dataesr/scientific-paragraphs-categorization huggingface dataset:
(e) Ba 2+ dissociation constants plotted against the membrane potential. Circle symbols represent the apparent K D values related to the inward Kir blockade, calculated from fits of Equation (1) as shown in (c). Triangle symbols correspond to the K D values computed from fits of fractional currents as presented in (d), after subtraction of the K + -independent blockade. The solid lines are fits of Equation (5) with δ = 0. 65 in [K + ] i = 110/[K + ] o = 3 mM, and 0. 75 in [K + ] i = 110/[K + ] o = 10 mM. The dashed lines show extrapolation of the K + -independent K D (v) relationship established for Ba 2+ on the outward Kir current (same data as in Figure 2h) Downloaded from https://physoc. onlinelibrary. wiley. com/doi/10. 14814/phy2. 15200 by Portail BibCNRS INSB, Wiley Online Library on [22/11/2022].
Introductory paragraph (150 words max) Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 16,498 cases and 182,142 controls. Burden tests were performed for protein-truncating and rare missense variants in 16,562 and 18,681 genes respectively. Associations between protein-truncating variants and breast cancer were identified for 7 genes at exome-wide significance (P<2. 5x10  -6  ): the five known susceptibility genes BRCA1, BRCA2, CHEK2, PALB2 and ATM, together with novel associations for ATRIP and MAP3K1. Predicted deleterious rare missense or protein-truncating variants were additionally associated at P<2.
Main Text (up to 2000 words) Breast cancer is the leading cause of cancer-related mortality for women worldwide. Genetic susceptibility to breast cancer is known to be conferred by common variants, identified through genome-wide association studies (GWAS), together with rarer coding variants conferring higher disease risks. The latter, identified through genetic linkage or targeted sequencing studies, include protein-truncating variants (PTVs) and/or some rare missense variants in ATM, BARD1, BRCA1, BRCA2, CHEK2, RAD51C, RAD51D, PALB2 and TP53 1. However, these variants together explain less than half the familial relative risk (FRR) of breast cancer 2. The contribution of rare coding variants in other genes remains largely unknown. Here, we used data from three large whole-exome sequencing (WES) studies, primarily of European ancestry, to assess the role of rare variants in all coding genes: the BRIDGES (Breast Cancer Risk after Diagnostic Gene Sequencing) dataset that included samples from eight studies in the Breast Cancer Association Consortium (BCAC), the PERSPECTIVE (Personalised Risk assessment for prevention and early detection of breast cancer: integration and implementation) dataset that included three BCAC studies (Supplementary Table  1 ), and UK Biobank.
After quality control (see methods), these datasets comprised 16,498 cases and 182,142 controls (Supplementary Table  2 ). We considered two main categories of variants: protein-truncating variants (PTVs) and rare missense variants (minor allele frequency <0. 001). Single variant association tests are generally underpowered for rare variants; however, burden tests, in which variants are collapsed together, can be more powerful if the associated variants have similar effect sizes 3. To further improve power, we incorporated data on family history of breast cancer (see methods)  4. Association tests were conducted for all genes in which there was at least one carrier of a variant (16,562 genes for PTVs and 18,681 genes for rare missense variants). In the combined analysis of PTVs, 18 genes were associated at P<0.
001 (Supplementary Table  3 , Figures  1 2 ). Of these, 7 met exome-wide significance (P<2. 5x10 -6 ), of which 5 are known breast cancer risk genes -BRCA1, BRCA2, CHEK2, PALB2 and ATM. Novel associations were identified for PTVs in MAP3K1 (P=6. 1x10  -8  ) and ATRIP (P=1. 8x10  -6  ). Of the other previously identified breast cancer susceptibility genes, nominally significant associations were observed for BARD1, CDH1 and RAD51D (Supplementary Table  4 ). There was no evidence for an excess of significant associations after allowing for the 7 exome-wide significant genes (Figure  2 ). However, 17 of the 18 associations at P<0. 001 correspond to an increased risk, compared with ~9. 5 that would be expected by chance. This imbalance suggests some of the other associations may be genuine. Within the BCAC dataset, we evaluated the association with subtypes of breast cancer; ER+ or ER-, PR+ or PR-, and triple-negative disease.
