3 research outputs found
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
Cell movement in the early phase of C. elegans development is regulated by a
highly complex process in which a set of rules and connections are formulated
at distinct scales. Previous efforts have shown that agent-based, multi-scale
modeling systems can integrate physical and biological rules and provide new
avenues to study developmental systems. However, the application of these
systems to model cell movement is still challenging and requires a
comprehensive understanding of regulation networks at the right scales. Recent
developments in deep learning and reinforcement learning provide an
unprecedented opportunity to explore cell movement using 3D time-lapse images.
We present a deep reinforcement learning approach within an ABM system to
characterize cell movement in C. elegans embryogenesis. Our modeling system
captures the complexity of cell movement patterns in the embryo and overcomes
the local optimization problem encountered by traditional rule-based, ABM that
uses greedy algorithms. We tested our model with two real developmental
processes: the anterior movement of the Cpaaa cell via intercalation and the
rearrangement of the left-right asymmetry. In the first case, model results
showed that Cpaaa's intercalation is an active directional cell movement caused
by the continuous effects from a longer distance, as opposed to a passive
movement caused by neighbor cell movements. This is because the learning-based
simulation found that a passive movement model could not lead Cpaaa to the
predefined destination. In the second case, a leader-follower mechanism well
explained the collective cell movement pattern. These results showed that our
approach to introduce deep reinforcement learning into ABM can test regulatory
mechanisms by exploring cell migration paths in a reverse engineering
perspective. This model opens new doors to explore large datasets generated by
live imaging.Comment: We revised the manuscript to make it clearer to follow. Please notice
that the Abstract shown in this page is slightly different than that in the
manuscript due to the limitation of 1920 characters in arxiv.or
Line Outage Detection and Localization via Synchrophasor Measurement
Since transmission lines are crucial links in the power system, one line
outage event may bring about interruption or even cascading failure of the
power system. If a quick and accurate line outage detection and localization
can be achieved, the system operator can take necessary actions in time to
mitigate the negative impact. Therefore, the objective of this paper is to
study a method for line outage detection and localization via synchrophasor
measurements. The density of deployed phasor measurement units (PMUs) is
increasing recently, which greatly improves the visibility of the power grid.
Taking advantage of the high-resolution synchrophasor data, the proposed method
utilizes frequency measurement for line outage detection and power change for
localization. The procedure of the proposed method is given. Compared with
conventional methods, it does not require the pre-knowledge on the system.
Simulation study validates the effectiveness of the proposed method
Augmenting C. elegans Microscopic Dataset for Accelerated Pattern Recognition
The detection of cell shape changes in 3D time-lapse images of complex
tissues is an important task. However, it is a challenging and tedious task to
establish a comprehensive dataset to improve the performance of deep learning
models. In the paper, we present a deep learning approach to augment 3D live
images of the Caenorhabditis elegans embryo, so that we can further speed up
the specific structural pattern recognition. We use an unsupervised training
over unlabeled images to generate supplementary datasets for further pattern
recognition. Technically, we used Alex-style neural networks in a generative
adversarial network framework to generate new datasets that have common
features of the C. elegans membrane structure. We also made the dataset
available for a broad scientific community