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29 results on '"Stirling, David R."'

1. CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools

Author

Weisbart, Erin, Tromans-Coia, Callum, Diaz-Rohrer, Barbara, Stirling, David R, Garcia-Fossa, Fernanda, Senft, Rebecca A, Hiner, Mark C, de Jesus, Marcelo B, Eliceiri, Kevin W, and Cimini, Beth A

Subjects
Quantitative Biology - Quantitative Methods
Abstract

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community., Comment: 17 pages, 2 figures, 1 table

Published
2023
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2. OME-Zarr: a cloud-optimized bioimaging file format with international community support

Author

Moore, Josh, Basurto-Lozada, Daniela, Besson, Sébastien, Bogovic, John, Bragantini, Jordão, Brown, Eva M., Burel, Jean-Marie, Casas Moreno, Xavier, de Medeiros, Gustavo, Diel, Erin E., Gault, David, Ghosh, Satrajit S., Gold, Ilan, Halchenko, Yaroslav O., Hartley, Matthew, Horsfall, Dave, Keller, Mark S., Kittisopikul, Mark, Kovacs, Gabor, Küpcü Yoldaş, Aybüke, Kyoda, Koji, le Tournoulx de la Villegeorges, Albane, Li, Tong, Liberali, Prisca, Lindner, Dominik, Linkert, Melissa, Lüthi, Joel, Maitin-Shepard, Jeremy, Manz, Trevor, Marconato, Luca, McCormick, Matthew, Lange, Merlin, Mohamed, Khaled, Moore, William, Norlin, Nils, Ouyang, Wei, Özdemir, Bugra, Palla, Giovanni, Pape, Constantin, Pelkmans, Lucas, Pietzsch, Tobias, Preibisch, Stephan, Prete, Martin, Rzepka, Norman, Samee, Sameeul, Schaub, Nicholas, Sidky, Hythem, Solak, Ahmet Can, Stirling, David R., Striebel, Jonathan, Tischer, Christian, Toloudis, Daniel, Virshup, Isaac, Walczysko, Petr, Watson, Alan M., Weisbart, Erin, Wong, Frances, Yamauchi, Kevin A., Bayraktar, Omer, Cimini, Beth A., Gehlenborg, Nils, Haniffa, Muzlifah, Hotaling, Nathan, Onami, Shuichi, Royer, Loic A., Saalfeld, Stephan, Stegle, Oliver, Theis, Fabian J., and Swedlow, Jason R.

Published
2023
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3. CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis

Author

Stirling, David R., Carpenter, Anne E., and Cimini, Beth A.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses., Comment: 5 pages, 1 figure

Published
2021
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4. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes

Author

Glunk, Viktoria, Laber, Samantha, Sinnott-Armstrong, Nasa, Sobreira, Debora R., Strobel, Sophie M., Batista, Thiago M., Kubitz, Phil, Moud, Bahareh Nemati, Ebert, Hannah, Huang, Yi, Brandl, Beate, Garbo, Garrett, Honecker, Julius, Stirling, David R., Abdennur, Nezar, Calabuig-Navarro, Virtu, Skurk, Thomas, Ocvirk, Soeren, Stemmer, Kerstin, Cimini, Beth A., Carpenter, Anne E., Dankel, Simon N., Lindgren, Cecilia M., Hauner, Hans, Nobrega, Marcelo A., and Claussnitzer, Melina

Published
2023
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5. Piximi - An Images to Discovery web tool for bioimages and beyond

Author

Moser, Levin M, primary, Gogoberidze, Nodar, additional, Papaleo, Andréa, additional, Lucas, Alice, additional, Dao, David, additional, Friedrich, Christoph A, additional, Paavolainen, Lassi, additional, Molnar, Csaba, additional, Stirling, David R, additional, Hung, Jane, additional, Wang, Rex, additional, Tromans-Coia, Callum, additional, Li, Bin, additional, Evans, Edward L, additional, Eliceiri, Kevin W, additional, Horvath, Peter, additional, Carpenter, Anne E, additional, and Cimini, Beth A, additional

Published
2024
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6. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

