dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of 44,438 individuals including 24,037 lung cancer patients and 20,401 health controls from European descent population were recruited in this study, which is by far the largest genetic interaction study in lung cancer to our knowledge. We successfully identified epistasis in RGL1:RAD51B in ALL and NSCLC lung cancer, SYNE1:RNF43 in ADE and FHIT:TSPAN8 in SQC risk development. None of the SNPs, from the significant SNP pairs, were revealed previously in main effect association analysis either from our discovery dataset (P > 0. 01) or from other published lung cancer GWAS reports. These findings reinforce that genetic epistasis among cancerrelated genes is a common mechanism involved in lung tumorigenesis and many latent genes contribute to lung cancer development through interacting with other modifier genes.
In summary, the gene expression analysis provides information about the functional role of the identified genetic interactions in lung cancer development, supporting the findings of this study and yield insights about the molecular mechanisms of lung carcinogenesis. In the gene network analysis, the IPA program constructed gene networks by searching the extensive records maintained in its library to find the genes that may directly or indirectly connected with the input "seeds" from interaction analysis. Interestingly, all the three significant epistasis--RGL1:RAD51B in ALL and NSCLC lung cancer, SYNE1:RNF43 in ADE and FHIT:TSPAN8 in SQC risk development--were displayed as interaction between networks (Figure  4  and Appendix 3 Supplementary Figure  2 ). These results suggest that www. oncotarget.
Significance p value threshold calculation Specifying a reasonable Bonferroni corrected p value threshold is an important step in analysis of high dimensional data. Among the 35,031 tested SNPs, some of them are not independent from each other because of linkage disequilibrium (LD). We applied the GEC (Genetic Type I error calculator) program to evaluate the eigenvectors of the correlation matrix and computed the number of independent SNPs in the analysis  [42]. We found there were 22,622 independent SNPs among the 35,031 tested SNPs and the number of pairwise interaction tests was 255,866,131. The Bonferroni corrected p value cutoff assuming independence among all the tests was computed as 0. 05/255,866,131 = 1. 95x10 -10. Any SNP pairs with joint interaction p value < 1. 95x10 -10 will be reported as significant findings.
Epistasis analysis In this proposed genetic interaction analysis, we followed a two-stage study design: we conducted the interaction analysis at discovery stage using OncoArray GWAS data, and then replicated the signals using independent Affymetrix and GELCC GWAS datasets. A two-step analysis strategy was adopted in the discovery stage: in step 1, an imprecise but fast pairwise epistasis analysis was conducted using the "fast-epistasis" option in PLINK program  [43]. This test was based on a Z-score test to compare the difference of SNP1-SNP2 allelic association between cases and controls. SNP pairs with p value < 1x10 -6 were submitted to a more stringent logistic regression analysis using formula (1) at step 2. An R program was used for logistic regression interaction analysis and the first three principal components (PCs) were adjusted in the analysis.
In order to harvest as many potential signals as possible in discovery study we relaxed the significance cutoff to 1x10 -5 in regression analysis. The SNP pairs with logistic regression interaction p value less than 1x10 -5 were further submitted to replication studies. logit (D) = β 0 + β 1 × snp1 + β 2 × snp2 + β 3 × snp1 × snp2 + ∑ β i x cov i (1) In the replication study, the same regression model specified by formula (1) was applied in the analysis. Considering the small sample size in the GELCC GWAS dataset (n = 1430) compared with the TRICL Affymetrix GWAS (n = 10,347), we performed a meta-analysis in the replication study to combine the information from both datasets. The SNP pairs with interaction meta-analysis p value < 0. 05 and consistent interaction effects (OR > or < 1 across different discovery and replication datasets) were reported as replicated signals.
Genotype imputation The genotypes from the three GWAS datasets came from different platforms and the overlaps among the SNP panels were limited. We used IMPUTE2 program to impute the genotype in replication datasets to increase the SNP overlaps between the discovery and replication datasets. For those candidate SNP pairs with meta-analysis p value < 0. 1 in each replication study we further imputed the flanking SNPs in ~30 kb of those candidate SNPs in the discovery data to increase the density of SNP markers. The 1000 Genomes Project Phase 3 release was used as the reference dataset  [44]. The output dosage file from IMPUTE2 was used as input in logistic regression analysis and the first three PCs were adjusted in the imputed genotype analysis.
