dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-Seq Data Analysis RNA was isolated from each group of cells including Mock, HERV-K env KO, and HERV-K env over-expression cells, and next generation sequencing (NGS) for transcriptome analysis was performed by clustering analysis of the DEGs based on the log 2 FPKM values and a heat map was generated using Pheatmap software (v1. 0. 8, available at http://cran. r-project. org/web/packages/pheatmap/index. html, accessed on 24 February 2021) with hierarchical clustering method (complete) functions. We selected genes that showed more than two expressional differences among each group. Genes with statistically significant differences in expression were identified using the ExDEGA program (Ebiogen, Seoul, Korea).
Acknowledgements We thank  Rueangrit Promdam ,  Areeruk Nilsai ,  Mattrakan Jitpalo  and  Kanchana Jantapaso  for offering various help in the field. We are most grateful for  Martin Ellis  for proof-reading the English. We also thank the  Division of Biological Science (Biology) ,  Faculty of Science, Prince of Songkla University , and  PSU-NHM  for providing the facilities and support. This study was supported by the  Faculty of Science Research Fund, Prince of Songkla University  (contract no. 1-2561-02-001 ), the  National Science and Technology Development Agency  ( FDA-CO-2563-11031-TH ), and the  Thailand Research Fund  ( RSA6280063 ).
Population resources and feeding management Data were collected between 2002 and 2016 at the Danish Cattle Research Centre (Foulum, Denmark) and shared as part of the GenTORE project (https://www. gentore. eu). Animals were Holstein cows with lactation rank ranging from first to third lactation. Cows were housed in a freestall barn with cubicles and slatted floor and milked in an automatic milking system (AMS; DeLaval, Tumba, Sweden) allowing free cow traffic. Data were collected during various trial periods, and cows were not nested within trial. They had ad libitum access to a partial mixed ration (PMR) varying in nutritional content in accordance with the particular trial in which the cows were involved. The PMR diets used during this period contained the following ingredients (typical g/kg DM): rapeseed meal (106), barley (135), dried sugerbeet pulp (51), grass silage (264), maize silage (422), urea (7) and minerals (18).
Phenotypes and data editing Data from 1 469 lactations of 740 cows were collected. A weekly measurement of average daily milk yield per cow was obtained from the average of daily milk yield records per cow in each week. Milk samples were taken weekly for analyses of fat and protein. On the same basis, the DM contents in PMR and concentrates were analyzed regularly and the compositions were aligned and merged with feed intake records to obtain weekly DM intake (DMI) values for individual cows. Animals were also automatically weighed at each milking so that BW records were averaged to obtain a weekly record of BW per cow in each week. Body condition score (BCS) was evaluated every 2 weeks and scored on a scale from 1 to 5. A corrected milk (cmilk) trait was created following the FAO formula which defines as a standard milk with 4.
This data set, that was used to conduct the main analyses, contains 40 619 records of cmilk, 40 662 records of weight, 42 177 records of DMI and 19 661 records of BCS, which represents about 73% of the overall records. Additionally, the overall records were used to assess the robustness of our model in case of missing data, and the details of this analysis are presented in Supplementary Material S1.
Acknowledgments GenTORE (www. gentore. eu) is a  Horizon 2020 project running from 1 June 2017 to 31 May 2022. This research received funding from the  European Union 's  H2020 Research and Innovation Program  under agreement No. 727213. The authors also would like to thank  Amélie Fischer ,  Philippe Faverdin ,  Isabel Casasus  and  Daniel Villalba  for their valuable advice on the work.
Sample Collection and DNA Extraction The baseline data at day one was not collected since the cecal content is minimal at this age. Instead, the first sampling was conducted on day 7. In addition, we took measures to avoid initial biases for all of the groups, such as all of the chicks being form the same source of hatchery, all of the chicks being of the same sex (male broilers) of equal average weight, the randomized allocation of the chicks to the pens, and the randomized allocation of the pens to the treatments. At days 7, 14, 28, and 42, one bird per pen (n = 6/group) was randomly selected and was euthanized by cervical dislocation, and both ceca were removed to obtain the cecal contents. The cecal contents were collected in cryovials, snap-frozen in liquid nitrogen, and stored at -80 • C.
