dataesr/scientific-paragraphs-categorization huggingface dataset:
Key innovations enable access to new adaptive zones and are often linked to increased species diversification. As such, innovations have attracted much attention, yet their concrete consequences on the subsequent evolutionary trajectory and diversification of the bearing lineages remain unclear. Water striders and relatives (Hemiptera: Heteroptera: Gerromorpha) represent a monophyletic lineage of insects that transitioned to live on the water-air interface and that diversified to occupy ponds, puddles, streams, mangroves and even oceans. This lineage offers an excellent model to study the patterns and processes underlying species diversification following the conquest of new adaptive zones. However, such studies require a reliable and comprehensive phylogeny of the infraorder. Based on whole transcriptomic datasets of 97 species and fossil records, we reconstructed a new phylogeny of the Gerromorpha that resolved inconsistencies and uncovered strong support for previously unknown relationships between some important taxa.
We then used this phylogeny to reconstruct the ancestral state of a set of adaptations associated with water surface invasion (fluid locomotion, dispersal and transition to saline waters) and sexual dimorphism. Our results uncovered important patterns and dynamics of phenotypic evolution, revealing how the initial event of water surface invasion enabled multiple subsequent transitions to new adaptive zones on the water surfaces. This phylogeny and the associated transcriptomic datasets constitute highly valuable resources, making Gerromorpha an attractive model lineage to study phenotypic evolution.
The same is also true for the subfamily Veliinae ("Veliidae"), in which Velia caprai was recovered as sister to all other clades of ("Veliidae" + Gerridae), while a clade containing species of Oiovelia and Paravelia is sister to Rhagovelia ("Veliidae": Rhagoveliinae), not to the rest of the subfamily. Furthermore, the clade (Microveliinae + Haloveliinae) is more closely related to Gerridae than to the other subfamilies of "Veliidae". Within Gerridae in the current sense (e. g. , Damgaard 2008b), Halobatinae was recovered as sister to all other subfamilies, and Rhagadotarsinae as sister to (Trepobatinae + [Gerrinae + {Charmatometrinae + Cylindrostethinae}]). These observations indicate that Microveliinae and Haloveliinae are not veliids and were consistently recovered regardless of dataset and methods we used (supplementary fig.
Time of Divergence of the Main Clades of Gerromorpha The split of Gerromorpha dated using six fossil calibration points obtained from 11 fossil records (supplementary table  S3 , Supplementary Material online) was estimated to have occurred during the Upper Permian about 259 million year ago (Mya) (95% CI 225. 55-296. 3 Mya) (fig. 2 ), consistent with other phylogenetic analyses  (Wang, et al. 2016 (Wang, et al. , 2021;; Johnson, et al. 2018). The divergence of the major clades of Gerromorpha is estimated to have started during the mid-Triassic about 202 Mya (95% CI 174. 16-232. 01 Mya) (fig. 2 ) when the clade (Gerridae + "Veliidae") split from ("Hydrometridae" + [Mesoveliidae + Hebridae + Heterocleptinae]) early during the evolution of Gerromorpha. Again, those relationships were found regardless of dataset and methods we used (supplementary figs.
S3 and S4, Supplementary Material online). This result is inconsistent with classic phylogenies using either only morphological characters or combined with few molecular markers that recovered (Gerridae + Veliidae) as a derived and later branching clade  (Andersen and Weir 2004; Damgaard 2008a ), but consistent with most recent phylogenetic analyses using larger datasets  (Wang, et al. 2016 (Wang, et al. , 2021;; Johnson, et al. 2018)  On the other side of the tree, the split between "Veliidae" (already excluding Microveliinae and Single Origin of Rowing as a Derived Mode of Locomotion in "Veliidae" and Gerridae Ancestral character state reconstruction shows a significant increase in the ratio of midleg to body length in both males and females (supplementary figs. S6 and S9, Supplementary Material online) at the base of Gerromorpha coinciding with the transition to water surface habitats  (Crumière, et al.
