dataesr/scientific-paragraphs-categorization huggingface dataset:
Key innovations and the diversification of Hymenoptera The association of four key innovations (parasitoidism, wasp waist, stinger, secondary phytophagy) with diversification in Hymenoptera was assessed using 30 models in the HiSSE (Hidden State Speciation and Extinction) framework (Supplementary Data 10), which can incorporate unobserved traits (hidden states) that may influence diversification rate together with the observed traits  48. We also Chronogram estimated using approximate likelihood in MCMCTREE with the PAMLv4. 9 package 113 from the nuc-70% matrix and topology C-1. All outgroups were pruned from the tree and alignment prior to divergence time estimation. Twelve calibration points were used, which are indicated by a white star. analyzed carnivory and phytophagy (i. e. , primary + secondary) as the fundamental feeding strategies, including parasitoidism and secondary phytophagy, respectively.
For a detailed list of results and model specifications, refer to Supplementary Data 10. Source data for this Table  can  be found in the Dryad repository at https://doi. org/10. 5061/dryad. 08kprr54m (folder 3. 3). may be influenced by sampling completeness and the number of alignment sites analyzed  78. We show that our results are largely congruent and robust across data sets of different sizes and types of analyses, and careful measures were taken to minimize the effect of taxon sampling. Our diversification analyses corrected for unsampled diversity on the clade level by using a backbone sampling frequency for accounting for the few lineages (i. e. families) missing from our analyses. As these were mainly lineages with low diversity, we believe their exclusion likely had little effect on the overall diversity estimates.
We further included six representatives of other insect orders as outgroups by mining UCEs in silico from published genomes: Coleoptera (Agrilus planipennis), Diptera (Aedes albopictus), Lepidoptera (Papilio glaucus), Hemiptera (Homalodisca vitripennis), Psocodea (Pediculus humanus corporis), and Blattodea (Blattella germanica). Supplementary Data 1 list voucher information and NCBI accession numbers for all sequences, while more detailed specimen data is provided for sequences newly released in this article. All specimens were collected with the required permits and in accordance with local regulations at the time of their collection, and vouchers have been deposited in major collections.
For 36 chalcidoid taxa, UCE data were generated at Center de Biologie et de Gestion des Populations (CBGP), Montpellier, France using the myBaits UCE Hymenoptera 1. 5Kv1 panel  82  with similar protocols  84  , and sequenced on an Illumina MiSeq instrument. UCE sequences for the six non-Hymenoptera outgroup taxa were captured from genome assemblies published on NCBI (www. ncbi. nlm. nih. gov, see Supplementary Data 1 for accession numbers), using scripts provided within the PHYLUCE package v. 1. 5. 0  99. We followed the tutorial "harvesting UCEs from genomes" (https://phyluce. readthedocs. io/en/latest/tutorials/tutorial-3. html), except we reduced the stringency of the minimum coverage parameter of the phyluce_probe_run_multiple_lastzs_sqlite script (-minCov = 50) and used the myBaits UCE Hymenoptera 2.
5Kv2P panel as input. We captured between 235 and 634 UCE loci for outgroup taxa. All UCE data were processed using scripts within the PHYLUCE package  99. We first trimmed the demultiplexed FASTQ data output for adapter contamination and low-quality bases using Illumiprocessor v2. 0. 7  100  , based on the package Trimmomatic v0. 32-1  101. We assembled the cleaned reads using the program Trinity (version trinityrnaseq_r20140717)  102  and a wrapper script (phyluce_assem-bly_assemblo_trinity. py). At this step, we combined assemblies from previously published sequences (including outgroups) with these newly generated assemblies and aligned these to enrichment baits using phyluce_assembly_match_contigs_to_probes. py (min_-coverage=50, min_identity=80), thereby creating a relational sqlite database containing the matched probes.
