dataesr/scientific-paragraphs-categorization huggingface dataset:
Genes and SNPs selection. The mRNA splicing-related genes were selected with the combination of two online databases commonly exploited in the gene-set enrichment analysis: Molecular Signatures Database (http://www. broadinstitute. org/gsea/index. jsp) and Genecards (http://www. genecards. org/). Overall, 206 mRNA splicing-related genes were selected (Supplementary Table  1 ), which included a total of 11,966 genotyped or imputed common SNPs extracted from these genes (include 2-kb upstream and downstream of genes) among the TRICL Consortium with the following inclusion criteria: 1) Genotyping call rate ≥ 90%; 2) Minor allele frequency (MAF) ≥ 5% among Europeans; and 3) Hardy Weinberg Equilibrium exact P value ≥ 10 -5. The detailed work-flow can be found in Supplementary Figure  1.
Statistical analysis. The associations between SNPs and lung cancer risk were evaluated using an additive genetic model by R (v2. 6), Stata (v10, State College, Texas, US) and PLINK (v1. 06) software  36. For the studies of ICR, MDACC, IARC, Toronto, NCI and Harvard, top significant principle components were included in the analysis to control for population stratification that might cause inflation of test statistics, while for the deCODE study, genomic control method was used to adjusted for population stratification. No population structure was found in the German Lung Cancer Study (GLC). With the application of the inverse variance method, a meta-analysis under the fixed and random-effects models was performed on the results of a log-additive model for 11,966 SNPs. In order to assess the heterogeneity among studies, the Cochran's Q statistic to test for heterogeneity and the I 2 statistic to quantify the proportion of the total variation due to the heterogeneity were calculated  31.
A fixed-effects model was applied, if no heterogeneity existed among studies (P Q-test > 0. 10 and I 2 < 25%); otherwise, a random-effects model was chosen. The Benjamini-Hochberg false discovery rate (FDR) procedure was employed for the correction of multiple testing with a cutoff value ≤ 0. 05. Regional association plots were generated with LocusZoom on the basis of 1000 Genomes European (EUR) reference data (phase I integrated release 3, March 2012)  37. Haploview v4. 2 was employed to construct the Manhattan and LD plot. All analyses were conducted with SAS (version 9. 1. 3; SAS Institute, Cary, NC, USA) unless specified otherwise. In silico SNP function annotation. To prioritize functional SNPs, three in silico tools: dbSNP func annotation (http://www. ncbi. nlm. nih. gov/projects/SNP/), SNPinfo (http://snpinfo.
1haracteristic information of the study populations. 1ICR: the Institute of Cancer Research Genome-wide Association Study, UK; 2 MDACC: the MD Anderson Cancer Center Genome-wide Association Study, US; 3 IARC: the International Agency for Research on Cancer Genome-wide Association Study, France; 4 NCI: the National Cancer Institute Genome-wide Association Study, US; 5 Toronto: the Lunenfeld-Tanenbaum Research Institute Genome-wide Association Study, Toronto, Canada; 6 GLC: German Lung Cancer Study, Germany; 7 TRICL: GWASs datasets combined by six GWASs of ICR, MDACC, IARC, NCI, Toronto and GLC; 8 Harvard: Harvard Lung Cancer Study, US; 9 deCODE: Icelandic Lung Cancer Study, Iceland; 10 AD: adenocarcinoma; 11 SQ: squamous cell carcinoma.
Abbreviations: TRICL: GWASs datasets combined by six GWASs of ICR, MDACC, IARC, NCI, Toronto and GLC; ICR: the Institute of Cancer Research Genome-wide Association Study, UK; MDACC: the MD Anderson Cancer Center Genome-wide Association Study, US; IARC: the International Agency for Research on Cancer Genome-wide Association Study, France; NCI: the National Cancer Institute Genome-wide Association Study, US; Toronto: the Lunenfeld-Tanenbaum Research Institute Genome-wide Association Study, Toronto, Canada; GLC: German Lung Cancer Study, Germany; Harvard: Harvard Lung Cancer Study, US; deCODE: Icelandic Lung Cancer Study, Iceland; AD: adenocarcinoma; SQ: squamous cell carcinoma;.
Acknowledgements The authors thank the investigators and participants of all TRICL-ILCCO studies (i. e. , ICR, MDACC,  IARC ,  NCI ,  Toronto, GLC, Harvard and deCODE ) for their important contributions. As Duke Cancer Institute members, QW and KO acknowledge support from the  Duke Cancer Institute  as part of the  P30 Cancer Center Support Grant  (Grant ID:  NIH CA014236 ). QW was also supported by a start-up fund from  Duke Cancer Institute, Duke University Medical Center. Yongchu Pan  was sponsored by  Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University. TRICL: This work was supported by the  Transdisciplinary Research in Cancer of the Lung (TRICL) Study ,  U19-CA148127  on behalf of the  Genetic Associations and Mechanisms in Oncology (GAME-ON) Network.
