dataesr/scientific-paragraphs-categorization huggingface dataset:
Democlavia p Democlavia p is the most species-rich (roughly 75% of demosponge species;  [38] ) and diverse of the major demosponge clades, and includes the traditional orders Agelasida, Astrophorida, Hadromerida, Halichondrida, Poecilosclerida, and Spirophorida  [48] , several of which are already thought to not be monophyletic (as discussed below). As such, the systematics of Democlavia p presents many challenges, but important breakthroughs are being made in understanding the phylogeny of this clade based on increasingly taxon-rich analyses of ribosomal RNA and mitochondrial CO1 data  [38]. Our nuclear housekeeping gene dataset and analyses provide an opportunity to test hypotheses arising from these alternative sets of data and suggest new hypotheses where previous results have provided no resolution.
Nevertheless, there are still key points of difference, for example the position of the freshwater Spongillida p clade, that remain to be tested by new datasets, and numerous open questions not yet satisfactorily answered by any phylogenetic analyses, such as the position of Tetractinellida p within Democlavia p , and the relationships among Keratosa p , Myxospongiae p , and the clade consisting of Democlavia p , Haploscleromorpha p , and Spongillida p. A final important advance of this study is that incorporates a diverse set of sponge systematicists engaged in transforming the taxonomy (both PhyloCode-based and more traditional approaches) used to describe demosponge diversity. As a new understanding of demosponge relationships emerges, the names -and possibly the rules by which we erect and use themmust change  [38] [39] [40] [41].
[44]. In many cases, however, it was necessary to use nested PCR primers if amplification and re-amplification of housekeeping gene products was not possible. Table  S3  provides primer sequences for nested amplifications of individual housekeeping genes. Primary or nested amplification products were cloned into PCR cloning vectors (pGEMH-T, Promega or TOPO TAH, Invitrogen) and individual clones were prepared for DNA sequencing using standard protocols. After editing and trimming vector sequences with GENEIOUS  [77] , DNA sequences were assessed for gene and sponge identity via BLASTX or BLASTP queries  [78] , followed by preliminary single-gene phylogenetic analyses under the likelihood framework described below. The identification of likely paralogs followed standard procedures based on the generation of trees including all the members of each gene family that could be identified in GenBank (via reciprocal blasting).
Within the context of these trees, paralogy groups were identified and only the sequences nesting within the selected orthology group were used. New sequences generated in this study have been submitted to GenBank (Table  1 ). Sequences are also available via the Porifera Tree of Life database (PorToL. org). In addition, voucher specimens for many of the sequences presented in Sperling et al. [18, 44]  were examined, resulting in several instances of updated taxonomic identification and classification (Table  1 ). Nucleotide sequences were translated and aligned using MUSCLE  [79]  and visualized in SEAVIEW (v. 4. 3)  [80]. In addition to the new sequences, the initial alignment included data for sponges that had already been published (Table  1 ). Also, five species for which transcriptome data exist were also added to the dataset.
Both mRNA and cDNA from Corticium candelabrum, Petrosia ficiformis and Sycon coactum were obtained using protocols available in Riesgo et al. [81]. Sycon ciliatum and Leucosolenia complicata sequences are derived from current genome and transcriptome sequencing projects for these species  [82]  and Adamska, unpublished). De novo assemblies of the reads obtained with Illumina GA (Illumina, Solexa, USA) were built with CLC Genomics Workbench 4 (CLCbio, MA, USA). Local blasts against the contig lists generated were used to search for the housekeeping genes. Initially, 50 outgroup taxa representing Bilateria, Ctenophora, Cnidaria, Placozoa and non-metazoan Opisthokonta were included in the analyses. However, preliminary phylogenetic analyses, conducted as described below, indicated that inferred demosponge relationships were robust to outgroup choice and therefore outgroups in the final dataset were reduced to the cnidarian taxa (Acropora, Metridium and Nematostella) and the placozoan Trichoplax.
Phylogenetic Analyses For all gene trees we investigated the presence of significant clustering information using Maximum Likelihood Mapping  [83]  as implemented in Treepuzzle V. 5. 2  [51]. The dataset was analyzed in both Bayesian and Maximum Likelihood (ML) frameworks. For the ML analyses, appropriate models of amino acid evolution were assessed using the Akaike Information Criterion (AIC), as implemented in ProtTest (v. 2. 4)  [49]. The computing cluster of the Smithsonian's Laboratories of Analytical Biology was used to run the parallelized version of RaxML  [84]  to search for maximum likelihood (ML) topologies. We assumed the model that best fit our data according to the second-order AIC (AICc-1) with the exception that a proportion of invariant sites was not estimated (according to a recommendation in the RaxML manual).
