Study of dispersion extraction for optical coherence tomography imaging

Dispersion mismatch of optical coherence tomography (OCT), caused by inherent dispersion of biological sample, leads to axial resolution degradation and image blurring. However, the dispersion of biological samples may carry characteristic information of itself. Therefore, it is useful and important to extract dispersion as an additional parameter of biological samples. Here, we study and compare five categories of conventional OCT dispersion extraction methods first. The results show that the iterative method has the best accuracy and stability. Furthermore, we propose an improved iterative method, which achieves higher accuracy and stability in simulation compared with conventional methods. Then, the dispersion values of optical glasses are extracted experimentally utilizing the proposed method. The extracted values are consistent well with reference values. Furthermore, we apply the proposed method for extracting the dispersion characteristics of zebrafish eye. With the proposed method, OCT-based analysis of dispersion characteristics could be a powerful tool in clinical practice.

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

On the Convergence Theory of Double K-Weak Splittings of Type II

Recently, Wang (2017) has introduced the K-nonnegative double splitting using the notion of matrices that leave a cone K ⊆ ℝⁿ invariant and studied its convergence theory by generalizing the corresponding results for the nonnegative double splitting by Song and Song (2011). However, the convergence theory for K-weak regular and K-nonnegative double splittings of type II is not yet studied. In this article, we first introduce this class of splittings and then discuss the convergence theory for these sub-classes of matrices. We then obtain the comparison results for two double splittings of a K-monotone matrix. Most of these results are completely new even for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K = \mathbb{R}_ + ^n$$\end{document}K=ℝ+n. The convergence behavior is discussed by performing numerical experiments for different matrices derived from the discretized Poisson equation.

Protein-protein interaction site prediction using random forest proximity distance

A front-end method based on random forest proximity distance (PD) is used to screen the test set to improve protein-protein interaction site (PPIS) prediction. The assessment of a distance metric is done under the assumption that a distance definition of higher quality leads to higher classification. On an independent test set, the numerical analysis based on statistical inference shows that the PD has the advantage over Mahalanobis and Cosine distance. Based on the fact that the proximity distance depends on the tree composition of the random forest model, an iterative method is designed to optimize the proximity distance, which adjusts the tree composition of the random forest model by adjusting the size of the training set. Two PD metrics, 75PD and 50PD, are obtained by the iterative method. On two independent test sets, compared with the PD produced by the original training set, the values of 75PD in Matthews correlation coefficient and F₁ score were higher, and the differences between them were statistically significant. All numerical experiments show that the closer the distance between the test data and the training data, the better the prediction results of the predictor. These indicate that the iterative method can optimize proximity distance definition and the distance information provided by PD can be used to indicate the reliability of prediction results.

Weak convergence theorem for a class of split variational inequality problems and applications in a Hilbert space

In this paper, we consider the algorithm proposed in recent years by Censor, Gibali and Reich, which solves split variational inequality problem, and Korpelevich's extragradient method, which solves variational inequality problems. As our main result, we propose an iterative method for finding an element to solve a class of split variational inequality problems under weaker conditions and get a weak convergence theorem. As applications, we obtain some new weak convergence theorems by using our weak convergence result to solve related problems in nonlinear analysis and optimization.

A Color-Doppler Shear-Wave-Imaging Phase-reconstruction Method Using Four Color Flow Images

This study investigates shear wave phase map reconstruction using a limited number of color flow images (CFIs) acquired with a color Doppler ultrasound imaging instrument. We propose an efficient reconstruction method to considerably reduce the number of CFIs required for reconstruction and compare this method with Fourier analysis-based color Doppler shear wave imaging. The proposed method uses a two-step phase reconstruction process, including an initial phase map derived from four CFIs using an advanced iterative algorithm of optical interferometry. The second step reduces phase artifacts in the initial phase map using an iterative correction procedure that cycles between the Fourier and inverse Fourier domains while imposing directional filtering and total variation regularization. We demonstrate the efficacy of this method using synthetic and experimental data of a breast phantom and human breast tissue. Our results show that the proposed method maintains image quality and reduces the number of CFIs required to four; previous methods have required at least 32 CFIs to achieve equivalent image quality. The proposed method is applicable to real-time shear wave elastography using a continuous shear wave produced by a mechanical vibrator.

