dataesr/scientific-paragraphs-categorization huggingface dataset:
Data resources On 1 September 2019, a photograph of a Mantodea male resembling Mantis religiosa (Linnaeus, 1758) was published, from the 9th district of Marseille (Bouches-du-Rhône, France) (Fig. 1 ). The author, as an expert entomologist, was in charge of validating this photograph, amongst many others, illustrating the different species of Mantodea living in France. First, the observed specimen was considered as a species different from those known from France. The author of the photograph was contacted. Two days later, the same or another specimen returned to the observer's balcony, attracted by the light in the evening. It was photographed, captured and sent to the author.
PAD versus WAL Pb-isotopic results from the 2-acid reverse Aqua Regia partial digestion (leaching) of rock pulp (PAD) were compared to those obtained from the QFIR WAL (WAL-Q) of crushed rock material. On Quantile-Quantile plots, the PAD data shows a distribution that is close to log-normal (Figure  4F ), while the WAL data sets are both log-normally distributed (Figure  5F ). The WAL-Q analyses provide less Pb extraction than does the PAD analyses (Figure  4A-C ). The geometric means for the PAD data versus the WAL-Q data are 0. 040 ppm versus 0. 009 ppm, 1. 661 ppm versus 0. 306 ppm, and 0. 762 ppm versus 0. 147 ppm for 204 Pb, 206 Pb, and 207 Pb, respectively (Table  1 ). These data means are closer in value to each other than the equivalent TAD versus PAD comparison. From the paired two-sample Student's t-Test of means (Figure  4F ), it is shown that the mean values for the PAD and WAL-Q data are significantly different, both at the 98% significance level ( 207 Pb shown) and the 95% level, with the t-critical value being much less than the absolute value of the t-statistic value.
Comparison of WAL Variants Pb-isotopic results from the SRC WAL (WAL-S) were compared to those obtained from the QFIR WAL (WAL-Q), both being carried out on crushed rock material. On Quantile-Quantile plots, both data sets show log-normal distributions (Figure  5F ). As shown by the statistical data in Table  1 , the WAL-S and WAL-Q analyses provide very similar amounts of Pb-isotope extraction (Figure  5A-C ), both providing less Pb extraction than the PAD, with the geometric means for the WAL-S data versus the WAL-Q data being 0. 010 ppm versus 0. 009 ppm, 0. 335 ppm versus 0. 306 ppm, and 0. 162 ppm versus 0. 147 ppm for 204 Pb, 206 Pb, and 207 Pb, respectively. From the paired two-sample Student's t-Test of means (Figure  5F ), it is shown that the mean values for the WAL-S and WAL-Q data are not significantly different, at the 98% significance level ( 204 Pb shown), with the t-critical value being greater than the t-statistic value.
g. , in both the PAD and WAL data for the Contact U prospect area, Figure  10C, D ) that display 'strongly anomalous' and 'highly anomalous' 207 Pb/ 206 Pb ratios. The WAL data show the same features as the PAD data. Importantly, there are abundant samples that contain only low amounts of U that return 'strongly anomalous' and 'highly anomalous' ratio values (Figure  10A, B ). Unlike the U mineralized areas, other exploration areas that have so far been found to be barren display at best only a few 'strongly anomalous' values highly subordinate to abundant background values ('barren', 'weakly anomalous') and lesser 'moderately anomalous' values.
Pb-Isotope Pathfinder Geochemistry: Contact U Prospect An evaluation of Pb-isotope pathfinder dispersion geochemistry as a mineralization vector for basement-hosted unconformity-related U mineralization was carried out on drill-hole samples from the area around the Contact U prospect using Pb-isotope data from both PAD and WAL sample digestion/leaching. The Contact prospect mineralization is hosted by Archean orthogneiss and is structurally underlain by a silicified quartz breccia unit. The mineralization envelope extends along the NE-trending Andrew Lake structural trend. Plots using the 206 Pb/ 204 Pb ratio or the 207 Pb/ 206 Pb ratio were used to illustrate the systematic trends that are observed in the Pb-isotope data from these samples. U versus 207 Pb/ 206 Pb ratio down-hole plots (Figure  11 ) combine the same downhole 207 Pb/ 206 Pb ratio data as presented in Figure  9  with down-hole U data (PAD-only).
