dataesr/scientific-paragraphs-categorization huggingface dataset:
Lastly, we used a long-term comparison period going back to 1958, which is the start of the consistent CPR time-series. This long-term period represents a multi-decadal perspective including the time periods before and after the 1980s regime shift. The assessment period of 2013-2017 was assessed at these three temporal scales using each indicator metric. For the Plankton Index metric, the period 2013-2017 was used to create the assessment period domain, and then data between  2004 and 2012, 1990 and 2012, and 1958 and 2012 , respectively, were overlaid and a Plankton Index value calculated. This analysis therefore covered both different discreet periods of time within the comparison period, and also different lengths of comparison period. For the LCBD metric, three time-series were created,  2004-2017, 1990-2017, and 1958-2017.
Tumor volume was calculated according to: V ¼ (p/ 6)Â(lÂwÂt) where l is the length, w the width, and t the thickness of the tumor as delimited on the B-mode images. The tumor size at day 0 was used for the initial randomization of mice into the 4 therapy groups. The DCE-US data sequences were acquired in Contrast Harmonic Imaging mode after retro-orbital injection of a 100 mL bolus of Luminity contrast agent (Luminity; Bristol-Myers Squibb, New York City, NY). The DCE-US data sequences were recorded in Raw Data-formatted files. Imaging data were first acquired 15 days after tumor cell injection which, with respect to treatment administration, is day 0 (prior to the first treatments). Imaging was then repeated on days 1, 2, 7, and 9 with respect to the treatment administration.
Statistical Analyses Statistical significance of differences in the AUC day to day was estimated using the Wilcoxon signed rank test. Comparison of the AUC was made on stable groups of mice (mice euthanized during the period of a comparison were excluded from the data included in the statistical analysis). Progression-free survival (PFS) was assessed from the initiation of treatment. Progression was defined as a maximum tumor dimension increase > 20% (RECIST 1. 1) or an AUC increase >10% (functional criteria). Overall survival (OS) was assessed based on the time from the initiation of treatment to the time of necessity to administer ethical end-point euthanasia. Survival was followed from days 15 to 29 with respect to tumor implantation. Significance of the differences in survival between treatment groups was assessed using the log-rank test.
that help predict the impact of variations on the structure, function and stability of the specific protein expressed by the gene associated with the disease. None of these central databases contain all the tools required for full clinical interpretation of variants. Second by contrast, gene variant databases, also known as locus-specific databases (LSDBs), that collect and display information on sequence variants (mutations) on a gene-by-gene basis are usually run by a consortium of collaborating researchers with scientific expertise in a particular gene or phenotype. LSDBs provide an invaluable tool for analysing variants, generally promoting submission of data and therefore enabling an accurate and up-to-date resource. Complete and up-to-date variant databases save valuable user time and stimulate effective diagnosis and research by providing access to curated standardized and annotated data, which would otherwise be scattered over many different resources.
Importantly, the curators check the data entered into the database, which increases data quality, as there are a significant number of reporting errors in published articles. In addition, LSDBs provide access to variant information that has been collected by direct database submission and would not otherwise be available as it becomes increasingly difficult to publish variant data. With the advent of next-generation sequencing of ever-increasing numbers of individuals and therefore increased identification of variants within genes, access to curated data incorporating structural, functional, evolutionary as well as phenotypic data becomes increasingly important in order to ascribe pathogenicity. Locus-specific databases for haemostatic genes were first established with the publication of collated data for variants in F9, 1 F8 2 and VWF  3  in the early 1990s.
The first web-based database for F8 appeared in 1996. 4 Over the years, several additional coagulation factor variant databases have also been developed (F7, 5 F10, F11  6  and fibrinogen [FGA, FGB, FGG]  7  ). Some allowed database searching and provided a number of additional resources, whereas others offered data in a downloadable Excel spreadsheet. These resources provided an invaluable resource to the haemostasis community and were highly cited. However, many of these databases were established and maintained by individuals and availability of time and funding had a significant impact on their ability to maintain the database as well as continuity of access to a stable URL. An LSDB loses its relevance once it is not maintained and updated on a regular basis. In response to these concerns, the European Association for.
Haemophilia and Allied Disorders (EAHAD) initiated a Coagulation Factor Variant Database Project with the aim of gathering together single gene variant databases involved in clinical bleeding disorders that would provide a single web portal to LSDBs for genes in haemostasis. The LSDBs would share a common architecture making navigation of the database(s) easier as well as providing greater support for maintenance of the sites. The availability of an up-to-date open access variant database is a crucial tool for diagnostic and research laboratories; therefore, there is free and unrestricted access to all the online EAHAD variant databases. However, in order to honour the legal requirements of medical/health information and data protection ensuring that no individuals can be identified from the data stored and distributed in the databases, no patient IDs or the geographical location of the submitting laboratory are publicly available (unless they are already in the public domain through published articles).
