dataesr/scientific-paragraphs-categorization huggingface dataset:
In cstacks, we used all the individuals in each species to build the catalog of loci. In rxstacks, we removed confounded loci with a conservative confidence limit of 0. 25, removed excess haplotypes from individuals and also removed any loci with a mean log likelihood <-10. We filtered SNPs in the populations module to retain a single random SNP per RAD-tag, remove any SNPs with a minor allele frequency (MAF) < 0. 01 or heterozygosity > 0. 5, remove any loci that were not present in all colonies and remove any SNPs that were not genotyped in at least 80% of individuals per colony. In the adegenet package  (Jombart, 2008; Jombart & Ahmed, 2011)  in R (R Core Team, 2013), we calculated whether SNPs were in Hardy-Weinberg equilibrium (HWE) in each colony and used vcftools v0. 1. 13 (Danecek et al.
| Contemporary population structure and summary statistics The number of private alleles in each colony was calculated with the populations module in Stacks, using the total SNP data sets for each species. We calculated the observed (H O ) and expected (H S ) heterozygosity for each colony with the neutral SNP data sets using genodive v2. 0b27  (Meirmans & Van Tienderen, 2004). We also used GenoDive to calculate AMOVA-based F ST estimates for each species using 999 permutations to calculate significance, and the Weir and Cockerham unbiased weighted F ST estimator  (Weir & Cockerham, 1984)  between all pairs of colonies within each species. This measure has been shown to be robust to small sample sizes when F ST is low  (Willing, Dreyer, & van Oosterhout, 2012). Significance was calculated using 10,000 permutations of the data and corrected for multiple tests using the sequential goodness of fit method (SGoF+) (Carvajal-Rodriguez & de Uña-Alvarez, 2011).
, 2016; Gilbert et al. , 2012; Janes et al. , 2017)  and that in order to gain insight into different levels of genetic structure, it is better to view and report multiple K-values. Therefore, we discuss our results for multiple values of K for each taxon, to fully understand the levels of structure in the data. Secondly, we performed PCAs for each species using the neutral SNP data sets. Allele frequencies were scaled and centred, and missing values were replaced with the species' mean allele frequency using the scaleGen function from adegenet. PCA was computed with the dudi. pca function from the ade4 v1. 7-11 package. The first three PCs were plotted against one another, but only the first two are shown, as population structure was not visible beyond PC2 in all species. Finally, we used DAPC, which can be used to describe genetic clusters by creating synthetic variables (discriminant functions) that maximize the variance among clusters while minimizing the variance within them.
| Phylogenetics and species delimitation of gentoo penguins To investigate the phylogeographic relationships among gentoo penguin colonies, we used the coalescent species tree approach implemented in the snapp package  (Bryant, Bouckaert, Felsenstein, Rosenberg, & RoyChoudhury, 2012)  in beast v. 2. 4. 0  (Bouckaert et al. , 2014). SNAPP infers species trees from unlinked biallelic markers, such as SNPs. The method calculates species tree likelihoods directly from the data by estimating the probability of allele frequency change across nodes, thus avoiding the necessity of finding and combining individual gene trees. Nevertheless, the method is highly computationally demanding; therefore, we selected two random individuals (i. e. , four haplotypes) per colony to include in the analysis and repeated the analysis twice with different individuals to ensure reproducibility.
To investigate whether described  (Stonehouse, 1970)  and putative (de Dinechin et al. , 2012) subspecies of gentoo penguins are reciprocally monophyletic, and therefore taxonomically valid, we used raxml v8. 2. 7  (Stamatakis, 2014)  to infer maximum-likelihood phylogenies among the full complement of gentoo penguin individuals using the total SNP data set. An ascertainment bias correction was applied to the likelihood calculations, as recommended when using SNPs to account for the lack of invariant sites  (Leaché, Banbury, Felsenstein, de Oca, & Stamatakis, 2015). When using an ascertainment bias correction, all potentially invariant sites must be removed from the data set. An alignment site consisting of only heterozygotes and homozygotes for a single allele (e. g. , Rs and As with no Gs) is considered potentially invariant by RAxML; therefore, we filtered out such sites using the Phrynomics R script (https://rstudio.
stat. washington. edu/ shiny/phrynomics/). After this filtering step, 5,871 SNPs remained in the data set. We conducted 20 independent maximum-likelihood tree inferences and then drew bootstrap supports from 1,000 replicates onto the best scoring topology. All searches were conducted under the GTRGAMMA nucleotide substitution model. To further investigate the validity of the proposed subspecies divisions within gentoo penguins  (de Dinechin et al. , 2012; Stonehouse, 1970) , we compared species delimitation models using the Bayes factor delimitation method BFD*  (Leaché, Fujita, Minin, & Bouckaert, 2014)  as implemented within the snapp package  (Bryant et al. , 2012)  in BEAST 2. 4. 3  (Bouckaert et al. , 2014). The BFD* method estimates the marginal likelihoods of competing species delimitation models using path sampling, such that the models can be ranked by their marginal likelihoods, with Bayes factors then used to assess support for the model rankings.