Sequencing and Data Analysis of Cecal Microbial Community Illumina MiSeq (Illumina, San Diego, CA, USA) paired-end sequencing was performed to determine the bacterial community composition of each sample using the 548F and 806R primers for the V4 region of the 16S rRNA gene amplicon library preparation. The MiSeq sequencing was performed according to standard Illumina protocol using a dual-indexing strategy for multiplexed sequencing  [22]. Raw sequencing data were received as the 250-bp length of each pair of the reads in FASTQ format for further processing. The Illumina data were analyzed with QIIME2 software version 2019. 10. 0  [23]. The reads were checked for quality and were subjected to a denoising method for the removal of low-quality reads and chimeras and for the correction of sequencing errors through the DADA2 plugin with default parameters.
The phylogenetic tree was constructed through the q2-phylogeny plugin and the taxonomy was assigned using the q2-feature-classifier plugin  [24]  through a pre-trained Naïve Bayes classifier based on the Greengenes v. 13_8 database. Alpha and beta diversity analyses were performed at a sequencing depth of 12,850 using the QIIME2 alpha and beta diversity plugins. Different metrics, such as observed operational taxonomic units (OTU), Pielou, and Shannon, were used to assess alpha diversity. The comparison of the alpha diversity metrics among the treatments was made through the two-way ANOVA test with the groups, time, and their interaction, which was followed by the Sidak test as a post hoc test for multiple comparisons. Beta diversity, a metric used for the comparison of microbial diversity between samples, was calculated with a weighted UniFrac metric.
The results were tested at each time point with a PERMANOVA test, and the multiple-test correction was completed with the Benjamini-Hochberg FDR method (q-values < 0. 05) in order to assess the community differences between the groups. Taxa plots were generated using the q2-taxa plugin (https://github. com/qiime2/q2-taxa, accessed 15 May 2021) to visualize the differences in the treatment groups at the phylum level. The relative abundance of microbiota between NC and other the treatments at the family and genus levels at days 7, 14, 28, and 42 was generated and ranked through Songbird software  [25]. These ranked differentials were used to pick suitable reference frames based on their presence across the most samples as the denominator for the log-ratio test using Qurro software, version v0.
Sequencing Data From the 144 samples, a total of 15,772,526 sequences were obtained, with mean of 109,531 sequences per sample. After DADA2 quality control processes, 9,317,770 sequences with a mean of 64,707 sequences per sample were retained. The samples were rarefied at 12,850 sequences per sample for even depth of analysis. The low read samples were removed, and the remaining 108 samples that reached the saturation plateau of the rarefaction curve were included for further analyses.
Probiotics Change Composition of Cecal Microbiota in Broiler Chickens To study changes of microbial taxa by probiotics, the abundance of microbiota was examined at days 7, 14, 28, and 42 of the broiler's life due to the importance of these time points in the development of stable microbiota. From the cecal samples from all of the treatments and time points, Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes were the dominant phyla, accounting for a major part of the total sequence reads (Figure  1a ). The families of Lachnospiraceae and Ruminococcaceae from the Phylum Firmicutes accounted for around 70% of the microbiota and showed higher differences among the treatment groups at day 14 than they did at the other timepoints (Figure  1b, c  The data of weighted UniFrac distance matrix were analyzed through a PERMANOVA test, and the results were corrected for significance through the Benjamini-Hochberg FDR method (q-values).
2017). With advances in sequencing technologies, a common strategy to resolve long-standing problematic nodes has been to analyse larger datasets (e. g. ,  Jarvis et al. 2014; Prum et al. 2015; McGowen et al. 2020) , typically using concatenation  (Gatesy and Springer 2014)  and/or multispecies coalescent (MSC) based approaches  (Edwards 2009; Liu et al. 2015; Jiang et al. 2020). The relative merits and disadvantages of both approaches have been strongly debated  (Bryant and Hahn 2020). Generally, concatenation appears to perform poorly when incomplete lineage sorting is high  (Degnan and Rosenberg 2006; Mendes and Hahn 2018) , whereas summary tree inferences based on the MSC are prone to errors and higher levels of uncertainty in the presence of uninformative loci  (Gatesy and Springer 2014; Xi et al.
2014; Springer and Gatesy 2016; Brown and Thomson 2017). Large multigene datasets can be further enhanced by filtering steps; for example, to reduce the effects of uninformative loci and other sources of error. Removing loci based on proportion of parsimony informative (PI) sites has been shown to improve the efficiency of summary species coalescence methods as well as increase topological concordance between inference methods  (Hosner et al. 2016; Manthey et al. 2016; Blom et al. 2017). Filtering can also be used to reduce systematic biases due to model misspecification, by removing model partitions that violate SRH assumptions  (Jermiin et al. 2004; Duchene et al. 2017; Naser-Khdour et al. 2019; Jermiin et al. 2020). Such violations may lead to systematic error that, unlike stochastic error, cannot be remedied simply by increasing the size of a dataset  (Ho and Jermiin 2004; Jermiin et al.
