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In this paper, we present a novel variational wavelet inpainting based on the total variation (TV) regularization and the l1-norm fitting term. The goal of this model is to recover incomplete wavelet coefficients in the presence of impulsive noise. By incorporating the Moreau envelope, the proposed model for wavelet inpainting can better handle the non-differentiability of the l1-norm fitting term. A modified primal dual fixed-point algorithm is developed based on the proximity operator to solve the proposed variational model. Moreover, we consider the existence of solution for the proposed model and the convergence analysis of the developed iterative scheme in this paper. Numerical experiments show the desirable performance of our method. [ABSTRACT FROM AUTHOR]

Background: The survival and proliferation of multiple myeloma (MM) cells in the bone marrow (BM) critically depend on interaction with stromal cells expressing the chemokine CXCL12. CXCL12 regulates the homing to the BM niche by mediating the transendothelial migration and adhesion/retention of the MM cells. The gamma isoform of CXCL12 (CXCL12γ) has been reported to be highly expressed in mouse BM and to show enhanced biological activity compared to the 'common' CXCL12α isoform, mediated by its unique extended C-terminal domain, which binds heparan sulfate proteoglycans (HSPGs) with an extraordinary high affinity. Here, we investigated the expression of CXCL12γ in human BM and studied its functional role in the interaction of MM cells with BM stromal cells (BMSCs). Methods: We assessed CXCL12γ mRNA and protein expression by human BMSCs using qPCR, flow cytometry, and immunohistochemistry. CRISPR-Cas9 was employed to delete CXCL12γ and the heparan sulfate (HS) co-polymerase EXT1 in BMSCs. To study the functional roles of BMSC-derived CXCL12γ and HSPGs in the interaction of MM cells with BMSCs cells, MM cell lines and primary MM cells were co-cultured with BMSCs. Results: We observed that CXCL12γ is expressed in situ by reticular stromal cells in both normal and MM BM, as well as by primary BMSC isolates and BMSC lines. Importantly, upon secretion, CXCL12γ, unlike the CXCL12α isoform, was retained on the surface of BMSCs. This membrane retention of CXCL12γ is HSPG mediated, since it was completely annulated by CRISPR-Cas9-mediated deletion of the HS co-polymerase EXT1. CXCL12γ expressed by BMSCs and membrane-retained by HSPGs supported robust adhesion of MM cells to the BMSCs. Specific genetic deletion of either CXCL12γ or EXT1 significantly attenuated the ability of BMSCs to support MM cell adhesion and, in addition, impaired their capacity to protect MM cells from bortezomib-induced cell death. Conclusions: We show that CXCL12γ is expressed by human BMSCs and upon secretion is retained on their cell surface by HSPGs. The membrane-bound CXCL12γ controls adhesion of MM cells to the stromal niche and mediates drug resistance. These findings designate CXCL12γ and associated HSPGs as partners in mediating MM–niche interaction and as potential therapeutic targets in MM. [ABSTRACT FROM AUTHOR]

Background: The survival and proliferation of multiple myeloma (MM) cells in the bone marrow (BM) critically depend on interaction with stromal cells expressing the chemokine CXCL12. CXCL12 regulates the homing to the BM niche by mediating the transendothelial migration and adhesion/retention of the MM cells. The gamma isoform of CXCL12 (CXCL12γ) has been reported to be highly expressed in mouse BM and to show enhanced biological activity compared to the 'common' CXCL12α isoform, mediated by its unique extended C-terminal domain, which binds heparan sulfate proteoglycans (HSPGs) with an extraordinary high affinity. Here, we investigated the expression of CXCL12γ in human BM and studied its functional role in the interaction of MM cells with BM stromal cells (BMSCs). Methods: We assessed CXCL12γ mRNA and protein expression by human BMSCs using qPCR, flow cytometry, and immunohistochemistry. CRISPR-Cas9 was employed to delete CXCL12γ and the heparan sulfate (HS) co-polymerase EXT1 in BMSCs. To study the functional roles of BMSC-derived CXCL12γ and HSPGs in the interaction of MM cells with BMSCs cells, MM cell lines and primary MM cells were co-cultured with BMSCs. Results: We observed that CXCL12γ is expressed in situ by reticular stromal cells in both normal and MM BM, as well as by primary BMSC isolates and BMSC lines. Importantly, upon secretion, CXCL12γ, unlike the CXCL12α isoform, was retained on the surface of BMSCs. This membrane retention of CXCL12γ is HSPG mediated, since it was completely annulated by CRISPR-Cas9-mediated deletion of the HS co-polymerase EXT1. CXCL12γ expressed by BMSCs and membrane-retained by HSPGs supported robust adhesion of MM cells to the BMSCs. Specific genetic deletion of either CXCL12γ or EXT1 significantly attenuated the ability of BMSCs to support MM cell adhesion and, in addition, impaired their capacity to protect MM cells from bortezomib-induced cell death. Conclusions: We show that CXCL12γ is expressed by human BMSCs and upon secretion is retained on their cell surface by HSPGs. The membrane-bound CXCL12γ controls adhesion of MM cells to the stromal niche and mediates drug resistance. These findings designate CXCL12γ and associated HSPGs as partners in mediating MM–niche interaction and as potential therapeutic targets in MM. [ABSTRACT FROM AUTHOR]

