dataesr/scientific-paragraphs-categorization huggingface dataset:
SNPs With G Â G Interactions Potentially Involved in Biological Regulatory Function In the RegulomeDB database, the abundant biological regulatory function was observed for 10 of 13 SNPs, including eQTL, transcription factor binding site, or DNase peak. Numerous enhancer histone marks and motifs changed were observed for the SNPs (Supplementary Table  17 ). In the eQTL analysis using the GTEx database of lung tissues, abundant regulatory relations in the human leukocyte antigen (HLA) region were identified for all eight SNPs in 6p21. 32 and 6p25. 2 (Supplementary Table  18 ), whereas no significant eQTLs were found for the others. Furthermore, we performed differential expression analysis with the GEO repository. For the three genes (C6orf10, CLPTM1L, and TERT) identified in the two-phase study, their expression levels were significantly up-regulated in the tumor tissues (Supplementary Fig.
5 ). In addition, BTNL2, which was also identified by the metaanalysis, was significantly differentially expressed between lung cancer tumor and normal tissues (Supplementary Fig. 6 ). By tumor mutational burden analysis of these 10 genes in tumor tissues with the LUAD-and LUSC-TCGA databases, on the basis of somatic mutations by next-generation sequencing, we have found that three genes, specifically, TERT, CLPTM1L, and CSMD1, presented high proportions of somatic mutations in the tumor cells (Supplementary Fig. 7 ). The findings may inspire novel targeted therapies of lung cancer.
Genetic Variants Significantly Enriched in Biological Pathways To biologically understand the genes mapped to epistatic SNPs in the screening models, we performed gene enrichment pathway analyses with the KEGG database for ever and never smokers separately. A total of 16 pathways were significant among never smokers, such as cell adhesion molecules and allograft rejection (Supplementary Fig. 10 ). For ever smokers, 22 pathways were identified, including the well-known pathways such as TH1 and TH2 cell differentiation, Notch signaling pathway, and leishmaniasis, indicating more biological pathways were involved in smoking behaviors leading to tumorigenesis (Supplementary Fig. 11 ).
Because smoking is a well-established risk factor for lung cancer risks and mortality, smoking cessation is never too late for smokers and the sooner the better; our results did not contradict this. Nevertheless, because lung cancer is such a complex disease, it is driven by multiple environmental, clinical, and genetic factors. 46 ence, smoking cessation alone may not be sufficient for lung cancer prevention. For example, previous respiratory diseases can also increase lung cancer risks, including emphysema and chronic obstructive pulmonary disease. 47, 48 Our study identified 212 SNPs that were associated with respiratory diseases (e. g. , chronic obstructive pulmonary disease and asthma) using the GWAS Catalog database (Supplementary Table  25 ). These identified biomarkers can share the same role in the common pathogenetic pathways both for respiratory diseases and lung cancer,  49  contributing to lung cancer risks in the presence or the absence of smoking.
Acknowledgments This study was funded by the  National Natural Science Foundation of China  ( 81820108028  to  Dr. H. Shen ,  81973142  to  Dr. Y. Wei ,  82103946  to  Dr. S. Shen ,  81922061  and  81973123  to  Dr. H. Ma ),  National Key Research and Development Program of China  ( 2016YFE0204900  to  Dr. F. Chen ),  Natural Science Foundation of Jiangsu Province  ( BK20191354  to  Dr. R. Zhang ),  China Postdoctoral Science Foundation  ( 2020M681671  to  Dr. S. Shen ),  Jiangsu Planned Projects for Postdoctoral Research Funds  ( 2020Z019  to  Dr. S. Shen ), the  U. S. National Institutes of Health  ( CA209414 ,  CA092824 , and  ES000002  to  Dr. D. C. Christiani ,  AG056764  to  Dr. Y. Li ), and  Priority Academic Program Development of Jiangsu Higher Education Institutions. Dr. R. Zhang  was partially supported by the  Qing Lan Project of the Higher Education Institutions of Jiangsu Province  and the  Outstanding Young Level Academic Leadership Training Program  of  Nanjing Medical University.