Genetic interactions on gene expression in the lung Identified epistasis on lung cancer risk were extended to gene expression levels using the lung eQTL dataset  [23] [24]. Briefly, lung specimens were collected from patients undergoing lung cancer surgery and stored at the biobank of the "Institut universitaire de cardiologie et de pneumologie de Québec" (IUCPQ). Genotyping was carried out using the Illumina Human1M-Duo BeadChip. Expression profiling was performed using an Affymetrix custom array (see GEO platform GPL10379). A total of 409 subjects passed genotyping and gene expression quality controls. Expression values were adjusted for age, sex and smoking status. Probe sets and SNPs implicated in the identified genetic interactions for lung cancer risk were selected including RGL1:RAD51B, RNF43:SYNE1, and FHIT:TSPAN8.
Gene set enrichment analysis Gene set enrichment analysis provided information about the biological implications underlying statistical findings in our genetic interaction analysis. We conducted canonical pathway and gene network analysis using Ingenuity Pathway Analysis (IPA, QIAGEN Inc. , https:// www. qiagenbioinformatics. com/products/ingenuity-pathway-analysis) software to explore the possible relationships among the candidate genes. The oncogenesisrelated genes from the SNP pairs with interaction metaanalysis p value < 0. 05 in replication study were provided as the focus genes in IPA. IPA searched the extensive records maintained in its Ingenuity Pathways Knowledge Base (IPKB) and created the canonical pathway based on well-known biological and molecular pathways. In gene network analysis, the IPA program utilized the provided list of focus gene as "seeds" and searched IPKB to find the genes that may directly or indirectly connected with the "seeds" and then created a gene network.
Reagents Hybrid human IFN-αB/D  [29] , which is highly active on mouse cells, was used for this study. Polyinosinic-polycytidylic acid (PIC; Sigma Aldrich; P9582) and siRNAs (Dharmacon) were transfected into cells using Neon Transfection System (Invitrogen). Primary antibodies were used according to manufacturer recommendation and were as follows: NCBP1 (Thermo Fisher Scientific, PA5-30098), NCBP3 (Atlas Antibodies; HPA008959), Histone H3 (Abcam; ab1791-100). For viral protein detection, we used antibodies against IAV (Millipore; AB1074) and VSV-G (Santa Cruz; sc-66180). Antibodies against NCBP2 were purified from serum of rabbits immunized with recombinant full-length protein purified from E. coli. Serum isolated from Ifit1-deficient mice immunized with full-length murine IFIT1 protein purified from E.
3 and down shift of 1. 8). Differential expression analysis was performed using the limma package in R  [38]. To identify the proteins, whose response to infection altered between Ncbp3-ko and -wt MEFs, the interaction term of the linear model was used. The criterion for significantly changing proteins was |log 2 (fold change)×log 10 (p value)|>3. ISGs were annotated using the INTERFEROME v2. 0 database  [39]  filtered for mouse fibroblast cells and a minimal fold change of 2. Graphs were plotted in R and adapted using Adobe Illustrator. Z-score was calculated using the following formula: (LFQ(protein)-LFQ(mean of sample)) / sd(sample). Kolmogorov-Smirnov test was performed using R version 3. 5. 1. GO-term enrichment analysis was performed on differentially expressed host proteins using quantifiable proteome as reference dataset on the level of protein groups using hypergeometric test in R version 3.
Statistical Analysis R-studio R software  [35]  was used to analyze the descriptive characteristics of the study participants. Univariate and multivariate logistic regressions were used to compute measures of association between cancer awareness as a binary outcome and the explanatory variables age, sex, district of residence, and HIV status. Binary outcome constituted Bknowledgeableŵ hich is a correct response to the known cancer risk factors or warning sign, and Bnon-knowledgeable^is the opposite.
Acknowledgments The authors are grateful to the research assistants for their carefulness and diligence in data collection and entry. We are also grateful to the governing authorities from region to ward level for their timely approval and support to the survey work. Most importantly, we thank all participants for their views to this subject. Disclaimer The funding agency did not take part in designing, data collection, analysis, or interpretation of the results, neither in writing or decision to submit this manuscript. Funding This research was supported by  NIH  Grant  BHIV  associated malignancies training project,^grant number  D43CA153722. Analysis and manuscript submission were supported by student travel award from the  School of Social Sciences, University of Tampere , Finland.
