316 research outputs found
Characterization of Hepatitis C Virus Infection of Hepatocytes and Astrocytes
Indiana University-Purdue University Indianapolis (IUPUI)Approximately 2.8% of the world population is currently infected with hepatitis C virus (HCV). Neutralizing antibodies (nAbs) are often generated in chronic hepatitis C patients yet fail to control the infection. In the first two chapters of this study, we focused on two alternative routes of HCV transmission, which may contribute to HCV’s immune evasion and establishment of chronic infection. HCV was transmitted via a cell-cell contact-mediated (CCCM) route and in the form of exosomes. Formation of HCV infection foci resulted from CCCM HCV transfer and was cell density-dependent. Moreover, CCCM HCV transfer occurred rapidly, involved all four known HCV receptors and intact actin cytoskeleton, and led to productive HCV infection. Furthermore, live cell imaging revealed the temporal and spatial details of the transfer process. Lastly, HCV from HCV-infected hepatocytes and patient plasma occurred in both exosome-free and exosome-associated forms and the exosome-associated HCV remained infectious, even though HCV infection did not significantly alter exosome secretion.
In the third chapter, we characterized HCV interaction with astrocytes, one of the putative HCV target cells in the brain. HCV infection causes the central nervous system (CNS) abnormalities in more than 50% of chronically infected subjects but the underlying mechanisms are largely unknown. We showed that primary human astrocytes (PHA) were very inefficiently infected by HCV, either in the free virus form or through cell-cell contact. PHA expressed all known HCV receptors but failed to support HCV entry. HCV IRES-mediated translation was functional in PHA and further enhanced by miR122 expression. Nevertheless, PHA did not support HCV replication regardless of miR122 expression. To our great surprise, HCV exposure induced robust IL-18 expression in PHA and exhibited direct neurotoxicity. In summary, we showed that CCCM HCV transfer and exosome-mediated HCV infection constituted important routes for HCV infection and dissemination and that astrocytes did not support productive HCV infection and replication, but HCV interactions with astrocytes and neurons alone might be sufficient to cause CNS dysfunction. These findings provide new insights into HCV infection of hepatocytes and astrocytes and shall aid in the development of new and effective strategies for preventing and treating HCV infection
Normalized Wolfe-Powell-type local minimax method for finding multiple unstable solutions of nonlinear elliptic PDEs
The local minimax method (LMM) proposed in [Y. Li and J. Zhou, SIAM J. Sci.
Comput., 23(3), 840--865 (2001)] and [Y. Li and J. Zhou, SIAM J. Sci. Comput.,
24(3), 865--885 (2002)] is an efficient method to solve nonlinear elliptic
partial differential equations (PDEs) with certain variational structures for
multiple solutions. The steepest descent direction and the Armijo-type
step-size search rules are adopted in [Y. Li and J. Zhou, SIAM J. Sci. Comput.,
24(3), 865--885 (2002)] and play a significant role in the performance and
convergence analysis of traditional LMMs. In this paper, a new algorithm
framework of the LMMs is established based on general descent directions and
two normalized (strong) Wolfe-Powell-type step-size search rules. The
corresponding algorithm framework named as the normalized Wolfe-Powell-type LMM
(NWP-LMM) is introduced with its feasibility and global convergence rigorously
justified for general descent directions. As a special case, the global
convergence of the NWP-LMM algorithm combined with the preconditioned steepest
descent (PSD) directions is also verified. Consequently, it extends the
framework of traditional LMMs. In addition, conjugate gradient-type (CG-type)
descent directions are utilized to speed up the NWP-LMM algorithm. Finally,
extensive numerical results for several semilinear elliptic PDEs are reported
to profile their multiple unstable solutions and compared for different
algorithms in the LMM's family to indicate the effectiveness and robustness of
our algorithms. In practice, the NWP-LMM combined with the CG-type direction
indeed performs much better than its known LMM companions.Comment: 27 pages, 9 figures; Accepted by SCIENCE CHINA Mathematics on January
17, 202
Nonmonotone local minimax methods for finding multiple saddle points
In this paper, by designing a normalized nonmonotone search strategy with the
Barzilai--Borwein-type step-size, a novel local minimax method (LMM), which is
a globally convergent iterative method, is proposed and analyzed to find
multiple (unstable) saddle points of nonconvex functionals in Hilbert spaces.