Data Availability ILCCO-Oncoarray data are available from: https:// www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi? study_id¼phs001273. v3. p2. TRICL data are available from: https://www. ncbi. nlm. nih. gov/projects/gap/cgibin/study. cgi?study_id¼phs001681. v1. p1. UK Biobank data are available from https://www. ukbiobank. ac. uk/. KEGG database are available from http://www. gseamsigdb. org/gsea/msigdb/genesets. jsp?collection¼CP: KEGG.
cereus group in Genbank. Pairs of CDSs that aligned over at least 70% of the length of the shortest sequence and with at least 75% nucleotide sequence identity were grouped in the same cluster, which resulted in 23,815 clusters representing distinct genes. Reads were mapped using bowtie2 (version 2. 2. 6, options "-N 1 -L 16 -R 4")  [26]  whose results were converted to bam format using SAMtools version 1. 9  [27]. Read counts on each allelic variant were obtained using HTSeq-count (version 0. 6. 1) [28]  and summed over allelic variants to obtain a single read count per gene per sample. To cope with sequence similarity between allelic variants of a same gene and fragmentation of the reference according to gene boundaries, R1 and R2 reads were aligned independently and use of HTSeq-count option "-a 0" allowed to count reads that aligned equally well on several allelic variants of a same gene.
Of note, since bowtie2 mapped each read on a single allelic variant, reads could not be counted more than once in the sum. Expression levels expressed as log 2 scaled rpkm (reads per kilobase per million mapped reads) were produced by the R package "edgeR" (version 3. 11) using the mean length of the genes in the cluster and a prior count of 1. Raw transcriptomic data and differential expression analysis are accessible through GEO Series accession number GSE168681 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc¼GSE171128).
Statistical model The strategy for statistical analysis of RNAseq data was to select genes to predict whether a strain is pathogenic y ¼ 1 or not y ¼ 0 and evaluate the prediction accuracy. We considered the logistic regression model with lasso penalty implemented in the R-package "glmnet", which allows the selection of a limited subset of genes whose expression is associated with strain pathogenicity  [29]. The package glmnet provides an interval cross validation procedure to select the penalty constant, which determines the number of selected genes. The prediction accuracy of the procedure was evaluated in a cross-validation framework where splitting in training and validation sets preserves the matching of the three replicates of each strain. For each replicate, the model provides a probability ẑ i to be pathogenic, and we considered the average value over the three replicates as the prediction probability of the strain.
Biomarker screen by PCR The seven marker genes were retrieved from at least 20 sequenced B. cereus strains from NCBI databases and aligned by CLC Main workbench7 software to identify two regions conserved across the strains. Within these regions, 20 bp primers were designed using the Beacon Designer software. For the majority of the selected genes, there were no perfectly conserved sequence and some bases had to be replaced with R (A/T), Y (C/T) or W (A/T) for primer design (Table  S3 ). For all the strains of the collection, a single colony was picked, resuspended in 100 mL Tris-EDTA NaCl buffer (TEN) and incubated at 98 C for 10 min. After centrifugation, 1 mL of supernatant was used as DNA matrix. The PCR mixture contained 1 mL DNA matrix, 0. 5 mM primer (forward and reverse), 10 mL DreamTaq Green PCR Master Mix (2X) (Thermo Scientific) in a final volume of 20 mL.
AUC analysis to select combinations of biomarkers The PCR data were pooled into a presence (1)/absence (0) table, which was then used as input for receiver operating characteristic (ROC) area under the curve (AUC) analysis facilitated by the webbased suite of tools hosted at www. combiroc. eu. The ROC-AUC analysis determines the combination of biomarkers, which will best differentiate the classes of samples input ('disease' versus 'non-disease' groups). Sets of biomarkers were selected based on their performance in sensitivity or specificity alone, or in combination as the AUC metric. Potential hits were filtered at 85% specificity and 85% sensitivity.
For characteristics missing in less than a third of patients, we imputed the most frequent value or the average value, for binary/categorical and numeric characteristics respectively. The patient cohort table was constructed using custom Python and R scripts using the tableone R package. Table  3. Comparison of significantly predictive proteins between survival prediction schemes. Proteins listed were significantly predictive of all-time survival post-transplant after accounting for site-of-origin. Those proteins that did not meet significance criteria are indicated by '-'. Predictive significance criteria were: AUROC > 0. 5, beta coefficient 95% confidence interval not containing the null association, and permutation beta coefficient interval containing the null association. Mass spectrometry analysis.