Sequence quality statistics were calculated for Trinity contigs and UCE contigs using phylu-ce_assembly_get_fastq_lengths and are summarized in Supplementary Data 1. We aligned the sequence data for individual UCE loci using MAFFT v7. 130b  103  through phyluce_assembly_seqcap_align. py (settings: max_divergence=0. 2, min-length=100, -no-trim). We performed internal trimming using Gblocks v0. 91b  104  and a phyluce wrapper script (phyluce_assembly_get_gblocks_trimmed _alignment_from_untrimmed. py), with the relaxed trimming settings b1 = 0. 5, b2 = 0. 5, b3 = 12, b4 = 7. From 2590 trimmed UCE alignments, we prepared three different matrices using sets of loci recovered for at least 50%, 60%, and 70% of taxa for further analyses using the script phyluce_a-lign_get_only_loci_with_min_taxa. py.
1118, 767 and 447 UCE loci were retained for analysis in a 50%, 60%, and 70% matrix, respectively. We have outlined the above deviations from default parameters for UCE processing only; for detailed documentation of this bioinformatics pipeline refer to https://phyluce. readthedocs. io/en/latest/tutorials/. Supplementary Fig. 1  provides a flowchart-style overview of our data treatments and data sets. To extract protein-coding loci from our captured loci, we followed a published pipeline  105  and used the required script available at https://github. com/marekborowiec/uce-to-protein, which uses BLASTX (https://blast. ncbi. nlm. nih. gov/Blast. cgi) to match unaligned UCE sequences to a reference protein database. Filtering of the protein-coding UCE loci retained 324 loci present in at least 50% of taxa, which were used for subsequent phylogenetic analyses.
Diversification and comparative analyses To assess potential shifts in diversification rates over time in Hymenoptera we used a sampling fraction approach in BAMM v2. 5  45, 46  and the associated R package BAMMtools v2. 1. 7  115  , as well as a taxonomic approach implemented in the MEDUSA function in the R package Geiger v2. 0. 7 116 in R v4. 0. 3. For BAMM, we created clade-specific sampling probabilities by assembling a richness matrix with the number of described species for all families of Hymenoptera included in our analyses. We predominantly used species estimates published in Huber  18  , except for the following groups for which the classification in that volume was outdated or did not correspond with natural monophyletic groupings. Within Aculeata we used the family-level classifications established by Pilgrim et al.
Clades were assigned to one of these categories based on the life strategy exhibited by most (>80-95%) members. We allowed polymorphism for Chalcidoidea, Formicidae, and Vespidae, for which such a fully binary choice would not be representative of the group. Our rationale is further described in the Supplementary Discussion; see also Supplementary Data 16 for trait coding and the references used to score the biology of each group. We used the rayDISC function in the R package corHMM v2. 1 in R v4. 0. 3 to reconstruct ancestral states using the reduced cladelevel phylogenies created for MEDUSA analyses. We performed reconstructions under the "equal rates" model (ER) and the "all rates different" model (ARD) and compared the fit of these models with a likelihood ratio test. We tested for state-dependent diversification associated with the four putative key innovations (wasp waist, stinger, parasitoidism, secondary phytophagy) and related traits (carnivory, phytophagy) in Hymenoptera using the HiSSE (Hidden State Speciation and Extinction) framework and associated R package  48  in R v4.
This framework addresses concerns about the false positive inference of state-dependent diversification when applying less complex models such as BiSSE  126, 128. We compared 30 models in the HiSSE framework, using the full set of models tested by Beaulieu and O'Meara  48  plus six models suggested in the HiSSE documentation and a study assessing the diversification of Squamates  129. We tested the fit of our data to a full HiSSE model with unconstrained parameters and 17 subsets with various constraints on transition and diversification rates. Four models excluded hidden states and modeled traitdependent diversification in a BiSSE-like fashion. Eight characterindependent null models, or CID-2 and CID-4 models (sensu Beaulieu & O'Meara  48  ), were further tested, also including an extension of the currently implemented CID-4 models in the HiSSE package allowing for nine transitions rates  129.
