dataesr/scientific-paragraphs-categorization huggingface dataset:
A sensitivity analysis will be performed to ensure robustness of results, adjusting for potential confounders through a multivariable linear regression model. The annual change in secondary endpoints will be investigated similarly, but these analyses are not formally powered, and so will be considered as secondary analyses. Correlations between continuous variables will be made using Pearson correlation coefficients if variables are normally distributed, or Spearman's rank correlation coefficients otherwise. Both intention to treat (based on the dialysis modality HD or HDF at the start of the study) and per protocol (at least 90% of all dialysis sessions must be in the assigned group) analyses will be performed. All analyses will be performed in SAS Version 9. 4 (SAS Institute Inc. , Cary, NC).
Sample The data for Waves 1 and 2 were collected via Prolific, a crowdsourcing community set up by researchers at the University of Oxford, UK, offering high quality participant pools. Respondents were recruited via the website which linked to our survey in Qualtrics. Wave 1 was administered on 31 May 2016. In total, 638 people finished the survey. The survey included three test questions (i. e. 'Please tick 2 here') to make sure respondents paid attention. Removing all respondents who incorrectly answered two or three of the three test questions (n ¼ 11), and those who participated twice (n ¼ 13), left a final sample of 614 respondents randomly assigned to one of the three medium types (Video games n ¼ 210, TV n ¼ 189, blogs n ¼ 215). This sample had a good distribution of gender (52% female), age (M ¼ 36.
The final PKS-SC should also be validated on an independent sample. In addition, further research could do additional convergent validity testing for the conceptual components with constructs that are more cognitive in nature, and could do more elaborate testing of discriminant validity. Thus, there is a need for further validation and testing of the scales. The R protocol of scale validation we adapted to develop the PKS-SC is freely available in the Online Appendix. Future research that employs these scales can use this procedure to test item and scale properties as part of their data analysis process. We believe that scale development and validation is a perpetual and ongoing process that needs to accompany theory development and testing, particularly in fast-moving fields such as that of persuasion knowledge and new forms of advertising.
We believe that the scales would benefit from further research, with more data collected, studying different types of sponsored content in different media, in different countries. It would be good to validate the PKS-SC again after several studies by merging data from different studies to also look at measurement invariance across different socio-demographic variables. In sum, this study fulfilled a strong need for standardized measures of the different components of persuasion knowledge that was fuelled by the increasing popularity of persuasion knowledge in the current literature on consumers and persuasion  (Ham, Nelson, and Das 2015). The PKS-SC enables accumulation of knowledge as true accumulation can only occur when standardized and validated measures are used.
Main Text Our previous work identified 19 genome-wide significant common variant signals in addition to APOE  9  , that influence risk for LOAD. These signals, combined with 'subthreshold' common variant associations, account for ~31% of the genetic variance of LOAD  2  , leaving the majority of genetic risk uncharacterized  10. To search for additional signals, we conducted a GWAS metaanalysis of non-Hispanic Whites (NHW) using a larger sample (17 new, 46 total datasets) from our group, the International Genomics of Alzheimer's Project (IGAP) (composed of four AD consortia: ADGC, CHARGE, EADI, and GERAD). This sample increases our previous discovery sample (Stage 1) by 29% for cases and 13% for controls (N=21,982 cases; 41,944 controls) (Supplementary Table  1  and 2, and Supplementary Note).
To sample both common and rare variants (minor allele frequency MAF ≥ 0. 01, and MAF < 0. 01, respectively), we imputed the discovery datasets using a 1000 Genomes reference panel consisting of. 36,648,992 single-nucleotide variants, 1,380,736 insertions/deletions, and 13,805 structural variants. After quality control, 9,456,058 common variants and 2,024,574 rare variants were selected for analysis (a 63% increase from our previous common variant analysis in 2013). Genotype dosages were analyzed within each dataset, and then combined with meta-analysis (Supplementary Figures  1  and 2  and Supplementary Table  3 ). The Stage 1 discovery metaanalysis was first followed by Stage 2 using the I-select chip we previously developed in Lambert et al (including 11,632 variants, N=18,845) and finally stage 3A (N=6,998).
