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.

Hepatocellular carcinoma-derived exosomes promote motility of immortalized hepatocyte through transfer of oncogenic proteins and RNAs

Abstract: Exosomes are increasingly recognized as important mediators of cell-cell communication in cancer progression through the horizontal transfer of RNAs and proteins to neighboring or distant cells. Hepatocellular carcinoma (HCC) is a highly malignant cancer, whose metastasis is largely influenced by the tumor microenvironment. The possible role of exosomes in the interactions between HCC tumor cell and its surrounding hepatic milieu are however largely unknown. In this study, we comprehensively characterized the exosomal RNA and proteome contents derived from three HCC cell lines (HKCI-C3, HKCI-8 and MHCC97L) and an immortalized hepatocyte line (MIHA) using Ion Torrent sequencing and mass spectrometry, respectively. RNA deep sequencing and proteomic analysis revealed exosomes derived from metastatic HCC cell lines carried a large number of protumorigenic RNAs and proteins, such as MET protooncogene, S100 family members and the caveolins. Of interest, we found that exosomes from motile HCC cell lines could significantly enhance the migratory and invasive abilities of non-motile MIHA cell. We further demonstrated that uptake of these shuttled molecules could trigger PI3K/AKT and MAPK signaling pathways in MIHA with increased secretion of active MMP-2 and MMP-9. Our study showed for the first time that HCC-derived exosomes could mobilize normal hepatocyte, which may have implication in facilitating the protrusive activity of HCC cells through liver parenchyma during the process of metastasis.

Capacitive Pressure Sensor with High Sensitivity and Fast Response to Dynamic Interaction Based on Graphene and Porous Nylon Networks.

Abstract: Flexible pressure sensors are of great importance to be applied in artificial intelligence and wearable electronics. However, assembling a simple structure, high-performance capacitive pressure sensor, especially for monitoring the flow of liquids, is still a big challenge. Here, on the basis of a sandwich-like structure, we propose a facile capacitive pressure sensor optimized by a flexible, low-cost nylon netting, showing many merits including a high response sensitivity (0.33 kPa–1) in a low-pressure regime (<1 kPa), an ultralow detection limit as 3.3 Pa, excellent working stability after more than 1000 cycles, and synchronous monitoring for human pulses and clicks. More important, this sensor exhibits an ultrafast response speed (<20 ms), which enables its detection for the fast variations of a small applied pressure from the morphological changing processes of a droplet falling onto the sensor. Furthermore, a capacitive pressure sensor array is fabricated for demonstrating the ability to spatial pres...

Complex Zernike Moments Features for Shape-Based Image Retrieval

Abstract: Shape is a fundamental image feature used in content-based image-retrieval systems. This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments (ZMs). As the rotation of an image has an impact on the ZM phase coefficients of the image, existing proposals normally use magnitude-only ZM as the image feature. In this paper, we compare, by using a mathematical form of analysis, the amount of visual information captured by ZM phase and the amount captured by ZM magnitude. This analysis shows that the ZM phase captures significant information for image reconstruction. We therefore propose combining both the magnitude and phase coefficients to form a new shape descriptor, referred to as invariant ZM descriptor (IZMD). The scale and translation invariance of IZMD could be obtained by prenormalizing the image using the geometrical moments. To make the phase invariant to rotation, we perform a phase correction while extracting the IZMD features. Experiment results show that the proposed shape feature is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. The proposed IZMD feature also outperforms the commonly used magnitude-only ZMD in terms of noise robustness and object discriminability.

Providing long-term participation incentive in participatory sensing

Abstract: Providing an adequate long-term user participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under asymmetry of current information. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% ∼ 90%) and social performance (e.g., increasing the social welfare by 15% ∼ 80%).