dataesr/scientific-paragraphs-categorization huggingface dataset:
The maximum size of these datasets ranged from 41,150 to 339,224 individuals. For variants available on the exome array with a known genome-wide association with a risk factor, we also compared the magnitude of the reported association with the risk factor to the observed association with CAD in our analysis. To identify any associations with other diseases or traits, we searched version 2 of the GRASP (Genome-Wide Repository of Associations between SNPs and Phenotypes) database (  14 ) and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalog  (15) , plus we collected all associations below 1 Â 10 À4. For all associations, we identified the lead variant for that trait or disease and calculated pairwise LD with the lead CAD-associated variant using the SNAP web server  (16).
(35) , suggest that modulating SR-B1 may be therapeutically beneficial. After adjusting for multiple testing, we found that slightly more than one-third of the CAD loci showed an association with traditional cardiovascular risk factors. Although the vast majority of associations were in the direction consistent with the epidemiological association of these risk factors with CAD, as noted in the previous text with respect to loci affecting the HDL cholesterol level, this should not be interpreted as implying that these loci affect CAD risk through an effect on the specific risk factor. Indeed, for variants available on the array with a known genome-wide association with these risk factors, we found a poor correlation between the magnitudes of their effect of the risk factor and their association with CAD in our dataset except for LDL cholesterol (Online Figure  4 ).
Chronic inflammation plays a key role in both the pathogenesis of CAD and of inflammatory bowel disease. It is therefore interesting to note the association of the same locus at 15q22 with CAD as well as Crohn's disease and ulcerative colitis (Table  3 ). Association of this locus with CAD at genome-wide significance was recently reported by the CARDIoGRAMplusC4D consortium (2) with the lead SNP (rs56062135) showing strong linkage disequilibrium (r 2 ¼ 0. 9) with the lead SNP (rs17293632) associated with inflammatory bowel disease. Both rs56062135 and rs17293632 lie in a region of w30 kb within the initial introns of the SMAD family member 3 gene (SMAD3), a signal transducer in the transforming growth factor-beta pathway. Indeed, rs17293632 was included on the exome array because of its known association with Crohn's disease and showed a significant association with CAD in our combined dataset (p ¼ 1.
Dr. Tardif has received research grants from Amarin, AstraZeneca, Merck, Pfizer, Eli Lilly, Sanofi, Servier, and DalCor; has received honoraria from Pfizer (to his institution), Servier, DalCor, and Sanofi (to his institution); and has received modest equity interest from DalCor. Dr. Kathiresan has financial/other relationships with Regeneron, Bayer, Catabasis, Merck, Celera, Genomics PLC, San Therapeutics, Novartis, Sanofi, AstraZeneca, Alnylam, Eli Lilly, Leerink Partners, and Noble Insights. All other authors have re- ported that they have no relationships relevant to the contents of this paper to disclose. A full list of acknowledgments and funding sources is included in the Online Appendix. Drs. Webb, Erdmann, Strirrups, Stitziel, Samani, Schunkert, Deloukas, and Kathiresan contributed equally to this work.
E // field maps calculated between the poloidal limiters were used to estimate poloidal distribution of RF-sheath DC potential V DC in this private region of the ILA. For this purpose we used the asymptotic version of the Self-consistent Sheaths and Waves for Ion Cyclotron Heating Slow-Wave (SSWICH-SW) code with radiating boundary conditions  [10]. These estimations can help to study the formation of RF sheaths around the antenna and even at distant locations (~3m away) and make association to some variations of the Be line emission that were measured spectroscopically on some of the A2 antenna limiters when the magnetic connection from observation point to the active ILA was moved via a q 95 scan  [7]. 2 Description of the simulations E // field maps were computed with TOPICA code. To perform simulation with the TOPICA code, the 3D curved model of ILA with limiters was placed into a recess and limited by an aperture.
