dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and Methods Nuclear and mitochondrial datasets used for the analyses Seven giraffe datasets were generated for our analyses using the sequences available in the NCBI nucleotide database: (1) the mtDNA-G507 dataset, which contains a mitochondrial fragment covering the whole cytochrome b (Cytb) gene and the 5'part of the control region (length = 1742 nucleotides [nt]) for 507 individuals (listed in Appendix B1. 1), and its reduced version including only the 82 different mitochondrial haplotypes, named mtDNA-GH82; (2) the mtDNA-GH82O3, in which the mtDNA-GH82 dataset was aligned to three the nuDNA-G137O3 dataset, in which the nuDNA-G137 dataset was aligned to the three outgroup species mentioned above (length = 17276 nt);  (7)  the nuclear haplotype dataset, named nuDNA-GH274, which was inferred from the nuDNA-G274 dataset using the PHASE v2.
Phylogenetic analyses The mtDNA-GH82O3 and nuDNA-G137O3 datasets were analysed with probabilistic methods. Bayesian inferences were conducted in MrBayes v3. 2. 6  [40]  by calculating the posterior probabilities (PP) after 10 7 Metropolis-coupled MCMC generations with tree sampling every 1000 generations and a burn-in of 25 %. Maximum Likelihood (ML) analyses were performed with PhyML v3. 1  [41]  and Bootstrap percentages (BP) were calculated after 1000 replicates. The GTR+I+G substitution model was applied for both methods, as suggested by the Likelihood calculations in jModeltest  [42]  based on the Akaike information criterion. Bayesian analyses were also performed for each of the 21 introns using the model of DNA substitution selected under jModeltest (Table  1 ).
STRUCTURE analyses Giraffe haplotypes were reconstructed from the nuDNA-G137O3 dataset for each of the 21 introns by applying the PHASE v2. 1 algorithm implemented in the software DNASP v5. 0  [39] , allowing for recombination and reducing the output probability threshold of conserved regions (CT) from 0. 9 by default to 0. 6. For each of the 21 introns, the haplotype information was used to code individuals sharing the same allele with a unique integer. Bayesian analyses of genetic admixture were run in STRUCTURE v. 2. 3. 4  [44]  to identify genetically homogeneous groups of individuals (populations of origin, K). The analyses were done as recommended by Gilbert et al. [45] , i. e. , number of MCMC generations = 200 000 and burn-in = 100 000 generations for K = 1-10 clusters. We applied several combinations of ancestry model, allele frequency and supporting information (Popdata) like the assignment of the subspecies (population identity/ POPID) or sampling location (LOCPRIOR model) for each individual.
[44] , where the log probability of the data (ln Pr (X|K) was plotted against a range of K values, and the optimal K was selected as the point at which the plot curvature plateaus. A regression curve and gridlines were added to the diagrams generated by STRUCTURE HARVESTER to help in determining the point of plateau. To assess the reliability of the results, CLUMPAK  [51]  was used to display the barplots from K = 1 to 10 for each of the 20 iterations by means of the implemented software DISTRUCT  [52].
Analyses of nuclear haplotypes The nuDNA-GH274 dataset (nuclear haplotypes inferred for 21 introns and 137 giraffes) was used to construct a median joined network using PopART v1. 7 following the distance criterion  [53]. The robustness of haplotype clusters was evaluated by bootstrapping (1000 replicates) under PAUP* v4b10  [43]  using either the Maximum Parsimony (MP) method (heuristic search, faststep option) or the Neighbor-Joining (NJ) method (GTR+I+G model), and under the Maximum Likelihood (ML) criterion using RAxML on CIPRES  [54]  (http://www. phylo. org). PopART was also used to construct a median joined network for each of the 21 introns. For six introns (i. e. CTAGE5, NUP155, OTOF, PLCE1, RASSF4 and SOS1), missing alleles were removed from the alignment to avoid any distortion of the results.
