Text-based Image Segmentation Methodology

AbstractIn computer vision, segmentation is the process of partitioning a digital image into multiple segments (sets of pixels). Image segmentation is thus inevitable. Segmentation used for text-based images aim in retrieval of specific information from the entire image. This information can be a line or a word or even a character. This paper proposes various methodologies to segment a text based image at various levels of segmentation. This material serves as a guide and update for readers working on the text based segmentation area of Computer Vision. First, the need for segmentation is justified in the context of text based information retrieval. Then, the various factors affecting the segmentation process are discussed. Followed by the levels of text segmentation are explored. Finally, the available techniques with their superiorities and weaknesses are reviewed, along with directions for quick referral are suggested. Special attention is given to the handwriting recognition since this area requires more advanced techniques for efficient information extraction and to reach the ultimate goal of machine simulation of human reading

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types

Body Composition of Reserve Officers Training Corps (ROTC) Cadets: A Comparison Across Three Techniques of Measurement

Background: Physical fitness is imperative for the Army Reserves Officers’ Training Corps (ROTC) cadets to be commissioned for active duty. This project compared the standard anthropometric assessment of ROTC cadets using tape measurement to body composition measurements using Air Displacement Plethysmography (ADP) from the Bod Pod and the seven- site Pollock Skin Fold Thickness (SFM) measurement. These results were compared to the norms established by the Army Physical Fitness Test (APFT), recommending to improve ROTC body measurement method. Method: Thirteen ROTC cadets were recruited from Central State and Cedarville Universities. Cadets were measured using the ADP (BODPOD) and SFM method. Tape measurements were provided by the ROTC program. Descriptive statistics were generated and classifications for each cadet were made based on APFT standards for the aforementioned methods. T-test and Bland-Altman tests were conducted to compare the classifications. Results: Numbers indicate, the three methods measuring body composition present different body fat outcomes for each candidate. The SFM overestimated body fat and the tape measurement underestimated body fat when the ADP measurement is used as a gold standard. Conclusion: Three individuals were classified as “Risky (low body fat)” by Tape Measurement but as “lean” or “moderately lean” by SFM and the “gold standard” ADP. These results indicate that the systematic underestimation by the tape measurement can cause unnecessary concern and follow up, using limited resources unnecessarily. We recommend that ROTC use ADP body fat testing whenever available to more accurately estimate body fat percentages for cadets. The second choice would be SFM