Author

Laber, Samantha, Strobel, Sophie, Mercader, Josep M., Dashti, Hesam, dos Santos, Felipe R.C., Kubitz, Phil, Jackson, Maya, Ainbinder, Alina, Honecker, Julius, Agrawal, Saaket, Garborcauskas, Garrett, Stirling, David R., Leong, Aaron, Figueroa, Katherine, Sinnott-Armstrong, Nasa, Kost-Alimova, Maria, Deodato, Giacomo, Harney, Alycen, Way, Gregory P., Saadat, Alham, Harken, Sierra, Reibe-Pal, Saskia, Ebert, Hannah, Zhang, Yixin, Calabuig-Navarro, Virtu, McGonagle, Elizabeth, Stefek, Adam, Dupuis, Josée, Cimini, Beth A., Hauner, Hans, Udler, Miriam S., Carpenter, Anne E., Florez, Jose C., Lindgren, Cecilia, Jacobs, Suzanne B.R., and Claussnitzer, Melina

Published
2023
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11. Bead-based approaches to CRISPR diagnostics

Author

Siddiqui, Sameed M, primary, Welch, Nicole L., additional, Nguyen, Tien G., additional, Razmi, Amaya, additional, Chang, Tianyi, additional, Senft, Rebecca, additional, Arizti-Sanz, Jon, additional, Mirhashemi, Marzieh E., additional, Stirling, David R., additional, Ackerman, Cheri, additional, Cimini, Beth A., additional, Blainey, Paul C., additional, Sabeti, Pardis C., additional, and Myhrvold, Cameron, additional

Published
2023
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12. REVERBa couples the circadian clock to hepatic glucocorticoid action

Author

Caratti, Giorgio, Iqbal, Mudassar, Hunter, Louise, Kim, Donghwan, Wang, Ping, Vonslow, Ryan M., Begley, Nicola, Tetley, Abigail J., Woodburn, Joanna L., Pariollaud, Marie, Maidstone, Robert, Donaldson, Ian J., Zhang, Zhenguang, Ince, Louise M., Kitchen, Gareth, Baxter, Matthew, Poolman, Toryn M., Daniels, Dion A., Stirling, David R., Brocker, Chad, Gonzalez, Frank, Loudon, Andrew S.I., Bechtold, David A., Rattray, Magnus, Matthews, Laura C., and Ray, David W.

Subjects
Cell receptors -- Health aspects, Energy metabolism -- Genetic aspects -- Health aspects, Circadian rhythms -- Genetic aspects -- Health aspects, Glucocorticoids -- Health aspects, Health care industry
Abstract

The glucocorticoid receptor (GR) is a major drug target in inflammatory disease. However, chronic glucocorticoid (GC) treatment leads to disordered energy metabolism, including increased weight gain, adiposity, and hepatosteatosis--all programs modulated by the circadian clock. We demonstrated that while antiinflammatory GC actions were maintained irrespective of dosing time, the liver was significantly more GC sensitive during the day. Temporal segregation of GC action was underpinned by a physical interaction of GR with the circadian transcription factor REVERBa and co-binding with liver- specific hepatocyte nuclear transcription factors (HNFs) on chromatin. REVERBa promoted efficient GR recruitment to chromatin during the day, acting in part by maintaining histone acetylation, with REVERBa- dependent GC responses providing segregation of carbohydrate and lipid metabolism. Importantly, deletion of Reverba inverted circadian liver GC sensitivity and protected mice from hepatosteatosis induced by chronic GC administration. Our results reveal a mechanism by which the circadian clock acts through REVERBa in liver on elements bound by HNF4A/HNF6 to direct GR action on energy metabolism., Introduction Glucocorticoids (GCs; cortisol in humans, corticosterone in rodents) are critical regulators of energy metabolism and immunity. Their secretion by the adrenal gland follows a circadian pattern, with serum concentrations [...]

Published
2018
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13. CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools

Author

Tromans-Coia, Callum, Diaz-Rohrer, Barbara, Weisbart, Erin, Stirling, David R, Garcia-Fossa, Fernanda, Senft, Rebecca A, Hiner, Mark C, de Jesus, Marcelo B, Eliceiri, Kevin W, and Cimini, Beth A

Subjects
FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository with examples of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community., 17 pages, 2 figures, 1 table

