Motivated by identifying the origin of the bias dependence of tension propagation, we investigate methods for measuring tension propagation quantitatively in computer simulations of driven polymer translocation. Here, the motion of flexible polymer chains through a narrow pore is simulated using Langevin dynamics. We measure tension forces, bead velocities, bead distances, and bond angles along the polymer at all stages of translocation with unprecedented precision. Measurements are done at a standard temperature used in simulations and at zero temperature to pin down the effect of fluctuations. The measured quantities were found to give qualitatively similar characteristics, but the bias dependence could be determined only using tension force. We find that in the scaling relation τ∼N^{β}f_{d}^{α} for translocation time τ, the polymer length N, and the bias force f_{d}, the increase of the exponent β with bias is caused by center-of-mass diffusion of the polymer toward the pore on the cis side. We find that this diffusion also causes the exponent α to deviate from the ideal value -1. The bias dependence of β was found to result from combination of diffusion and pore friction and so be relevant for polymers that are too short to be considered asymptotically long. The effect is relevant in experiments all of which are made using polymers whose lengths are far below the asymptotic limit. Thereby, our results also corroborate the theoretical prediction by Sakaue's theory [Polymers 8, 424 (2016)2073-436010.3390/polym8120424] that there should not be bias dependence of β for asymptotically long polymers. By excluding fluctuations we also show that monomer crowding at the pore exit cannot have a measurable effect on translocation dynamics under realistic conditions.
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BMC Bioinformatics
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Department of Radiology, University of Minnesota, Minneapolis, MN, 55455, USA.
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Division of Public Health Sciences, Washington University in St Louis, 660 S. Euclid Ave, St Louis, MO, 63110, USA.
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Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO, United States.
In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications in neuro-oncology, including guiding clinical decisions and improving patient management. However, the lack of clarity on how AI arrives at predictions has hindered its clinical translation.
View Article and Find Full Text PDFMethods Enzymol
January 2025
Faculty of Biology, Technion - Israel Institute of Technology, Technion City, Haifa, Israel. Electronic address:
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January 2025
Institute for Studies in Medicine History, Persian and Complementary Medicine, Iran University of Medical Science, Tehran, Iran; Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran; Student Research Committee, Iran University of Medical Sciences, Tehran, Iran. Electronic address:
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