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.

Beclin 1 and autophagy are required for the tumorigenicity of breast cancer stem-like/progenitor cells

Abstract: Malignant breast tissue contains a rare population of multi-potent cells with the capacity to self-renew; these cells are known as cancer stem-like cells (CSCs) or tumor-initiating cells. Primitive mammary CSCs/progenitor cells can be propagated in culture as floating spherical colonies termed 'mammospheres'. We show here that the expression of the autophagy protein Beclin 1 is higher in mammospheres established from human breast cancers or breast cancer cell lines (MCF-7 and BT474) than in the parental adherent cells. As a result, autophagic flux is more robust in mammospheres. We observed that basal and starvation-induced autophagy flux is also higher in aldehyde dehydrogenase 1-positive (ALDH1(+)) population derived from mammospheres than in the bulk population. Beclin 1 is critical for CSC maintenance and tumor development in nude mice, whereas its expression limits the development of tumors not enriched with breast CSCs/progenitor cells. We found that decreased survival in autophagy-deficient cells (MCF-7 Atg7 knockdown cells) during detachment does not contribute to an ultimate deficiency in mammosphere formation. This study demonstrates that a prosurvival autophagic pathway is critical for CSC maintenance, and that Beclin 1 plays a dual role in tumor development.

Flight Time Minimization of UAV for Data Collection Over Wireless Sensor Networks

Abstract: In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV’s total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV’s speed, and the sensors’ transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor’s transmit power follows a water-filling policy and the UAV’s speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present the insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV’s optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but it is inversely proportional to the data upload requirement.