dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We derived probability distributions for the number of extant species in the past. Given the phylogeny of a group, these expressions provide estimates of how the species richness of this group varied through time and a confidence interval around these estimates. We implemented these expressions in the R-package RPANDA  (Morlon et al. , 2016) , which should help evolutionary biologists derive diversity curves for groups of interest. We provided (and implemented) two expressions, the first one corresponding to the case when there is a priori knowledge of the total number of extant species in the clade, and the second one corresponding to the case when there is a priori knowledge of the probability that an extant species is represented in the phylogeny. In practice, current likelihood models of diversification require providing a sampling faction, which is computed by dividing the number of species represented in the phylogeny by the total number of species known for the group.
This implies that species diversity in clades will rise and fall over time, particularly if a similar group of organisms (e. g. members of that clade) does adapt and outcompetes the species that do not change. Whether this happened for Archaeobatrachia is unclear. Biogeographic analyses show that Neobatrachia most likely originated during the splitting of Pangaea in two, with most archaeobatrachian lineages staying on Laurasia, and Neobatrachia originating on Gondwana  (Feng et al. , 2017; Roelants and Bossuyt, 2005). Subsequent neobatrachian colonization of North America and Eurasia likely happened much later than our inferred declined of Archaeobatrachia (Fig. 6 ;  Feng et al. (2017) ). Moreover, our analyses show that the decline of Archaeobatrachia is associated with a failure to speciate rather than with increased extinction rates, a pattern that has previously been observed in mammals but whose causes are not well understood  (Quental and Marshall, 2013).
Duration of protection after AL and AS-AQ treatment in different trial sites We analyzed 4214 individual participant data from randomized clinical trials in 12 sites obtained from the WorldWide Antimalarial Resistance Network (WWARN) data platform  (25)  with the consent of investigators or sponsors. The median age in the study population was 2. 8 years (IQR 1. 5-4. 2). Patients were followed up until at least day 28 and assessed for the presence of reinfection, using PCR to distinguish reinfecting parasites from recrudescence of the original infection. The time to reinfection in these trials is only in part determined by the duration of protection conferred by the drug. This is because individuals do not immediately become reinfected after the protection ends, but rather enter an "at-risk" state.
Factors affecting the duration of prophylaxis To investigate which factors affect the duration of prophylaxis after AS-AQ and AL treatment and might explain the heterogeneity between trial sites, the data were further analyzed by accelerated failure time regression models. As expected, EIR (as estimated for each site by the HSMM analysis) was strongly associated with time to reinfection (Table  2 ). We therefore adjusted for EIR before testing the effect of any additional variables. Treatment arm had a small and significant effect on time to reinfection overall, with AS-AQ being associated with a 1. 09-fold increase in time to reinfection (95% CI 1. 05-1. 13) compared to AL, after adjusting for log EIR. We explored the effect of molecular markers associated with parasite sensitivity to AL and AS-AQ.
0 days is in good agreement with our previous estimate of 13. 8 days which was obtained from analysis of a completely different dataset of clinical trials in six sites in Africa (4) (although the impact of 86Y and 76T was not previously investigated). In the current analysis we found a more rapid decline of protection over time after AL treatment than AS-AQ (Figure  1 ), and a similar rapid decline after AL was seen in our previous analysis. The resolution of data informing this profile of post-treatment prophylaxis is not perfect, with most patients observed only weekly after day 7. In 4 of the trial sites in the current analysis, no tests for reinfection were done until day 14  (23). Nevertheless given the very low proportion of individuals reinfected at earlier times in the other sites it is unlikely that many reinfections were missed.
Data WWARN invited investigators to contribute individual-level patient data for this meta-analysis (79) if their studies fulfilled the following criteria: randomized controlled trials of uncomplicated P. falciparum malaria; AS-AQ and AL being compared; follow-up to at least day 28, with at least one follow-up visit at day 14 and another before day 28; 100 or more participants per study site or more than 28 days follow-up; polymerase chain reaction (PCR)-adjusted efficacy available; at least 95% PCR-adjusted treatment efficacy in both study arms; PCR-unadjusted cure rates of <95% in at least one trial arm by day 28 (to indicate sufficient number of reinfections to inform analysis on post-treatment prophylaxis); standard dose regimens of AL and AS-AQ (we included studies regardless whether AS-AQ was given as a fixed-dose combination or not); and known dosage taken for each patient.
