Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic respons

The Digital Fish Library: Using MRI to Digitize, Database, and Document the Morphological Diversity of Fish

Museum fish collections possess a wealth of anatomical and morphological data that are essential for documenting and understanding biodiversity. Obtaining access to specimens for research, however, is not always practical and frequently conflicts with the need to maintain the physical integrity of specimens and the collection as a whole. Non-invasive three-dimensional (3D) digital imaging therefore serves a critical role in facilitating the digitization of these specimens for anatomical and morphological analysis as well as facilitating an efficient method for online storage and sharing of this imaging data. Here we describe the development of the Digital Fish Library (DFL, http://www.digitalfishlibrary.org), an online digital archive of high-resolution, high-contrast, magnetic resonance imaging (MRI) scans of the soft tissue anatomy of an array of fishes preserved in the Marine Vertebrate Collection of Scripps Institution of Oceanography. We have imaged and uploaded MRI data for over 300 marine and freshwater species, developed a data archival and retrieval system with a web-based image analysis and visualization tool, and integrated these into the public DFL website to disseminate data and associated metadata freely over the web. We show that MRI is a rapid and powerful method for accurately depicting the in-situ soft-tissue anatomy of preserved fishes in sufficient detail for large-scale comparative digital morphology. However these 3D volumetric data require a sophisticated computational and archival infrastructure in order to be broadly accessible to researchers and educators

Views to a war: systematic differences in media and military reporting of the war in Iraq

The quantitative study of violent conflict and its mechanisms has in recent years greatly benefited from the availability of detailed event data. With a number of highly visible studies both in the natural sciences and in political science using such data to shed light on the complex mechanisms underlying violent conflict, researchers have recently raised issues of systematic (reporting) biases. While many sources of bias are qualitatively known, biases in event data are usually not studied with quantitative methods. In this study we focus on a unique case - the conflict in Iraq - that is covered by two independently collected datasets: Iraq Body Count (IBC) reports of civilian casualties and Significant Action (SIGACT) military data. We systematically identify a number of key quantitative differences between the event reporting in the two datasets and demonstrate that even for subsets where both datasets are most consistent at an aggregate level, the daily time series and timing signatures of events differ significantly. This suggests that at any level of analysis the choice of dataset may substantially affect any inferences drawn, with attendant consequences for a number of recent studies of the conflict in Iraq. We further outline how the insights gained from our analysis of conflict event data have broader implications for studies using similar data on other social processes.publishe