85 research outputs found
Data-Driven Modeling and Forecasting of Chaotic Dynamics on Inertial Manifolds Constructed as Spectral Submanifolds
We present a data-driven and interpretable approach for reducing the
dimensionality of chaotic systems using spectral submanifolds (SSMs). Emanating
from fixed points or periodic orbits, these SSMs are low-dimensional inertial
manifolds containing the chaotic attractor of the underlying high-dimensional
system. The reduced dynamics on the SSMs turn out to predict chaotic dynamics
accurately over a few Lyapunov times and also reproduce long-term statistical
features, such as the largest Lyapunov exponents and probability distributions,
of the chaotic attractor. We illustrate this methodology on numerical data sets
including a delay-embedded Lorenz attractor, a nine-dimensional Lorenz model,
and a Duffing oscillator chain. We also demonstrate the predictive power of our
approach by constructing an SSM-reduced model from unforced trajectories of a
buckling beam, and then predicting its periodically forced chaotic response
without using data from the forced beam.Comment: Submitted to Chao
Nonlinear Model Reduction to Fractional and Mixed-Mode Spectral Submanifolds
A primary spectral submanifold (SSM) is the unique smoothest nonlinear
continuation of a nonresonant spectral subspace of a dynamical system
linearized at a fixed point. Passing from the full nonlinear dynamics to the
flow on an attracting primary SSM provides a mathematically precise reduction
of the full system dynamics to a very low-dimensional, smooth model in
polynomial form. A limitation of this model reduction approach has been,
however, that the spectral subspace yielding the SSM must be spanned by
eigenvectors of the same stability type. A further limitation has been that in
some problems, the nonlinear behavior of interest may be far away from the
smoothest nonlinear continuation of the invariant subspace . Here we remove
both of these limitations by constructing a significantly extended class of
SSMs that also contains invariant manifolds with mixed internal stability types
and of lower smoothness class arising from fractional powers in their
parametrization. We show on examples how fractional and mixed-mode SSMs extend
the power of data-driven SSM reduction to transitions in shear flows, dynamic
buckling of beams and periodically forced nonlinear oscillatory systems. More
generally, our results reveal the general function library that should be used
beyond integer-powered polynomials in fitting nonlinear reduced-order models to
data.Comment: To appear in Chao
Accuracy of generalized gradient approximation functionals for density functional perturbation theory calculations
We assess the validity of various exchange-correlation functionals for
computing the structural, vibrational, dielectric, and thermodynamical
properties of materials in the framework of density-functional perturbation
theory (DFPT). We consider five generalized-gradient approximation (GGA)
functionals (PBE, PBEsol, WC, AM05, and HTBS) as well as the local density
approximation (LDA) functional. We investigate a wide variety of materials
including a semiconductor (silicon), a metal (copper), and various insulators
(SiO -quartz and stishovite, ZrSiO zircon, and MgO periclase).