The work was additionally supported by a  Helmholtz-DAAD fellowship  ( A/07/97379  to MNT) and by the  NIH  ( U19CA148127 ). The  KORA Surveys  were financed by the  GSF , which is funded by the  German Federal Ministry of Education, Science, Research and Technology and the State of Bavaria. The Lung Cancer in the Young study (LUCY) was funded in part by the  National Genome Research Network (NGFN) , the  DFG  ( BI576/2-1 ;  BI 576/2-2 ), the  Helmholtzgemeinschaft (HGF)  and the  Federal office for Radiation Protection (BfS :  STSch4454 ). Genotyping was performed in the  Genome Analysis Center (GAC) of the Helmholtz Zentrum Muenchen. Support for the Central Europe ,  HUNT2/Tromsø  and CARET genome-wide studies was provided by  Institute National du Cancer, France. Support for the HUNT2/Tromsø genome-wide study was also provided by the  European Community  (Integrated Project DNA repair,  LSHG-CT-2005-512113 ), the  Norwegian Cancer Association and the Functional Genomics Programme of Research Council of Norway.
Support for the Central Europe study, Czech Republic, was also provided by the  European Regional Development Fund  and the  State Budget of the Czech Republic (RECAMO ,  CZ. 1. 05/2. 1. 00/03. 0101 ). Support for the CARET genome-wide study was also provided by grants from the  US National Cancer Institute ,  NIH  ( R01 CA111703  and  UO1 CA63673 ), and by funds from the  Fred Hutchinson Cancer Research Center. Additional funding for study coordination, genotyping of replication studies and statistical analysis was provided by the  US National Cancer Institute  ( R01 CA092039 ). The lung cancer GWAS from Estonia was partly supported by a  FP7 grant  ( REGPOT245536 ), by.
Bands were detected with ECL chemiluminescence kit (Thermo Fisher scientific). Intensity of bands was then determined by densitometry, using the ImageJ software (https://imagej. nih. gov/ij/). 2. 9. RNA Extraction. Snap frozen tissues were ground in liquid nitrogen by a mortar and pestle then total RNA was isolated using TRIzol according to manufacturer's instructions (Thermo Fisher Scientific, Grand Island, NY, USA). The quantity of RNA was measured using the NanoDrop® ND-1000 UV-Vis spectrophotometer. RNA purity was assessed using the absorbance ratio of 260 to 280 nm, where a value of 1. 8-2. 0 indicated good-quality RNA. Finally, RNAs were treated by Deoxyribonuclease I (Thermo, USA) to remove contaminating genomic DNA. 2. 10. Real-Time qPCR. To quantify differences in mRNA expression, gene expression was monitored using SYBR ® Green PCR Master Mix (Bio-Rad, Hercules, CA, USA) to quantify the gene expression of interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) genes.
Tissue images were acquired using a laser scanning confocal microscope (LSM710, Zeiss, Germany). Quantification was done on kidney sections obtained from 5 different animals per condition. A total of 3 different fields of view were quantified per section. The "particle analysis" plugin in the ImageJ software (https://imagej. nih. gov/ij/) was used to obtain DHE fluorescence integrated density (FID), which represents ROS generating cells, and the Hoechst FID, which represents total cells in the tissue section. A ratio was calculated for DHE FID/Hoechst FID for each image. Finally, ratios from all images were averaged and results were analyzed by 2-way ANOVA followed by multiple pairwise analysis using Tukey's post hoc test. 2. 13. Periodic Acid Schiff (PAS) Staining. This dye is commonly used to stain carbohydrates and polysaccharides, such as glycogen, producing a pink color.
Statistical Analysis. Results are expressed as the mean ± SEM. Statistical comparisons were performed using 2-way ANOVA followed by multiple pairwise analysis using Tukey's post hoc test in order to determine statistical significance. The p value was determined and values for p < 0 05, p < 0 001, and p < 0 0001 ( * , * * , and * * * , resp. ) were considered significant. Microsoft Excel and GraphPad Prism software were used to perform statistical analysis.
