dataesr/scientific-paragraphs-categorization huggingface dataset:
Their considerable molecular divergence nevertheless supports the classification of the two modern sloth genera into distinct families. Within two-toed sloths, the new mitochondrial genome sequence obtained for the Southern two-toed sloth (Choloepus didactylus) appears almost identical to the reference mitogenome (NC_006924) deposited in GenBank (99. 8% pairwise identity). As expected, the Hoffmann's twotoed sloth (Choloepus hoffmanni) is more divergent (pairwise distance of 7. 2% with Cho. didactylus). The divergence time between the two toed-sloth species is estimated at about 9  Ma (fig. 2 and table 2). Choloepus hoffmanni presents two disjunct northern and southern populations. A recent study estimated the divergence between northern and southern   (Steiner et al. 2010). This evidence coupled with the significant variation in chromosome number observed in South American Choloepus (2n = 53-67; Hayssen 2010) indicates the need for a taxonomic review of both Cho.
variegatus (fig. 1 ) from which it diverged some 8  Ma (fig. 2 and table 2). By sequencing a palethroated three-toed sloth (B. tridactylus) specimen from French Guiana, where only this species occurs, we were able to confirm that the GenBank reference mitogenome (NC_006923) was originally misidentified as B. tridactylus and in fact belongs to the brown-throated three-toed sloth (B. variegatus), as previously shown by Moraes-  Barros et al. (2011). Indeed, our newly sequenced B. variegatus specimen from Peru (MVZ 155186) is 97. 3% identical to NC_006923, whereas the pairwise distance with B. tridactylus reaches 9. 5%. The divergence date between B. variegatus and B. tridactylus is estimated here around 6 Ma (fig. 2  and table 2 ). However, similar to the Hoffmann's two-toed sloth, the brown-throated three-toed sloth includes divergent mitochondrial lineages (Moraes-  Barros et al.
2  and table 2 ). This profound dichotomy appears fully compatible with previous estimates  (Delsuc et al. 2004 (Delsuc et al. , 2012) )  and confirms the rationality of dividing anteaters into two distinct families Cyclopedidae (Cyclopes) and Myrmecophagidae (Tamandua and Myrmecophaga), as proposed by  Barros et al. (2008). This taxonomic distinction also reflects the numerous morphological differences observed between the two main anteater lineages  (Gaudin and Branham 1998). Regarding lesser anteaters, our new sequence of the southern tamandua (Tamandua tetradactyla) from French Guiana confirmed the identification of the GenBank reference mitogenome (NC_004032), the two sequences being 99. 4% identical (fig. 1 ). However, our mitogenomic data revealed a very limited genetic differentiation between northern (Tamandua mexicana) and southern (Ta.
2002 (Delsuc et al. , 2003 (Delsuc et al. , 2012))   1 ) is congruent with noncoding nuclear data in supporting the paraphyly of the tribe Priodontini by grouping Tolypeutes with Cabassous to the exclusion of Priodontes (PP CAT = 1/PP PART = 1/BP PART = 88). This suggests that the morphological characters related to fossoriality used to define this tribe might either have been acquired convergently by giant and naked-tailed armadillos or, more probably, represent symplesiomorphies inherited from a fossorial ancestor. Concerning Tolypeutini, we collected the first molecular data for the flagship Brazilian three-banded armadillo (Tolypeutes tricinctus). This endangered endemic of the North-Eastern Brazilian Caatinga biome was chosen as a mascot to increase awareness about biodiversity and ecosystem conservation.
Illumina Sequencing The 33 libraries were sequenced on 3 different lanes. The first run including 19 libraries was processed on an Illumina Genome Analyzer IIx using 72 bp single-end reads. The second run of four libraries was also processed on an Illumina Genome Analyzer IIx but using 72 bp paired-end reads. These first two runs were subcontracted to Ambry Genetics (Aliso Viejo, CA, USA). The third run of 10 libraries was run on an Illumina HiSeq 2500 instrument with 100 bp paired-end reads at the Donnelly Sequencing Centre of the University of Toronto (Canada). Initial data processing and base calling, including extraction of cluster intensities, was.
