抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

/pdf/tensor-decompositions-and-applications-46vl2ih5gn.pdf

Tensor Decompositions and Applications

We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted p-penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem. Use of such p-penalized problems with p < 2 is often advocated when one expects the underlying ideal noiseless solution to have a sparse expansion with respect to the basis under consideration. To compute the corresponding regularized solutions, we analyze an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm. © 2004 Wiley Periodicals, Inc.

/pdf/an-iterative-thresholding-algorithm-for-linear-inverse-565i4q2jj2.pdf

An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission. >

/pdf/image-coding-using-wavelet-transform-426c6qtjbf.pdf

Image coding using wavelet transform

This paper presents a systematic approach to the description of spatial resolution of seismic experiments and migration (or inversion) algorithms.

Spatial Resolution of Migration Algorithms

A shift-invariant multiresolution representation of signals or images using dilations and translations of the autocorrelation functions of compactly supported wavelets is proposed. Although this set of functions does not form an orthonormal basis, a number of properties of the autocorrelation functions of the compactly supported wavelets make them useful for signal and image analysis. Unlike wavelet-based orthonormal representations, the representation has symmetric analyzing functions, shift invariance, natural and simple iterative interpolation schemes, and a simple algorithm for finding the locations of the multiscale edges as zero crossings. A noniterative method is developed for reconstructing signals from their zero crossings (and slopes at these zero crossings) in the representation. This method reduces the problem to that of solving a system of linear equations. >

https://amath.colorado.edu/pub/wavelets/papers/SAI-BEY-1993.pdf

Multiresolution representations using the auto-correlation functions of compactly supported wavelets

https://amath.colorado.edu/pub/wavelets/papers/BEY-SAN-2005.pdf

Wave propagation using bases for bandlimited functions

: A class of vector-space bases is introduced for the sparse representation of discretizations of integral operators. An operator with a smooth, non-oscillatory kernel possessing a finite number of singularities in each row or column is represented in these bases as a sparse matrix, to high precision. A method is presented that employs these bases for the numerical solution of second-kind integral equations in time bounded by O(nlog squared n) , where n is the number of points in the discretization. Numerical results are given which demonstrate the effectiveness of the approach, and several generalizations and applications of the method are discussed.

/pdf/wavelets-for-the-fast-solution-of-second-kind-integral-3v45w57q0d.pdf

Wavelets for the Fast Solution of Second-Kind Integral Equations

https://amath.colorado.edu/pub/wavelets/papers/SA-MA-BE-2003.pdf

Gregory Beylkin

Papers

Spatial Resolution of Migration Algorithms

Multiresolution representations using the auto-correlation functions of compactly supported wavelets

Wave propagation using bases for bandlimited functions

Wavelets for the Fast Solution of Second-Kind Integral Equations

A fast reconstruction algorithm for electron microscope tomography.