dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses All values were presented as mean ± SEM (standard error of the mean) from at least three independent experiments, except for the StAZ bioassay, for which the number of larvae and independent batches are detailed in the figure legends. Multiple comparisons among groups were performed using one-way analysis of variance (ANOVA) followed by a Dunnett's post-test/Newman-Keuls post-test, or using a Kruskal-Wallis test followed by a Dunn's post-test using GraphPad Prism8 software (GraphPad Software, San Diego, CA, USA). Differences were considered significant when p < 0. 05 (*), p < 0. 01 (**), p < 0. 001 (***).
Acknowledgments: We first wish to thank  BIOSIT  ( UMS BIOSIT, Rennes, France ), notably  Marie-Madeleine Gueguen , for providing zebrafish eggs. We are also very grateful to MRiC platforms ( UMS BIOSIT, Rennes, France ), notably,  Stéphanie Dutertre  and  Xavier Pinson , for their help with confocal microscopic imagery. We would also like to thank  Karin Tarte  ( UMR U1236-MICMAC, Rennes, France ) for access to the  Fluidigm-Biomark HD System Technology. In addition, we are grateful to  Catherine Lavau  for her help in the English editing process.
Fig. 2. a) Scheme of the Inductive Power Transfer  [11] , b) OLEV bus in South Korea  [18]  and a typical DWPT charging system for EVs  [19]. Fig. 3. Principle of trench-based construction for integration of inductive charging systems in pavements (after  [20] ). rated power of 30 kW for the "Zoe" (Renault) and "DS3" (Stellantis) cars and 90 kW for the "MASTER" (Renault) van. They can be defined as Z2 and Z3 class according to standards like J2954  [14] , IEC 61980  [15] , ISO 19363  [16]  and even IEC 63243  [17]  (standard concerning DWPT which is being drafted). Two technologies of coils are assessed in INCIT-EV work package 4, but for space limitations, only the primary charging coil solution developed by VEDECOM is presented here. Using the ERS system provided by VEDECOM, several solutions for the integration of the inductive charging coils in pavements were considered  [20].
Structural transient thermo-viscoelastic simulations The transient structural simulations are based on finite element calculations using the software Cast3M, with thermo-mechanical coupling. The calculations are performed in 2D, for a transverse section of the road structure, considering the plane strain formulation. The input data are the geometry, the material properties, the boundary conditions, the initial and loading conditions, like the heat flux generated by the coil, and climatic parameters (i. e temperature of ambient air, solar radiation, longwave radiation and wind speed). The outputs are the temperature fields and mechanical response of the road structure.
Materials and thermal parameters The parameters related to the thermo-viscoelastic properties of the two asphalt materials and of the Plastiroc viscoelastic resin are reported in Table  2. Representations by Prony series (GMM elements) and by the WLF equation are used to characterize the materials. Parameters were obtained from laboratory complex modulus tests, performed according to standard EN 12697-26 and analysed using the software Viscoana-lyse®  [36]. The reference temperature was set at 15 • C. For the thermal properties of the asphalt materials, generic properties published in  [37]  were used. The thermal properties of the Plastiroc were obtained from laboratory tests. The materials used for the coil block, the VA60 resin and the soil are assumed linear elastic. The thermo-mechanical properties of these materials are summarized in Table  3.
Acknowledgments The authors want to thank the European commission for within the  INCIT-EV  project this research. This project is founded by the  European Union 's  Horizon 2020 Research and Innovation program , under the Grant Agreement N •  875683 ; it brings together 33 partners (industrials, universities, institutes, city authorities, start-ups, SMBs). The missions include the analysis and user involvement, the development of the control platforms, the development of innovative solutions, and the integration of developments in demonstration areas.
Peter Davies. After several washes, cells were incubated with anti-mouse or anti-rat or anti-rabbit fluorescein-conjugated secondary antibodies (dilution 1:500, Invitrogen) and cell nuclei were stained with DAPI (Sigma-Aldrich). After mounting the cover glass into glass slides, the slides were analyzed under Zeiss Observer Z1 microscope (Zeiss, Germany). Images obtained were further analyzed using ImageJ software  (53).