Raw mass spectrometric data were analyzed using Proteome Discoverer 2. 2 to perform database search and TMT reporter ions quantification. patients, to generate and test from a distribution of prediction metrics from random survival assignment. Comparison of the bootstrap and permutation prediction distributions allows for prediction and feature importance comparisons between real and randomly distributed data while accounting for over-fitting during these prediction tasks. We evaluated the significance of each marker to predict patient survival by comparing the 200 feature importance values from the bootstrap and the permutation prediction distributions. The p-values generated in this comparison represent protein marker prediction in our cohort compared with random patient survival. We tested differences in the bootstrap and permutation distributions with the 2-sample Kolmogorov-Smirnov test.
Data availability All code for the analyses can be found on the following website: https:// github. com/ ngian gre/ kng1_ analy sis. We included a versioned release of our code at https:// doi. org/ 10. 5281/ zenodo. 69697 03 and at the https:// github. com/ ngian gre/ kng1_ analy sis/ tree/2. 0. 0. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD031426.
LUminometric Methylation Assay Analyses The global DNA methylation level of whole blood samples or tissues of individuals was measured using the LUMA assay  (Karimi et al. , 2006a; Karimi et al. , 2006b). Genomic DNA was extracted from blood samples using a high-salt extraction method  (Roussot et al. , 2003)  and from tissue samples using NucleoSpin Tissue kit (Macherey Nagel). DNA was digested by EcoRI + HpaII or EcoRI + MspI restriction enzymes (New England Biolabs) and then analyzed using a Q24 Pyromark sequencer (Qiagen). MspI and HpaII have the same recognition site (CCGG), whereas HpaII is inhibited by the presence of a 5-methylcytosine. EcoRI (recognition site: GAATTC) was used as internal control for normalization. Runs were analyzed with PyroMark Q24 1. 0. 10 software (Qiagen). The dispensation order for nucleotides was GTGTCACATGTGTG.
Statistical Analysis The fixed effects of breed, sex, litter size (singleton, twice or triplet), date of sampling, CBC and tissue were tested, when relevant, by mixed linear models using the MIXED procedure of SAS software  (SAS, 2008). Only the fixed effects and their interactions for which p < 0. 05 were considered as significant and retained in the different models presented below. For global DNA methylation rate of the blood and the complete blood count, the equation was: GDMR ijk or CBC ijk μ + date_sampling i + breed j + sex k + a ij k + e ijk (model 1) (1) with date_sampling i (5 levels), breed j (3 levels) and sex k (2 levels) as fixed effects, a ijk the animal repeated across sampling (as random effect), and e ijk the residual (as random effect). For the global DNA methylation rate of the blood considering the complete blood count, the equation was: GDMR ij μ +CBC ij + breed i + sex j + a ij + e ij (model 2) (2) with CBC ij as the covariable, with breed i (3 levels) and sex j (2 levels) as fixed effects, a ij the animal repeated across sampling (as a random effect), and e ij the residual (as a random effect).
Tissue-to-Tissue GDMR Correlation At slaughter, using the GDMR dataset for 30 animals and 19 tissues (12 somatic tissues, three additional reproductive tissues specific to each sex and a blood sample), we looked for correlations of GDMR between tissues. The significant correlations ranged from -0. 56 (p = 0. 04) between ovary and adrenal gland, to 0. 55 (p = 0. 03) between medulla kidney and seminal vesicle (Supplementary Table  S3 ). Using the blood previously sampled from birth to 4 months of age, the only significant strong correlation found was 0. 63 between GDMR of ovary and GDMR of blood at 3 months of age (p = 0. 02).
39 ± 2. 56% using the LUMA technology. Whereas WGBS accounts for all the CpG of the genome, the LUMA technology only considers the CpG located at the CCGG restriction site. In silico, we counted 5 994 593 CCGG sites on the ovine reference genome, which gives a reliable approximation of the methylated cytosine (mC) for CG of the entire genome but may explain the lower values found with the LUMA technology than with WGBS. In the female tissues of the reproductive axis (ovary, uterus, and oviduct), GDMR was around 70% and corresponded to the average values obtained for the other somatic tissues. In contrast, the three male tissues (testis, epididymis, and seminal vesicle) had a lower average GDMR of around 60%. It would be interesting to compare this observation with the significant effect of sex in blood showing lower GDMR in males, but we found no similar data in the literature with which to compare our results to interpret the difference between sexes.