The mechanical properties of titanium alloys result from their complex multi-scale microstructural features, including micron scale precipitates and millimeter scale microtextured regions (MTRs). While previous investigations have revealed that the presence of mm-scale MTRs can degrade mechanical properties, particularly fatigue, the accompanying strain localization processes that operate at the microscale within the α grains in MTRs are not well understood. The present work is a mechanistic investigation of MTRs using crystal plasticity simulations of mm 3 -scale experimentally captured and synthetically generated 3D microstructure datasets. The explicit modeling of both the α grains and MTRs in Ti-6Al-4V enables assessment of the effect of microtexture and local structure variations within the MTR on overall deformation behavior and the onset of plastic slip in MTRs.
Two major technical obstacles have prevented multi-scale simulations of deformation of titanium alloys. First, the collection and reconstruction of large mm 3 -scaled 3D microstructural volumes necessary to capture both the MTRs and an accurate description of the microscale microstructural elements, including the primary α grains, is a technical challenge. The recent development of 3D data collection techniques including mechanical serial sectioning  [20]  , plasma focused ion beam  [21]  , high energy X-ray diffraction microscopy  [22]  and femtosecond laser ablation  [23]  now permits the collection of datasets large enough for the analysis of the effects of MTRs on mechanical properties. The second challenge is the ability to simulate the deformation behavior of these large 3D datasets within a crystal plasticity framework.
The 3D reconstruction was performed using DREAM. 3D [30]. Briefly, the reconstruction process required a registration (slice alignment step) and a 2 °misorientation tolerance for segmentation to define the grains boundaries. Only the α phase was considered due to the small size and the low fraction of retained β phase at room temperature. Grain averaged Euler angles were used in the following. The dimensions of the 3D gathered dataset are 198 μm x 715. 5 μm × 508. 5 μm. It contains about 21 million voxels distributed over more than 57,0 0 0 α grains with experimentally captured morphologies and crystallographic orientations. The crystal orientations relative to the Z-axis are plotted on the surface of the reconstructed 3D dataset in Fig. 1   The 3D dataset was collected from the identical tensile specimen tested in a prior study  [6].
Synthetic microstructure generation The effects of MTRs on the global and local behaviors were isolated by comparison with a carefully designed synthetic dataset. The macrotexture was kept nominally constant while MTRs were removed, in order to prevent any effect of the texture at the dataset scale. An approach that is outlined in more detail elsewhere for use with 2D data was employed  [18]. It consists of reassigning the crystallographic orientation of a given grain in a dataset to a different random grain of the dataset. The crystalline orientation relative to the Z -axis is plotted on the surface of the synthetic 3D dataset in Fig. 1  (b) after complete redistribution of the crystallographic orientations. MTRs, such as visible in Fig. 1 (a) , have successfully been removed. The macrotexture of the synthetic dataset is presented in Fig.
7 reported in  [7]. The value of n , which is related to the strain rate sensitivity, was taken from  [38 , 39]. A summary of the parameters used for the calculations and the associated values are indicated in Table  1. In order to reproduce the kinematic boundary conditions, an additional layer with infinite compliance was added on the free surface of the dataset. A displacement along the Z direction was applied to simulate tensile loading at a strain rate of 10 -4 s -1.
Significant differences depending on the degree of microtexture in the tested microstructural volume are observed. The experimental stress-strain curve obtained at a similar strain rate is also presented and reveals a good agreement between simulated and experimental behaviors. plastic strain, is presented in Fig. 4  (b) and (c) for total strains of 0. 65% and 0. 71%. Plastic strain hotspots on the surface of the TriBeam dataset are indicated with dashed boxes and the corresponding locations are reported on the DIC strain field. The associated crystallographic orientation maps are shown in Fig. 4 (d)  and (e). A careful comparison of both orientation maps reveals some differences, which result from difficulties in experimentally capturing the near surface microstructure in the early stages of the ablation process using TriBeam tomography (typically a few μm).