9 All variants described in the EAHAD databases therefore conform to HGVS guidelines and are reported in relation to reference sequences (RefSeq) maintained by the American National Centre for Biotechnology Information (NCBI). Variants are described using cDNA and protein reference sequences; however in future releases, variants will also be described using a locus genomic reference (LRG) sequence. This description is currently available from the Leiden Open Variation Database (LOVD; lovd. nl) to which we mirror all variants described in the EAHAD database. Sequence variants differing from the reference sequence are denoted by a prefix, c. for cDNA and p. for amino acid variants. The A of the ATG initiator methionine is utilized as the sequence start point +1 and similarly the first methionine is numbered +1.
This differs from some legacy numbering of haemostasis proteins where the signal peptide (and sometimes pro-peptide) was numbered negatively and amino acid numbering started from the beginning of the mature protein. Both HGVS and 'Legacy' numbering are provided for the F7, F8 and F9 databases. The CoagBase web page (coagb ase. org) provides information for coagulation factor genes including gene names, RefSeq as well as an indication of the difference between legacy and HGVS amino acid numbering schemes.
| DATABA S E S TRUC TURE The databases (Table  1 ) are built on a common architecture using a MySQL platform, and their interface was developed using HTML, CSS, JavaScript, Perl and PHP. They are available at dbs. eahad. org. In order to encourage the ethos of data sharing and open access to resources, the variant data in the databases are shared with LOVD (lovd. nl 10 ), which is a freely available gene-centred collection of DNA variant data. The databases allow simple searches of variants based on nucleotide or amino acid numbering (HGVS and legacy), variant type or location within the gene. In addition, advanced search options allow further refinement of searches based on variant effect, protein domain, severity and inhibitor status (where known). A search returns the unique variants at the selected position in the gene or protein as a list with links where appropriate to in-depth analysis of the variation and anonymized patient information (Figure  1 ).
Unlike other variant DBs, the EAHAD databases store and display all cases reported regardless of how many individual reports of the identical genetic variant have been reported, allowing users to survey phenotypic variability among dozens or even hundreds of cases with the same variant. It is possible to visualize/export the data either as list of unique variants at the selected position, or as a multiple patient list showing all individuals with a particular variant at that position, allowing between-case comparisons to assess variability of presentation. This also allows the assessment of the frequency of reporting a particular variant; however, it should be noted that this frequency may be biased by extensive family studies, founder effects in certain ethnic groups and does not represent the frequency within the affected population.
In addition, the database presents statistics and graphics on all the variants in the database by specific type of variant, by protein domain and by disease severity (Figure  2 ). There is also a variant map of all missense variants mapped onto the amino acid sequence of the protein. Predicting whether or not a variant is pathogenic may not be straightforward, especially for synonymous and missense variants or for those occurring in intronic sequences or promoter regions of the gene. The databases provide access to in silico analyses that may be useful in the prediction of variant pathogenicity of missense variants using open access software packages (Table  2 ). The minor allele frequency (MAF) of variants in large population studies such as the Genome Aggregation Database (gnomAD; gnomad.
The databases do not currently assign pathogenicity classification for individual variants but in future will link directly to ClinVar (ncbi. nlm. nih. gov/clinvar) which is an open access database that reports curated information and likely pathogenicity on gene variants. EAHAD curators are working as co-chairs or members of relevant curation panels that input into ClinVar. Evaluation of variant frequencies from large population data sets can provide strong evidence that a variant is benign or can identify one that is sufficiently rare to be considered a candidate pathogenic variant. The EAHAD variant databases link to the MAF of a variant in gnomAD database (based on exome and genomes of 141 456 individuals  11  ). MAF values >0. 05 in a control population may be considered stand-alone support for a benign interpretation.
| Future developments We support the ethos of open access and data sharing, and we encourage researchers and clinicians to contribute to the project by submitting their variant data to the relevant databases via the submission forms on the EAHAD Coagulation Factor Variant Project home page (dbs. eahad. org). We have made all reasonable efforts to ensure that the information contained in the databases and displayed on the web pages is accurate; however, the database may contain errors and we would be grateful if users could inform us of any that they find. The development of a common database architecture will allow other coagulation factor variant databases to be developed. Databases for F10 and F11 are planned as well as FGA, FGB and FGG, responsible for FX, FXI and fibrinogen deficiency, respectively.
| CON CLUS ION The analysis of genotype-phenotype relationships in rare diseases such as the bleeding disorders associated with deficiencies in FVII, FVIII, FIX and VWF requires international collaboration and standardized reporting. The establishment of the EAHAD Coagulation Factor Variant Database Project has generated a common database architecture and web-based platform that includes analytical tools to assess the structure/function consequence of variants. This has allowed the implementation of updated variant databases for F7, F8, F9 and VWF. The variant databases will be an invaluable resource for scientists and clinicians in the field of haemostasis.
