Kowsari, K. | bioRxiv Science

bioRxiv2023

Myelin ensheathment and drug response of oligodendrocytes are modulated by stiffness of artificial axons

Myelination is a key biological process wherein glial cells such as oligodendrocytes wrap myelin around neuronal axons, forming an insulative sheath that accelerates signal propagation down the axon. A major obstacle to understanding myelination is the challenge of visualizing and reproducibly quantifying this inherently three-dimensional process in vitro. To this end, we previously developed Artificial Axons (AAs), a biocompatible platform consisting of 3D-printed hydrogel-based axon mimics designed to more closely recapitulate the micrometer-scale diameter and sub-kilopascal mechanical stiffness of biological axons. First, we present our platform for fabricating AAs with tunable axon diameter, stiffness, and inter-axonal spacing. Second, we demonstrate that increasing the Youngs modulus E or stiffness of polymer comprising the AAs increases the extent of myelin ensheathment by rat oligodendrocytes. Third, we demonstrate that the responses of oligodendrocytes to pro-myelinating compounds are also dependent on axon stiffness, which can affect compounds efficacy and the relative ranking. These results reinforce the importance of studying myelination in mechanically representative environments, and highlight the importance of considering biophysical cues when conducting drug screening studies.

neuroscience↗

bioRxiv2018

GaKCo: a Fast GApped k-mer string Kernel using COunting

String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size ({Sigma}) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O({Sigma}M). We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger{Sigma} and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the{Sigma} M term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets). 1

bioinformatics↗