Any gaps in the contig assemblies were progressively filled by extending the contigs using iterations of the mapping procedure on the consensus sequence as implemented in Geneious. Final mitochondrial genomes were annotated by alignment with published xenarthran mitogenomes, and protein-coding regions were checked to confirm no indels or stop codons were present. The 33 new xenarthran mitogenomes have been deposited in GenBank under accession numbers KT818523-KT818555.
We also repeated these three rate-variable models with a linear dependence with temperature (because we have no prior expectation about how speciation or extinction might vary with temperature). These models rely on a past environmental variable describing how the environment varied through time. For temperature, we relied on the well-known Cenozoic temperature curves published by  Zachos et al. (2001 Zachos et al. ( , 2008)). We used the R package pspline to reconstruct smooth lines of the paleo-data for the environmental variable. In other words, a smooth line is introduced in the birth-death model to represent the paleoenvironment through time, and at each point in time the model refers to this smooth line to obtain the value of the temperature. Given the dated phylogeny, the model then estimates speciation and extinction rates as a function of this value  (Condamine et al.
BAMM is implemented in a C++ command line program and the BAMMtools R package  (Rabosky, Grundler, et al. 2014). We set 4 MCMC running for 10 million generations and sampled every 10,000 generations. Other parameters were set to default values except the Poisson process prior that we set to 1. 0 following the authors' recommendation  (Rabosky, Grundler, et al. 2014). We performed four independent runs (with a burn-in of 15%) using different seeds, and we used effective sample size to assess the convergence of the runs, considering values above 200 as indicating convergence. The posterior distribution was used to compute the best global rates of diversification through time, to estimate the configuration of the diversification rate shifts by evaluating alternative diversification models as compared by Bayes factors.
Acknowledgments This work largely benefited from the help of the following individuals and institutions who provided tissue samples: Francois  Catzeflis (Institut des Sciences de l'Evolution, Montpellier, France) ,  Jean-Francois Mauffrey ,  Philippe Gaucher ,  Eric Hansen ,  Francois Ouhoud-Renoux, Jean-Christophe Vi e, Philippe Cerdan ,  Michel Blanc , and  Rodolphe Paow e (French Guiana) ,  Sergio Vizca ıno  ( Museo de La Plata, La Plata, Argentina ),  Jorge Omar Garc  ıa and  Rodolfo Rearte  ( Complejo Ecol ogico Municipal, Presidencia Roque S aenz Peña, Argentina ),  Daniel Hern andez  ( Facultad de Ciencias, Universidad de la Rep ublica, Montevideo, Uruguay ),  John Trupkiewicz  ( Philadelphia Zoo, USA ),  Darrin Lunde  ( National Museum of Natural History, Washington, USA ),  Jim Patton  and  Yuri Leite  ( Museum of Vertebrate Zoology, Berkeley, USA ),  Ross MacPhee  ( American Museum of Natural History, New York, USA ),  Jonathan Dunnum  and  Joseph Cook  ( Museum of Southwestern Biology, Albuquerque, USA ),  Donna Dittman  and  Mark Hafner  ( Louisiana State University Museum of Natural Science, Baton Rouge, USA ),  Gerhard Haszprunar  and  Michael Hiermeier  ( Zoologische Staatssammlung M€ unchen, Munich, Germany ),  G eraldine V eron  ( Museum National d'Histoire Naturelle, Paris, France ), Agust ın  Jim enez-Ruiz ,  Guido Valverde , and  Guillermo P erez-Jimeno.