The use of transcriptome datasets is even more affordable and realistic as whole genome sequencing of a large number of species is still costly and requires accurate genome annotation. Drawbacks of phylotranscriptomics based on next-generation sequencing include difficulties to obtain complete gene sequences, limited data for weakly expressed or non-expressed genes and uncertainties in gene number mainly due to the presence of multiple isoforms. However, many of these potential problems can be mitigated with a proper design of sampling, good specimen preservation, enough sequencing depth, and thirdgeneration sequencing technologies. Transcriptomebased phylogenomics have been already used with success in the study of Hemiptera  (Johnson, et al. 2018) , true water bugs  (Wang, et al. 2021 ) and, as presented here, in the semi-aquatic bugs.
MBE Hebrus nipponicus (Gerromorpha), and Stenopirates sp. (Encicocephalomorpha) were sequenced independently using Illumina Hiseq 2000 paired end 90 nucleotides, while Halovelia sp. (Gerromorpha) was sequenced using Illumina Hiseq 2000 paired end 150 nucleotides  (Wang, et al. 2016). Finally, Oncopeltus fasciatus (Pentatomomorpha) gene transcripts were obtained from official gene set v1. 1  (Panfilio, et al. 2019). Hoplitocoris sp. (Enicocephalomorpha), and Ceratocombus sp. (Dipsocoromorpha) transcriptomes were downloaded from  (Johnson, et al. 2018). As with newly sequences transcriptomes, we extracted long ORFs with TransDecoder 3. 0. 1  (Hass and Papanicolau 2013)  and filtered with CD-HIT  (Li and Godzik 2006; Fu, et al. 2012)  to create a reduced dataset and limit the computational burden.
Contaminant Removal A list of 73,737,464 sequence identifiers (gi) for archaea, bacteria, other, unclassified, and viruses, were e-fetched from NCBI database to detect potential contaminants. A second list of potential contaminant sequences from Gryllus and Acheta (34,738) was created because manual checks of first gene clusters detected traces of their ribosomal RNA in some samples. The origin of this contamination is brown crickets used as a food source for all Gerromorpha species bred in the lab. We searched potential contaminant from each source independently using BLASTN  (Altschul, et al. 1990 (Altschul, et al. , 1997) )  (option: -db nt -gilist contaminant. txt -dust yes -evalue 1e-5 -max_target_seqs = 1000). We then recovered the id of any hit with an identity >90 and coverage >25% (for archaea, bacteria, other unclassified and viruses) and a minimum hit identity of 90 for Gryllus and Acheta (no minimum coverage).
Orthologous Gene Clusters We generated three different datasets of orthologous genes using OrthoFinder (Emms and Kelly 2019), SiLiX  (Miele, et al. 2011) , and BUSCOs  (Simao, et al. 2015; Waterhouse, et al. 2018). OrthoFinder: All transcriptomic sequences were used as input (option -f). Hierarchical Ortho Groups (HOGs) were recovered and those not containing at least 80% of the 104 species filtered out. The 4,181 remaining clusters (out of 181,933) were used as input clusters in the next steps. SiLiX: Transcripts from coding proteins were aligned all-against-all using BLASTP  (Altschul, et al. 1990; 1997)  masking low complexity regions and threshold of 1e-5. Output results were then clustered for homologous sequences using the software package SiLiX 1. 2. 11 which implements an ultra-efficient algorithm based on single transitive links with alignment coverage constraints  (Miele, et al.
2011 ). We ran SiLiX using stringent values of minimum 70% of identity and 80% overlap to define the clusters (options: -i 0. 7 -r 0. 8 -l 100 -m 0. 5). The 3,258,665 protein-coding transcripts from all species yielded a total of 1,702,195 clusters, including singletons. Clusters where then filtered to remove those that fail to contain at least 80% of the species. A total of 3,174 clusters were retained. Nevertheless, while OrthoFinder applies a phylogenetic reconstruction per gene to distinguish the orthologs from the paralogous, and BUSCOs uses by definition only single-copy orthologs, SiLiX clusters might contain orthologous and paralogous genes (hence a possible explanation for the reduced number of clusters compared with OrthoFinder). BUSCOs: Finally, we generated a third dataset of singlecopy orthologous genes using BUSCOs results we used to assess transcriptome completeness on which we removed potential contaminants.
