recorded followed by selection of representative sequences in order. Up to 10 sequences were sampled from each phylum. The sequence sampling ceased when a total of 80 sequences were selected or the entire list of significant hits was read through.
Branch supports were calculated using the Shimodaira-Hasegawa method  59. Branch-site selection tests. The corresponding protein sequences that composed the OG were aligned with MAFFT v7  60  and a codon alignment for each OG was then created with TranslatorX 61 using the nucleotide CDS sequence and aligned protein data as a guide. MaxAlign v1. 1  62  was used in codon mode to remove poorly aligned sites and sequences. Any taxa removed from the OG by MaxAlign were then removed from the phylogenetic tree (above) using PareTree (http://emmahodcroft. com/PareTree. html). The branch-site selection test implemented in codeml (part of the PAML package v. 4. 8a)  63  was then used to test for positive selection on three branches of the phylogeny leading to the "wood", "blast" and "root" fungi (see above)  9.
If all taxa within a particular clade (i. e. both Pseudohalonectria lignicola and Ophioceras dolichostomum or "wood" saprophytic fungi)  9  were removed by PareTree, the OG was ignored for that particular test. The log-likelihoods generated by codeml under a null hypothesis of neutral evolution (omega fixed at 1) and the alternative (ML estimate of omega) were compared using a likelihood ratio test to a chi-square distribution (df = 1) and false discovery rate (FDR) tests were performed in R using the Bioconductor package. Ortholog groups with a corresponding test statistic (FDR ≤0. 01) were retained. Gene Ontology terms were assigned to each gene in each OG using Blast2GO  64  , and annotated further against the NCBI nr database using BLASTp. A Fisher's exact test was conducted in Blast2GO to find enriched (or over-represented) GO terms (p ≤ 0.
However, avirulence effectors trigger defense responses in hosts with cognate R genes and block disease. From various strains of P. oryzae, over 200 effector and effector candidate genes have been identified, some of which belong to multigenic families  28, 67. Of these, we selected 28 well-characterized effector genes and their homologs to survey for their presence in 13 sequenced genomes of nine Magnaporthales species and four closely related fungal pathogens including Colletotrichum species and Fusarium species. TE identification. Each of the nine Magnaporthales genomes, plus the genome for Falciphora oryzae, were searched for TEs using the REPET vr. 2. 5 pipeline 33 according to the authors' documentation (https://urgi. versailles. inra. fr/Tools/REPET). The pipeline begins with the TEdenovo script, which uses a series of classifiers to identify a set of de novo TEs which are combined to form a database for use with the second TEannot script.
This latter stage is run twice in order to perform a complete genome-wide annotation using multiple classifiers. A step is also performed which employs tBLASTx and BLASTx from the BLAST + vr. 2. 2. 31 package  68  to search Repbase vr. 20. 05  69  , a curated data bank of TEs. Both stages were run using default settings and also incorporated the Pfam vr. 27. 0 70 data bank. After identifying TEs throughout the genome, final annotation was exported as a GFF3 file using an option to merge overlapping TE entries. Counts of TE superfamilies were made for each genome using these files according to the Wicker classification scheme  71. According to recommendations by Wicker et al. 71  , sequences less than 80 bp in length were excluded to avoid misclassification. Other TEs removed included potential host genes and predictions made from tBLASTx and BLASTx with less than 70% sequence identity reported.
As some genomes had many hits classified by family and order, but listed as "unknown" for superfamily (e. g. DTX, an unknown Class II terminal inverted repeat), we ran an additional tBLASTx search (e-value: 0. 01, percent query coverage per hsp: ≥70%, minimum percent identity of hits: ≥70%) against Repbase for these sequences. If a top hit matching the above search criteria had the same family and order as the original REPET prediction, we reassigned the superfamily based on the matching Repbase entry. Additional details about the REPET pipeline can be found in Flutre et al. 33. Cross-genome comparison of REPET de Novo TEs. Similarity across genomes for TEs predicted by the REPET TEdenovo pipeline (excluding incomplete and potential host gene sequences) was checked using hierarchical clustering with the h-cd-hit-est program from the CD-HIT Suite Server (http://weizhongli-lab.
org/ cd-hit/)  72. The 580 initial sequences were grouped (reverse compliments considered) using iterative cutoffs of 90%, 85% and 80% sequence identity to form 347 clusters. Custom scripts were then used to identify the number of different taxa present in each cluster based on sequence definitions. Representative sequences for each cluster were then blasted against the NCBI nucleotide (nt) database using Blast2GO 64 for annotation. Repeat-induced point mutation identification. TE families for each genome were searched for signatures of RIP activity using the RIPCAL vr. 2 program  73, 74. The program was only used to check for RIP within individual TE families which contained a minimum of ten sequences, with one or more sequence ≥300 bp (the default RIPCAL scanning subsequence length), using both the alignment with ClustalW 75 and di-nucleotide frequency-based methods.
These cutoff criteria were established to minimize computational requirements, as several of the genome assemblies were relatively fragmented. RIP was considered present if di-nucleotide frequencies matched the indices: (TpA/ApT) ≥0. 89 and (CpA + TpG)/(ApC + GpT) ≤1. 03  76  and visual inspection of RIPCAL alignments showed one or more peaks for (CA ← → TA) + (TG ← → TA) mutations  73. Additional details about the RIPCAL program can be found in Hane and Oliver  73.
Acknowledgements This work is funded by the  National Science Foundation  ( DEB 1145174 ) to NZ and DB, the  Rutgers Center for Turfgrass Science , the  Rutgers School of Environmental and Biological Sciences , and was the outcome of an international symposium hosted by NZ and DB at  Rutgers University. We thank  Ralph Dean ,  Nicole Donofrio ,  Mark Farman ,  Li-Jun Ma ,  Thomas Mitchell ,  Kerry Pedley ,  Youliang Peng ,  Barbara Robbertse ,  Antonis Rokas ,  Darren Soanes ,  Nicholas Talbot ,  Didier Tharreau ,  Michael Thon ,  Barbara Valent , and  Jin-Rong Xu  for participanting the symposium and/or discussion.