Acknowledgments This work was supported by grants from the  Medical Practice Plan (MPP)  at  AUB  (grant title " Effect of Second Hand Smoking (SHS) on Cardiac and Vascular Smooth Muscle Remodeling: A Targeted and Global Approach. "Lead PI: Firas Kobeissy, co-PIs:  Asad Zeidan  and  Ahmad Husari ), from  Lebanese National Council for Scientific Research  ( Kazem Zibara ), from  AUB URB  ( Firas Kobeissy ), and from  Lebanese University  grant (Kazem Zibara).
A number of genes that contribute to the domestication traits of cultivated rice have been identified. These include Sh4, Rc, PROG1 and LABA1, which are associated with non-shattering rachis, white pericarp, erect growth and barbless awns, respectively. The mutations giving rise to the ''domestication alleles'' of these genes are either invariable in cultivated rice, or have variability that is strictly associated with the phenotypic trait. This observation forms the basis to those current rice domestication models that envisage a single origin for the domesticated phenotype. Such models assume that the domestication alleles are absent or rare in wild rice, emerged under cultivation and spread across all rice groups by introgressive hybridization. We examined whole-genome sequencing datasets for wild and cultivated rice to test the former two assumptions.
2007; Sang and Ge 2007; Izawa 2008; Huang et al. 2012; Gross and Zhao 2014 ). Here we examine the occurrence of the sh4, prog1, rc and laba1 alleles in O. rufipogon by searching published whole-genome sequencing datasets, and based on our findings suggest that the possibility that independent rice groups obtained identical domestication alleles directly from the wild population needs to be considered.
Methods We downloaded raw sequence data for 1543 wild and cultivated rice accessions  (Huang et al. 2012 ) from the Sequence Read Archive (ERP001143, ERP000729, ERP000106) and converted each file into FASTQ format using the fastq-dump command in sratoolkit 2. 3. 5. The full FASTQ dataset consisted of 9. 174 billion Illumina reads totalling 2. 129 TB and was processed on a Linux platform. Low-quality regions were removed by Trimmomatic-0. 33  (Bolger et al. 2014). These data sets have low sequencing depth (mean 19 and 29 for O. sativa and O. rufipogon, respectively;  Huang et al. 2012)  which does not allow reconstruction of entire genes or ungapped haplotypes. Nonetheless, known single nucleotide polymorphisms (SNPs) or indel variants can be scored in a subset of the samples where sequencing reads are available for the particular locus.
Given the sample size (460 accessions of wild rice; 519 subsp. indica; 482 subsp. japonica; 30 aus; 5 aromatic; 47 other/unassigned), estimates of allelic frequencies can be obtained for subsp. indica, subsp. japonica and wild rice, despite the prevalence of missing data. To determine indel variants of the Rc gene, we searched each of the 1543 trimmed FASTQ datasets using the grep command with 30 bp words (perfect match) spanning the 14 bp indel site in the Rc gene (5 0 -AAAGGCGCAAGTGGATGCCATCCAAGGTGA-3 0 and reverse complement, matching the variant with deletion; 5 0 -CAAGTGGAACGCGAAAAGTCGGT GCCATCC-3 0 and reverse complement, matching the wild type). Identified matches were further verified by aligning reads to reference sequences of the Rc gene with and without the deletion. We also searched for reads perfectly matching a 30 bp sequence spanning the 1 bp indel site in the LABA1 coding sequence (5 0 -AGCCATGGCTCTACTCAGTCTCGGTTCAG G-3 0 and reverse complement, matching the variant with deletion; 5 0 -AGCCATGGCTCTACTCCAGT CTCGGTTCAG-3 0 and reverse complement, matching the wild type), and verified these matches against the reference sequences for the LABA1 gene.
To obtain diversity estimates for the rc, laba1, sh4 and prog1 haplotypes, we searched for variability in 10 kb windows surrounding the causative mutations utilising the SNP matrix published by  Huang et al. (2012)  (downloaded from the Rice Haplotype Map Project database). The diversity was calculated as the average number of polymorphic sites in a 10 kb window surrounding the causative mutation, weighted per examined accession and the proportion of nonmissing data. A maximum-parsimony tree of the Sh4 locus was constructed from the data published by  Zhu et al. (2012)  using dnapars program in the PHYLIP package with default parameters  (Felsenstein 2005). Only the parsimony-informative sites were used and [1 bp gaps were recoded as single events. A majority consensus tree was constructed from 5171 equally parsimonious trees.