Background: The survival and proliferation of multiple myeloma (MM) cells in the bone marrow (BM) critically depend on interaction with stromal cells expressing the chemokine CXCL12. CXCL12 regulates the homing to the BM niche by mediating the transendothelial migration and adhesion/retention of the MM cells. The gamma isoform of CXCL12 (CXCL12γ) has been reported to be highly expressed in mouse BM and to show enhanced biological activity compared to the 'common' CXCL12α isoform, mediated by its unique extended C-terminal domain, which binds heparan sulfate proteoglycans (HSPGs) with an extraordinary high affinity. Here, we investigated the expression of CXCL12γ in human BM and studied its functional role in the interaction of MM cells with BM stromal cells (BMSCs). Methods: We assessed CXCL12γ mRNA and protein expression by human BMSCs using qPCR, flow cytometry, and immunohistochemistry. CRISPR-Cas9 was employed to delete CXCL12γ and the heparan sulfate (HS) co-polymerase EXT1 in BMSCs. To study the functional roles of BMSC-derived CXCL12γ and HSPGs in the interaction of MM cells with BMSCs cells, MM cell lines and primary MM cells were co-cultured with BMSCs. Results: We observed that CXCL12γ is expressed in situ by reticular stromal cells in both normal and MM BM, as well as by primary BMSC isolates and BMSC lines. Importantly, upon secretion, CXCL12γ, unlike the CXCL12α isoform, was retained on the surface of BMSCs. This membrane retention of CXCL12γ is HSPG mediated, since it was completely annulated by CRISPR-Cas9-mediated deletion of the HS co-polymerase EXT1. CXCL12γ expressed by BMSCs and membrane-retained by HSPGs supported robust adhesion of MM cells to the BMSCs. Specific genetic deletion of either CXCL12γ or EXT1 significantly attenuated the ability of BMSCs to support MM cell adhesion and, in addition, impaired their capacity to protect MM cells from bortezomib-induced cell death. Conclusions: We show that CXCL12γ is expressed by human BMSCs and upon secretion is retained on their cell surface by HSPGs. The membrane-bound CXCL12γ controls adhesion of MM cells to the stromal niche and mediates drug resistance. These findings designate CXCL12γ and associated HSPGs as partners in mediating MM–niche interaction and as potential therapeutic targets in MM. [ABSTRACT FROM AUTHOR]

