dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction: The Stroke and Cognition consortium (STROKOG) aims to facilitate a better understanding of the determinants of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders (VCD). Methods: Longitudinal studies with 75 participants who had suffered or were at risk of stroke or TIA and which evaluated cognitive function were invited to join STROKOG. The consortium will facilitate projects investigating rates and patterns of cognitive decline, risk factors for VCD, and biomarkers of vascular dementia. Results: Currently, STROKOG includes 25 (21 published) studies, with 12,092 participants from five continents. The duration of follow-up ranges from 3 months to 21 years. Discussion: Although data harmonization will be a key challenge, STROKOG is in a unique position to reuse and combine international cohort data and fully explore patient level characteristics and outcomes.
Stroke and Cognition consortium (STROKOG) is a recently established consortium of prospective post-stroke/ TIA studies from around the world, including, at the time of writing, 25 studies with up to 12,092 patients from 16 countries and 25 institutions worldwide. By harmonizing data and conducting individual participant data (IPD) meta-analyses on studies from around the world, STRO-KOG aims to better understand the longitudinal course of poststroke cognitive impairment and investigate differences in the prevalence of VCD as well as risk and protective factors between different countries and ethnic groups. In IPD meta-analysis, the original research data are sought directly from the researchers responsible for each study rather than extracting summary (aggregate) data from study publications or from investigators.
The original data can then be re-analyzed centrally and combined, if appropriate, in meta-analyses. There are various advantages of conducting IPD meta-analyses over standard meta-analysis, such as the ability to investigate patient level characteristics, adjust for the same confounding factors across studies, address new research questions, and reduce publication bias  [17, 18]. By harmonizing and conducting analyses on a rich collection of individual participant data, STROKOG can examine risk factors on the patient level and apply standardized methods of statistical analyses and dementia diagnosis that could produce more accurate prevalence estimates and more detailed information on the risk factors for VCD and the impact stroke has on cognitive decline. It is anticipated that the findings of STROKOG will help guide and optimize preventative strategies and health policy worldwide.
Membership Studies are eligible to participate in STROKOG if they meet the following membership criteria: 1. Prospectively recruited patients with stroke, TIA, or high-stroke risk (such as patients with atrial fibrillation, hypertension, genetic disorders such as CADA-SIL and so forth). 2. Are longitudinal, with a minimum of two waves of assessments conducted or planned. 3. Have a minimum sample size of 75. 4. The major outcome measures include dementia and/or cognitive impairment and/or cognitive decline. Official enrollment requires the study lead investigator to sign a memorandum of understanding (MoU) which specifies a willingness to share nonidentifiable data for joint analyses. Studies that have restrictions on data use and data sharing may also participate in STROKOG by conducting in-house analyses using STROKOG protocols and providing summary data for pooling.
At the time of writing, there are 24 officially enrolled member studies of STROKOG and one unofficial member study which plan to obtain participants' permission to share data before signing the MoU. These studies and their key demographic characteristics are shown in Table  1. Supplementary Table  S1  shows some salient findings from each member study. They represent 16 different countries, including one lower middle income country from across five continents (Africa, Asia, Australia, Europe, and North America). In total, there are 12,092 participants, and for most studies, patients were recruited consecutively as they were admitted to hospital with a recent stroke. Approximately half of the studies admitted participants with a recent TIA, and those with a previous stroke were typically not excluded.
Ethics The overall STROKOG project has been approved by the Human Research Ethics Committee (HREC) of the University of New South Wales (UNSW), Sydney (reference number: HC14359). Member studies are responsible for obtaining approval (if necessary) from their local institutional review board for the sharing of data. Although de-identified data are not considered Protected Health Information by the National Institutes of Health of the United States, ethics review boards differ in their approach to this matter. A protocol for the deidentification of data has been developed. Additionally, each new project presented to STROKOG must be approved by an institutional review board and the HREC of UNSW informed about the project.