A total of 16 individuals were included in the analysis, such that each putative subspecies unit within our species delimitation models had a minimum of four representatives. Therefore, we included four individuals from each of the following: (d) all colonies grouped except for Kerguelen. We used the neutral SNP data set for the BFD* analysis, as required for coalescent-based methods. SNPs that were no longer polymorphic within the reduced data set of individuals were removed, leaving 8,116 SNPs. In SNAPP, we specified a gamma prior distribution for the theta parameter, with alpha = 1 and beta = 1000, for a prior mean of 0. 001 on theta. This value was chosen to reflect the 0. 1% sequence divergence observed among gentoo penguin alleles known to belong to a single subspecies. The speciation rate, lambda, was fixed at 495, as calculated from the tree height and number of tips using the Python script yule.
py (https://github. com/joaks1/pyule). Initial exploratory path sampling analyses were conducted to determine an appropriate number of steps to produce stability of the marginal likelihood, with 72 deemed more than sufficient. Path sampling analyses of 72 steps were then conducted for each of four species delimitation models with 100,000 MCMC generations following 10,000 pre-burn-in. Finally, to estimate divergence times among the major gentoo lineages, we performed time-calibrated Bayesian phylogenetic analyses of published mitochondrial hypervariable region (HVR) sequences for 47 gentoo penguins, with the topology constrained to that inferred using the SNP data set, using BEAST 2. 4. 4  (Bouckaert et al. , 2014). Representatives from Kerguelen (n = 7), the Falkland Islands (n = 10), South Georgia (n = 10), the South Shetlands (n = 10) and the West Antarctic Peninsula (n = 10) were included, along with chinstrap penguins (n = 3) as the outgroup.
Accession numbers for these sequences can be found in the Supplementary Information. The nucleotide substitution model was specified as HKY with four gamma categories. We used the Yule tree prior with a strict molecular clock calibrated with the divergence of chinstrap and gentoo penguins, estimated at 3. 17 Ma (95% HPD: 1. 69 -4. 94)  (Gavryushkina et al. , 2017). The topology was constrained to that resolved by our RAxML and SNAPP analyses of the full data set. Two independent analyses, from different random number seeds, were performed to ensure reproducibility of the posterior distribution. The MCMCs were run for 150 million generations and convergence of the posteriors confirmed using tracer v1. 6  (Rambaut & Drummond, 2007). A maximum clade credibility tree with mean node heights was estimated from each posterior after removing the first 10% of samples as burn-in.
| Genotyping We genotyped 376 individuals across five penguin species, representing 32 breeding colonies (Figure  1 ). To ease interspecific comparisons, we refer to island or archipelago names rather than colonies (Supporting information Tables  S1, S2 ). RAD-Seq yielded an average of 11. 6 million reads per individual, with 97. 1% retained after quality control. Alignment to reference genomes, SNP calling and filtering generated high-coverage, neutral SNP data sets (Table  1 ). Median sequencing depth per SNP ranged from 26. 7 in chinstrap penguins to 32. 1 in emperor penguins (Table  1 , Supporting information Figure  S1 ). Sequencing depth of individuals was similar among species (Supporting information Figure  S2 , Table  S3 ), as was the distribution of minor allele frequencies (Supporting information Figure  S3 ).
There was no evidence for lane effects in our data: Sequencing depth did not vary significantly among lanes, the locations of SNPs called within reads were even and similar across lanes, and the patterns of population structure that we found showed no relationship to the lanes on which individuals were sequenced. Given the large number of outliers in the gentoo penguin data set, we investigated whether loci putatively under selection were influencing patterns of genetic differentiation by comparing pairwise F ST values from both the neutral and total SNP data sets (Supporting information Tables  S4, S5 ). The differentiation patterns were the same; therefore, we used the neutral SNP data set for all subsequent analyses.
| Levels of intraspecific variation among species The mean minor allele frequency (MAF) did not differ among species (Table  1 ). Measures of genetic differentiation based on F ST were therefore comparable  (Jakobsson, Edge, & Rosenberg, 2013). It should be noted that the data set MAFs were low (0. 062-0. 091), as expected for biallelic SNP data sets generated from RAD-Seq. The maximum values of F ST were therefore mathematically constrained to small values  (Jakobsson et al. , 2013) , and relatively low, yet statistically significant, observations of F ST should not be misinterpreted as a lack of genetic differentiation. AMOVA-based estimates of F ST were small but significant for king, emperor, chinstrap and Adélie penguins (F ST = 0. 002-0. 003, Table  1 ; Figure  2 ), suggesting that genetic differentiation is present across the range of each species.