ACKNOWLEDGEMENTS: The authors are grateful to members of the  Jockers laboratory  for stimulating discussions and comments on the project. We thank  Aurelien Cordier  for his expert technical help in the initial phase of the project. The work was supported by  Servier Laboratories ,  Institut National de la Santé et de la Recherche Médicale (INSERM)  and  Centre National de la Recherche Scientifique (CNRS).
Marmoset saccades Marmoset eye position was tracked as previously described using an EyeLink 1000 (SR Research, Osgoode, ON, Canada) camera to capture binocular eye position at sampling rate of 1 kHz (500 Hz for each eye)  10, 27  and saccades were detected with a velocity threshold  10. Twentyfour small (20 by 16 dva) and 24 large (40 by 32 dva) images from the McGill Calibrated Color Image Database  95  were presented per session on a CRT monitor (85 Hz refresh rate) 50 cm away randomly interleaved them with unrelated 1-s fixation trials using Maestro software suite (https://sites. google. com/a/srscicomp. com/maestro/). In separate sessions, the images were presented with their luminance contrast scaled down uniformly at either 5 or 10% of the original contrast. Data from images of both sizes were combined for contrast-related results.
Statistics and reproducibility All statistical tests were nonparametric based on the median and error bars were based on bootstrap analysis of the median by resampled data 1000 times, allowing repeats, to produce surrogate datasets of the same size. The 160th and 840th samples were used for the standard error of the median for all results. For data we collected on humans, marmosets, and mice, we present the entire distribution of all subjects grouped or single subject medians as single solid data points, an outline connecting the standard error of the median of grouped data across rate and size distributions, and an outline connecting the 25th and 75th quartiles of grouped data across rate and size distributions. For receptive field, temporal frequency and saccade data published previously by other authors, we present the median of grouped data or medians of single subjects as open circles and a dashed outline connecting the 25th and 75th quartiles of grouped data across y-and x-axis distributions.
Pictures were selected from the natural images from the McGill Calibrated Color Image Database 95. For the first 20 runs, participants randomly explored the screen with either the 80 full-sized selected images covering 40 by 32 dva of the visual field or the same 80 images center-cropped covering 20 by 16 dva of the visual field. For the next 20 runs, each participant explored 40 images selected randomly from the same set of 80 images, but with their luminance contrast scaled down uniformly at either 5 or 10% of the original contrast. These images covered 40 by 32 dva of the visual field during the first 10 runs and 20 by 16 dva of the visual field in the last 10 runs. Data from images of both sizes were combined for contrast-related results.
and 8-12 small (40 by 32 dva) and 8-12 large (80 by 64 dva) images/per session were presented before or after 2-3 training sessions. Images from the McGill Calibrated Color Image Database 95 were presented onto a screen 22 cm away with a DLP LED projector. Binocular eye movements were tracked using custom software to analyze 250 by 250-pixel images collected at 20-30 frames per second with cameras aligned with the orbital axis of each eye. Since mice have a much higher contrast threshold and saturation point than primates, for image contrast experiments, we had them view natural images scaled down to 25 and 50% of the original contrast.
; https://doi. org/10. 1101/2023. 06. 20. 23291538 doi: medRxiv preprint to other countries (Extended Data Fig. 1 ). Principal Component Analysis (PCA) performed on the proportions of the six primary SBS mutation classes (C>A, C>G, C>T, T>A, T>C, T>G) in each sample identified a distinct cluster of mainly Romanian and Serbian cases and a further cluster of mainly Japanese cases (Extended Data Fig. 2 ). The results, therefore, clearly demonstrate geographical variation of somatic mutation loads and patterns in ccRCC. To investigate the mutational processes contributing to the geographical variation in mutation burdens we extracted mutational signatures and estimated the contribution of each signature to each ccRCC genome. Ten signatures with strong similarity to a reference signature in the Catalogue of Somatic Mutations in Cancer (COSMIC) database were extracted: SBS1, due to deamination of 5-methylcytosine  13  ; SBS2 and SBS13, due to cytosine deamination by Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) DNA editing enzymes  13  ; SBS4, due to tobacco smoke mutagens 14 ; SBS5, due to an endogenous mutational process in which mutations accumulate with age 14 ; SBS12, of unknown cause; SBS18, due to DNA damage by reactive oxygen species 14 ; SBS21 and SBS44, due to defective DNA mismatch repair  14, 15  ; and SBS22, due to Aristolochic acid exposure  16, 17.