Pb-Isotope Pathfinder Geochemistry: Contact U Prospect An evaluation of Pb-isotope pathfinder dispersion geochemistry as a mineralization vector for basement-hosted unconformity-related U mineralization was carried out on drill-hole samples from the area around the Contact U prospect using Pb-isotope data from both PAD and WAL sample digestion/leaching. The Contact prospect mineralization is hosted by Archean orthogneiss and is structurally underlain by a silicified quartz breccia unit. The mineralization envelope extends along the NE-trending Andrew Lake structural trend. Plots using the 206 Pb/ 204 Pb ratio or the 207 Pb/ 206 Pb ratio were used to illustrate the systematic trends that are observed in the Pb-isotope data from these samples. 6C, D ). However, several other drill-holes, located southwest and northeast along strike of the U prospect, are also barren (e.
g. , maximum of 26 ppm U in CONT-25, CONT-26, CONT-27; Figure  6C, D ), but they return some promising 'strongly anomalous' ratio values and are located within an interesting low-gravity anomaly (Figure  2B ). The WAL-S data from Contact (Figure  6D ) show the same features as the PAD data (Figure  6C ). a single sample containing 'excess Pb', located immediately above the upper mineralized zone. The WAL-S data (Figure  7E ) show the same features as the PAD data. The 206 Pb/ 204 Pb versus 238 U/ 206 Pb 'excess Pb' diagrams for the Contact U prospect (Figure  8C ,D) show a much weaker 'excess Pb' trend than do either the Bong or End deposits (Figure  8A, B ). Interestingly, most of the mineralized samples from Contact plot in the upper right quadrant of the diagram, below the 50 Ma growth curve, with very high 206 Pb/ 204 Pb ratio values (800 to 25,000).
TAD Versus PAD It is well-known that the total digestion (TAD) data are of limited use in element dispersion evaluation, because they incorporate Pb sourced from the decay of U present in resistate minerals resident within the rock (e. g. , zircon) that will mask the mobile (adsorbed) Pb isotope signal. Nevertheless, a comparison was carried out between the TAD data and the PAD data because such data are routinely available in multi-element ICP-MS analytical packages. Both data sets show near-log-normal distributions, but the values obtained from TAD are significantly greater than those obtained from PAD (Figure  3 ) as a result of the total dissolution of the rock compared to partial dissolution. A consequence of the TAD is the lithology-dependent liberation of additional elemental content from resistate minerals, like zircon, tourmaline, and other silicate minerals, compared to the less aggressive leaching of the PAD.
The Pb-isotopes released from these minerals are related to the formation of these rock-forming minerals and not to any later primary or secondary dispersion that is the target for the Pb-isotope pathfinder exploration method. The observed difference between digestion results is greater in basement metamorphic material than in orthoquartzitic Thelon and Athabasca sandstone because of the greater quantities of these resistate minerals in the basement lithologies. The mean values for the TAD and PAD data are significantly different at both the 95% and 98% significance levels, thus it is statistically unlikely that the TAD data and the PAD data come from distributions with equal population means. Use of isotope ratios eliminates some of the effects of the quantity differences observed between the digestion methods, but the 204 Pb data have lower associated analytical precision than the other Pb-isotopes, due to their generally nearer-to-detection limit abundances (Pers.
Comm. , R. Millar, SRC-GA, 2015), and there is relatively poor correlation of the TAD and PAD 204 Pb results (Figure  3 ). Because of the lower precision, ratios using 204 Pb (e. g. , 206 Pb/ 204 Pb; Figure  3D ) are less reliable, they show greater scatter on the TAD versus PAD plots, and thus they provide less confidence in interpretation for mineral exploration. The 207 Pb/ 206 Pb ratio data are more robust, with the data showing a relatively consistent TAD versus PAD relationship, and less, although still relatively broad, data scatter especially in the middle of the plot (Figure  3E ). Overall, these features indicate that the TAD data should not be used in Pb-isotope pathfinder exploration in sandstone or basement. The PAD data are better suited to Pb-isotope pathfinder exploration; however, they are also obtained from a type of Aqua Regia digestion that is more aggressive than a WAL.
