More Like this

1. Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics

   https://doi.org/10.1093/sysbio/syad025  

   Girón, Jennifer C ; Tarasov, Sergei ; González Montaña, Luis Antonio ; Matentzoglu, Nicolas ; Smith, Aaron D ; Koch, Markus ; Boudinot, Brendon E ; Bouchard, Patrice ; Burks, Roger ; Vogt, Lars ; et al ( April 2023 , Systematic Biology)

   Abstract The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multi-species anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to (1) use controlled vocabularies and create semi-automated computer-parsable insect morphological descriptions; (2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and (3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies. 

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2. Terrestrial Parasite Tracker indexed biotic interactions and review summary

   https://doi.org/10.5281/zenodo.6761707  

   Poelen, Jorrit H. ; Seltmann, Katja C. ; Campbell, Mariel ; Orlofske, Sarah A. ; Light, Jessica E. ; Tucker, Erika M. ; Demboski, John R ; McElrath, Tommy ; Grinter, Christopher C ; Diaz-Bastin, Rachel ; et al ( January 2022 , Zenodo)

   PLEASE CONTACT AUTHORS IF YOU CONTRIBUTE AND WOULD LIKE TO BE LISTED AS A CO-AUTHOR. (this message will be removed some time weeks/months after the first publication)

   Terrestrial Parasite Tracker indexed biotic interactions and review summary.

   The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.

   This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open source software.

   If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.

   Funding:
   The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926

   References:
   Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.