METhODS Methods and any associated references are available in the online version of the paper. Accession codes. Genome assemblies and annotations for the organisms used in this study are available via the JGI fungal genome portal MycoCosm  22  (Supplementary Tables  2  and 3 ). In addition, the newly sequenced genome assemblies and annotations have been deposited in GenBank under the following accession codes and BioProjects, respectively: A. muscaria Koide BX008, JMDV00000000 and PRJNA207684; Gymnopus luxurians FD-317 M1, JJNP00000000 and PRJNA68535; H. cylindrosporum h7, JMDQ00000000 and PRJNA207849; Hydnomerulius pinastri MD 312, JMSK00000000 and PRJNA207871; Hypholoma sublateritium FD-334 SS-4, JMSJ00000000 and PRJNA70685; L. amethystina LaAM-08-1, JMSL00000000 and PRJNA196025; O. maius Zn, JMDP00000000 and PRJNA74727; P.
involutus ATCC 200175, JOMD00000000 and PRJNA60449; Paxillus rubicundulus Ve08. 2h10, JMDR00000000 and PRJNA243391; P. croceum F 1598, JMDN00000000 and PRJNA61203; Pisolithus microcarpus 441, JMDM00000000 and PRJNA60815; Pisolithus tinctorius Marx 270, JMDO00000000 and PRJNA207840; Plicaturopsis crispa FD-325 SS-3, JOMB00000000 and PRJNA207847; Scleroderma citrinum Foug A, JMDU00000000 and PRJNA207859; S. vermifera MAFF 305830, JMDS00000000 and PRJNA207844; Sphaerobolus stellatus SS14, JOMA00000000 and PRJNA207858; S. luteus UH-Slu-Lm8-n1, JMSM00000000 and PRJNA242126; T. calospora MUT 4182,  l e t t e r s JMDT00000000 and PRJNA207843). Electronic files of the phylogenomic and phylogenetic analyses have been deposited in the Dryad Repository under doi:10. 5061/dryad. f2g0f. The complete gene expression data sets have been submitted to Gene Expression Omnibus (superseries GSE63947).
ACKNOWLEDGMENTS This material is based on work conducted by the  US Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , supported under contract no. DE-AC02-05CH11231. This work was also supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  Genomic Science Program  ( Plant-Microbe Interactions  project) funded by the  US Department of Energy, Office of Science, Biological and Environmental Research  (contract  DE-AC05-00OR22725 ), the  Lorraine Region Council  (to F. M. ),  US National Science Foundation  grants  DEB-1208719  and  DEB-0933081  (both to D. S. H. and  DEB-1021606  (to A. P. )), the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig  ( DFG FTZ 118 ) and the  German Science Foundation  (DFG, grant  BU941/20-1 ) (to F.
Genome Sequencing Genomic DNA was extracted from COL parental strain and mreCD deletion mutants and sequenced using the Illumina MiSeq system (Instituto Gulbenkian de Ciência, Oeiras, Portugal). 250-bp paired end reads with 100x average coverage were generated. Sequence reads were then assembled and analyzed with SeqMan NGen 1 and SeqMan Pro 1 (Version 12. 0. DNAS-TAR. Madison, WI) software. COL genome (NCBI Accession NC_002951. 2) was used as a reference template. The specific variations of the deletion mutants were identified by comparison with the respective parental strain. Low quality variations with read frequencies below 50% were removed from the dataset. Growth curves of S. aureus strains. Growth of mreCD inducible mutants was analyzed by growing the cells overnight at 37°C in TSB medium supplemented with appropriate antibiotics and 0.
Statistical analysis GraphPad Prism 6 (GraphPad Software) was used to perform the statistical analysis. The differences in cellular volume between each of the mutants and the parental strain and the comparisons between NADA-labeling fluorescence ratios were evaluated by unpaired student's ttests. p-values 0. 05 were considered significant for all analysis performed and were indicated with asterisks: Ã p 0. 05, ÃÃ p 0. 01 and ÃÃÃ p 0. 001.