Multispecies coalescent analyses Three coalescent-based approaches were applied to infer species boundaries within the genus Giraffa: (1) the "Species Tree Ancestral Reconstruction" template (*BEAST  [19] ),  (2)  the extension of the *BEAST model called "Species Tree and Classification Estimation, Yarely" (STACEY)  [24] , and (3) the Bayesian Phylogenetics and Phylogeography program (BPP v. 3. 2  [23, 55] ) (see specifications for each program in Table  1 ). We estimated the species-tree phylogeny using the coalescent algorithm implemented in BEAST v. 2. 4. 4  [56]  in order to consider an alternative to the traditional concatenated phylogenetic approach (see Kubatko and Degnan  [57]  for caveats concerning concatenation). Inferences were based on the nuDNA-G274O6 dataset using an a priori assignment at the level of individuals, i.
e. by assigning for each of the 137 giraffes two alleles for the 21 introns. We assumed an uncorrelated lognormal molecular clock for all 21 loci. For each marker, we selected the best suited substitution model inferred in jModeltest  [42]  (Table  2 ). Analyses were run with 2x 10 8 generations, with trees sampled every 5000 steps. The. log files were analysed with Tracer v1. 7  [58]  to assess the convergence of model parameters (effective sample size [ESS] > 200). The species tree was summarized as a Maximum Clade Credibility tree in TreeAnnotator v. 1. 10 [59]  after discarding 25% as burn-in. The nuDNA-G274 and nuDNA-G274O6 datasets were further used for species delimitation analyses using the STACEY template implemented in BEAST v2. 4. 4. STACEY represents an improvement to the DISSECT model  [60]  to infer a "species or minimal clusters tree" (SMC) under the birth-death-collapse tree prior and without the requirement of a guide tree.
0 for the tree prior, a lognormal distribution with a mean of 4. 6 and a standard deviation of 2 to the growth rate prior and a uniform distribution for the relative death rate prior with a lower bound of -0. 5 and an upper bound of 0. 5. The dataset was partitioned by the 21 genes, with independent strict clock models and individual assignment of the best suited substitution model to each gene (Table  2 ). Each analysis was run for 2. 5 x 10 8 generations and convergence of parameters was assessed in Tracer v1. 7  [58]. Subsequently, the most supported number of distinct clusters was estimated using SpeciesDelimitationAnalyser v1. 8. 0  [24]  by analysing the species trees with a burn-in of 25 % and the default collapse height of 0. 0001. Species delimitation analyses with BPP v3. 2 were based on a reduced dataset comprising only 66 giraffes due to software limitation.
Seventy-one individuals were excluded from the original dataset using the three following criteria: (1) 14 individuals with missing data, (2) 39 individuals sharing the same haplotype and (3) 18 individuals characterized by a long terminal branch in the Bayesian tree. We analysed the support for each of the five taxonomic hypotheses depicted in Fig. 4  (see results for more details). First, we applied the A00 algorithm, the simple MSC model with the species tree fixed to explain the acceptance proportions of MCMC moves  [23]  under the default gamma prior values G  (2, 2000)  for the tau (τ, root divergence time) and theta (θ, genetic difference among taxa). Then, we assessed the support for each putative species using the A11 algorithm  [55]. The three species model priors (SMP 1, 2 and 3) were tested.
Nuclear and mitochondrial pairwise distances The nuDNA-G137 dataset (16968 nt) and mtDNA-GH82 dataset (1742 nt) were used to calculate pairwise distances in PAUP* v4b10  [43]  (Appendix D). For the nuclear dataset, we performed calculations considering the five taxonomic hypotheses summarized in Fig. 4. For the mtDNA dataset, we primarily performed calculations based on the three main mitochondrial haplogroups, named N, E and S depicted in Fig. 1B , but considered also the five possible hypotheses of species delimitation shown in Fig. 4.
Multispecies coalescent analyses We constructed a MSC species-tree from the nuDNA-G274-O6 dataset using *BEAST. The topology is similar to the supermatrix topology of Fig. 1A  Species delimitation analyses based on BPP provided maximal support (PP = 1) for all species recognized according to the 3S, 4S, and 5S hypotheses (Fig. 4 ). The same results were.
Population genetic analyses support the 3S hypothesis The assessment of population genetic structure has become indispensable in evolutionary biology and conservation to reveal hidden biodiversity. Among freely accessible software provided for this task, STRUCTURE  [26]  is the most commonly used program, with 17473 citations in Web of Science (January 2019). Using Bayesian inference, STRUCTURE is a model-based clustering method to detect population structure and assign individuals to K populations  [26]. However, many published results based on STRUCTURE are not reproducible because the genotypes were not available or the parameters used for the analyses were not fully detailed by the authors  [45, 61]. The program STRUCTURE was previously used to infer genetic structure in giraffe populations, using either genotypes from 14 microsatellite loci of 381 individuals  [29]  or PHASED alleles of seven introns for 105 giraffes  [38]  or rather the extended dataset of 21 introns for 137 individuals  [12].