05 and P < 1 x 10 -5 or variants with MAF ≥ 0. 05 and P < 5 x 10 -6 from genome regions not covered on the Stage 2 custom array (11,710 cases and 14,133 controls) (Supplementary Table  6 ). For Stages 1, 2, and 3, samples did not overlap. Stage 1 single variant-based association analysis was conducted on genotype dosages modeling for an additive genotype model and adjusting for age (defined as age-at-onset for cases and age-at-last exam for controls), sex and population substructure using PCs  8. The score test was implemented on all case-control datasets. This test was shown to be optimal for metaanalysis of rare variants due to its balance between power and control of type 1 error  9. Family datasets were tested using the R package GWAF  10  , with generalized estimating equations (GEE) implemented for common variants (MAF ≥ 0.
Results of this model were compared to the inverse-variance weighted meta-analysis, and results that differed by more than 3 logs on both P-values were removed from further analysis. Regression coefficients for rare variants can at times be unstable  12  , and this step attempted to control for these problematic variants by using a second method of meta-analysis that may be less sensitive to certain properties of rare variant analysis. In total, 11 variants were removed through this comparison, and most results showed very little difference in P-values between the two methods. An additional 106 variants with high heterogeneity between studies (defined as I 2 > 75) were removed. Figures for association signals were generated with LocusZoom software  13. Genome-wide summary statistics are available from The National Institute on Aging Genetics of Alzheimer's Disease (NIAGADS) website (https://www.
A panel of ancestry-informative markers (AIMs), was used to perform PCA analysis with SMARTPCA from EIGENSOFT 4. 2 software  16  , and individuals with non-European ancestry were excluded. Variant quality control was also performed separately in each country including removal of variants missing in more than 10% of individuals, having a Hardy-Weinberg P value in controls lower than 1 x 10 -6 , or a P value for missingness between cases and controls lower than 1 x 10 -6. Please see Lambert et al. for a more detailed description of the QC procedures followed in Stage 2 analysis. After quality control, 18,845 individuals (8,362 cases and 10,483 controls) were available for the stage 2 analysis. The same quality control measures were applied to data for the Stage 3 variants attained from follow-up genotyping.
The Aβ-centered network pathway analysis used a curated list of Aβ processing related genes from Campion et al. 48 Thirty-two Aβrelated gene sets and all 335 genes combined (see Campion et al. 48 for details) were run in MAGMA pathway analysis on both common (MAF ≥ 0. 01) and rare (MAF < 0. 01) variant summary results. The combined dataset of 37,635 individuals from the ADGC were used as a reference set for LD calculations in these analyses. Validation of prioritization method. Evaluation of the prioritization of the risk genes in genomewide loci was done using STRINGdb  49  , Jensen Diseases 50 , Jensen Tissues 51 , and the ARCHS4  52  resource via the EnrichR 53 tool. We evaluated both the 469 genes set list and the prioritized 68 Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing -Tables  Table 1.
Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www. fnih. org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional.
Nord Pas de Calais. This work was supported by INSERM, the National Foundation for Alzheimer's disease and related disorders, the Institut Pasteur de Lille and the Centre National de Génotypage, the JPND PERADES and the FP7 AgedBrainSysBio. The Three-City Study was performed as part of collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects.
DCR is a Wellcome Trust Principal Research fellow. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer's and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomed-ical Centre and their work was supported by the NIHR Queen Square Dementia BRU. LASER-AD was funded by Lundbeck SA. The Bonn group would like to thank Dr. Heike Koelsch for her scientific support. The Bonn group was funded by the German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant number 01GI0102, 01GI0711, 01GI0420.