Acknowledgments We thank  Michael VanNieuwenhze  and  Yves V. Brun  ( Indiana University ) for the generous gift of NADA;  João M. Monteiro  ( ITQB ) for the construction of pFAST2 plasmid;  Ana Raquel Pereira  (ITQB) and  Nathalie Reichmann  ( ITQB ) for critically reading the manuscript;  Helena Veiga ,  Patricia Reed  and  Gonçalo Covas ,  ITQB , for assistance in molecular biology, protein purification and peptidoglycan purification techniques, respectively. We thank  Thierry Meylheuc  from the  Microscopy and Imaging Platform MIMA2  ( INRA, France ) for SEM observations and  Arnaud Chastanet  ( Micalis Institute, INRA Jouy-en-Josas ) for support for SEM observations and assistance with the preparation of the SEM Samples. We thank  Christine Longin  from the  MIMA2 MET-UMR 1313 GABI  ( Equipe Plateformes, INRA, Jouy-en-Josas ) for TEM observations.
This work was supported by a joint international grant from the  Fundação para a Ciência e Tecnologia (FCT ,  FCT-ANR/BEX-BCM/0150/2012  to M. G. P) and the  French National Research Agency  ( ANR-12-ISV3-0004-01  to R. C. -L. ). A. C. T. is the recipient of a doctoral fellowship from  FCT  ( SFRH/ BD/52204/2013 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
miR-221 expression varies in different breast cancer subtypes and is correlated with clinical outcome Given the linkage between normal breast cell subpopulations and tumor subtypes  [2, 3] , we examined miR-221 expression in different subtypes of breast cancer cells: luminal (T47D, MCF7), basal (SUM149, HCC1954), and Claudin-low (SUM159, SUM1315, MDA-MB-231) and established primary breast cancer xenografts in nonobese diabetic/severe combined immunodeficiency (NOD/ SCID) mice [MC1 (Claudin-low), UM1 (Claudin-low) and UM2 (Basal)]  [6]. qRT-PCR analysis revealed that miR-221 was barely detected in luminal type, and highly expressed in basal and Claudin-low type of breast cancer (Figure  3A ). Since cancer cell lines and tumor xenografts may not biologically recapitulate the primary tumor, we investigated miR-221 levels in a publicly available breast cancer microarray dataset (Enerly dataset  [20] ).
To identify the direct targets of miR-221, these results were compared with potential miR-221 targets predicted by miRWALK, a program combining ten miRNA target prediction databases  [23]. Four candidate target genes (ATXN1, FNDC3A, ERBB3, PSD3) were overlapped (Figure  6A ). ATXN1 (Ataxin-1), a polyglutamine protein that alters cell morphology by interacting with microtubules during neuronal development  [24, 25] , drew particular attention. ATXN1 was found to occupy the promoter region of E-cadherin gene and hence activate its expression  [24]. ATXN1 showed opposite mRNA patterns against miR-221 in normal breast cell subpopulations (Figure  6C ), breast cancer cell lines (Figure  6D ) and breast cancer subtypes from the Enerly dataset (Figure  6E ). MCF7 cell line was chosen for further experiments owing to the low basal level of miR-221 (Figure  3A ) and high level of ATXN1 (Figure  6B ).
MCF10A is a phenotypically flexible normal breast cell line representing functional and transcriptional features of both basal progenitor and luminal cells  [13, 18]. Likewise, miR-221 over-expression formed more mesenchymal MCF10A cells and miR-221 repression favored luminallike phenotype. This might be explained by the opposite expression pattern of estrogen receptor against miR-221 in these subpopulations and the negative regulatory loop between estrogen receptor and miR-221  [27]. Given the importance of estrogen receptor in mammary gland development, we propose miR-221 is involved in regulating the differentiation of normal breast epithelial cells. We also found miR-221 was differentially expressed in subtypes of breast cancer and correlated with clinical outcome. Based on the data from breast cancer cell lines and a large public dataset, we found miR-221 level was higher in claudin-low and basal type of breast cancer than in luminal type of cancer, which is similar to its pattern in normal breast stem-like, myoepithelial and luminal cells.
