University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Polyphenol-Containing Nanoparticles: Synthesis, Properties, and Therapeutic Delivery

Abstract: Polyphenols, the phenolic hydroxyl group-containing organic molecules, are widely found in natural plants and have shown beneficial effects on human health. Recently, polyphenol-containing nanoparticles have attracted extensive research attention due to their antioxidation property, anticancer activity, and universal adherent affinity, and thus have shown great promise in the preparation, stabilization, and modification of multifunctional nanoassemblies for bioimaging, therapeutic delivery, and other biomedical applications. Additionally, the metal-polyphenol networks, formed by the coordination interactions between polyphenols and metal ions, have been used to prepare an important class of polyphenol-containing nanoparticles for surface modification, bioimaging, drug delivery, and disease treatments. By focusing on the interactions between polyphenols and different materials (e.g., metal ions, inorganic materials, polymers, proteins, and nucleic acids), a comprehensive review on the synthesis and properties of the polyphenol-containing nanoparticles is provided. Moreover, the remarkable versatility of polyphenol-containing nanoparticles in different biomedical applications, including biodetection, multimodal bioimaging, protein and gene delivery, bone repair, antibiosis, and cancer theranostics is also demonstrated. Finally, the challenges faced by future research regarding the polyphenol-containing nanoparticles are discussed.

Heterogeneous reactions of mineral dust aerosol: implications for tropospheric oxidation capacity

Abstract: . Heterogeneous reactions of mineral dust aerosol with trace gases in the atmosphere could directly and indirectly affect tropospheric oxidation capacity, in addition to aerosol composition and physicochemical properties. In this article we provide a comprehensive and critical review of laboratory studies of heterogeneous uptake of OH, NO3, O3, and their directly related species as well (including HO2, H2O2, HCHO, HONO, and N2O5) by mineral dust particles. The atmospheric importance of heterogeneous uptake as sinks for these species is assessed (i) by comparing their lifetimes with respect to heterogeneous reactions with mineral dust to lifetimes with respect to other major loss processes and (ii) by discussing relevant field and modeling studies. We have also outlined major open questions and challenges in laboratory studies of heterogeneous uptake by mineral dust and discussed research strategies to address them in order to better understand the effects of heterogeneous reactions with mineral dust on tropospheric oxidation capacity.

3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation

Abstract: Learning 3D global features by aggregating multiple views is important. Pooling is widely used to aggregate views in deep learning models. However, pooling disregards a lot of content information within views and the spatial relationship among the views, which limits the discriminability of learned features. To resolve this issue, 3D to Sequential Views (3D2SeqViews) is proposed to more effectively aggregate the sequential views using convolutional neural networks with a novel hierarchical attention aggregation. Specifically, the content information within each view is first encoded. Then, the encoded view content information and the sequential spatiality among the views are simultaneously aggregated by the hierarchical attention aggregation, where view-level attention and class-level attention are proposed to hierarchically weight sequential views and shape classes. View-level attention is learned to indicate how much attention is paid to each view by each shape class, which subsequently weights sequential views through a novel recursive view integration. Recursive view integration learns the semantic meaning of view sequence, which is robust to the first view position. Furthermore, class-level attention is introduced to describe how much attention is paid to each shape class, which innovatively employs the discriminative ability of the fine-tuned network. 3D2SeqViews learns more discriminative features than the state-of-the-art, which leads to the outperforming results in shape classification and retrieval under three large-scale benchmarks.