Taxon Sampling, Marker Selection, DNA Extraction, and Sequencing For generating new sequence data, we sampled 114 pteropodid species (135 individuals) from 43 genera, representing 57% and 96% of the respective total extant species-and genus-level diversity (IUCN Red List 2019). Two rare genera were not included due to unavailability of suitable material (Aproteles and Mirimiri). All tissue samples came from museum and other zoological collections (Supplementary Table  S1  available on Dryad at https://doi. org/10. 5061/dryad. 7m0cfxpq5). We designed a new bait set comprising exons of 1545 putative protein-coding nuclear genes, selected to encompass a range of Gene Ontology (GO) http://mc. manuscriptcentral. com/systbiol categories (Supplementary Table  S2  available on Dryad) using QuickGO  (Binns et al.
Data Assembly and Sequence Alignment For each nuclear protein-coding gene, Illumina reads were processed to construct multiple sequence alignments (MSA) using HybPhyloMaker 1. 6. 4 (Fer and Schmickl 2018). Off-target intronic sequences were trimmed to reduce alignment errors; however, off-target mitochondrial (mt) reads were recovered (see Picardi and Pesole 2012) using the pipeline HybPiper 1. 3. 1  (Johnson et al. 2016). We added published sequences for four mt loci (CO1, CYTB, 12S, 16S) from five additional species (Aproteles bulmerae, Casinycteris campomaanensis, Epomophorus anselli, Epomops buettikoferi and Mirimiri acrodonta) and two species (Epomophorus minimus and Hypsignathus monstrosus) for which we were unable to recover mitochondrial data using sequence capture (Supplementary Table  S3  available on Dryad).
Each nuclear and mt locus was individually aligned using MAFFT  (Katoh and Standley 2013). We excluded nuclear loci with < 50% species completeness and > 60% missing data. We integrated orthologous sequences (NCBI Genbank) of two outgroup species, Hipposideros armiger and Rhinolophus sinicus, representatives of the closely-related families Hipposideridae and Rhinolophidae respectively. We visually inspected and corrected alignments for indels, incorrect reading frames, and premature stop codons in BioEdit 7. 0. 5  (Hall 1999) , and retained 1455 CDSs as our baseline nuclear data matrix (referred to as 'all loci') for downstream analyses.
Phylogenetic Analyses and Tests for Model Misspecification We inferred phylogenetic trees using maximum likelihood (ML) with IQ-TREE  (Nguyen et al. 2015 , Chernomor et al. 2016 , Hoang et al. 2018 ) on our concatenated nuclear 'all loci' matrix as well as our complete mitochondrial dataset. We then reduced the 'all loci' dataset using a filtering strategy based on the parsimony informativeness (PI) content within each locus  (Meiklejohn et al. 2016). This resulted in three additional modified CDS datasets including loci with ≥ 18% (PI18, n=1088 loci), 22% (PI22, n=728 loci) and 27% (PI27, n=341 loci) of PI sites, reflecting the respective first quartile value (17. 3%), median (21. 3%) and third quartile (25. 8%) of PI variation in our baseline dataset. Thus, four nuclear CDS datasets and one concatenated mtDNA data set were used in subsequent analyses.
Details of loci assembly, alignment and filtering strategy are available in the Supplementary Materials and Methods. We also reduced our 'all loci' dataset by removing partitions that violated SRH assumptions. First, best-fit partitioning schemes and models of molecular evolution were determined with PartitionFinder 2. 1. 1  (Lanfear et al. 2016 ) using a partition-by-locus scheme for nuclear data sets and partition by locus and codon position for the mt dataset. Then, for each partition in each nuclear data set ('all loci', PI18, PI22, PI27), we tested for violation of the SRH assumptions. While new non-SRH models have been recently developed to account for data that have evolved under non-SRH conditions (e. g. ,  Zou et al. 2012; Woodhams et al. 2015) , they are not easy to use and require large computation power (e.
