The Experts below are selected from a list of 28059 Experts worldwide ranked by ideXlab platform
Emma Lejeune - One of the best experts on this subject based on the ideXlab platform.
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fm track a fiducial marker tracking software for studying cell mechanics in a three dimensional environment
SoftwareX, 2020Co-Authors: Emma Lejeune, Alex Khang, Jacob Sansom, Michael S SacksAbstract:Abstract Tracking the deformation of fiducial markers in the vicinity of living cells embedded in compliant synthetic or biological gels is a powerful means to study cell mechanics and mechanobiology in three-dimensional environments. However, current approaches to track and quantify three-dimensional (3D) fiducial marker displacements remain ad-hoc, can be difficult to implement, and may not produce reliable results. Herein, we present a compact software package entitled “FM-Track,” written in the popular Python Language, to facilitate feature-based particle tracking tailored for 3D cell micromechanical environment studies. FM-Track contains functions for pre-processing images, running fiducial marker tracking, and post-processing and visualization. FM-Track can thus aid the study of cellular mechanics and mechanobiology by providing an extensible software platform to more reliably extract complex local 3D cell contractile information in transparent compliant gel systems.
Michael S Sacks - One of the best experts on this subject based on the ideXlab platform.
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fm track a fiducial marker tracking software for studying cell mechanics in a three dimensional environment
SoftwareX, 2020Co-Authors: Emma Lejeune, Alex Khang, Jacob Sansom, Michael S SacksAbstract:Abstract Tracking the deformation of fiducial markers in the vicinity of living cells embedded in compliant synthetic or biological gels is a powerful means to study cell mechanics and mechanobiology in three-dimensional environments. However, current approaches to track and quantify three-dimensional (3D) fiducial marker displacements remain ad-hoc, can be difficult to implement, and may not produce reliable results. Herein, we present a compact software package entitled “FM-Track,” written in the popular Python Language, to facilitate feature-based particle tracking tailored for 3D cell micromechanical environment studies. FM-Track contains functions for pre-processing images, running fiducial marker tracking, and post-processing and visualization. FM-Track can thus aid the study of cellular mechanics and mechanobiology by providing an extensible software platform to more reliably extract complex local 3D cell contractile information in transparent compliant gel systems.
Dieu Tien Bui - One of the best experts on this subject based on the ideXlab platform.
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an automated Python Language based tool for creating absence samples in groundwater potential mapping
Remote Sensing, 2019Co-Authors: Omid Rahmati, Davoud Davoudi Moghaddam, Vahid Moosavi, Zahra Kalantari, Mahmood Samadi, Saro Lee, Dieu Tien BuiAbstract:Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine ...
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an automated Python Language based tool for creating absence samples in groundwater potential mapping
Remote Sensing, 2019Co-Authors: Omid Rahmati, Davoud Davoudi Moghaddam, Vahid Moosavi, Zahra Kalantari, Mahmood Samadi, Saro Lee, Dieu Tien BuiAbstract:Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming Language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.
Omid Rahmati - One of the best experts on this subject based on the ideXlab platform.
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an automated Python Language based tool for creating absence samples in groundwater potential mapping
Remote Sensing, 2019Co-Authors: Omid Rahmati, Davoud Davoudi Moghaddam, Vahid Moosavi, Zahra Kalantari, Mahmood Samadi, Saro Lee, Dieu Tien BuiAbstract:Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine ...
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an automated Python Language based tool for creating absence samples in groundwater potential mapping
Remote Sensing, 2019Co-Authors: Omid Rahmati, Davoud Davoudi Moghaddam, Vahid Moosavi, Zahra Kalantari, Mahmood Samadi, Saro Lee, Dieu Tien BuiAbstract:Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming Language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.
Steven Bird - One of the best experts on this subject based on the ideXlab platform.
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nltk the natural Language toolkit
Meeting of the Association for Computational Linguistics, 2006Co-Authors: Steven BirdAbstract:The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural Language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python Language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.
