dataesr/scientific-paragraphs-categorization huggingface dataset:
2012 ); (iii) rmdup_collapsed from paleomix was used for removing PCR duplicates for collapsed reads and picard tools version 2. 18. 0 (http://broad insti tute. github. io/picar d/) for uncollapsed reads; and (4) post-mortem DNA damage was assessed using mapdamage version 2. 2. 1  (Jónsson et al. , 2013)  and pmdtools version 0. 50  (Skoglund et al. , 2014). The expected number and size of DNA fragments obtained following enzymatic digestion were estimated in silico applying fragmatic  (Chafin et al. , 2018)  to the EquCab3. 0 reference genome. Here, all three combinations of enzymatic restrictions could be investigated using two analyses only, as HpaII and MspI target the same restriction site. The preseq version 2. 0. 3  (Daley & Smith, 2013)  functions c_curve (step size in extrapolations =10,000) and lc_extrap (maximum extrapolation =50,000,000, step size in extrapolations =10,000) were used to estimate library complexity profiles.
These latter analyses should therefore be considered as exploratory only. We have also plotted the size distribution of on-target reads (based only on collapsed reads for precision), and analysed enrichment folds (number of reads mapped after the procedure/number of reads mapped prior to the procedure) for captures with PstI-MseI probes. These data were further analysed using generalized linear models (GLMs) with inverse Gaussian distribution and the original endogenous content, level of PCR duplicates and age of the samples as independent variables. The relationship between enrichment-folds and read length was analysed using a GLM with Poisson distribution, stratified by sample, using the lme4 R package  (Bates et al. , 2015). For probe reads, additional statistics such as the %GC content, CpG content and fragment length were calculated.
| Protocol automation Automated solutions to aDNA analyses have gained increasing interest over the last few years  (Rohland et al. , 2015; Slon et al. , 2017) , with the benefit of increased efficiency and reproducibility  (Holland & Davies, 2020). However, investment costs for acquiring liquid-handling robots can be extremely prohibitive and not available outside of large-scale facilities. To provide an automation solution for most laboratories, we developed an automated hyRAD protocol that can be run on the inexpensive liquid-handling devices produced by Opentrons. Our automated implementation reduced hands-on time to 30 min per capture session, vs. 3 hr when carried out manually by skilled laboratory staff, while achieving similar performance (Figure  8 ). The developed protocol is available at https://github.
DATA AVA I L A B I L I T Y S TAT E M E N T The data sets generated for this study can be accessed in the ENA (Accession No. PRJEB43744). The laboratory protocol is available at https://doi. org/10. 17504/ proto cols. io. bywxpxfn. The program for piloting the Opentrons OT-2 robot can be accessed at https://github. com/Tomas zSuch an/opent rons-hyRAD/ tree/v1. 0.
5 High-output 150 cycle kit (75 bp paired-end reads, Illumina Inc. , Cambridge, UK). A total of 455 308 427 reads (average of 12 647 456 6 2 461 518 reads per sample) were generated for the 36 liver samples. Reduced representation bisulfite sequencing. A total of 100 ng of total genomic liver DNA was diluted and processed using the Premium Reduced Representation Bisulfite Sequencing (RRBS) Kit (Diagenode, Denville, New Jersey) as per the manufacturer's instructions. Libraries were sequenced to 75-bp single end on a NextSeq 500 (Illumina, California). Data were aligned to the rat reference genome Rn6 with Bismark  (Krueger and Andrews, 2011). A total of 650 358 053 reads (average of 18 065 501 6 4 846 025 reads per sample) were generated. DNA damage assay. DNA damage was measured in the liver and kidneys of rats exposed to a dose of 50 mg/kg bw/day of glyphosate or to MON 52276 at the same glyphosate equivalent dose.
Solvent concentration was the same in all wells and never exceeded 1% dimethyl sulfoxide (DMSO). Statistical analysis. ToxTracker assay data analysis was performed using ToxPlot, a dedicated data analysis software package created to import raw GFP reporter gene expression data from the flow cytometry measurements, calculate GFP induction levels and cytotoxicity, and perform statistical analysis of the data. The ToxTracker assay is considered to give a positive response when a compound induces at least a 2-fold increase in GFP expression in any of the 6-cell assay systems. Only GFP induction at concentrations that do not cause >75% cytotoxicity is used for the ToxTracker analysis. A total of 3 independent repeat experiments in either the absence or presence of metabolic activation of the test substances and the control compounds cisplatin, diethyl maleate, tunicamycin, and aflatoxin B1 were performed.
