Naor Granik

TL;DR: A neural network is implemented to classify single-particle trajectories by diffusion type: Brownian motion, fractional BrownianMotion and continuous time random walk, and the applicability of the network architecture for estimating the Hurst exponent for fractionalBrownian motion and the diffusion coefficient for Brownianmotion on both simulated and experimental data is demonstrated.

TL;DR: A neural network is implemented to classify single particle trajectories according to diffusion type –Brownian motion, fractional Brownian motion (FBM) and Continuous Time Random Walk (CTRW), and the net is used to estimate the Hurst exponent for FBM, and the diffusion coefficient for BrownianMotion, demonstrating its applicability on simulated and experimental data.

TL;DR: In this paper, an oligo-library and machine learning-approach was applied to characterize the sequence and structural determinants of binding of the phage coat proteins of bacteriophages MS2 (MCP), PP7 (PCP), and Qβ (QCP) to RNA.

TL;DR: It is demonstrated that chemotaxis can be detected within minutes using intrinsic optical measurements of silicon femtoliter well arrays (FMAs) using phase-shift reflectometric interference spectroscopic measurements (PRISM) of the wells, enabling label-free, real-time quantification of the number of trapped bacteria cells in the optical readout.

TL;DR: In this paper, the authors demonstrate that PP7-coat protein (PCP) and Qβ-coat protein (QCP) co-expressed with synthetic long non-coding RNA (slncRNA) encoded with multiple CP binding sites self-assemble into phase-separated synthetic RNP (SRNP) granules within E coli cells and in vitro SNRP granules exhibit hallmarks of RNP granule formation including phase separation from a semi-dilute liquid in vitro, and retention of structure after cell lysis.