Browsing by Subject "Open source software"

Now showing 1 - 4 of 4
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    Academic laboratory information management system: a tool for science and computer science students
    (2011-07-08) Lerch, Spencer; Merchant, Mahesh; Wild, David; Doman, Thompson N.
    Proof of Concept - An Academic LIMS application: The aim of this project is the creation of an open-source, freeware LIMS application that can be used in an academic setting as a teaching tool for both chemistry and computer science students. The LIMS package will combine an application, developed using VB.NET, to manage the data with other open-source or freeware programs such as MySQL and WEKA. The numerous commercial chemical informatics applications available are useful tools to learn how to manage data from a user's standpoint. However, they are not readily available to the average student, nor do they offer a great understanding into how they were developed from a programmer's frame of mind. There is a great void here that, if filled can greatly help the academic community.
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    OC_Finder: Osteoclast Segmentation, Counting, and Classification Using Watershed and Deep Learning
    (Frontiers Media, 2022) Wang, Xiao; Kittaka, Mizuho; He, Yilin; Zhang, Yiwei; Ueki, Yasuyoshi; Kihara, Daisuke; Biomedical Sciences and Comprehensive Care, School of Dentistry
    Osteoclasts are multinucleated cells that exclusively resorb bone matrix proteins and minerals on the bone surface. They differentiate from monocyte/macrophage lineage cells in the presence of osteoclastogenic cytokines such as the receptor activator of nuclear factor-κB ligand (RANKL) and are stained positive for tartrate-resistant acid phosphatase (TRAP). In vitro osteoclast formation assays are commonly used to assess the capacity of osteoclast precursor cells for differentiating into osteoclasts wherein the number of TRAP-positive multinucleated cells is counted as osteoclasts. Osteoclasts are manually identified on cell culture dishes by human eyes, which is a labor-intensive process. Moreover, the manual procedure is not objective and results in lack of reproducibility. To accelerate the process and reduce the workload for counting the number of osteoclasts, we developed OC_Finder, a fully automated system for identifying osteoclasts in microscopic images. OC_Finder consists of cell image segmentation with a watershed algorithm and cell classification using deep learning. OC_Finder detected osteoclasts differentiated from wild-type and Sh3bp2 KI/+ precursor cells at a 99.4% accuracy for segmentation and at a 98.1% accuracy for classification. The number of osteoclasts classified by OC_Finder was at the same accuracy level with manual counting by a human expert. OC_Finder also showed consistent performance on additional datasets collected with different microscopes with different settings by different operators. Together, successful development of OC_Finder suggests that deep learning is a useful tool to perform prompt and accurate unbiased classification and detection of specific cell types in microscopic images.
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    Open Data and Open Code for Big Science of Science Studies
    (2013) Light, Robert P.; Polley, David E.; Börner, Katy
    Historically, science of science studies were/are performed by single investigators or small teams. As the size and complexity of data sets and analyses scales up, a “Big Science” approach (Price, 1963) is required that exploits the expertise and resources of interdisciplinary teams spanning academic, government, and industry boundaries. Big science of science studies utilize “big data”, i.e., large, complex, diverse, longitudinal, and/or distributed datasets that might be owned by different stakeholders. They apply a systems science approach to uncover hidden patterns, bursts of activity, correlations, and laws. They make available open data and open code in support of replication of results, iterative refinement of approaches and tools, and education. This paper introduces a database-tool infrastructure that was designed to support big science of science studies. The open access Scholarly Database (SDB) (http://sdb.cns.iu.edu) provides easy access to 26 million paper, patent, grant, and clinical trial records. The open source Science of Science (Sci2) tool (http://sci2.cns.iu.edu) supports temporal, geospatial, topical, and network studies. The scalability of the infrastructure is examined. Results show that temporal analyses scale linearly with the number of records and file size, while the geospatial algorithm showed quadratic growth. The number of edges rather than nodes determined performance for network based algorithms.
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