Journal: Cell reports

Article Title: Recursive feedback between matrix dissipation and chemo-mechanical signaling drives oscillatory growth of cancer cell invadopodia

doi: 10.1016/j.celrep.2021.109047

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: 280 kDa Matrigel Corning Cat# 354230 Calcium Sulfate (CaSO4) Sigma Aldrich Cat# 255696-10G Lipofectamine 3000 ThermoFisher Scientific Cat# L3000075 OptiMEM ThermoFisher Scientific Cat# 31985062 EGF Peprotech, Inc. Cat# AF-100-15 Puromycin ThermoFisher Scientific Cat# A1113803 Neomycin ThermoFisher Scientific Cat# 21810031 Fluoresceinamine Sigma Aldrich Cat# 20162 Critical commercial assays MycoAlert Mycoplasma Detection Kit Lonza Cat# LT07-118 GeneJet Plasmid Miniprep kit ThermoFisher Scientific Cat# K0701 Deposited data Measured invadopodia dynamics of MDA-MB-231 This paper https://doi.org/10.17632/tp7fkkxk44.2 Measured circularity of HT-1080 cells Wisdom et al., 2018 https://doi.org/10.17632/tp7fkkxk44.2 Experimental models: Cell lines MDA-MB-231 ATCC ATCC® HTB-26 HT-1080 cells ATCC ATCC® CCL-121 Recombinant DNA RFP-LifeAct Gift from A. Dunn N/A Software and algorithms Simulation of invadopodia dynamics This paper N/A High throughput invadopodia dynamics analysis Chang et al., 2020 N/A MATLAB Mathworks https://www.mathworks.com/products/matlab.html Imaris Oxford Instruments https://imaris.oxinst.com/ ImageJ ImageJ https://imagej.net/Welcome Other Stress-controlled rheometer TA Instruments AR2000EX Open in a separate window KEY RESOURCES TABLE A chemo-mechanical model for invadopodia dynamics in 3D matrices is developed Oscillations occur when timescales for extension and myosin dynamics are comparable Matrix plastic strain accumulates by cyclic ratcheting during invadopodia growth High matrix plasticity facilitates the oscillatory growth of invadopodia

Techniques: Blocking Assay, Virus, Recombinant, Modification, Plasmid Preparation, Software, High Throughput Screening Assay

Journal: Sensors (Basel, Switzerland)

Article Title: Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.

doi: 10.3390/s25092676

Figure Lengend Snippet: Figure 15. Reinforcement learning reference tracking.

Article Snippet: The agent creation, training, and deployment were performed using the MATLAB Reinforcement Learning toolbox (https://www.mathworks.com/products/reinforcement-learning.html, accessed on 30 March 2025).

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.

doi: 10.3390/s25092676

Figure Lengend Snippet: Figure 16. Reinforcement learning reference tracking (real no train).

Article Snippet: The agent creation, training, and deployment were performed using the MATLAB Reinforcement Learning toolbox (https://www.mathworks.com/products/reinforcement-learning.html, accessed on 30 March 2025).

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.

doi: 10.3390/s25092676

Figure Lengend Snippet: Figure 17. Reinforcement learning reference tracking (real).

Article Snippet: The agent creation, training, and deployment were performed using the MATLAB Reinforcement Learning toolbox (https://www.mathworks.com/products/reinforcement-learning.html, accessed on 30 March 2025).

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.

doi: 10.3390/s25092676

Figure Lengend Snippet: Figure 18. Reinforcement learning reference tracking error.

Article Snippet: The agent creation, training, and deployment were performed using the MATLAB Reinforcement Learning toolbox (https://www.mathworks.com/products/reinforcement-learning.html, accessed on 30 March 2025).

Techniques: