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Mouse models play a crucial role in understanding the genetic and genomic basis of cancer and for the development of novel cancer therapies. Because data and information about these models are distributed and heterogeneous, gaining an appreciation for which models have been developed and what their characteristics are presents a major challenge for cancer researchers. The Mouse Models of Human Cancer Database (MMHCdb; http://tumor.informatics.jax.org) solves this challenge by providing a comprehensive, expertly curated on-line information resource about genetically engineered (GEMMs) and inbred mouse models of human cancer. In partnership with the European Bioinformatics Institute (EMBL/EBI), MMHCdb has also developed and implemented PDX Finder, a global catalog of Patient Derived Xenograft models (https://www.pdxfinder.org). Both MMHCdb and PDX Finder employ metadata standards to simplify the process of searching for relevant mouse models by diverse criteria including cancer type, type of strain, and genetic alteration. MMHCdb contains more than100,000 records on tumor frequency data for over 7,600 different mouse strains. These data are linked to over 4,700 references, 2,900 pathology reports, and 6,900 images. PDX Finder provides researchers with one stop access to information about more than 2,800 PDX models in repositories from around the world. MMHCdb and PDX Finder are supported, in part, by NIH/NCI CA089713. MMHCdb was formerly called the Mouse Tumor Biology database (MTB). Citation Format: Carol J. Bult, Debra M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg. Mouse models of human cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 435.

CORRIGENDUM: InterMOD: integrated data and tools for the unification of model organism research

Model organisms are widely used for understanding basic biology and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

We describe the design and implementation of QBISM (Query By Interactive, Spatial Multimedia), a prototype for querying and visualizing 3D spatial data. Our medical image application is focused on the brain mapping requirements for multimodality relationships across multiple subjects. It incorporates data describing both structure and function. It includes data structures that describe anatomy, physiology, coordinates using rendered imagery and statistical output. The system is built on top of the Starburst DBMS extended to handle spatial data types, specifically, scalar fields and arbitrary regions of space within such fields. In this paper we list the requirements of the application, discuss the logical and physical database design issues, and present timing results from our prototype. We observed that the DBMS' early spatial filtering results in significant performance savings because the system response time is dominated by the amount of data retrieved, transmitted, and rendered.

We describe the design and implementation of QBISM (Query By Interactive, Spatial Multimedia), a prototype for querying and visualizing 3D spatial data. Our driving application is in an area in medical research, in particular, Functional Brain Mapping. The system is built on top of the Starburst DBMS, extended to handle spatial data types, and, specifically, scalar fields and arbitrary regions of space within such fields. In this paper we list the requirements of the application, discuss the logical and physical database design issues, and present timing results from our prototype. We observed that the DBMS’ early spatial filtering results in significant performance savings because the system response time is dominated by the amount of data retrieved, transmitted, and rendered.

SummaryMental health staff may have limited exposure to emergencies associated with obsessive–compulsive disorder (OCD) during postgraduate training. The first time they encounter a person in the midst of severe obsessions, or one who has compulsively self-harmed in response to such obsessions, might be when working on call covering the emergency department. This educational article presents the lived experience of one of the authors as a clinical scenario. The scenario is then used to illustrate the severity of disability and the rates of self-harm and suicide-related mortality caused by OCD. The recognition and assessment of OCD is described, along with what helps in emergency situations. Written informed consent was obtained for the publication of clinical details.

Describes the design and implementation of QBlSM (Query By Interactive, Spatial Multimedia), a prototype for querying and visualizing 3D spatial data. The first application is in an area in medical research, in particular, Functional Brain Mapping. The system is built on top of the Starburst DBMS extended to handle spatial data types, specifically, scalar fields and arbitrary regions of space within such fields. The authors list the requirements of the application, discuss the logical and physical database design issues, and present timing results from their prototype. They observed that the DBMS' early spatial filtering results in significant performance savings because the system response time is dominated by the amount of data retrieved, transmitted, and rendered. >