5,296 research outputs found
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
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