The dynamic safety factor is calculated using the pseudo-static method, taking into account the excess pore water pressure that develops during cyclic loading into the granular soil of the earth dam upstream face. The results of the parametrical analytical study were also compared to the results of numerical simulations of the dam seismic stability trough pseudostatic method. The numerical simulations were done with three different software: PLAXIS and ABAQUS (based on the finite element method) and GEOSTAB (deals with the problem at the limit equilibrium using the simplified Bishop method). At the end, on one hand, we were able to describe how and at what level of the dam upstream face the sliding occurs, and on the other hand, we were able to underline the adequate combination between the dam geometric parameters and the mechanical soil characteristics which may ensure seismic stability.
, 2011) and large-scale shaking table test  (Yuan et al. , 2014). Until now, the research works are still in progress; even if some questions are solved, some others remain, and the complete seismic design of an earth dam is still an open question. Stability and safety of earthen dams during an earthquake event are of primary concern. The study of the earth dam's stability under seismic conditions is a challenging problem for geotechnical engineers. In this paper, seismic analysis of an earthen dam is carried out using analytical methods (DYNANSTA software developed here and GEOSTAB software) and software based on finite element method (PLAXIS and ABAQUS). 2 Literature review  Fellenius (1936)  developed an ordinary method of slices where the slip area is a circular arc. This method has been extended by other studies under static and pseudostatic conditions  (Bishop, 1955; Morgenstern and Price, 1965; Janbu, 1973; Sarma, 1973; Spencer, 1973).
3. Analysis of the pore pressure generated by an earthquake according to the method developed by Sarma. Based on these hypotheses and on the limit equilibrium theory, an analytical approach has been developed to study the pseudo-static or seismic stability of an earthen dam constituted of two refills. The upstream and downstream zones are composed of sandy materials, and the waterproof, inclined central core is composed of clayey materials. Once the pore pressure DU due to seismic loading is evaluated, the determination of the seismic pseudostatic safety factor, F d , which is a function, among others, of this excess pore pressure DU can be done. In our analytical study, all the step calculations are considered in a computer program DYNANSTA that was developed by us. This allows to deduce the effects of several parameters related to the soil characteristics, and to the dam geometry, on its seismic behaviour.
Effect of the slope angle β The curves in Figs 9 and 10 show that the slope has a dominant role in the structure's stability. The safety factor decreases gradually when the inclination of the slope increases. For k = 0. 15 and β = 21°, the difference between the safety factor given by the two software programs and our calculation is around 20% compared to PLAXIS and 69. 4% compared to GEOSTAB. It is also noted that for a seismic coefficient equal to 0. 3, when the slope angle increases from 21° to 70°, the stability is reduced by 75% in this study and by 31% in the analysis conducted by the Bishop method. This reduction (see Fig. 9 ) is about 83%, 90% and 100% for the coefficients 0. 2, 0. 1 and under static conditions, respectively. Therefore, the failure mechanism is really important in this studied case.
Statistical Analysis The means of at least four independent experiments were calculated and normalized to culture medium. Results are expressed in percentage (for cell viability) or fold change (for P2X7 receptor activation and hormonal quantitation) compared with culture medium and presented as means of at least three independent experiments ± standard error of the means. After a D'Agostino-Pearson omnibus normality test to check the normal distribution of the data, a one-way ANOVA followed by a Dunnett's test were performed (α risk = 5%) using GraphPad Prism 8 software (San Diego, CA, USA). Thresholds of significance were * p < 0. 05, ** p < 0. 01, *** p < 0. 001 and **** p < 0. 0001 compared to the culture medium. In all the experiments, the culture medium and control solvent were not statistically different.
Discussion The objective of the present study was to assess the potential placental toxicity of forskolin in vitro. Forskolin is available, without any medical advice, over-the-counter in pharmacies and as natural supplements sold online principally as a fat burner or appetite suppressant. Pregnant women concerned about limiting their weight gain may be tempted to try these natural products, but natural does not mean safe. Natural supplements containing forskolin generally recommend an intake of 50 mg of forskolin a day, which represents 122 µM forskolin daily. Using SwissADME software, the log Po/w is 2. 67,.