Tracer analysis The tracer used for free-flowing experiments was semolina coloured thanks to an iodine ethanol solution: this tracer has the same d 50 than the original semolina and was detected by image analysis. A given zone sampled from the blender was homogenised and poured on a 19 cm diameter plate. 8 pictures were then taken at random locations with an x50 magnifying microscope and the images were analysed thanks to a Matlab © routine. The tracer concentration was obtained thanks to a calibration curve. The tracer used for cohesive experiments was talc, since no colorimetric techniques proved satisfying for tracing purposes. The talc used had characteristics close to that of lactose. Its mass fraction in the samples was determined by thermogravimetry analysis: each sampled zone was homogenised and 12 samples of 2g each were sampled randomly from it, before being heated at 650°C for 2h.
Computations We performed density functional theory (DFT) calculations (T = 0 K) and molecular dynamics (MD) simulations (T a 0 K) using the cp2k program suite. 28 In all computations, the PBE functional  29  was used together with the D3 dispersion correction scheme. 30 The Gaussian Plane Wave Method 31 was used to solve the electronic structure. Core electronic states were modelled with GTH pseudopotentials  32  and the valence electrons were described using molecularly optimized polarized double-z basis functions. 33 The electronic density was described using a plane wave basis with an energy cutoff of 500 Ry. More details can be found in the ESI. †.
Introduction Quantitative grain shape analysis is a strong proxy for determining sediment transport history and depositional environments. As demonstrated in earlier works, grain angularity can be quantified by determining the roughness coefficient (Rc) from Fourier harmonics range  [2, 3, 5, 8, 11]. We present MorpheoLV, a well-documented and publicly available code written in Matlab devoted to the computation of roughness coefficients from unique grain images. The new key feature is the opportunity to analyse a collection of samples of grain images all at once to compute roughness variations along a distance or a duration, for instance. Benchmark images proposed in  [1]  are processed for both validation and interpretation purposes. A 25. 8 m jumbo piston core, NBP1203 JPC36, was taken from a ∼100 m thick sedimentary drift deposit from the Perseverance Drift on the northern Antarctic Peninsula continental shelf.
Roughness calculation with MorpheoLV MorpheoLV (LV for Light Version) is a small (about 500 lines), well-documented Matlab code derived from MOR-PHEO  [1]. The new features are roughness calculations following  [5]  and the opportunity to analyse a collection of samples of grain images, all at once. Comparing to MORPHEO, MorpheoLV analyses unique grain images with more speed by a reduction in complexity of the image processing part of the code. Steps of a grain image analysis (outline and centroid determination, polar decomposition and Fourier analysis using NbHarms harmonics) are described with detail in  [1]  and documented in the downloadable archive 1. Note that images input to MorpheoLV are assumed to come with a jpeg or a tiff extension, while MorpheoLV's output images are png or Matlab figures.
Sample analysis The roughness analysis can be applied to a sample of grains with "common history", collected at the same place, the same depth or observed at the same time for instance, and stored in the same folder. The function, MorpheoLV_Sample('SampleName',NbHarms) considers any files in the folder named 'SampleName' with a jpeg or a tiff extension as part of the grain image sample. MorpheoLV analyses them, individually, as described in subsection 2. 1. The mean roughness coefficient Rc a-b and the standard deviation for the sample of grains are output in the Matlab command window. MorpheoLV produces its own histograms to plot the distribution of the roughness coefficient values by using predetermined classes for a fair comparison between the different samples of the same collection. User-defined classes can be edited in the function MorpheoLV_Histo.
Grain microtexture with SEM The ikaite crystals found in the core, were studied only under the SEM to determine their elemental composition and to capture an image, see Fig. 1. Grain shape analysis, to provide clues on past climate records, on ikaite crystals was not performed due to the mineral's tendency to degrade to glendonite overtime and its in-situ formation, which does not provide evidence on transport history  [12]. SEM images are segmented by adapting the Matlab's tutorial for cell detection, then analysed as described at §2. 1. Roughness coefficients reported in Tab. 2. As expected, the overall angularity/roundness property is well captured by the first ten harmonics, while roughness and texture are better described by the higher harmonics. The coefficient Rc 21-40 , with a very small value for the euhedral crystal, is the most appropriate to reveal the very smooth texture of such a grain.
12, 13 The Cu-binding affinity for the phenol-triazole ligands are thus signicantly stronger than those reported for the quinoline-triazole analogues. 29 A Cu-competition assay was then performed in which 2 eq. of each phenol-triazole ligand was added to pre-formed Cu-Ab 1-16 and Cu-Ab 1-42 species. The Cu complex of the phenol-triazole ligands displays an absorption peak at ca. 320 nm (Fig. S7 -S9 †), and upon addition of ligand to solutions containing Cu-Ab 1-42/1-16 , an increase at 320 nm is observed. This data suggests an interaction of the phenol-triazole ligands with Cu. A baseline increase in the Ab 1- 42 experiments is likely due to aggregate formation and associated light scattering. On the basis of this data we expect the phenol-triazole ligands to have the appropriate Cu-binding affinity to interact with Cu in the presence of the Ab peptide.