The EAHAD Variant Database Project was established in 2011 and is administered by a Steering Group, a Curator Group and individual Variant Database Advisory Committees; further details of the composition of these groups can be found on the EAHAD Coagulation Factor Variant Database Project home page (dbs. eahad. org). Each individual variant database has a lead curator and a curator who are supported by further experts in the field. One of the important roles of the curators is to ensure that variants are correctly described with the correct nomenclature relative to the correct reference sequence. An increasingly important task of the curator(s) is also to suggest where possible the potential functional consequences of a variant. Current EAHAD curators are working as co-chairs or members of relevant ClinGen Variant Curation Expert Panels (clini calge nome.
Mutation Database (hgmd. cf. ac. uk 12  ), literature searches and direct submission by reporting laboratories. All variants incorporated into the new databases were verified for accuracy, and HGVS nomenclature was generated and checked with Mutalyzer (mutal yzer. nl 13 ). Each variant was linked to the original publication describing the variant(s). Finally, any duplicated cases were removed. Identifying duplicate accounts of the same patient can be problematic as multiple published accounts of the same cases can occur and there may be no explicit reference to previous publications and/or the use of a unique laboratory patient identifier. If there was any doubt over duplication of reporting an individual, then both records were maintained in the database. Future updates to the databases will be made F I G U R E 1 Screenshot of the in-depth variant analysis following a search of the Factor VIII Gene Variant Database for variants in codon 415 (legacy 396) showing the predicted consequence of variant c.
1244C>T (p. Ala415Val) using in silico prediction programmes, structural implications and location of the variant within the FVIII structure and finally multiple amino acid sequence alignments across primates. Further multiple sequence alignments are available from the AA Alignments tab at the top of the page. The case information for this variant is also shown demonstrating the range of laboratory data and ascribed bleeding severity in eight reported individuals F I G U R E 2 Screenshot of the database statistics available on the Factor VIII Gene Variant Database f8-db. eahad. org/statistics. html. php illustrating the percentage of: variants by type; variants within specific protein domains; and cases by disease severity. Similar information is available on the factor IX and VII websites on a yearly basis by continued literature searches.
Direct submission of variant cases to EAHAD database curators is highly encouraged, and electronic submission forms are available on the EAHAD Variant Database Project home page (dbs. eahad. org). All new submissions are validated by the expert curator(s) before they are added to the relevant database. 6 | UTILIT Y OF E AHAD DATABA S E SIt is recognized that the availability of standardized, validated DNA variant and phenotypic data is a crucial tool for diagnostic and research laboratories in the accurate classification of disease severity, and in order to make assessments of likely pathogenicity. The establishment of the EAHAD Coagulation Factor Variant Database Project has enabled the development of a single platform for genespecific variant databases for the rare bleeding disorders. It has allowed the development of three new online databases for variants in the genes encoding FVII, FVIII and FIX and links with the existing database for variants in the gene for VWF.
One of the important advances has been the conversion of legacy descriptions of variant data to a standardized nomenclature as recommended by the HGVS and the use of reference sequences to allow unambiguous mapping of the positions of variants. The new databases have been updated substantially with new variants. The databases for F7 (f7-db. eahad. org), F8 (f8-db. eahad. org) and F9 (f9-db. eahad. org) currently have 221, 2015 and 2591 unique variants in the respective genes corresponding to 728, 8424 and 3713 individual cases (accessed 18 July 2019).
In silico tools used to predict the consequence of missense variants MAF = 0. 1632) that are not pathogenic in themselves but are associated with substantially reduced levels of FVII and therefore when co-inherited with other variants can have a significant impact on the overall phenotype. The databases do not contain detailed clinical information but provide information on the ascribed severity of bleeding based on the submitter's observations when available. Where available, laboratory data from individual cases have been included. There are a number of validated assays that measure functional activity or quantify plasma concentrations of coagulation factors. This type of data straddles the boundary between clinical phenotype and in vitro functional data but must be interpreted in the context of the pathophysiological levels associated with particular diseases.
For example, in the case of FVII activity it is essential to use only data obtained using human tissue factor (TF; thromboplastin) as the trigger for coagulation. The use of TF from animal sources or recombinant versions thereof may result in discrepant and misleading in vitro results that are inconsistent with assays performed with human TF and the bleeding phenotype of the individual concerned. 16 Thus, the species of TF (unknown, rabbit or human) used to generate the FVII activity values (FVII:C) presented in the F7 variant database are indicated where known. Similarly, it is well recognized that certain missense variants in F8 are associated with 'assay discrepancy' such that the values obtained with two-stage or chromogenic assays are substantially lower than the result with the one-stage assay.
