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.

Effect of Stromal Cells in Tumor Microenvironment on Metastasis Initiation.

Abstract: The cellular environment where tumor cells reside is called the tumor microenvironment (TME), which consists of borders, blood vessels, lymph vessels, extracellular matrix (ECM), stromal cells, immune/inflammatory cells, secreted proteins, RNAs and small organelles. By dynamically interacting with tumor cells, stromal cells participate in all stages of tumor initiation, progression, metastasis, recurrence and drug response, and consequently, affect the fate of patients. During the processes of tumor evolution and metastasis initiation, stromal cells in TME also experience some changes and play roles in both the suppression and promotion of metastasis, while the overall function of stromal cells is beneficial for cancer cell survival and movement. In this review, we examine the effects of stromal cells in TME on metastasis initiation, including angiogenesis, epithelial-mesenchymal transition (EMT) and invasion. We also highlight functions of proteins, RNAs and small organelles secreted by stromal cells in their influences on multiple stages of tumor metastasis.

Sparse Bayesian Extreme Learning Machine for Multi-classification

Abstract: Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.

The Formyl Peptide Receptors: Diversity of Ligands and Mechanism for Recognition

Abstract: The formyl peptide receptors (FPRs) are G protein-coupled receptors that transduce chemotactic signals in phagocytes and mediate host-defense as well as inflammatory responses including cell adhesion, directed migration, granule release and superoxide production. In recent years, the cellular distribution and biological functions of FPRs have expanded to include additional roles in homeostasis of organ functions and modulation of inflammation. In a prototype, FPRs recognize peptides containing N-formylated methionine such as those produced in bacteria and mitochondria, thereby serving as pattern recognition receptors. The repertoire of FPR ligands, however, has expanded rapidly to include not only N-formyl peptides from microbes but also non-formyl peptides of microbial and host origins, synthetic small molecules and an eicosanoid. How these chemically diverse ligands are recognized by the three human FPRs (FPR1, FPR2 and FPR3) and their murine equivalents is largely unclear. In the absence of crystal structures for the FPRs, site-directed mutagenesis, computer-aided ligand docking and structural simulation have led to the identification of amino acids within FPR1 and FPR2 that interact with several formyl peptides. This review article summarizes the progress made in the understanding of FPR ligand diversity as well as ligand recognition mechanisms used by these receptors.