dataesr/scientific-paragraphs-categorization huggingface dataset:
In turn, P [d i , a i |g i = h] is the likelihood for genotype i. Furthermore, P [g i = h|p(x i , y i , t i )] is a binomial distribution, where n represents the ploidy level, which in this case is 2: P [g i = h|p(x i , y i , t i )] = n h p(x i , y i , t i ) h (1 -p(x i , y i , t i )) n-h (6) Then, the likelihood of the entire data can be computed as L(d, a) = M i=1 L(x i , y i , t i ) ( 7 ) where M is the total number of individuals for which we have data, d is the vector containing the derived read count for each individual and a is the vector containing the ancestral read count for each individual. We computed genotype likelihoods directly on the BAM file read data, using the SAMtools genotype model  (Li, 2011)  implemented in the software ANGSD  (Korneliussen et al. , 2014). When only randomly sampled pseudohaploid allele counts are available, we used a Bernoulli sampling likelihood (conditional on the genotype g i ) on the left-hand side of equation 5 instead.
Implementation The above described model was implemented in R version 3. 6. To numerically solve the differential equations and obtain maximum likelihood estimates, we used the libraries deSolve  (Soetaert et al. , 2010) , ReacTran  (Soetaert & Meysman, 2012)  and bbmle  (Bolker & R Development Core Team, 2020). Scripts containing the code used in this paper are available on github: https://github. com/RasaMukti/stepadna.
Acknowledgments We thank  Graham Gower ,  Evan Irving-Pease ,  Montgomery Slatkin  and the members of the  Racimo group  for helpful comments and advice. FR and RM were funded by a  Villum Fonden Young Investigator  award to FR (project no. 00025300 ). FR was also supported by a  Lundbeckfonden grant  ( R302-2018-2155 ) and a  Novo Nordisk   Fonden grant  ( NNF18SA0035006 ) to the  GeoGenetics Centre. TSK was funded by a  Carlsberg grant  ( CF19-0712 ). JN was funded by  NIH  grant  R01 GM132383.
Methods Using modeling and simulations, we investigated the effects of increasing recombination rate and/or modifying the shapes of recombination landscapes on a program that introgresses the allele of a donor parent at a target locus into a recurrent parent by successive backcrosses. All the corresponding results were obtained in the context of Brassica rapa landscapes (modified or not). Our computer codes were written in the programming language R  [31] , both for producing individual-based simulations of forward-in-time backcross breeding schemes and for all associated analyses.
Acknowledgements We thank  S. Mezmouk ,  E. Jenczewski ,  J. C. Motamayor ,  D. Livingston ,  C. E. Durel  and  R. Mercier  for constructive comments and discussions and  A. M. Chèvre  and  D. Boichard  for their helpful comments on the manuscript. We also acknowledge the very useful suggestions provided by the two anonymous referees.
Funding This work has benefited from a  French State grant  (Saclay Plant Sciences, reference n°  ANR-17-EUR-0007 , EUR SPS-GSR), managed by the  French National Research Agency  under an " Investments for the Future" program  (reference n°  ANR-11-IDEX-0003-02 ) which co-funded with KWS, MARS, and Secobra the salary of ET and support was also provided by the  Ecole Doctorale FIRE -Program Bettencourt.
The lack of a widely accepted comparative methodology to establish a coherent diagnosis of hominin species recognition and consensus-based definition of the genus Homo demonstrates that a comprehensive phylogenetic analysis to address these issues is long overdue  (Strait et al. , 2015). The objective of the present study is to present a methodology combining a cladistic Maximum Parsimony (MP) protocol and a Phylogenetic Networks (PN) method applied to a comprehensive hominin dataset of craniodental characters. Our approach will aim to clarify the phylogenetic definition(s) of the genus Homo and test the hypothesis of phylogenetic reticulation in its mode of evolution. The methodology is described in the section experimental design of Transparent Methods, and the analytical framework is schematically summarized in Figure  S1.
To carry out our study with the greatest number of hominin taxa and morphological traits, and to enable the comparison of its results with those of an alternative methodology, we selected the supermatrix assembled by  Dembo et al. (2016)  among large published datasets of pre-established species. This supermatrix of 391 craniodental characters was compiled by the authors from matrices used in 14 previous studies, with some alterations made to a mix of trait observations as described in their publication. They analyzed it using primarily Bayesian phylogenetic methods compared with a bootstrap MP analysis. All craniodental datasets in paleoanthropological studies are based on morphological characters observed on hominin fossils and coded subjectively by researchers. Some might consider that either the choice of characters is not adequate, or the coding might be missing, incorrect, or subject to observational biases.