Research projects and data The Sydney team will initiate the first project, collate study data and conduct the initial data harmonization and IPD meta-analyses across the different studies. STROKOG will follow guidelines for rigorous and effective harmonization  [58]  as well as for IPD meta-analyses and reporting  [17, 59]. Original data sets supplied by members and the harmonized data sets, which will include derived data, will be held by the Sydney team on a secure database at the University of New South Wales. A STROKOG RSC member can propose a research project, which need to undergo an approval process by the RSC. If the prospective proposer of a project is not a member of the RSC or affiliated with a member study, the proposal can still go ahead if it is sponsored by a member of the RSC.
A student may similarly request data for a research project if sponsored by a member of the RSC. Once approved, the proposer will be advised to form a Work Group for the specific project, with a clearly identified project leader. Other members of the consortium may volunteer to be part of the Work Group if they have a clear interest and expertise in the research project. The Work Group will address and be responsible for the planning, analysis, and publication of the approved project. Data held by STROKOG will be released to the Work Group; newly joined and existing member studies will be requested to provide additional de-identified data. Each cohort can opt-in or opt-out of participation; optingin represents a commitment to collaborate on the project. The central STROKOG data repository at UNSW, Sydney, will hold a copy of the data sets used for each project.
Challenges Large international consortia face general challenges that include efficient communication among personnel involved with studies across different countries and a need for additional funding to enable the further use of previously collected data. Such challenges have been previously described by other consortia (e. g. , by CHARGE  [60] ). Challenges more specific to STROKOG are those associated with data harmonization, many of which have also been previously described  [61, 62]. Neuropsychological test data are likely to be the most challenging set of data to be harmonized across studies due to heterogeneity in test instruments, administration, and criteria for defining impairment; however, the Sydney team has had experience harmonizing large data sets from diverse  y Some studies also obtained PET scans from a subgroup of their participants: GRECOG-VASC (amyloid for 100 patients with poststroke cognitive deficit), STROKDEM (at 1 year), DEDEMAS (amyloid/FDG for incident dementia cases), CANVAS (amyloid at 3 years), STRIDE (PiB, n 5 50) and DSDB (SPECT, n 5 36).
z ASPIRE-S is an unofficial member that is planning to obtain participants' permission to share data outside of Ireland before signing the MoU. x For the GRECOG-VASC study, only the 200 first patients from the Amiens center are included for STROKOG and the VASCOG criteria was used except for the threshold of cognitive impairment which was set at the 5th percentile. k PROSPER shares data at the summary level and not individual participant data. studies, for example, for the COSMIC consortium  [63]. There exists much heterogeneity in measurement instruments used by different studies. Even when studies have used similar tests, different versions of tests were used, and tests might have been administered in nonstandard ways. Owing to differences in culture and language, questions and responses may have been worded differently.
Advantages The benefits of being able to reuse, combine, and compare data from multiple cohorts are obvious, and this approach has been recommended by the Lancet REWARD Campaign  [64]. Cohort studies focused on stroke and cognition are expensive, labor-intensive, and very demanding of researchers' and participants' time. Multiple factors may limit the study sample size and duration. There already exists a wealth of data from previous or current studies that are underutilized and can be put to further use. Standard meta-analysis is one way of analyzing the combined cohorts of existing studies, but it is limited to published results and specific research questions. STROKOG's approach in harmonizing individual participant data from various studies is an economical use of previously collected data to explore both existing and novel research questions.
Having access to a rich collection of individual participant-level data from international studies, STROKOG is in a unique position to apply IPD metaanalyses techniques and fully explore and examine patient   Cracow study conducted neuropsychological tests under the five domains but these data were lost and not available. level characteristics and outcomes, which in turn could lead to facilitating a better understanding of various aspects of post-stroke cognitive impairment. A consortium such as STROKOG has the added benefit of informing new researchers of the need to standardize their measures and methods so that they are in step with other researchers in the same field. This is particularly important for stroke and dementia, as many researchers from low and middle income countries have recently become interested in this topic and are mounting studies that would benefit from comparability with existing studies.