Ligand-Ab peptide interactions The direct interaction of the phenol-triazole ligands with monomeric Ab 1-40 was investigated using 2-D 1 H-15 N SOFAST-HMQC NMR experiments. 29, 57 The less aggregation prone Ab 1-40 peptide length was used here to limit aggregation during data collection and ensure all shis are solely the result of peptideligand interactions and not peptide aggregation. Incubation with one of POH, PMorph, or PTMorph resulted in small but detectable chemical shi changes for specic peptide residues (see ESI for experimental details †). Interestingly, POH was determined to exhibit signicant chemical shi changes distributed across the entire peptide length, including Ab residues E3, D7, Y10, V18, F20, G33, V36, and G38 (Fig. 4 ). In the case of PMorph, signicant chemical shi changes were observed for Ab residues D7, F19, D23, and N27 (Fig.
Acknowledgements This work was supported by a  Natural Sciences and Engineering Research Council (NSERC)   Discovery Grant , a  Michael Smith Career Investigator Award , and a  New Investigator Grant  from the  Alzheimer's Association  ( NIRG-15-362537 ) and  Brain Canada  (to T. S. ); the  Alzheimer's Society of Canada for a Biomedical Doctoral Scholarship  and the  Alberta Prion Research Institute (APRI)  and  Alzheimer Society of Alberta and Northwest Territories (ASANT)  for the Ed and Joyce Lyons postdoctoral fellowship (to M. R. J. ); an international postdoctoral grant from the  Swedish Research Council  (Dnr:  350-2012-239  (to C. D. );  ENS Cachan  for a doctoral fellowship (E. M. ); an operating grant from the  Canadian Institutes of Health Research (CIHR)  (to V. W. Y. ); APRI and ASANT research grants (to S.
T. and P. K. S. ); and  Tier I CRC  (to P. K. S. ). This work was also supported by the  National Research Foundation of Korea  Grant funded by the  Korean Government  ( NRF-2014S1A2A2028270 ) (to M. H. L. and A. R. ) and partly by the  National Institutes of Health  ( AG048934  to A. R. ).
Recently, the Schizophrenia Working Group of the Psychiatric Genomics Consortium (SWG-PGC) GWAS (2014) identified 108 genetic loci associated with SCZ, as well as several other markers that failed to reach genome-wide significance, suggesting a polygenic foundation to SCZ, with many genetic variants of individually small effect contributing to the overall phenotypic variation  (International Schizophrenia Consortium. , 2009). Strong evidence also exists for a polygenic basis for BD, with a strong overlap in the genetic variants associated with  SCZ and BD (International Schizophrenia Consortium, 2009;  Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013;  Ripke et al. , 2013). Using the summary data from the SWG-PGC GWAS (including alleles associated with the risk of SCZ as well as their effect sizes) as the training dataset, PGRS-SCZ can be created in an independent sample.
Risk variants in the independent sample which are common to the training dataset are identified, these are then weighted by the effect sizes reported in the SWG-PGC GWAS and summed across individual genotypes in the independent sample  (International Schizophrenia Consortium. , 2009; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Euesden et al. , 2015). Higher positive scores indicate a greater polygenic risk for SCZ disorder. Several studies have investigated the effect of these PGRS for SCZ (PGRS-SCZ) on clinical and cognitive phenotypes  (McIntosh et al. , 2013; Stepniak et al. , 2014; Whalley et al. , 2016). Thus far, structural neuroimaging phenotypes have been assessed with regard to the first SWG-PGC GWAS data, which identified 7 associated loci (Schizophrenia Psychiatric Genome-Wide Association Study Consortium, 2011), with inconsistent results  (Terwisscha van Scheltinga et al.
A second aim of the current study is to determine if a relationship exists between PGRS-SCZ, created using the most recent PGC SCZ data, and CT. Within the current study, global, frontal and temporal regions of CT were analysed to determine their relationship with PGRS-SCZ and environmental risk associated with SCZ. We hypothesised that both PGRS-SCZ and an accumulation of environmental risk factors would be inversely associated with cortical thinning in these regions, for both the patients and controls separately and when assessing differences between patients and controls. Due to the aforementioned overlap between structural findings, environmental, and genetic risk factors, and in order to increase power within the sample, SCZ and BD patient data were combined into one patient groups for analyses.