Considering the limitation of machine and technology, we study the stability for nonlinear impulsive control system with some uncertainty factors, such as the bounded gain error and the parameter uncertainty. A new sufficient condition for this system is established based on the generalized Cauchy–Schwarz inequality in this paper. Compared with some existing results, the proposed method is more practically applicable. The effectiveness of the proposed method is shown by a numerical example. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is characterized by the expansion of malignant plasma cells in the bone marrow (BM). Most MMs display aberrant Wnt/β-catenin signaling, which drives proliferation; however, they lack oncogenic Wnt pathway mutations, suggesting activation by autocrine Wnt ligands and/or paracrine Wnts from the BM microenvironment. Expression of the heparan sulfate (HS) proteoglycan syndecan-1 is a hallmark of MM. Syndecan-1 is a critical player in the complex reciprocal interaction between MM cells and their BM niche, mediating growth factor/cytokine binding and signaling by its HS chains. Here, by means of CRISPR/Cas9-mediated knockout and doxycycline-inducible short hairpin RNA-mediated knockdown of EXT1, a critical enzyme for HS polymerization, we demonstrate that the HS chains decorating syndecan-1 mediate aberrant Wnt pathway activation in MM. HS-deficient MM cells exhibited strongly decreased autocrine Wnt/β-catenin pathway activity and reduced Wnt pathway-dependent proliferation. In addition, we demonstrate that Wnts bind to the HS side chains of syndecan-1 and that this binding contributes to paracrine Wnt pathway activation through the Wnt receptor Frizzled (Fzd). Furthermore, in an HS-dependent fashion, syndecan-1 also binds osteoblast-produced R-spondin, which represses Fzd degradation by activation of LGR4, an R-spondin receptor aberrantly expressed on MM cells. Costimulation with R-spondin and its binding to HS chains decorating syndecan-1 are indispensable for optimal stimulation of Wnt signaling in MM. Taken together, our results identify syndecan-1 as a crucial component of the Wnt signalosome in MM cells, binding Wnts and R-spondins to promote aberrant Wnt/β-catenin signaling and cell growth, and suggest HS and its biosynthetic enzymes as potential targets in the treatment of MM. [ABSTRACT FROM AUTHOR]

The unrestrained growth of tumor cells is generally attributed to mutations in essential growth control genes, but tumor cells are also affected by, or even addicted to, signals from the microenvironment. As therapeutic targets, these extrinsic signals may be equally significant as mutated oncogenes. In multiple myeloma (MM), a plasma cell malignancy, most tumors display hallmarks of active Wnt signaling but lack activatingWnt-pathway mutations, suggesting activation by autocrine Wnt ligands and/or paracrine Wnts emanating from the bone marrow (BM) niche. Here, we report a pivotal role for the R-spondin/leucine-rich repeat-containing G protein-coupled receptor 4 (LGR4) axis in driving aberrant Wnt/ β-catenin signaling in MM. We show that LGR4 is expressed by MM plasma cells, but not by normal plasma cells or B cells. This aberrant LGR4 expression is driven by IL-6/STAT3 signaling and allows MM cells to hijack R-spondins produced by (pre)osteoblasts in the BM niche, resulting in Wnt (co)receptor stabilization and a dramatically increased sensitivity to auto- and paracrine Wnts. Our study identifies aberrant R-spondin/LGR4 signaling with consequent deregulation of Wnt (co)receptor turnover as a driver of oncogenic Wnt/ β-catenin signaling in MM cells. These results advocate targeting of the LGR4/R-spondin interaction as a therapeutic strategy in MM. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive active contour model is proposed for image segmentation, which is built based on fractional order differentiation, level set method and curve evolution. The energy functional for the proposed model consists of three terms: fitting term, regularization tern and penalty term. By incorporating the fractional order fitting term, the novel fitting term can describe the original image more accurately, and be robustness to noise. In order to ensure stable evolution of the level set function, a penalty tern is added into the proposed model. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. Experimental results for both synthetic and real image show desirable performance of our method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a fractional order total variation (TV) regularization functional for image super-resolution, the role of which is to better handle the texture details of image. This regularization functional is then incorporated into a variational formulation with an image fidelity term and the usual TV regularization that can efficiently preserve the discontinuities and image structures. The resulting evolution equation is the gradient descent flow that minimizes the overall functional. The proposed model has been applied to eight real images with promising results; unlike the existing TV-based image super-resolution models, the proposed model does not suffer from block artifacts, staircase edges and false edge near the edges. [Copyright &y& Elsevier]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Total variation based regularization and wavelet frame based regularization are two different methods for image restoration. Moreover, nonconvex regularization has advantages over convex regularization for preserving edges in images. In this paper, a variational model consisting of two nonconvex regularizers based respectively on total variation and wavelet frame is proposed for multiplicative noise removal. The proposed model combines the advantages of the nonconvex regularization and the hybrid regularization. So it can more effectively preserve edges and details in images than any single regularization can. The alternating minimization method is proposed to solve the novel model. Although the corresponding minimization problem is nonconvex, the convexity of all subproblems is guaranteed under some mild conditions. Therefore, we study the convergence property of the algorithm. Numerical experiments have proved that the proposed model with the corresponding algorithm outperforms some state-of-the-art models in terms of the peak signal-to-noise ratio and the relative error of the restored images. [ABSTRACT FROM AUTHOR]

