dataesr/scientific-paragraphs-categorization huggingface dataset:
The filtered supernetwork was inferred from the 1103 1-to-1 ortholog gene tree set, and only bipartitions that received more than 80% bootstrap support in gene tree analyses were included. Edge lengths and colors are by their weight, a measure of prevalence of the bipartition that the edge represents among the gene trees. Ellipses with dashed outlines indicate increased complexity at putative locations of WGDs. and Papilionoideae rather than a pan-legume WGD (Fig. 2a, b ). Missing data could also affect identification of which parental lineages were involved in an ancient allopolyploid event and which subfamilies are derived from it. However, given that GRAMPA takes gene tree topology into account, the inference that allopolyploidy is more likely than autopolyploidy is likely robust, and moreover, none of the other results reject allopolyploidy.
Solid lines represent the species tree topology; dashed lines connect to the putative second parental lineage of the allopolyploid, with hypothetical extinct lineages indicated with a †. Caes = Caesalpinioideae, Cerc = Cercidoideae, Detar = Detarioideae, Dial = Dialioideae and Pap = Papilionoideae. MAPS analysis (three ingroup and three outgroup taxa for the analysis of the putative Caesalpinioideae WGD) and without the sort of extensive gene tree filtering we performed here. Reanalysis of the One Thousand Plant Transcriptomes Initiative (2019) gene trees with Notung and Phyparts suggests that their data also do not support a Caesalpinioideae-specific WGD (Supplementary Appendix S2 available on Dryad).
Estimating the Timeline of Legume Evolution Our analyses suggest that the legume crown age dates back to the Maastrichtian or Early Paleocene, potentially within 1 or 2 million years before or after the KPB (Fig. 5 , Supplementary Figs. S6-S13 , Table  S3  available on Dryad), although such high precision is unwarranted due to the idiosyncrasies of the molecular clock. These results update those of  Lavin et al. (2005) ,  Bruneau et al. (2008), and Simon et al. (2009)  and provide the first age estimates for legumes based on nuclear genomic data. The FLC analyses (i. e. , assuming 3, 6, or 8 different clade-specific substitution rates) even suggest that potentially only a single legume ancestor crossed the KPB giving rise to the six subfamilies during the early Paleocene, conforming to a "hard explosive" model.
S15  available on Dryad) whilst still respecting hard maximum and minimum bounds of fossil constraints on calibrated nodes, as suggested by our results. It is also clear from running analyses without data, that the marginal age prior on the (uncalibrated) legume crown node is poorly informed, with the 95% HPD interval between 80. 03 and 109. 70 Ma (Fig. 5b  and Supplementary Table  S3  available on Dryad), the minimum of which is much older than the oldest legume fossils, presumably caused by overly conservative maximum bounds on calibrated nodes  (Phillips 2015). UCLN and RLC analyses also inferred relatively high substitution rates for some deep branches in the outgroup during the Lower Cretaceous, relative to more derived and terminal branches (Supplementary Figs. S6 and S8 available on Dryad), presumably to satisfy the poorly informed marginal priors.
Phillips (2015)  suggested that setting less conservative maxima on priors could remedy this problem, but our analysis with such prior settings shows little effect (Supplementary Figs. S7 and S16k available on Dryad), with some of the deepest branches still showing much higher substitution rates. Since there is no evidence for, nor any reason to assume that substitution rates along those branches should be elevated relative to terminal branches, we conclude that this is caused by overfitting rate heterogeneity across branches under the influence of the marginal prior. Furthermore, the RLC analyses fitted c. 45 local clocks across the phylogeny, a high number relative to the 142 branches in the tree (implying a separate clock for every ∼3 branches on average), which is also indicative of overfitting.
This could also be seen as evidence that the data are not the product of clock-like evolution, but it becomes difficult to estimate how much the clock deviates if the marginal prior on node ages is too influential. FLC analyses provide a more pragmatic approach by defining local clocks based on root-to-tip length distributions across clades and pruning outlier taxa (see Methods and Supplementary Fig. S5  available on Dryad). This approach largely accounts for the violation of the molecular clock but does not relax the clock such that the marginal prior on node ages is given excessive weight relative to the molecular signal. Furthermore, because the genes we selected are reasonably clock-like and highly informative, it is desirable that these data inform the node ages with sufficient weight.