The presence of SBS22 was strongly correlated with that of new signatures SBS_I, DBS_D, and ID_C (Extended Data Fig. 3 4 5 ) which are, therefore, also probably due to Aristolochic acid exposure. SBS_I, like SBS22, is composed predominantly of T>A mutations. The signature identified previously as SBS22, has therefore been renamed SBS22a, and SBS_I has been named SBS22b. The two signatures may be due to different subsets of Aristolochic acids, and/or to different metabolites, which induce slightly different mutational patterns. All these signatures exhibited their highest mutation loads away from recognized BEN zones (Fig. 3 ) indicating that a substantial proportion of the population over a wide geographical area of Eastern Europe has been subject to mutagenesis due to Aristolochic acid exposure.
The sources of this exposure are uncertain. SBS12 was present in 72% of Japanese and 2% of non-Japanese ccRCC (p-value=4. 7 × 10 -78 ) (Extended Data Fig. 6h ). Compared to the mutation burdens imposed by Aristolochic acid in ccRCC, SBS12 contributed modest mutation loads. SBS12 is composed predominantly of T>C substitutions and exhibits strong transcriptional strand bias with more T>C mutations on the transcribed than untranscribed strands of protein coding genes. Transcriptional strand bias is typically caused by activity of transcription-coupled nucleotide excision repair acting on bulky DNA adducts due to exogenous mutagenic exposures such as tobacco smoke chemicals 14 , ultraviolet light 14 , Aristolochic acids  16  , and aflatoxins  21. Assuming that transcription-coupled repair of DNA adducts is responsible for the SBS12 strand bias, the adducts are likely on adenine.
SBS12 was previously reported in hepatocellular carcinomas  5, 14  and additional analysis of existing datasets revealed strong SBS12 enrichment in hepatocellular carcinomas from Japan when compared to other countries (p-value=3. 8 × 10 -15 ; Supplementary Note). The results, therefore, indicate that exposure to an agent contributing SBS12 mutations to kidney and liver cancer is common in Japan and rare elsewhere. The agent responsible for SBS12 is unknown. Although population-specific endogenous production of the mutagen cannot be excluded, the precedents provided by other mutational signatures with strong transcriptional strand bias suggest that it is likely of exogenous origin. A polymorphism in aldehyde dehydrogenase 2 known to impair metabolism of alcohol to aldehydes and common in Japan did not associate with levels of SBS12 (although power is limited due to the relatively small number of Japanese cases).
SBS40a, SBS40b, and SBS40c were present in ccRCC from all 11 countries. The countryspecific average mutation burdens of SBS40a and SBS40b positively associated with countryspecific ASRs of kidney cancer incidence (p-value=0. 0022 and p-value=5. 1 × 10 -18 , respectively; Extended Data Fig. 7a ; Fig. 4a ), with the highest mutation loads in the Czech Republic and Lithuania. Indeed, when excluding the outlier effect of Romania and Serbia, SBS40b was largely responsible for association of country-specific average total SBS burdens with kidney cancer ASR (p-value=6 × 10 -5 ; Extended Data Fig. 8a ). Kidney cancer incidence rates also vary between the regions of the Czech Republic and SBS40b mutation burdens differed significantly between these (p-value=0. 011; Fig  4b, c ), with the highest attribution in the highest risk region.
SBS40b exhibits modest transcriptional strand bias and, assuming that transcription-coupled repair of DNA adducts is responsible, the adducts underlying SBS40b are likely on pyrimidines. Insertion and deletion (indel) signatures ID5 and ID8, which together contributed ~60% of the indel mutation burden on average, were also strongly associated with country-specific kidney cancer ASR (p-value=1. 3 × 10 -10 and p-value=6. 2 × 10 -9 , respectively, Extended Data Fig. 7b, c ). Signatures ID5 and ID8 correlated with each other (0. 78), as well as with SBS40b (0. 81 and 0. 74, respectively) indicating that they likely all constitute products of the same underlying mutational process. Thus, the burdens of the full complement of mutation types generated by this mutational process correlate with age-adjusted kidney cancer incidence rates.
Cases were excluded if coverage was below 30X for tumor or 15X for normal tissue. For evenness of coverage, the median over mean coverage (MoM) score was calculated. Tumors with MoM scores outside the range of values determined by previous studies to be appropriate for whole genome sequencing (0. 92 -1. 09) were excluded. Conpair 2 (https://github. com/nygenome/Conpair) was used to detect contamination, cases were excluded if the result was greater than 3% 3. A total of 962 cases passed all criteria and were included in subsequent analysis.