WAL Variants While the PAD data are better suited to Pb-isotope pathfinder exploration than TAD data, the use of WAL data over PAD data has been said by  [10]  to be more useful in U exploration. This is because the weak acid leaches are less aggressive and they are carried out on a size-fraction material that targets clay minerals, rather than on ground sample pulp in which the clay mineral content has been diluted by the major rock-forming minerals. In this study, two variants of the WAL were used. The WAL data from QFIR were obtained following leaching by 2% nitric acid, while the WAL data from the SRC were obtained following leaching by 5% nitric acid. Both data sets show log-normal distributions and the WAL-S and WAL-Q analyses provide very similar amounts of Pb-isotope extraction (Figure  5A-C ; Table  1 ), both providing less Pb extraction than the PAD.
PAD Versus WAL: Bong and End Deposits As for the TAD versus PAD Pb-isotope results comparison, the values obtained from PAD are significantly greater than those obtained from either of the WALs (Figure  4 ) as a result of the partial dissolution of rock-forming minerals in the PAD compared to the extraction of only adsorbed mobile metals by the WAL. The PAD data set shows a near-log-normal distribution, while the WAL data are log-normally distributed; also, as for the TAD versus PAD comparison, the PAD versus WAL comparison indicates that it is statistically unlikely that the PAD data and the WAL data come from distributions with equal population means. The 204 Pb PAD and WAL data values are also more poorly correlated than those for 206 Pb and 207 Pb, with greater data scatter in the 204 Pb results.
As observed above, use of isotope ratios eliminates some of the effects of the quantity differences seen between the PAD and WAL digestion methods, but ratios using 204 Pb are less reliable and show greater scatter ( 206 Pb/ 204 Pb ratio, Figure  4D ) relative to the 207 Pb/ 206 Pb ratio data (Figure  4E ), with a relatively consistent PAD to WAL relationship. A variety of data plots using the 206 Pb/ 204 Pb ratio and the 207 Pb/ 206 Pb ratio were used in an empirical manner to illustrate the similarities between the PAD data set and the WAL data set. The "Holk" 'excess Pb' diagrams ( 206 Pb/ 204 Pb-p vs. 238 U*/ 206 Pb-p plot) for non-mineralized samples from the Bong deposit, End deposit, and the Contact prospect (Figure  8A-C ) show that many data points follow a path that is broadly between the ~50 Ma radiogenic Pb growth curve and the 250 Ma growth curve.
1. 'Strongly anomalous' and 'moderately anomalous' ratio values are present, in both the PAD and WAL data, above the 105 m wide mineralized interval, while below the mineralized interval, 'weakly anomalous' and 'moderately anomalous' values are present. In all of the Pb-isotope ratio diagrams presented, it has been demonstrated that the Pb-isotope data show systematic trends associated with dispersion from the U mineralization. The PAD data from non-mineralized samples also show the same data patterns as the WAL data, even though the absolute quantities of the Pb-isotopes extracted from the samples differ significantly between the PAD and WAL leaches. For example, the Holk 'excess Pb' diagram for the Contact PAD data (Figure  8C ) is essentially the same as the one showing the WAL data (Figure  8D ), depicting the same features, particularly the same pattern of data points falling into the 'excess Pb' field.
The PAD data also show the same features as the WAL data in the other illustrative Pb-isotope ratio diagrams (Figure  6 , Figure  7 , Figure  9 , Figure  10 , and Figure  11 ). While the WAL leach targets adsorbed mobile metals for extraction and the PAD treatment leaches these metals, as well as some metals from partially dissolved non-resistate minerals, it is apparent that the dilution effect on the Pb-isotopic signature from partial dissolution of the major rock-forming minerals is not great. Thus, in practice, it appears that the PAD data can be used as a proxy for WAL data if WAL data is unavailable, particularly the 207 Pb/ 206 Pb data.
Conclusions In general, total-digestion Pb-isotope data should not be used in Pb-isotope pathfinder U exploration. The two varieties of Weak Acid Leaches (SRC, QFIR) provided data that were not significantly different. Either WAL variety can be used in U exploration using Pb-isotope pathfinder geochemistry. The partial digestion Pb-isotope data for the basement-hosted U deposits of the Kiggavik area provide results that are very similar to those obtained for the WAL data. Both sets of data produced interesting vectoring results in scatterplots, down-hole plots, and plan views. In the absence of WAL Pb-isotope data, the partial digestion Pb-isotope data can be used for drill-hole-and prospect-scale evaluation and exploration vectoring purposes. Diagrams using the 206 Pb/ 204 Pb and 207 Pb/ 206 Pb ratios show systematic trends useful in unconformity-type U exploration.