New studies therefore would have the benefit of validated measures and better comparability. There is also the potential for the STROKOG database to be made available to nonconsortium researchers following consortium-based publications and with the approval of the RSC and the ethics review board. The scientific benefits of making large databases available to researchers worldwide have been demonstrated by the more than 250 publications that have resulted from the sharing of ADNI (Alzheimer's Disease Neuroimaging Initiative) data  [66]. The research community that STROKOG proposes to create is likely to pay other dividends. It is expected that the productivity of the group will increase over time in quality and quantity; junior researchers will be supported, new ideas and lines of investigation will be generated and scientific interchange and collaboration will be facilitated.
Specific projects The first project aims to describe the prevalence and profile of poststroke cognitive impairment, measured between 1 and 6 months after a stroke or TIA, in diverse geographical and ethnocultural settings as represented by the STROKOG member cohorts. The project is currently underway and is being coordinated by the Sydney team. A project proposal was approved by the RSC, and the following data are currently being requested: While many of the existing member studies have relevant data to contribute to the more general topics (such as 1 to 4 noted above), the more specific topics (5 to 8) will be best addressed by growing the STROKOG membership base. Therefore, we encourage any investigators with a study meeting the eligibility criteria to contact STROKOG and become a member of the consortium.
Interpretation: The STROKOG consortium brings together international longitudinal cohort studies of cognitive decline and dementia following stroke or TIA. By harmonizing data and conducting individual participant meta-analyses, STROKOG aims to better understand the determinants and manifestations of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders. 3. Future directions: STROKOG will conduct individual projects investigating research topics such as the prevalence of VCD, rates of cognitive decline, determinants of cognitive impairment and the frequency of depression in relation to cerebrovascular disease. STROKOG invites other longitudinal studies on stroke/TIA patients and cognitive impairment to join, contribute to the database and participate in collaborative research.
Acknowledgments ASPIRE-S: The authors acknowledge the ASPIRE-S Steering Group members:  Professor Hannah McGee ,  Royal College of Surgeons in Ireland ;  Professor Emer Shelley ,  Royal College of Surgeons in Ireland ;  Professor Peter Kelly ,  Mater Misericordiae University Hospital, Dublin ;  Dr Eamon Dolan ,  Connolly Hospital ,  Dublin. BIOSTROKE  and STROKDEM:  French Health Ministery , the  Vascog FHU , the ' Fondation Coeur et Art eres '. CANVAS: CANVAS researchers wish to acknowledge support from the  NHMRC  ( APP1020526  and  APP1094974 ),  Sidney  and  Fiona Myer Foundation  and the Collie Trust. CASPER: The  CASPER  project team consists of  Ms. Syenna Schievink ,  Ms Elles Douven ,  Dr Sebastian K€ ohler ,  Dr Pauline Aalten ,  Dr Julie Staals ,  Professor Robert van Oostenbrugge , and  Professor Frans Verhey.
Neocortical Volume 1y, 3y 1 y, 3 y NINDS- (2014) [23] After Stroke AIREN (CANVAS) Cognitive Function UK 2002-2012 355 3m; 1-8y 3 mo; 1-8 y 3 mo, 2 y Yes Allan et al. After Stroke (annually) (annually) (2011) [12] (CogFAST-UK) Cognitive Function Nigeria 2010- 217 3m 3 mo 3 mo DSM-IV, Akinyemi et al. After Stroke Nigeria ongoing (small AHA/ASA (2014) [24] (CogFAST-Nigeria) subset only) Cognitive Outcome Singapore 2009-2017 400 3-6m, 1y, 3-6 mo, 1 y, Initial (small DSM-IV Dong et al. After Stroke (planned) 3-4y, 5y, 3-4 y, 5 y, subset only) (2012) [25] (COAST) 6y, 7y 6 y, 7 y Cracow Stroke Poland 2000-2001 250 3m, 12m 3 mo, 12 mo No DSM-IV Klimkowicz et al. Database (Cracow) (2004; 2006) [26,27] Determinants of Germany 2011-2019 600 3m, 6m, 6 mo, 1 y, Initial, 6 mo; DSM-V Wollenweber et al. Dementia After (planned) 1y, 2y, 3y, 3 y, 5 y; 3 y, 5 y (2013) [28] Stroke 4y, 5y (TICS (DEDEMAS) only: 3 mo, 2 y, 4 y) Durban Stroke Data South Africa 1992-1998 1000 3m, 6m, 12m Initial, 3, 6, Initial DSM-IV Hoffmann (1998; Bank (DSDB) 13 mo 2001) [29,30] Epidemiologic study of USA 1988-1999 585 Initial, 3 mo-10 y No Modified Desmond et al.
