Cell sheet morphogenesis characterizes key developmental transitions and homeostasis, in vertebrates and throughout phylogeny, including gastrulation, neural tube formation and wound healing. Dorsal closure, a process during Drosophila embryogenesis, has emerged as a model for cell sheet morphogenesis. ∼140 genes are currently known to affect dorsal closure and new genes are identified each year. Many of these genes were identified in screens that resulted in arrested development. Dorsal closure is remarkably robust and many questions regarding the molecular mechanisms involved in this complex biological process remain. Thus, it is important to identify all genes that contribute to the kinematics and dynamics of closure. Here, we used a set of large deletions (deficiencies), which collectively remove 98.5% of the genes on the right arm of Drosophila melanogaster’s 2nd chromosome to identify “dorsal closure deficiencies”. Through two crosses, we unambiguously identified embryos homozygous for each deficiency and time-lapse imaged them for the duration of closure. Images were analyzed for defects in cell shapes and tissue movements. Embryos homozygous for 47 deficiencies have notable, diverse defects in closure, demonstrating that a number of discrete processes comprise closure and are susceptible to mutational disruption. Further analysis of these deficiencies will lead to the identification of at least 30 novel “dorsal closure genes”. We expect that many of these novel genes will identify links to pathways and structures already known to coordinate various aspects of closure. We also expect to identify new processes and pathways that contribute to closure.
Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with “expansion of query expression” using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.
IMPORTANCE According to the National Residency Matching Program's biennial Charting Outcomes in the Match (NRMP ChOM) reports, the mean number of research items of matched allopathic dermatology applicants has nearly tripled since 2007, rising from 5.7 to 14.7. Research items are self-reported by applicants and serve as an approximation of research output. Because the NRMP research items field is unverified and reported as an aggregate of several different research pursuits, it may not be an accurate representation of applicant research output. OBJECTIVE To determine if the rise in NRMP-reported data is associated with a rise in verifiable, indexed publications from matched allopathic dermatology applicants from 2007 to 2018. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study including a bibliometric analysis on accepted applicant research output among 2234 matched allopathic dermatology applicants, with a total of 6229 publications, in dermatology residency programs for the years
Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.
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