dataesr/scientific-paragraphs-categorization huggingface dataset:
Integrating biomolecular databases- The integration of biomolecular databases has faced a number of complex problems, such as mapping things that are related but not identical and mapping things that are similar but not identical. Extending the single identifier approach to more complicated structures (e. g. proteins and genes), requires expanding queries to handle all possible synonyms used within different databases. This is difficult because the nomenclature for biomolecules is still not totally standardized. However, some success has been achieved in establishing a common API for a given type of resource, facilitating integration by alleviating the need for schema matching. Essentially, the API defines a common schema and if all resources of the same kind are compliant with the API, the main burden of semantic mapping is met.
Data are typically transformed into a common data model (e. g. relational database or a triple store); examples of this approach are PubChem (https://pubchem. ncbi. nlm. nih. gov/) and ChEMBL (https://www. ebi. ac. uk/chembl/) databases. The Open PHACTS project (https://www. openphacts. org/) provides a common API to a variety of pharmacological data sets. It does not, however, normalize to a single data model, but addresses non-uniformity at the API level  (Williams et al. , 2012). The European Bioinformatics Institute Resource Description Framework (EBI-RDF) platform uses another approach, maintaining multiple RDF repositories for different resources and allowing federated searching across all of them  (Jupp et al. , 2014). Entities in the EBI-RDF platform are assigned equivalent identifiers, with identifiers.
Integrating public life sciences databases-Life sciences data resources provide further insight into the handling of integration of complex data inherent in living biological systems. In these systems, additional complexity is added by the large number of variables needed to fully characterize a living system. The Syngenta federated search system  (Eyres, 2013)  is an example of addressing the challenge of integrating internal company data with public life science databases. The system has moved from data warehousing (even if that offers faster reporting) towards federated search technologies. The architecture includes several internal relational database repositories, translated into RDF dynamically via D2RQ (http://d2rq. org/), and provides adapters in order to combine all internal and external data resources into a distributed SPARQL endpoint.
, 2001; Marshall et al. , 2012). The Open PHACTS project has taken this approach and developed an Identifier Mapping Service (IMS) that links databases using IRI-based identifiers  (Batchelor et al. , 2014). Services such as identifiers. org and the IMS itself provide routes to convert between alphanumeric identifiers (e. g. CHEBI:33128) and IRI-based identifiers (http:// www. ebi. ac. uk/chebi/searchId. do?chebiId=CHEBI:33128) as defined in the ChEBI ontology  (Hastings et al. , 2015b). Once these links are operational, allowing comparison of data for a set of similar or identical materials, the cross-comparison can be used for automated data curation. During curation, automated comparisons could be enabled to automatically generate warnings that point the user towards other studies reported in other data sources that contradict those being curated.
, 2013)  focused on achieving data integration goals. The advantage of having a group such as the GAIN is that the synergy and diversity of data resources necessary to test integration approaches would be present. For example, the breadth of nanomaterials entities (see Phase 1 below) would be rich enough so models developed would encompass relevant domains. In the stakeholder survey, all stakeholders agreed to participate in a Global Alliance pending availability of funding and time. Active groups include the stakeholders previously identified plus the NanoSafety Cluster (NSC, http://nanosafetycluster. eu/) Databases Working Group (along with participation in other NSC working groups), the US-EU Communities of Research working group on Databases and Computational Modeling for NanoEHS (https://us-eu.
Chemical and Agent Repositories Repositories such as PubChem, ChemSpider, ChEBI, and vendor repositories like Sigma Aldrich to obtain information on chemicals used in nanomaterial compositions; integrate with small molecule repositories like DrugBank  (Knox et al. , 2011)  to compare a small molecule (e. g. magnevist) with a nanomaterial formulation that associates with the small molecule (e. g. dendrimer magnevist complex) Modeling tools Modeling and simulation tools as well as 3D structural modeling tools. Integrating with modeling and simulation tools will assist in modeling the effects of nanomaterial size, shape, and other properties on bio-distribution and toxicity; integrating with 3D modeling tools such as The Collaboratory for Structural Nanobiology https://ncifrederick. cancer. gov/dsitp/abcc/abcc groups/simulation-and-modeling/collaboratory-for-structural-nanobiology/to facilitate the display on nanomaterial structures in 3D leveraging a Protein Data Bank (PDB) file, offering prediction options for adverse effects of nanomaterials  (Chomenidis et al.