Brown et al. [29]  suggested the existence of at least six species, but the optimal K was not determined using either the method of Evanno et al. [50]  or that of Pritchard et al. [44] , and their results are not reproducible, because the microsatellite data were not made available. According to Winter et al. [12] , "K = 4 shows four well resolved groups and is supported as best fitting number of clusters by several statistical methods", but they did not provide any details on the model and method used for their STRUCTURE analyses. Using the same dataset, comprising allelic information of 21 nuclear introns for 137 giraffes, we tested 16 different models under STRUCTURE in order to shed more light on giraffe population structure. Considering the method of Evanno et al. [50] , 58% of the analyses provided support for two distinct populations of origin (K = 2), 25% for three distinct clusters (K = 3), and only 17% confirmed the result obtained by Winter et al.
This problem explains the highly variable root position in our mitochondrial analyses: with MrBayes, the first haplogroup to diverge is either S (Fig. 1B , PP = 0. 37; BP = 60) or E (if the two bovid species are excluded as outgroup taxa, data not shown, PP = 0. 55); with BEAST, haplogroups E and S are found to be sister-groups (PP = 0. 74), as in the mitochondrial tree of Fennessy et al. [38]. The nuclear dataset provided more signal for resolving basal relationships within Giraffa. As indicated in Fig. 1 , our phylogenetic analyses supported a sister-group relationship between G. giraffa and G. tippelskirchi (PP = 0. 82; BP = 71). This node was found monophyletic with 6 independent markers (C1orf74, DDX1, COL5A2, SAP130, USP33 and USP54). By comparison, SuperTRI analyses clearly showed that the two other hypotheses (either G.
Its grouping with Rothschild's giraffes may be explained by interbreeding between reticulata and rothschildi either in zoos  [68]  or in the wild, as field observations have documented the occurrence of reticulata X rothschildi hybrid phenotypes in Kenya  [69]. Unfortunately, these hybrid individuals or populations were not yet studied for nuclear genes. The mitochondrial haplotype RET9, which was detected by Brown et al. [29]  in a single reticulated giraffe (accession number: EU08821), is intriguing because it is divergent from all other sequences of haplogroup N. We propose two hypotheses to explain its divergence. The first hypothesis assumes the retention of ancestral haplotypes in wild populations of reticulated giraffes; it will be confirmed if identical or similar haplotypes are discovered in other reticulated giraffes.
In the fifth column are listed the markers supporting each taxonomic hypothesis in the separate analyses of 21 introns and mtDNA, as well as the support values obtained from supermatrix and SuperTRI analyses ("-": not found). In the sixth column are detailed the mean pairwise distances between individuals of the same taxon calculated using either nuDNA data (concatenation of 21 introns, above) or mtDNA (below) (Since all the mitochondrial sequences of the subspecies giraffa belong to haplogroup E, they were considered as tippelskirchi for distance comparisons; see paragraph 4. 5 for discussion on mtDNA introgression). In the seventh column are shown the distribution maps of bovid genera with a similar geographic pattern of speciation.
phylogenetic trees obtained from the separate analyses of the 21 independent introns are detailed in Appendix B and the Bayesian nuclear tree of the concatenated dataset (17276 nt) is depicted in Fig. 1A. The results of other analyses (ML bootstrap [BP] and SuperTRI indices [SBP/MPP/NRep]) are indicated only for the nodes supported by posterior probability (PP) values ≥ 0. 9, as well as for nodes discussed in the text (e. g. , subspecies). The monophyly of Giraffa is supported by all analyses and almost all markers separately (NRep = 20), and the genus is diagnosed by 158 exclusive synapomorphies in the nuclear genes. Within Giraffa, 19 nodes are supported by PP ≥ 0. 9 in the Bayesian tree of the nuDNA supermatrix (Fig. 1A; Appendix B2. 22), but only three of them are associated with BP > 90: (1) the clade here named G.
found with the three species model priors (SMP1, 2 or 3; Appendix E1). The further division of G. tippelskirchi into two separate taxa, i. e. G. tippelskirchi sensu stricto and G. thornicrofti (6S hypothesis) was only weakly supported (PP SMP1 = 0. 26; PP SMP2 = 0. 4; PP SMP3 = 0. 34). Phylogenetic analyses of the mitochondrial fragmentThe Bayesian tree reconstructed from the mtDNA-GH82O3 dataset (1776 nt) is shown in Fig. 1B. It shows the existence of three major geographic haplogroups: northern (N), eastern + southeastern (E), and southwestern (S) giraffes. The N haplogroup is supported by both Bayesian and bootstrap analyses (PP = 1; BP = 93). It includes all haplotypes detected for G. camelopardalis s. s. A, as well as one divergent haplotype of G. tippelskirchi (TIP15, EU088334) sequenced by Brown et al.