Published
2023

15. OME-Zarr: a cloud-optimized bioimaging file format with international community support

Author

Moore, Josh, primary, Basurto-Lozada, Daniela, additional, Besson, Sébastien, additional, Bogovic, John, additional, Bragantini, Jordão, additional, Brown, Eva M., additional, Burel, Jean-Marie, additional, Casas Moreno, Xavier, additional, de Medeiros, Gustavo, additional, Diel, Erin E., additional, Gault, David, additional, Ghosh, Satrajit S., additional, Gold, Ilan, additional, Halchenko, Yaroslav O., additional, Hartley, Matthew, additional, Horsfall, Dave, additional, Keller, Mark S., additional, Kittisopikul, Mark, additional, Kovacs, Gabor, additional, Küpcü Yoldaş, Aybüke, additional, Kyoda, Koji, additional, le Tournoulx de la Villegeorges, Albane, additional, Li, Tong, additional, Liberali, Prisca, additional, Lindner, Dominik, additional, Linkert, Melissa, additional, Lüthi, Joel, additional, Maitin-Shepard, Jeremy, additional, Manz, Trevor, additional, Marconato, Luca, additional, McCormick, Matthew, additional, Lange, Merlin, additional, Mohamed, Khaled, additional, Moore, William, additional, Norlin, Nils, additional, Ouyang, Wei, additional, Özdemir, Bugra, additional, Palla, Giovanni, additional, Pape, Constantin, additional, Pelkmans, Lucas, additional, Pietzsch, Tobias, additional, Preibisch, Stephan, additional, Prete, Martin, additional, Rzepka, Norman, additional, Samee, Sameeul, additional, Schaub, Nicholas, additional, Sidky, Hythem, additional, Solak, Ahmet Can, additional, Stirling, David R., additional, Striebel, Jonathan, additional, Tischer, Christian, additional, Toloudis, Daniel, additional, Virshup, Isaac, additional, Walczysko, Petr, additional, Watson, Alan M., additional, Weisbart, Erin, additional, Wong, Frances, additional, Yamauchi, Kevin A., additional, Bayraktar, Omer, additional, Cimini, Beth A., additional, Gehlenborg, Nils, additional, Haniffa, Muzlifah, additional, Hotaling, Nathan, additional, Onami, Shuichi, additional, Royer, Loic A., additional, Saalfeld, Stephan, additional, Stegle, Oliver, additional, Theis, Fabian J., additional, and Swedlow, Jason R., additional

Published
2023
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17. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

Author

Laber, Samantha, primary, Strobel, Sophie, additional, Mercader, Josep-Maria, additional, Dashti, Hesam, additional, Ainbinder, Alina, additional, Honecker, Julius, additional, Garborcauskas, Garrett, additional, Stirling, David R., additional, Leong, Aaron, additional, Figueroa, Katherine, additional, Sinnott-Armstrong, Nasa, additional, Kost-Alimova, Maria, additional, Deodato, Giacomo, additional, Harney, Alycen, additional, Way, Gregory P., additional, Saadat, Alham, additional, Harken, Sierra, additional, Reibe-Pal, Saskia, additional, Ebert, Hannah, additional, Zhang, Yixin, additional, Calabuig-Navarro, Virtu, additional, McGonagle, Elizabeth, additional, Stefek, Adam, additional, Dupuis, Josée, additional, Cimini, Beth A., additional, Hauner, Hans, additional, Udler, Miriam S., additional, Carpenter, Anne E., additional, Florez, Jose C., additional, Lindgren, Cecilia M., additional, Jacobs, Suzanne B. R., additional, and Claussnitzer, Melina, additional

Published
2021
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19. Additional file 1 of CellProfiler 4: improvements in speed, utility and usability

Author

Stirling, David R., Swain-Bowden, Madison J., Lucas, Alice M., Carpenter, Anne E., Cimini, Beth A., and Goodman, Allen

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Additional file 1: Figure S1. Execution times of all modules within the Cell Painting example pipeline. Measured as per-image CPU time taken for each module in the Cell Painting assay protocol (n = 48). I/O loading operations in the Images module are not recorded by these measurements.

Published
2021
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20. A postdoctoral training program in bioimage analysis

Author

Cimini, Beth A., Tromans-Coia, Callum, Stirling, David R., Sivagurunathan, Suganya, Senft, Rebecca A., Ryder, Pearl V., Miglietta, Esteban, Llanos, Paula, Jamali, Nasim, Diaz-Rohrer, Barbara, Dasgupta, Shatavisha, Cruz, Mario, Weisbart, Erin, and Carpenter, Anne E.

Abstract

We herein describe a postdoctoral training program designed to train biologists with microscopy experience in bioimage analysis. We detail the rationale behind the program, the various components of the training program, and outcomes in terms of works produced and the career effects on past participants. We analyze the results of an anonymous survey distributed to past and present participants, indicating overall high value of all 12 rated aspects of the program, but significant heterogeneity in which aspects were most important to each participant. Finally, we propose this model as a template for other programs which may want to train experts in professional skill sets, and discuss the important considerations when running such a program. We believe that such programs can have extremely positive impact for both the trainees themselves and the broader scientific community.