g. ,  Betancur-R et al. 2013 ). An alternative method is to identify data that evolved under non-SRH conditions before performing phylogenetic reconstruction. We used matched-pairs tests of homogeneity  (Jermiin et al. 2017)  implemented in IQ-TREE 2. 0-rc1  (Naser-Khdour et al. 2019). Partitions rejecting the SRH assumptions (P < 0. 05) were discarded before performing ML phylogenetic inferences. In addition to concatenation analysis, coalescent-based species tree summary methods were performed with ASTRAL-III  (Mirarab et al. 2014; Zhang et al. 2018 ) and MP-EST  (Liu et al. 2010 ) on all gene trees, as well as the reduced PI datasets. Individual nuclear ML gene trees, based on the full coding sequence, inferred with IQ-TREE were used as inputs. Branch support as local posterior probabilities (LPP;  Sayyari and Mirarab 2016) , and quartet support for the main topology and alternative topologies were calculated with ASTRAL.
Tree Discordance and Gene Genealogy Interrogation To summarise discordance among ML gene trees, we estimated bipartitions based on topology using PhyParts  (Smith et al. 2015)  with the function phypartspiecharts. py (https://github. com/mossmatters). Internode Certainty All (ICA) values were calculated by comparing the individual ML gene trees to both the estimated concatenated 'all loci' tree and the ASTRAL species tree. We also quantified the gene concordance factor (gCF) and site concordance factor (sCF) in IQ-TREE 1. 7-beta7  (Minh et al. 2020). The concatenated 'all loci' tree was used as the reference tree with 100 quartets computed to estimate sCF values. Individual genes in concordance with specific branches of the reference tree were identified using the --cfverbose option. Conflict was visualised using a cloudogram generated via the DensiTree function in the R package Phangron v.
Ancestral State Reconstruction of Diet To reconstruct the evolutionary history of nectar feeding, we performed ancestral state reconstructions (ASR) of diet (frugivory vs. nectarivory) based on Topology 1 in which longstanding problematic nodes were resolved (see Result Fig. 1 ). For classifying nectar-feeding lineages, we followed  Freeman (1995)  supplemented with  Bergmans (1989) ,  Sheherazade et al. (2019)  and  Almeida et al. (2020). We used a Bayesian stochastic character mapping approach in the R package phytools version 0. 7-47  (Revell 2012)  with the function make. simmap (Bollback 2006). Akaike information criteria (AIC) were used to assess which of the three available hierarchical models (ER, SYM, and ARD) was the most appropriate for our data. Analyses were then run using the best model for 10,000 simulation replicates, with all other options set as default.
RESULTS AND DISCUSSION Using sequence capture, we successfully recovered an average of 1536. 9 loci (99. 4%) per sample (n = 135). Following manual examination of our alignments, we discarded 90 problematic loci, yielding a final dataset of 1455 CDSs spanning 2,791,426 base-pairs ('all loci' dataset). Filtering the 'all loci' dataset on parsimony informativeness reduced the numbers of CDSs to 1088, 728 and 341 based on PI thresholds of ≥18%, 22% and 26%, respectively. Using PartitionFinder 2 we also divided the 1455 loci into 1028 partitions, of which 473 partitions met SRH assumptions (555 failed) based on a matched-pairs test of symmetry (MPTS) (Table  1 ) and we included in our 'SRH loci' dataset. Finally, we recovered sequences from up to 15 mitochondrial loci, totalling 13,950 bp, with a mean of 106 taxa (min = 65 and max = 131) (Table  1 ; Supplementary Tables S1, S4-S6 available on Dryad).
patterns of weak support. Specifically, concordance analysis using PhyParts showed low percentages of gene trees recovering nodes A-F (ICA: 0. 09-0. 10; Fig. 1 ; Supplementary Fig. S14  and Table  S8  available on Dryad). We repeated the PhyParts concordance analysis using Topology 2 and Topology 3, and found that a similar number of genes supported these backbone relationships (Figure  2 ; Supplementary Figs. S15-S16 available on Dryad). Concordance analyses performed on Topology 1 using IQ-TREE also indicated that nodes A-F were supported by few gene trees (gCF=10. 55-25. 57; mean gCF=15. 95) and a low proportion of parsimony-informative sites (sCF=31. 14-40. 41; mean sCF=35. 68), despite very high bootstrap support (mean BS=100) (Figure  1 ). Furthermore, no alternative to Topology 1 emerged from the inferred gene trees, likely reflecting limited phylogenetic signal in individual loci (i.