Ontology/Taxonomy Resources To obtain an up-to-date database of ontologies in a table-type format so that one can easily review them. This includes resources such as the NCI Thesaurus http://evs. nci. nih. gov/ftp1/NCI_Thesaurus (Sioutos et al. , 2007) , BioPortal http:// bioportal. bioontology. org/ (Noy et al. , 2009) , and Ontobee http://www. ontobee. org/. This will allow databases to link to term references and accession numbers. (N. B. : as discussed above, ontology annotations support data integration. Hence integration of two resources with ontology terms supports wider integration.
Acknowledgements and funding sources The authors would also like to acknowledge the  Nano WG  leaders for their time and expertise in providing the necessary tools that supported collaboration on this article. Authors would like to give special thanks to  Dr. Mervi Heiskanen , the Nano WG lead from the  NCI Center for Biomedical Informatics and Information Technical (CBIIT)  and  Dr. Stephanie Morris , from the  NCI Office of Cancer Nanotechnology Research (OCNR)  for their leadership and support for  Nano WG  initiatives including the development of this article and funding of its Open Access publication status. The first three authors are listed in order of contributions. The remaining authors, excluding the last, are listed alphabetically. The views, opinions, and content in this article are those of the authors and do not necessarily represent the views, opinions, or policies of their respective employers or organizations.
ncbi. nlm. nih. gov/geo/) to achieve high-throughput functional genomics data Non-nanotechnology resource Description or example Life Sciences and Life science and chemistry databases in general, containing information about human biology (both experimental Chemistry Databases data, as well as knowledgebases) and chemistry (functionality, chemical structure, etc. ) (Kim et al. , 2015; Wang et al. , 2014); needed to inform the design of new nanomaterials to avoid potential negative influences on human health Image archives The National Biomedical Imaging Archive (NBIA) (https://imaging. nci. nih. gov/ncia/login. jsf); the Cancer Image Archive (TCIA) (http://www. cancerimagingarchive. net/), or other image archives to display MRIs or other image modalities of subjects in which nanomaterials are used for diagnostic and/or therapeutic purposes; a "public domain" image archive illustrating images used in articles (e.
g. SEM pictures), would assist in visualizing particle characterizations (see http://www. enanomapper. net/ library/image-descriptor-tutorial) Image Contrast Agent The Molecular Imaging and Contrast Agent Database (MICAD) (https://www. ncbi. nlm. nih. gov/books/NBK5330/) to Repository obtain information on image contrast agents to compare with nanomaterials used in diagnostic imaging Model Organisms Repository Clinical Trials Management Systems (CTMS).
g. SNLJJNM CAshleyACSNano2012-01. CEINT Mapping directly Under development Uses ontologies Nanomaterial associated to Includes some from NBI such data source and assigned a non-nanomaterial data set; curating as MO, NPO, UO, unique identifier data literature ChEBI, and data; directly eNanoMapper; also integrating data compatible with from customized ISA- templates TAB-Nano. built with active collaborators CSSP/NIPHE, Commonly uses the Provides a list a Uses field headings Identifier assigned based on No Netherlands OCHEM fields available for as particle core composition database for storing toxicity data a means of uploading controlling toxicity data vocabulary DECHEMA No Relational model Uses the scientific Not a central issue of the No documented in wording for DECHEMA work Kühnel et al.
Collection of MDR-TB isolates From 2009 to 2012, a total of 324 MDR-TB isolates were collected from 31 tuberculosis (TB) hospitals in Shanghai, China. All of these isolates were MIRU-VNTR genotyped, and 122 clustered MDR-TB strains had undergone WGS previously  10. Here, to determine the presence of compensatory mutations in VNTR nonclustered MDR-TB strains, we randomly selected 105 isolates from the remaining 202 VNTR nonclustered MDR-TB isolates to sequence the full-length genes rpoA, rpoB, and rpoC. The combined results of the 227 Shanghai MDR-TB strains were termed the "Shanghai dataset". Meanwhile, we obtained WGS records of 8331 M. tb isolates of global origin from the European Nucleotide Archive (ENA) (Supplementary Table  1 ), and 1346 of these strains were identified as MDR-TB (Supplementary Table  3 ).