For the structural properties, we find that PBEsol and WC are the closest to
the experiments and AM05 performs only slightly worse. All three functionals
actually improve over LDA and PBE in contrast with HTBS, which is shown to fail
dramatically for -quartz. For the vibrational and thermodynamical
properties, LDA performs surprisingly very good. In the majority of the test
cases, it outperforms PBE significantly and also the WC, PBEsol and AM05
functionals though by a smaller margin (and to the detriment of structural
parameters). On the other hand, HTBS performs also poorly for vibrational
quantities. For the dielectric properties, none of the functionals can be put
forward. They all (i) fail to reproduce the electronic dielectric constant due
to the well-known band gap problem and (ii) tend to overestimate the oscillator
strengths (and hence the static dielectric constant)
Rheumatoid arthritis and the risk of chronic kidney diseases: a Mendelian randomization study
BackgroundThe extra-articular lesions of rheumatoid arthritis (RA) are reported to involve multiple organs and systems throughout the body, including the heart, kidneys, liver, and lungs. This study assessed the potential causal relationship between RA and the risk of chronic kidney diseases (CKDs) using the Mendelian randomization (MR) analysis.MethodIndependent genetic instruments related to RA and CKD or CKD subtypes at the genome-wide significant level were chosen from the publicly shared summary-level data of genome-wide association studies (GWAS). Then, we obtained some single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which are associated with RA in individuals of European origin, and had genome-wide statistical significance (p5 × 10−8). The inverse-variance weighted (IVW) method was the main analysis method in MR analysis. The other methods, such as weighted median, MR–Egger, simple mode, and weighted mode were used as supplementary sensitivity analyses. Furthermore, the levels of pleiotropy and heterogeneity were assessed using Cochran’s Q test and leave-one-out analysis. Furthermore, the relevant datasets were obtained from the Open GWAS database.ResultsUsing the IVW method, the main method in MR analysis, the results showed that genetically determined RA was associated with higher risks of CKD [odds ratio (OR): 1.22, 95% confidence interval (CI) 1.13–1.31; p < 0.001], glomerulonephritis (OR: 1.23, 95% CI 1.15–1.31; p < 0.000), amyloidosis (OR = 1.43, 95% CI 1.10–1.88, p < 0.001), and renal failure (OR = 1.18, 95% CI 1.00–1.38, p < 0.001). Then, using multiple MR methods, it was confirmed that the associations persisted in sensitivity analyses, and no pleiotropy was detected.ConclusionThe findings revealed a causal relationship between RA and CKD, including glomerulonephritis, amyloidosis, and renal failure. Therefore, RA patients should pay more attention to monitoring their kidney function, thus providing the opportunity for earlier intervention and lower the risk of progression to CKDs
Transient mTOR Inhibition Facilitates Continuous Growth of Liver Tumors by Modulating the Maintenance of CD133+ Cell Populations
The mammalian target of the rapamycin (mTOR) pathway, which drives cell proliferation, is frequently hyperactivated in a variety of malignancies. Therefore, the inhibition of the mTOR pathway has been considered as an appropriate approach for cancer therapy. In this study, we examined the roles of mTOR in the maintenance and differentiation of cancer stem-like cells (CSCs), the conversion of conventional cancer cells to CSCs and continuous tumor growth in vivo. In H-Ras-transformed mouse liver tumor cells, we found that pharmacological inhibition of mTOR with rapamycin greatly increased not only the CD133+ populations both in vitro and in vivo but also the expression of stem cell-like genes. Enhancing mTOR activity by over-expressing Rheb significantly decreased CD133 expression, whereas knockdown of the mTOR yielded an opposite effect. In addition, mTOR inhibition severely blocked the differentiation of CD133+ to CD133- liver tumor cells. Strikingly, single-cell culture experiments revealed that CD133- liver tumor cells were capable of converting to CD133+ cells and the inhibition of mTOR signaling substantially promoted this conversion. In serial implantation of tumor xenografts in nude BALB/c mice, the residual tumor cells that were exposed to rapamycin in vivo displayed higher CD133 expression and had increased secondary tumorigenicity compared with the control group. Moreover, rapamycin treatment also enhanced the level of stem cell-associated genes and CD133 expression in certain human liver tumor cell lines, such as Huh7, PLC/PRC/7 and Hep3B. The mTOR pathway is significantly involved in the generation and the differentiation of tumorigenic liver CSCs. These results may be valuable for the design of more rational strategies to control clinical malignant HCC using mTOR inhibitors
Symmetrized two-scale finite element discretizations for partial differential equations with symmetric solutions
In this paper, a symmetrized two-scale finite element method is proposed for
a class of partial differential equations with symmetric solutions. With this
method, the finite element approximation on a fine tensor product grid is
reduced to the finite element approximations on a much coarse grid and a
univariant fine grid. It is shown by both theory and numerics including
electronic structure calculations that the resulting approximation still
maintains an asymptotically optimal accuracy. Consequently the symmetrized
two-scale finite element method reduces computational cost significantly.Comment: 22 page
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