A new conjugate gradient method based on the modified secant equations

Abstract. Based on the secant equations proposed by Zhang, Deng and Chen, we propose a new nonlinear conjugate gradient method for unconstrained optimization problems. Global convergence of this method is established under some proper conditions

A new difference of anisotropic and isotropic total variation regularization method for image restoration

Total variation (TV) regularizer has diffusely emerged in image processing. In this paper, we propose a new nonconvex total variation regularization method based on the generalized Fischer-Burmeister function for image restoration. Since our model is nonconvex and nonsmooth, the specific difference of convex algorithms (DCA) are presented, in which the subproblem can be minimized by the alternating direction method of multipliers (ADMM). The algorithms have a low computational complexity in each iteration. Experiment results including image denoising and magnetic resonance imaging demonstrate that the proposed models produce more preferable results compared with state-of-the-art methods

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time

A primal-dual algorithm framework for convex saddle-point optimization

Abstract In this study, we introduce a primal-dual prediction-correction algorithm framework for convex optimization problems with known saddle-point structure. Our unified frame adds the proximal term with a positive definite weighting matrix. Moreover, different proximal parameters in the frame can derive some existing well-known algorithms and yield a class of new primal-dual schemes. We prove the convergence of the proposed frame from the perspective of proximal point algorithm-like contraction methods and variational inequalities approach. The convergence rate O ( 1 / t ) $O(1/t)$ in the ergodic and nonergodic senses is also given, where t denotes the iteration number

Synergistic Induction of Erlotinib-Mediated Apoptosis by Resveratrol in Human Non-Small-Cell Lung Cancer Cells by Down-Regulating Survivin and Up-Regulating PUMA

Background/Aim: Treatment of human non-small-cell lung cancer (NSCLC) often involves uses of multiple therapeutic strategies with different mechanisms of action. Here we found that resveratrol (RV) enhanced the anti-tumor effects of epidermal growth factor receptor (EGFR) inhibitor erlotinib in NSCLC cells. Methods: Cell viability was measured by MTT assay and clonogenicity assay. Western blot was applied to assess the protein expression levels of target genes. Cell apoptosis was monitored by AnnexinV-FITC assay and sub-G1 population assay. Intracellular ROS were measured by flow cytometric analysis. Cell caspase activities were carried out by fluorometric assays. Results: Exposure of H460, A549, PC-9 and H1975 cells to minimal or non-toxic concentrations of RV and erlotinib synergistically reduced cell viability, colony formation and induced cell apoptosis. Furthermore, RV synergistically enhanced erlotinib-induced apoptosis was involved in ROS production. Additionally, co-treatment with RV and erlotinib repressed the expressions of anti-apoptosis proteins, such as survivin and Mcl-1, whereas promoted p53 and PUMA expression and caspase 3 activity. Moreover, the combination was also more effective at inhibiting the AKT/mTOR/S6 kinase pathway. Subsequently, small interfering RNA (siRNA) depletion of PUMA and overexpression of survivin significantly attenuated NSCLC cells apoptosis induced by the combination of the two drugs. Conclusion: Our findings suggested that RV synergistically enhanced the anti-tumor effects of erlotinib in NSCLC cells were involved in decrease of survivin expression and induction of PUMA expression. In conclusion, based on the observations from our study, we indicated that the combined administration of these two drugs might be considered as a novel therapeutic regimen for treating NSCLC

Concomitant Targeting of Multiple Key Transcription Factors Effectively Disrupts Cancer Stem Cells Enriched in Side Population of Human Pancreatic Cancer Cells

<div><p>Background</p><p>A major challenge in the treatment of pancreatic ductal adenocarcinoma is the failure of chemotherapy, which is likely due to the presence of the cancer stem cells (CSCs).</p> <p>Objective</p><p>To identify side population (SP) cells and characterize s-like properties in human pancreatic cancer cell lines (h-PCCLs) and to exploit the efficacy of concomitant targeting of multiple key transcription factors governing the stemness of pancreatic CSCs in suppressing CSC-like phenotypes.</p> <p>Methods</p><p>Flow cytometry and Hoechst 33342 DNA-binding dye efflux assay were used to sort SP and non-SP (NSP) cells from three h-PCCLs: PANC-1, SW1990, and BxPc-3. The self-renewal ability, invasiveness, migration and drug resistance of SP cells were evaluated. Expression of CSC marker genes was analyzed. Tumorigenicity was assessed using a xenograft model in nude mice. Effects of a complex decoy oligonucleotide (cdODN-SCO) designed to simultaneously targeting Sox2, Oct4 and c-Myc were assessed.</p> <p>Results</p><p>CSCs were enriched in the side proportion (SP) cells contained in the h-PCCLs and they possessed aggressive growth, invasion, migration and drug-resistance properties, compared with NSP cells. SP cells overexpressed stem cell markers CD133 and ALDH1, pluripotency maintaining factors Nanog, Sox2 and Oct4, oncogenic transcription factor c-Myc, signaling molecule Notch1, and drug resistant gene ABCG2. Moreover, SP cells consistently demonstrated significantly greater tumorigenicity than NSP cells in xenograft model of nude mice. CdODN–SOC efficiently suppressed all CSC properties and phenotypes, and minimized the tumorigenic capability of the SP cells and the resistance to chemotherapy. By comparison, the negative control failed to do so.</p> <p>Conclusion</p><p>The findings indicate that targeting the key genes conferring the stemness of CSCs can efficiently eliminate CSC-like phenotypes, and thus may be considered a new approach for cancer therapy. Specifically, the present study establishes the combination of Sox2/Oct4/c-Myc targeting as a potential anti-pancreatic cancer agent worthy of further studies in preclinical settings.</p> </div