Multidrug resistance of non-adherent cancer cells

Metastases are the cause of 90% of human cancer deaths. Cancer in _situ_ can usually be effectively removed by surgery. Once cancer cells disseminate from the original site and start to circulate in blood, lymph, or other body fluids, the disease becomes almost incurable. Here we show that cancer cells in a non-adherent, 3-dimentional growth pattern are highly drug resistant compared to their adherent counterparts that grow in monolayer, attaching to the wall of tissue culture plates. The non-adherent cancer cells retain the adhering potential and can attach to an appropriate surface to reacquire adherent phenotype. Once the non-adherent cancer cells become attached, they regain drug response, similar to the original adherent cells. A significant increase in the expression of CD133, CD44, Nanog, survivin, and thymidylate synthase was observed in the non-adherent cancer cells compared to their adherent counterparts, which may underlie the mechanisms of multidrug resistance of the cells. Since the non-adherent cancer cells cultured in vitro resemble the circulating metastatic cells in vivo in that both cells exhibit suspended non-adherent phenotype, possess re-attaching potential, and are highly drug resistant, we suggest that circulating metastatic cells can attach to an appropriate surface to gain adherent phenotype and subsequently acquire drug sensitivity. We propose that devices coated with cell attachment materials or small particles of extracellular matrix and collagen that mimic the structural framework of real human tissues to which cells can attach and grow may be able to stabilize the circulating metastatic cells. Once the metastatic cells undergo attachment and become adherent, they gain drug sensitivity and can be killed by anticancer drugs that are either administered to the blood or conjugated to the devices

Active contours driven by local and global intensity fitting energy with application to SAR image segmentation and its fast solvers

In this paper, we propose a novel variational active contour model based on Aubert-Aujol (AA) denoising model, which hybrides geodesic active contour (GAC) model with active contours without edges (ACWE) model and can be used to segment images corrupted by multiplicative gamma noise. We transform the proposed model into classic ROF model by adding a proximity term. Inspired by a fast denosing algorithm proposed by Jia-Zhao recently, we propose two fast fixed point algorithms to solve SAR image segmentation question. Experimental results for real SAR images show that the proposed image segmentation model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images with multiplicative gamma noise. The proposed fast fixed point algorithms are robustness to initialization contour, and can further reduce about 15% of the time needed for algorithm proposed by Goldstein-Osher.Comment: 20 pages,28 figures. arXiv admin note: substantial text overlap with arXiv:2312.08376, arXiv:2312.0936

SAR image segmentation algorithms based on I-divergence-TV model

In this paper, we propose a novel variational active contour model based on I-divergence-TV model to segment Synthetic aperture radar (SAR) images with multiplicative gamma noise, which hybrides edge-based model with region-based model. The proposed model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images. We incorporate the global convex segmentation method and split Bregman technique into the proposed model, and propose a fast fixed point algorithm to solve the global convex segmentation question[25]. Experimental results for synthetic images and real SAR images show that the proposed fast fixed point algorithm is robust and efficient compared with the state-of-the-art approach.Comment: 22 pages,28 figures. arXiv admin note: substantial text overlap with arXiv:2312.0837

A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms

In this paper, we propose a novel locally statistical variational active contour model based on I-divergence-TV denoising model, which hybrides geodesic active contour (GAC) model with active contours without edges (ACWE) model, and can be used to segment images corrupted by multiplicative gamma noise. By adding a diffusion term into the level set evolution (LSE) equation of the proposed model, we construct a reaction-diffusion (RD) equation, which can gradually regularize the level set function (LSF) to be piecewise constant in each segment domain and gain the stable solution. We further transform the proposed model into classic ROF model by adding a proximity term. Inspired by a fast denoising algorithm proposed by Jia-Zhao recently, we propose two fast fixed point algorithms to solve SAR image segmentation question. Experimental results for real SAR images show that the proposed image segmentation model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images with multiplicative gamma noise. The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the time required for the SBRD model based on the Split Bregman technique.Comment: 18 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2312.11849, arXiv:2312.08376, arXiv:2312.0936

Asymptotic properties of spiked eigenvalues and eigenvectors of signal-plus-noise matrices with their applications

This paper is to consider a general low-rank signal plus noise model in high dimensional settings. Specifically, we consider the noise with a general covariance structure and the signal to be at the same magnitude as the noise. Our study focuses on exploring various asymptotic properties related to the spiked eigenvalues and eigenvectors. As applications, we propose a new criterion to estimate the number of clusters, and investigate the properties of spectral clustering