Taken together, these three factors could mean that the time frame for early legume evolution appears too long in our results, with (some of the) subfamily ages likely being slightly older than estimated here, and divergence of the subfamilies happening nearly simultaneously  (Koenen et al. 2020) , rather than spanning the c. 3-5 million years inferred here (Fig. 5a  and Supplementary Figs. S6-S13 available on Dryad). On the other hand, the time frame over which successive speciation events cause ILS depends primarily on the asymptotic effective population sizes (N e ) of the daughter species and their mean generation times, which can both be high for woody perennials, the most likely ancestral habit of Leguminosae. Reciprocal monophyly of sequences sampled from two species becomes highly likely when the number of generations since speciation is substantially larger than N e  (Rosenberg 2003) , which could require millions of years if N e ≥ 10,000 and the generation time ≥ 100 years.
Substantial ILS (c. 30% gene trees deviating from the species tree) is well documented among genera Homo, Pan, and Gorilla  (Scally et al. 2012)  despite the 4 million years separating the two speciation events. Similar observations in plant groups with long generation times and moderately large N e  (Copetti et al. 2017; Chen et al. 2019)  suggests this is also common in long-lived woody plants. Hence, the substantial gene tree conflict for the main legume lineages  (Koenen et al. 2020 ) could be due to ILS assuming that successive speciation events occurred within a few millions of years, as inferred here (Fig. 5a  and Supplementary Figs. S6-S13 available on Dryad). The placement of Cercidoideae and Detarioideae fossils within the stem or crown groups of these subfamilies, and hence the timing of their origins, remains uncertain (Supplementary Appendix S1 available on Dryad).
While stem age estimates of each subfamily are remarkably close to each other, crown age estimates are strikingly different (Supplementary Table S3 available on Dryad). Caesalpinioideae are found to have the oldest crown age (late Paleocene), followed by Papilionoideae with a crown age in the Early Eocene. Overall, the subfamily age estimates suggest that early diversification of the legume subfamilies coincided with Paleocene biotic recovery, the Eocene climatic optima and Oligocene turnover in response to global cooling.
8 33. 9 Q Q2 Z SG Acacieae/Ingeae SG Acacia s. s. SG Caesalpinioideae Flattened polyads with 16 pollen grains; Brazil, Colombia, Cameroon and Egypt o Polyads with pseudocolpi; Australia p Bipinnate leaves; Colombia q 33. 9 23 58 Wing et al. (2009))s (2000)03)available on Dryad for detailed discussion of these fossil calibrations. CG = Crown group; SG = Stem group; Ma = Million years ago. aNumbers26,27,38,48,94,105,112,116,122and133refer to calibrations fromMagallón et al. (2015)as listed in their Supplementary Information Methods S1; letters A, D, F, G, I2, M2, and Q refer to calibrations fromBruneau et al. (2008)and/orSimon et al. (2009). bMagallónetal. (2015)andreferencestherein. cPriorsetasnormal with standard deviation of 1. 0, and truncated between minimum and maximum bounds of 113 and 136 Ma, respectively.
Background: Although anti-inflammatory agents could theoretically have anticancer properties, results from cohort studies on nonsteroidal anti-inflammatory drugs (NSAIDs) and breast cancer (BC) risk are inconsistent. Methods: We investigated the association between NSAID use and BC incidence in the French E3N prospective cohort, which includes 98,995 women born between 1925 and 1950 and insured by a health insurance plan that covers mostly teachers. Self-reported information on lifestyle and medical history has been collected biennially by questionnaires and matched with data from a drug reimbursement database covering the period 2004-2014. Women who self-reported current NSAID use in the 2000 or 2002 questionnaires or with at least two reimbursements in any previous 3-month period were defined as exposed to NSAIDs.