Funding: This research was funded by  AREVA Resources Canada Inc. Acknowledgments: This study began as a result of discussions between the authors and the  late Kurt Kyser of the Queen's Facility for Isotope Research (QFIR) at Queen's University. The authors thank  Orano Canada Inc. (formerly  AREVA Resources Canada Inc. ) for geological and logistical support in the field, as well as for the analytical support, especially  Patrick Ledru  and  John Robbins. John Robbins  is also thanked for providing comments and suggestions for improving the manuscript. The  Saskatchewan Research Council's Geoanalytical Laboratory  is also thanked for performing the lithogeochemical analyses, for their in-kind contribution of a set of WAL leaches and Pb-isotope analyses, and for the comments and suggestions provided by  Rob Millar.
Thus, we measured the contribution of smoking-associated methylation signatures and genome-by-methylation interactions to trait variation. We performed analyses in both sexes jointly and independently and also including genome-by-smoking-by-sex interactions, and we showed that omics data can be exploited as proxies for environmental exposures to improve our understanding of complex trait architecture. We observed that using an appropriate set of CpG sites, methylation can be used to model trait variation associated with smoking, and genome-by-smoking interactions suggesting potential applications for better prediction and prognosis of complex disease and expanding these modelling approaches to other environments and traits.
UK Biobank We sought to replicate the results observed in Generation Scotland with data from the UK Biobank cohort (UKB). Analyses were run in four sub-cohorts for computational reasons (G1, G2, G3 and G4, grouping individuals in geographically close recruitment centres; for more information see Methods and Supplementary Table  3 ), with the two sexes considered jointly and separately in three different analyses (the sample size of these groups permitted estimates to be obtained with the two sexes separately). Individual sub-cohort analyses were meta-analysed. The estimated contributions of self-reported smoking status (and other covariates) to trait variation in UK Biobank are shown in Supplementary Table  2. These were similar to the ones observed in Generation Scotland, varying between 0.
Data. Generation Scotland. We used data from Generation Scotland: Scottish Family Health Study (GS)  51, 52. Ethical approval for the study was given by the NHS Tayside committee on research ethics (ref: 05/s1401/89). Governance of the study, including public engagement, protocol development and access arrangements, was overseen by an independent advisory board, established by the Scottish government. Research participants gave consent to allow both academic and commercial research. Individuals were genotyped with the Illumina HumanOmniExpressExome-8 v1. 0 or v1. 2. We used PLINK version 1. 9b2c  53  to exclude SNPs that had a missingness > 2% and a Hardy-Weinberg Equilibrium test P < 10 -6. Markers with a minor allele frequency (MAF) smaller than 0. 05 were discarded. Duplicate samples, individuals with gender discrepancies and those with more than 2% missing genotypes were also removed.
The resulting data set was merged with the 1092 individuals of the 1000 Genomes population  54  and a principal component analysis was performed using GCTA  55. Individuals more than 6 standard deviations away from the mean of principal component 1 and principal component 2 were removed as potentially having African/Asian ancestry as shown in Amador et al. 56. After quality control, individuals had genotypes for 519,819 common SNP spread over the 22 autosomes. Of the ~24,000 individuals in GS, the number of individuals with complete information for smoking and other covariates was 18,522 so we used this core set of samples for the analyses in order to allow comparisons between the models, we refer to this set of samples as GS18K. UK Biobank. Data access to UK Biobank was granted under MAF 19655.
The UK Biobank database include 502,664 participants, aged 40-69, recruited from the general UK population across 22 centres between 2006 and 2010  57. They underwent extensive phenotyping by questionnaire and clinic measures and provided a blood sample. All participants gave written informed consent, and the study was approved by the North West Multicentre Research Ethics Committee. Phenotypes and genotypes were downloaded direct from UK Biobank. UK Biobank participants were genotyped on two slightly different arrays and quality control was performed by UK Biobank. The two are Affymetrix arrays with 96% of SNPs overlap between both. Further information about the quality control can be found in the UK Biobank website (https://www. ukbiobank. ac. uk/register-apply/). Only genetically white British individuals were used in the analyses.