MTRs with a dominant [0 0 01] orientation thus favor the presence of stress hotspots and are preferential locations for them. The ZZ component of the plastic strain tensor, which is denoted as ɛ P ZZ in the following, at an applied stress of 852 MPa is plotted in Fig. 7  (b) on the surface of the TriBeam 3D dataset. For convenience, the crystallographic orientation relative to the loading direction is reported in Fig. 7 (a). The maximum magnitude reached is 7. 3 × 10 -3. At this loading step, the plastic strain field is highly heterogeneous. This reveals slip activation restricted to favorable microstructural configurations. Insights into the effect of MTRs on the plastic strain distribution are obtained considering Fig. 6 (c ) where the voxels with ɛ P ZZ lower than 10 -3 are hidden. The spatial distribution of the plastic strain at this loading step is significantly influenced by the presence of MTRs.
At this loading step, the average total strain and the average plastic strain of the synthetic microstructure are 7. 4 × 10 -3 and 1. 4 × 10 -4 , respectively. Higher average total strain and average plastic strain values are observed in the TriBeam dataset. ɛ P ZZ also locally reach higher values in the experimentally collected microstructure. The maximum plastic strain is 3. 6 × 10 -3 for the synthetic microstructure versus 7. 3 × 10 -3 for the TriBeam instantiated microstructure. These observations are in good agreement with the lower proof stress displayed by this microstructure. However, the stress fields in the TriBeam microstructure have both higher and lower magnitudes of σ ZZ than the synthetic microstruc- A comparison of the spatial location of these voxels and the location of voxels associated with significant plastic strain indicates that these low magnitudes are plastically deforming grains.
Also, the evolution of the stress magnitude with respect to the declination angle differs significantly from the one in the elastic regime. A major drop in the stress values is observed for angles around 45 °and a minor drop for angles around 90 °. These angles correspond to the crystallographic orientations with basal and prismatic Schmid factors of 0. 5. Slip activation and the resulting plastic strain induce a local stress relaxation that leads to the observed stress drop. This is confirmed by the plot of the magnitude of ɛ P ZZ averaged over each grain as a function of the declination angle shown in Fig. 12 (c ). The stress drops, and their intensities, seem tightly related to the magnitude of the plastic strain. Two peaks are noticed: for an applied stress of 850 MPa, (c) the magnitude of ɛ P ZZ for an applied stress of 850 MPa (the insert shows the highest values on the surface of the dataset).
More stress heterogeneities and higher plastic strain exist in the TriBeam 3D dataset despite a similar applied stress. a major peak for a declination angle about 45 °and a minor peak for a declination angle about 90 °. Since the 45 °peak is associated with basal slip and the 90 °peak is associated with prismatic slip, then it reveals that a higher plastic strain is experienced by grains where basal slip operates, at least in the early stages of deformation. A similar feature was recently reported from experimental characterizations  [30]. Regarding the effect of MTRs, the magnitudes of the peaks for the experimentally collected microstructure are higher than the magnitudes of the peaks for the synthetic microstructure. While the plastic strain in grains deforming by basal slip reaches 1. 4 × 10 -2 in the 3D TriBeam microstructure, the plastic strain remains below 4 × 10 -3 for the synthetic microstructure.
Grains deforming by prismatic slip reach 1 × 10 -2 plastic strain in the 3D TriBeam microstructure versus 3 × 10 -3 in the synthetic microstructure. Therefore, the presence of MTRs favors stress heterogeneities in the elastic regime, however also allow higher plastic strain magnitudes in the plastic regime. Grains where slip is active were identified using a criterion based on an average plastic strain magnitude higher than 10 -3. The crystallographic orientations of these grains are plotted with respect to the loading axis on an inverse pole figure in Fig. 12 (d). For both 3D datasets, two distinct orientation domains are associated with slip activity. Considering the Schmid factor iso-contours reported in  [40]  , these regions match with orientation domains associated with high Schmid factors for basal and prismatic slip systems.
ture for a similar applied stress, showing that localization and heterogeneous loading are occurring. Further evidence of the increased stress heterogeneities in the 3D TriBeam dataset are in the distribution frequency of σ ZZ , shown in Fig. 11 , at applied stresses of 750 MPa and 925 MPa. 1. 5 MPa bins are used for these plots. The 750 MPa and 925 MPa load steps were selected in order to show the deformation behavior in the elastic and early plasticity regimes. A significantly higher spread in σ ZZ values is present in the experimentally collected microstructure at 750 MPa. Considering frequencies higher than 0. 2%, the values range from 716 MPa to 805 MPa for the TriBeam microstructure versus 716 MPa to 784 MPa for the synthetic microstructure. Two peaks are noticed for the experimentally collected dataset: 733 MPa and 793 MPa.