In addition, over half of the prospective studies relied on self-reported data on NSAID use, prone to exposure misclassification, while studies exploring the associations between NSAID use and breast cancer risk using medico-administrative healthcare databases  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]  had very limited data on potential confounding factors and were not able to evaluate whether the associations differed by specific breast cancer subtypes. Furthermore, there is a lack of epidemiological studies considering the use of other drugs as potential confounders/effect modifiers even though NSAIDs are frequently used concomitantly with other drugs such as proton pump inhibitors (PPIs)  [18, 19]. We therefore investigated the association between NSAID use and breast cancer incidence, overall and by breast cancer subtypes, risk factors, and use of other drugs, in the French prospective E3N (Etude Epidémiologique auprès de femmes de la Mutuelle Générale de l'Education Nationale) cohort in which there are both self-reported information on lifestyle, medical, and reproductive factors, and drug reimbursement data for the period 2004-2014.
E3N cohort The E3N cohort includes 98,995 French women born between 1925 and 1950 and insured by the health insurance scheme that covers all workers in the national education system, most of whom are teachers  [20]. In 1990, these volunteers provided their informed consent and completed the first questionnaire on their lifestyle, as well as lifetime medical and reproductive history. Every 2-3 years thereafter, participants completed selfadministered questionnaires to mostly update this information and address the occurrence of medical events. Furthermore, for each cohort member, the health insurance plan provided data on all outpatient reimbursements for health expenditure since January 1, 2004. The study was approved by the French National Commission for Data Protection and Privacy.
Exposure to NSAIDs We considered all deliveries of NSAIDs since January 1, 2004 (as listed in Additional file 1, Table  S1 ). Of note, aspirin used at low dose (pills of 325 mg or less) is not a NSAID but an antiplatelet drug. The following data were extracted for each NSAID delivery: date of drug purchase, molecule, route of administration (oral, rectal, parenteral), number of pills/ ampules per package, and dose per pill/ampule. E3N participants also reported whether they were currently using anti-inflammatory drugs at least 3 three times per week in the 2000 and 2002 questionnaires. We defined as "NSAID ever users" women with at least one NSAID reimbursement since January 1, 2004, or who self-reported NSAID current use in the 2000 or 2002 questionnaires. We defined as "NSAID recurrent users" women with at least two reimbursements during any previous 3-month period since January 1, 2004, or who self-reported NSAID current use in the 2000 or 2002 questionnaires.
Covariates Parameters considered as potential confounders are listed in Table  1. Some information on these parameters originated from the biennial self-administered questionnaires sent before year 2004 (educational level  [1990] , breastfeeding  [1990 and 1992]  , current smoking status, lifetime personal history of benign breast disease, current alcohol intake, self-report of a mammogram performed during the previous follow-up cycle, and lifetime histories of arthrosis, rheumatism, arthritis, polyarthritis, spondyloarthritis, or migraine). Number of consultations with the doctor during the preceding 6 months and drugs more likely to be used among NSAID users [paracetamol (Anatomical Therapeutic Chemical class (ATC, http://www. whocc. no/atc_ddd_index/) code: N02BE01), corticosteroids (H02AB), PPIs (A02BC), and symptomatic slow-acting drugs for osteoarthritis such as glucosamine (M01AX05), diacerein (M01AX21), oxaceprol (M01AX24), chondroitin sulfate (M01AX25), or avocado and soybean oil (M01AX26)] were identified using the drug reimbursement database which contains information starting from January 1, 2004.
Statistical analysis Multivariable Cox regression models with age as the time scale and stratified by birth cohort (in 5-year categories) were used to estimate hazard ratios (HRs) for the association of NSAID exposure with breast cancer incidence, overall and by breast cancer subtypes. Exposure was considered as a time-varying parameter as well as other factors issued from the reimbursement database and covariates updated during follow-up. Consequently, participants classified as exposed to NSAIDs (either recurrent or occasional) or other drugs at the end of follow-up contributed follow-up as non-exposed until filling their first reimbursement (occasional) or their second reimbursement within a 3-month period (recurrent). Our time-varying approach also allowed us to update cumulative duration/dose and time since last and first use during follow-up.
Undiagnosed breast cancer may cause symptoms resulting in an increased use of drugs a few months prior the diagnosis. A Danish study published in 2017 suggested that a 6-month lag time would be sufficient to minimize any reverse causation bias for most drug-cancer associations, including breast cancer  [24]. Thus, exposure and other variables coming from the reimbursement database such as MHT, number of consultations, and other drugs were lagged by 6 months. Changing the lag time to 0, 1 year, or 2 years did not make a difference in the estimates (data not shown). Educational level, recent mammogram (self-report of a mammogram performed during the previous follow-up cycle), and known risk factors for breast cancer were systematically included in the multivariable models [BMI, physical activity level, lifetime history of breast benign disease, family history of breast cancer, age at menarche, age at menopause, parity, lifetime use of oral contraceptives, lifetime use of MHT, and alcohol consumption].