For that investigation we used the same antenna model and plasma settings as mentioned in the previous text. E // field maps were calculated on the first surface that was located 5 mm in front of the leading edge of the poloidal limiters. In that case simulations were performed for only top half array (0 -π/2) and only bottom half array (0 -π/2) antenna feedings. For estimation of the poloidal distribution of the rectified DC plasma potential V DC between the poloidal limiters of the ILA we used the asymptotic version of the SSWICH-SW code with radiating boundary conditions. It was chosen because it is rapid, robust, always provides systematically one solution for each set of input parameters and convergence is guaranteed. It is suitable for relative comparisons of antenna designs and electrical settings.
Principal disadvantages of the code are related to number of approximations made on the original physical model. Parallel DC plasma conductivity is taken as infinite while transverse plasma conductivity is neglected in the simplified model of DC plasma biasing. The potential V DC is homogeneous all along the open field lines and depends only on the voltages V RF at both ends of these field lines. Main input to the SSWICH-SW code is the E // field map evaluated between the poloidal limiters. Simple 3dimensional parallelepiped simulation domain with walls normal to the straight magnetic field lines and only slow wave, responsible for excitation of the RF sheaths when the side walls are normal to B 0 , were only adopted. Sketch of the simulation domain can be found in  [11]. Fig. 2. Electron and ion density profiles with varying q 95 that were measured during shot #90454 with Li beam emission spectroscopy.
Peak detection algorithms follow two strategies: derivative techniques or matched filter response  (Szymanska et al 2016; Yi et al 2016). A deconvolution step is used to separate overlapping peaks in order to improve peak detection  (Johnsen et al 2017). Furthermore, a de-isotoping step is used to cluster the isotopic peaks corresponding to the same chemical feature to clean the data matrix. Alignment of the detected features across different samples aims to remove intersample shifts, and several alignment algorithms have been developed  (Smith et al 2013; Szymanska et al 2016). The data dimensionality has to be reduced to make them applicable to instruments paired with MS. Different strategies enable data compression such as binning and the Bsearch of regions of interest (ROI)^methods that are the most adequate hyphenated MS data sets.
A comparison of some peakpicking algorithms used in untargeted MS-based metabolomics have been reported  (Rafiei and Sleno 2015). XCMS is an open access mass spectrometry data processing software. It is widely used in the metabolomics community. It was developed in response to the growing need for user-friendly software to process complex untargeted metabolomic data  (Smith et al 2006; Gowda et al 2014). It has been designed as a solution for the entire untargeted metabolomic workflow ranging from the raw data processing to direct metabolite assignment through integrated and automated METLIN database queries. The platform has been recently upgraded with data streaming capabilities to support high-throughput, cloud-based data processing, and systems biology analyses  (Huan et al 2017). NMR data preprocessing typically includes baseline correction, alignment, and binning.
Metabolite annotation and characterization The identification of the discriminant metabolites is an important step in metabolomics. The introduction of high-resolution mass spectrometers and accurate mass measurements that facilitate access to the chemical formula of the detected peaks has considerably accelerated this step. The combined use of quadrupole ion traps for sequential fragmentation experiments provides additional structural information needed to identify metabolites of interest. However, MS using soft ionization techniques such as electrospray methods, exhibits high variability in the fragmentation profiles generated on different devices due to the lack of standardized ionization conditions, thus limiting the construction of universal spectral data banks such as those obtained by electron ionization or NMR  (Cui et al 2008).
This issue could be addressed using standardized ionization conditions such as electron based ionization techniques that are highly reproducible across MS systems worldwide and across different vendors. Indeed, in MS, one or more chemical formulas can be generated if high-resolution instruments are used, which provides a first element for carrying out an interrogation of the existing databases. The acquisition of fragmentation spectra at this stage enables us to discriminate the responses obtained previously on the basis of the produced ions or neutral losses, characteristic of chemical groups. Given the importance of the identification step, standardization elements have been proposed to harmonize metabolite identification data. Thus, identification standards have been defined within the framework of the Metabolomics Standards Initiative according to the available information on the metabolite to be characterized  (Sumner et al 2007).
