dataesr/scientific-paragraphs-categorization huggingface dataset:
For the filtering step, we use PhylteR  ( 8 ). This tool allows the detection and removal of outlier sequences in a set of gene alignments by iteratively removing taxa from the gene trees (inferred from these alignments) to optimise a score of concordance between all gene trees. Phyl-teR is particularly well adapted to remove potential erroneous sequences in CDS alignments since erroneous sequences will lead to abnormal phylogenetic placements or unexpectedly long phylogenetic branches. Since PhylteR requires gene trees, steps 2-5 of the OMM pipeline (Figure  1  ) were designed to quickly produce a first set of accurate gene trees for the CDSs downloaded from NCBI. First, we used the MACSE subprogram 'trimNonHomologousFragments' to eliminate potential contamination in every single-copy orthologous gene (Figure  1  , step 2).
This subprogram detects non-homologous fragments that often result from annotation errors and correspond to remaining intron fragments or untranslated regions (UTRs). Then, the sequences of each orthologous gene were aligned using MAFFT  ( 26 )  and a soft alignment cleaning was performed using HMMcleanNucl. pl ( 7 ) (Figure  1  , step 3). The best evolutionary model for each gene was then chosen using ModelFinder as implemented in IQTREE v2. 1. 3 (-m MFP) and followed by gene tree reconstruction. Node supports were evaluated using ultrafast bootstraps estimated by IQ-TREE (-bb 1000; ( 9 )). Finally, using the phylogenetic gene trees inferred from all CDSs as reference (combined in five batches of ∼3k markers to save computation time), PhylteR was able to detect 28,886 (1. 01%) problematic sequences leading to strange-behaving phylogenetic branches.
In most cases, Phyl-teR just removed a few sequences of a gene dataset to fix it, most likely corresponding to paralogous sequences. In seven extreme cases, where too many problematic sequences were detected in an alignment, PhylteR discarded the corresponding gene entirely (Figure  1  , step 5: 15,880 versus 15,873 at step 1). Because of the potential impact of the presence of erroneous sequences in previous alignment and cleaning steps (Figure  1  , steps 2 and 3), we decided to remove the outlier sequences detected by PhylteR from the raw CDSs extracted from NCBI and restart alignments and cleaning from scratch (Figure  1  , step 6). The new alignment step was performed using the OMM_MACSE v12. 01 pipeline implemented in a Singularity container  ( 27 ) ; the '-MACSE_min_MEM_length 8' option was used to save RAM when needed; Figure  1 , step   7 ).
Database access and query While the full database can be downloaded from the website, to facilitate user experience, the OMM website allows users to search for markers in two different ways (Figure  2 a ), either by searching for one specific gene or by filtering the database using several parameters to download subsets. On one hand, specific gene alignments can be access through the search of their NCBI gene ID ('search marker' page) or by using a BLAST research ('blast' page, 31 ), based on nucleotide (blastn or tblastx) or amino acid sequences (tblastn). On the other hand, from the main page, one can access the 'query' page in which the database can be filtered by selecting specific ranges of evolutionary indicator values (relative evolutionary rate, percentage of G+C at third codon position, α shape parameter [ distribution], and alignment length) or specific conditions ( e.
g. genes present in all species, in a list of species, or in species belonging to a specific taxonomic group; markers part of the mammalian BUSCO genes; markers located on a given Human chromosome). For example, for the default query values, we have 339 CDSs located on Human chromosome 1, but this number goes up to 811 when the maximum GC3 percentage is fixed to 60. For each CDS, OrthoMaM_v12 provides immediate access to many evolutionary features and information with a dedicated page (Figure  2  ). To present all information provided by the OMM website interface for each single-copy proteincoding gene included in the database, we used the page dedicated to the ACE2 gene as an example. This gene is of particular medical significance as the ACE2 receptor is used by the S AR S-CoV-2 and other coronaviruses to enter mammalian cells; several evolutionary analyses have been conducted for this gene to predict functionally important sites and to identify other potentially susceptible mammalian species besides humans  ( 32 ,33 ).