We also used RaxML to conduct bootstrap analyses (400 replicate searches) to assess nodal support. We searched for ML topologies using each gene separately as well as all genes combined. We analyzed the combined data a) assuming a single model for all the data and b) by assigning most appropriate models to each gene partition (mixed models). Bayesian analyses were performed using the site-heterogeneous CAT-GTR+gamma in Phylobayes 3. 3b  [85]. This model was selected because Bayesian model selection, performed using 10fold cross-validation  [86] , showed that CAT-GTR best fitted our dataset, outperforming CAT, GTR and LG. The considered models were: WAG, LG, GTR, CAT, and CAT-GTR (all models used a gamma correction to account for rate heterogeneity among sites). The CAT based models (in this case CAT and CAT-GTR  [86] ) are mixture models developed to better take into account site-specific features of protein evolution.
These models are thus expected to fit the data better than homogeneous time reversible models like LG and GTR  [86]. Indeed, CAT based models have previously been shown to fit amino acid datasets better than other models and they have been shown to be highly effective at reducing systematic biases, like long branch attraction, which are well known to be very pervasive in deep time phylogenetics. In Phylobayes two independent analyses were run for 30,000 cycles sampling every 100 points. The analyses were considered converged when the largest discrepancy observed across all bipartitions (i. e. the maxdiff statistics) dropped below 0. 15, despite the Phylobayes manual's suggestion that a chain has reached convergence when maxdiff ,0. 3. Support values for the nodes recovered in the CAT-GTR analysis are expressed as posterior probabilities.
Comparisons were made between the different single-gene topologies and the Bayesian topology to the ML tree derived from the overall data. In addition, nodal differences were calculated, as measured by the root-mean-squared distance, in Topd (v. 3. 3) [87]. Taxa that were missing data for some genes were pruned from the combined tree prior to calculating nodal differences. Topd was also used to conduct randomization analyses to test whether similarities between the various topologies and the combined ML topology were not greater than expected by chance. Finally, further ML searches were conducted by sequentially excluding the three genes that subtend the trees that are most distant from the tree derived from the concatenated dataset, as measured by subtracting the random nodal difference from the actual nodal difference.
To further investigate the extent to which the principal signal  [53]  in the single-genes corroborated the results of concatenated Bayesian and ML analyses, we performed a supertree analysis. The supertree was built using the Matrix Representation with Parsimony method  [53]. Input trees used for this analysis were, for each gene, the 400 bootstrap trees derived (see above) under ML. This set of 2800 input trees was bootstrapped to generate 100 replicate datasets, each of which scored 2800 trees using the software CLANN  [88]. For each bootstrapped dataset a bootstrap supertree was recovered and a majority rule consensus of the recovered bootstrap supertrees was built to estimate nodal support. Finally, analyses were performed to test for tree reconstruction artifacts. More precisely we investigated the potential effect of long-branch attraction and compositional attraction on our results.
We first investigated the effect of using alternative model of evolution on our results. We thus built trees (within a Bayesian framework) using models (WAG, LG, GTR, CAT, and CAT-GTR, each with a gamma correction) providing different levels of fit to the data and compared the trees we obtained. We tested whether the taxa in our dataset were compositionally heterogeneous performing a posterior predictive analysis (see for example  [18] ) of compositional heterogeneity using Phylobayes under the CAT-GTR model. The posterior predictive analysis indicated that several taxa displayed a biased composition of their sites. This, if not addressed, can cause compositional artifacts. To test whether our results were affected by compositional biases we performed two analyses. First we analysed (under CAT-GTR) a dataset from which all compositionally heterogeneous taxa were excluded.
This experiment has the downside of excluding potentially important taxa. Accordingly, a second experiment was performed in which our dataset was recoded using the Dayhoff scheme. Dayhoff recoding can alleviate compositional artifact, and a posterior predictive analysis of our Dayhoff-recoded dataset was performed (under CAT-GTR) to evaluate whether further compositionally biased taxa remained after the application of Dayhoff recoding. Finally, our Dayhoff recoded dataset was analysed using both a site-homogeneous (GTR) and a site heterogeneous (CAT-GTR) model. To test for the potential effect of long-branch attraction artifacts we identified fast evolving sites in our dataset using the program Tiger  [89]. After that, sites that Tiger deemed as being fast evolving (bins 7 to 10) were excluded and the slowly evolving sites analysed in isolation.
