Dataset Information

Image and data processing algorithms for identifying cell-bound membrane vesicle trajectories and movement information.

ABSTRACT: This DIB article provides details about the trajectory identification and data processing algorithms used in the article "Dynamic single-vesicle tracking of cell-bound membrane vesicles on resting, activated, and cytoskeleton-disrupted cells" (Zhang et al.) [1]. The algorithm identifies vesicles on cell membranes from series of undyed grayscale images captured by the confocal microscope based on contrast differences and then trajectories of vesicles are obtained by analyzing their positions in consecutive images. Once the trajectories have been obtained, other quantitative movement information, such as moving speed, direction and acceleration, are derived by standard dynamic relations.

SUBMITTER: Xu Y

PROVIDER: S-EPMC6327075 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Image and data processing algorithms for identifying cell-bound membrane vesicle trajectories and movement information.

Xu Ye Y Zhang Wendiao W Chen Yong Y Shan Wenzhe W

Data in brief 20181228

This DIB article provides details about the trajectory identification and data processing algorithms used in the article "Dynamic single-vesicle tracking of cell-bound membrane vesicles on resting, activated, and cytoskeleton-disrupted cells" (Zhang et al.) [1]. The algorithm identifies vesicles on cell membranes from series of undyed grayscale images captured by the confocal microscope based on contrast differences and then trajectories of vesicles are obtained by analyzing their positions in c ...[more]

PMID: 30671507

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Project description:BackgroundSymptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes.ObjectivesWe aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations.MethodsFirst, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types.ResultsCompared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent.DiscussionUsing our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

Dataset Information

Image and data processing algorithms for identifying cell-bound membrane vesicle trajectories and movement information.

Publications

Image and data processing algorithms for identifying cell-bound membrane vesicle trajectories and movement information.

Similar Datasets

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