General information and links to other databases First, at the top of the marker page (Figure  2 a ), the common name of the gene is provided (ACE2: angiotensin-converting enzyme 2 isoform 1 precursor). Then, nine blue boxes D 533 provide global information about the gene. The first five blocks (first line) give access to Ensembl, Uniprot, NCBI, HGNC and Protein Atlas databases in which information about each gene (through different identifiers: ENSG00000130234, Q9BYF1, 59272, HGNC:13557, ENSG00000130234-ACE2, respectively) is present. The four next boxes (second line) show the length of the alignment (2901 nucleotides), the number of species for which the gene is available (184 out of 190), a link to multi-codon alignment for the given gene as provided by the Zoonomia project, and a link to orthoDB details if the gene is included in the BUSCO list for mammals (mammalia_odb10, 34 ).
Note that Zoonomia genomes have been / will be progressively integrated in NCBI. Since we strongly rely on the NCBI orthology, we prefer to wait until the genomes are annotated by the NCBI consortium to fully integrate them in OrthoMaM. In the meantime, we provide in the help page, a script to merge the OrthoMaM and Zoonomia raw files and run the OrthoMaM pipeline on the merged file. Then, six tabs provide additional information and resources associated with the gene: 'Phylogenetic information', 'NT alignment', 'AA alignment', 'Rooted ML tree', 'Sequence details', 'Download'.
Evolutionary descriptors The first tab, called 'Phylogenetic Information' (Figure  2 b ), displays the base frequencies, the substitution matrix under a GTR model, the site variability (percentage of variable sites at each codon position), and additional evolutionary descriptors estimated through a phylogenetic approach. These evolutionary descriptors are incorporated in the database, and some of them can be used to query CDSs (see 'Query' section below). First, the 'Relative evolutionary rate' (RER) is estimated with ERaBLE  ( 29 ). The relative evolutionary rate of a CDS is important to evaluate its usefulness in analyses of phylogenetics and molecular evolution. Faster-evolving genes will be more suitable for genomic comparisons at smaller taxonomic scales, while slower-evolving genes will be more suitable at deeper taxonomic scales.
Rooted ML tree For each OrthoMaM marker, a ML phylogenetic tree with branch lengths (i. e. phylogram) is provided (Figure  2 e ). It represents a synthesis of the base composition, substitution pattern, and among-site substitution rate heterogeneity of the corresponding CDS alignment. Phylogenetic trees were rooted through a sequential re-rooting procedure implemented in bio++ ( 37 ), using either monotremes, or marsupials, afrotherians, xenarthrans, laurasiatheria and euarchontoglires, in this order until one species to use as outgroup is present. To improve tree readability, the major clades have been coloured using the APE R-package ( 38 , see Figure  2  ). To zoom in, the user can click left on the tree, or click right and open a new tab where to zoom in or save the image. The rooted gene trees in Newick format are available for download.
Results and discussion The earliest versions of OrthoMaM based on Ensembl also included individual orthologous exon alignments in addition to CDSs. This feature was temporarily abandoned from v10 because of the incorporation of NCBI data. In future versions, we plan on reinstating the exon database since powerful programs such as Minimap2  ( 39 )  and Miniprot  ( 40 )  now allow efficiently extracting exons from genomic data  ( 41 ). Future developments also include the implementation of a command line API allowing to interrogate the database.
Funding Agence Nationale de la Recherche  [ CEBA :  ANR-10-LABX-25-01 ,  CEMEB :  ANR-10-LABX-0004 ,  CoCoAlSeq :  ANR-19-CE45-0012  to C. S. ];  European Research Council  [Con-vergeAnt:  ERC-2015-CoG-683257  to FD]. Funding for open access charge:  Agence Nationale de la Recherche  [ CEBA :  ANR-10-LABX-25-01 ].
