Depixelation and image restoration with meta-learning in fiber-bundle-based endomicroscopy.

In order to efficiently remove honeycomb artifacts and restore images in fiber-bundle-based endomicroscopy, we develop a meta-learning algorithm in this work. Two sub-networks are used to extract different levels of features. Meta-training is employed to train the network with small amount of simulated training data, enabling the optimal model to generalize to new tasks not seen in the training set.

A novel RSW&TST framework of MCPs detection for abnormal pattern recognition on large-scale time series and pathological signals in epilepsy.

To quickly and efficiently recognize abnormal patterns from large-scale time series and pathological signals in epilepsy, this paper presents here a preliminary RSW&TST framework for Multiple Change-Points (MCPs) detection based on the Random Slide Window (RSW) and Trigeminal Search Tree (TST) methods. To avoid the remaining local optima, the proposed framework applies a random strategy for selecting the size of each slide window from a predefined collection, in terms of data feature and experimental knowledge. For each data segment to be diagnosed in a current slide window, an optimal path towards a potential change point is detected by TST methods from the top root to leaf nodes with O(log3(N)).

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic).

A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.

Although Kolmogorov-Smirnov (KS) statistic is a widely used method, some weaknesses exist in investigating abrupt Change Point (CP) problems, e.g. it is time-consuming and invalid sometimes.

Kinetic theory approach to modeling of cellular repair mechanisms under genome stress.

Under acute perturbations from outer environment, a normal cell can trigger cellular self-defense mechanism in response to genome stress. To investigate the kinetics of cellular self-repair process at single cell level further, a model of DNA damage generating and repair is proposed under acute Ion Radiation (IR) by using mathematical framework of kinetic theory of active particles (KTAP). Firstly, we focus on illustrating the profile of Cellular Repair System (CRS) instituted by two sub-populations, each of which is made up of the active particles with different discrete states.

Dynamic modeling of cellular response to DNA damage based on p53 stress response networks.

Under acute perturbations from the outside, cells can trigger self-defensive mechanisms to fight against genome stress. To investigate the cellular response to continuous ion radiation (IR), a dynamic model for p53 stress response networks at the cellular level is proposed. The model can successfully be used to simulate the dynamic processes of double-strand breaks (DSBs) generation and their repair, switch-like ataxia telangiectasia mutated (ATM) activation, oscillations occurring in the p53-MDM2 feedback loop, as well as toxins elimination triggered by p53 stress response networks.