Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m 6 A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m 6 A in LUAD patients is critical. In this study, m 6 A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m 6 Arelated lncRNA model. Kaplan-Meier analysis, principalcomponent analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m 6 A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m 6 Abased lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.

The novel copolymers of acrylamide (AM) with complex pseudorotaxane monomer (BAMACB) of butyl ammonium methacrylate (BAMA) and cucurbit[6]uril (CB[6]) were prepared via free‐radical polymerization in aqueous solution. The copolymers containing pseudorotaxane (PAM/BAMACB) were characterized by 1H‐NMR, FTIR, elemental analysis, TGA, and DSC. The glass transition temperature (Tg) of the copolymer PAM/BAMACB are higher than that of the copolymer of acrylamide and butyl ammonium methacrylate (PAM/BAMA) because of the enhanced rigidity and the bulky steric hindrance of BAMACB side chain in PAM/BAMACB. The molecular weights of copolymer PAM/BAMACB were obtained via static light scattering. The hydrodynamic radii of coils or aggregates were investigated by dynamic light scattering. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 5999–6008, 2008

This paper proposes an image encryption algorithm based on a chaotic map and information entropy. Unlike Fridrich’s structure, the proposed method contains permutation, modulation, and diffusion (PMD) operations. This method avoids the shortcoming in traditional schemes of strictly shuffling the pixel positions before diffusion encryption. Information entropy is employed to influence the generation of the keystream. The initial keys used in the permutation and diffusion stages interact with each other. As a result, the algorithm acts as an indivisible entity to enhance security. Experimental results and security analyses demonstrate the good performance of the proposed algorithm as a secure and effective communication method for images.

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In this paper, we present a chaotic image encryption algorithm in which the key stream is generated by nonlinear Chebyshev function. The novel method of designing pseudorandom chaotic sequence is carried out with the created secret keys depending on with each other. We then make multiple permutation of pixels to decrease the strong correlation between adjacent pixels in original plain image. Further, a two-dimensional Chebyshev function is considered to avoid known-plaintext and chosen-plaintext attacks in diffusion process, i.e., even with a one-bit change in original plain image, the encrypted image would become different greatly. Simulation results are given to show that the proposed method can offer us an efficient way of encrypting image.