MAT cannot resolve 10. 2% of the quartets. (PDF) Figure  S6  Maximum Likelihood Mapping shows PFK has signal to resolve unambiguously over 71% of the quartets that make up the PFKtree. PFK cannot resolve 20. 6% of the quartets. (PDF) Figure  S7  Maximum Likelihood Mapping shows TPI has signal to resolve unambiguously over 76% of the quartets that make up the TPI-derived tree. TPI cannot resolve 15. 8% of the quartets. (PDF) Table  S1  Results of the Posterior Predictive Analysis of the combined data set (all 7 genes) under the CAT GTR model. Taxa with a star are heterogeneous in composition. (PDF) Table  S2  An analysis of the Dayhoff recoded dataset (still under CAT-GTR). As expected, nearly all the heterogeneity is gone (compared to Table  S1 ). (PDF) Table  S3  Nested primers used to facilitate amplifications of 5 of the 7 genes analyzed in this work.
Acknowledgments We are indebted to the many sponge biologists who contributed samples, expertise, and sequences to this project. Special thanks go to  Sebastian Joffre  and  Crystal Romero  for their assistance in sponge RNA preparations,  Sebastian Basile ,  Tandace Burkhart  and  Renee Potens  with sequence data curation and bioinformatics analyses, and  Chris Freeman  for assistance in collecting specimens. We thank  Martin Dohrmann  for identifying hexactinellid samples and providing helpful comments on an earlier version of this MS. We also thank  Gonzalo Giribet  and  Sally Leys  for guidance provided to obtain all the transcriptomic sequencing for the species  Petrosia ficiformis, Sycon coactum, and Corticium candelabrum.
5G ) or 40 mM (Fig. 5H ) of A2E. Cells tended to round up, to lose their confluence and their density seemed to decrease. To quantify the respective cell viability and characterize possible degenerative events caused by light conditions, we used the Apotox-Glo TM assay (Promega) on RPE cells. For each individual well, we measured 1) cell viability, 2) cell apoptosis and 3) cell necrosis. For each A2E concentration and each light condition, the measures were averaged from 4 wells and normalized with respect to the dark control (%). Measurements for each individual light condition were reproduced in at least 4 and up to 6 independent experiments. All data were normalized to the control condition without A2E in the medium and maintained in darkness. This normalization was required to give equivalent weight to all experiments because cell densities slightly varied between experiments and only 5 different light conditions were simultaneously tested in one experiment.
Statistical analysis Statistical analysis of light effect on RPE cell viability, caspase-3/7 activities and necrosis were performed using Graph Pad Prism 6 software. When precised, statistical analysis was performed using t-test. Otherwise, one-way ANOVA was used to compare variances between groups at each A2E concentration. In case of significant differences, the means for each wavelength was compared to the dark control group with a Dunnett multiple comparison test. Differences were considered significant at *p,0. 05, **p,0. 01 and ***p,0. 001.
This research programm received French public subsidies from  OSEO  ( DESCARTES  project) and was also supported by  INSERM, UniversitePierre et Marie Curie (Paris VI) ,  CNRS , the  Fondation Ophtalmologique A. de Rothschild (Paris) , the  Federation des Aveugles de France , the  City of Paris , the  Regional Council of Ile-de-France  and  ESSILOR International. This work performed in the frame of the LABEX  LIFESENSES  [reference  ANR-10-LABX-65 ] was also supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
rs11075995 is not correlated with any of the previously reported SNPs associated with obesity at genome-wide significant levels in GIANT or with rs17817449 (P = 3. 7 × 10 -60 , based on 123,864 subjects in GIANT; ref. 15). However, rs11075995 is associated with body mass index (BMI), both in GIANT (P = 1. 51 × 10 -6 , based on 121,427 subjects) and our control population (P = 2. 8 × 10 -5 , based on 20,952 controls in iCOGS; data not shown). Analyses adjusting and stratifying by BMI on the basis of 3,071 ER-negative cases and 20,130 controls from 19 studies genotyped on the iCOGS array indicated that the association between rs11075995 and ER-negative disease is not explained or modified by our measure of BMI (BMI-adjusted OR = 1. 16, 95% CI = 1. 09-1. 24, P = 1. 1 × 10 -5 ; P interaction = 0.