All the 84 animals (7 groups of 12 animals) were subjected to an analysis of food and water consumption, body and organ weights, a serum biochemistry analysis, and a histopathological evaluation of the kidneys and liver. We focused our mRNA-Seq, small RNA-Seq, RRBS, and DNA damage analysis on 36 samples from the cohort of rats exposed to the EU NOAEL (50 mg/kg bw/ day) for MON 52276 and glyphosate, as well as the untreated controls. Food and water consumption, as well as body and organ weights, were analyzed with Kruskal-Wallis test with Dunnett's post hoc comparisons with STATA 10 software. Incidence of non-neoplastic lesions was evaluated with a Fisher's exact test (1-and 2-tailed; 1-sided results were also considered, since it is well established that only an increase in incidence can be expected from the exposure, and incidence in the control group are almost always 0).
The analysis of linear trend, for incidence of pathological lesions, was obtained using the Cochran-Armitage trend test  (OECD, 2011; Shockley and Kissling, 2018). Statistical significance for the serum biochemistry data was evaluated using a 1-way ANOVA with post hoc Tukey honestly significant difference. The mRNA-seq data were analyzed with Salmon  (Patro et al. , 2017). This tool was used to quantify transcript abundance by mapping the reads against a reference transcriptome (Ensembl Release Rattus Norvegicus 6. 0 cDNA fasta). The Salmon output was then imported into R version 4. 0 (R Core Team, 2019) using the Bioconductor package tximport. We created a transcript database containing transcript counts, which was used to perform a differential gene expression analysis using DESeq2  (Love et al.
, 2014). Genes with l < 5 counts in 5 samples were removed. Unnormalized raw counts were used to create the DESeq2 model. Tables of outcome data were generated using the DESeq2 results function to create a results table with log2-fold changes, Wald test p value and adjusted p values for multiple comparisons made according to the Benjamini-Hochberg procedure. The contrast argument of results function was then used to make pair-wise comparisons between untreated and treated samples. Differentially expressed genes (DEGs) were identified by DESeq2 with a cutoff of more than 1. 5-fold change and an adjusted p value of <. 05. We finally used goseq to perform a gene ontology of biological processes and a Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis accounting for transcript length biases using the gene annotations included in the R package org.
Rn. eg. db 3. 11. 4 (Young et al. , 2010). The relationship between the different significant GO terms was visualized using the REVIGO web server  (Supek et al. , 2011). DNA methylation calls from RRBS data were extracted with Bismark  (Krueger and Andrews, 2011). The output from Bismark was then imported into R and analyzed with Methylkit  (Akalin et al. , 2012). DNA methylation calls were annotated using RefSeq gene predictions for rats (rn6 release) with the package genomation  (Akalin et al. , 2015). Other annotations were retrieved using the genome-wide annotation for rat tool org. Rn. eg. dbR package version 3. 8. 2. Statistical analysis was performed with logistic regression models fitted per CpG using Methylkit functions. p Values were adjusted to Q-values using the SLIM method  (Wang et al.
The output of miRge3. 0 was then imported in R (v4. 0. 0) for statistical analysis. The miRNA counts were used to perform a differential gene expression analysis using DESeq2 in a similar manner as described above for the mRNA-seq data  (Love et al. , 2014). TargetScan 7. 1 was used to predict biological targets of miRNAs among glyphosate-altered mRNAs by searching for the presence of conserved sites that match the miRNA seed region  (Lewis et al. , 2005). The resulting miRNA-mRNA network was visualized using the R package for network visualization visNetwork. We ultimately analyzed the different datasets of liver molecular profiles with an orthogonal partial least squares discriminant analysis (OPLS-DA) to evaluate the predictive ability of each omics approach. OPLS-DA can be used to distinguish the variability corresponding to the experimental perturbation from the portion of the data that is orthogonal; that is, independent from the experimental perturbation.
