dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction and expression analysis Cellular RNA was purified with the Nucleospin RNA Kit (Macherey-Nagel) or Qiagen RNeasy Mini Kit (Qiagen); gene expression was analyzed by real-time PCR as described  [39]  with specific primers (Supplementary Table  I ). TaqMan ® Array Human TGFB Pathway (ThermoFisher Scientific) analysis followed the manufacturer's instructions on the ABI 7900 HT instrument (Applied Biosystems, California, USA). The Affymetrix Genechip system U133 plus 2. 0 and the GeneChip 3'IVT Express protocol were used. Microarray hybridization, scanning, and data normalization was performed by the Uppsala University Array Platform according to standard procedures (http://www. medsci. uu. se/klinfarm/a rrayplatform/). Two independent samples for each cell clone were analyzed, and the replicate is denoted as "bis".
To identify genes that were significantly regulated by Snail, we compared U2987MG-Snail to U2987MG-mock cells and filtered gene expression based on an adjusted p < 0. 01 using geWorkbench  [40]. The U2987MG expression profiles have been deposited to Array Express https://www. ebi. ac. uk/arra yexpress/ with accession number E-MTAB-6151.
Immunofluorescence microscopy Immunofluorescence protocol has been described previously  [13]. The pSMAD1/5, SMAD2, pSMAD2, and SMAD3 antibodies used were as described under immunoblotting. Random (5-10) pictures were taken with a Nikon Eclipse 90i microscope (20× objective) at the same exposure time for each protein. ImageJ64 10. 2 software (National Institutes of Health, Bethesda, MD, USA) was used to quantify pixels in the nuclear area.
Chromogen intensity was quantified using the reciprocal intensity (r) method  [45] , where: r = 255-y, 255 being the maximum pixel intensity of unstained area (as measured by the standard intensity function in the Fiji software) and y = mean intensity of the selected tumor area. Reciprocal intensity was quantified from one to six fields of three mice per group; the number of fields depended on the tumor size, and all analyses were performed without any blinding.
Acknowledgements We thank  Bengt Westermark  and  Karin Forsberg-Nilsson  ( Dep. Immunology, Genetics Pathology, Uppsala University ) for human GBM cells;  Kuber Sampath  ( Sanofi-Genzyme Research Center, Framingham ) for recombinant BMP7;  Kohei Miyazono  ( Tokyo University ) and  Takeshi Imamura  ( Ehime University ) for SMAD vectors;  Antonio Garcia de Herreros  ( IMIM, Barcelona ) for Snail vectors;  Per-Henrik Edqvist  ( Dep. Immunology, Genetics, Pathology, Uppsala University ) and SciLifeLab Tissue Profiling platform for IHC analysis; and  Hanna Göransson Kultima (Dep. Medical Sciences, Uppsala University) and Bioinformatics Infrastructure for Life Sciences, Uppsala University  for assistance with microarray analysis. We thank  George Divolis  and members of our group for suggestions and discussions.
, 2008). Our model suggests that the increased robustness of the organism could help restore the evolvability of systems affected by a high degree of complexity (large number of traits), consistent with the previous suggestion that selection may overcome the random drift in populations that are sufficiently large  (Ohta, 1992)  in a range of trait complexity  (Gros et al. , 2009). Within the family of the FGMs, our model's ability to detect the generalist and to perform regression of the trait space complexity based on relatively small datasets is unique and could open up an avenue for the validation of FGM predictions, such as the link between robustness and trait space complexity in different organisms  (Gros et al. , 2009). In a more general context, our results on the role of robustness in evolvability are unique.
Phenotypic profiling of aneuploid yeast strains Phenotypic profiling was performed as described in  (Pavelka et al. , 2010). Briefly, 4 μl of the normalized (OD 600 = 0. 1) aneuploid and euploid cultures was spotted onto various Nunc OmniTray agar plates with a Biomek FX liquid handler. A list of chemicals and conditions used can be found in Supplemental Table  S1. After 3-10 d of growth, the plates were scanned and the images were processed with a custom R script  (Shah et al. , 2007)  to obtain the mean spot intensity.