The dataset has a large percentage of missing data, due in part to the fragmentary nature of some specimens of the late Miocene-Pliocene paleospecies, which may have an impact on the results. However, for comparative purposes with the findings of  Dembo et al. (2016)  we have not made any changes to the list of characters and the coding and thus have taken the data at face value. The sample of taxa consists of two extant outgroup hominoids (Gorilla gorilla and Pan troglodytes) and the following 22 hominins covering the geological period from late Miocene to Holocene (Figure  S2 ): Sahelanthropus tchadensis, Ardipithecus ramidus, Australopithecus anamensis, Australopithecus afarensis, Australopithecus garhi, Kenyanthropus platyops, Australopithecus africanus, Australopithecus sediba, Paranthropus boisei, Paranthropus robustus, Paranthropus aethiopicus, Homo naledi, Homo rudolfensis, Homo habilis, African Homo erectus, Georgian Homo erectus, Asian Homo erectus, Homo floresiensis, Homo antecessor, Homo heidelbergensis, Homo neanderthalensis, and H.
Selection of MPMAX optimal scenario Our MP protocol is implemented with the PAUP software  (Swofford, 2020). Based on the dataset of 391 morphological characters of 22 hominin paleospecies and 2 outgroup taxa, we execute 16 MP runs by heuristic search with the four main character types, uncertainty or polymorphic setting for multistate taxa and either equally weighed or reweighed characters, and generate 16 phylogenetic MP trees (Figures  S3-S6 ) with the main results summarized in Table  1. These MP trees represent 16 hypothetical scenarios of what hominin evolution might have been in a dichotomous phylogenetics perspective based on the dataset. To assess the branch support of each the 16 MP trees, we also show in Figures S3-S6 the bootstrap 50% majority rule consensus trees executed with the same parsimony settings.
Comparative validation of MPMAX optimal scenario In order to validate the MPMAX scenario, we compare it with the results of a Bayesian inference-based Markov chain Monte Carlo (BMCMC) analysis with the same dataset  (Dembo et al. , 2016). We call the tree from Figure  2  of this study, the BMCMC Dembo tree, and compare the proportions of bootstrap replicates P boot of the MPMAX bootstrap tree (Figure  S6F ) to the posterior probability values P post of the BMCMC Dembo tree on the basis of their equivalence (Transparent Methods 5). We use an elliptic graphical representation for the comparative analysis  (Hennig, 1966, Figure 18, p. 71). For illustrative purposes, we highlight in  Figure S7  the similarities between the elliptic representation and the MP cladogram of Figure  1. With the exception of the inverted topological position of S.
We also applied the Popperian elimination of evolutionary scenarios protocol using the bootstrap consensus trees of Figures S11B, S11C, S11E, and S11G, with the results shown in Table  S5. Compared with the MPMAX scenario that shows the highest tree RI of 0. 8805 (Table  1 ) and only one inconsistency (Table  2 ), all the Dembo scenarios with the split character dataset are phylogenetically suboptimal and clearly not as coherent and informative as evidenced by their MP tree RIs (Figure  S11 ) and their number of Popperian elimination inconsistencies (Table  S5 ). As a matter of fact, in  The comparison of both phylogenies shows that the MPMAX scenario (Figure  2A ) has a systematically higher support than the Bayesian BMCMC Dembo scenario (Figure  2B ) in practically all the sister groups, and a clearer and higher resolution concordant, we believe, with generally accepted paleoanthropological paradigms.
rudolfensis associated with the H. erectus group instead of being in a polytomy. The fact that for three algorithms the tree RI = CI = 1 is proof that the selection of apomorphies by the character RI = 1 is effective and robust. In the analysis runs with the full dataset, homoplasies generate a background noise that varies depending on the algorithmic constraints used (character type, multistate taxa, and weighing/reweighing) and is related to the tree topographies. We define this background noise as the difference between the best RI from the run with 74 apomorphies having a value = 1 (Figure  3A ) and the tree RI with value <1 from an analysis run with 391 characters (Table  1 ); in short, the background noise of a run with the full dataset is (1 -RI). Conceptually, it represents the contribution of the 317 homoplastic characters to the diminution of the RI between the two types of analyses.