Real time implementation of anti-scatter grid artifact elimination method for high resolution x-ray imaging CMOS detectors using Graphics Processing Units (GPUs)

Scatter is one of the most important factors effecting image quality in radiography. One of the best scatter reduction methods in dynamic imaging is an anti-scatter grid. However, when used with high resolution imaging detectors these grids may leave grid-line artifacts with increasing severity as detector resolution improves. The presence of such artifacts can mask important details in the image and degrade image quality. We have previously demonstrated that, in order to remove these artifacts, one must first subtract the residual scatter that penetrates through the grid followed by dividing out a reference grid image; however, this correction must be done fast so that corrected images can be provided in real-time to clinicians. In this study, a standard stationary Smit-Rontgen x-ray grid (line density - 70 lines/cm, grid ratio - 13:1) was used with a high-resolution CMOS detector, the Dexela 1207 (pixel size - 75 micron) to image anthropomorphic head phantoms. For a 15 × 15 cm field-of-view (FOV), scatter profiles of the anthropomorphic head phantoms were estimated then iteratively modified to minimize the structured noise due to the varying grid-line artifacts across the FOV. Images of the head phantoms taken with the grid, before and after the corrections, were compared, demonstrating almost total elimination of the artifact over the full FOV. This correction is done fast using Graphics Processing Units (GPUs), with 7-8 iterations and total time taken to obtain the corrected image of only 87 ms, hence, demonstrating the virtually real-time implementation of the grid-artifact correction technique.

Interpreting the flock algorithm: a reply to Anderson & Barry (2015)

Anderson & Barry (Molecular Ecology Resources, 2015, 10, 1020-1030) compared a reprogrammed version of flock (Duchesne & Turgeon , Molecular Ecology Resources, 2009, 9, 1333-1344), flockture, to a particular model of structure (Pritchard , Genetics, 2000, 155, 945-959) that they propose is equivalent to flock, a non-MCMC, non-Bayesian algorithm. They conclude that structure performs better than flockture at clustering individuals from simulated populations with very low level of differentiation (FST c. 0.008) based on 15 microsatellites or 96 SNPs. We rather consider that both algorithms failed, with proportions of correct allocations lower than 50%. The authors also noted the slightly better performance of flockture with SNPs at intermediate FST values (c. 0.02-0.04) but did not comment. Finally, we disagree with the way the processing time of each program was compared. When compared on the basis of a run leading to a clustering solution, the main output of any clustering algorithm, flock, is, as users can readily experience, much faster. In all, we feel that flock performs at least as well as structure as a clustering algorithm. Moreover, flock has two major assets: high speed and clear, well validated, rules to estimate K, the number of populations. It thus provides a valuable addition to the set of tools at the disposal of the many researchers dealing with real empirical data sets.

FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter

Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

LSRN: A PARALLEL ITERATIVE SOLVER FOR STRONGLY OVER- OR UNDERDETERMINED SYSTEMS

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to min _{x∈ℝ ⁿ} ‖Ax - b‖₂, where A ∈ ℝ ^{m × n} with m ≫ n or m ≪ n, and where A may be rank-deficient. Tikhonov regularization may also be included. Since A is involved only in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and LSRN automatically speeds up when A is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size ⌈γ min(m, n)⌉ × min(m, n), where γ is moderately larger than 1, e.g., γ = 2. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results show that on a shared-memory machine, LSRN is very competitive with LAPACK's DGELSD and a fast randomized least squares solver called Blendenpik on large dense problems, and it outperforms the least squares solver from SuiteSparseQR on sparse problems without sparsity patterns that can be exploited to reduce fill-in. Further experiments show that LSRN scales well on an Amazon Elastic Compute Cloud cluster.