   GloBI Data Review Report

   Datasets under review:
    - University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. accessed via https://github.com/EMTuckerLabUMMZ/ummzi/archive/6731357a377e9c2748fc931faa2ff3dc0ce3ea7a.zip on 2022-06-24T14:02:48.801Z
    - Academy of Natural Sciences Entomology Collection for the Parasite Tracker Project accessed via https://github.com/globalbioticinteractions/ansp-para/archive/5e6592ad09ec89ba7958266ad71ec9d5d21d1a44.zip on 2022-06-24T14:04:22.091Z
    - Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology accessed via https://github.com/globalbioticinteractions/bpbm-ent/archive/c085398dddd36f8a1169b9cf57de2a572229341b.zip on 2022-06-24T14:04:37.692Z
    - Texas A&M University, Biodiversity Teaching and Research Collections accessed via https://github.com/globalbioticinteractions/brtc-para/archive/f0a718145b05ed484c4d88947ff712d5f6395446.zip on 2022-06-24T14:06:40.154Z
    - Brigham Young University Arthropod Museum accessed via https://github.com/globalbioticinteractions/byu-byuc/archive/4a609ac6a9a03425e2720b6cdebca6438488f029.zip on 2022-06-24T14:06:51.420Z
    - California Academy of Sciences Entomology accessed via https://github.com/globalbioticinteractions/cas-ent/archive/562aea232ec74ab615f771239451e57b057dc7c0.zip on 2022-06-24T14:07:16.371Z
    - Clemson University Arthropod Collection accessed via https://github.com/globalbioticinteractions/cu-cuac/archive/6cdcbbaa4f7cec8e1eac705be3a999bc5259e00f.zip on 2022-06-24T14:07:40.925Z
    - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via https://github.com/globalbioticinteractions/dmns-para/archive/a037beb816226eb8196533489ee5f98a6dfda452.zip on 2022-06-24T14:08:00.730Z
    - Field Museum of Natural History IPT accessed via https://github.com/globalbioticinteractions/fmnh/archive/6bfc1b7e46140e93f5561c4e837826204adb3c2f.zip on 2022-06-24T14:18:51.995Z
    - Illinois Natural History Survey Insect Collection accessed via https://github.com/globalbioticinteractions/inhs-insects/archive/38692496f590577074c7cecf8ea37f85d0594ae1.zip on 2022-06-24T14:19:37.563Z
    - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via https://github.com/globalbioticinteractions/min-umsp/archive/3f1b9d32f947dcb80b9aaab50523e097f0e8776e.zip on 2022-06-24T14:20:27.232Z
    - Milwaukee Public Museum Biological Collections Data Portal accessed via https://github.com/globalbioticinteractions/mpm/archive/9f44e99c49ec5aba3f8592cfced07c38d3223dcd.zip on 2022-06-24T14:20:46.185Z
    - Museum for Southern Biology (MSB) Parasite Collection accessed via https://github.com/globalbioticinteractions/msb-para/archive/178a0b7aa0a8e14b3fe953e770703fe331eadacc.zip on 2022-06-24T15:16:07.223Z
    - The Albert J. Cook Arthropod Research Collection accessed via https://github.com/globalbioticinteractions/msu-msuc/archive/38960906380443bd8108c9e44aeff4590d8d0b50.zip on 2022-06-24T16:09:40.702Z
    - Ohio State University Acarology Laboratory accessed via https://github.com/globalbioticinteractions/osal-ar/archive/876269d66a6a94175dbb6b9a604897f8032b93dd.zip on 2022-06-24T16:10:00.281Z
    - Frost Entomological Museum, Pennsylvania State University accessed via https://github.com/globalbioticinteractions/psuc-ento/archive/30b1f96619a6e9f10da18b42fb93ff22cc4f72e2.zip on 2022-06-24T16:10:07.741Z
    - Purdue Entomological Research Collection accessed via https://github.com/globalbioticinteractions/pu-perc/archive/e0909a7ca0a8df5effccb288ba64b28141e388ba.zip on 2022-06-24T16:10:26.654Z
    - Texas A&M University Insect Collection accessed via https://github.com/globalbioticinteractions/tamuic-ent/archive/f261a8c192021408da67c39626a4aac56e3bac41.zip on 2022-06-24T16:10:58.496Z
    - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/825678ad02df93f6d4469f9d8b7cc30151b9aa45.zip on 2022-06-24T16:12:29.854Z
    - University of Hawaii Insect Museum accessed via https://github.com/globalbioticinteractions/uhim/archive/53fa790309e48f25685e41ded78ce6a51bafde76.zip on 2022-06-24T16:12:41.408Z
    - University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC accessed via https://github.com/globalbioticinteractions/unhc/archive/f72575a72edda8a4e6126de79b4681b25593d434.zip on 2022-06-24T16:12:59.500Z
    - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via https://github.com/globalbioticinteractions/unl-nsm/archive/6bcd8aec22e4309b7f4e8be1afe8191d391e73c6.zip on 2022-06-24T16:13:06.914Z
    - Data were obtained from specimens belonging to the United States National Museum of Natural History (USNM), Smithsonian Institution, Washington DC and digitized by the Walter Reed Biosystematics Unit (WRBU). accessed via https://github.com/globalbioticinteractions/usnmentflea/archive/ce5cb1ed2bbc13ee10062b6f75a158fd465ce9bb.zip on 2022-06-24T16:13:38.013Z
    - US National Museum of Natural History Ixodes Records accessed via https://github.com/globalbioticinteractions/usnm-ixodes/archive/c5fcd5f34ce412002783544afb628a33db7f47a6.zip on 2022-06-24T16:13:45.666Z
    - Price Institute of Parasite Research, School of Biological Sciences, University of Utah accessed via https://github.com/globalbioticinteractions/utah-piper/archive/43da8db550b5776c1e3d17803831c696fe9b8285.zip on 2022-06-24T16:13:54.724Z
    - University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection accessed via https://github.com/globalbioticinteractions/uwsp-para/archive/f9d0d52cd671731c7f002325e84187979bca4a5b.zip on 2022-06-24T16:14:04.745Z
    - Giraldo-Calderón, G. I., Emrich, S. J., MacCallum, R. M., Maslen, G., Dialynas, E., Topalis, P., … Lawson, D. (2015). VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids research, 43(Database issue), D707–D713. doi:10.1093/nar/gku1117. accessed via https://github.com/globalbioticinteractions/vectorbase/archive/00d6285cd4e9f4edd18cb2778624ab31b34b23b8.zip on 2022-06-24T16:14:11.965Z
    - WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection accessed via https://github.com/globalbioticinteractions/wis-ih-wirc/archive/34162b86c0ade4b493471543231ae017cc84816e.zip on 2022-06-24T16:14:29.743Z
    - Yale University Peabody Museum Collections Data Portal accessed via https://github.com/globalbioticinteractions/yale-peabody/archive/43be869f17749d71d26fc820c8bd931d6149fe8e.zip on 2022-06-24T16:23:29.289Z

   Generated on:
   2022-06-24

   by:
   GloBI's Elton 0.12.4 
   (see https://github.com/globalbioticinteractions/elton).