A perfect tree RI = 1 means that phylogenetically there is no noise in the run with the reduced apomorphy dataset, whereas there is noise with the runs executed with the full dataset, and the MPMAX R15 scenario is the one with the least noise. As in the present study, it has been observed that more polytomies become apparent when the number of homoplastic characters describing the taxa decreases and the number of most parsimonious trees increases substantially  (Caparros, 1997). Polytomies are thought to be generally uninformative, but they will result in being of utmost importance in the PN analysis, i. e. , neutralizing the background noise will allow to conduct the PN analysis. Polytomies are prevalent among late Miocene-Pliocene hominin species upstream from Au. africanus in both consensus trees because most of the apomorphous character-state changes in that part of the tree are ambiguous with uncertain assignments (Figure  S10 ), and to the many missing characters that contribute to the multiplication of trees of minimal length.
This group could be split possibly into two sub-groups (S. tchadensis/Ar. ramidus) and (K. platyops/Au. garhi/Au. afarensis), with Au. anamensis and Au. africanus in intermediate positions. The characters of the dataset are not sufficiently informative to discriminate more precisely among these taxa. On the other hand, as in MPMAX (Figure  1 ) we observe the appearance of recurrent and very stable sister groups across the four MP character type runs  (Fitch, Wagner, Camin-Sokal, or Dollo)   3A  or as a sister taxon of the Her group in Figure  3B. Given that the consensus tree of Figure  3A  has the highest tree score R = 1, we may infer that H. rudolfensis is more closely linked to (PreHs, Her) than H. habilis as proposed by  Leakey et al. (2012). The MP analysis with 74 apomorphies would indicate that the evolutionary topography of Figure  1  downstream of Au.
Phylogenetic Networks (PN) analysis We use the SplitsTree software  (Huson and Bryant, 2006)  to compute the majority consensus tree, consensus network, and reticulate network based on the MMPT dataset. Common combinations of sister groups sharing homologous characters are displayed in the unrooted majority consensus tree (Figure  4A ), which is topographically equivalent to the one of Figure  3A , i. e. , well-supported dichotomous branches PreHs (F), Her (G), NalSed (I), and Par (J) are identified, whereas the least-supported groups are identically expressed as polytomies. The conflicting data patterns of polytomies are highlighted in the consensus network (Figure  4B ) by means of parallelograms, with high intensity in the group up to Au. africanus Tree and network computations were executed with the program SplitsTree  (Huson and Bryant, 2006)  with input of 3,213 trees (MMPT) from Dollo parsimony run with 74 apomorphous characters.
These apomorphies were selected based on their character RI = 1 from the output (Table  S1 ) of the MPMAX run with the full dataset. correlating with a high number of incompatibilities essentially due to a substantial proportion of ambiguous characters difficult to position at the nodes, and missing data. In the reticulate network (Figure  4C ) we observe three evolutionary patterns. First, there is no evidence of tree-like or web-like ancestral relationships among the two groups (S. tchadensis-Ar. ramidus, Au. anamensis) and (Au. garhi, Au. afarensis, K. platyops), but a chronological punctuated succession of taxa that eventually would give rise to the emergence of Au. africanus shown here as an isolated single edge  (White et al. , 2015). The conflicting character incompatibilities, low discriminating power of the state changes, and uncertain direction of character transformations of these variable morphoclines may be interpreted as the expression of evolutionary adaptive events that would favor the classification of anagenetic grade instead of clade for these hominin taxa.
Taxonomic definition of the genus Homo We addressed first the issues of number of species within the Homo hypodigm, their relatedness. and the definition of the genus Homo with an MP protocol. Based on the complete dataset of pre-established species with 391 features, the MP analysis allowed us to identify three hypothetical genus Homo phylogenetic definitions from the MPMAX scenario (Figure  1 ), well supported by bootstrap replicate values (Figure  2A ), as follows: genus Homo sensu lato = D that includes among others H. rudolfensis and H. habilis, genus Homo sensu stricto = E that excludes them, and a broader definition including H. naledi and Au. sediba we call genus Homo sensu amplo = C. Furthermore, regardless of the constraints used in the prior analysis (character types, multistate taxa settings, and reweighing of characters or not), the MP runs with 74 apomorphous characters produced consensus trees (Figure  3 ) that confirm the existence of four coherent monophyletic sister groups: Par = J= ((P.