The AgeCoDe study group was supported by the German Federal Ministry for Education and Research grants 01 GI 0710, 01 GI 0712, 01 GI 0713, 01 GI 0714, 01 GI 0715, 01 GI 0716, 01 GI 0717. Genotyping of the Bonn case-control sample was funded by the German centre for Neurodegenerative Diseases (DZNE), Germany. The GERAD Consortium also used samples ascertained by the NIMH AD Genetics Initiative. HH was supported by a grant of the Katharina-Hardt-Foundation, Bad Homburg vor der Höhe, Germany. The KORA F4 studies were financed by Helmholtz Zentrum München; German Research Center for Environmental Health; BMBF; German National Genome Research Network and the Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the Heinz Nixdorf Foundation (Dr. Jur. G. Schmidt, Chairman) and BMBF.
Coriell Cell Repositories is supported by NINDS and the Intramural Research Program of the National Institute on Aging. We acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the MRC and the Wellcome Trust which was genotyped by the Wellcome Trust Case Control Consortium and the Type-1 Diabetes Genetics Consortium, sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International. pour la Recherchesur Alzheimer", Paris, France. The research leading to these results has received funding from the program "Investissementsd' avenir" ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Agence Institut Hospitalo-Universitaire-6.
Genotyping and Quality Control. Datasets for Stage 2 analysis were obtained from previous genotyping from Lambert et al. 2013 7 of 11,632 single nucleotide variants genotyped using Illumina iSelect technology. Eight variants from Stage 3A were genotyped using Taqman technology. Stage 3B included 23 variants included as part of Sequenom MassArray iPLEX panels and 10 additional variants genotyped using Taqman technology.
Acknowledgements ADGC. The  National Institutes of Health, National Institute on Aging (NIH-NIA ) supported this work through the following grants: ADGC,  U01 AG032984 ,  RC2 AG036528 ; Samples from the  National Cell Repository for Alzheimer's Disease (NCRAD) , which receives government support under a cooperative agreement grant ( U24 AG21886 ) awarded by the  National Institute on Aging (NIA) , were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible; Data for this study were prepared, archived, and distributed by the  National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania  ( U24-AG041689-01 );  NACC ,  U01 AG016976 ;  NIA LOAD (Columbia University) ,  U24 AG026395 ,  U24 AG026390 ,  R01AG041797 ;  Banner Sun Health Research Institute   P30.
The Bonn samples are part of the  German Dementia Competance Network (DCN)  and the  German Research Network on Degenerative Dementia (KNDD) , which are funded by the  German Federal Ministry of Education and Research  (grants KND:  01G10102 ,  01GI0420 ,  01GI0422 ,  01GI0423 ,  01GI0429 ,  01GI0431 ,  01GI0433 ,  04GI0434 ; grants KNDD:  01GI1007A ,  01GI0710 ,  01GI0711 ,  01GI0712 ,  01GI0713 ,  01GI0714 ,  01GI0715 ,  01GI0716 ,  01ET1006B ). Markus M Nothen  is a member of the  German Research Foundation (DFG) cluster of excellence ImmunoSensation. Funding for Saarland University  was provided by the  German Federal Ministry of Education and Research (BMBF) , grant number  01GS08125  to  Matthias Riemenschneider. The  University of Washington  was supported by grants from the  National Institutes of Health  ( R01-NS085419  and  R01-AG044546 ), the  Alzheimer's Association  ( NIRG-11-200110 ) and the  American Federation for Aging Research  ( Carlos Cruchaga  was recipient of a New Investigator Award in Alzheimer's disease).
Brigham Young University  was supported by the  Alzheimer's Association  ( MNIRG-11-205368 ), the  BYU   Gerontology Program  and the  National Institutes of Health  ( R01-AG11380 ,  R01-AG021136 ,  P30-S069329-01 ,  R01-AG042611 ). We also acknowledge funding from the  Institute of Neurology , UCL, London who were supported in part by the  ARUK  via an anonymous donor, and by a fellowship to  Dr Guerreiro. Seripa ,  Urbano  and Masullo's participation in the study was completely supported by  Ministerodella Salute", I. R. C. C. S. Research Program , Ricerca Corrente 2015-2017, Linea n. 2 "Malattiecomplesse e terapie innovative" and by the "5 × 1000" voluntary contribution. AddNeuromed is supported by  InnoMed , an  Integrated  Project funded by the  European Union   Sixth Framework programme priority   FP6-2004-LIFESCIHEALTH-5 ,  Life Sciences, Genomics and Biotechnology for Health.