Somatic variant calling Variant calling was performed using the standard Sanger bioinformatics analysis pipeline (https://github. com/cancerit). Copy number profiles were determined first using the algorithms ASCAT  4  and BATTENBERG  5  , where tumor purity allowed. SNV were called with cgpCaVEMan  6  , indels were called with cgpPINDEL 7 , and structural rearrangements were called using BRASS. CaVEMan and BRASS were run using the copy number profile and purity values determined from ASCAT where possible (complete pipeline, n=857). Where tumor purity was insufficient to determine an accurate copy number profile (partial pipeline, n=105), CaVEMan and BRASS were run using copy number defaults and an estimate of purity obtained from ASCAT/BATTENBERG. For SNV additional filters (ASRD >= 140 and CLPM ==0) were applied to remove potential false positive calls.
Mutational signature analysis Mutational signatures were extracted using two algorithms, SigProfilerExtractor (https://github. com/AlexandrovLab/SigProfilerExtractor), based on nonnegative matrix factorization, and mSigHdp 10 (https://github. com/steverozen/mSigHdp), based on the Bayesian hierarchical Dirichlet process. For SigProfilerExtractor, de novo mutational signatures were extracted from each mutational matrix using SigProfilerExtractor with nndsvd_min initialization (NMF_init="nndsvd_min") and default parameters (v1. 1. 9)  11. Briefly, SigProfilerExtractor deciphers mutational signatures by first performing Poison resampling of the original matrix with additional renormalization (based on a generalized mixture model approach) of hypermutators to reduce their effect on the overall factorization  11.
Where possible, SigProfilerExtractor matched each de novo extracted mutational signature to a set of previously identified COSMIC signatures  12  , for SBS-1536 signatures this requires collapsing the 1536 classification into the standard 96 substitution type classification with six mutation classes having single 3' and 5' sequence contexts (Supplementary Table  5 ). This step makes it possible to distinguish between de novo signatures which can be explained by a combination of the known catalog of mutational process (which have not been completely separated during the extraction), and those which have not been previously identified. mSigHdp extraction of SBS-96 and ID-83 signatures was performed using the suggested parameters and using the country of origin to construct the hierarchy. SigProfilerExtractor's decomposition module was subsequently used to match mSigHdp de novo signatures to previously identified COSMIC signatures  12.
Attribution of activities of mutational signatures The de novo (SigProfiler) and COSMIC signature (SigProfiler and mSigHdp) activities were attributed for each sample using the MSA signature attribution tool (v2. 0, https://gitlab. com/s. senkin/MSA)  13. For COSMIC attributions, only COSMIC reference signatures, which were identified in the decomposition of de novo signatures, where included in the panel for attribution, in addition to de novo signatures which could not be decomposed into COSMIC reference. At its core, the tool utilizes the nonnegative least squares (NNLS) approach minimizing the L2 distance between the input sample and the one reconstructed using available signatures. To limit false positive attributions, automated optimization procedure was applied by repeated removal of all signatures that do not increase the L2 similarity of a sample by >0.
Evolutionary analysis Subclonal architecture reconstruction was performed using the DPClust R package v2. 2. 8  5, 15  , after obtaining cancer cell fraction (CCF) estimates by dpclust3p v1. 0. 8 (https://github. com/Wedge-lab/dpclust3p) based on the variant allele frequency provided by the somatic variant callers and the copy number profiles determined by the BATTENBERG algorithm. Only tumors with at least 40% purity according to BATTENBERG were considered for further evolutionary analysis. For each tumor with at least one subclone, the respective somatic mutations were split into clonal and subclonal mutations using the most probable cluster assignment for each mutation as per the DPClust output. Mutations not assigned to a cluster by DPClust were removed from further analysis. Clusters centered at a CCF>1.