Computational tools such as CAMERA  (Kuhl et al 2012) , ProbMetab  (Silva et al 2014) , AStream  (Alonso et al 2011) , and MetAssign  (Daly et al 2014)  have been developed for metabolite annotation. These methods mainly use m/z, retention time, adduct patterns, isotope patterns, and correlation methods for metabolite annotation. However, in MS the detected m/z ion and MS database matching is insufficient for unambiguous charcterization. Although retention time prediction are still used to improve identification confidence, complementary orthogonal information is required for reliable assignment of chemical identity, such as retention time matching and molecular dissociation patterns compared to authentic standards  (Sumner et al 2007). For reliable characterization, a solution may be in a multidimensionnal framework based on orthogonal information integration, which may include accurate mass m/z, chromatographic retention time, MS/MS spectra patterns, CCS, chiral form, and peak intensity.
Furthermore, hybrid strategies, including pathway network and analysis methods, could enhance metabolite characterization through different metrics integration, including data-driven network topology, chemical features correlation, omics data, and biological databases. Such a multidimensional approach may permit the chemical characterization by merging both extended chemical information and biological context. The Human Metabolome Database (HMDB) was first introduced in 2007 and is currently the most comprehensive, organism-specific metabolomic database. It contains NMR and MS spectra, quantitative, analytical, and physiological information about human metabolites. It also contains associated enzymes or transporters and disease-related properties. The HMDB is a fully searchable database with many built-in tools for viewing, sorting and extracting metabolites information features.
Biological databases are important for mapping different metabolic pathways (Table  1 ). Conceptual framework of pathway analysis is illustrated in Fig. 2. Indeed, pathway analysis or metabolite set enrichment analysis (MSEA) are methodologically based on the gene set enrichment analysis approach, previously developed for pathway analysis of gene-expression data  (Khatri et al 2012; Garcia-Campos et al 2015). There are three distinct methods for performing MSEA: overrepresentation analysis (ORA), quantitative enrichment analysis (QEA), and singlesample profiling (SSP)  (Xia and Wishart 2010; Garcia-Campos et al 2015; Xia et al 2015). An important advantage of computational metabolomics lies in the use of correlations among feature signals to map chemical identity. Since metabolites are interconnected by a series of biochemical reactions to build the network of metabolites, they can be interrogated using network-based analytical tools.
Since this step is challenging, a novel approach, named Mummichog, has been proposed by Li et al to reboot the conventional metabolomic workflow  (Li et al 2013). This method predicts biological activity directly from MS-based untargeted metabolomics data without a priori identification of metabolites. The idea behind this strategy is combining network analysis and metabolite prediction under the same computational framework, which significantly reduces the metabolomics workflow time. Based on spectral peaks, the computational prediction of metabolites yields several hits; thus, a Bnull^distribution can be estimated by how these predicted metabolites, retrieved from a metabolomics experiment, map to all known metabolite reactions through interrogating databases. Despite most annotations being false, the biological meaning underpinning the data drives enrichment of the metabolites.
On one hand, it unveils hidden data structure in experimental data through differential expression using statistical metrics. On the other hand, it uses prior knowledge retrieved through biological databases and literature. Pathway analysis combines these two pillars to interpret the experimental findings  and Cortassa 2015). Recently, Cortassa et al suggested a new approach, distinct from flux balance analysis or metabolic flux analysis, that takes into account kinetic mechanisms and regulatory interactions  (Cortassa et al 2015). Since metabolites are often involved in multiple pathways, biologically-mediated labeling is particularly informative in such cases. Given the dynamics and compartmentation that characterize the metabolism, isotopic labeling is poised as an appealing approach to unambiguously track metabolic events.