Nucleic acid concentrations were measured, and the DNA quality was evaluated on agarose gel. Sequencing was performed on DNA samples obtained before and after sand flies feeding. Short-insert paired-end libraries were prepared with the KAPA Hyper Prep Kit (Kapa Biosystems). The libraries were then sequenced using TruSeq SBS Kit v3-HS (Illumina Inc. ), in paired end mode, 2 x 101bp, in a fraction of one sequencing lane of an HiSeq2000 flowcell v3 (Illumina Inc. ) according to standard Illumina operation procedures at Centro Nacional de Análisis Genómico (CNAG). Genome sequencing and genome assembly data. The whole genome sequencing datasets described in this study were deposited in the Sequence Read Archive (SRA)  [23]  and available at the accessions reported in Table  S1. All bioinformatics analyses were performed considering the L.
Computational analyses. The read alignment, the quantification of chromosomes, genomic bins and genes, and the single nucleotide variant analyses were performed with the GIP pipeline and the giptools toolkit (version 1. 0. 9) [20]. The mapping statistics are provided in Table  S2  and the GIP parameters are available in Dataset S1. Briefly, WGS reads were mapped by GIP using BWA-mem (version 0. 7. 17)  [24]. The resulting alignment files were then sorted, indexed and reformatted by GIP using Samtools (version 1. 8)  [25]. Read duplicates were removed by GIP using Picard MarkDuplicates (http://broadinstitute. github. io/picard) (version 2. 18. 9). A minimum read alignment MAPQ score of 50 was enforced by GIP to select genes for cluster analysis and to call for SNVs. A full description of GIP pipeline steps and parameters are available from its online documentation (https://gip.
) gambiae and in A. aquasalis following infection with the parasite Plasmodium falciparum  [62]  and Plasmodium vivax  [63] , respectively. Even though Leishmania infection itself seems not to elicit detectable ROS inside the infected sand fly gut  [64]  a possible role of base-level ROS in affecting Leishmania genome integrity is supported by indirect evidence. First, the sand fly genome encodes for a member of the NAPDH oxidase family responsible for the generation of ROS (termed dual oxidase, DuOx) and several ROS detoxification enzymes, including superoxide dismutase and catalase (https://vectorbase. org/vectorbase/app/record/dataset/TMPTX_llonJacobina). Second, sand flies are able to produce immune protective ROS as shown in Lutzomyia (L. ) longipalpis orally infected with the pathogenic bacteria Serratia marcescens  [64].
Acknowledgements We thank  Lucy Glover  and  Shaden Kamhawi  for helpful discussions. This study was supported by a seeding grant from the  Institut Pasteur International Department  to the  LeiSHield Consortium  and the  EU   H2020  project  LeiSHield-MATI -REP-778298-1. Prague group is supported by  European Regional Development Fund  (project  CZ. 02. 1. 01/0. 0/0. 0/16_019/0000759 ). Isabelle Louradour is financed by a  Roux-Cantarini fellowship  ( S-FB14002-74A ).
2010; Barrat and Cattuto 2013 and references therein). Some of the approaches that have been proposed in the statistics literature to model instantaneous interactions include  Corneli et al. (2017)  and  Matias et al. (2018) , however we note that these approaches rely on extensions of the stochastic blockmodel  (Nowicki and Snijders 2001) , and not on the LPM. Another relevant strand of literature focuses instead on modeling this type of data using Hawkes processes (see  Junuthula et al. 2019  and references therein). We propose our new Continuous Latent Position Model (CLPM) both for the projection model framework and for the distance model framework. In our approach, each of the nodes is characterized by a latent trajectory on the real plane, which is assumed to be a piece-wise linear curve.
The interactions between any two nodes are modeled as events of a inhomogeneous Poisson point process, whose rate is determined by the instantaneous positions of the nodes, at each point in time. The piece-wise linear curve assumption gives sufficient flexibility regarding the possible trajectories, while not affecting the purely continuous nature of the framework, in that the rate of the Poisson process is not piece-wise constant. This is a major difference with respect to other approaches that have been considered  (Corneli et al. 2017  and one of the approaches of  Matias et al. 2018). We propose a penalized likelihood approach to perform inference, and we use optimization via gradient descent to obtain optimal estimates of the model parameters. We have created a software that implements our estimation method, which is publicly available from CLPM GitHub repository (2021).
