Anion Recognition by Charge Neutral Electron-deficient Arene Receptors

Anion-? interactions are newly emerging non-covalent interactions and have attracted considerable attention from both theoreticians and supramolecular chemists. This short review article summarizes the recent advances in anion recognition using charge neutral electron-deficient aromatic compounds

Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer learning can enhance classification performance and generalization ability. In this study, we proposed a novel cross-scale framework that integrates transfer learning and deep learning for urban land cover mapping. The framework utilizes pre-trained deep learning models, trained on diverse urban datasets, as a foundation for classification. These models are then finetuned using transfer learning techniques on smaller urban datasets, tailoring them to the specific characteristics of the target urban context. To evaluate the effectiveness and feasibility of the proposed framework, extensive evaluations are conducted across different cities and years. Performance metrics such as accuracy and dice score are employed to assess the framework\u27s classification capabilities. The results of this study contribute to advancing the field of urban classification by demonstrating the effectiveness and feasibility of the cross-scale framework. By combining transfer learning and deep learning, the framework improves classification accuracy, efficiency, and scalability in urban land cover mapping tasks. Leveraging the strengths of transfer learning and deep learning holds great promise for accurate and efficient urban land cover mapping, providing valuable insights for urban planning and resource management decision-making

Lattice dynamics and elastic properties of alpha-U at high-temperature and high-pressure by machine learning potential simulations

Studying the physical properties of materials under high pressure and temperature through experiments is difficult. Theoretical simulations can compensate for this deficiency. Currently, large-scale simulations using machine learning force fields are gaining popularity. As an important nuclear energy material, the evolution of the physical properties of uranium under extreme conditions is still unclear. Herein, we trained an accurate machine learning force field on alpha-U and predicted the lattice dynamics and elastic properties at high pressures and temperatures. The force field agrees well with the ab initio molecular dynamics (AIMD) and experimental results, and it exhibits higher accuracy than classical potentials. Based on the high-temperature lattice dynamics study, we first present the temperature-pressure range in which the Kohn anomalous behavior of the ${\Sigma}$4 optical mode exists. Phonon spectral function analysis showed that the phonon anharmonicity of alpha-U is very weak. We predict that the single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E,G), and polycrystalline sound velocity ($C_L$,$C_S$) have strong heating-induced softening. All the elastic moduli exhibited compression-induced hardening behavior. The Poisson's ratio shows that it is difficult to compress alpha-U at high pressures and temperatures. Moreover, we observed that the material becomes substantially more anisotropic at high pressures and temperatures. The accurate predictions of alpha-U demonstrate the reliability of the method. This versatile method facilitates the study of other complex metallic materials.Comment: 21 pages, 9 figures, with Supplementary Materia

A convenient tandem one-pot synthesis of donor-acceptor-type triphenylene 2,3-dicarboxylic esters from diarylacetylene

A tandem one-pot method for the direct synthesis of polysubstituted triphenylene 2,3-dicarboxylic esters with different substitution patterns was developed by enyne metathesis of diarylacetylene, followed by Diels–Alder, aromatization and a cyclization cascade

Polyphenolic natural products as photosensitizers for antimicrobial photodynamic therapy: recent advances and future prospects

Antimicrobial photodynamic therapy (aPDT) has become a potent contender in the fight against microbial infections, especially in the context of the rising antibiotic resistance crisis. Recently, there has been significant interest in polyphenolic natural products as potential photosensitizers (PSs) in aPDT, given their unique chemical structures and inherent antimicrobial properties. Polyphenolic natural products, abundant and readily obtainable from natural sources, are generally regarded as safe and highly compatible with the human body. This comprehensive review focuses on the latest developments and future implications of using natural polyphenols as PSs in aPDT. Paramount polyphenolic compounds, including curcumin, hypericin, quercetin, hypocrellin, celastrol, riboflavin, resveratrol, gallic acid, and aloe emodin, are elaborated upon with respect to their structural characteristics, absorption properties, and antimicrobial effects. Furthermore, the aPDT mechanism, specifically its targeted action on microbial cells and biofilms, is also discussed. Polyphenolic natural products demonstrate immense potential as PSs in aPDT, representing a promising alternate approach to counteract antibiotic-resistant bacteria and biofilm-related infections

Interleukin-18 and IL-18BP in inflammatory dermatological diseases

Interleukin (IL)-18, an interferon-γ inducer, belongs to the IL-1 family of pleiotropic pro-inflammatory factors, and IL-18 binding protein (IL-18BP) is a native antagonist of IL-18 in vivo, regulating its activity. Moreover, IL-18 exerts an influential function in host innate and adaptive immunity, and IL-18BP has elevated levels of interferon-γ in diverse cells, suggesting that IL-18BP is a negative feedback inhibitor of IL-18-mediated immunity. Similar to IL-1β, the IL-18 cytokine is produced as an indolent precursor that requires further processing into an active cytokine by caspase-1 and mediating downstream signaling pathways through MyD88. IL-18 has been implicated to play a role in psoriasis, atopic dermatitis, rosacea, and bullous pemphigoid in human inflammatory skin diseases. Currently, IL-18BP is less explored in treating inflammatory skin diseases, while IL-18BP is being tested in clinical trials for other diseases. Thereby, IL-18BP is a prospective therapeutic target

Wideband design of compact monopole-Like circular patch antenna using modal analysis

In this paper, we present a systematic approach to design a compact dual-mode monopole-like patch antenna using characteristic mode analysis (CMA). The modal analysis of a slotted circular patch structure incorporating a new shorting pin loading technique is presented. To achieve a compact monopole-like antenna with wideband operation, it is demonstrated that the first two significant modes with monopole-like patterns are the most suitable ones for dual-mode excitation. Based on the analysis of the modal currents and electric fields, four groups of shorting pins and four slots are introduced to individually tune the two modes, which facilitates the optimization. The effects of these slots and shorting pins on the resonant frequencies of the two modes are analyzed in detail. Finally, a CPW T-junction power divider is applied to simultaneously excite these two modes and suppress the undesired modes. Apart from a more compact form factor and higher gain than existing work, it also features a competitive gain-bandwidth per volume ratio