Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy

Here we describe the use of a new open-source software package and a Raspberry Pi® computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide – a component of Rifater®, used in the treatment of tuberculosis – and its reduced derivative piperazine-2-carboxamide.

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TPVTrack v1.0: a watershed segmentation and overlap correspondence method for tracking tropopause polar vortices

Geoscientific Model Development ◽

10.5194/gmd-11-5173-2018 ◽

2018 ◽

Vol 11 (12) ◽

pp. 5173-5187 ◽

Cited By ~ 1

Author(s):

Nicholas Szapiro ◽

Steven Cavallo

Keyword(s):

Software Package ◽

Unstructured Grids ◽

Test Cases ◽

Watershed Segmentation ◽

Spatial Characterization ◽

Modular Software ◽

Mean Structure ◽

Spatial Shape ◽

Physical Features ◽

Effective Representation

Abstract. A new free modular software package is described for tracking tropopause polar vortices (TPVs) natively on structured or unstructured grids. Motivated by limitations in spatial characterization and time tracking within existing approaches, TPVTrack mimics the expected dynamics of TPVs to represent their (1) spatial structure, with variable shapes and intensities, and (2) time evolution, with mergers and splits. TPVs are segmented from the gridded flow field into spatial objects as restricted regional watershed basins on the tropopause, described by geometric metrics, associated over time by overlap similarity into major and minor correspondences, and tracked along major correspondences. Simplified segmentation and correspondence test cases illustrate some of the appeal, sensitivities, and limitations of TPVTrack, including effective representation of spatial shape and reduced false positive associations in time. Tracked TPVs in more realistic historical conditions are consistent in bulk with expectations of life cycle and mean structure. Individual tracks are less reliable when discriminating among multiple overlaps. Modifications to track other physical features are possible, with each application requiring evaluation.

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An open source software package for primality testing of numbers of the form p2^n+1, with no constraints on the relative sizes of p and 2^n

10.7287/peerj.preprints.27396 ◽

2018 ◽

Author(s):

Tejas R. Rao

Keyword(s):

Open Source ◽

Open Source Software ◽

Software Package ◽

Primality Testing ◽

Fermat Number ◽

Open Source Software Package ◽

Sierpinski Numbers ◽

Efficient Software

We develop an efficient software package to test for the primality of p2^n+1, p prime and p>2^n. This aids in the determination of large, non-Sierpinski numbers p, for prime p, and in cryptography. It furthermore uniquely allows for the computation of the smallest n such that p2^n+1 is prime when p is large. We compute primes of this form for the first one million primes p and find four primes of the form above 1000 digits. The software may also be used to test whether p2^n+1 divides a generalized fermat number base 3.

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ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis

Bioinformatics ◽

10.1093/bioinformatics/bty313 ◽

2018 ◽

Vol 34 (18) ◽

pp. 3238-3240 ◽

Cited By ~ 8

Author(s):

Anliang Wang ◽

Xiaolong Yan ◽

Zhijun Wei

Keyword(s):

Open Source ◽

Software Package ◽

Bioimage Analysis

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Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images

BioMed Research International ◽

10.1155/2014/248505 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 43

Author(s):

Yu-Hua Dean Fang ◽

Chien-Yu Lin ◽

Meng-Jung Shih ◽

Hung-Ming Wang ◽

Tsung-Ying Ho ◽

...

Keyword(s):

Open Source ◽

Texture Analysis ◽

Open Source Software ◽

Software Package ◽

Tumor Heterogeneity ◽

Locally Advanced ◽

Image Texture ◽

Pixel Intensity ◽

Open Source Software Package ◽

Molecular Images

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project.Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies.Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmeanfor outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmeanand TLG (0.6 and 0.52, resp.).Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use athttp://code.google.com/p/cgita.

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