The groups so defined are theoretically similar to the three prototypical types of clusters used to evaluate the role of drug resistance in transmission  11. In the Shanghai dataset, 12 transmission clusters were determined as C-type, 18 as N-type, and six as M-type (Fig. 2a ); in the Global dataset, 26 transmission clusters were determined as C-type, 84 as N-type, and seven as M-type.
Compensated MDR-TB strains did not cause larger transmission clusters If the putative compensatory mutations stimulated the transmission of MDR-TB, then C-type clusters would be expected to be larger than N-type clusters, but in the Shanghai strains, the C-type clusters were not larger than either the N-type or the M-type clusters (Wilcoxon ranksum test, P = 0. 4053 and 0. 9587, respectively) (Fig. 2b ). An analysis of the size of the clusters in the global MDR-TB strains yielded similar results (Wilcoxon rank-sum test, P = 0. 3816 and 0. 2277, respectively) (Fig. 2c ). Notably, the largest clusters observed in both the Shanghai and Global datasets (with seven and 23 linked cases, respectively) were caused by MDR-TB strains without compensatory mutations (Fig. 2b, c ). Moreover, we did not find any particular compensatory mutation that was associated with larger transmission clusters in either dataset.
If this is true, then clustered MDR-TB strains should have wider  drug-resistance spectra (number of drugs to which they are resistant) than those of nonclustered MDR-TB strains. To test this inference, we compared the drug-resistance spectra of the clustered versus nonclustered MDR-TB strains in the Global dataset. This analysis demonstrated that the median number of antibiotics to which nonclustered MDR-TB strains were resistant was 4. 22 (4. 10 ~4. 34), while for clustered MDR-TB strains it was 5. 13 (5. 05 ~5. 21) (Student's t-test, P < 0. 0001, Fig. 3b ). In addition, as a single M. tb strain can accumulate multiple mutations that confer resistance to a single antibiotic (evolution of high-level resistance)  [12] [13] [14]  , we further compared the numbers of drug-resistanceconferring mutations in these two groups.
MDR-TB datasets A total of 324 MDR-TB isolates were collected from 1 January 2009 to 31 December 2012, in Shanghai (the most populous city in China) during a population-based observational study  10. MIRU-VNTR genotyping (12 loci) was performed for all MDR-TB strains, and the clustered strains were further subjected to WGS  10. In the present study, we further sequenced the entire gene sequences of the rpoA, rpoB, and rpoC genes in 105 nonclustered MDR-TB strains to detect putative compensatory mutations. The PCR amplicons were sent for Sanger sequencing, and the results were analyzed to detect putative compensatory mutations using Geneious (http://www. geneious. com/). A total of 8331 published whole-genome sequences of M. tb strains were downloaded from the ENA (http://www. ebi. ac. uk/ena).
Identification of putative compensatory mutations The maximum likelihood method was used to reconstruct the phylogenetic trees with 500 bootstrap repeats using MEGA (v6. 06)  34  , and the phylogenetic tree was further visualized by FigTree (http://tree. bio. ed. ac. uk/ software/figtree/). Putative compensatory mutations in the rpoA, rpoB, or rpoC genes were identified through the following criteria: (1) nonsynonymous mutations in the rpoA, rpoB or rpoC genes that were present in only rifampicin-resistant strains; (2) each putative compensatory mutation must have arisen at least twice independently (parallel selection). We wrote a Python script to implement these criteria with the phylogenetic tree and a mutation matrix, and the Python script was uploaded to GitHub.