Prototypes of a special conformal load‐bearing antenna array (CLAA) which has nondevelopable surface, are designed, fabricated, and tested, and the effect of the substrate curvature radius on its EM performance is also researched in this work. A novel three‐dimensional (3‐D) printing technology and fabrication equipment based on micro‐droplet spraying and metal laser sintering are proposed to create patch array and divider network on a non‐developable curved rigid substrate. In order to compare with conventional technology (such as chemical etching), a planar CLAA prototype with two patches, operating frequency at 5GHz, is designed and fabricated by two different technologies, the surface roughness, fabrication tolerance, and EM performance are tested and compared. Finally, a spherical CLAA prototype with eight patches, operating frequency at 13GHz, is designed and fabricated by the novel 3D printing, measured EM performance demonstrate the applicability of additive manufacturing for this special CLAA. [ABSTRACT FROM AUTHOR]

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich's helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ (x _ , λ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _. In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software "ASTRA toolbox." We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images. [ABSTRACT FROM AUTHOR]

• Proposed a noise-robust convolution sparse representation model that can extract good features from noisy images. • Develop a fast algorithm to solve our model in the spatial domain. • Experimental results show that our method can get good denoised images and fused images. In this paper, we propose a noise-robust online convolutional coding model for image representation, which can use the noisy images as training data. Then an alternating algorithm is utilized to convert the model into two sub-problems, the image pursuit problem and the dictionary learning problem. For the image pursuit problem, the Gauss elimination method is used to solve the equation set which is derived by the Euler equation and discrete Fourier transform. For the dictionary learning problem, a gradient-descent flow is derived to solve it. Experimental results show that our method can output more meaningful feature representations compared to the related models while the training data was corrupted by Poisson noise. [ABSTRACT FROM AUTHOR]

Highlights • We propose a binary level set variational model involving L 1 data term for noisy image segmentation. • We design a three-step time-splitting scheme to numerically solve our model. • The proposed model is robust to impulse noise (i.e., salt & pepper and random-valued) and effective for some real images. Abstract In this paper, we propose a variational model in binary level set framework for segmenting two-phase images with impulse noise. In this model, the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set. The energy functional consists of three terms, i.e., the data term that is defined by L 1-norm metric, the regularization term that is defined by Dirichlet energy of level set function, and the penalty term that punishes the deviation of level set function from binary function. We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently, in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence. We present some experimental results that are performed on simulated and real images, which demonstrate that our model is very robust to impulse noise and has the best performance compared with five relevant models. [ABSTRACT FROM AUTHOR]

In this paper, we present a new variant of level set methods and then propose a ternary variational level set model involving L 0 gradient regularizer and L 0 function regularizer in discrete framework, following the Chan-Vese model for image segmentation. Different from the existing level set methods, we use the 0.5-level set of a ternary function whose values are within {0, 0.5, 1} to implicitly represent the interfaces between subregions and use L 0 counting operator to discretely measure the length of interfaces and the area of foreground subregions. The proposed model can be regarded as a discrete form of the Chan-Vese model. Based on the half-quadratic splitting method, we design an alternating minimization algorithm to solve our model efficiently. Experimental results show that the proposed method has good performance for segmentation of images with severe noise, outliers or low contrast. [ABSTRACT FROM AUTHOR]

It is well known that an image is the pixel-wise multiplication of two components (illumination and reflectance) that are described as intrinsic images. To enhance the visual quality of a degraded image with uneven illumination and low local contrast, it is an effective way to extract the illumination or reflectance from the image and correct the unevenness of illumination. In this paper, we propose a variational model to estimate the illumination and reflectance from the degraded image for the uneven illumination correction. The proposed model is solved by the alternating minimization scheme and the augmented Lagrange method. We give some theoretic analysis of our model and convergence analysis of the algorithm. Experimental results show that the proposed model can achieve excellent performance in the correction of the uneven illumination. [ABSTRACT FROM AUTHOR]