Individual patient data from eligible studies were shared, collated and standardized using previously described methodology  (80). For the present analyses, we used data on PCR-confirmed reinfections as well as the proportion of patients who were not reinfected during follow up, to estimate the duration of chemoprophylaxis. Time of reinfection is included in the analysis so that different follow up times between studies are accounted for (see also below). Patients who experienced PCR-confirmed recrudescence were excluded. In two studies (in Tororo, Uganda & Sikasso, Mali, see Table  1 ) the patients were followed up longitudinally across several episodes, and consequently treated multiple times within short intervals. We only used the first treatment episode and follow-up data collected before the next episode from these studies in order to avoid confounding of our results by residual drug levels from a previous treatment.
For sensitivity analysis, we also tried assuming additional age-independent variation in exposure to mosquito bites, with the distribution of relative biting rates across people following a log-normal distribution. We used informative priors on the lognormal distribution of bites of mean=1 and variance=1. 76 as previously estimated  (66). A number of HSMM variants were fitted via MCMC (Markov-Chain Monte Carlo), using the JAGS ("Just Another Gibbs Sampler") software for Bayesian inference in conjunction with the "rjags" package using R statistical software  (83). The likelihood calculation took into account the interval-and right-censoring of observations in the data. EIR values εadult for each site were estimated simultaneously with the other parameters, with moderately informative gamma priors with median as predicted by MAP (27) (Table  1 ) and a shape parameter of 1.
Accelerated failure time models: In order to identify which factors influence the time until a reinfection is detected, we used accelerated failure time models, as implemented in the "survival" package in R (84 were assumed for continuous variables. We tested for interactions between AL or AS-AQ treatment, prevalence of the pfmdr1 86Y mutant versus N86 wild type parasites and pfcrt 76T mutant versus K76 wild type parasites, since there is evidence of differential effects of each drug on these parasite genotypes  (9, 11). We tested for an effect of different formulations of AS-AQ, i. e. fixed dose combination (from Sanofi), blister pack or loose dose (see also Table  1  for dose information). For AL, all included studies used the same fixed dose combination from Novartis. We calculated weight-for-age Z scores for patients under 5 years old according to the WHO age and gender specific reference values, using the WHO Anthro software in R  (85).
Software and data availability Analysis code in R and the transmission model executable file are fully available online at https://github. com/lucyokell/duration_protection_AL_ASAQ, as are the data underlying the figures: (Zenodo data repository DOI 10. 5281/zenodo. 3339215). The source code for the transmission model in C++ will be made available on Github prior to any full manuscript submission. The original individual level clinical trial data is available upon request from WWARN (https://www. wwarn. org/accessing-data). Requests must be approved by the data contributor and the WWARN Malaria Data Access Committee.
Acknowledgements We thank  H Bukirwa ,  C Nabasumba ,  B Schramm  and  Sanofi  for providing data from the original clinical trials. We thank  Christian Nsanzabana  at the  Swiss Tropical and Public Health Institute  for technical support, and  Francois Bompart ,  Valerie Lameyre  and  Muriel Mannechez  at  Sanofi  for providing comments on the manuscript. This work was supported by  Medicines for Malaria Venture. LCO also acknowledges funding from a  UK Royal Society Dorothy Hodgkin fellowship , the  Bill & Melinda Gates Foundation , a joint fellowship from the  UK Medical Research Council (MRC)  and the  UK Department for International Development (DFID)  under the  MRC/DFID  Concordat agreement, and joint Centre funding from the  UK Medical Research Council  and  DFID  ( MR/R015600/1 ).
Acknowledgements This study is a contribution to project Nr. 200021_169627  funded by the  Swiss National Science Foundation. Rosemarie Roth  (Zürich) kindly helped with photography of the holotype. We acknowledge the  SOLEIL Synchrotron  for provision of synchrotron radiation facilities and we would like to thank the  DiffAbs beamline team and Clément Jauvion (MNHN, Paris)  for assistance during the experiments. We greatly appreciate the technical help provided by the photographers  Mariah Slovacek  and  Steve Thurston  ( American Museum of Natural History, New York ). We thank  Jessica Utrop  ( Yale University, New Haven, Connecticut ) who examined the specimen using RTI and  Carmella Cuomo  ( University of New Haven, Connecticut ) who helped in the interpretation of the results. Kallie Moore  ( University of Montana Paleontology Center Research Collection Missoula, Montana ) and  Albert Kollar  ( Carnegie Museum of Natural History, Pittsburgh, Pennsylvania ) kindly put specimens at our disposal.
Data availability All data generated or analyzed during this study are included in this published article (and its supplementary information files). The specimen is kept in the American Museum of Natural History in New York, with the numbers AMNH 43264 (part) and AMNH 50267 (counterpart). Reflectance Transformation Images, *. rti and *. ptm of the holotype AMNH 43264 are provided as Supplementary Data. To view these images and change the illumination, the required software RTIViewer is available at http://culturalheritageimaging. org/What_We_Offer/Downloads/View/. These RTI and PTM files have been deposited in Figshare  72, 73.