A total of 1,869 clusters containing sequences for at least 80% of the species were recovered (out of 2,510 BUSCOs in OrthoDB v10 Hemiptera). Once the clusters of orthologous genes were defined for each method, we applied a custom PERL script to select a single sequence per species as multiple sequences might be present for a given species in the cluster. First, we selected for each species the transcript with cumulative best scores against the transcripts of other species in an all-against-all BLASTP. Manual checking of the SiLiX clusters results showed the existence of some chimeric clusters (containing two or more different genes) due to long conserved domains. To resolve this, in a second step we sorted the transcripts by value, selected the transcripts with highest and lowest cumulative score to start and assigned the rest of transcripts to one or the other based on their hit P-values.
This effectively divided the transcripts in two lists. Third, we selected the longest list and repeated the same list division process again. Finally, for each missing species in the longest list we selected the best hit transcript in the cluster. This method allowed us to select the more conserved transcripts for each species and manual check showed that it effectively solved the problems of chimeric SiLiX clusters. Manual checking of clusters alignments also showed the presence of gaps that might be filled with other transcripts of the cluster. This situation might be due either a real gene split in two, or most probably, due to a de novo transcriptome assembly artifact. As trying to fill the gaps or merge transcripts might increase the alignment size across species, we decided to remain cautious and avoid possible propagation of errors from a chimeric transcript in one species (i.
Alignment, Selection of Conserved Blocks and Check for Model Violation Partitions Prior to phylogeny reconstruction each of the transcript clusters from OrthoFinder, SiLiX, and BUSCOs were aligned using MAFFT  (Katoh and Standley 2013) , and we used GBLOCKS  (Castresana 2000)  to retrieve conserved Transcriptome-based phylogeny of the semi-aquatic bugs • https://doi. org/10. 1093/molbev/msac229 MBE codon regions (options: -t = c -b5 = h). We then concatenated per gene the blocks covering >50% of the gene size and at least 100 nucleotides. This resulted in 1,645, 1,923 and 1,329 gene blocks for OrthoFinder, SiLiX, and BUSCOs, respectively. To avoid partitions that can violate common assumptions in most substitution models, we tested each cluster to verify if nucleotic data was stationary, reversible, and homogeneous (SRH)  (Naser-Khdour, et al.
2019). A total of 686, 851, and 528 gene clusters passed the SRH test at position 1 and 2 in OrthoFinder, SiLiX, and BUSCOs, respectively. Third codon position was discarded as it only fulfilled SRH condition in up to three clusters. By not considering position 3 we also avoided possible substitution saturation problems in our analyses. Finally, for all genes which position 1 and 2 passed the SRH test, we generated an aminoacid dataset from their conserved codon regions. Nucleotide dataset contained  261,936, 374,586 and 203,920.
We used same parameters to reconstruct nucleotides trees from amino acids merged partitions (supplementary figs. S15-S17, Supplementary Material online). Maximum-Parsimony approach: To reconstruct the trees using maximum-parsimony, we used TNT  (Giribet 2005)  on amino acid sequences with 100 replications using TBR branch swapping (option: mult = replic 100 tbr) to get branch lengths, and resampling 1,000 times the matrix to calculate group support (option: resample replications 1,000) (supplementary figs. S18-S20, Supplementary Material online). Once all reconstructions done, we used TreeShrink package  (Mai and Mirarab 2018)  to detect outlier long branches in all our reconstructed trees. A single long branch corresponding to Neogerris hesione was detected, although not in all reconstructed trees.
Additionally, although Velia caprai was consistently branching sister to the rest of ("Vellidae" + Gerridae), we wondered if its position could explain the early divergence found between those two groups and the (Hydrometridae + Hebridae + Mesoveliidae). To test this hypothesis and discard any possible artifact of those two species on tree reconstruction, we removed those two species from OrthoFinder data and repeated the tree reconstruction using IQ-TREE for aminoacid and nucleotide sequences as described previously (supplementary figs. S21 and S22, Supplementary Material online). Finally, we also built complementary trees with IQ-TREE for BUSCOs data with no partition merging (option: -bb 1,000 -bsam GENESITE) to test robustness of the merging results (supplementary figs. S23 and S24, Supplementary Material online).
