Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Capsule Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

Colistin and its role in the Era of antibiotic resistance: an extended review(2000–2019)

Abstract: Increasing antibiotic resistance in multidrug-resistant (MDR) Gram-negative bacteria (MDR-GNB) presents significant health problems worldwide, since the vital available and effective antibiotics, including; broad-spectrum penicillins, fluoroquinolones, aminoglycosides, and β-lactams, such as; carbapenems, monobactam, and cephalosporins; often fail to fight MDR Gram-negative pathogens as well as the absence of new antibiotics that can defeat these "superbugs". All of these has prompted the reconsideration of old drugs such as polymyxins that were reckoned too toxic for clinical use. Only two polymyxins, polymyxin E (colistin) and polymyxin B, are currently commercially available. Colistin has re-emerged as a last-hope treatment in the mid-1990s against MDR Gram-negative pathogens due to the development of extensively drug-resistant GNB. Unfortunately, rapid global resistance towards colistin has emerged following its resurgence. Different mechanisms of colistin resistance have been characterized, including intrinsic, mutational, and transferable mechanisms.In this review, we intend to discuss the progress over the last two decades in understanding the alternative colistin mechanisms of action and different strategies used by bacteria to develop resistance against colistin, besides providing an update about what is previously recognized and what is novel concerning colistin resistance.

MiR-34a inhibits proliferation and migration of breast cancer through down-regulation of Bcl-2 and SIRT1

Abstract: MicroRNA-34a(miR-34a), a pivotal member of the p53 network, was found to be down-regulated in multiple types of tumors and further reported as a tumor suppressor microRNA. However, the profile and biological effects of miR-34a in breast cancer are still unclear. In this study, we aimed to determine the effect of miR-34a on the growth of breast cancer and to investigate whether its effect is achieved by targeting Bcl-2 and SIRT1. We examined miR-34a levels in breast cancer cell lines and breast cancer specimens by qRT-PCR. Proliferation assay, apoptosis assay, and morphological monitoring were performed to assess the tumor suppression effect of miR-34a in breast cancer cell lines. Western blotting was used to identify the targets of miR-34a. We also investigated the anti-tumor effects of the treatment combining miR-34a with 5-FU in breast cancer cells. We found that miR-34a expression was down-regulated in 5 breast cancer cell lines compared with the immortalized normal mammary epithelial cell line 184A1, and was also down-regulated by almost 50 % in breast cancer samples compared with their corresponding adjacent non-malignant breast tissues. Ectopic restoration of miR-34a in breast cancer cells suppressed cells proliferation, invasion, and induced apoptosis. Bcl-2 and SIRT1 as the targets of miR-34a were found to be in reverse correlation with ectopic expression of miR-34a. Furthermore, the treatment combining miR-34a with 5-FU significantly showed more efficient anti-tumor effects than single treatment of miR-34a or 5-FU. Since miR-34a functions as tumor suppressor microRNA in breast cancer, modulating miR-34a level in breast cancer was suggested to be a new and useful approach of breast cancer therapy.