Genotypic drug resistance detection and identification of transmission clusters Drug-resistance-associated mutations in M. tb were obtained from the database TBDReaMDB and articles reporting rifampicin-resistance mutations (Supplementary Table  2 ). M. tb isolates carrying any characterized rifampicin-resistance mutation were identified as rifampicin-resistant strains, whereas those isolates with additional isoniazid-resistance mutations were determined as MDR-TB strains. To determine the drugresistance spectra of the MDR-TB isolates in the Global dataset, we additionally identified drug-resistanceassociated mutations for nine drugs (amikacin, capreomycin, ethambutol, ethionamide, fluoroquinolones, isoniazid, kanamycin, para-aminosalicylic acid, pyrazinamide, streptomycin) by inspection of their wholegenome sequences.
Any isolates with genetic distances of less than 12 SNPs were classified into a cluster 10. To compare the collection time distributions between the isolates in the clustered and nonclustered groups, we determined the collection time of each isolate as "days to the end of the study duration (31 December 2012)". For example, if an isolate was collected on 1 January 2011, then its collection would be counted as "365 (days in 2010) + 366 (days in 2012) = 731 days". A Python script was written for screening transmission clusters in both the Shanghai and Global datasets, and this Python script was uploaded to GitHub.
Acknowledgements We are grateful to  Dr. Ted Cohen  for the helpful suggestions and discussion of this work. This study was funded by the  National Science and Technology Major Project of China  ( 2017ZX10201302  and  2018ZX10715012  to Q. G. ),  National Natural Science Foundation of China  ( 91631301 & 81661128043  to Q. G. ,  81701975  to Q. L. , and  81402727  to C. Y. ),  NSFC-BRICS  grant ( 81761148027 ),  Sanming project of Medicine in Shenzhen  ( SZSM201611030 ) and  JSGG20170413142559220.
Finding participants for secondary prevention of Alzheimer's Disease Finding participants for Alzheimer's Disease (AD) trials is challenging  [1]. This is particularly the case for studies with prodromal or preclinical AD participants, because these persons may not seek care for their problems and are unaware of the presence of amyloid pathology. An increasing number of AD trials aims to delay the onset of dementia in prodromal and preclinical AD. Traditional ad-hoc recruitment strategies, such as advertising in newspapers, result in a costly, labour-intensive and long recruitment process with many screen failures. Novel pre-selection and patient recruitment strategies are warranted. Online registries or the use of existing data sources may help to facilitate recruitment  [2]. EPAD Registry makes use of existing data sources for recruitment in a virtual registry in order to speed up recruitment , reduce recruitment efforts, and reduce screen failures.
The EPAD Registry preselects participants from ongoing cohort studies and uses data from these cohorts for prescreening. The EPAD Registry involves several steps (figure  1 ). First the collaboration with an ongoing cohort study representative is established. Second a minimal dataset from the ongoing cohort study is created that can be queried in a software tool, called PREPAD. Potential participants are identified and invited for the EPAD-LCS by the EPAD study team. The results of these efforts are monitored.
Selection of and engagement with ongoing cohort studies Cohorts that are selected for the EPAD Registry fulfil the following criteria: they are willing to provide participants for the EPAD-LCS, include participants without dementia over the age of 50, collected data suitable for prescreening, and have consent to contact their participants about the EPAD-LCS. In return the ongoing cohort studies will receive the data collected within the EPAD project for their participants. When interest is expressed by a cohort representative, cohort characteristics are collected online either in the European Medical Information Framework (EMIF) or Dementia Platform UK (DPUK) catalogues  [4, 5]. Legal contracts are developed to cover interactions between cohorts and EPAD. These contracts cover the use of the virtual registry for EPAD purposes and receiving the EPAD data of their own participants.
Minimal dataset and PREPAD query platform Each cohort is asked to provide a minimal dataset of variables that can be used to preselect participants with an increased risk for AD. The variables comprise age, gender, education, apolipoprotein E ε4 (APOE ε4) genotype, family history of dementia, diagnosis of cognitive disorder, CSF biomarkers, MRI hippocampal atrophy, memory test scores, and baseline and longitudinal minimal mental state examination (MMSE) scores. At least 4 of the above variables are required. The minimal dataset of each cohort is harmonized. Cohort specific harmonisation rules are run with every update. Cohort representatives were supported by a small EPAD Registry workgroup consisting of software developers and AD-researchers. AD-researchers used mock files to define harmonisation rules and shared those with the developers supporting the harmonisation.
