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1. Learning to Locomote with Artificial Neural-Network and CPG-based Control in a Soft Snake Robot

   https://doi.org/10.1109/IROS45743.2020.9340763  

   Liu, Xuan; Gasoto, Renato; Jiang, Ziyi; Onal, Cagdas; Fu, Jie (October 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to the pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots. 

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2. Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning

   https://doi.org/10.1038/s41467-021-26278-9  

   Ni, Peng; Huang, Neng; Nie, Fan; Zhang, Jun; Zhang, Zhi; Wu, Bo; Bai, Lu; Liu, Wende; Xiao, Chuan-Le; Luo, Feng; et al (October 2021, Nature Communications)

   Abstract In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequenceArabidopsis thalianaandOryza sativausing both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes. 

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3. Conformal irregular-grid metasurfaces comprising a dense array of free-standing dipoles

   Kwon, Do-Hoon (July 2022, 2022 IEEE International Symposium on Antennas and Propagation)

   For large or infinite metasurfaces, a design tech- nique for a dense array of subwavelength resonators on an irregular grid is presented. For a given incident wave, the desired induced dipole moment distribution determines the local electric field that excites individual meta-atoms. The interaction field that accounts for mutual coupling is evaluated via a combination of discrete coupling from nearby resonators and continuous sheet current coupling from far-separated resonators. Meta-atoms placed on an irregular grid can be treated, greatly enhancing the flexibility in surface profile in practical conformal metasurfaces. 

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4. An Adaptive Frequency Central Pattern Generator for Synthetic Nervous Systems

   Nourse, William; Quinn, Roger D; Szczecinski, Nicholas S (July 2018, Lecture notes in computer science)

   Generators (CPGs) are being used for controlling the complicated gaits and timing required for stable walking. Traditionally, these models are precisely designed for oscillation at a set of specific frequencies and phase relationships, which while easier to design is not conducive to robust and stable walking. In recent years, work has been done on designing adaptive models of CPGs. These CPGs are able to exhibit complex behaviors such as learning the resonant dynamics of a system to improve walking stability, as well as using mathematical learning rules to learn arbitrary signals and embed their relationships within the system. This work explores the possibility of implementing an adaptive frequency CPG with a similar behavior to these systems, using conductance-based models of dynamic non-spiking neurons connected as a synthetic nervous system (SNS). 

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5. Locally correlated kinetics of post-replication DNA methylation reveals processivity and region specificity in DNA methylation maintenance

   https://doi.org/10.1098/rsif.2022.0415  

   Ren, Honglei; Taylor, Robert B.; Downing, Timothy L.; Read, Elizabeth L. (October 2022, Journal of The Royal Society Interface)

   DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of maintenance methylation activity among neighbouring CpGs is critical to stability over cellular generations; however, the mechanism is unclear. We used mathematical models and stochastic simulation to analyse data from experiments that probe genome-wide methylation of nascent DNA post-replication in cells. We find that DNA methylation maintenance rates on individual CpGs are locally correlated, and the degree of this correlation varies by genomic regional context. By using theory of protein diffusion along DNA, we show that exponential decay of methylation rate correlation with genomic distance is consistent with enzyme processivity. Our results provide quantitative evidence of genome-wide methyltransferase processivity in vivo . We further developed a method to disentangle different mechanistic sources of kinetic correlations. From the experimental data, we estimate that an individual methyltransferase methylates neighbour CpGs processively if they are 36 basepairs apart, on average. But other mechanisms of coupling dominate for longer inter-CpG distances. Our study demonstrates that quantitative insights into enzymatic mechanisms can be obtained from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modelling. 

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