DATA AVA I L A B I L I T Y The Illumina reads are available from the NCBI Sequence Read Archive: https://www. ncbi. nlm. nih. gov/bioproject/ PRJNA384210 for emperor penguins; https://www. ncbi. nlm. nih. gov/bioproject/ PRJNA343012 for king penguins; https://www. ncbi. nlm. nih. gov/ bioproject/PRJNA493660 for gentoo, Adélie, and chinstrap penguins. Additional materials are available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 7c0q8 for python script filter. py and the king penguin SNP data set; https://doi. org/10. 5061/dryad. 4s7t3 for the emperor penguin SNP data set; https://doi. org/10. 5061/dryad. bs30388 for Adélie, gentoo (neutral and full), and chinstrap penguin SNP data sets, plus input files for SNAPP, BFD*, and RAxML.
02 TeV measurements. The MBTS system detects charged particles over 2. 1 ≲ η ≲ 3. 9 using two hodoscopes of counters positioned at z = ±3. 6 m. The FCal consists of three sampling layers, longitudinal in shower depth, and covers 3. 2 < η < 4. 9. The ZDC are positioned at ±140 m from the IP, detecting neutrons and photons with η > 8. 3. This analysis uses approximately 7 µb -1 and 470 µb -1 of Pb+Pb data at √ s NN = 2. 76 and 5. 02 TeV, respectively, recorded by the ATLAS experiment at the LHC. The 2. 76 TeV data were collected in 2010, while the 5. 02 TeV data were collected in 2015. The ATLAS trigger system  [33]  consists of a level-1 (L1) trigger implemented using a combination of dedicated electronics and programmable logic, and a high-level trigger (HLT) implemented in generalpurpose processors.
Charged-particle tracks and primary vertices  [37]  are reconstructed from hits in the ID. Tracks are required to have p T > 0. 5 GeV and η < 2. 4. For the 2. 76 TeV data, tracks are required to have at least nine hits in the silicon detectors with no missing pixel hits and not more than one missing SCT hit, taking into account the presence of known dead modules. For the 5. 02 TeV data, tracks are required to have at least two pixel hits, with the additional requirement of a hit in the first pixel layer when one is expected, at least eight SCT hits, and at most one missing hit in the SCT. In addition, for both datasets, the point of closest approach of the track is required to be within 1 mm of the primary vertex in both the transverse and longitudinal directions  [38]. The efficiency, (p T , η), of the track reconstruction and track selection criteria is evaluated using Pb+Pb Monte Carlo events produced with the HIJING event generator  [39].
Results The presentation of the results is structured as follows. Section 7. 1 presents the results for r n n;1 and R n n;2 and the comparison between the two collision energies. Section 7. 2 shows the results for r n n;k for k > 1. The scaling relation from Eq. (  4 ) is tested and the contributions from v n FB asymmetry and event-plane twist are estimated. Results for the mixed-harmonic correlators, Eqs. (  7 )-(  9 ), are presented in Section 7. 3 and checked for compatibility with the hydrodynamical picture. The measurements are performed using charged particles with 0. 5 < p T < 3 GeV, and the reference flow vector is calculated with 4. 0 < η ref < 4. 9. Most results are shown for the √ s NN = 5. 02 TeV Pb+Pb dataset, which has better statistical precision. The results for the √ s NN = 2.
76 TeV Pb+Pb dataset are shown only for r n n;1 and R n n;2. 7. 1 r n n;1 and R n n;2 at two collision energies  20-30% centrality range, and is more pronounced in both more central and more peripheral collisions. This centrality dependence is the result of a strong centrality dependence of the v 2 associated with the average elliptic geometry  [47]. The decreasing trend at √ s NN = 2. 76 TeV is slightly stronger than that at √ s NN = 5. 02 TeV, which is expected as the collision system becomes less boost-invariant at lower collision energy  [24]. decrease as a function of η is observed for both correlators, and the rate of the decrease is approximately  independent of centrality. This centrality independence could be due to the fact that v 3 and v 4 are driven mainly by fluctuations in the initial state.