Discussion The objective of the present study was to assess the potential placental toxicity of forskolin in vitro. Forskolin is available, without any medical advice, over-the-counter in pharmacies and as natural supplements sold online principally as a fat burner or appetite suppressant. Pregnant women concerned about limiting their weight gain may be tempted to try these natural products, but natural does not mean safe. Natural supplements containing forskolin generally recommend an intake of 50 mg of forskolin a day, which represents 122 µM forskolin daily. Using SwissADME software, the log P o/w is 2. 67, meaning a high probability of absorption and consequently the gastrointestinal absorption of forskolin is high. The tested concentrations seem to be in the same order as this intake, especially as it has been demonstrated that chemicals are more concentrated in the placenta than in maternal tissues  [28].
As alien invasive species are a key driver of biodiversity loss, understanding patterns of rapidly changing global species compositions depends upon knowledge of biological invasion dynamics and trends. The Ponto-Caspian region is among the most notable donor regions for aquatic invasive species in Europe. Using macroinvertebrate time series collected over 52 years  at 265 sites across 11 European countries, we examined the occurrences, invasion rates and interspeci c interactions of freshwater Ponto-Caspian fauna, as well as biotic homogenization trends in European waterways. According to our data, Ponto-Caspian macroinvertebrates increased from two species in 1972 to 29 species in 2012 and we found a greater richness of invading Ponto-Caspian macroinvertebrates in datasets collected closer to the source region.
Data collection To investigate the presence of Ponto-Caspian species in European freshwater streams and rivers, we used a recently collated database containing 1,816 macroinvertebrate time series from 22 European countries  (Haase et al. , in review). Each time series contained samples collected in a minimum of eight, not necessarily consecutive, sampling years. Individual time series were sampled from the same geographic location (hereafter, site) in all years. Survey methods (e. g. kick sampling) varied among time series, but were kept constant within each time series (Table  S1 ). Each time series was thus harmonized to have a comparable sampling effort over time, with sampling occurring once annually within the same threemonth season (fall, spring and summer) and reaching consistent species-level taxonomic resolution for macroinvertebrate species.
Accumulation of Ponto-Caspian macroinvertebrates species over time To characterize temporal change in the number of Ponto-Caspian macroinvertebrate species across Europe and thus test our rst hypothesis, we plotted the number of species over time using the "loess" smoother, grouping the number of species by year. We extracted the rst record of each Ponto-Caspian macroinvertebrate species in each time series and plotted the cumulative richness as an estimator of rates of new invasions, using the "stat_ecdf" function in the ggplot2 R package  (Wickham et al. , 2016).
Patterns of Ponto-Caspian invasions We assigned Ponto-Caspian macroinvertebrate species to major groups (i. e. Amphipoda, Annelida, Bivalvia, Gastropoda, Hirudinea, Hydrozoa, Isopoda, Mysid, Oligochaeta and Tricladida; Table  S2 ) to identify the presence of potential taxonomic patterns in Ponto-Caspian invasions over time. To test our second hypothesis there is an inverse relationship between the numbers of these Ponto-Caspian species and the distance to their native ranges, we calculated the distance between each site and the Ponto-Caspian region, following the river network (including canals) using the "riverdist" function of riverdist R package  (Tyers, 2022). To standardize distances, we used the Danube delta as the native range for all time series, except for those from Latvia, for which we used the Dnieper delta  (Karatayev et al.
, 2008; Lipinskaya et al. , 2020)  and for those from the United Kingdom, which were calculated following the marine route that connects Great Britain with the Netherlands and subsequently via the invasion corridors along the river networks  (Gallardo & Aldridge, 2015). We evaluated this relationship statistically using Spearman's correlations, as the data did not satisfy the assumption of normality. To test our third hypothesis of whether Ponto-Caspian macroinvertebrate species facilitate other invasions from that region and to quantify changes in invasion rates, we recorded the rst year of occurrence (i. e. the rst detection) of each species in each time series, noting that detection may not re ect establishment status over time. We then compared the year of the rst occurrence of each Ponto-Caspian macroinvertebrate with the most recent preceding year in which another such species appeared in the same time series.