The R package ropls version 1. 20. 0 was used with a nonlinear iterative partial least squares algorithm  (Th evenot et al. , 2015). Prior to analysis, experimental variables were centered and unit-variance scaled. Since OPLS-DA methods are prone to overfitting, we assessed the significance of our classification using permutation tests (permuted 1000 times). Data availability. All the raw data are made available in Dryad (https://doi. org/10. 5061/dryad. 1g1jwstxk). The study metadata describing the different identifiers linking the different analyses (Supplementary File 1), as well as data for water consumption (Supplementary File 2), food consumption (Supplementary File 3), body weights (Supplementary File 4), pathology (Supplementary File 5), and serum biochemistry (Supplementary File 6) is made available.
The raw data for the liver transcriptomics are available at the GEO accession number GSE157426. Transcriptome count data (Supplementary File 7), and gene expression statistics (Supplementary Files 8 and 9), GO enrichments (Supplementary Files 10 and 11), and KEGG pathway enrichments (Supplementary Files 12 and 13), are available for MON 52276 and glyphosate, respectively. The raw data for the small RNA profiling are available at the GEO accession number GSE182559. Quantification files from Salmon are available (Directory transcriptomes). Small RNA count data (Supplementary File 14), predicted new miRNAs (Supplementary File 15), as well as summary statistics for MON 52276 (Supplementary File 16), and glyphosate groups (Supplementary File 17) are also provided. Count data for all UMIs are provided (Directory smallRNA).
C t values for the expression of genes in kidneys are provided as Supplementary File 18. The raw data from the RRBS analysis are available at the GEO accession number GSE157551. Methylation summary statistics are available for MON 52276 (Supplementary File 19) and glyphosate (Supplementary File 20). Bismark files with the number of C bases that are methylated or unmethylated at each genomic coordinate are provided (Directory methylation). We also provide the results of the DNA damage assay for each animal (Supplementary File 21). The code used to perform the statistical analysis is made available as supplementary data (Supplementary File 22).
miRNA Profiles Suggests That Regulation of Gene Expression by MON 52276 Occurs Partly Posttranscriptionally We also undertook a profiling of small RNAs in the same liver samples to assess if gene expression changes could be linked to this epigenetic system. An average of 49. 7% of the total number of good quality reads was classified as miRNA, whereas 30. 4% were unclassified (Figure  7A ). The remaining reads were tRNA (7. 4%) and mRNA (9. 8%). These included 677 unique miRNAs, which were identified in the miRNA database, and 9 novel predicted miRNAs. Statistical analysis showed that miR-22 and miR-17 had their levels decreased by exposure to MON 52276, whereas miR-30 had its levels decreased by glyphosate, and miR-10 had its levels increased by glyphosate. We then evaluated whether the mRNA, which have been found to have their levels altered were potentially regulated by these miRNAs (Figure  7B ).
However, no genotoxic activity was detected in the 6 ToxTracker mES reporter cell lines for glyphosate (Figure  2 ),  OPLS-DA models were performed for each set of omics data. We show estimates of the total explained variation (R2X), variations between the different groups (R2Y), and the average prediction capability (Q2). We assessed the significance of our classification using permutation tests. New estimates of R2Y and Q2 values were calculated from this 1000 times permuted dataset (p-values pR2Y and pQ2 for permuted R2Y and Q2, respectively). which indicates that glyphosate does not act as a direct genotoxicant or a mutagen. These data taken together suggest that DNA damage from glyphosate or MON 52276 exposure could be the result of organ damage from oxidative stress and concomitant inflammatory processes, which can be induced at least in part by the observed fatty liver condition as well as necrosis.
TSC and LAM: the success of registry-based clinical research A rare disease is defined, from an epidemiological and regulatory perspective, as any condition determined to be of low prevalence or number of cases. In the European Union, this is defined as not more than five affected persons per 10 000  [4] , while in the United States it is defined as <200 000 patients affected, in Japan as <50 000 and in Australia <2000  [5]. Low prevalence, scarcity of knowledge and phenotype heterogeneity have restricted research into rare diseases, including both TSC and LAM. Registries of rare diseases were implemented to describe the natural history and outcomes of such diseases, to support research and to establish a database for evaluating drugs and other therapies; in addition, these seek to connect patients, families and clinicians  [6].