The program HDECAY determines the partial decay widths and branching ratios of the Higgs bosons within the Standard Model with three and four generations of fermions, including the case when the Higgs couplings are rescaled, a general two-Higgs doublet model where the Higgs sector is extended and incorporates five physical states and its most studied incarnation, the minimal supersymmetric Standard Model (MSSM). The program addresses all decay channels including the dominant higher-order effects such as radiative corrections and multi-body channels. Since the first launch of the program, more than twenty years ago, important aspects and new ingredients have been incorporated. In this update of the program description, some of the developments are summarized while others are discussed in some detail.
NEW VERSION PROGRAM SUMMARY 1. Introduction The FORTRAN code HDECAY  [1] , released more than twenty years ago (on arXiv in April 1997)  1  , addresses a crucial issue in the phenomenology of the Higgs particles which, in the context of the Standard Model (SM) of particle physics, have been predicted long ago  [4]  and were only discovered in 2012 at the CERN Large Hadron Collider (LHC)  [5]. Indeed, the strategies for Higgs bosons searches at high-energy colliders, such as the LHC, exploit various Higgs decay channels. The detection strategies depend not only on the experimental setup (for instance hadron versus lepton colliders) but also on the theoretical scenarios: the SM or some of its extensions, such as the two-Higgs doublet model (2HDM), or the Minimal Supersymmetric Standard Model (MSSM), or variants such as including a fourth generation of fermions, or Higgs particles with rescaled couplings to fermions and gauge bosons.
In the 2HDM, to cope naturally with the fact that the observed Higgs boson is SM-like, one invokes the so-called alignment limit  [11]  in which only one Higgs doublet gives masses to the V = W/Z bosons. In this case, the mixing angle α is such that the Higgs couplings of one of the CP-even Higgs bosons are the same as the ones of the SM Higgs state. In this case too, the second CP-even state will no longer couple to massive gauge bosons as also does the pseudoscalar A in general. It was, and still is, of vital importance to have reliable predictions for the branching ratios of the Higgs boson decays for these theoretical models. The program HDECAY calculates the Higgs boson partial decay widths and the decay branching ratios within the SM, 2HDM and MSSM scenarios 2. In its first version, the main features of the program were as follows: -Included are all decay channels that are kinematically allowed and which have branching ratios larger than 10 -4 , i.
The program, written in FORTRAN77, provided a very flexible and convenient use, fitting to all options of phenomenological relevance. The basic input parameters, fermion and gauge boson masses and their total widths, coupling constants and, in the MSSM, soft SUSY-breaking parameters can be chosen from an input file hdecay. in. In this file several flags allow switching on/off or changing some options [e. g. choosing a particular Higgs boson, including/excluding the multi-body or SUSY decays, or including/excluding some specific higher-order QCD corrections]. All the relevant information is given in the original publication  [1]  to which we refer for details. However, since the first release of the original version of the program some bugs have been fixed, a number of improvements and new theoretical calculations have been implemented.
Earlier important modifications were documented, besides those of Refs. [12, 14]  where the special extensions SUSY-HIT and eHDECAY were discussed, in three reports of the Les Houches Workshops in 1999, 2009 and 2013, Refs. [27, 28, 29]. The logbook of all modifications and the most recent version 6. 52 of the program can be found on the web page http://tiger. web. psi. ch/hdecay/. The most important updates are summarized in the next section.
Pre-Higgs discovery updates Before Higgs boson discovery, most of the modifications of the original program have been made in the context of the MSSM. Until 1999 the following changes have been performed  [27] : -Added links to the FeynHiggsFast routine which provides the masses and couplings of the MSSM Higgs bosons up to two-loop order in the diagrammatic approach  [30] , and, in the framework of the SUSY-HIT program  [12] , to the SUSPECT routine for the renormalisation group evolution and for the proper electroweak symmetry breaking in the minimal supergravity model  [31]. -Implemented Higgs boson decays to a gravitino and neutralino or chargino in gauge mediated SUSY breaking models  [32]  and SUSY-QCD corrections to Higgs boson decays to q q pairs (in particular bottom quarks)  [33].
