Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

Texture pattern classification has long been an essential issue in computer vision (CV). However, texture is a kind of perceptual concept of human beings in scene observation or content understanding, which cannot be defined or described clearly in CV. Visually, the visual appearance of the complex spatial structure (CSS) of texture pattern (TP) generally depends on the random organization (or layout) of local homogeneous fragments (LHFs) in the imaged surface. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for distinctive CSS feature characterization to achieve good classification performance. This work presents an image statistical modeling-based TP identification (ISM-TPI) method. It firstly makes a theoretical explanation of the Weibull distribution (WD) behavior of the LHFs of the imaged surface in the imaging process based on the sequential fragmentation theory (SFT), which consequently derives a symmetrical WD model (SWDM) to characterize the LSD of the TP’s SS. Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. The effectiveness of the proposed ISM-TPI method is verified by extensive experiments on three texture image databases. The classification results demonstrate the superiority of the proposed methods over several state-of-the-art TP classification methods

Customizable metal-phenolic supraparticles based on rationally designed building blocks

Metal-phenolic networks (MPNs) as a versatile platform for particle engineering have been well developed due to their integrated benefits of both metal ions and phenolic molecules. However, the approaches to broaden their applications are limited due to the single-driving force from the coordination of these two components. Herein, we developed a universal approach to introducing programmable assembles into MPNs to form metal-phenolic supraparticles based on the rationally designed phenolic building blocks. These as-prepared building blocks can first assemble into primary nanoparticles driven by various controllable intermolecular interactions (i.e., metal-organic coordination, host-guest interaction, and hydrophobic interaction), followed by particle assembly with metal ions to coat on different templates. The introduction of multiple assembly modalities into phenolic building blocks enriches the functionalities of these metal-phenolic supraparticles, such as dual-pH responsibility, light-controllable permeability, and rapid fluorescence labeling of living cells. Our work provides a conceptual and practical paradigm for customizing MPNs with hierarchical structures by importing various assembly strategies via rationally designed phenolic building blocks

Systemic Tumor Suppression via Macrophage‐Driven Automated Homing of Metal‐Phenolic‐Gated Nanosponges for Metastatic Melanoma

Abstract Cell‐based therapies comprising the administration of living cells to patients for direct therapeutic activities have experienced remarkable success in the clinic, of which macrophages hold great potential for targeted drug delivery due to their inherent chemotactic mobility and homing ability to tumors with high efficiency. However, such targeted delivery of drugs through cellular systems remains a significant challenge due to the complexity of balancing high drug‐loading with high accumulations in solid tumors. Herein, a tumor‐targeting cellular drug delivery system (MAGN) by surface engineering of tumor‐homing macrophages (Mφs) with biologically responsive nanosponges is reported. The pores of the nanosponges are blocked with iron‐tannic acid complexes that serve as gatekeepers by holding encapsulated drugs until reaching the acidic tumor microenvironment. Molecular dynamics simulations and interfacial force studies are performed to provide mechanistic insights into the “ON‐OFF” gating effect of the polyphenol‐based supramolecular gatekeepers on the nanosponge channels. The cellular chemotaxis of the Mφ carriers enabled efficient tumor‐targeted delivery of drugs and systemic suppression of tumor burden and lung metastases in vivo. The findings suggest that the MAGN platform offers a versatile strategy to efficiently load therapeutic drugs to treat advanced metastatic cancers with a high loading capacity of various therapeutic drugs