The diversification rate and trait reconstruction results were plotted and summarized for the best-scoring models. We also employed a recent direct extension of the HiSSE framework in the same R package (but using v2. 1. 9 in R v4. 2. 2), the MiSSE (Missing State Speciation and Extinction) model. MiSSE executes a trait-free version of the HiSSE model and thereby focuses only on the impact of unobserved, hidden states on the diversification dynamics of a clade  130. We automated the process of fitting MiSSE models by using the "MiSSEGreedy" function and possible. -combos=generateMiSSEGreedyCombinations(), using the default for stop. deltaAICc=10 and a sampling fraction of 0. 005 based on our sampling of Hymenoptera (765 of 152,691 described species). We performed analyses using both topA-0 and C-1 topologies and summarized and plotted tip rates for the best-scoring model across both phylogenies.
Acknowledgements Laboratory work was conducted in and with the support of the L. A. B. facilities of the  National Museum of Natural History. We acknowledge general support for molecular work from L. A. B. staff and thank  Michael Lloyd  for assistance in laboratory methods. Many thanks to  Taina Litwak  for the illustrations and  Matt Bertone  for the images of wasps. Phylogenomic analyses utilized the  Smithsonian Institution High-Performance Cluster (SI/HPC ). Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  USDA. USDA  is an equal opportunity provider and employer. This study was supported by the  National Science Foundation  ( DEB-1555905 , S. G. B. ) and a  Smithsonian Institute for Biodiversity Genomics and Global Genome Initiative  grant (S.
G. B. , M. L. B. , and B. B. B. ), as well as a grant from the  GGI Peer-Review Awards Program  (B. F. S. ). R. R. K. , M. W. G. , and  M. L. B. , as well as part of the sequencing effort, were funded by  SEL-ARS. B. B. B. was supported by the  Museum für Naturkunde, Berlin , during the analyses and writing stages of this project. J. Y. R. and AC were supported by the  INRAe SPE department. B. F. S. was funded by a  GGI Peter Buck Postdoctoral Fellowship (Smithsonian Institution ) during much of this work. E. J. T. was supported by the  Florida Department of Agriculture and Consumer Services, Division of Plant Industry.
Data availability The raw sequence reads newly generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject accession code PRJNA811764 and PRJNA632862. The UCE sequence data from prior publications used in this study are available under BioProject accession codes PRJNA379583, PRJNA248919, PRJNA495844, PRJNA814466, PRJNA606284, PRJNA647791, PRJNA625490, and PRJNA473845; the genome assemblies used in this study are available under accession codes GCA_001444175, GCA_ 000699045, GCA_000762945, GCA_000696855, GCA_000006295, and GCA_000931545. The raw sequence data can also be accessed using individual accession numbers given in Supplementary Data 1. Source data for this study (assembled contig files, data matrices, tree and log files for phylogenetic analyses; input and output files for FcLM; R code and input and results files for comparative analyses) are available in the Dryad repository at https://doi.
9package113. Node numbers refer to Fig. 2. -refers to a split between two lineages, whereas + indicates a combined clade of two or more lineages. For an extended version of this table, see Supplementary Data 6. Source data for this Table can be found in the Dryad repository at https://doi. org/ 10. 5061/dryad. 08kprr54m (folder 2. 2).
The low poloidal field near this secondary X-point can lead to longer connection lengths, and there can be significant power sharing between the multiple outer divertor targets-depending on the separation of the two separatrixes and strength of cross-field transport  [8, 9]. Furthermore, the secondary X-point can be located at higher major radius than the primary, creating a magnetic flux expansion and lowering heat flux densities at the targets (similar to the Super-X divertor configuration  [10] ). Finally, the low poloidal field and local minimum in total field created by the secondary X-point could aid in detachment control  [11, 12]. Despite the potential of Snowflake and XPT divertors, neither geometry has been widely simulated in codes with kinetic neutrals, particularly in the simulation code SOLPS-ITER  [13, 14].
