1,721,200 research outputs found
Short and highly efficient synthetic promoters for melanoma-specific gene expression.
Full text linkHere, we report the construction and functional analysis of synthetic promoters designed for gene therapy applications requiring strong and specific gene expression in melanoma cell lines. We have analysed the transcriptional activity of different combinations of two transcriptional regulatory modules, a melanocyte-specific element from the human tyrosinase promoter and a cell-cycle-specific element from the human alpha-fetoprotein promoter. Transient expression assays in different cell lines show that several of these composite synthetic promoters can drive a strong and selective expression of a reporter gene in melanoma cell, providing us with a new powerful tool for gene therapy of melanomas
Behavioral portfolio optimization via cumulative prospect theory with a symmetric alternating direction method of multipliers
No full textAmidst prevailing uncertainties in investment landscapes and heterogeneous investor risk attitudes toward gains and losses, this study investigates behavioral portfolio selection under a flexible investment horizon. We employ cumulative prospect theory (CPT) to model preferences, integrating mean-variance criteria with asymmetric risk behaviors. By extending the mean-variance framework, our model balances exploiting existing opportunities and exploring new assets to derive adaptive strategies. The optimization problem is solved using the symmetric alternating direction method of multipliers and the pooling-adjacent-violators algorithm, chosen for their efficacy in handling non-convexity and ordinal constraints. The optimal number of new assets to explore is determined via an integer programming problem, solved with a modified particle swarm optimization algorithm. In addition, we incorporate environmental, social, and governance (ESG) metrics to evaluate their impact on sustainable behavioral portfolios. Empirical analyses using real-world equity datasets demonstrate that strategic exploration enhances returns, reduces portfolio risk, and improves investment efficiency. The effectiveness of the proposed algorithm is also illustrated. The results highlight the value of adaptive horizon planning, ESG integration, and CPT preferences in portfolio optimization, offering actionable insights for investors navigating dynamic markets
Cartoon-texture evolution for two-region image segmentation
Full text linkTwo-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. (SIAM J Appl Math 66(5):1632–1648, 2006) designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model
On the Adaptive Penalty Parameter Selection in ADMM
No full textMany data analysis problems can be modeled as a constrained optimization problem characterized by nonsmooth functionals, often because of the presence of (Formula presented.) -regularization terms. One of the most effective ways to solve such problems is through the Alternate Direction Method of Multipliers (ADMM), which has been proved to have good theoretical convergence properties even if the arising subproblems are solved inexactly. Nevertheless, experience shows that the choice of the parameter (Formula presented.) penalizing the constraint violation in the Augmented Lagrangian underlying ADMM affects the method’s performance. To this end, strategies for the adaptive selection of such parameter have been analyzed in the literature and are still of great interest. In this paper, starting from an adaptive spectral strategy recently proposed in the literature, we investigate the use of different strategies based on Barzilai–Borwein-like stepsize rules. We test the effectiveness of the proposed strategies in the solution of real-life consensus logistic regression and portfolio optimization problems
Gene expression profiles and molecular subtyping of hereditary haemochromatosis using cDNA microarray.
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