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π-Conjugated chiral nanorings with intriguing electronic structures and chiroptical properties have attracted considerable interests in synthetic chemistry and materials science. We present the design principles to access new chiral macrocycles (1 and 2) that are essentially built on the key components of main-group electron-donating carbazolyl moieties or the π-expanded aza[7]helicenes. Both macrocycles show the unique molecular conformations with a (quasi) figure-of-eight topology as a result of the conjugation patterns of 2,2',7,7'-spirobifluorenyl in 1 and triarylamine-coupled aza[7]helicene-based building blocks in 2. This electronic nature of redox-active, carbazole-rich backbones enabled these macrocycles to be readily oxidized chemically and electrochemically, leading to the sequential production of a series of positively charged polycationic open-shell cyclophanes. Their redox-dependent electronic states of the resulting multispin polyradicals have been characterized by VT-ESR, UV-vis-NIR absorption and spectroelectrochemical measurements. The singlet (ΔES-T = -1.29 kcal mol-1) and a nearly degenerate singlet-triplet ground state (ΔES-T(calcd) = -0.15 kcal mol-1 and ΔES-T(exp) = 0.01 kcal mol-1) were proved for diradical dications 12+2• and 22+2•, respectively. Our work provides an experimental proof for the construction of electron-donating new chiral nanorings, and more importantly for highly charged polyradicals with potential applications in chirospintronics and organic conductors.

Variable camber wing is one of the key research directions of advanced aircraft in the future. The inhe-rent contradiction between the rigidity of wing structure and the bending deformation ability is the difficulty in the design of variable camber wing structure. In this paper, a new structure and function integrated variable camber wing structure is designed. Based on the inductive motion mechanism of mimosa in nature, on the one hand, a double-layer array M-type elastic connector is arranged in the elastic deformation zone of the variable camber ribs to meet the requirements of structural stiffness and flexible deformation. On the other hand, the aerodynamic muscle is embedded in the ribs to meet the airfoil deformation drive while maintaining the overall airfoil structural stiffness. Through simulation analysis and experimental test of the prototype, the results show that the variable camber wing design can meet the camber change effect of 13.5° upward and 9.5° downward, thus verifying the feasibility of the variable camber wing structure design.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC. [ABSTRACT FROM AUTHOR]