dataesr/scientific-paragraphs-categorization huggingface dataset:
So on one hand comparative analyses of site-frequency spectra among animals did not reveal any effect of N e on the strength of gBGC, while on the other hand the analysis of the substitution pattern in mammals is consistent with a N e effect. Also a bit surprisingly, the estimated range of variation of B across mammals (0. 1-5,  Lartillot, 2013)  is wide enough to contain all the estimates of B reported in any species of animals so far. Lartillot (2013)  analysed a subset of currently annotated mammalian orthologs (1329 exons in the largest data set), and importantly, relatively ancient divergences, at the family or order level, thus capturing the average effect of gBGC across dozens of million years. Here we analyse a large set of genes from closely related species in four families of mammals, thus accessing a high-resolution map of the effect of gBGC on coding sequences, both in time and across the genome.
Estimating B For each branch we estimated the population gBGC coefficient B = 4N e b and its variation based on synonymous WS, SW and SSWW synonymous substitution counts. Various models were fitted to the data via the maximum likelihood (ML) method, assuming that the mutation process is known (TCA  Smith et al. , 2018). Model M1 assumes a constant intensity of gBGC, B, among and within genes. Model M2 considers two categories of genes, each with its own gBGC intensity, assumed to be shared by all sites within a gene. M2 led to a rejection of M1 by a likelihood ratio test (p -val < 0. 05) in 36 branches out of 40. The M3z model assumes three categories of genes, which we below denote "cold" (B = 0), "mild", and "hot". M3z rejected M1 in 39 branches out of 40, and M2 in 27 branches. There was, therefore, strong evidence for a variable B across genes in this data set.
003). This is consistent with the hypothesis of a high ancestral N e in this taxon, as also suggested by fossil data and d N /d S -based reconstructions  (Figuet, Ballenghien, Lartillot, et al. , 2017; Figuet, Romiguier, et al. , 2014). Our study could not confirm the report by  Romiguier, Ranwez, et al. (2010)  and Lartillot (2013) of a particularly strong gBGC in bats, tenrecs and lagomorphs due to the unavailability of fully-sequenced, closely related species in sufficient numbers in these taxa. The estimated genome average B was in the nearly neutral zone in the four families analysed here. Even so, gBGC was found to be pervasive and strongly impact the substitution process in coding sequences. Fitting a three-category, hotspot model across genes, we estimate that 30 to 60% of the WS synonymous substitutions can be attributed to gBGC in mammals.
Sequence data Mammalian coding sequence alignments were downloaded from the Orthomam v10 database  (Scornavacca et al. , 2019). The four families of mammals represented by at least six species in OrthoMam v10 were selected, namely Hominidae (six species, 11,859 genes), Cercopithecidae (eleven species, 10,834 genes), Bovidae (seven species, 9527 genes), and Muridae (six species, 11,758 genes). In Bovidae, the Bos indicus sequences were not considered since this taxon is a subspecies of Bos taurus. In all four families the phylogenetic histories of the sampled species are well documented, with the exception of the unresolved relationship between cattle, yak and bison  (Fabre, Hautier, et al. , 2012; Fabre, Rodrigues, et al. , 2009; Hassanin et al. , 2012; Vanderpool et al. , 2020, Fig1). Nodes were dated based on the TimeTree website (http://www.
Clustering: simulations We downloaded from the Ensembl database coding sequence annotations in one representative species per family, namely Homo sapiens (Hominidae), Macaca mulatta (Cercopithecidae), Bos taurus (Bovidae) and Mus musculus (Muridae), disregarding coding sequences shorter than 400 bp. Then we simulated substitution data in a hypothetical branch by iteratively sampling the location of third-codon-position substitutions across coding sequences using the following method: (initiation:) randomly sample the location of the first substitution among the third codon positions of all genes; (iteration:) -with probability 1 -p clust , randomly sample the location of the (n + 1) th substitution among the third codon positions of all genes; -with probability p clust , randomly sample the location of the (n + 1) th substitution in the neighborhood of the n th substitution (clustered substitutions).
For each gene, the expected number of WS substitutions in the absence of gBGC was estimated as: m * 1 = l W µ W S T (18) and the excess WS substitutions due to gBGC were estimated as (n * 1 -m * 1 )/m * 1. No depletion of SW substitutions is expected under M3sh (compare equations 15 and 18). Finally, each gene was assigned to one of the gBGC categories by selecting the category k maximising the following posterior probability: φ 1 (n 1 |B k , T )φ 2 (n 2 |B k , T )φ 3 (n 3 |B k , T ) (19) All these calculations were achieved separately for the 40 branches of the data set.