Post-Higgs discovery modifications: summary In the subsequent years and during LHC operation, a large amount of work was devoted to improve the program. Many of the changes were made in order to meet the experimental needs and the recommendations of the LHC Higgs cross section working group. By 2013, i. e. a year after the Higgs discovery, very important modifications and additions to the program were made. They are summarized below. -Inclusion of the leading SUSY-QCD and electroweak corrections to all effective down-type fermion Yukawa couplings, i. e. for the µ, τ, s in addition to the bottom quark according to Refs. [18, 45]. In the MSSM the sneutrino mass parameters of the first two generations are allowed to be different from the third generation. -Inclusion of the two-loop QCD corrections to top decays  [46].
Rescaled Higgs couplings In 2013, the program HDECAY has been substantially modified (version 6. 40) in order to cope with the possibility of modified Higgs couplings to fermions and massive gauge bosons. This was required by the LHC collaborations which started to measure precisely the Higgs production cross sections and the decay branching ratios, allowing to derive strong constraints on these couplings. To compare the experimental measurements with the theory predictions in the SM, it was convenient to allow for the variation of the different Higgs couplings to the other particles in a systematic way. The inclusion of rescaled Higgs couplings to SM particles has been done according to the simplified effective interaction Lagrangian L int ∋ - ψ c ψ m ψ ψψ + 2c W m 2 W W +µ W - µ + c Z m 2 Z Z µ Z µ H v + α s 8π c gg G aµν G a µν + α 8π c γγ F µν F µν + √ αα 2 4π c Zγ F µν Z µν H v (4) where G aµν , F µν and Z µν are the field strength tensors of the gluon, photon and Z-boson fields.
The higher-order corrections are contained in the factors δ tot V which, up to two-loop order, read δ tot(1) V = δ (1) u + δ (1) V , δ tot(2) V = δ (2) u + δ (2) V + δ (1) u δ (1) V. ( ) 10 The one-loop expressions for a single SU(2) doublet of heavy fermions with masses m A , m B read  [62, 63]  δ (1) u = N c X A 7 6 (1 + x) + x 1 -x ln x , δ (1) V = -2N c X A (1 + x), (11) where x = m 2 B /m 2 A , X A = G F m 2 A /(8 √ 2π 2 ), and N c = 3 or 1 for quarks or leptons, respectively. The results for the two-loop corrections δ tot(2) V can be found in  [64]  for the QCD corrections of O(α s G F m 2 f ) and in  [63]  for the EW ones of O(G 2 F m 4 f ). The corrected partial decay width is then given by Γ NLO ≈ Γ LO 1 + δ (1) Γ + δ (2) Γ = Γ LO 1 + 2δ tot(1) V + (δ tot(1) V ) 2 + 2δ tot(2) V. (12) In the case of Higgs decays into SM fermions, the decay widths Γ(H → f f ) in the HDECAY code include, besides the SM corrections, the 4th generation approximate NLO and NNLO EW corrections in the heavy-fermion limit according to  [63]  and mixed NNLO EW/QCD corrections according to  [64].
This should be kept in mind when calculating decay widths involving Higgs self-couplings. The program also tests for vacuum stability, perturbative unitarity and the compatibility with the S and T parameters and gives out a warning if these are not fulfilled. Higher-order SM EW corrections do not factorize from the LO result and cannot be readily included for the 2HDM. The higher-order EW corrections to all relevant 2HDM Higgs boson decays have only become available recently  [68] , and not been implemented in HDECAY yet. This introduces unavoidable uncertainties, which can be estimated from the size of the known EW SM corrections to be up to 5-10% for several partial decay widths. Differences of this magnitude compared to the most precise values for the SM are therefore expected even in the decoupling/alignment limit.
Additional direct corrections as ∆ b should in principle enter the Higgs couplings but because M S is taken to be very large, they are assumed to have a small impact in the hMSSM and are ignored. This, however, strongly depends on the size of the µ parameter and should be taken with caution for large values of tan β. Another important set of couplings are the Higgs self-couplings and in the hMSSM, they are again given in terms of β and α, with the latter fixed by tan β, M A and M h as in Eq. (  19 ), but contain additional genuine radiative corrections that can be derived from the input parameters, too, since they are related to ∆M 2  22. The calculation of the Higgs branching ratios within the hMSSM are performed by HDECAY starting with version 6. 40. The program takes M h as input and obtains M H and α from the hMSSM prescriptions.