The data were used to build an online database for data mining and visualization  (Shin et al 2014). The effectiveness of multi-omic approaches has been recently illustrated by van Karnebeek et al. The authors reported a disruption of the N-acetylneuraminic acid pathway in patients with severe developmental delay and skeletal dysplasia using both genomics and metabolomics approaches. Variations in the NANS gene encoding the synthase for N-acetylneuraminic acid were identified  (van Karnebeek et al 2016). This elegantly highlights how systemic approaches may address IMD complexity and allow their diagnosis  (Argmann et al 2016). For more details on mGWAS studies, the reader may refer to recent reviews  (Kastenmuller et al 2015; Suhre et al 2016). Figure  3  shows how laboratory workflow using high-throughput analytical technologies, integrative bioinformatics, and computational frameworks will reshape IMD investigations.
Perspectives in clinical metabolomics translation Despite spectral information becoming available in the literature or in spectral databases, metabolite identification is still a challenging task  (Goodacre et al 2007). However, metabolite identification remains a central issue in metabolomics prior to embracing complete clinical translation. No software is currently available to automate the identification step. Furthermore, metabolite identification is mandatory for absolute quantitation especially in MS-based methods requiring the use of stable isotope-labeled internal standards. Some data-driven alternatives have been developed to elucidate metabolite structure associations such as correlation-based network and modularity analysis. The association structure can be used to identify MS ions derived from the same metabolite  (Broeckling et al 2014)  or to identify biotransformations  (Kind and Fiehn 2010).
However, these knowledge-based approaches may be hampered by their limits for addressing the entire chemical space and limited coverage of metabolome databases. Another limitation lies in the cost for targeted analyses, which cannot reasonably be expected to support measurement of tens of thousands of chemicals in large populations. Thus, more efforts are needed to overcome this issue. However, in IMD a few hundred key metabolites may be defined for large-scale screening. Standardized and validated protocols are a prerequisite for metabolic phenotyping technologies. Harmonization of the sample preparation, processing, analysis, and reporting, using validated and standardized protocols, is mandatory  (Chitayat and Rudan 2016; Kohler et al 2016). Standardized protocols are particularly helpful for untargeted metabolomics.
In targeted methods, since each analyte is known and quantified, technology versatility is less important. Despite substantial efforts to standardize untargeted metabolomics methods, there are still no universally adopted protocols, particularly for MS-based strategies. This situation is due to the diverse and ever-changing analytical platform. The community and journals may take a lead in standardization by aligning it to community-published standards, such as the Metabolomics Standards Initiative  (Sumner et al 2007) , and data repisotories to encourage open metabolomic data, such as MetaboLights database at the EBI. All these endeavors aim to develop infrastructures and frameworks standardize terminology, data structure, and analytical workflows  (Levin et al 2016). Finally, addressing these standardization issues is essential for regulatory compliance, which is a prerequisite for any clinical implementation.
Conclusion Translating metabolomic data into actionable knowledge is the ultimate goal. Particular attention should be paid to computational tools for multidimensional data processing. There is an urgent need for more databases with validated and curated MRM transitions for targeted metabolites. Furthermore, for untargeted metabolomics, larger libraries and curated MS/ MS spectra for metabolite identification are needed. Hybrid strategies including pathway and network analysis methods could enhance metabolite characterization through integration of different metrics, including data-driven network topology, chemical features correlation, omics data, and biological databases. Such multidimensional approaches may improve the chemical characterization by combining both extended chemical information and biological context.
Biological databases KEGG (Kyoto Encyclopedia of Genes and Genomes) http://www. genome. jp/kegg (Kanehisa et al 2016) HumanCyc (Encylopedia of Human Metabolic Pathways) http://humancyc. org (Romero et al 2005) MetaCyc (Encyclopedia of Metabolic Pathways) http://metacyc. org (Caspi et al 2008) Reactome (A Curated Knowledgebase of Pathways) http://www. reactome. org (Vastrik et al 2007) SMPDB (Small Molecule Pathway Database) http://www. smpdb. ca (Jewison et al 2014) Virtual Metabolic Human Database https://vmh. uni. lu (Thiele et al 2013) Wikipathways http://www. wikipathways. org (Kelder et al 2012) Pathway and networks analysis and visualization BioCyc-Omics Viewer http://biocyc. org (Caspi et al 2016) iPath http://pathways. embl. de (Yamada et al 2011) MetScape http://metscape. ncibi. org (Karnovsky et al 2012) Paintomics http://www.