Data processing of aneuploid Saccharomyces cerevisiae response to stress conditions The initial raw data for S. cerevisiae aneuploid growth was obtained from in-lab archives resulting from experiments performed for  Pavelka et al. (2010). The image analysis and the assembly pipeline were kept intact. Consistent with  Pavelka et al. (2010) , the density of the colony on the plate following 7 d of growth was used as a proxy for the fitness. The raw data are available in Supplemental Table  S2  (final_growth_values. csv), with strain names and stress names consistent with the original publication. This data extraction was performed using custom Python 2. 7 code located at https:// github. com/chiffa/Screening_analysis, commit ebe0eb8. First the spot intensity values were retrieved by running growth_assay_ fitting.
py. The resulting data, summarized in lastvalues. csv, were analyzed with custom Python 2. 7 and Wolfram Mathematica code, located at https://github. com/chiffa/General_Adaptability_Model, commit 624f1f7. Figure  2E  and Supplemental Figure  4A  were generated directly by running Jin_data_re_analysis. py, whereas Figure  2F  was generated directly by running Jin_Data_Regression. nb on Jin_out_haploid-gen. csv data output by Jin_data_re_analysis. py. p values for Figure  2F  were calculated from the r 2 correlation coefficient and sample number. The formula t r n r 2 1 2 = --was used to find the t-statistic value and the p value corresponding to a twotailed Student's test was used to determine the p value from the t value  (Miles and Banyard, 2007). The same procedure was applied to retrieve the p value from the r 2 correlation coefficient in the breast cancer cell lines analysis.
Breast cancer cell lines collection response to the drug treatments The initial data used for breast cancer cell lines analysis were obtained from  Daemen et al. (2013). The additional file 9 -"gb-2013-14-10-r110-s9. tsv" was downloaded, renamed "gb-breast_cancer. tsv," and analyzed with a custom Python 2. 7 pipeline located at https://github. com/chiffa/Pharmacosensitivity_growth_assays, commit b31eaa1. The first run with the "readers. py" script extracted the dilution series for every drug-cell line combination pair. The second run with "post_processing. py" used the data structures created by the first run to generate Figure  2G  and Supplemental Figure  4B. Once BT483 was identified as a generalist, normalization to it was performed and the mean and SD of normalized log-fitness were computed and stored in "BC_analysis.
csv" file (Supplemental Table  S3 ). This file was fed into the same Jin_Data_Regression. nb script as in the case of Saccharomyces cerevisiae, which generated Figure  2H. p values were generated in the same way as for aneuploid S. cerevisiae data. Conceptually, the "readers. py" script performed the following steps: Read the OD readings for blank wells on each plate, use it to calculate the instrument noise for a given plate, and discard plates that had too high an instrument noise. Pooled noise from the plates was used to estimate the instrument-based error margin of the measurements. Read the ODs for wells without drugs. Based on them, the normalized maximum is calculated. To determine the fitness, we used the total decrease in OD compared with the no-drug conditions. Due to the large size of the confidence interval for the cell line fitness in response to a drug at a given concentration, as well a strong batch effect, ODs from wells that contained the same cell lines submitted to the same drug at the same concentration were pooled together.
Simulation The simulations were performed with a custom Mathematica script, available at https://github. com/chiffa/General_Adaptability_Model, commit d66c8e0, Supporting_Simulations. nb file. The simulations consisted of a sampling of positions in the heterogeneous population in the N-dimensional trait space, based on a multinormal distribution. For the simulations of anisotropic space, the covariance matrix was constructed by sampling from a lognormal distribution with a mean of 0 and a dispersion of 1. After the positions were computed and saved, a set of random new optimum values were generated, with a fixed distance from the center of the population. The distance between the positions of subpopulations and optimums were computed and fed into the fitness function G. Direct output results were used in Figure  1G.
ACKNOWLEDGMENTS We thank  O. Tenaillon ,  S. Matuszewski ,  G. Fischer ,  J. E. Gern ,  R. Yu ,  D. Dahlen ,  A. Baryshnikova , and  D. Georgess  for insightful discussions and  O. Tenaillon ,  H. -J. Tsai ,  S. Matuszewski , and  G. Fischer  for comments on the manuscript. This work was supported by Grant  R35 GM118172  from the  National Institutes of Health  to  R. L. Any  custom software used in this publication and data sets used to obtain the results in this manuscript are listed in Materials and Methods and are in open access in the indicated GitHub repositories.
A large-scale multilocus sequence data matrix was also obtained for Pontoscolex spp. specimens using the Anchored Hybrid Enrichment (AHE) method. Multilocus phylogenetic and phylogenomic analyses, combined with species delimitation methods; including single locus (mPTP, ABGD) and multilocus (BPP) approaches, revealed congruent results. Four cryptic species were supported within the P. corethrurus species complex, and four potentially new species within the genus Pontoscolex. One widespread lineage (L1), within P. corethrurus complex was observed in the current population of Fritz Müller's garden where P. corethrurus was first described in 1856. Cryptic lineages were observed in sympatry at several localities. This, in combination with observed heteroplasmy in COI gene in one population raises an important question of reproductive isolation between these species.