e. low gDF1 and gDF2 values; Supplementary Table  S9  available on Dryad). To evaluate the alternative topologies in greater depth, we reordered the branching pattern of the conflicting nodes B-D relative to the other early splits, and identified and compared support for 105 possible topologies using Gene Genealogy Interrogation (GGI) (Supplementary Fig. S17  available on Dryad). We found that each of the 105 hypotheses was supported by at least one gene, and a large number of alternative topologies (n=36) received reasonable support (>15 loci). While no topology showed strong support above all others, Topology 1 was consistently the best supported based on rank of p-values (46 of 1455 loci; 3. 16%). In contrast, Topologies 2 and 3 were supported by 39 (2. 68%) and 31 (2. 13%) loci respectively, although both of these were outperformed by alternatives.
For each of these three topologies, only a subset of loci was significantly better than the second ranked locus (5, 4, and 1 loci, respectively). We also compared each of these topologies to a null hypothesis in which the five major lineages were placed as a polytomy at the crown node of the Pteropodidae (Figure  2b ; also see Alda et al. 2019), and found that none of the loci supported a resolved topology over the polytomy. On the other hand, more than a third of loci supported the polytomy over one of the alternatives (36%; 524 out of 1455 loci), with 74 showing significant support (Supplementary Table  S10  available on Dryad). Finally, we performed coalescent analysis in ASTRAL using both the set of all gene trees of rank 1 (n = 1455), and a subset of these that showed statistical support above rank 2 (P < 0.
05; n = 54). In both cases, the inferred tree was consistent with Topology 1 with the exception http://mc. manuscriptcentral. com/systbiol of the relationship of Notopteris, which in the latter analysis was recovered as a sister taxon to the Nyctimeninae (Supplementary Figs. S18 and S19 available on Dryad). By revealing that multiple evolutionary scenarios receive some degree of support, these results highlight the long-standing difficulties that researchers have faced in resolving the early splits in this group of mammals. Our analyses of concordance and node interrogation suggest that such uncertainty likely stems from two main causes. First, many single genes (around a third of those examined) appear to contain too little information for resolving ancestral nodes separated by short branches, a pattern that characterises the pteropodid tree.
Diversification and Diet Our analyses of a large molecular dataset confirm the early rapid divergence of the major fruit bat lineages. The early diversification of this group has previously been attributed to a shift in feeding habits coupled with the potential loss of laryngeal echolocation  (Amador et al. 2018; Almeida et al. 2020) , although such scenarios are speculation given the highly depauperate fossil record (e. g. ,  Gunnell and Manthi 2018). The ultimate drivers of this early diversification remain obscure, particularly since it appears to have occurred ~25 to 20 million years ago, long after the radiation of angiosperms. One scenario is that the phenotypic changes associated with a transition to frugivory, combined with their ability to fly, means that early pteropodids would have been http://mc.
manuscriptcentral. com/systbiol able to realise the ecological opportunities presented by an unevenly distributed and ephemeral resource such as fruiting trees. Indeed, frugivory was previously linked to high rates of diversification in primates  (Gómez and Verdú 2012)  and phyllostomid bats  (Rojas et al. 2012) , and it is noteworthy that these groups all exhibit visual and/or olfactory adaptations for detecting fruit  (Zhao et al. 2009; Hayden et al. 2014; Wang et al. 2020). Within the Pteropodidae, a best-fitting model (ER) of diet evolution supported seven independent transitions from frugivory to nectarivory (Fig. 3 ; Supplementary Figure  S21  available on Dryad). Nectar-feeding is relatively rare in vertebrates, and our understanding of its evolution in pteropodids has changed markedly with improved resolution of systematic relationships (e.
Implications for Systematics The inferred relationships among the main pteropodid lineages differ substantially from published phylogenetic trees (e. g. ,  Giannini and Simmons 2005; Almeida et al. 2011; 2020; Fig. 3). Given that these earlier analyses were based on eight loci (~8000 bp), representing <1% of the dataset generated here (~2,791,500 bp), we attribute the greater resolution and support for basal nodes in our trees to the much larger volume of data. We found that the tribes Pteralopini and Melonycterini grouped together (BS = 100, Fig. 1 ; Topologies 1-3) and not with other genera of the Pteropodini with which they have been traditionally allied  (Bergmans 1997; Almeida et al. 2020). Of particular interest was the phylogenetic position of the genus Eidolon, which has long been problematic and unstable, even across molecular studies (e.