Acknowledgements The author is grateful to  Carina Mugal ,  Fanny Pouyet ,  David Castellano , an anonymous reviewer,  Laurent Duret ,  Nicolas Lartillot ,  Sylvain Glémin ,  Benoît Nabholz  and  Jonathan Romiguier  for very useful comments, and to  Adam Eyre-Walker  for sharing data on de novo mutation in humans. This work was supported by  Agence Nationale de la Recherche  project  ANR-19-CE12-0019  (HotRec). Version 5 of this preprint has been peer-reviewed and recommended by  Peer Community In Genomics  (https://doi. org/10. 24072/pci. genomics. 100012).
MODEL In this article, a dual-scale model was constructed to investigate the influence of microstructure parameters on the thermal shock of the material as shown in Figure  1. In step one, apparent fracture strength and apparent fracture energy were obtained from RVE models by uniaxial tensile loading at microscale; in step two, the apparent fracture strength and energy will be the input to macroscale model for thermal shock simulation. T 0 is the temperature of model, and T ∞ is the cooling environmental temperature. In our simulation, alumina was taken as an example, and the mechanical parameters are listed in Table  1. In addition, to obtain the average properties as the input data of the macroscale model, calculation of the RVE model at the microscale was based on the following assumptions  (Ehre and Chaim, 2008; Gaida et al.
ACKNOWLEDGMENTS The authors thank the  National Key R&D Program of China  (Grant No. 2017YFB0703200 ),  National Natural Science Foundation of China  (Grant Nos. 51702100  and  51972268 ), and  China Postdoctoral Science Foundation  (Grant No. 2018M643075 ). The authors also acknowledge the  Northwestern Polytechnical University High Performance Computing Center  for the allocation of computing time on their machines.
The behavior of a A356+0. 5wt. %Cu alloy used to manufacture cylinder heads was studied. Samples were solutionized, quenched and aged at 200 °C for 0. 1, 1, 10 and 100 hours. TEM characterization showed that for the short aging durations (up to 10 hours), the dominating hardening precipitates were β rods, while for the long aging duration (100 hours), the dominance shifted to the Q-phase (Q , Q precipitates). The length and diameter of the β rods were measured to produce size distributions which were later used to calibrate and validate the precipitation model. The physics-based precipitation kinetics model relies on classical nucleation/growth/coarsening equations adapted for the precipitation of Mg-Si precipitates in the aluminum matrix. Indirect coupling to Thermo-Calc software was used in order to determine the essential thermodynamic variables such as the driving force for precipitation and the solubility product for the model.
Si Cu Mg Sr Fe Ti Other Nominal 7 0. 5 0. 3 0. 01 < 0. 15 < 0. 15 < 0. 1 Measured 6. 63 0. 52 0. 36 0. 0067 0. 114 0. 136 0. 054 Figure  1  shows a plot of the volume fraction of the different phases as a function of temperature obtained using Thermo-Calc software and TCAL5 database. The solvus temperature of the β-Mg 2 Si is 446 °C, thus defining the solution heat treatment temperature window between 446 and 562 °C, i. e. the solidus temperature. Solvus temperatures of the phases as given by Thermo-Calc are compared to values from literature in Table  2. This is used to validate the representativeness of the TCAL5 database for the studied alloy. Figure  1  shows that up to temperatures of 407 °C, the Q-phase is the stable phase, which is in good agreement with the literature  [3, [15] [16] [17] [18] [19].
Length and diameter distributions of rod-shaped β precipitates were created for samples aged at 200 °C during 0. 1, 1 and 10 h. It is important to note that it is not possible to measure the dimensions in more than one direction for the same precipitate due to their morphology and orientation. Thus, an average aspect ratio cannot be extracted. Instead, the mean aspect ratio is used in this study. The TEM images were imported into the commercial software ImageJ. After calibration, the precipitates were measured manually in order to characterize the size distributions. The latter are expressed as number fractions with respect to size bins of equal amplitude. For each distribution of size a total number, ranging from 285 to 520, of precipitates was measured. The experimental distribution of rod-shaped β for each sample in terms of diameter and length is shown and discussed in Section 4.