Databases like the "The Human Gene Mutation Database" (HGMD; http://www. hgmd. org/) constitute a comprehensive collection of mutations in genes underlying or associated with human inherited diseases. The database is routinely accessed and utilized by next generation sequencing (NGS) project researchers, human molecular geneticists, neurologists and genetic counselors. Interpretation of genetic results is challenging, especially in multifactorial diseases like common epilepsies. The HGMD comprises more than 1000 disease-associated mutations annotated for the SCN1A gene (accessed Dec/2014). Here, we investigated a already whole-exome sequenced cohort of 448 patients with a broad range of common genetic epilepsies, for SCN1A mutations listed as disease associated in the HGMD. We re-evaluated the identified variants' evidence for pathogenicity by in silico prediction, segregation, literature review for clinical, genetic and functional relevance and assessment of allele frequencies in healthy individuals.
Patients and sequencing Patients of European and Turkish ancestry were recruited at several centers from Germany, Austria, Italy, Finland, Canada and Turkey. We included Turkish patients since SCN1A variant frequencies do not show correlation with the ethnicity when compared between Asian and European patients  [4]. Samples from The Rotterdam Study  [15]  not specifically screened for European ethnicity, which were handled in the same way as our cases, i. e. using the same enrichment and sequencing methods, served as controls for the whole exome dataset. The controls are drawn from the population and are not evaluated for seizure disorders. Informed consent for whole exome sequencing was obtained from all participants or legal representatives respectively. The two institutions in charge of the reported analysis are the University hospitals in Tübingen (for "genetic generalized epilepsy") and Giessen (for "genetic focal epilepsy"), Germany.
HGMD annotation and assessment of the literature Variant annotation was performed using the HGMD Professional 2013. 4-15th December 2013 Version  [17]. We assessed original reports cited by the HGMD for each identified HGMD patient mutation. We re-analyzed the mutations, and if the original report used the sequence of a shorter transcript of the SCN1A gene for numbering we numbered the variant accordingly to the RefSeqGene (NM_001165963. 1; which represent the longest transcript and reference protein NP_001159435. 1) used in our data set.
In silico prediction Functional prediction scores were obtained from the dbNSFP database version 2. 8 (http://sites. google. com/site/jpopgen/dbNSFP, accessed 01/2015). In total we used six prediction scores (SIFT, Polyphen-2-HVAR, Polyphen-2-HDIV, Mutation Assessor, FATHMM, LRT) and two conservation scores (GERP++, PhyloP). We did not use the dbNSFP generated ensemble scores, as they integrate the prediction of the tool Mutation Taster, which itself incorporates HMGD entries. We classified the variant as "damaging" when the majority of the tools predicted a functional effect for the variant (i. e. a minimum of 5 out of 8 tools). Topology and domain structure were taken from UniProt entry P35498.
Case vs. control HGMD analysis We analyzed sequence data of 448 epilepsy patients and 734 controls for SCN1A variants previously reported as disease causing collected by the HGMD. Our mutation screening identified eight distinct HGMD missense mutations (Table  1 : p. R27T, p. R28C, p. R604H, p. R542Q, p. T1174S, p. T1250M, p. E1308D, p. R1928G) affecting in total 17 unrelated epilepsy patients (17/ 448 = 3. 80%) compared to six HGMD variants affecting 10 control individuals (10/ 734 = 1. 36%). None of these patients were carrier of a rare loss of function variant in a known epilepsy gene (S2 Table ). The SCN1A HGMD variant frequency was therefore slightly enriched in the patient cohort (P = 8. 30 x 10 -3 ). A high allele frequency in public databases would argue against pathogenicity of the identified SCN1A HGMD variants identified in our patients.