Flow Cytometry Dissected brain samples were finely minced by scalpel blade in ice-cold Hanks Balanced Salt Solution HBSS (Sigma-Aldrich), centrifuged (400 g, 5 min, 4 • C) resuspended and incubated for 1 h at 37 • C in digestion cocktail (50 U/ml collagenase, 8. 5 U/ml dispase, 100 ug/ml Nα-Tosyl-L-lysine chloromethyl ketone hydrochloride, 5 U/ml DNaseI in HBSS). Tissue was dissociated using a Dounce homogeniser and the enzymatic reaction terminated by addition of equal volume HBSS containing 10% fetal bovine serum. Homogenates were centrifuged (400 g, 5 min, 4 • C) and pellets resuspended in 35% Percoll (GE Healthcare, Sweden), overlaid with HBSS then centrifuged (800 g, 45 min, 4 • C). The cell pellet was resuspended in FACS buffer (DPBS containing 0. 1% low endotoxin BSA). Cell suspensions were added to a 96-well plate and low affinity Fc receptors  Data for all samples were acquired using a LSR Fortessa (BD Biosciences) and analyzed using Summit (Dako) or FlowJo software.
Transcriptomic Profiling and Computational Analysis Microarray assays were performed by Edinburgh Genomics, University of Edinburgh (https://genomics. ed. ac. uk/) as described previously  (46). Total RNA was labeled using the IVT Express Kit (Affymetrix). First-strand cDNA was synthesized and converted to double-stranded DNA template for transcription and synthesis of aRNA incorporating a biotinconjugated nucleotide. aRNA was purified and fragmented prior to hybridization on Affymetrix arrays. Biotin-labeled aRNA was hybridized to the whole mouse genome HT MG-430 PM array plate (Affymetrix, CA, USA) representing >39,000 transcripts, using the GeneTitan multi-channel instrument (Affymetrix). Microarray datasets were normalized by the Robust Multiarray Averaging (RMA) method in Affymetrix Expression Console (Affymetrix, CA, USA) and analysis performed using the ht_mg-430_pm.
na36 annotation release (Affymetrix). Genes differentially expressed among treatment groups were determined by ANOVA with FDR correction using Affymetrix Transcriptome Analysis Console (TAC) and differentially expressed genes visualized by heat map. Transcripts significantly altered in expression (FDR q < 0. 05, fold change ≥1. 5) in LPS compared to PBS-treated mice were determined in TAC and log-transformed data visualized by volcano plot. For gene coexpression network analysis, a pairwise transcript-to-transcript coexpression matrix was computed in Graphia Pro (https://kajeka. com/graphia-professional/; Kajeka, UK) from the set of differentially-expressed transcripts among all treatments (determine in TAC as above) using a Pearson correlation threshold r ≥ 0. 95. A network graph was generated where nodes represent individual probesets (transcripts/genes), and edges between them the correlation in expression pattern across treatment groups.
The graph was clustered into discrete groups of transcripts sharing similar expression profiles using the Markov clustering algorithm in Graphia (inflation = 2. 2). Transcript content of key clusters was inspected manually. Cluster enrichment for Gene Ontology (GO) biological processes and cell components was determined in DAVID (https://david. ncifcrf. gov/) using default criteria. Microarray datasets are available at NCBI GEO with accession GSE122815.
Quantitative PCR cDNA was synthesized from DNase-treated RNA samples using Superscript III Reverse Transcriptase (Life Technologies) according to manufacturer's instructions. Primers were designed using Primer BLAST (http://www. ncbi. nlm. nih. gov/tools/primer-blast/) and validated to ensure adequate efficiencies. Primer sequences were as follows: Trem1 forward 5 ′ -CTGGTGGTGACCAAGGGTTC-3 ′ ; Trem1 reverse 5 ′ -CTTGGGTAGGGATCGGGTTG-3 ′ ; Trem2 forward 5 ′ -CTGCTGATCACAGCCCTGTCCCAA-3 ′ ; Trem2 reverse 5 ′ -CCCCCAGTGCTTCAAGGCGTCATA-3 ′ ; Gapdh was used as the housekeeping gene. cDNA was mixed with forward and reverse primers, ROX reference dye and Platinum SYBR Green qPCR Supermix-UDG in DNase/RNase-free water and qPCR cycles performed on a Stratagene Mx3005P thermocycler (Agilent) as follows: hot-start denaturation cycle 95 • C for 10 min, 40 cycles of amplification at 95 • C for 15 s, 60 • C for 20 s, and 72 • C for 1 min, followed by one cycle of 95 • C for 1 min, 55 • C for 30 s, and 95 • C for 30 s.