Precipitation kinetics model Precipitation kinetics models usually focus on the description of three main processes: nucleation, growth and coarsening. Moreover, three different types of precipitation models can be found in literature: phase field, molecular dynamics and particle size distribution models. This study focuses on size distribution models, mostly for calculation efficiency. The input variables to the precipitation kinetics model are the alloy composition, the thermal history and thermodynamic quantities such as the driving force for precipitation, equilibrium concentrations, etc. In the frame of this study, these variables were provided using an indirect coupling of the model with the Thermo-Calc software and the TCAL5 database. The model also uses a number of physical parameters such as the diffusivities of chemical elements.
The time dependency of incubation is therefore inconsistent with the above statement as phase transformation may occur in different stages. In order to cover all possibilities, simulations were conducted with and without the incubation term and a negligible difference in the results. Therefore, it was decided not to use the incubation term in the final set of constitutive equations. The evaluation of these equations requires the knowledge of the volumetric precipitation driving force ∆g v which depends on temperature and on the molar concentration of solute in the matrix. The molar concentration of solute in the matrix was obtained using Thermo-Calc and the TCAL5 database. It is worth noting that although the precipitates are identified as β , the precipitation sequence is simplified to the equilibrium phase β-Mg 2 Si.
Such an assumption finds its justification in the fact that in the TCAl5 database, thermodynamic data are more reliable when it comes to stable phases rather than metastable phases. It is assumed thus that the variation of ∆g v for β has a similar temperature and composition dependence as β-Mg 2 Si. The input of these values from Thermo-Calc into the precipitation model is performed through an indirect coupling method. First, the values of the precipitation driving force of β-Mg 2 Si as a function of temperature and solute content in the matrix are tabulated. It was made sure that the ranges of temperature and composition encompasse the ranges simulated in this study. Subsequently, all the results were mapped using a single Taylor function that was implemented into the model (Figure  3 ). The mathematical expression of the functiom is given by Eq.
(  8 ) where f is representing the driving force ∆g v , x is representing the inverse of the temperature 1/T (K), and y is representing the logarithm of the molar fraction of Mg in the matrix log (C α M g ).                          f (x, y) = ax -1 + by + cx -2 + dy 2 + eyx -1 + f a = -3. 65 × 10 4 J m -3 K -1 b = -1. 03 × 10 7 J m -3 c = -4. 45 × 10 2 J m -3 K -2 d = -2. 98 × 10 4 J m -3 e = 4. 46 × 10 5 J m -3 K -1 f = 2. 04 × 10 9 J m -3 (8) 0. 0015 0. 002 0. 0025 0. 003 0. 0035 0. 004 -14 -11 -8 -5 -3e+09 -2e+09 -1e+09 0 1e+09 Δg v (J/m 3 ) TCAL5 data Fit function 1/T(K) log(Mg α ) Δg v (J/m 3 ) Figure  3 : Fit function (Eq. (  8 )) of the precipitation driving force of β-Mg 2 Si in the aluminum matrix extracted from Thermo-Calc/TCAL5. This indirect coupling method has the advantage of being efficient in terms of computation time when compared to a direct coupling to Thermo-Calc.
[26]  (Eq. (  10 )): Eq. (  9 ) implies that both Mg and Si diffusion fluxes in the aluminum matrix should give the same growth rate of β phase. This requires the addition of a thermodynamic and a kinetic constraints to fully define the growth rate. f (λ) = 2 √ λ 2 -1 The thermodynamic constraint consists of the tie-line equations in the Al-Si-Mg diagram expressing the equilibrium between α-Al and β phase. Tie-lines are calculated using Thermo-Calc and TCAL5 database are shown in Figure  5a  for temperatures ranging from 273. 15 to 723. 15 K. The solubility product K s = (C α,eq M g ) 2. C α,eq Si for the β phase is expressed as a function of the molar fractions of Mg and Si in the matrix at equilibrium, C α,eq M g and C α,eq Si respectively. Therefore, plotting log(K s ) against the inverse of the temperature bring these tie-lines into one straight line that can be fitted with a simple linear function (Figure  5b ).
Log(C α,eq Mg )  The kinetic constraint comes by enforcing the fact that the flux of solute respects the difference in diffusivities between Mg and Si and of the stoechiometry of the precipitate. Therefore, the growth rate expressed by Eq. (  9 ) must be the same whether it is calculated using the Mg or the Si supersaturation. Log(C α,eq Si ) 0 -1 -2 -3 -4 -5 -6 -7 -8 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 (a) One of the simplifications used in this work is the collapse of the precipitation sequence into a single equivalent phase considered to be β-Mg 2 Si. The metastable precursors to this phase are therefore not considered and all the aforementioned thermodynamic data are extracted from Thermo-Calc for the stable phase only. This simplification was used in the pioneering implementations of precipitation modelling by Myhr et al.