This is probably due to that primary normal breast cells and MCF10A cells possess more than one cell phenotype, so, the loss of myoepithelial/basal-like cells by miR-221 knock-down can be compensated by the expansion of luminal cells induced. EMT and the converse process of mesenchymalto-epithelial transition (MET) play essential roles during mammary tissue development. EMT induced cell plasticity participate in the invasion of the terminal end buds (TEBs) as expanding mammary ducts penetrate into the surrounding stroma. We found typical EMT morphology and activation of EMT-related genes after induction of miR-221 in MCF10A and MCF7 cells in vitro and in vivo. Using public miRNA datasets, miRNA profiling and luciferase reporter assay, we found the EMT-related gene, ATXN1, was the target of miR-221 in the phenotypical regulation of breast cells.
qRT-PCR for miRNA and mRNA Total RNA for the miRNA and mRNA experiment were extracted from the above cells using a Qiagen miRNeasy Kit (217004) and RNeasy Kit (74104) (Valencia, CA, USA), respectively. qRT-PCR was performed using a TaqMan MicroRNA Reverse Transcription Kit and/or TaqMan PCR Kit (Applied Biosystems, Foster City, CA, USA) and the Applied Biosystems 7900HT Sequence Detection System (Applied Biosystems). The appropriate TaqMan probes were purchased from Applied Biosystems for qRT-PCR quantification of miR-221, E-cadherin, N-cadherin, Vimentin, Twist, Snail, Slug, and Oct. PCR reactions for each sample were run in triplicate. The number of cycles required for amplification to reach the threshold limit, the Ct-value was used for quantification. RNU24 was used as an endogenous control for miRNA data normalization, and TBP was used as an endogenous control for other gene normalization.
Tumor size was measured twice a week. The animals were euthanized when the tumors were approximately 1-1. 5 cm at their largest diameter. Part of each tumor was then formalin fixed and paraffin embedded for histology and tumor fragments were enzymatically digested and filtered to generate single cells for CD44/CD24 FACS staining. Secondary tumor formation was assessed by injection into female NOD/SCID mice with 10 2 , 10 3 , 10 4 cells from DOX-treated and untreated tumors, in absence of DOX water afterwards. Tumor initiating frequency was calculated by limiting dilution analysis  [21]  and the L-Calc Software  [32]  (StemCell Technologies Inc. , Vancouver, BC, Canada). When assessing the miR-221 level in breast cancer cells, human primary breast cancer xenografts MC1, UM1 and UM2 were used in addition to the established cell lines, as previously described  [6].
Microarray data analysis Public array dataset: data types, platforms and analyses are as previously described  [33]. Normalized miRNA and mRNA profiling data and clinical information were downloaded from the GEO database (GSE19536: Enerly dataset  [20] ). Samples were cross-referenced by tumor IDs. Patients were divided into high and low groups according to the median value of miR-221. The log-rank test was used for survival analysis. Transcriptional profiling: affymetrix human U133 Plus 2. 0 was used. Preparation of the cRNA, hybridizations, washes, and detection were conducted according to the manufacturer's protocols (http://www. affymetrix. com/index. affx). Expression data were analyzed by the Robust Multichip Average (RMA) method in R with Bioconductor and associated packages  [33]. miRNA family data and predicted target gene list crossed from 10 miRNA databases were downloaded from the miRWALK website (http://www.