We are grateful to the  Wellcome Trust  for awarding a  Principal Research Fellowship  to Rubensztein ( 095317/Z/11/Z ). Matthias Riemenschneider  was funded by the  BMBF NGFN  Grant  01GS08125. BN supported by  FondazioneCassa di Risparmio di Pistoia e Pescia  (grants  2014. 0365 , 2011. 0264  and  2013. 0347 ). Harald Hampel  is supported by the  AXA Research Fund , the  Fondation Universite Pierre et Marie Curie  and the "Fondation.
Data Availability Stage 1 data (individual level) for the GERAD cohort can be accessed by applying directly to Cardiff University. Stage 1 ADGC data are deposited in a NIAGADS-and NIA/NIH-sanctioned qualified-access data repository. Stage 1 CHARGE data are accessible by applying to dbGaP for all US cohorts and to Erasmus University for Rotterdam data. AGES primary data are not available owing to Icelandic laws. Genome-wide summary statistics for the Stage 1 discovery are available from The National Institute on Aging Genetics of Alzheimer's Disease (NIAGADS) website (https://www. niagads. org/). Stage 2 and stage 3 primary data are available upon request.
Stage 2 and 3 3 Genotyping and Quality Control. Datasets for Stage 2 analysis were obtained from previous genotyping from Lambert et al. 2013 7 of 11,632 single nucleotide variants genotyped using Illumina iSelect technology. Eight variants from Stage 3A were genotyped using Taqman technology. Stage 3B included 23 variants included as part of Sequenom MassArray iPLEX panels and 10 additional variants genotyped using Taqman technology.
| Samples The whole-genome sequences used in this study were obtained from 176 pure haploid males, representing 117  A. m. iberiensis, 28 A. m. carnica and 31 A. m. ligustica (DH and MAP, unpublished data; Parejo et al. , 2016)  sampled across a wide geographical range (Figure  1 ). All samples were sequenced on an Illumina HiSeq 2500 with an aimed sequencing depth of 10× per individual. Mapping and variant calling were performed following best practices (see Supporting Information for details). To assess subspecies ancestry and purity of all individuals included in the initial whole-genome data set (see Supporting Information for details), we inferred model-based admixture proportions (Q-values) for K = 1 to 5 clusters with 10,000 iterations using the software ADMIXTURE v1. 3. 0  (Alexander, Novembre, & Lange, 2009).
| Selection and genomic information of highly informative SNPs Having assessed the potential effects of sampling bias, we were able to follow Anderson's simple training and holdout method without incorporating a significant bias when selecting highly informative SNPs (Figure  2 ). Accordingly, highly informative SNPs for estimating C-lineage introgression into A. m. iberiensis were selected using the training data set (88 A. m. iberiensis and 44 C-lineage individuals). A total of 18,272 SNPs were fixed (F ST = 1) (Table  S3 , Figure  S3 ), an increase of 7,181 fixed SNPs compared to that calculated from the complete data set (117 A. m. iberiensis data set and 59 C-lineage individuals). While these SNPs were not fixed in the complete data set, they were still highly differentiated (F ST ≥0.
m. carnica and 21 A. m. ligustica) were used as the training data set for selecting informative SNPs. The most informative SNPs were identified from F ST values (fixed SNPs, F ST = 1), calculated in PLINK between A. m. iberiensis and C-lineage individuals using the training data set. To uncover the putative functional role of the highly differentiated SNPs, we used SNPeff 4. 3 (Cingolani et al. , 2012)  and the NCBI honeybee annotation version 102 (Pruitt et al. , 2013). Subsequently, we performed a gene ontology (GO) analysis in the DAVID v. 8. 0 database (Huang, Sherman, & Lempicki, 2009)  considering the GO terms of the biological process (BP), molecular function (MF), cellular component (CC) (Gene Ontology Consortium, 2015) and the KEGG pathway (Kanehisa, Sato, Kawashima, Furumichi, & Tanabe, 2016).
