Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

Antimicrobial peptides CS-piscidin-induced cell death involves activation of RIPK1/PARP, and modification with myristic acid enhances its stability and tumor-targeting capability

Abstract Ovarian cancer (OC) is a highly lethal gynecological malignancy, often diagnosed at advanced stages with limited treatment options. Here, we demonstrate that the antimicrobial peptide CS-piscidin significantly inhibits OC cell proliferation, colony formation, and induces cell death. Mechanistically, CS-piscidin causes cell necrosis by compromising the cell membrane. Furthermore, CS-piscidin can activate Receptor-interacting protein kinase 1 (RIPK1) and induce cell apoptosis by cleavage of PARP. To improve tumor targeting ability, we modified CS-piscidin by adding a short cyclic peptide, cyclo-RGDfk, to the C-terminus (CS-RGD) and a myristate to the N-terminus (Myr-CS-RGD). Our results show that while CS-RGD exhibits stronger anti-cancer activity than CS-piscidin, it also causes increased cytotoxicity. In contrast, Myr-CS-RGD significantly improves drug specificity by reducing CS-RGD toxicity in normal cells while retaining comparable antitumor activity by increasing peptide stability. In a syngeneic mouse tumor model, Myr-CS-RGD demonstrated superior anti-tumor activity compared to CS-piscidin and CS-RGD. Our findings suggest that CS-piscidin can suppress ovarian cancer via multiple cell death forms and that myristoylation modification is a promising strategy to enhance anti-cancer peptide performance. Graphical Abstrac