ATR-ATRIP is involved in the process that activates checkpoint signalling when single-stranded DNA is detected following the processing of DNA double-stranded breaks or stalled replication forks  12, 13. SAMHD1 promotes the degradation of nascent DNA at stalled replication forks, limiting the release of single-stranded DNA  14. SAMHD1 also encodes dNTPase that protects cells from viral infections  15  and is frequently mutated in multiple tumor types, including breast cancer. Pathology information was available in the BCAC dataset for 9 carriers of MAP3K1 PTVs, 14 carriers of ATRIP PTVs and 46 carriers of SAMHD1 PTVs or predicted deleterious rare missense variants. These data suggest a higher proportion of low-grade, low-stage ductal breast cancer for MAP3K1 carriers, but high-grade, low-stage ductal breast cancer for ATRIP carriers (Supplementary Table  9 ).
4). These results demonstrate that large exome sequencing studies, combined with efficient burden analyses, can identify additional breast cancer susceptibility genes. The excess of positive associations at P<0. 001 indicates that further genes should be identifiable through large datasets: the heritability analyses suggest the number of associated genes might be of the order of 130. Further replication in larger datasets will also be necessary to provide more precise estimates for variants in the novel genes ATRIP, MAP3K1 and SAMHD1, to define the set of variants in these genes associated with breast cancer and the clinic-pathological characteristics of tumors in variant carriers. The heritability analyses suggest that most of the contribution of PTVs is mediated through the five genes BRCA1, BRCA2, ATM, CHEK2 and PALB2, commonly tested for in clinical cancer genetics  16.
Barcoded pooled libraries of 96 samples were sequenced on each lane of a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq Xp 4-Lane Kit (2x100bp). For PERSPECTIVE, library preparation was conducted using Agilent SureSelect Human all exon V7 (48. 2Mb). Barcoded libraries of 88 samples were sequenced on a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq Xp4-lane Kit (2x100bp). The same pipeline for variant calling was applied to both the BRIDGES and PERSPECTIVE data and followed the GATK (Genome Analysis Toolkit) best practices. Briefly, raw sequence data (FASTQ format) were pre-processed to produce BAM files. This involved alignment to the reference genome, identification and removal of duplicate read pairs from the same DNA fragments, and base recalibration. The base recalibration included the generation of a base quality score recalibration table, later applied to the read bases to adjust their quality scores and increase the accuracy of the variant calling algorithms.
An intermediate and informal Quality Control was performed for a sanity check, including coverage and alignment mapping metrics. Variants were then called using Haplotype Caller for the whole exome. This was later split into chromosomes for the analysis due to file size constraints. Variants were further filtered using Variant Quality Score Recalibration (VQSR), a proprietary algorithm from GATK that applied new calibration scores independently at SNP and indel variant levels. From the final dataset, samples and variants were excluded based on coverage, allelic balance, and Hardy-Weinberg equilibrium, using the same filtering as for UK Biobank. We also excluded samples where the genotypes were inconsistent with previous array genotyping or targeted sequencing data  1, 2. The BRIDGES study sequenced 6,912 samples, of which 3,461 cases and 3,200 controls remained in the final dataset after QC.
Data preparation For both the UK Biobank and BCAC datasets, Ensembl Variant Effect Predictor (VEP) was used to annotate variants  21. Annotations included the 1000 genomes phase 3 allele frequency, sequence ontology variant consequences and exon/intron number. For each gene, the MANE Select  22  transcript was used if it was available for that gene, or the RefSeq Select transcript  23. Annotation files were used to identify PTVs and rare (allele frequency <0. 001 in both the 1000 genomes dataset and the current dataset) missense variants. PTVs in the last exon of each gene were excluded as these are generally predicted to escape Nonsense-Mediated mRNA Decay (NMD). VEP was also used to annotate missense variants by Combined Annotation Dependent Depletion score (CADD)  24. Here CADD ≥20 was used to define variants predicted to be deleterious.
Burden Test analysis Association analyses were carried out for each gene separately for PTVs, rare missense variants, and predicted deleterious rare missense variants (defined by CADD score ≥20) and PTVs combined. The main association analyses were burden tests in which genotypes were collapsed to a 0/1 variable based on whether samples carried a variant of the given class. That is, 𝐺 𝑖 = 1 𝑖𝑓 ∑ 𝑔 𝑖𝑗 𝑝 𝑗=1 > 0 𝑎𝑛𝑑 0 𝑖𝑓 ∑ 𝑔 𝑖𝑗 𝑝 𝑗=1 = 0 where 𝑔 𝑖𝑗 = 0, 1, 2 is the number of minor alleles observed for sample i at variant j, and p is the number of variants in the gene (thus, heterozygous and homozygous carriers were combined). Different statistical tests were considered by simulation, and the best method was chosen for each dataset. For the BCAC datasets, we used an exact conditional test  25, 26  , stratified by country and library preparation method (BRIDGES vs Perspective).