The reason for the scenario considered is due to reports that under different conditions the use of ‡ Up to this time, in June 2013, fourteen chemicals had been declared as RCAs by States Parties since the Convention had entered into force in 1997. The SAB was informed by the OPCW of the identity of these chemicals but not which State Parties had declared them, in line with OPCW condentiality rules. § In 2001 the SAB's Temporary Working Group on Analytical Procedures had drawn up a list of RCAs and old/abandoned chemical warfare agents to be considered for inclusion in the OPCW Central Analytical Database (OCAD). For the purposes of implementing the Convention, old chemical weapons are oen considered to be chemical weapons produced between 1925 and 1946 that have deteriorated to the extent that they can no longer be used as chemical weapons.
2  With the OPCW Designated Laboratory network providing an off-site sample analysis capability, the OCAD has served as a database for on-site use, especially for inspections to commercial facilities subject to declaration and inspection as part of the industry verication obligations of States Parties. 25 The OCAD includes analytical data for chemicals relevant to the Convention. The 2018 edition of the database contains mass spectra and retention indices for more than 4400 chemicals, nuclear magnetic resonance spectra for nearly 300 chemicals, and infrared spectra for more than 750 chemicals. { The list is not provided here, but did contain those chemicals assessed by the SAB, which appear in Tables  S1  and S2   certain types of RCAs has been linked to longer term health effects. 9, 13, 35 verall, the SAB considered 60 chemicals that included: (a) 14 declared as RCAs since entry into force of the Convention; (b) chemicals identied as potential RCAs from a list of "riot control agents and old/abandoned chemical weapons" to be considered for inclusion in the OPCW Chemical Agent Database (OCAD) (this list had been draed by the SAB's Temporary Working Group on Analytical Procedures in 2001 (ref.
Future considerations Every ve years the States Parties convene a Conference to review the operation of the Convention (a 'Review Conference') to which the SAB provides a report on developments in science and technology to assist the States Parties in their deliberations with science advice to better inform decision making. The SAB's report summarises science and technology and scientic advances deemed of relevance to the Convention over the veyear period since the last Review Conference. The report makes recommendations to the OPCW Director-General and States Parties that are intended to further fortify the implementation of the Convention. The next Review Conference, the fourth since entry into force of the treaty, will be held in The Hague in November 2018. The SAB's science and technology report to this conference 54 includes the following recommendations mentioning RCAs and CNS-acting chemicals and advocating inclusion of their analytical information into the OCAD: 'Appropriate analytical data for chemicals that may pose a risk to the Convention or that are needed to help differentiate permitted activities from prohibited activities should be added to the OCAD.
This could include isotopically-labelled relatives and stereoisomers of scheduled compounds, salts of scheduled chemicals, toxic industrial chemicals, CNS-acting chemicals, riot control chemicals, bioregulators [endogenous chemicals that regulate biological/life processes, such as the neuropeptide substance P], toxins, and unscheduled chemicals that have been identied as posing a risk to the Convention. '  [54] [55] [56]  'Technical discussions of so-called "incapacitating chemicals" or central nervous system-acting (CNS) chemicals remain exhausted. The SAB sees no value in revisiting this topic as scientic facts remain unchanged since the SAB rst considered the issue. In view of the increasing availability of such chemicals, the Secretariat [OPCW] should be prepared to develop capabilities that could be required to conduct missions involving an alleged use of CNS-acting chemicals for hostile purposes, including sample collection and the addition of analytical data to the OPCW Central Analytical Database (OCAD).