In fact, though snowflakes have been simulated in codes such as EMC3-EIRENE  [15] [16] [17] , they have only been simulated once before in SOLPS-ITER  [18] , and XPTs have never been simulated previously using the code due to the rigidity of the physics modules and grid generators  [14]. The benefits of using SOLPS-ITER include the complex kinetic treatment of neutrals through its coupling to the EIRENE Monte-Carlo solver, the ability to activate drifts and currents, and the extensive history and benchmarking of the code  [14]. In this work we propose a new generalised method for simulating both X-point target and low-field side snowflake divertors within SOLPS-ITER, which may allow more widespread simulation of these alternative geometries; furthering the understanding of such divertors. Building on work performed in  [18] , the modifications to SOLPS-ITER have been generalized and are integrated with the rest of the code (version 3.
Methodology When running a SOLPS-ITER simulation of a new geometry, it is first necessary to generate relevant input files. This is typically done using a combination of the DivGeo software for basic input specification, the standard grid builder CARRE  [19] , and other preprocessing routines. After generating the relevant input files, the physics simulations can be run, which for SOLPS-ITER is typically a coupled simulation of the fluid solver B2. 5 and the kinetic Monte Carlo code EIRENE  [20]. However, previous versions of SOLPS-ITER only work for an expected list of geometries, including single null (SN) divertors, double null divertors, slab geometries, annular geometries, limited geometries and stellarator islands. For more unconventional grids such as the XPT, there are two main issues that prevent using this standard workflow.
First, the physics modules of SOLPS-ITER expect a double null geometry if a grid is given that contains two X-points. The second issue is that the grid builder CARRE is unable to build grids using equilibria with two X-points not connected to the same O-point. Consequently, to run a SOLPS-ITER simulation of a Snowflake or XPT requires solutions to these two issues. Solutions to these challenges have been developed throughout this work and will be presented in the following section.
Modifying SOLPS-ITER source The first challenge to be solved before XPT or Snowflakes can be simulated in SOLPS-ITER is the fact that the fundamental physics modules are not expecting such geometries. Instead, when two X-points are detected in the geometry, SOLPS-ITER assumes the grid is a double null. These assumptions are present because the B2. 5 plasma grid must be rectangular when mapped into poloidal and radial index coordinates. The introduction of X-points or more than two targets introduces 'cuts' into the grid, where the poloidal neighbour of a cell is not the cell that is adjacent in physical space. To prepare the geometry correctly, SOLPS-ITER assumes a certain order of Xpoint and target cuts. If two disconnected X-points are detected, the code assumes the ordering is that of a disconnected double null (DDN), which is shown in figure  1.
Because the ordering of XPT cuts are different from what SOLPS-ITER expects, a standard run of the code will fail if an X-point target or Snowflake grid is provided. To overcome this, several B2. 5 preprocessing routines, and B2. 5-EIRENE interfacing routines were edited to detect whether a grid has a DDN or an XPT (equivalently a low-field side snowflake) ordering of primary and secondary X-point cuts. If a DDN is detected, the code will run in its unedited form, expecting the cut order shown in figure  1. If an XPT or snowflake is detected, however, new code has been written to unpack grid data according to an ordering of cuts shown in figure  2. With this modified code, XPTs and low-field side snowflakes (both plus and minus) can be simulated without taking away from any previous SOLPS-ITER functionality.
To do this, we have used INGRID  [21] , an open-source grid generator than can use snowflake and X-point target equilibria, among others. After the grid data is generated from INGRID and the species and surface data is obtained from DivGeo, the third step is to manually convert the surface data obtained from DivGeo and grid data into SOLPS-ready input files using custom python routines. After this third step, all geometrydependent input files should be generated, and the remaining geometry-independent input files can be written in the usual way. The standard preprocessing routines can then be run, followed by submission of the simulation itself until convergence is achieved. Converged SOLPS-ITER simulations have been obtained for a snowflake-minus, snowflake-plus, and for the first time for an XPT geometry.
