A Kinetic Model for Cell Damage Caused by Oligomer Formation

It is well-known that the formation of amyloid fiber may cause invertible damage to cells, while the underlying mechanism has not been fully uncovered. In this paper, we construct a mathematical model, consisting of infinite ODEs in the form of mass-action equations together with two reaction-convection PDEs, and then simplify it to a system of 5 ODEs by using the maximum entropy principle. This model is based on four simple assumptions, one of which is that cell damage is raised by oligomers rather than mature fibrils. With the simplified model, the effects of nucleation and elongation, fragmentation, protein and seeds concentrations on amyloid formation and cell damage are extensively explored and compared with experiments. We hope that our results can provide a valuable insight into the processes of amyloid formation and cell damage thus raised.Comment: 16 pages+ 5 figures for maintext; 8 pages+ 4 figures for Supporting Material

Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity

A novel control design framework is proposed for a class of non-Lipschitz nonlinear systems with quantized states, meanwhile prescribed transient performance and lower control design complexity could be guaranteed. Firstly, different from all existing control methods for systems with state quantization, global stability of strict-feedback nonlinear systems is achieved without requiring the condition that the nonlinearities of the system model satisfy global Lipschitz continuity. Secondly, a novel barrier function-free prescribed performance control (BFPPC) method is proposed, which can guarantee prescribed transient performance under quantized states. Thirdly, a new \textit{W}-function-based control scheme is designed such that virtual control signals are not required to be differentiated repeatedly and the controller could be designed in a simple way, which guarantees global stability and lower design complexity compared with traditional dynamic surface control (DSC). Simulation results demonstrate the effectiveness of our method

Mobility of TX100 suspended multiwalled carbon nanotubes (MWCNTs) and the facilitated transport of phenanthrene in real soil columns

AbstractThe transport behavior of TX100 suspended multiwalled carbon nanotubes (MWCNTs) through different soil columns as well as their effects on the mobility of phenanthrene was systematically studied. Results showed that the mobility of MWCNTs varied with soils, which was found to be correlated positively to the average soil particle diameters and soil sand contents, while correlated negatively to soil clay contents. The retention of MWCNTs on soil columns is most likely due to surface deposition and physical straining. Co-transport of phenanthrene with MWCNTs was tested in three selected soils (soil HB, DX and BJ), where MWCNTs could act as carriers of phenanthrene and enhance the mobility of phenanthrene in soils. However, during passing through the soil columns phenanthrene initially adsorbed onto MWCNTs could be partially “stripped” off. In soil with the lowest phenanthrene sorption affinity and highest water velocity (soil HB), only 8.5% phenanthrene was desorbed during transport, suggesting that a strong MWCNT-associated phenanthrene mobile may occur in this soil. More than 80% of phenanthrene was stripped off in soils with higher sorption affinity (soil DX and BJ), indicating the limitation of the co-transport of phenanthrene and MWCNTs in such soils

RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery

The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative information-based method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.Comment: This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytic

Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach

Author's accepted manuscript.Available from 16/05/2022.acceptedVersio