DNA Methylation data. DNA methylation data is available for a subset of 9,537 participants from the GS cohort, as part of the Stratifying Resilience and Depression Longitudinally (STRADL) project  58. From those, we used N = 8,821 individuals that had complete information for all the same set of covariates as used in the smoking status analysis. We refer to this subset of individuals as GS9K. DNA methylation was measured at 866,836 CpGs from whole blood genomic DNA, using the Illumina Infinium MethylationEPIC array. Quality control was performed using R (version 3. 6. 0)  59 , and packages shinyMethyl 60 and meffil  61. We removed outliers based on overall array signal intensity and control probe performance and samples showing a mismatch between recorded and predicted sex. We removed samples with more than 0.
Acknowledgements The authors want to acknowledge the  Medical Research Council (MRC) UK  for funding (core funding:  MC_UU_00007/10 ,  MC_PC_U127592696  and  MC_PC_U127561128 ), and a  Wellcome Trust   Investigator Award  to AMM ( 220857/Z/20/Z ). Generation Scotland has received core funding from the  Chief Scientist Office of the Scottish Government Health Directorates   CZD/16/6  and the  Scottish Funding Council   HR03006. Genotyping of the GS samples was carried out by the  Genetics Core Laboratory  at the  Wellcome Trust Clinical Research Facility, Edinburgh , Scotland and was funded by the  UK MRC  and the  Wellcome Trust  ( Wellcome Trust   Strategic Award  " STratifying Resilience and Depression Longitudinally " (STRADL) Reference  104036/Z/14/Z ). We are grateful to all the families who took part, the general practitioners, and the  Scottish School of Primary Care  for their help in recruiting them, and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants and nurses.
Data availability Generation Scotland data are available from the MRC IGC Institutional Data Access / Ethics Committee for researchers who meet the criteria for access to confidential data. Generation Scotland data are available to researchers on application to the Generation Scotland Access Committee (access@generationscotland. org). The managed access process ensures that approval is granted only to research which comes under the terms of participant consent which does not allow making participant information publicly available. UK Biobank data are available from: https://www. ukbiobank. ac. uk/register-apply/.
3C ) for LB-injected (Fig. 3D ) and noLB-injected (Fig. 3E ) animals independently. To avoid possible overfitting, we used several methods in combination. First, we performed cross-validation by splitting the dataset into two parts: a training and a testing set of data. Eighty percent of the data were randomly selected to train the networks (and in-dependently for each network), while the 20% remaining were used to evaluate the networks. Then, to evaluate the robustness of the quality of prediction for a given set, we repeated this cross-validation step 50 times for every set of three input variables (each network was trained and tested using a different partition of the dataset; total number of networks, 35,428,050). Last, we generated random data and used them as input for the MLP. As expected, performances were significantly lower compared to our actual dataset (fig.
Compared to the RBD pull-down assay, the NanoBit assay is quantitative and can be used to calculate affinity and kinetic parameters. We determined with the NanoBit assay that rocaglamide inhibits KRAS activation with an IC50 of 24. 8 nM (Fig. 1C ), and that 100 nM of rocaglamide produced 50% inhibition by 50 minutes (Fig. 1D , Fig. S1E ). The results also show that rocaglamide inhibits KRAS4A activated either by EGF stimulation or by gain of function mutations (Fig. S1F  and G ). Recent studies led to the successful development of a KRAS G12C inhibitor which entered clinical trial (https://clinicaltrials. gov/ct2/show/NCT03600883). For further validation of our NanoBiT assay, we included KRAS G12C inhibitor (ARS-1620) in our experiments  (Janes et al. , 2018). As shown in Fig. 1E , ARS-1620 strongly inhibited KRAS G12C mutants while the G12V mutant was only modestly inhibited by the treatment.
Soft agar colony formation assay 1. 5% agarose solution was mixed with 2x growth medium (20% FBS, with or without 100 nM rocaglamide) and placed with 1. 5 ml of 0. 75% agarose/1x growth medium in 6 well plates. To solidify agarose, plates were incubated at room temperature for at least 10 min. HCT-116 and ASPC-1 cells were diluted in 2x growth medium (20% FBS, with or without 100 nM rocaglamide) and mixed with 0. 9% agarose solution. 1. 5 ml of cell suspension in 0. 45% agarose in 1x growth medium was added to the bottom agarose layer. The cells seeded in soft agar were cultured for 2 to 4 weeks followed by crystal violet staining. The images were taken using a ChemiDoc Touch (Bio-Rad) and the number of colonies was counted by Image J software. Soft agar colony formation assays were also performed in 96 well plates to simplify the quantification of colonies.