Ethnicity was not adjusted for as within each country ethnicity was constant. This method had greater power than the Mantel-Haenszel test  27  and Wald test from logistic regression  28  , and the type-1 error rate was closest to the specified significance level. The exact conditional test was also used for case-control analyses for subtypes and case-only analysis when comparing subtypes e. g. , oestrogen receptor status. Two genes with very common PTVs, NUDT11 and ZNF598, were excluded because missing genotypes (which were treated as non-carriers) led to spurious associations even though the variants passed QC filtering. AFF1 was also removed as the PTV frequency was high in PERSPECTIVE but rare in BRIDGES and UKB. This was likely due to a single PTV artefact within the PERSPECTIVE dataset.
All analyses adjusted for the first 10 principal components. For genes on chromosome X, only females were used in the analysis. To combine the results from the BCAC and UK Biobank datasets in a meta-analysis, the association tests for each gene were converted to Z-scores. The combined Z-score was defined as: 𝑍 𝑀 = ∑ 𝑤 𝑗 𝑍 𝑗 𝑗 √𝑤 𝑗 2. Here, 𝑍 𝑀 is the combined z-score, 𝑍 𝑗 is the z-score for study j and 𝑤 𝑗 is the weight associated with study j. A standard meta-analysis would define the weights 𝑤 𝑗 using inverse variance or effective sample sizes. However, the effect sizes from the BCAC and UK Biobank may not be comparable, since the BCAC studies oversampled for family history and early age at onset, which may have increased the estimated effect. Furthermore, the UK biobank analysis incorporated family history, which changes the effective sample size as well as the effect estimate.
Therefore, we defined weights by using the associations in the known risk gene CHEK2 as a standard: we rationalised that the CHEK2 PTVs provided the best standard as the association is well established and the odds ratio is highly reproducible  1, 29, 30. Moreover, the odds ratio (~2-2. 5) was representative of the size of effects we hoped to detect for other genes. Thus, we defined (𝑤 1 , 𝑤 2 ) = ( is the ratio of z-scores for CHEK2 for the BCAC dataset and UK Biobank. A similar approach was applied for the meta-analysis of the other variant categories. 𝑍 𝑀 -scores were plotted in Manhattan plots and associated P-values plotted in quantile-quantile plots. The lambda statistic for inflation in the test statistics (based on the median chi-squared statistic) was 0. 638 for UK Biobank, 0. 549 for BCAC and 0.
Acknowledgements Sequencing and analysis for this project were funded by the  European Union 's  Horizon 2020 Research and Innovation Programme (BRIDGES : grant number  634935 ), the  PERSPECTIVE I&I  project (funded by the  Government of Canada  through  Genome Canada  and the  Canadian Institutes of Health Research , the  Ministère de l'Économie et de l'Innovation du Québec through Genome Québec , the  Quebec Breast Cancer Foundation ,  Agilent Technologies Canada Inc and Illumina Canada Ulc)  and the  Wellcome Trust  [grant no:  v203477/Z/16/Z ]. BCAC is funded by the  European Union   Horizon 2020 Research and Innovation Programme  (grant numbers  634935  and  633784  for  BRIDGES  and  B-CAST  respectively), the  PERSPECTIVE I&I  project and via the  Confluence  project which is funded with intramural funds from the  National Cancer Institute Intramural Research Program, National Institutes of Health.
UK biobank The UK Biobank is a population-based prospective cohort study of more than 500,000 samples. More detailed information on the UK Biobank is given elsewhere  17, 18. WES data for 200,000 samples were released in October 2020  19. QC metrics were applied to Variant Call Format files  19. At the genotype level, SNPs were excluded with sequencing depth <7 or heterozygous allele balance <0. 15 or >0. 85. Indels were excluded with sequencing depth <10 or allele balance <0. 2 or >0. 8. On males' X chromosomes, depth filters were reduced to 5 for SNPs and 7 for indels. Samples with missing calls for >15% and variants with missing calls for >15% of samples were excluded. Variants with Hardy Weinberg Equilibrium p-value 10 -15 were also removed. Samples with disagreement between genetically determined and self-reported sex, sex aneuploidy, or excess relatives in the dataset were excluded.