In addition to our own sequenced controls we investigated the SCN1A HGMD variant frequencies further using the ExAc, a publicly accessible variant database of 60706 unrelated individuals. All eight variants were found in individuals collected in the ExAc database (Table  1 ). After combining our in-house control and ExAc data in a meta-analysis, only the p. T1174S variant remained significantly enriched after correction for multiple testing (Table  1 ) in epilepsy patients supporting p. T1174S as the only identified SCN1A genetic risk factor for epilepsy in our cohort.
Replication analysis p. T1174S Overall, after the initial re-evaluation of the identified SCN1A HGMD variants, we suggest that only the p. T1174S variant is potentially a epilepsy associated variant. The p. T1174S variant is enriched in patients, segregates with the phenotype in families and published functional data support pathogenicity of the variant (Table  2 ). A major factor in our variant classification is comparing the allele frequency differences between patients and controls. To further assess the frequency of p. T1174S alleles in epilepsy patients and replicate our association, we investigated an additional epilepsy cohort provided by the CeGaT diagnostic lab using targeted sequencing. We identified seven out of 777 epilepsy patients (0. 90%) carrying the p. T1174S SCN1A mutation.
The molecular analysis revealed two divergent effects: a positive shift of the activation curve and deceleration of recovery from fast inactivation-consistent with a loss of function, and an increase of persistent current (I (NaP))-consistent with a gain of function  [19]. On the other hand, the p. T1174S variant was only predicted to be damaging by one in silico program (FATHMM, Table  3 ) and was inherited by unaffected parent in the replication cohort five out of seven times. Besides the unclear role of the p. T1174S variant, the majority of our investigated SCN1A HGMD variants cannot be classified as clearly pathogenic. Based on our results, we assume that a significant fraction of patients diagnosed with pathogenic SCN1A mutations may actually not carry an SCN1A variant of relevance. The role of SCN1A missense mutations in the pathogenesis of common epilepsies may thus be overstated (in general and e.
g. studies in Table  2 ). Previous conclusions were frequently based on comparisons of allele frequencies between patients and small control datasets often without molecular follow up of the variant (Table  2 ). By accessing large gene mutation databases of non-epileptic individuals (e. g. http:// exac. broadinstitute. org) we show that previous studies have been underpowered. In order to draw a definitive conclusion about pathogenicity for variants in common epilepsy syndromes, which are also present in healthy individuals, functional studies are mandatory (e. g. functional characterization of patient neurons derived form induced pluripotent cells). The conclusions drawn from our study can be translated to other fields of research. Several prediction tools  [35] [36] [37] [38] [39]  evaluating the pathogenic potential of DNA sequence alterations are trained with variant lists extracted from the HGMD.
Acknowledgments We thank all participants and their families for participating in this study. The authors would like to thank the  Exome Aggregation Consortium  and the groups that provided exome variant data for comparison. A full list of contributing groups can be found at (http://exac. broadinstitute. org/). H. L. , B. N. , P. N. , T. S. and F. Z. received grants by the  EuroEPINOMICS programme  (DFG grant numbers: HL:  LE1030/11-1 , BN:  BN416/5-1 , PN:  NU50/8-1 , TS:  SA434/5-1 ,  FWF  grant number: FZ:  1643-B09 ) within the EUROCORES framework of the  European Science Foundation (ESF). The generation and management of genomics data for the Rotterdam Study are supported by the  Netherlands Organization of Scientific Research NOW Investments  (nr. 175. 010. 2005. 011 , 911-03-012 ) and the  Netherlands Genomics Initiative (NGI) / NOW  project nr.
050-060-810  ( Netherlands Consortium  for Healthy Ageing; NCHA). We thank the members of the  Genomics Lab  and the ERGO support team for their help in sampling the data and in creating the database, the study participants, the staff of the  Rotterdam Study  and the participating general practitioners and pharmacists. The  Rotterdam Study  is funded by  Erasmus Medical Center  and  Erasmus University, Rotterdam ,  Netherlands Organization for the Health Research and Development (ZonMw) , the  Research Institute for Diseases in the Elderly (RIDE) , the  Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports , the  European Commission (DG XII) , and the  Municipality of Rotterdam. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.