Angle is an intuitive and important property for representing corners. This fact motivates us to present a novel angle-based corner detector, named Eigenvector-based Angle Estimator (EAE). EAE estimates the angle of each point in a contour via computing the eigenvectors of the covariance matrix of boundary points over a small Region of Support (RoS). Since EAE is sensitive to uniform scaling due to the fixed RoS, an enhanced version of EAE named Weighted EAE (WEAE) is proposed. WEAE achieves robustness to uniform scaling by weighting the boundary points using their distances from the target point. Experimental results demonstrate that EAE and WEAE can efficiently achieve promising performance in comparisons with several recent state-of-the-art approaches under two commonly used evaluation metrics, namely, Average Repeatability (AR) and Localization Error (LE). [ABSTRACT FROM AUTHOR]

EXTL3 regulates the biosynthesis of heparan sulfate (HS), important for both skeletal development and hematopoiesis, through the formation of HS proteoglycans (HSPGs). By whole-exome sequencing, we identified homozygous missense mutations c.1382C>T, c.1537C>T, c.1970A>G, and c.2008T>G in EXTL3 in nine affected individuals from five unrelated families. Notably, we found the identical homozygous missense mutation c.1382C>T (p.Pro461Leu) in four affected individuals from two unrelated families. Affected individuals presented with variable skeletal abnormalities and neurodevelopmental defects. Severe combined immunodeficiency (SCID) with a complete absence of T cells was observed in three families. EXTL3 was most abundant in hematopoietic stem cells and early progenitor T cells, which is in line with a SCID phenotype at the level of early T cell development in the thymus. To provide further support for the hypothesis that mutations in EXTL3 cause a neuro-immuno-skeletal dysplasia syndrome, and to gain insight into the pathogenesis of the disorder, we analyzed the localization of EXTL3 in fibroblasts derived from affected individuals and determined glycosaminoglycan concentrations in these cells as well as in urine and blood. We observed abnormal glycosaminoglycan concentrations and increased concentrations of the non-sulfated chondroitin disaccharide D0a0 and the disaccharide D0a4 in serum and urine of all analyzed affected individuals. In summary, we show that biallelic mutations in EXTL3 disturb glycosaminoglycan synthesis and thus lead to a recognizable syndrome characterized by variable expression of skeletal, neurological, and immunological abnormalities. [ABSTRACT FROM AUTHOR]

In this paper, we propose a variational model for the limited-angle computed tomography (CT) image reconstruction and convert it into an end-to-end deep network. We first use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms, the second refines the CT images, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approximate the solutions of the first two subproblems and, thus, obtain an end-to-end deep network for the limited-angle CT image reconstruction. Our network tackles both the sinograms and the CT images, and can simultaneously suppress the artifacts caused by the incomplete data and recover fine structural information in the CT images. Experimental results show that our method outperforms the related methods for the limited-angle CT image reconstruction. • A variational model utilizing the prior information both in the frequency and spatial domains is proposed. • A new end-to-end deep network is proposed by using CNNs to approximate the solution of the proposed model. • A subnetwork in the Fourier transform domain is developed to complete the limited-angle sinograms. • Experiments show that our method outperforms the related methods for limited-angel CT reconstructions. [ABSTRACT FROM AUTHOR]

We present a corner-detection method named arc length-based angle estimator (AAE). Different from most of the existing approaches, AAE focuses on employing angle detection for finding corners, because angle is an important measure for discrete curvature. AAE provides a new robust solution to the estimation of the K-cosine. In AAE, the K-cosine estimation issue in the x,y space is considered as the problem of the slope estimations in the s, x and s, y spaces, where s is the arc length. Then, weighted least square fitting is employed to address such a slope estimation issue. Experimental results demonstrate that AAE can achieve promising performance in comparison with some recent state-of-the-art approaches under two commonly used evaluation metrics, namely average repeatability and localization error criteria. [ABSTRACT FROM AUTHOR]