We have implemented the estimation algorithm and visualization tools in a software repository, called CLPM, which is publicly available (CLPM GitHub repository 2021). Moreover, as it can be seen in Eq. (  9 ), the log-likelihood is additive in the number of nodes. Potentially, this remark allows one to speed up the inference of the model parameters by means of stochastic gradient ascent  (Bottou 2010). Indeed, let us introduce ψ 1 ,. , ψ n such that ψ i (β, Z) : = 1 2 n j=1 j =i     e∈E ij (β-z i (τ e ) -z j (τ e ) 2 )   - T 0 e β-z i (s)-z j (s) 2 ds   - 1 2σ 2 K k=1 z i (η k ) -z i (η k-1 ) 2 - 1 2σ 2 0 z i (η 0 ) 2 and a discrete random variable Ψ(β, Z) such that π := π i := P{Ψ(β, Z) = ψ i (β, Z)|Z} = 1 n , ∀i ∈ {1,. , n} where we stress that the above probability is conditional to Z and given the model parameter β.
Dynamic block structures Simulation study 1. In this first experiment, we use a data generative mechanism that relies on a dynamic blockmodel structure for instantaneous interactions  (Corneli et al. 2017 ). We specifically focus on a special case of a dynamic stochastic blockmodel where we can have community structure, but we cannot have disassortative mixing, i. e. the rate of interactions within a community cannot be smaller than the rate of interactions between communities. In this framework, the dynamic stochastic blockmodel approximately corresponds to a special case of our distance CLPM, whereby the nodes clustered together essentially are located nearby. In the generative framework that we consider the only node-specific information is the cluster label, hence, this structure is not as flexible as the CLPM as regards modeling node's individual behaviours.
So, our goal here is to obtain a latent space visualization for these data, and to ensure that CLPM can accurately capture and highlight the presence of communities. An aspect of particular importance is how CLPM reacts to the creation and dissolution of communities over time: for this purpose, our generated data includes changes in the community structure over time. For this setup, we consider the time interval [0, 40] (for simplicity we use seconds as a unit measure of time), and divide this into 4 consecutive time segments of 10 seconds each. In each of the 4 time segments, 60 nodes are arranged into different community structures. Thus, any changes in community structure are synchronous for all nodes and they happen at the endpoints of a time segment. The rate of interactions between any two nodes is determined by their group allocations in that specific time segment.
Now, in dimension two, this clearly can only happen with no more than two communities for the projection model. By contrast, the distance model does not have this limitation, and it can more easily accommodate a large number of completely separated communities. We use this argument to favour the use of the distance CLPM in two dimensions, in our applications. However, we note that the projection model may be of interest in higher dimension (d >> 2) for purposes other than visualization (e. g. clustering or sub-space tracking). Technical details regarding the simulation's parameters, including penalization terms    and number of change points, can be consulted on the CLPM code repository. Simulation study 2. In the second simulation study, we use again a blockmodel structure, however, in this case we approximate a continuous time framework by defining very short time segments, and letting the communities change from one time segment to the next.
Applications In this section, we illustrate our approach over 3 real datasets, highlighting how we can characterize the trajectories of individual nodes, the formation and dissolution of communities, and other types of connectivity patterns. From the simulation studies, we have pointed out that the distance model generally provides a more convenient and appropriate framework to study these aspects of the data. In addition, it is also easier to interpret, so, we only show the results for the distance model and redirect the reader to the associated code repository where the complete results can be found.