Likelihood Mapping Analyses A final additional likelihood mapping analysis was performed to confirm early split of ("Hydrometridae"+ Hebridae + Mesoveliidae) and ("Veliidae"+Gerridae) as well as sister position of "Hydrometridae" to (Hebridae + Mesoveliidae). To do so, we used IQ-TREE (option: -n 0 -lmap 4,000 -m TEST -mset LG, JTT, JTTDCMUT, MTZOA, DAYHOFF, WAG) to test possible topologies between: Outgroups, Hydrometrinae, (Hebridae + Mesoveliidae + Heterocleptinae) and ("Veliidae"+ Gerridae) (supplementary fig. S25A  and B , Supplementary Material online). Similar to phylogenetic reconstruction we tested possible topologies with and without Velia caprai with similar results (supplementary fig. S25C  and D , Supplementary Material online, respectively). Results show that our reconstructed tree topology is well supported, but almost 25% of the quartets support alternative topology with (Hebridae + Mesoveliidae + MBE Heterocleptinae) being sister to (Hydrometrinae + ["Veliidae"+Gerridae]) which reinforces the importance of using large datasets when possible.
To decide which model fit better our data, we used "geiger::fitDiscrete" function. We checked AICc values and selected the best transition model for each dataset based on a likelihood ratio test. To do so we compared the square distribution with the degrees of freedom to calculate a P-value of the likelihood ratio test (Command example: P_value_ ER_versus_SYM < -pchisq(abs(2*(ER_geiger$opt$lnL-SYM_geiger$opt$lnL)), abs(SYM_geiger$opt$k-ER_geiger $opt$k), lower. tail = FALSE)). Main figures were composed based on best transition model. Additionally, we also computed maximum-parsimony ancestral reconstruction using Rstudio package "castor" (Louca and Doebeli 2018) with transition costs "all_equal" and "exponential". All model results can be found summarized in supplementary figs. S27-S30, Supplementary Material online.
Acknowledgements We thank  Lois Taulelle  and  Hervé Gilquin  for providing access to computing resources in the  Pôle Scientifique de Modélisation Numérique (PSMN)  at the  ENS Lyon ; and  Chérif Kabir  for managing the computing resources of the  Institut de Génomique Fonctionnelle de Lyon (IGFL)  including BLAST site hosting. We thank  Jakob Damgaard  for the discussions on Gerromorpha phylogeny and evolution over the years. We thank  François Bonneton  for discussion and comments. We also thank  Qiang Xie  for data sharing and discussion.
Spectra The rest-frame UV spectroscopic data from which the ALPINE sample is selected are combined from various large surveys. Of the 105 ALPINE galaxies in the COSMOS field, 84 spectra are obtained from DEIMOS at the Keck Observatory in Hawaii  (Capak et al. 2004; Mallery et al. 2012; Hasinger et al. 2018) , and the remaining spectra are obtained from VIMOS at the VLT in Chile  (Le Fèvre et al. 2005; Tasca et al. 2017). Their spectral resolutions are different, with R ∼230 for VIMOS and R ∼3200 for DEIMOS. See  Faisst et al. (2020b)  for more details on the spectra of ALPINE galaxies.
Image Stacking and Flux Measurements Since none of the ALPINE galaxies are individually detected in the 3 and 1. 4 GHz images, we carry out a stacking analysis. In order to systematically compare the radio luminosity with the IR luminosity and perform SED fitting, we stack FIR continuum maps from ALMA and available optical/NIR/mid-IR (MIR) imaging. Two stacking methods that are commonly used are the mean or median. The former method is preferred for a population that exhibits an approximately Gaussian flux distribution, and requires the absence of close neighbors that contribute appreciable flux. On the other hand, median stacking is insensitive to the presence of outliers, and has the advantage that all the data can be used without qualifications from neighboring objects (e. g. ,  Carilli et al.
2008; Karim et al. 2011; Man et al. 2016; Leslie et al. 2020) , although it builds the S/N more slowly. As our data do not present ideal conditions for mean stacking, we adopt the median stacking method for all available bands. We require accurate source positions in order to stack the images at the locations of the galaxies. Faisst et al. (2020b)  measured the astrometric offset of the individual ALPINE galaxies between the COSMOS2015 catalog and the Hubble Space Telescope (HST) images that aligned to the Gaia reference frame. We adopt these Gaia-corrected coordinates as the locations of the galaxies for all images, except the 3 GHz images. A small systematic astrometric offset has been identified by a positional matching of the 3 GHz sources with the COSMOS2015 catalog, using a search radius of 0 8  (Smolčić et al.