5 and ones where chromosome X contributed the highest number of mutations were deemed artifactual, and the respective mutations were removed. Samples with a total number of clonal or subclonal mutations below 256 were also removed. Additionally, samples with poor separation between the clonal and subclonal distributions (e. g. , subclone centered at a CCF>0. 80) were removed. Finally, only samples that had both a clone and at least one subclone post-filtering were retained for further analysis. This yielded a total of 223 samples, each with clonal and subclonal mutations. SigProfilerAssignment (v0. 0. 13) (https://github. com/AlexandrovLab/SigProfilerAssignment) was used to identify the activity of each mutational signature in each clone/subclone, and these activities were then normalized by the total number of mutations belonging to the clone/subclone (i.
internationalgenome. org/data/) were filtered and merged with ccRCC genotype data based on the pruned set of variants present in both datasets. ADMIXTURE was run on the merged genotype data with k=3, which would correspond to the three ancestral continental population groups that likely reflect the participants of our study. The ccRCC cases with European genetic fraction greater than 80% by the ADMIXTURE analysis were selected for the polygenic risk scores (PRS) analyses. To complement the ADMIXTURE analysis, PCA was run on the same samples. The initial genotype data based on whole-genome sequence from 849 ccRCC cases with European genetic background consisted of biallelic SNPs with MAF >0. 01% (to exclude ultrarare variants; N ~ 30 million variants). After basic quality control, variants with missing genotype rate of greater than 5% (N=7,519,196 variants) with strong deviation from Hardy-Weinberg equilibrium (p-values<10 -8 , N=220,862) were excluded.
For each GWAS trait, we restricted our analyses to the biallelic SNPs with minor allele frequency (MAF) greater than 1% in the 1000 genomes reference for European populations. For the selection of the independent genome-wide significant hits (p-values<5 × 10 -8 ) of each GWAS summary statistic used to generate the PRS, SNPs were clumped (r 2 =0. 1 within a LD window of 10 MB) using PLINK (v1. 9b, www. cog-genomics. org/plink/1. 9/) based on the 1000 genomes European reference population genotype data (N=489; ~ 10 million variants). Where a selected GWAS hit was not found in ccRCC genotype data, we extracted proxies (r 2 >0. 8 in 1000 genomes) also present in ccRCC dataset where possible (Supplementary Table  11 ). The variance of each genetic trait explained by the genetic variants were calculated as previously suggested  25.
Untargeted metabolomics association with signatures Of the 962 subjects from the main analysis, 901 subjects were included in this sub-study -all Japanese samples (n=36) as well as few cases from Czech Republic (n=13), Romania (n=5) and Russia (n=3) were not included due to lack of available plasma samples. Samples were randomized and analyzed as two independent analytical batches. Analysis was performed with a UHPLC-QTOF-MS system that consisted of a 1,290 Binary LC and a 6,550 QTOF mass spectrometer equipped with Jet Stream electrospray ionization source (Agilent Technologies), using previously described methods  27. Pre-processing was performed using Profinder 10. 0. 2. 162 and Mass Profiler Professional B. 14. 9. 1 software (Agilent Technologies). A "Batch recursive feature extraction (small molecules)" process was employed for samples and blanks to find [M+H] + ions.
As with univariate models, regression models were used for the least zero-inflated signatures (<30% of zeros) while classification models were used for all other signatures, with restriction to Romanian and Serbian samples for SBS22a and SBS22b. Importance was estimated from the total decrease in node impurities from splitting on the variable, averaged over all trees. Node impurity was measured by the Gini index for classification, and by residual sum of squares for regression. The significance of importance metrics for Random Forest models were estimated by permuting the response variable (https://github. com/EricArcher/rfPermute). Features considered for identification, along with their highly correlated counterparts, were searched in Human Metabolome Database (HMDB), LipidMaps, Metlin, and Kegg.
Acknowledgements The authors would like to thank  Laura O'Neill ,  Kirsty Roberts ,  Katie Smith ,  Maisie Farenden ,  Siobhan Austin- Guest and the staff of DNA Pipelines at the  Wellcome Sanger Institute  for their contribution. We are grateful for the support provided by  Maja Milosevic ,  Christophe Lallemand ,  Helene Renard ,  Aude Bardot ,  Andreea Spanu  and  Nivonirina Robinot  as well as  IARC General Services , including the  Laboratory Services and Biobank  team led by  Zisis Kozlakidis , the Section of Support to Research overseen by  Tamas Landesz  and the Evidence Synthesis and Classification Section led by  Ian Cree , under  IARC  regular budget funding. The authors would like to thank  Gislaine Bergo ,  Riley Cox  and  Juliana Oliveira  for help with data/sample preparation and processing.