As expected, setting C β i = 1 in Eq. (  12 ), makes Eq. (  12 ) identical to Eq. (11). It is worth noting that Du et al. [33]  used a different approach to evaluate the effect of curvature on interfacial equilibrium concentrations. It consisted in constructing what the authors referred to as a "Gibbs-Thomson phase diagram" using Thermo-Calc. It is obtained by increasing the Gibbs free energy of the precipitating phase in the database by 2γκV m , with κ being the curvature and V m the precipitate molar volume. The inbuilt energy minimization algorithm of Thermo-Calc then produces a new phase diagram taking into account the curvature. This calculation is then performed for the entire range of curvature values in order to produce the full curvature-dependent diagram. The model then uses a table look-up technique to navigate the diagram and access the required values.
Since Mg is only consumed to form β-Mg 2 Si precipitates (the formation of Q phase is neglected in this study) and since solutionizing temperatures are always higher than the solvus temperature, C 0 M g is considered constant in the model and equal to the mean alloy content. The same cannot be said about C 0 Si because Si contributes to the formation of both β-Mg 2 Si and the eutectic Si phase. This is shown in Figure  6  where the yellow dash dash dotted curve represents the equilibrium Si concentration in the matrix and the red curve the equilibrium molar fraction of the Si phase (eutectic Si), according to TCAL5 database. Below the β-Mg 2 Si solvus temperature, the amount of Si that is available to the formation of β-Mg 2 Si is not a constant and does not correspond to the mean Si content in the alloy, since Si required to form the eutectic phase has to be taken into account.
Therefore, C 0 Si (blue short dashed curve in Figure  6 ) was obtained by eliminating the amount of Si that goes into the formation of the Si phase from the mean Si content in the alloy, and one can see that C 0 Si is temperature-dependent. It was fit and implemented in the model as such. The corresponding assumption is that as soon as Si phase is formed, the matrix is instantly depleted from Si. Si ) (blue curve) with respect to temperature, obtained using the equilibrium concentration of Si in the aluminum matrix α phase (yellow curve) and the molar fraction of the Si phase (eutectic Si) (red curve), calculated using Thermo-Calc software and TCAL5 database.
Concluding remarks This work was centered around modelling precipitation hardening in an A356+0. 5wt. %Cu cast aluminum alloy for cylinder head applications. The main results can be summarized as follows: • The use of TEM characterization shows that for short aging durations at 200 °C (up to 10 hours), the dominating hardening precipitates is β rods, while for the long aging duration (up to 100 hours), the dominance shifts to the Q-phase system (Q , Q precipitates). The length and diameter of the β rods were measured to produce size distributions. • The precipitation model relies on classical nucleation/growth/coarsening equations. Indirect coupling to the Thermo-Calc software is used to input into the model essential thermodynamic variables such as the driving force for precipitation and the solubility product.
One microliter of the DNA extracted using sonication, boiling method or commercial kits and 10-20 ng of DNA extracted using CTAB method was used for PCR. Spoligotyping and 24 loci MIRU-VNTR typing were performed according to standard protocols  19, 20. TB Profiler database (http:// tbdr. lshtm. ac. uk/) was used for genotypic detection of drug resistance. MDR, pre-XDR and XDR phenotypes were defined according to the World Health Organization definitions: MDR are strains resistant to isoniazid and rifampicin; XDR-resistant to isoniazid, rifampicin, fluoroquinolone, and a second-line injectable agent; pre-XDR-resistant to isoniazid, rifampicin and either a fluoroquinolone or a second-line injectable agent. Bioinformatics and phylogenetic analysis. Phylogenetic analysis of the ancient Beijing strains based on the WGS data obtained in our laboratory or retrieved from SRA NCBI, was described previously  15  and the list of the accession numbers of the analyzed genomes is provided in the same publication.
In brief, short sequencing reads were mapped to the reference genome of M. tuberculosis H37Rv (NC_000962. 3) by using the Burrows-Wheeler Aligner v. 0. 7. 12  21. Single nucleotide polymorphism (SNP) calling was performed by SAMtools v. 0. 1. 19  22. Variable call format (vcf) files were annotated by BSATool v. 0. 1  23. Graphical presentation of the trees was done using TreeGraph (http:// treeg raph. bioin fweb. info) and iTOL v3 (http:// itol. embl. de). Obtained VCF files were used for analysis. Single nucleotide polymorphisms (SNPs) with quality scores ≥ 20 were used for phylogenetic analysis with IQTREE software (http:// www. cibiv. at/ softw are/ iqtree) by using ModelFinder (http:// www. iqtree. org/ Model Finder) with the GTR2 + FO + G4 substitution model and 1,000 rapid bootstrap replicates.