The minimal dataset is uploaded on a regular basis to the PREPAD software tool. PREPAD was developed to search these minimal datasets. It was adapted from an existing data discovery platform to allow for querying federated datasets and allow complex queries  [6]. To ensure participants remain anonymous in the EPAD Registry, a software called 'Deridiom' was created that generates 'Derivative IDs' (derIDs). Deridiom converts cohort local identification numbers to derIDs. Searching participants with PREPAD involves a number of steps. First an algorithm is defined that aims to identify participants, according to the needs of the EPAD-LCS and EPAD-PoC. The algorithm is tailored for each cohort allowing selection of participants in different risk-stages for AD. An algorithm can for example be a decreased memory score and age over 65.
Recruitment for EPAD-LCS via EPAD Registry As of the first of June 2017, 2433 participants of the EPAD Registry were pre-selected, of whom 75% were invited for EPAD-LCS screening by the team of the collaborating cohorts. The main reason for not contacting a participant was a known exclusion criterion. Most cohorts chose to contact participants by phone. Thus far around 15% of the subjects selected were suitable and agreed to undergo EPAD-LCS screening. This percentage may increase as more of the participants in the EPAD Registry will be considered for EPAD-LCS screening. Reasons of potential participants not entering EPAD-LCS screening are variable and partly dependent on collaborating cohort type. We will monitor uptake prospectively and report on this in detail once we have sufficient data available.
DISCUSSION The EPAD Registry provides a novel recruitment strategy. It is sufficiently flexible as we can adapt the screening algorithms to the type of data collected in a cohort and the type of participants needed for future EPAD-PoCs. The collaborating cohorts have different levels of information and draw on different populations. We chose not to define strict criteria for collaboration, but instead to use the data that are available within the cohorts. This approach leads to collaboration with more cohorts than would have been possible otherwise. However, a limitation is that risk estimates vary over cohorts. The efficacy of the approach will be monitored and reported on in the future. This includes the recruitment rate and the characteristics of population recruited, which can then be compared to other strategies.
It may well be that this approach is particularly effective for specific populations of volunteers of patients. The adaptation of existing software led to a fast implementation. Plans for further development of the EPAD Registry functions entail extending PREPAD with other risk factors and inclusion of information on exclusion criteria. We also intend further automatization of the harmonization process. Our approach for recruitment and prescreening differs from those used in other studies and trials aimed at preventing disease progression in preclinical AD. Examples are the Early trial with a BACEinhibitor from Janssen and another BACE inhibitor trial, the A4 study, that stepwise screen individuals from over 65 years old found via advertisements or a website. Additionally for the Early trial, persons between 60 and 64 that have an additional risk factor, being a positive family history for AD or being an APOE ε4 gene carrier can be screened  [14, 15].
FUNDING The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  n°115736 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. Generation Scotland received core support from the  Chief Scientist Office of the Scottish Government Health Directorates  [ CZD/16/6 ] and the  Scottish Funding Council  [ HR03006 ]. Genotyping was carried out by the  Genetics Core Laboratory  at the  Wellcome Trust Clinical Research Facility, Edinburgh , Scotland and was funded by the  Medical Research Council UK  and the  Wellcome Trust  (Reference 104036/Z/ 14/Z ).
This method is being extended to simulations of the CGM, with increased resolution to capture the impact of outflows on accreting structure (see Sec. 4. 4). Improved numerical techniques such as those mentioned above have also meant a slight division in the field of line modeling, between groups would who specialize in creating the photoionization codes and groups that apply these codes to hydrodynamical simulations. The photoiononization codes have evolved to also calculate line emission and are kept updated as experimental values for collision strengths, chemical rate coefficients and photoelectric heating rates are being revised and databases of atomic and molecular line data increase in size [e. g. [25] [26] [27] [28] [29] [30] [31] [32]. Similar to the case of PDRs, significant progress has been achieved over the last few years in modelling nebular emission from ionized regions around young star clusters, AGN and post-AGN stellar populations in a full cosmological context.