In PubAnnotation, document-level information is represented as attributes of the document itself. INCEpTION models document-level metadata using subtypes of "AnnotationBase" (a built-in UIMA type representing an annotation anchored on an object without specifying what type of object [text, video, audio, etc. ] it is. The type "Annotation" used for annotations on text is a subtype of "An-notationBase"). PubAnnotation does not support complex attributes with nested features (e. g. , "first name" and "last name" components in an "author" attribute). Structured attributes are modeled in UIMA as subtypes of "TOP" (The root of the built-in UIMA type hierarchy, custom subtypes of which are typically used to model data structures which are not annotations) and can be converted to PubAnnotation format using an attribute naming conventions.
PubAnnotaton ↔ LAPPS Grid Data interoperability between PubAnnotation and the LAPPS Grid faces many of the same challenges as interoperability between PubAnnotation and INCEpTION; therefore, many of the solutions for INCEpTION ↔ PubAnnotation conversion can be used for the LAPPS Grid as well. As noted before, PubAnnotation's annotation model is schemaless or may optionally rely on an external resource, e. g. , a RDF schema or OWL ontology. While the LAPPS Grid recommends the use of the annotation types defined in the WSEV, this is not a strict requirement, and any string or URI can be used to specify the annotation type. This flexibility allows for a trivial "syntactic" mapping between the PubAnnotation JSON format and LIF (This is also true for mapping to a UIMA schema, assuming that the type names follow Java naming conventions).
However, the semantics of PubAnnotation annotations (i. e. , are they POS tags, named entities, etc. ) are usually not specified; therefore, it is in many cases necessary to apply LAPPS Grid tools (annotation/feature renamers) to annotations imported from PubAnnotation in order to massage data so that it is accessible by other tools. To import annotations from the LAPPS Grid to PubAnnotation, type information must be retained, as discussed above in "PubAnnotaton ↔ INCEpTION" for the case of importing annotations from INCEpTION. For LAPPS Grid annotations that reference types in the WSEV via a URI, the URI can be used as the value of obj in order to preserve the link between the LAPPS Grid annotation and its definition in the schema. For LAPPS Grid annotations that do not reference types in the WSEV, we can apply the same solution as previously discussed for INCEpTION, that is, LAPPS Grid type information can be preserved in PubAnnotation using the "obj" element.
To accommodate this difference, annotations from all LIF views in an annotation document are collapsed into a single collection of annotations (denotations/relations/attributes) in PubAnnotation, retaining information about dependencies between LIF views in atrributes. In the reverse direction, annotations from each project in a PubAnnotation collection can be safely placed into its own view in LIF. Like INCEpTION, LIF allows complex types including nested features; here we apply the solution implemented for PubAnnotation to INCEpTION conversion, that is, using a set naming convention when generating PubAnnotation attribute names from LIF features. In summary, there are two main issues that must be addressed to achieve PubAnnotation/LAPPS Grid interoperability. The first is a requirement that PubAnnotation allow for retention of LIF metadata so that this information is not lost in a round trip transaction between the two platforms.
On the INCEpTION side, there is presently no concept of grouping annotations by provenance. Thus, when ingesting LAPPS Grid data to INCEpTION, the information encoded in the LIF views identifying the producer (software that produced the annotations) is currently dropped; to enable round-tripping between the LAPPS Grid and INCEpTION, some means to preserve this information in the INCEpTION representation must be implemented. Similarly, INCEpTION does not allow multiple annotations of the same type, and so only the latest LIF view containing a particular annotation type is included. Again, means to represent the information from multiple LIF views involving the same annotation type in IN-CEpTION would demand creating a special mechanism to accommodate this information, if a round trip between the two platforms is desired.
PubAnnotation recommends that a project should contain only one type of annotation (e. g. , "NamedEntity") or a set of closely related annotation types (e. g. , "POS" tags and "Dependency" relations), e. g. , to avoid visual clutter when viewing the annotations. Depending on the needs of the user, INCEpTION projects may contain many annotation types. When viewing annotations, the user may choose in INCEpTION which types to display and which to hide. For the sake of simplicity, we consider that one INCEpTION project is exported as one PubAnnotation project and do not attempt to split INCEpTION projects up into multiple PubAnnotation projects, even if that means that viewing the annotated data using PubAnnotation's native Tex-tAE visualization may be inconvenient.