Individuals from this site were considered as topotypes (i. e. , a specimen originating from the type locality of the species or subspecies to which it is thought to belong) for P. corethrurus. GenBank sequences for mitochondrial and nuclear markers corresponding to P. corethrurus were also added to our data, including cytochrome oxidase I (COI) (JN036370, JQ279700, AB543229, JN887898, JN185607, JQ279698, JQ279699, JN887896, JN887897), 16S rDNA gene (JN793524, AB543235, JN887906, JN887905), and the 28S rDNA gene (AY101571, KJ912259). These sequences were from Australia, Brazil, French Guiana, India and Japan. Two specimens of P. (Pontoscolex) spiralis (only known from a few locations in the Caribbean;  Borges and Moreno, 1990; James and Gamiette, 2016)  from Guadeloupe and Puerto Rico were added.
Phylogenetic trees The 16S rDNA, COI, 28S rDNA and ITS2 alignments were concatenated using Seaview v. 4. 5. 4 and haplotypes were generated using DnaSP v. 5  (Librado and Rozas, 2009). Topological and branch-length congruence of the mitochondrial (COI and 16S rDNA) and nuclear (ITS2 and 28S rDNA) datasets were tested using Concaterpillar 1. 8a  (Leigh et al. , 2008). Concaterpillar uses the likelihood ratio test for congruence developed by  Huelsenbeck et al. (1996)  in a hierarchical framework to identify congruent subsets of genes. DNA sequences were analyzed under the GTR model proposed by the software. The mitochondrial and nuclear datasets were found to be topologically congruent (raw P-value=0. 17, Weibullsmoothed P-value=0. 16). They were also congruent with each other according to the branch length congruence test (P=0.
16). Absence of significant incongruence between partitions (p = 0. 72) was also showed by the incongruence length difference (ILD) test of  Farris et al. (1995) , also known as the partition homogeneity test, in PAUP v. 4  (Swofford, 2002). Heuristic searches were performed on 500 pseudoreplicates with ten random additions of taxa, holding one tree per iteration to a maximum of 100 trees. Phylogenetic analyses were thus performed on the concatenated sequences. The best fitting evolutionary model was selected with MrModelTest  (Nylander, 2016)  using the Akaike Information Criterion for each individual partition and for the total concatenated dataset. For Bayesian Inference, appropriate models of substitution per gene was: GTR + G, HKY + I + G, GTR + I and GTR + G for the 16S rDNA, COI, 28S rDNA and ITS2 datasets, respectively.
The Bayesian search was carried out with MrBayes v. 3. 1. 2 (Ronquist and Huelsenbeck, 2003)  using four simultaneous Markov chains, 2 million generations and sampling every 100 generations, with a burn-in of 2000. Resulting p-files were examined in Tracer v. 1. 6 (Rambaut et al. , 2014)  to evaluate convergence and to ensure sufficient burn-in for the trees. Maximum likelihood (ML) analysis was performed with PhyMl online analysis (http://www. atgc-montpellier. fr/phyml/, Guindon et al. , 2010)  for the concatenated dataset using the global GTR + I + G model of substitution using default parameters. Clade support was assessed using bootstrap with 1000 pseudoreplicates. Trees were visualized and edited using FigTree v. 1. 4. 2. (Rambaut, 2014).
Molecular species delimitation analyses For species delimitation, we followed a two-step procedure: we first used single locus methods applied to the complete COI haplotype dataset (79 haplotypes) in order to infer species hypotheses, which were tested in a second step using multilocus analyses applied to the concatenated dataset from the 4 genes (63 sequences out of 1474; i. e. , those specimens for which we had the 4 genes sequences) in order to confirm putative species. For single locus analysis, the Automatic Barcode Gap Discovery (ABGD) method developed by  Puillandre et al. (2012)   they are typically more stable on a wide range of prior values and are generally closer to the number of groups described by taxonomists than recursive partitions (i. e. , primary partitions assume a single gap for the entire data set, while the recursive partition splits the primary partitions into secondary partitions, and so on until no further splittings occur)  (Puillandre et al.