Only a small number of pathogenic variants are frequently found in affected individuals, thus making genotypephenotype correlation difficult. The Leiden Open Variation Database (LOVD) is a free and regularly updated web-based open-source database founded by the Leiden University Medical Centre in the Netherlands, which hosts many of the known TSC1 and TSC2 variants together with their assigned significance (benign, pathogenic or unknown significance) (https://databases. lovd. nl/shared/genes?search_ name=tuberous%20sclerosis). It enables physicians and geneticists to interpret the results of the genetic studies in patients with TSC. LVOD catalogued 11 866 variants with 1121 unique variants for TSC1 and 3251 unique variants for TSC2. Penetrance is almost complete. Mutations within TSC1 are predominantly small truncating nonsense and insertion or deletion mutations, whereas TSC2 mutations are more frequently missense mutations and large rearrangements  [39].
ACKNOWLEDGEMENTS The authors thank  Nicola Giacobbo  and the referee,  Pavel Ivanov , for discussions and comments on the manuscript. R. N. acknowledges support from  UKRI/EPSRC  through a  Stephen Hawking Fellowship  ( EP/T017287/1 ). E. R. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  681601  and  No 864965 ). D. G. is supported by  European Union 's  H2020   ERC Starting Grant  No. 945155-GWmining ,  Leverhulme Trust  Grant No. RPG-2019-350 , and  Royal Society  Grant No. RGS-R2-202004. G. R. acknowledges support from the  Netherlands Organisation for Scientific Research  ( NWO , program number  016. Veni. 192. 233 ) and from an  STFC Ernest Rutherford Fellowship  (grant number  ST/T003855/1 ).
Comparative transcriptomics of the legs at the fifth nymphal instar, the developmental stage where we detected a burst of growth, first revealed a global difference in gene expression profiles between the 3 pairs of legs in both sexes at this developmental window (S3A Fig). When comparing the first versus third legs, we found that the third legs diverged more from the first legs in males than they did in females, both in terms of number of differentially expressed genes and in the degree of differential expression (S3 and S4 Figs). Overall, these data indicate that leg exaggeration in M. longipes males is associated with a higher number and higher degree of differential expression of leg-biased genes in males compared to females. We used these datasets to select and test the role of candidate genes in leg exaggeration.
Genes that are up-regulated in the third legs of females (in comparison with first legs of females) were enriched in signaling and metabolic pathways as well as response to stimuli and cell communication processes. Among the genes and pathways that were hypothesized to play a role in the regulation of growth-related exaggerated traits  [3] , many were identified in our datasets as differentially expressed between legs within the same sex or between the sexes (S4 Table; see  [24]  for sex-biased genes). To test the role of these genes in trait exaggeration, we conducted an RNAi screen targeting about 30 candidates representing transcripts that were either leg-biased, sex-biased (up-regulated in male's or female's third legs), broadly expressed, or tissue-specific (S5 Table ). This functional screen identified 2 genes, BMP11 (also known as GDF11) and Ultrabithorax (Ubx), the depletion of which unambiguously and reproducibly resulted in altered scaling relationships in the males.
Some of the other genes tested were lethal or did not produce any detectable effect, thus preventing conclusive analyses of scaling relationships (S5 Table ). Ubx is a tissuespecific Hox protein known to be confined to the second and third thoracic segments in water striders  [25] [26] [27] [28]. In our comparative transcriptomics dataset, Ubx is absent from the first legs, lowly expressed in the second legs, and highly expressed in the third legs of both sexes (S5A Fig), thus confirming its tissue-specific expression in M. longipes. BMP11 is a known secreted growth regulator with a systemic effect in vertebrates  [29, 30] ; however, its expression in waters striders is unknown. A phylogenetic analysis of the BMP gene family clustered this sequence from many water striders with BMP11 of humans and mice, thus confirming their homology (S6 Fig;  S1  and S2 Data).
For this we selected genes with FPKM > 2 in at least 1 of the 3 legs. The filtering process and PCA analysis were performed in males and females separately (S3 and S4 Figs). For the PCA, we also corrected both for line and replicate effects that are intrinsic to our experimental design  [24]. The latter effect matched the days where RNA was extracted from each sample. We corrected for both effects, using Within Class Analysis, with the R package 'ade4' version 1. 7-15  [39].