ACM Hypertext conference dataset The ACM Hypertext 2009 conference was held over three days in Turin, Italy, from June 29 to July 1. At the conference, 113 attendees wore special badges which recorded an interaction whenever two badges were facing each other at a distance of 1. 5 meters or less, for at least 20 seconds. For each of these interactions, a timestamp was recorded as well as the identifiers of the two personal badges. This interaction dataset was first analysed by  Isella et al. (2011) , and is publicly available from Hypertext 2009 network dataset -KONECT (2017). Similarly to  Corneli et al. (2016) , we focus our analysis on the first day of the conference. On the first day, the main events that took place included a poster session in the morning (starting from 8 a. m. ), a lunch break around 1 p.
m. , and a cheese and wine reception in the evening between 6 p. m. and 7 p. m. We use our distance CLPM to provide a graphical representation of these data, and to note how the model responds to the various gatherings that happened during the day. We set a change point (η k ) every fifteen minutes. Figures  8  and 9  provide a number of snapshots highlighting some of the relevant moments of the day. The complete results, shown as a video, can be found in the code repository that accompanies this paper. We can see that, in the morning, there is a high level of mixing between the attendees. The visitors tend to merge and split into different communities that change very frequently and very randomly. These communities reach a high level of clusteredness, which signals that the participants of the study are mixing into different groups.
Reality mining The reality mining dataset  (Eagle and Pentland 2006)  is derived from the Reality Commons project, which was run at the Massachusetts Institute of Technology (MIT) from 14 September 2004 to 5 May 2005. The dataset describes proximity interactions in a group of 96 students, collected primarily through bluetooth devices. An overview of this network dataset is also given by Rastelli (2019). In the context of this paper, the proximity interactions can be reasonably considered as instantaneous interactions, due to the study being 9 months long. With our latent space representation, we aim at highlighting the patterns of connections of the students during the study, and any social communities that arise and how these change over time. Figure  10  shows a few snapshots of our fitted distance CLPM.
London bikes Infrastructure networks provide an excellent example of instantaneous interaction data. In this section we consider a network of bike hires which is collected and publicly distributed by Transport for London: Cycle hire usage data 2012-2015. We focus on a specific weekend day (Sunday 6 September 2015), and study the patterns of interactions between all bike hire stations in London. The bike hire stations correspond to the nodes of our network, whereas an instantaneous interaction between two nodes at time t simply means that a bike started a journey from one station towards the other, at that time (we consider undirected connections). In Figure  11  we show a collection of snapshots at some critical time points during the day, for the distance model. The complete results, shown as a video, can be found in the code repository that accompanies this paper.
Although there are a total of 818 stations that are active in this dataset, we provide a visualization for the 60 most active stations only. However, we emphasize that the results were obtained using the whole dataset. In addition, we highlight with a different color the 3 stations with the highest number of interactions, overall. These stations are: The first aspect that we notice is that the latent space expands during inactive times, and it contracts during busy hours. The contractions and expansions are not homogeneous, rather they highlight the presence of dense and less dense clusters of stations. The estimate for the intercept parameter is -5. 2, and the dispersion of the points in the latent space is not particularly large. This signals that the latent space characterization is not having a very strong effect on the rate of interactions, and the model does not capture much variability in the rates of interactions.
Conclusions We have introduced a new time-continuous version of the well known and widely used latent position model, as an extension which can model instantaneous interactions between entities. We have proposed a new methodology which provides good flexibility while also allowing for an efficient inferential framework. The methodology is implemented in our software CLPM which accompanies this paper and is publicly available. This provides an essential additional tool for practitioners that are interested in deriving latent space visualizations from observed instantaneous interaction data. The framework that we propose is highly inspired by the work of  Hoff et al. (2002) , and by the vast literature that has followed in this direction. Our work combines some crucial theoretical and statistical aspects of latent position modeling, with a pragmatic approach to inference and visualization of the results.
Background With the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not.
Background Although mating control is essential to genetic improvement in a honeybee breeding program  [1] , its practical application is not straightforward, due to queens' behavioral and anatomical peculiarities. Indeed, natural mating occurs during flight, typically at a few tens of meters high, where drones and young queens from several kilometers around gather together  [2]. In order to control the genetic origin of mates in a breeding program, virgin queens and mature drones can be geographically isolated on mating stations, or alternatively, artificial insemination can be used. At mating stations used in selective breeding, usually a group of sister drone-producing queens (DPQs) descending from a single dam is used to produce all the drones of a mating station. This group is referred to as a pseudo sire (PS) and is registered in the pedigree.