Published
2024
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25. Trinuclear copper(II) complexes with bridging (N–N) diazine ligands: structural and magnetic studies

Author

Zhao, Liang, Thompson, Laurence K., Xu, Zhiqiang, Miller, David O., and Stirling, David R.

Abstract

The polytopic ligands 2poap and po2p have a linear disposition of coordination pockets suitably disposed to bind metal ions in close proximity and produce linear trinuclear copper(II) complexes. The complexes [Cu3(2poap − 2H)(CH3CO2)4]·3H2O (1), [Cu3(2poap − 2H)(H2O)(DMF )3(CH3OH)2](BF4)4 (2), and [Cu3(po2p − H)2(H2O)2](ClO4)4·2H2O (3) have a linear arrangement of Cu(II) centres linked by a trans arrangement of N–N single bonds. Each metal is assigned a dx2 − y2 magnetic ground state, and the trans diazine bridging arrangement leads to strong antiferromagnetic coupling in complete agreement with previous magnetostructural correlations relating the twist of the copper magnetic planes around the N–N bond to the exchange integral.

Published
2001

26. The protective effects of continuous and interval exercise in athletes with exerciseinduced asthma

Author

MCKENZIE, DONALD C., MCLUCKIE, SANDRA L., and STIRLING, DAVID R.

Abstract

MCKENZIE, D. C., S. L. MCLUCKIE, and D. R. STIRLING. The protective effects of continuous and interval exercise in athletes with exercise-induced asthma. Med. Sci. Sports Exerc.,Vol. 26, No. 8, pp. 951–956, 1994. To determine the effect of two forms of warm-up on postexcrcise bronchoconstriction in athletes with exercise-induced asthma, 12 moderately trained persons with asthma (age = 26.5 ± 2.2 yr; height = 169.2 ± 2.6 cm; weight = 64.3 ± 2.6kg; VO2max= 52.7 ± 1.3 ml · kg-1.min-1) were tested under three experimental conditions: continuous warm-up (CW), interval warm-up (IW), and control (C). CW consisted of 15 min of treadmill running at a velocity corresponding to 60 VO2maxfollowed by an exercise challenge test (ET = 6 min at 90 VO2max). IW involved 8 X 30-s runs (1.5 min rest between bouts of exercise), at an intensity equivalent to 100 VO2maxfollowed by an ET. C consisted of only the ET. FEV1‘FVC, and MMEFR were measured prior to the experimental conditions, repeated before the ET, and every 2 min during a 25-min passive recovery period, using a Breon spirometer. Postexercise changes in pulmonary function were recorded as the largest decrease in FEV1’FVC, and MMEFR during the recovery period, and expressed as a percentage of baseline values. Significant differences were detected in FEV1(34. 6, 16.7, 29.7: P = 0.009), FVC (30.0, 10.7, 21.0: P = 0.03), and MMEFR (50.0, 30.2, 43.4: P = 0.05), in comparing C, CW, and IW, respectively. Scheffe's test detected significance (P< 0.05) between C and CW for all three dependent variables; no statistical significance between C and IW or IW and CW occurred. These data indicate that a continuous warm-up of 15 min at 60 VO2maxcan significantly decrease postexercise bronchoconstriction in moderately trained athletes.

Published
1994

27. Piximi - An Images to Discovery web tool for bioimages and beyond.

Author

Moser LM, Gogoberidze N, Papaleo A, Lucas A, Dao D, Friedrich CA, Paavolainen L, Molnar C, Stirling DR, Hung J, Wang R, Tromans-Coia C, Li B, Evans EL 3rd, Eliceiri KW, Horvath P, Carpenter AE, and Cimini BA

Abstract

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Published
2024
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28. CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools.

Author

Weisbart E, Tromans-Coia C, Diaz-Rohrer B, Stirling DR, Garcia-Fossa F, Senft RA, Hiner MC, de Jesus MB, Eliceiri KW, and Cimini BA

Abstract

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.

Published
2023

29. OME-Zarr: a cloud-optimized bioimaging file format with international community support.

Author

Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Moreno XC, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Yoldaş AK, Kyoda K, de la Villegeorges ALT, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, and Swedlow JR

Abstract

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Published
2023
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