paintomics. org (Garcia-Alcalde et al 2011) Pathos http://motif. gla. ac. uk/Pathos (Leader et al 2011) Pathvisio http://www. pathvisio. org (Kutmon et al 2015) VANTED http://vanted. ipk-gatersleben. de (Rohn et al 2012) IMPaLA http://impala. molgen. mpg. de (Kamburov et al 2011) MBROLE 2. 0 http://csbg. cnb. csic. es/mbrole2 (Lopez-Ibanez et al 2016) MPEA http://ekhidna. biocenter. helsinki. fi/poxo/mpea (Kankainen et al 2011) Mummichog http://clinicalmetabolomics. org/init/default/software (Li et al 2013) PIUMet http://fraenkel-nsf. csbi. mit. edu/PIUMet/ (Pirhaji et al 2016) 3Omics http://3omics. cmdm. tw/ (Kuo et al 2013) InCroMAP http://www. ra. cs. uni-tuebingen. de/software/InCroMAP/ (Wrzodek et al 2013) Multifunctional tools MetaboAnlayst http://www. metaboanalyst. com (Xia et al 2015) XCMS online https://xcmsonline.
Protein kinase CK2, initially designated as casein kinase 2, is an ubiquitously expressed serine/threonine kinase. This enzyme, implicated in many cellular processes, is highly expressed and active in many tumor cells. A large number of compounds has been developed as inhibitors comprising different backbones. Beside others, structures with an indeno[1,2-b]indole scaffold turned out to be potent new leads. With the aim of developing new inhibitors of human protein kinase CK2, we report here on the generation of common feature pharmacophore model to further explain the binding requirements for human CK2 inhibitors. Nine common chemical features of indeno[1,2-b]indole-type CK2 inhibitors were determined using MOE software (Chemical Computing Group, Montreal, Canada). This pharmacophore model was used for database mining with the aim to identify novel scaffolds for developing new potent and selective CK2 inhibitors.
Using this strategy several structures were selected by searching inside the ZINC compound database. One of the selected compounds was bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene-7,10,12-trione), a natural compound which is produced by several kinds of fungi. This compound was tested on human recombinant CK2 and turned out to be an active inhibitor with an IC 50 value of 1. 24 µM.
Virtual screening or "mining" of databases is considered as an important tool in drug discovery. The aim in using in silico techniques is to reduce the costs and time to discover new active compounds, since reducing the period of a new drug from research laboratory to the market by one year can reduce the costs by millions of dollars. To develop a pharmacophore model is an important concept in computer-aided drug design (CADD), as this method can reduce the complexity of molecular interactions between compound and target to a set of few features. This method was successfully applied in drug discovery  [14] [15] [16] [17] [18] [19]  and many molecular modeling software are available among them the Molecular Operating Environment (MOE). In this study a pharmacophore model for human protein kinase CK2 was constructed on the basis of 50 indeno  [1,2-b] indoles and used to identify new hits by mining the ZINC compound database  [20].
Some of those compounds turned to be highly active with IC50 values in the low nanomolar range. Molecular modeling is widely used for drug design and development, with the aim of finding new lead structures for the treatment of different diseases. Virtual screening or "mining" of databases is considered as an important tool in drug discovery. The aim in using in silico techniques is to reduce the costs and time to discover new active compounds, since reducing the period of a new drug from research laboratory to the market by one year can reduce the costs by millions of dollars. To develop a pharmacophore model is an important concept in computer-aided drug design (CADD), as this method can reduce the complexity of molecular interactions between compound and target to a set of few features.
This method was successfully applied in drug discovery  [14] [15] [16] [17] [18] [19]  and many molecular modeling software are available among them the Molecular Operating Environment (MOE). In this study a pharmacophore model for human protein kinase CK2 was constructed on the basis of 50 indeno  [1,2-b] indoles and used to identify new hits by mining the ZINC compound database  [20].