, 2012). Another single locus analysis was the multi-rate Poisson Tree Process (mPTP) analysis which is a model that accommodates different rates of coalescence within clades  (Kapli et al. , 2017). We performed the mPTP analysis on the COI ML tree reconstructed by PhyMl, using the online mPTP implementation http://mptp. h-its. org/#/tree. We then tested the species hypotheses inferred from ABGD and mPTP using the multilocus Bayesian Phylogenetics and Phylogeography (BPP) v. 3. 1 analysis applied to a dataset combining all four genes sequenced: 16S rDNA, COI, 28S rDNA and ITS2. BPP uses the multispecies coalescent to analyze DNA sequence data, and can accommodate incomplete lineage sorting and uncertainty in the topology of the species tree  (Rannala and Yang, 2003; Yang, 2002). We used the joint species delimitation and species-tree inference (unguided species delimitation), for which the topology from the concatenated Bayesian reconstruction was used as a guide tree, and species numbers were used based on the ABGD and mPTP approaches.
Phylogenomic approach We also produced a genome wide multilocus sequence dataset for eight of the Pontoscolex species sampled for the phylogenetic analysis described above. Using the results of the latter, one specimen per lineage was selected for phylogenomic study, plus the Rhinodrilidae outgroup taxa Andiorrhinus sp. and Urobenus sp. For this analysis, Cunha et al. (2014) lineage samples were not available. We used Anchored Hybrid Enrichment (AHE) following  Lemmon et al. (2012)  and  Lemmon and Lemmon (2013) , and the workflow as described here: University (unpublished data). In total, 594 anchor loci were targeted in the probe kit. After merging the overlapping read pairs  (Rokyta et al. , 2012) , reads were mapped to the target regions using Helobdella, Dendrobaena, and Mesenchytraeus as references (for details of the assembly, and orthology methods, see  Hamilton et al.
, 2016). In order to avoid contamination caused by indexing error, consensus sequences resultin from the assembly were screened to remove those originating from fewer than 650 loci. Orthology was assessed on the resulting consensus sequences using a neighbor-joining approach based on alignment-free pairwise sequence distances. Finally, alignments were performed in MAFFT (v7. 023b, with -genafpair and -maxiterate 1000 flags,  Katoh and Standley, 2013)  and trimmed to remove poorly aligned regions. After read assembly, quality control, alignment and alignment trimming, sequence data were used to derive phylogenetic trees using ASTRAL  (Mirarab et al. , 2014; Mirarab and Warnow, 2015) , which calculates individual gene trees and assembles them into a supertree; and PhyMl online (http://www. atgc-montpellier.
Results of COI K2P genetic distances between the 9 putative species are shown in http://purl. org/phylo/treebase/phylows/study/TB2:S22250?x-access-code=3cfa49fbd797d19078a0fe3adbd8525d&format=html. Missing data made up 25. 12% of the matrix. ML tree topology was exactly the same to those obtained from four genes except that lineage L4 was absent in phylogenomic analyses (no samples provided). All nodal support values are 1000 except for the node connecting P. spiralis to the tree which is 888. The phylogenomic trees (Figure A. 2 of the Supplementary data for ML tree) were highly similar to the concatenated 4 gene trees.
The length of the alignment was shorter than for the ITS2 alignment mentioned earlier (371 vs. 409 bp), because only P. corethrurus complex sequences were used for this analysis and these contained no indels or gaps in the alignment. Heterozygous ITS2 sites were observed for 192 sequences from the P. corethrurus complex. The analysis was done on the 38 haplotypes used in the tree resulting from the analysis of the concatenated dataset. Each lineage of the P. corethrurus complex was separated in the haplotype network except for haplotype 5 which was shared between L3 and L4 and was observed in the following locations; Furnas (Azores), Rabung and Hualtu (India), National Taiwan University (Taiwan) in L4, Mancha, Tlalcotlen and Hayas (Mexico), and Palmar de la Jarreta (Cuba) in L3. Interestingly, three specimens from one site in Taiwan (National Taiwan University) showed heteroplasmy for COI mitochondrial gene.
, 2010; Roe and Sperling, 2007; Will and Rubinoff, 2004). Alternatively, multilocus analysis constitutes a valuable source of information for species delimitation because it lowers the risk of incorrect results based on single locus datasets  (Dupuis et al. , 2012; Roe et al. , 2010). Our multilocus analysis with 4 genes was run with different values of the priors for the population size (θs) and the divergence time at the root (τ0)  (Rannala, 2015)  as it has been shown that different parameters (particularly θ priors) give different results i. e. , smaller values give higher probabilities for species splits  (Rannala, 2015). Our study confirms this prediction; as θ prior was smaller, higher probability was observed for L2 and L3 (PP=1 compared to PP=0. 99). Other PP values for all the other lineages in both analyses were the same (PP=1).