SNPs located in repetitive genome regions and in PE and PPE genes were removed from the analysis due to possible misalignments. To assess the significance of amino acid substitutions, we used 4 parameters: Tang U-index, Grantham D-distance, BLOSUM62 distance, and Dayhoff log odds. They were calculated using in-house scripts, in particular, the snpMiner2 program  24. Real-time PCR. Three SNPs at genome positions 170505, 451510 and 398918 specific of the Beijing 1071-32 cluster were tested by real-time PCR assays that were run under the same cycling conditions. Oligonucleotide primers and FAM and HEX labeled probes are listed in Table  1. Table  1. Oligonucleotides primers and labeled probes used for detection of three synonymous SNPs specific of the Beijing 1071-32-cluster. wt, wild type; mut, mutant; LNA, locked nucleic acid.
Results Identification of Beijing 1071-32-cluster specific SNPs. Phylogenetic analysis of 184 WGS samples of ancient Beijing sublineages demonstrated that all isolates with Beijing 1071-32 VNTR profile were also grouped within a monophyletic branch on the WGS tree  15  (Fig. S1 ). This branch included only Russian isolates (Omsk region in Siberia, St. Petersburg in Northwestern Russia, Samara in Central Russia). For the purposes of the present study, we reanalyzed the genome variation data underlying this tree and identified SNPs that were specific for this branch that we termed "Beijing 1071-32-cluster", i. e. 1071-32 and its single/double locus variants (Fig. S2 ). In total, 121 SNPs were identified: 15 in intergenic regions and 106 in the coding sequences. These latter included 56 nonsynonymous (2 nonsense and 54 missense) and 39 synonymous mutations.
tuberculosis genome diversity GMTV database  28. They were not detected in the non-Beijing isolates and were detected only in 6 Beijing isolates from Samara, Central Russia included in the above WGS analysis i. e. isolates of this Beijing 1071-32-cluster itself. Real-time PCR: optimization, validation and screening. Three SNPs were targeted by three separate real-time PCR reactions run simultaneously under the same cycling conditions. Initial optimization was performed with DNA samples with available WGS and VNTR data that represented different VNTR clusters of the Beijing genotype and H37Rv reference strain. Clear discrimination between wild type and mutant alleles was observed for all three tested SNPs (see an example for one SNP in Fig. 2 ). The DNA used for analysis was extracted from cultured bacteria using different methods but this did not affect the performance of the assay.
Discussion This study was undertaken to develop a practical approach to robust detection and prospective surveillance of the MDR/pre-XDR cluster within M. tuberculosis Beijing genotype emerging in Russia  14. WGS-based analysis of the Russian and global datasets demonstrated that this Beijing 1071-32 cluster also presents a separate branch on the WGS-based tree  15. In terms of large-scale sublineages, this cluster belongs to the early ancient Beijing sublineage characterized by deleted RD181 and wild type mutT2-58 and mutT4-48 codons (Fig. 1 ). In terms of drug resistance, it bears a characteristic signature of six drug resistance mutations associated with resistance to the four first-line drugs  15. Additional mutations in different genes were also present in most isolates of that branch and were associated with resistance to one or more second-line drugs, leading to pre-XDR or XDR genotype (Fig.
S2 ). Most noteworthy and alarmingly, this cluster included only MDR isolates. For example, even the notorious Russian epidemic B0/W148-cluster reportedly included not only MDR but polyresistant isolates resistant to STR and RIF or INH  8, 29, 30. On the whole, based on the VNTR analysis, Beijing 1071-32-cluster isolates were mostly detected in Russia with clearly increasing prevalence in Omsk, Siberia but these isolates were also present in other Russian regions and FSU countries in Central Asia and Transcaucasia (Fig. 3 ). In the large Beijing genotype VNTR dataset  31  , this VNTR profile 1071-32 was identified in the isolates from Armenia (1 isolate) and Abkhazia (5 isolates). When we additionally searched for the three neutral SNPs and six resistance mutations in the large Beijing genotype WGS dataset previously compiled and described in Perdigão et al.