Excess relatives were identified by considering pairs of individuals with kinship>0. 17. If one individual in a pair was a case and one was a control then the case was preferentially selected; otherwise, one individual was selected at random. Genetic ancestry was estimated using genetic principal components and the Gilbert-Johnson-Keerthi distance algorithm  20. If genetic principal components were not available, self-reported ethnicity was used. Samples of ancestry other than European were excluded. The final dataset for analysis included 181,992 samples with 100,068 Females. Cases were defined by having invasive breast cancer (ICD-10 C50) or carcinomain-situ (D05), as determined by linkage to national cancer registration (NCRAS), or self-reported breast cancer. Both prevalent and incident cases were included.
Moreover, the selection rules impose that a-type transitions (μ a = 2. 156(3) D) occur within each inversion state, while b-type transitions (μ b = 0. 7914(6) D) connect inversion states of different symmetry (see ref 32 for the dipole moment determination). Together with cyclopropenone and propynal, propadienone is one of the three most stable isomers of the [H 2 C 3 O] family. 53 Despite this, it is the only one of the three that has not been detected in the ISM,  22, 54  thus violating the minimum energy principle. Several astronomical searches have been carried out, spanning from starless cores to molecular clouds,  22, 55  but only upper limits on the molecular abundance of propadienone could be retrieved so far. These searches have been performed relying on the spectroscopic information collected in the Cologne Database for Molecular Spectroscopy (CDMS).
Therefore, it has been produced directly in situ by exploiting the methodology described in the literature, i. e. , through gas-phase pyrolysis of acrylic anhydride,  62, 63  and using The Journal of Physical Chemistry A the apparatus described in the Experimental Details. Given its relatively high vapor pressure, the precursor (acrylic anhydride) was kept in a water/ice bath at 0 °C to facilitate the flow regulation inside the pyrolysis system. The experimental conditions were adjusted by monitoring the signal-to-noise ratio (S/N) of the J Kd a ,Kd c = 10 1,10 -9 1,9 transition pair, predicted around 85. 8 GHz by the CDMS catalog. The best working conditions are those collected in Table  1  (oven temperature, 660 °C; pressure inside the absorption cell, 7-10 mTorr). Guided by the spectroscopic data available in the literature  32, 49, 60, 61  complemented by our computational study (see the Computational Methods), we successfully recorded the rotational spectrum of propadienone in selected ranges in the 80-115, 240-315, and 350-400 GHz frequency intervals.
Finally, the nudge treatment had a total of n = 48 participants and 16 hybrid groups. Moreover, the human control treatment was part of another experiment 19 and consisted of n = 72 participants divided into 12 groups of 6 participants. Due to the COVID- 19 situation, we were unable to perform more sessions of the customize and nudge. Nevertheless, our current data already hints towards interesting phenomena. For all treatments, we stored the result of the experiment, the configuration over all rounds and the response to a final questionnaire. In the delegate (and customize) treatment we also stored, for each participant, the selected (customized) agent and the time taken to select it. For the nudge treatment, we collected the contribution at each round, the actions of the other members of the group in the previous round, the content of the private and public accounts at each round, the predictions of the total content of the public account after each round, and the time taken to make each decision.
Acknowledgements The experiments were financed by the  NESTA 2018 Collective Intelligence  Grant "Hybrid intelligent solutions for collective risk situations". E. F. D. was supported by an  F. W. O. ( Fonds Wetenschappelijk Onderzoek) Strategic Basic Research (SB) Ph. D. Grant  (No. G. 1S639. 17N ), I. T. is supported by the  DELICIOS  project funded by the  F. W. O. Grant No. G054919N. R. S. and E. F. D. were supported by an  FNRS  Grant (No. 31257234 ). E. F. D. is also supported by an  FNRS  Grant for chargé de recherge (No. 40005955 ). J. G. is supported by an F. W. O. postdoctoral Grant. T. L. is supported by the F. W. O. project with Grant No. G. 0391. 13N , the F. N. R. S. project with Grant.