SPARC-like X-point target results For this study a SPARC lower XPT geometry was simulated to verify the modified code and workflow. In this geometry the two separatrixes are 0. 4 mm apart mapped to the outer midplane, and the XPT simulation was compared to a lower SN with a similar plasma current. The XPT grid files were generated using the novel workflow in section 2, whereas the SN input files were generated using the standard CARRE workflow as a benchmark for comparison. PFC contours for both simulations are based upon those of SPARC, but are modified to allow a wider simulation domain. When the relevant input files were obtained, both simulations were run with the same coupled SOLPS-ITER routines. Both grids were roughly matched in terms of radial boundaries; the SN extending from -7. 4 mm to +1.
Conclusions Throughout this work, a new way of simulating X-point target and low-field side snowflake divertors in SOLPS-ITER is presented. The SOLPS-ITER source has been edited to accept multiple X-point alternative divertors, whilst still maintaining functionality for standard divertors. INGRID, an interactive Python grid generator has been used to generate the grid data, which is then converted into SOLPS-ITER ready input files using custom python routines. Results and analysis from SOLPS-ITER simulation of a TCV-like snowflake plus, a SPARC-like X-point target, and MAST-U-Like snowflake minus equilibria have been presented, and demonstrate the robustness of the new workflow. Consistency of these simulations with predictions from the two-point model supports the validity of the results presented.
Acknowledgments This work has been part-funded by the  RCUK Energy Programme  [Grant No. EP/T012250/1 ], and in part performed under the auspices of the  U. S. DoE  by  LLNL  under Contract  DE-AC52-07NA27344. This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No. 101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the  European Commission  can be held responsible for them. The views expressed herein do not necessarily reflect those of the  ITER Organization. The authors would like to acknowledge the TCV team for the snowflake plus equilibrium,  Commonwealth Fusion Systems  for the SPARC equilibria, the  MAST-U  team for the snowflake minus equilibria.
serosim is an open source R package designed to aid inference of serological surveys, by simulating data arising from user-specified vaccine and infection-generated antibody kinetics processes using a random effects model. Serological surveys are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological surveys and new analytical techniques used to interpret them are increasingly widespread. This expansion creates a need for tools to simulate serological surveys and the processes underlying the observed titer values, as this will enable researchers to identify best practices for serological survey design, and provide a standardized framework to evaluate the performance of different inference methods.
serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological survey sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological surveys, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.
By simulating synthetic datasets from different complex generative models where the true, full complexity of the data generating process is specified but not observable, such frameworks can be used to assess the performance and tradeoffs of new analytical techniques. The serosim package allows users to simulate observed biomarker quantities arising from a user-specified set of between-and within-host processes. These simulations provide datasets to evaluate different statistical methods and study designs, allowing users to specify and adjust model inputs to best represent the population and disease system(s) of interest. We envisage three main use cases for serosim: • Plan serosurvey sampling strategies by simulating various observation processes (varying sample sizes, sampling frequency and assay characteristics) and analyzing the inferences that can be made given the quality of the generated data (e.
g. , power analyses). • Uncover the underlying immunological and epidemiological mechanisms responsible for observed biomarkers (e. g. , antibody titers) by comparing simulations under different models and parameter values to existing serological survey results. • Generate synthetic serological data from complex, user-specified generative models to assess the performance and trade-offs of inference models. Here, we explain the structure of the serosim package and its implementation. We then outline the components required for the main function, runserosim, explain their purpose and structure, outline useful tools available within serosim to aid in their construction and provide a simple example case study. Additionally, we direct users to two additional case study vignettes, available in the package, structured in the same order as the following methods subsections to assist users in constructing the required simulation inputs under different contexts.