The Effect of the IR SED Templates Since only a single monochromatic FIR data point is available from our observations, our estimated IR luminosity depends largely on the choice of IR SED template. Thus, we test whether a different IR SED template would change the IR luminosity enough to dramatically alter the recovered q TIR. As mentioned in  Béthermin et al. (2020) , several works have constructed IR SED empirical templates up to z ∼ 4  (Béthermin et al. 2015 , Álvarez-Márquez et al. 2016; 2017; Schreiber et al. 2018; Álvarez-Márquez et al. 2019). Here, we consider several other SED templates that might be representative of the average IR SED of our ALPINE galaxies, and calculate the difference between the fiducial L IR and the L IR using our primary IR SED template. As a reminder, the primary IR SED template is adopted from  Béthermin et al.
L. S. acknowledges the  National Natural Science Foundation of China (NSFC)  grant No. 12003030  and  Fundamental Research Funds for the Central Universities. G. L. acknowledges research grants from the  China Manned Space Project  (No. CMS-CSST-2021-A06  and No. CMS-CSST-2021-A07 ), as well as the  NSFC  grant No. 11421303. W. F. acknowledges the  NSFC  grant No. 11773024. This work is also based in part on observations taken by the  3D-HST Treasury Program  ( GO 12177  and  12328 ) with the  NASA/ESA HST , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Part of the material presented herein is based upon work supported by the  National Aeronautics and Space Administration  under  NASA  grant no. 80NSSC21K0986. This material is based upon work supported by the  National Science Foundation  under grant No.
1908422. M. B. gratefully acknowledges support from the  ANID   BASAL  project  FB210003  and the  FONDECYT regular grant   1211000. G. C. J. acknowledges  ERC Advanced Grants 695671 "QUENCH"  and  789056  " First Galaxies ," as well as support from the  Science and Technology Facilities Council (STFC). M. R. acknowledges support from  Narodowe Centrum Nauki  ( UMO-2020/38/E/ST9/00077 ). E. S. thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant # 1829740 , the  Brinson Foundation , and the  Moore Foundation. E. I. acknowledges funding from  ANID   FONDECYT Regular 1221846. This paper is based on data obtained with the  ALMA Observatory , under Large Program  2017. 1. 00428. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  MOST , and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
Bovine paratuberculosis is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is mainly transmitted between herds through movement of infected but undetected animals. Our objective was to investigate the effect of observed herd characteristics on Map spread on a national scale in Ireland. Herd characteristics included herd size, number of breeding bulls introduced, number of animals purchased and sold, and number of herds the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds. A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography.
Materials and methods A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland  [10, 24]. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography. Herds were connected with each other through observed animal trade movements  [17]. A comprehensive dataset on cattle trade movements at a national scale in Ireland was used, comprising information for each moved animal (e. g. , age at move, sex, breed, date of move, reason for the move, and source and destination herd). Details on the datasets used, and on the processes occurring at withinand between-herd scale are presented hereafter.
Data Data were extracted from the Animal Identification and Movement system (AIM) of the Irish Government's Department of Agriculture, Food and the Marine. The AIM database comprises records on bovine births, movements and movement types  [17, 27]. Movement data and demographic data (e. g. , animal ID, sex, breed, date of birth) were extracted for the 10-year period 1 st January 2009-31 st December 2018. Herds included in our dataset, also referred to as the studied metapopulation, were dairy herds classified as such using a similar methodology to Brock et al. [28]. In general, European dairy herds are organized into groups of animals that are a similar age. Each group has its own housing and management that influence how, and with which other animals, they can interact  [29]. The model was developed to simulate this contact structure for typical European dairy herds  [9, 24, 30].
Therefore, only dairy herds were included. Only herds with more than half of their animals of dairy breed (Additional file 1) were selected. Furthermore, more than 30% of the animals had to be female. With these two criteria, we were able to separate mixed herds (with female and male animals of dairy and beef breed) from store dairy male herds (high proportion of males that are of dairy breed  [28] ) and from store beef mixed herds (high proportion of beef animals that could be either males or females  [28] ). Next, herds were selected based on completeness of movement data (data were considered complete when data were available for a herd for each year of the study period). To simplify the simulations and analyses, we chose to exclude herds that closed or started during the study period (2127 herds).