PPIs alter (increase or decrease depending on the molecule) the absorption of other drugs by increasing the intragastric pH and interfering with the metabolism of other drugs  [44, 45]. Surprisingly, several reviews concluded that few studies were published about potential drug interactions between PPIs and NSAIDs  [45, 46]. These studies focused on the concomitant use of these two drugs and showed no influence of PPIs on the pharmacokinetics of NSAIDs  [45, 46]. However, PPI use might increase the risk of an imbalance in gut microbiota composition, even 8 weeks after PPI treatment  [47, 48]. This change in microbiota composition has been shown to exacerbate NSAID side effects  [49]. The main strengths of this study included its prospective design and the use of information from a drug reimbursement database to identify NSAID exposure, which avoids differential recall bias between cases and noncases and minimizes exposure misclassification.
However, because NSAID is mostly used sporadically (as noted in a study from the French National Health Insurance Claims  [54] ), our statistical power remained limited to evaluate the NSAID-breast cancer associations for a long duration of use. In addition, the period of follow-up was inadequate to examine the NSAID-breast cancer associations in different windows of susceptibility such as postpartum or perimenopause  [55, 56]. Since data on NSAID reimbursements were combined with self-reported data on lifestyle, reproductive, and medical factors, we were able to take into consideration potential confounders and to examine interactions of NSAID use with breast cancer risk factors, comorbidities, and other drugs. However, although we adjusted for many potential confounders, we cannot exclude residual confounding.
However, we assumed that women who need NSAIDs chronically will go to the doctor to get reimbursed. We defined recurrent users as women with at least 2 reimbursements during any 3month period which would suggest that they took the drug. Self-reported data on NSAID use, which therefore included NSAIDs purchased over-the-counter and captured only taken medications, were only available before year 2004 and consequently were used to assess prevalent use of NSAIDs.
Acknowledgements The research was carried out using data from the  Inserm (French National Institutes for Health and Medical Research)   E3N  cohort, which was established and maintained with the support of the  Mutuelle Générale de l'Education Nationale (MGEN) ,  Gustave Roussy , and the  French League against Cancer (LNCC). We are grateful to the study participants for their continued participation and to medical practitioners for providing pathology reports. We also thank all members of the  E3N-EPIC study group , in particular  Rafika Chaït ,  Ghizlane Esselma ,  Marie Fangon ,  Pascale Gerbouin-Rérolle ,  Lyan Hoang ,  Roselyn Gomes  and the data management team, and  Amandine Gelot  for data management and/or technical assistance. The work reported in this paper was performed during  Agnès Fournier 's term as a Visiting Scientist at the  International Agency for Research on Cancer.
5 min and held for four min. Column was primed with 100% LC solvent A for three min between samples. Metabolites were directed to an ACQUITY TQ detector mass spectrometer equipped with an electrospray ionization source set to negative ion mode (Waters, Milford, MA). Specific analytes were detected via multiple reaction monitoring and quantified using a standard curve of known concentration using MassLynx software (Waters, Milford, MA). Each standard (Cayman Chemical, Ann Arbor, MI) and internal standard was resolved at the follow retention time (RT) with the following mass transitions: arachidonic acid, RT 12. 52.
Acknowledgements We thank  Stephanie Anderson ,  Ari Dehn ,  Karlee Markovich ,  Austin Orecchio  and  Anne Yu  for all zebrafish animal care,  Ken Poss  for providing the Tg(cmlc2:GFP), Tg(tcf21:Dsred) and Tg(fli1a:GFP) fish, and  Lalita Ramakrishnan  for sharing the Tg(mpeg1:YFP) reporter strains. William Schott  at the  Jackson Laboratory  provided the technical support for FACS experiments. The Yin laboratory research on heart regeneration is supported by grants from the  National Institute of General Medical Sciences  ( P20 GM104318 ,  P20 GM103423 ), the  American Heart Association Scientist Development Grant  ( 11SDG7210045 ) and the  MacKenzie Foundation  ( 17003 ).