Statistical analyses All statistical analyses were performed in RStudio 0. 99. 486. Comparisons of mean trait size and trait variance were performed on log-transformed data and used for scaling relationship comparisons. Winged individuals are rare and have different scaling relationships. We therefore excluded them from the analysis. We used standardized major axis (SMA) as well as linear models (ANCOVA with body size as covariate) to assess differences in scaling relationships (using R package 'smatr' version 3. 4-8 and ANOVA in R, respectively,  [42, 43] ). Coefficients of variation were calculated using the R package 'goeveg' version 0. 4. 2. To test for differences in fight frequency and fight duration, we used a generalized linear model with Gamma distribution, considering replicates as batch effects.
longipes (M), M. sp. (N), and M. pulchella (O). Statistical analyses can be found in S6 Table. Comparisons of adult static allometries for each species between controls and BMP11 RNAi for males and females are shown in the keys below graphs. Linear regressions were fitted to the log-transformed datasets. Samples sizes for each species are indicated in the panels. Statistical analyses were also performed on log-transformed data and can be found in S6 Table. The data underlying this figure may be found in S6 and S7 Tables. BMP11, Bone Morphogenetic Protein 11; RNAi, RNA interference.
longipes (M), M. sp. (N), and M. pulchella (O). Statistical analyses can be found in S6 Table. Comparisons of adult static allometries for each species between controls and BMP11 RNAi for males and females are shown in the keys below graphs. Linear regressions were fitted to the log-transformed datasets. Samples sizes for each species are indicated in the panels. Statistical analyses were also performed on log-transformed data and can be found in S6 Table. The data underlying this figure may be found in S6 and S7 Tables. BMP11, Bone Morphogenetic Protein 11; RNAi, RNA interference. https://doi. org/10. 1371/journal. pbio. 3001157. g003.
Acknowledgments We thank  Leticia Arias  and  Pascale Roux  for help with measurements,  Juliette Mendes  for help with insect rearing,  Felipe Moreira  for help with fieldwork and species identification,  Romina Retamal-Figueroa  for technical assistance,  Francois Leulier ,  Dali Ma  for comments on the manuscript, and  Gael Yvert ,  Kevin Parsons , and  Roberto Arbore  for discussions. We thank  Lois Taulelle  and  HerveGilquin  for providing access to computing resources in the  Pole Scientifique de Modelisation Numerique (PSMN) at the ENS Lyon.
Previous trends in heatwaves Figure  3  shows the observed trends in TXx in the GHCN-D dataset over 1900-2019. The stations were selected on the basis of a long time series, at least 50 years of data, and being at least 2 • apart. The trend is defined as the regression on the global mean temperature, so the numbers represent how much slower or faster than the global mean the temperature has changed. Individual stations with different trends than nearby stations usually have inhomogeneities in the observational method or local environment. The negative trends in eastern North America and parts of California are well-understood to be the result of land use changes, irrigation and changes in agricultural practice  (Donat et al. , 2016 (Donat et al. , , 2017;; Thiery et al. , 2017; Cowan et al.
Observational data The dataset used to represent the heatwave is the ERA5 reanalysis  (Hersbach et al. , 2019)  from the ECMWF at 0. 25 • resolution. A very rapid analysis performed directly after the heatwave and published on https://www. worldweatherattribution. org/ western-north-american-extreme-heat-virtually-impossible-without-human-caused-climate-change/ used ERA5 extended by 70 the ECMWF operational analysis and the ECMWF forecast. There is only a very minor difference in the heatwave amplitude between the two analyses and this makes no difference to the rounded return period used in this study and the analysis results are therefore not affected. Temperature observations were collected to directly assess the probability ratios and return periods associated with the event for the three major cities in the study area; Portland, Seattle, and Vancouver.
Two long records exist adjacent to downtown Vancouver, but they are both very exposed to the Georgia Strait that influenced observations due to local onshore flow during the peak of the event. A record was chosen further inland at New Westminster. The observations start in 1875 but here are data gaps  1882-1893, 1928, 1980-1993. The data for Portland International Airport and Seattle-Tacoma International Airport were gathered from the Global Historical Climatology Network Daily (GHCND;  Menne et al. , 2012)  while data for New Westminster were gathered from the Adjusted Homogenized Canadian Climate Dataset (AHCCD) for daily temperature  (Vincent et al. , 2020). This station's record is a composite of data from three locations in two nearby cities with location changes in  1966 and 1980. From 1874 to 1966, ,  the station operated at an elevation of 118 m near the centre of New Westminster.