Data represent means ± SEM of 3 wells. (*p < 0. 05). (E) Transcriptional array level of ATXN1 in Enerly dataset was higher in luminal A type of breast cancer (n = 41) than in basal type of tumors (n = 15, p = 0. 0044). (F) mRNA level of EMT related genes (E-cadherin, Snail, Twist, Vimentin) were examined after knocking down ATXN1 in MCF7 cells. Relative mRNA expression was normalized to GAPDH. Data represent means ± SEM of 3 wells. (*p < 0. 05). (G) Percentage of stem-like cells (CD24-CD44+) was examined after knocking down ATXN1 in MCF7 cells by shRNA lentivirus. Data represent means ± SEM, (*p < 0. 05, n = 3). (H) Sequence alignment of miR-221 and the target site (wild-type and mutant) in 3' UTRs of ATXN1. (I, J) Reporter assay in HEK-293FT (I) and MCF7 cells (J), with co-transfection of Wt or mutreporter and control-miR, or miR-221 mimic as indicated.
ACKNOWLEDGMENTS We thank  Dr. Stephen Ethier  for generously providing the breast cancer cell lines SUM149 and SUM159. We also thank the  University of Michigan Flow Cytometry Core ,  Microarray Core  ( Joe Washburn  and  Craig Johnson ), Vector Core, Molecular Imaging Core, and  Pathology Core  for invaluable assistance. This work was supported by  CAS  stem cell grant  XDA01040410 ,  NSFC  grants  81472741  and  81322033 (S. L. ) and 81402091(J. K. ),  Susan G   Komen career catalyst award   KG110316  and the  Fundamental Research Funds for the Central Universities   WK2070000034 (S. L. ),  Stand Up 2 Cancer  (M. W. and G. H. ),  Guangdong Department of Science & Technology Translational Medicine Center  grant  2011A080300002 (P. L. ). This Research was also supported by  Kyungpook National University Research Fund , 2012 (HSH).
Implementation Throughout, we use the term phylogenetic tree to mean unranked phylogenetic tree shape. Below, we describe how to construct the spectral density profile of a phylogenetic tree, how to compute the spectral distance between trees, and how to cluster trees based on this distance. We also describe how to identify modalities within a given phylogenetic tree and to compute associated support values. We implemented these functionalities in the R package RPANDA freely available on CRAN  (37).
We simulated birth-death trees with constant speciation (0. 2) and extinction (0. 05) rates, with 20 time units each, using the R package TESS  (41). Trees were pruned of extinct lineages and discarded if fewer than 20 lineages survived to the present. A total of 530 trees remained. We constructed the MGL and nMGL and corresponding spectral densities of each tree. The corresponding skewness and kurtosis were computed as µ  , respectively, where µ i is the ordinary ith moment of the distribution. Negative and positive skewness reflect a relative abundance of large and small λ, respectively. Lower and higher kurtosis reflect an even and uneven distribution of λ values, respectively. We compared the principal λ, skewness, and kurtosis of spectral density profiles of each simulated tree to 4 classical measures on these trees: species richness, phylogenetic diversity, the γ statistic  (16) , and Colless' index  (32).
Phylogenetic diversity was measured as the sum of phylogenetic branch length (7) using the R package picante  (42). γ is a popular summary statistic reflecting the stem-to-tip structure of a tree: negative γ values characterize stemmy trees while positive values characterize tippy trees  (16). Colless' index is a measure of the lineage-to-lineage structure of a tree: smaller Colless' indices characterize balanced trees, while larger indices characterize imbalanced trees  (32). The γ statistic and Colless' index were calculated using the R packages ape and apT reeshape, respectively.
Finally, we cluster the results with energy-based hierarchical and k-medoids clustering, defining the optimal number of clusters by both an expectation-maximization based on the Bayesian Information Criterion (BIC) and medoid partitioning. Energy-based hierarchical clustering is a particularly powerful tool for maximizing among-cluster means and minimizing within-cluster means  (43)  and can show partitioning at different levels of resolution. K-medoids clustering, on the other hand, makes no soft assignments, so each spectral density profile is assigned to a single cluster, and each assignment is given a support estimate based on silhouette width  (44). In order to check the performance of clustering phylogenies based on their spectral density profiles, we implemented the method on a set of trees simulated under different diversification processes using the R package T ESS.