Rhizosphere samples were centrifuged at a relative centrifugal force (RCF) of 3,714, and a total soil mass of no more than 250 mg was transferred from each tube into a sterile Qiagen PowerSoil (Qiagen, Valencia, CA, USA) bead tube; a similar soil mass was collected for the plantless soil controls. DNA was extracted following the manufacturer's instructions. Extracted DNA was shipped to the Marine Biological Laboratories (Woods Hole, MA, USA) for amplification and sequencing of the V4-V5 (primers 518f and 926r) region of the 16S rRNA gene (52) on an Illumina MiSeq system (paired-end 2 Â 250; Illumina, San Diego, CA, USA). Sequence and data analysis. We used Trimmomatic version 0. 36  (53)  to remove adapters and primer sequences. We used default settings in the R package dada2 (ver. 1. 5. 8) to filter and trim reads based on quality (forward reads trimmed to 220 bp and reverse reads trimmed to 210 bp), estimate the error rate using 1,000,000 reads, dereplicate reads, infer amplicon sequence variants (ASVs), merge paired-end reads, remove chimeras, and assign unique sequences to taxa using the SILVA 16S database (ver.
128)  (54, 55). We removed ASVs corresponding to chloroplasts, mitochondria, and eukaryotes and ASVs for which the SILVA 16S database could not determine a kingdom-level classification. All other ASVs assigned an "NA" taxonomic classification at any level were retained. Next, we ascertained the relative abundance of taxa by quantifying unique sequences and amplicon sequence variants using the default settings in the microbiome R package  (56). We used adonis (permutational multivariate analysis of variance using distance matrices) on Jaccard (presence-absence) and Bray-Curtis (abundance) dissimilarities between community samples to characterize the effects of genotype (Col-2x and Col-4x versus Ler-2x and Ler-4x) and ploidy (Col-2x and Ler-2x versus Col-4x and Ler-4x), and calculated Shannon's diversity index to characterize intra-and intergenotype variation, using the vegan and phyloseq R packages  (57) (58) (59).
DMM was specified in the Stan probabilistic programming language and implemented through the rstan (ver. 2. 18. 2) package in the R statistical computing environment (ver. 3. 6. 0) (63, 64). For each of four chains, the sampler was run for 1,500 steps as a burn-in period and was followed by an additional 1,000 steps (a total of 4,000 samples were drawn from the posterior distributions of focal parameters [p from each sampling group]). The Gelman-Rubin statistic was computed to measure convergence among chains  (65). Separate models were constructed for the following comparisons: all diploid versus all tetraploid hosts, Columbia versus Landsberg erecta, and Columbia tetraploid versus all other treatments (pooled). For taxa that differed credibly among our treatment groups, we used NCBI BLAST to confirm 16S rRNA identifications generated using the SILVA database  (66, 67).
We report species-level identification in cases where reads align 100% with entries in the 16S rRNA sequence database and report the bacterial family if genus-level resolution was not corroborated by SILVA  (68). To assess effects on plant performance in experiment 2, we used fixed-effect, three-way ANOVA, where inoculum, genotype, and block were the explanatory variables. For purposes of data representation, residuals were plotted after statistically accounting for the effect of genotype and block. Finally, we used planned comparisons to contrast plant performance between plants grown in the Col-2x versus Col-4x inocula and Ler-2x versus Ler-4x inocula. Data availability. Raw sequences were uploaded to the NIH NCBI Short Read Archive under project ID PRJNA474006. Code for all analyses is available via the Dryad database (DOI: 10.
Those more abundant in the rhizosphere of all other treatment groups (inocula) appear below the gray line. The data depicted in this figure are sequencing data describing the rhizospheres from plants in each treatment group and are the data referenced in Fig. 1 and 2. Log 10 fold changes were calculated from relative abundance estimates obtained through hierarchical Bayesian modeling of rhizosphere associated ASV read counts following processing described in Materials and Methods. Points represent individual ASVs within families.
ACKNOWLEDGMENTS Computing was performed in the Teton Computing Environment at the  Advanced Research Computing Center, University of Wyoming, Laramie, WY  (https://doi. org/10. 15786/M2FY47 ). We thank  Lindsay Leverett  for collecting soil and  Meredith Pratt  and  Ryan Pendleton  for help with experimental and greenhouse maintenance. We thank two anonymous reviewers for comments on an early version of the manuscript. This work was supported by the  National Science Foundation  grants  IOS-1444571  to C. W. and L. M. and  EPS-16557226  to C. W. and C. A. B. We report no conflicts of interest.