912; data not shown). Furthermore, conditional analyses indicated that the ER-negative disease-specific signal (rs11075995) is independent of rs17817449 (Supplementary Table  2 ). This finding adds to the increasing evidence of distinct signals at the same locus for different subtypes of cancers occurring at the same site, including, for example, 5p15. 33 (TERT-CLPTM1L)  16  and 14q24. 1 (RAD51B, also known as RAD51L1)  8  in breast cancer and 5p15. 33 (TERT-CLPTM1L)  16  and HNF1B 17 in ovarian cancer. Detailed fine mapping of known and newly identified breast cancer-associated regions will be required to determine whether additional subtype-specific signals exist in these regions. In an attempt to investigate the likely genes responsible in the observed risk associations, we examined associations between SNPs with available genotype (rs4245739, rs12710696 and rs6678914) and RNA expression in data from 382 primary breast tumors, including 81 ERnegative samples in The Cancer Genome Atlas (TCGA) database.
ER-negative breast cancer GWAS Three GWAS of ER-negative breast cancer were conducted in populations of European ancestry by National Cancer Institute (NCI) BPC3 (refs. 7,18), TNBCC  5, 6  and C-BCAC. ER-negative status for BPC3 and C-BCAC cases was determined from review of medical records or state cancer registry information. TNBCC focused on triple-negative cases, defined as individuals with ER-negative, PR-negative and HER2-negative breast cancer using data from medical records  5, 6. The BPC3 GWAS included 2,188 ER-negative cases and 26,477 controls from 8 studies (CPSII, EPIC, MEC, NHS, NHSII, PLCO, PBCS and WGHS), geno-typed using different versions of Illumina SNP arrays  7, 18. A total of 1,718 triple-negative cases from 11 studies (ABCTB, BBCC, DFCI, FCCC, GENICA, HEBCS, MARIE, MCBCS, MCCS, POSH and SBCS) were genotyped for the TNBCC GWAS using Illumina SNP arrays  5.
Data for TNBCC controls (N = 3,670) were obtained from a Finnish study (HEBCS) and publicly available controls of European ancestry from the United States (CGEMS), Germany (KORA), Australia (QIMR) and the UK (Wellcome Trust Case Control Consortium 2, WTCCC2) genotyped using Illumina arrays  5. Samples from the four latter studies are not counted in the total number of TNBCC studies because they only provided controls for other studies. C-BCAC performed a meta-analysis of 9 GWAS that included data on 10,052 breast cancer cases and 12,575 controls  8. Five studies (ABCFS, MARIE, HEBCS, SASBAC and UK2) provided data on ER status from medical records or cancer registries and contributed data on 702 ER-negative cases and 7,713 controls of European ancestry. All C-BCAC studies were genotyped with versions of Illumina arrays.
Control data for C-BCAC were obtained from individual studies or publicly available data. Standard genotyping quality control procedures were performed for each GWAS as previously described  5, 7, 8. Estimated per-allele log(OR) and standard error were calculated for each SNP using unconditional logistic regression on allele counts (dosages), as implemented in ProbABEL  19. Analyses were adjusted by study, country of origin or principal components as previously described  5, 7, 8. Analyses assumed a log-additive genetic model, and P values were based on the 1-degree-of-freedom Wald test. Quantile-quantile plots from each GWAS showed no substantial evidence for cryptic population substructure or differential genotype calling between cases and controls. The estimated inflation factor (λ) was 1.
SNPs successfully genotyped on iCOGS but not included on the chips used for the GWAS were imputed within each GWAS before combining results with iCOGS data. Imputation was performed within each study and genotyping array using the HapMap Phase 2 CEU reference panel and MACH software package v1. 0. SNPs with low imputation quality (r 2 < 0. 3) or minor allele frequency (MAF) < 1% were excluded.
iCOGS genotyping Samples for follow-up analyses were drawn from 50 studies participating in BCAC (40 from populations of predominantly European ancestry (including CTS, DEMOKRITOS, NBCS, NBHS, OSUCCG, RPCI and SKKDKFZS from TNBCC), 9 of Asian ancestry and 1 of African-American ancestry) with information on ER status. Most breast cancer cases in BCAC studies have not been tested for BRCA1 mutations; however, the frequency of mutations in the studied populations is expected to be low. Samples were genotyped as part of the COGS Project using a custom Illumina Infinium array (iCOGS) at four genotyping centers (Supplementary Table  1 ). The most common source of data for ER, PR and HER2 status was medical records, followed by immunohistochemistry performed on tumor tissue microarrays (TMAs) or whole-section tumor slides.