In contrast, only one main peak at 740 MPa is observed for the synthetic dataset. An inflexion in the 930 MPa range indicates that a secondary overlapping peak might be present at stress values around 760 MPa. These observations unambiguously reveal a higher degree of stress heterogeneities in the experimental TriBeam microstructure in the elastic regime, with a very similar conclusion being drawn from the closely matching distributions in the early plasticity regime shown in Fig. 11 (b). However, an additional plateau is present for both microstructures in the domain ranging from 825 MPa to 890 MPa.
7E, F ). The incidence of glioma has been reported to be significantly higher in male than in female patients  20. This was also observed in the TCGA database that we analysed, where 55% GII-III patients (291/530) and 65% GBM patients (97/150) were males (Supp. Fig. S6A, B ). No differences were observed in the median survival of female vs. male GII-III patients (F: 94. 5 mos. ; M: 81. 1 mos. ) or GBM patients (F: 14 mos. ; M: 14 mos. ) (Supp. Fig. S6C ). The expression levels of PRLR mRNA did not show significant differences between GII-III and GBM patients in both cohorts of patients (Fig. 8A ; Supp. Fig. S8A ). The percentage of PRL + samples in GII-III and GBM patients was similar between men [GII-III/PRL + : 12% (8-16%); GBM/PRL + : 28% (19-38%)] and women [GII-III/PRL + : 13% (9-18%); GBM/PRL + : 23% (12-36%)] and the upregulation of PRL mRNA levels in GBM with respect to GII-III samples was found in both female and male patients (Fig.
This upregulation in PRL expression according to glioma grade was also observed when we stratified patient populations by their biological sex. Interestingly, the positive correlation between PRL and PRLR mRNA levels was only observed in female PRL + GBM patients, suggesting that a positive feedback between these proteins may underlie sexual dimorphism. The sexual dimorphism in the incidence of GBM has been extensively reported, with a male/female ratio of ~1. 6  20  , which is in agreement with the distribution of patients in the database we analysed (65% male vs. 35% female). Although we did not detect differences in the survival of male and female patients, a recent report indicates that the 5-year cancer-specific survival rate of female patients is significantly higher than in males  38.
Methods patients and datasets. RNA-Seq expression data of PRL, PRLR and MMP-2 (Illumina HiSeq 2000 RNA sequencing platform) from 530 GII-III samples and 150 GBM samples were obtained from The Cancer Genome Atlas (TCGA) and analysed using UCSC Xena browser (https://doi. org/10. 1101/326470)  63. This database provides quantitative gene expression information and a compelling list of patients' characteristics, including their clinical parameters and survival rates (Supp. Fig. S5 ). Drugs. Dulbecco's Modified Eagle's Medium (DMEM; 12800017), Penicillin-Streptomycin, Trypsin-EDTA (0. 05%) and Lipofectamine 2000 were obtained from Gibco (Invitrogen, Carlsbad, CA); fetal bovine serum (FBS) and horse serum (HS) from Natocor (Córdoba, Argentina); cisplatin from Microsules (Buenos Aires, Argentina); and temozolomide and ovine prolactin from Sigma (St.
Scratch assay. Rat GBM C6 cells and human GBM LN229 cells were seeded with or without PRLR-A in 24-well plates for 24 h. A wound was then performed by scratching the confluent cell culture wells with a micropipette tip. Afterwards, cells were washed with PBS and reincubated with or without PRLR-A in complete DMEM without serum. Lastly, cells were photographed at different time-points for up to 10 h and the wound area was measured using ImageJ Software. Zymography. Rat C6 and human U251-MG GBM cells were incubated for 48 h in the presence of 100 ng/ ml PRL. Conditioned media was collected and MMP gelatinocytic activity was assessed by zymography. 3 μl of medium was loaded onto 10% acrylamide gel containing 0. 2% gelatine and run at 120 V. Gels were then washed with 50 mM Tris-HCl pH 7. 5 in 2.