The opposite effect with a lower one-stage result is much less common but may also occur. This is of clinical significance as reliance on one assay might lead to a diagnosis of mild haemophilia A being missed. Variants known to be associated with 'assay discrepancy' are labelled as such in the database and on clinical reports. Furthermore, by collecting these variants together in the database insights into their localization in the FVIII tertiary structure have been gained which allows prediction of whether a novel variant might have a similar effect.
TA B L E 2 2 In silico tools used to predict the consequence of missense variants MAF = 0. 1632) that are not pathogenic in themselves but are associated with substantially reduced levels of FVII and therefore when co-inherited with other variants can have a significant impact on the overall phenotype. The databases do not contain detailed clinical information but provide information on the ascribed severity of bleeding based on the submitter's observations when available. Where available, laboratory data from individual cases have been included. There are a number of validated assays that measure functional activity or quantify plasma concentrations of coagulation factors. This type of data straddles the boundary between clinical phenotype and in vitro functional data but must be interpreted in the context of the pathophysiological levels associated with particular diseases.
Molecular Dynamics Simulations Simulations of Hemoglobin (Hb): The Hb-simulations (for the sequence see Figure  S1 ) were described previously,  4  and only the necessary points without technical details are reported here. The molecular dynamics trajectories were run in cubic water boxes with box lengths 90 Å, 120 Å, and 150 Å which are analyzed in the following. Each simulation was run for 1 µs or longer and for selected box sizes, additional repeat simulations were carried out. 6 The trajectories analyzed in the present work are those from Ref. 4  and the reader is referred to that manuscript for additional details on the production runs. Simulations of Melittin: MD simulations of the melittin dimer were carried out using CHARMM 11 c45a1 and the CHARMM36 force field. 12 The TIP3P water model was used, the same as that used for Hb.
The dimer structure (PDB:2MLT)  13  was used as the starting structure. It was solvated in a cubic water box of length 51. 051 Å (4066 water molecules) In addition, simulations with a box length of 60 Å were performed to assess whether, in analogy to Hb, there were effects of increased solvent box sizes on the stability, dynamics and water structuring of melittin dimer. A 16 Å cut-off was applied with a Particle Mesh Ewald scheme  14  and a 1 fs time step was used in the MD simulations. Although more physically realistic fixed point charge water models exist (e. g. TIP4P), the use of TIP3P here is mandatory for consistency because the CHARMM force field was parametrized with it. It is certainly of interest to include water polarization in corresponding simulations,  15  but such a study is beyond the scope of the present work.
The number of vertices can be determined in both data and simulation. Further details on the relationship between µ and N vertices are provided in Section 5. 3. The studies presented in this paper focus on the PU conditions in 2016, though the trends towards higher PU scenarios with up to 70 simultaneous interactions are explored as well. The trigger paths used for the data taking are mentioned in each section. Samples of simulated events are used to evaluate the performance of the PU mitigation techniques discussed in this paper. The simulation of standard model events composed uniquely of jets produced through the strong interaction, referred to as quantum chromodynamics (QCD) multijet events, is performed with PYTHIA v8. 212  [13]  in standalone mode using the Lund string fragmentation model  [14, 15]  for jets.
The PU data sample is recorded with a zero-bias trigger that randomly selects a fraction of the collision events, corresponding to an integrated luminosity of 3. 18 nb -1. The distribution of the number of PU interactions in both subsets of data is comparable to the one in the whole data sample collected in 2016. Figure  2  shows the distribution of the three main variables used in PUPPI for data and simulation. The upper left plot presents the distribution of α for charged particles from the LV and the PU vertices and for neutral particles with |η| < 2. 5 in the jet sample. The separation power of the variable α between particles from the LV and PU vertices for charged particles can be deduced from this figure. The majority of the charged particles from PU vertices have an α value below 8, whereas only a small fraction of particles have higher values.
Both techniques suppress both QCD-like and stochastic PU jets, though the observables used for neutral particle rejection are primarily sensitive to stochastic PU jets. The performance of the PU jet rejection for both PUPPI and CHS is evaluated in Z+jets events in data and simulation. The jet recoiling against the Z boson provides a pure sample of LV jets, whereas additional jets are often from PU collisions. The Z+jets events are selected by requiring two oppositely charged muons with p T > 20 GeV and |η| < 2. 4 whose combined invariant mass is between 70 and 110 GeV. Jets that overlap with leptons within ∆R(lepton, jet) < 0. 4 from the Z boson decay are removed from the collections of particle-and reconstruction-level jets. In simulation jets are categorized into four groups based on the separation from particle-level jets and their constituents.