PubAnnotation ↔ LAPPS Grid Documents from PubAnnotation are made available in the LAPPS Grid as data sources, that is, services that provide documents for processing by other services. Documents are retrieved from PubAnnotation using their PubMed ID number. Currently, the LAPPS Grid does not provide any search or query mechanisms for PubAnnotation, and therefore users must determine document ID numbers via other means-for example, by using the PubAnnotation search facility (http://pubannotation. org/search) or the LAPPS Grid AskMe service (https://services. lappsgrid. org/eager/ask). The documents may be downloaded from PubAnnotation as text documents with no annotations or with annotations from PubAnnotation projects included (if available). Annotated documents can also be sent back to the PubAnnotation platform for publication and alignment with the canonical text, so that the annotations are available along with all other published annotations of the same text for others to use.
Because INCEpTION allows users to create their own annotation layers and features, we need to ensure that recommenders called from the LAPPS Grid can only be configured for layers whose types are compatible. For this, we leverage the metadata information given by the LAPPS Grid service API to prune recommenders that are not compatible. Note that while INCEpTION can use LAPPS Grid services to generate annotation suggestions for the human to correct, but there is currently no way of feeding the corrections directly back to the services for re-training. However, we intend to configure another trainable recommender within INCEpTION, which learns based on the suggestions from the LAPPS Grid services that the user has explicitly accepted or corrected. INCEpTION internally uses UIMA CAS as a syntactic format, whereas the LAPPS Grid uses its LIF (JSON-LD) format.
Discussion Our analysis reveals that interoperability across platforms intended to facilitate the creation and use of resources and NLP applications for accessing and mining scientific publications is feasible, due to the many commonalities in the representation of linguistically annotated data that have been adopted by frameworks developed over the past decade. Pairwise interoperability among the three platforms is sufficient to enable the use of specific features by making use of underspecification, limited manual configuration, and/or conventions (best practices). For example, the fact that PubAnnotation is schemaless allows LAPPS and INCEpTION to set up conventions to encode platform-specific metadata using the PubAnnotation model. An example of minimal manual configuration is the interoperability between INCEpTION and LAPPS where the user needs to specify the annotation type and feature carrying the predicted labels.
Acknowledgments The LAPPS Grid is supported by  US National Science Foundation  grants  NSF-ACI 1147944 ,  NSF-ACI 1147912 ,  NSF-EAGER 181123 , and the  Andrew K. Mellon Foundation ; the LAPPS Grid is also supported by the  NSF -funded Jetstream cloud computing environment. INCEpTION is supported by the  German Research Foundation  under grant No. EC503/1-1  and  GU 798/21-1. PubAnnotation is supported by the Database Integration Coordination Program funded by  National Bioscience Database Center (NBDC) of Japan Science and Technology Agency (JST).
The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set.
It has been the work of the IMEx Consortium  11  to capture such information into a single data set and thus make it available for researchers to re-use and re-analyse. IMEx Consortium annotators follow a detailed curation model, capturing not only full details of the experiment (including interaction detection method, participant identification method and the host organism) but also a description of the constructs used. This may include the coordinates of deletion mutants used to derive a minimum binding domain and also the effect of point mutations. Databases in the Consortium perform detailed, archival curation of published literature and also receive pre-publication data through direct submissions. This close collaboration with data producers often entails access to unpublished details in the data, such as experiments reporting mutations that have no effect on interactions, which enables the capture of added value for the scientific community.
Results Data curation and quality control. The IMEx Consortium databases have been collecting point mutation data for over 14 years, which has resulted in a sizeable data set of almost 28,000 fully annotated events (www. ebi. ac. uk/intact/resources/ datasets#mutationDs). The IMEx resources curate interaction data into structured database fields, and from there into community standard interchange formats, and each observation is described using controlled vocabulary terms. Mutations are mapped to the underlying protein sequence in UniProtKB and updated in line with changes to that sequence, to ensure that they stay mapped to the correct amino acid residue with every proteome release. In order to make the mutations data set more accessible to the biomedical scientist, the Consortium has released it in a tabdelimited format (Table  1  and Supplementary Table  1 ), which includes details of the position and the amino acid change of the mutation, the molecules in the interaction and the effect of the mutation on the interaction, as well as additional fields containing contextual information.
Additionally, a data-update pipeline has been specifically developed to ensure the accuracy of the annotation of mutation events as interaction participant features (Supplementary Figure  1 ). The construction of this pipeline has been made possible by the creation of specific fields capturing sequence changes in our recently developed standard format PSI-MI XML3. 0  12. It is run in coordination with the IntAct database monthly protein update procedure, which ensures synchronization with Uni-ProtKB  13  and automatically shifts feature positions if there are changes in referenced protein sequences. The pipeline is applied to the entire data in the IntAct database (www. ebi. ac. uk/intact), in which all IMEx data, and also legacy data generated by the IntAct, MINT, DIP and UniProt curation groups is housed (see Supplementary Information, sections 'Initial re-curation of mutation data in IMEx' for details on re-annotation and 'Automated quality control pipeline for mutation entries in IMEx' for data-update procedures).