Experimental Design and Statistical Analysis Estimates of sample size were determined from previous data  (30)  to achieve a power of 0. 8 at P < 0. 05. Data were analyzed using GraphPad Prism v6 (GraphPad Software Inc. , CA, USA). One-way ANOVA with post-hoc correction was used for comparisons across multiple groups (Bonferroni) or to compare selected groups to control (Dunnett). For quantitative PCR, data were analyzed using a one sample t-test compared to PBS (reference value of 1 for the PBS group). Design and analysis of transcriptome data are described above. Analysis was performed unware of allocation to experimental group however no formal blinding procedure was used. Data are presented as mean ± SEM unless otherwise stated. * P < 0. 05, * * P < 0. 01, * * * P < 0. 001, and * * * * P < 0.
Lysosomes also act as a convergence point for autophagy-and phagocytosis-derived intra-and extra-cellular materials, and pro-resolving functions of macrophages, including phagocytosis, were associated with enhanced autophagy  (67). A further important function for lysosomal proteases is the processing of peptide antigens for presentation on MHC class II  (49). Our data showed a marked switch in the classes of MHC genes induced from the induction (MHC-I) to resolution (MHC-II) clusters. This may be similar to the enrichment of resolutionphase macrophages in the peritoneum for MHC-II-related genes  (68)  and suggests antigen presentation and links between innate and adaptive immune processes may be important for resolution  (65). Although we did not assess lymphocyte subsets in the current study, increased expression of several T cell and B cell chemoattractants in LPS-challenged mice was evident in our transcriptome dataset.
Our data reveal a global rewiring of the proteome and transcriptome during adult intestinal stem cell differentiation, with the large majority of dynamic protein expression being accompanied by a change in the corresponding transcript. Furthermore, we show that the Polycomb machinery represses a small but important subset of transcripts during adult intestinal stem cell differentiation. Finally, we identified the nuclear receptor hepatocyte nuclear factor 4 gamma (Hnf4g) as a major driver of enterocyte-specific gene expression patterns in intestinal organoids. Remarkably, loss of Hnf4g results in a partial loss of enterocyte-specific gene expression and an increase in secretory cells such as goblet cells in organoids and in the mouse small intestine. These datasets provide a rich resource for the community.
Global rewiring of the proteome and transcriptome during adult intestinal stem cell differentiation To study gene expression dynamics in the different organoid cultures at the mRNA and protein level, we integrated mass spectrometry-based proteomics and RNA sequencing data. We made use of spike-ins, both at the mRNA and at protein level, thus facilitating absolute quantification of the proteome and transcriptome. Reassuringly, both at the protein and at mRNA level, intestinal stem cell markers are significantly enriched and depleted in CV and EN, respectively, while known differentiation markers show opposite dynamics  (Figs 2A and EV1A, Dataset EV1). As expected, both stem cell and differentiation markers are expressed in the ENR organoid culture. Interestingly, while the EN organoids are strongly enriched for enterocyte markers Vil1  (Sato et al, 2011) , Alpi  (Tetteh et al, 2016) , Ptk6  (Haegebarth et al, 2006), Krt20 and Anpep (Merlos-Suarez et al, 2011) , other differentiation markers are not enriched in EN compared to ENR.
As reported before  (Schwanhausser et al, 2011) , absolute mRNA and protein copy numbers correlate poorly when compared within the same sample (R 2 = 0. 40, Fig  EV2A ). However, here we show that transcriptomic changes between intestinal cell fates lead to correlated changes at the proteome  (Fig 2A). As expected, the stem cell-enriched gene expression signature is significantly enriched for proteins involved in cell cycle and DNA replication, while enterocyte-enriched organoids express many proteins involved in different metabolic pathways  (Fig 2B). This likely reflects the nutrient absorption function of mature enterocytes in vivo. To investigate the consequences of these abundant metabolic programs, we performed mass spectrometry-based lipidomics and metabolomics on the cell-typeenriched organoids and integrated this with gene expression dynamics  (Fig EV1B, Dataset EV2).
First, we verified that there are significant differences in protein translation and degradation rates between stem cell-enriched and enterocyteenriched organoids  (Fig EV2B). Next, we compared the protein to mRNA levels individually between  CV and EN organoids (Fig 2C, Dataset EV3). Importantly, we see many known regulatory proteins of stem cell maintenance with significantly higher protein to mRNA ratios in the CV organoids. This could suggest that post-transcriptional regulation of transcription factors such as E2f4 and Sox9 might contribute to adult stem cell fate.