Breast cancer cases in the BCAC follow-up with missing data on ER status and cases from one study (PBCS) that included only ER-positive cases are excluded from this report. Studies were required to provide ~2% of samples in duplicate. The iCOGS chip included a total of 211,155 SNPs selected in collaboration with other consortia of BRCA1 and BRCA2 mutation carriers (CIMBA), ovarian cancer (OCAC) and prostate cancer (PRACTICAL). Genotype calling and quality control analyses were conducted by a single analysis center at the University of Cambridge 8. A total of 13,276 SNPs proposed by the combined ER-negative GWAS yielded high-quality genotype data (5,738 from BPC3, 4,628 from TNBCC and 2,910 from C-BCAC).
Statistical analysis After quality control exclusions  8  , BCAC follow-up data were analyzed using the Genotype Library and Utilities (GLU) package to estimate per-allele ORs and standard errors for each SNP using unconditional logistic regression. Analyses were stratified by ancestry (European, Asian or African). For samples of European ancestry, BCAC follow-up analyses were adjusted for seven principal components (the first six plus an additional component to reduce inflation for the LMBC study). GWAS and BCAC follow-up results were combined using inverse variance-weighted fixedeffects meta-analysis, as implemented in METAL  20. Forest plots showing study-specific estimates and fixed-effects meta-analysis for SNPs showing genome-wide significance were drawn using the command metan in STATA v.
Acknowledgments The authors wish to thank all the individuals who took part in these studies and all the researchers, clinicians and administrative staff who have enabled this work to be carried out. We are very grateful to Illumina, in particular  J. Stone ,  S. McBean ,  J. Hadlington ,  A. Mustafa  and  K. Cook , for their help with designing the array. BCAC is funded by  Cancer Research UK  ( C1287/A10118  and  C1287/A12014 ) and by the  European Community 's  Seventh Framework Programme  under grant agreement  223175  ( HEALTH-F2-2009-223175 ) (COGS). Meetings of BCAC have been funded by the  European Union European Cooperation in Science and Technology (COST)  programme ( BM0606 ). BPC3 is funded by  US National Cancer Institute  cooperative agreements  U01-CA98233 ,  U01-CA98710 ,  U01-CA98216  and  U01-CA98758  and the  Intramural Research Program  of the  US National Institutes of Health (NIH) / National Cancer Institute, Division of Cancer Epidemiology and Genetics.
TNBCC is supported by  Mayo Clinic Breast Cancer Study (MCBCS)  ( US NIH  grants  CA122340  and a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 )), grants from the  Komen Foundation  for the Cure and the  Breast Cancer Research Foundation. Genotyping on the iCOGS array was funded by the  European Union  ( HEALTH-F2-2009-223175 ),  Cancer Research UK  ( C1287/A10710 ),  US NIH  grant  CA122340 , the  Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer program  ( J. Simiard  and D. E. ) and  Ministry of Economic Development, Innovation and Export Trade of Quebec  grant  PSR-SIIRI-701  ( J. Simiard , D. E. and P. H. ). J. Simiard holds the  Canada Research Chair in Oncogenetics.
Plasmid constructs Helper plasmid. The V5PB pDNA (6. 4 kbp) encodes the V5 tagged PB transposase surrounded with the 5' and 3' untranslated terminal regions of the Xenopus laevis β-globin gene and inserted into the pCS2+ vector (Life Technologies, Paisley, UK). The V5PB sequence (2067 bp) did not contain any AU-rich elements, 5' stem loops, premature translation termination codons or introns (GenBank accession number: EF587698), and was synthesized by ATG:biosynthetic, Merzhausen, Germany. Donor plasmids. pBSK ITR5'-Neo R -ITR3' (5. 2 kbp) was built by introducing the PB 3' and 5' inverted terminal repeats (ITRs) (http://piggybac. bio. nd. edu/GenBank%20Format/pBSII-ITR1. 1k) into the pBluescript SK plasmid. The neomycin phosphotransferase gene (Neo R , 1. 5 kbp) was cloned between the two ITRs.