   Note that all files ending with .tsv are files formatted 
   as UTF8 encoded tab-separated values files.

   https://www.iana.org/assignments/media-types/text/tab-separated-values

   
   Included in this review archive are:

   README:
     This file.

   review_summary.tsv:
     Summary across all reviewed collections of total number of distinct review comments.

   review_summary_by_collection.tsv:
     Summary by reviewed collection of total number of distinct review comments.

   indexed_interactions_by_collection.tsv: 
     Summary of number of indexed interaction records by institutionCode and collectionCode.

   review_comments.tsv.gz:
     All review comments by collection.

   indexed_interactions_full.tsv.gz:
     All indexed interactions for all reviewed collections.

   indexed_interactions_simple.tsv.gz:
     All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.

   datasets_under_review.tsv:
     Details on the datasets under review.

   elton.jar: 
     Program used to update datasets and generate the review reports and associated indexed interactions.

   datasets.zip:
     Source datasets used by elton.jar in process of executing the generate_report.sh script.

   generate_report.sh:
     Program used to generate the report

   generate_report.log:
     Log file generated as part of running the generate_report.sh script
    

    

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3. Twitter-Based Knowledge Graph for Researchers

   Vincent, Kyle ; Meno, Emma ( May 2020 , Virginia tech)

   null (Ed.)

   The Twitter-Based Knowledge Graph for Researchers project is an effort to construct a knowledge graph of computation-based tasks and corresponding outputs. It will be utilized by subject matter experts, statisticians, and developers. A knowledge graph is a directed graph of knowledge accumulated from a variety of sources. For our application, Subject Matter Experts (SMEs) are experts in their respective non-computer science fields, but are not necessarily experienced with running heavy computation on datasets. As a result, they find it difficult to generate workflows for their projects involving Twitter data and advanced analysis. Workflow management systems and libraries that facilitate computation are only practical when the users of these systems understand what analysis they need to perform. Our goal is to bridge this gap in understanding. Our queryable knowledge graph will generate a visual workflow for these experts and researchers to achieve their project goals. After meeting with our client, we established two primary deliverables. First, we needed to create an ontology of all Twitter-related information that an SME might want to answer. Secondly, we needed to build a knowledge graph based on this ontology and produce a set of APIs to trigger a set of network algorithms based on the information queried to the graph. An ontology is simply the class structure/schema for the graph. Throughout future meetings, we established some more specific additional requirements. Most importantly, the client stressed that users should be able to bring their own data and add it to our knowledge graph. As more research is completed and new technologies are released, it will be important to be able to edit and add to the knowledge graph. Next, we must be able to provide metrics about the data itself. These metrics will be useful for both our own work, and future research surrounding graph search problems and search optimization. Additionally, our system should provide users with information regarding the original domain that the algorithms and workflows were run against. That way they can choose the best workflow for their data. The project team first conducted a literature review, reading reports from the CS5604 Information Retrieval courses in 2016 and 2017 to extract information related to Twitter data and algorithms. This information was used to construct our raw ontology in Google Sheets, which contained a set of dataset-algorithm-dataset tuples. The raw ontology was then converted into nodes and edges csv files for building the knowledge graph. After implementing our original solution on a CentOS virtual machine hosted by the Virginia Tech Department of Computer Science, we transitioned our solution to Grakn, an open-source knowledge graph database that supports hypergraph functionality. When finalizing our workflow paths, we noted some nodes depended on completion of two or more inputs, representing an ”AND” edge. This phenomenon is modeled as a hyperedge with Grakn, initiating our transition from Neo4J to Grakn. Currently, our system supports queries through the console, where a user can type a Graql statement to retrieve information about data in the graph, from relationships to entities to derived rules. The user can also interact with the data via Grakn's data visualizer: Workbase. The user can enter Graql queries to visualize connections within the knowledge graph. 

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4. Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

   https://doi.org/10.3390/rs13183672  

   Uhl, Johannes H. ; Leyk, Stefan ; Li, Zekun ; Duan, Weiwei ; Shbita, Basel ; Chiang, Yao-Yi ; Knoblock, Craig A. ( September 2021 , Remote Sensing)

   Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available. 

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5. Classifying the unknown: Insect identification with deep hierarchical Bayesian learning

   https://doi.org/10.1111/2041-210X.14104  

   Badirli, Sarkhan ; Picard, Christine Johanna ; Mohler, George ; Richert, Frannie ; Akata, Zeynep ; Dundar, Murat ( April 2023 , Methods in Ecology and Evolution)

   Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species.

   We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species.

   With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species).

   Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.

    

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