Compared to traditional LMMs with monotone search strategies, this approach,
which does not require strict decrease of the objective functional value at
each iterative step, is observed to converge faster with less computations.
Firstly, based on a normalized iterative scheme coupled with a local peak
selection that pulls the iterative point back onto the solution submanifold, by
generalizing the Zhang--Hager (ZH) search strategy in the optimization theory
to the LMM framework, a kind of normalized ZH-type nonmonotone step-size search
strategy is introduced, and then a novel nonmonotone LMM is constructed. Its
feasibility and global convergence results are rigorously carried out under the
relaxation of the monotonicity for the functional at the iterative sequences.
Secondly, in order to speed up the convergence of the nonmonotone LMM, a
globally convergent Barzilai--Borwein-type LMM (GBBLMM) is presented by
explicitly constructing the Barzilai--Borwein-type step-size as a trial
step-size of the normalized ZH-type nonmonotone step-size search strategy in
each iteration. Finally, the GBBLMM algorithm is implemented to find multiple
unstable solutions of two classes of semilinear elliptic boundary value
problems with variational structures: one is the semilinear elliptic equations
with the homogeneous Dirichlet boundary condition and another is the linear
elliptic equations with semilinear Neumann boundary conditions. Extensive
numerical results indicate that our approach is very effective and speeds up
the LMMs significantly.Comment: 32 pages, 7 figures; Accepted by Journal of Computational Mathematics
on January 3, 202
Convergence analysis of a spectral-Galerkin-type search extension method for finding multiple solutions to semilinear problems
In this paper, we develop an efficient spectral-Galerkin-type search
extension method (SGSEM) for finding multiple solutions to semilinear elliptic
boundary value problems. This method constructs effective initial data for
multiple solutions based on the linear combinations of some eigenfunctions of
the corresponding linear eigenvalue problem, and thus takes full advantage of
the traditional search extension method in constructing initials for multiple
solutions. Meanwhile, it possesses a low computational cost and high accuracy
due to the employment of an interpolated coefficient Legendre-Galerkin spectral
discretization. By applying the Schauder's fixed point theorem and other
technical strategies, the existence and spectral convergence of the numerical
solution corresponding to a specified true solution are rigorously proved. In
addition, the uniqueness of the numerical solution in a sufficiently small
neighborhood of each specified true solution is strictly verified. Numerical
results demonstrate the feasibility and efficiency of our algorithm and present
different types of multiple solutions.Comment: 23 pages, 7 figures; Chinese version of this paper is published in
SCIENTIA SINICA Mathematica, Vol. 51 (2021), pp. 1407-143
Second-order flows for approaching stationary points of a class of non-convex energies via convex-splitting schemes
The use of accelerated gradient flows is an emerging field in optimization, scientific computing and beyond. This paper contributes to the theoretical underpinnings of a recently-introduced computational paradigm known as second-order flows, which demonstrate significant performance particularly for the minimization of non-convex energy functionals defined on Sobolev spaces, and are characterized by novel dissipative hyperbolic partial differential equations. Our approach hinges upon convex-splitting schemes, a tool which is not only pivotal for clarifying the well-posedness of second-order flows, but also yields a versatile array of robust numerical schemes through temporal and spatial discretization. We prove the convergence to stationary points of such schemes in the semi-discrete setting. Further, we establish their convergence to time-continuous solutions as the time-step tends to zero, and perform a comprehensive error analysis in the fully discrete case. Finally, these algorithms undergo thorough testing and validation in approaching stationary points of non-convex variational models in applied sciences, such as the Ginzburg-Landau energy in phase-field modeling and a specific case of the Landau-de Gennes energy of the Q-tensor model for liquid crystals
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