, 2013; Willsey et al. , 2013). We present a systematic description of this resource, including demographics, gene expression across development, gene co-expression modules, and eQTLs. We describe interactions between these factors and comparisons with the BrainSpan dataset, cell-typespecific genes, and loci associated with neuropsychiatric traits and disorders. Our analysis replicates the late-fetal transition observation, a dramatic shift in gene expression between mid-fetal development and infancy  (Li et al. , 2018) ; refines the timing of this event; and delineates the degree to which each gene is involved. We also identify 252,629 cis-eQTLs affecting 8,421 genes and classify their effects as prenatal-predominant, postnatalpredominant, or constant across brain development. Finally, we identify eQTLs that colocalize with genome-wide association study (GWAS) loci, linking specific genes to neuropsychiatric phenotypes.
Data Processing RNA-seq reads were aligned and converted to log base 2 counts per million (log 2 CPM) per gene (STAR Methods), with 23,782 genes meeting minimum expression criteria. We restricted further analysis to these 23,782 cortically expressed genes, of which 16,296 (68. 5%) encode proteins, whereas 7,486 (31. 5%) are noncoding, including long noncoding RNA (lncRNA) (12. 6% of total) and antisense (9. 2% of total) genes (Table  S2 ). For the 14 samples also profiled in BrainSpan  (Li et al. , 2018) , gene expression was highly correlated per sample and per gene (Figure  S1 ). In both datasets, the first principal component of gene expression is strongly correlated with developmental age (Figures  2A  and S1 ). All samples were genotypically concordant between the WGS and RNA-seq data  (Regier et al.
Co-expression Modules in the Developing Human Cortex Tofurthercharacterizetherelationshipsbetweenthe23,782cortically expressed genes, we applied a weighted gene co-expression network analysis (WGCNA)  (Langfelder and Horvath, 2008)  to define 19 consensus modules that included 10,459 genes (Figures  3A  and S3 ; Table  S3 ). As expected, genes within each module shared functional roles (Figure  3B ), temporal trajectories of geneexpression(Figures3C and3D),regulatory transcriptionfactors (Figure  S3 ), and cell type enrichment  (Figures 3E and 3F). Module preservation analysis using BrainSpan data  (Li et al. , 2018)  identified similar co-expression patterns across brain regions, especially independent DLPFC samples (Figure  3G ). Similar to transitional genes (Figure  2 ), multidimensional scaling of the module eigengenes demonstrated that developmental age accounted for 44.
These discovery rates are in line with similarly sized cohorts (Figure  S5 ). The union of these three analyses identified 252,629 eQTLs of 8,421 eGenes (Table  S5 ). As expected, the eQTLs are enriched for markers of active transcription derived from the human brain (Figure  S5 ;  Li et al. , 2018; Reilly et al. , 2015; Kundaje et al. , 2015). We find that eQTL effect size and direction are correlated with prenatal whole brain  (O'Brien et al. , 2018 ) (Pearson's r = 0. 73, p ≤ 1 × 10 -16 ; Figure  5A ) and postnatal (adult) frontal cortex  (Aguet et al. , 2017 ) (Pearson's r = 0. 73, p ≤ 1 × 10 -16 ; Figure  5A ) from independent datasets.
Temporal Predominance of eGenes Most eGenes have more than one eQTL (5,538 of the 8,421, 65. 8%). Defining the top eQTL per eGene as that with the lowest FDR-significant p value in any of the three sample sets (Table  S5 ), we identified 2,977 (35. 4% of total) constant eGenes, 1,691 (20. 1%) prenatalpredominant eGenes, and 1,145 (13. 6%) postnatal-predominant eGenes (Figure  5B ). The remaining 2,608 eGenes (31. 0%) trend toward prenatal (25. 1%) or postnatal (5. 9%) effects. Because of linkage disequilibrium (LD), cis-eQTLs for an eGene are likely to have a similar direction and magnitude of effect; accordingly, the temporal category of the top eQTL matched the majority of eQTLs for 88. 2% of all eGenes (7,425 eGenes; Figure  S5 ). To validate the prenatal-and postnatal-predominant eQTLs, we evaluated their performance in independent datasets.