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: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Pharmacogenomics and the Yin/Yang actions of ginseng: anti-tumor, angiomodulating and steroid-like activities of ginsenosides

Abstract: In Chinese medicine, ginseng (Panax ginseng C.A. Meyer) has long been used as a general tonic or an adaptogen to promote longevity and enhance bodily functions. It has also been claimed to be effective in combating stress, fatigue, oxidants, cancer and diabetes mellitus. Most of the pharmacological actions of ginseng are attributed to one type of its constituents, namely the ginsenosides. In this review, we focus on the recent advances in the study of ginsenosides on angiogenesis which is related to many pathological conditions including tumor progression and cardiovascular dysfunctions. Angiogenesis in the human body is regulated by two sets of counteracting factors, angiogenic stimulators and inhibitors. The 'Yin and Yang' action of ginseng on angiomodulation was paralleled by the experimental data showing angiogenesis was indeed related to the compositional ratio between ginsenosides Rg1 and Rb1. Rg1 was later found to stimulate angiogenesis through augmenting the production of nitric oxide (NO) and vascular endothelial growth factor (VEGF). Mechanistic studies revealed that such responses were mediated through the PI3K→Akt pathway. By means of DNA microarray, a group of genes related to cell adhesion, migration and cytoskeleton were found to be up-regulated in endothelial cells. These gene products may interact in a hierarchical cascade pattern to modulate cell architectural dynamics which is concomitant to the observed phenomena in angiogenesis. By contrast, the anti-tumor and anti-angiogenic effects of ginsenosides (e.g. Rg3 and Rh2) have been demonstrated in various models of tumor and endothelial cells, indicating that ginsenosides with opposing activities are present in ginseng. Ginsenosides and Panax ginseng extracts have been shown to exert protective effects on vascular dysfunctions, such as hypertension, atherosclerotic disorders and ischemic injury. Recent work has demonstrates the target molecules of ginsenosides to be a group of nuclear steroid hormone receptors. These lines of evidence support that the interaction between ginsenosides and various nuclear steroid hormone receptors may explain the diverse pharmacological activities of ginseng. These findings may also lead to development of more efficacious ginseng-derived therapeutics for angiogenesis-related diseases.

Highly Efficient Visible Colloidal Lead-Halide Perovskite Nanocrystal Light-Emitting Diodes

Abstract: Lead-halide perovskites have been attracting attention for potential use in solid-state lighting Following the footsteps of solar cells, the field of perovskite light-emitting diodes (PeLEDs) has been growing rapidly Their application prospects in lighting, however, remain still uncertain due to a variety of shortcomings in device performance including their limited levels of luminous efficiency achievable thus far Here we show high-efficiency PeLEDs based on colloidal perovskite nanocrystals (PeNCs) synthesized at room temperature possessing dominant first-order excitonic radiation (enabling a photoluminescence quantum yield of 71% in solid film), unlike in the case of bulk perovskites with slow electron–hole bimolecular radiative recombination (a second-order process) In these PeLEDs, by reaching charge balance in the recombination zone, we find that the Auger nonradiative recombination, with its significant role in emission quenching, is effectively suppressed in low driving current density range

Wolf search algorithm with ephemeral memory

Abstract: In computer science, a computational challenge exists in finding a globally optimized solution from a tremendously large search space. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods have been extensively studied in the past, and progressively extended in order to suit a wide range of optimization problems. Researchers recently have invented a collection of heuristic optimization methods inspired by the movements of animals and insects (e.g., Firefly, Cuckoos, Bats and Accelerated PSO) with the advantages of efficient computation and easy implementation. This paper proposes a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The contribution of the paper is twofold: 1. for verifying the efficacy of the WSA the algorithm is tested quantitatively and compared to other heuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms; 2. The WSA is investigated with respective to its memory requirement. Superior results are observed in most tests.

SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention

Abstract: Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods

Excellent energy density of polymer nanocomposites containing BaTiO3@Al2O3 nanofibers induced by moderate interfacial area

Abstract: Inorganic/polymer nanocomposites, using one-dimensional (1D) core–shell structure BaTiO3@Al2O3 nanofibers (BT@Al2O3 nfs) as fillers and poly(vinylidene fluoride) (PVDF) as the polymer matrix, have been prepared. The core–shell structure BT@Al2O3 nfs have been synthesized via coaxial electrospinning. The breakdown strength (Eb) and discharged energy density of the nanocomposites can be significantly improved by creating an insulating Al2O3 shell layer with moderate dielectric constant on the surfaces of BT nanofibers to form a moderate interfacial area. The Al2O3 shell layer could effectively confine the mobility of charge carriers, which reduces energy loss by reducing the Maxwell–Wagner–Sillars (MWS) interfacial polarization and space charge polarization between the fillers and the polymer matrix. As a result, the nanocomposite films filled with 5 vol% BT@Al2O3 nfs exhibit a excellent discharge energy density of 12.18 J cm−3 at 400 MV m−1, which is ≈254% over bare PVDF (4.8 J cm−3 at 350 MV m−1) and ≈1015% greater than the biaxially oriented polypropylenes (BOPP) (≈1.2 J cm−3 at 640 MV m−1). The work here indicates that this promising state-of-the-art method of preparing high energy density nanocomposites can be used in the next generation of dielectric capacitors.