Biophysical Society Bulletin | September 2023

Call for Papers

Deadline for submission: November 30, 2023

Editors: Tamar Schlick (New York University) and Guowei Wei (Michigan State University) Special Issue: Machine Learning in Biophysics

With the pervasive usage of artificial intelligence tools in all aspects of our lives, biophysicists can certainly feel ahead of the curve from having developed and applied such tools for over a decade for important problems in biophysics. As the application scope has increased, machine learning algorithms have undoubt edly improved in sophistication, efficiency, and utility. It is impres sive today to realize how much can be deduced or predicted on the basis of large datasets without explicit programming. Yet, like every tool, caveats apply, and the best applications require a good understanding of the methods and their limitations. For a special volume dedicated to machine learning, Biophysical Journal invites contributions that address both algorithms and applications for a wide range of problems in biophysics. Article categories accepted include computational tools and research articles. If you are interested in writing a perspective or review article, please contact the editors, Tamar Schlick (schlick@nyu.edu) and Guowei Wei (weig@msu.edu), for pre-approval.

For more information, visit biophysics.org/biophysical-journal

Editor’s Pick Biophysical Reports Time-resolved burst variance analysis Ivan Terterov, Daniel Nettels, Dmitrii E. Makarov, Hagen Hofmann

“Single-molecule fluorescence spectroscopy, particularly in combination with Förster resonance energy transfer, has been extremely successful in quantifying

the dynamics of biomolecules. A toolbox of different methods is available to date that extracts dynamic information from the stream of photons emitted from donor and acceptor dyes. Yet, some of these methods require long integration times. In others, the presence or absence of dynamics is difficult to judge by eye and only fits with kinetic models providing this informa tion. Therefore, we extended the popular method of burst variance analysis (BVA) to overcome some of these limitations. The new method, termed time-resolved BVA, quantifies dynamics from 5 μs to 5 ms at high accuracy with as little as 5,000 bursts. Static and dynamic heterogeneity can be distinguished from each other, and even dynamics slower than the diffusion time can be quantified. Time-resolved BVA is a natural extension of classical BVA and therefore is easy to implement by researchers in the field of single-molecule Förster resonance energy transfer.” Version of Record Published July 6, 2023 DOI: https:/doi.org/10.1016/j.bpr.2023.100116

September 2023

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