It is noteworthy that two sequences from GenBank for COI (JN887896 and JN887897) and 16S (JN887906 and JN887905) genes, labeled as P. corethrurus from India, clustered with this lineage. This result highlights the risk of mistaking P. sp. 3 with corethrurus and reveals that P. sp. 3 is also present in India. Moreno (2004)emphasized that a source of misidentification could be the use of the quincunx formation of setae as diagnostic character for P. corethrurus. The quincunx formation indeed exist in P. sp. 1, P. sp. 2 and P. sp. 3 as well. Apart from P. spiralis, regular setae all over the body was also observed for P. sp. 4 which was found in Manaus (Brazil). We assume that P. sp. 4 belongs to either Meroscolex or Pontoscolex subgenus.
Acknowledgement  S. Taheri  acknowledges the  University Paris-Est and French government by Campus France  for her PhD grant. The authors thank the  Fritz Müller Museum of Blumenau  for allowing collecting on property grounds and the funding agencies for support of this study:  G. Brown ,  E. da Silva ,  M. Bartz  and  S. James thank CNPq  for fellowship support;  L. Cunha ,  D. Stanton  and  G. Brown  acknowledge support of  NERC ,  EU ,  Fundação Araucária  and the  Newton Fund ;  D. Baretta  and  M. Bartz  acknowledge  FAPESC  support;  C. -H. Chang  thanks support from  National Science Foundation, USA  ( EF-1550795 ). We would like to thank Patrick Lavelle for sampling specimens from Peru. We are grateful to  Michelle Kortyna ,  Sarah Banker , and  Alyssa Bigelow  for laboratory assistance and to  Jean-Emmanuel Longueville ,  Sean Holland ,  Kirby Birch , and  Ameer Jalal  for bioinformatics support.
sp. 1, P. sp. 2, P. sp. 3 and P. sp. 4 by P. spiralis. Moreover, the topotypes clustered with lineage L1 and the samples from the Azores were grouped with both L1 and L4. Consequently, this clade was representative of the P. corethrurus complex. Sequences from GenBank were all grouped with L1, but four sequences from India (COI sequences JN887896 and JN887897, and 16S sequences JN887906 and JN887905) fell within P. sp. 3 lineage. All the lineages had quincunx formation on the tails, except P. spiralis and P. sp. 4 that had regular A, B, C, and D setae all.
In an attempt to extend our observations to other plants we analyzed the existing GRO-maize data similarly to the PRO-cassava data. We identified 4,135 (Supplemental file S2) enhancer candidate regions using dREG and again observed a clear pattern of bi-directional transcription (Fig 3A  , 3B ). The transcription of these candidates was conserved across different genotypes as the PRO-maize data generated in our study showed transcription signals in the same regions (Fig. S4 ). We analyzed the transcription pattern of 1,495 (Supplemental file S3) enhancer candidates identified in a separate maize study  30  using an approach that integrated genome-wide methylation data, chromatin accessibility (DNAse-seq), and histone marks (H3K9ac). The GROmaize reads mapped against these candidates also showed bi-directional transcription (Fig 3C  , 3E ).
Processing and read alignment The fastq files were scanned for any residual adapter sequence (5' TGGAATTCTCGGGTGCCAAGG -3') using fastx_clipper from the FASTX_toolkit (http://hannonlab. cshl. edu/fastx_toolkit/), and the 3' molecular barcode was removed. Reads were trimmed to a maximum length of 36bp, and the reverse complement was calculated because the Hiseq apparatus starts sequencing from the 5' end. All downstream alignments were performed using Bowtie2  39. The PRO-seq method described before is not exclusive to transcripts produced by the nuclear DNA Polymerase II, therefore the reads were aligned to the chloroplast genome to eliminate transcripts produced in this organelle. The remaining reads were mapped to the Manihot esculenta reference genome v6. 1 (www. phytozome. com). Low-quality alignments were filtered and only reads mapping once to the genome were considered for further analysis (Table  S1 ).
Pro-seq read distribution The percentage of the cassava genome transcribed was calculated using bedtools (Figure  S7A ). A saturation curve, which calculates the number of unique positions covered as a function of read depth was obtained using the bed-metric scripts (https://github. com/corcra/bed-metric. git) (Figure  S7B ). Normalized BigWig files representing the mapped reads were used to visualize each strand of the genome separately in the Integrative Genomics Viewer (IGV)  41. The Metagene plots, histograms, peak scanning and gene expression values were generated using the HOMER software  42  and meta-gene maker (https://github. com/bdo311/metagene-maker). Since the cassava genome is not readily available to work with HOMER, feature annotations were created separately, and the HOMER config files were modified.