CONCLUSION The field of molecular anthropology has made significant advances in the past decade with the sequencing of the Neanderthal genome, the identification of the extinct lineage Denisova, and multiple evidence of introgression between H. sapiens and these two extinct human groups. Despite exceptional fossil discoveries in the last few years such as Denisova, Dmanisi, Au. sediba, H. naledi, Homo floresiensis, and recently Homo luzonensis, progress in paleoanthropology has not kept pace conceptually in redrawing and clarifying the human tree  (Scerri et al. , 2019). The last significant phylogenetic study on hominin evolution by  Dembo and co-authors (2016)  relied on a Bayesian inference-based Markov chain Monte Carlo (BMCMC) method using the most complete up-to-date craniodental features dataset available.
In the present study using the same dataset, we show that our MP protocol, backed by a bootstrap analysis, produces a more coherent hominin phylogenetic reconstruction (Figure  1 ), in accord with broadly accepted paleoanthropological paradigms, than the Bayesian BMCMC method, and illustrates three definitions of the genus Homo. However, the explanatory utility of commonly used dichotomous tree-like models of phylogenetic reconstruction is limited, as they do not explain the nature of underlying evolutionary processes and mode of evolution of the genus Homo. The molecular proof today of introgression in the genus Homo is well established, with documented cases of genetic admixture in recent and extinct Homo groups  (Nielsen et al. , 2017; Steinru ¨cken et al. , 2018; Villanea and Schraiber, 2019).
africanus, which may be considered by some as a linear punctuated phyletic evolutionary process  (White et al. , 2006). However, based on the dataset we show that the evolutionary events that gave rise to the species of the genus Homo are best represented by a Most Recent Common Ancestor rooted phylogenetic reticulate network. This reticulation might be interpreted as probable hybridization processes that led to the emergence of the extant species H. sapiens, further to the extinction of other paleospecies of the genus Homo. The results from our PN analysis come to a certain extent in support of recent findings of introgression and hybridization emerging in paleogenomic research of the genus Homo and confirm a paradigm shift in the study of human evolution in favor of reticulation  (Winder and Winder, 2014).
Limitations of the study and future avenues of research Based on a methodological analogy from molecular biology, the present research relying on phenotypic features represents an attempt to combine a tree-based phylogenetic reconstruction method in the study of human evolution with a PN approach. The interpretation of the topography of the resulting most parsimonious tree and network may be subject to debate if one is to argue with the quality of the dataset; however, the phylogenetic information sheds some light on the much debated issue of the emergence of the genus Homo, and its reticulate mode of evolution. Further work will be focused on the causal influence of the morphological apomorphous novelties on the nodes of the Homo hybridization subnetwork, and what phylogenetic information can be extracted from the tangles reflecting polytomous incompatibilities in the section of the network covering the late Miocene-Pliocene taxa up to Au.
africanus, in order to elucidate whether there were directional linear adaptive changes of an anagenetic nature. A future avenue for research will also be to supplement the dataset with postcranial characters. Postcranial data would be helpful to confirm the intermediate phylogenetic position of Au. sediba and H. naledi between australopiths and Homo genus sensu lato, shed some light on the placement of H. habilis and H. floresiensis as inferred by Argue et al. (  2017 ), and refine our understanding of the appearance and modes of bipedal locomotion. The diagnosis of hominin species in the present dataset is based on cranial remains, as is generally the case in paleoanthropology. However, we must stress that it is often difficult to assign postcranial remains to a specific species when several contemporaneous species have been identified in a site, but are not associated with cranial remains.
Experimental design The objective of our study is to present a conceptual phylogenetic reconstruction framework, summarized schematically in Figure  S1 , that combines tree-based Maximum Parsimony (MP) cladistics with a web-like Phylogenetic Networks (PN) method. We will test its application by using the phenotypic craniodental dataset of  Dembo et al. (2016) , with the aim of clarifying the taxonomic definition(s) of the genus Homo and ascertaining whether its mode of evolution leading up to the emergence of Homo sapiens was of a web-like reticulate nature or not. Our methodological approach follows three logical steps as follows: 1. Implementation of a tree-based MP protocol with the complete dataset to select the most parsimonious tree scenario out of 16 numerical runs based on various algorithmic constraints.
This scenario will highlight hypothetical ancestor-descendant relationships among the 22 hominin species that will serve to suggest phylogenetic definitions of the genus Homo. We use the PAUP software  (Swofford, 2020)  for this step. 2. Intermediate step to establish a methodological link between the MP and PN analyses. We execute an MP run based on apomorphies identified from the most parsimonious tree obtained in step 1 and generate multiple most parsimonious trees (MMPT) as input for the PN analysis. This run will serve to verify the phylogenetic signal of the most coherent scenario from step 1. We use the PAUP software  (Swofford, 2020)  for this step. 3. PN analysis based on input of MMPT from step 2 that produces a consensus tree, a consensus network and a reticulate network. From the reticulate network we will infer whether or not the phylogenetic relationships among the hominin species of the dataset are of a reticulate nature.
Execution of 16 MP tree runs with original dataset There are four main character types available to conduct an MP analysis (Transparent Methods 1). In most MP published studies in palaeoanthropology, authors commonly use the unordered character type (Fitch parsimony) or ordered type (Wagner parsimony), or a combination of both  (Collard and Wood, 2000; Strait and Grine, 2004) , whereas the ones favoring convergent homoplasies (Camin-Sokal parsimony) or reversals (Dollo parsimony) are never selected. This common approach would indicate that authors make a priori assumptions about the process of evolution with regard to character-state changes. Many authors tend to discard polymorphic characters in multistate taxa. Ignoring polymorphism results in a loss of information  (Wiens, 1999) ; fortunately, this is not the case in the present dataset that includes several taxa showing all the states for some specific characters.
Validation of MPMAX scenario by comparing MP and BMCMC results Given that we use the same dataset as  Dembo et al. (2016) , contrasting the results of the BMCMC analysis from Dembo et al. (2016, Figure  2 ) with the MP results of the most parsimonious scenario MPMAX of the present study (Figure  1 ) ought to be an empirical objective to interpret the data. However, the two methods are not directly comparable since MP is a numerical method based on mathematical graph theory  (Darlu and Tassy, 2019) , while BMCMC is a statistical method  (Goloboff and Pol, 2005). The measure of coherence of MP such as the tree RI is not comparable to the posterior probabilities produced by BMCMC. An indirect way of comparing the two methods is to execute a bootstrap run with the same settings as the MPMAX run, and compare the proportions of bootstrap replicates values P boot of the bootstrap tree (Figure  S6E ) to the posterior probability values P post of the BMCMC tree.
Execution of MP run with 74 apomorphous characters The character retention index ri is a measure of the phylogenetic information content of a character  (Farris, 1989b , Transparent Methods 6, Figure  S8 ), i. e. , it determines whether it is an apomorphy or a homoplasy. From the output of the MPMAX run (Table  S1 ), the dataset may be split in two: 74 apomorphies (Table  S2 ) with character ri = 1 (18. 93%), and 317 homoplasies with character 0 < ri < 1 (81. 07%). The fundamental principle of Phylogenetic Systematics is that the evolutionary history of taxa is reconstituted from characters by the sharing of apomorphies while homoplasies, resemblance not due to inheritance from a common ancestor, represent features of an adaptive nature and are therefore phylogenetically less informative: "The more characters certainly interpretable as apomorphous (not characters in general) are present in a number of different species, the better founded is the assumption that these species form a monophyletic group  (Hennig, 1966) ".
Tree-based Maximum Parsimony (MP) Maximum Parsimony is an integral part of the Phylogenetic Systematics school of thoughts, commonly known in taxonomy as cladistics  (Hennig, 1996). In essence, it is the analysis of individual characters (anatomical or genetic) free to evolve independently with the application of the principle of maximization of the number of novelties on the cladogram (tree), and the consequent discovery of the hierarchical order of the various groups of species of the dataset. By its computational nature it allows one to identify clearly the character-state changes that support the various clades (branches), and thus enhances the evolutionary analysis of observed anatomical modifications, be they of an adaptive nature or the result of epigenetic processes. Transparent Methods 1.