A Novel Model of Conforming Delaunay Triangulation for Sensor Network Configuration

Delaunay refinement is a technique for generating unstructured meshes of triangles for sensor network configuration engineering practice. A new method for solving Delaunay triangulation problem is proposed in this paper, which is called endpoint triangle’s circumcircle model (ETCM). As compared with the original fractional node refinement algorithms, the proposed algorithm can get well refinement stability with least time cost. Simulations are performed under five aspects including refinement stability, the number of additional nodes, time cost, mesh quality after intruding additional nodes, and the aspect ratio improved by single additional node. All experimental results show the advantages of the proposed algorithm as compared with the existing algorithms and confirm the algorithm analysis sufficiently

Distance-dependent plasmon-enhanced fluorescence of upconversion nanoparticles using polyelectrolyte multilayers as tunable spacers

Lanthanide-doped upconversion nanoparticles (UCNPs) have attracted widespread interests in bioapplications due to their unique optical properties by converting near infrared excitation to visible emission. However, relatively low quantum yield prompts a need for developing methods for fluorescence enhancement. Plasmon nanostructures are known to efficiently enhance fluorescence of the surrounding fluorophores by acting as nanoantennae to focus electric field into nano-volume. Here, we reported a novel plasmon-enhanced fluorescence system in which the distance between UCNPs and nanoantennae (gold nanorods, AuNRs) was precisely tuned by using layer-by-layer assembled polyelectrolyte multilayers as spacers. By modulating the aspect ratio of AuNRs, localized surface plasmon resonance (LSPR) wavelength at 980 nm was obtained, matching the native excitation of UCNPs resulting in maximum enhancement of 22.6-fold with 8 nm spacer thickness. These findings provide a unique platform for exploring hybrid nanostructures composed of UCNPs and plasmonic nanostructures in bioimaging applications

Hypoxia-inducible factor 1-mediated regulation of PPP1R3C promotes glycogen accumulation in human MCF-7 cells under hypoxia

AbstractHundreds of genes can be regulated by hypoxia-inducible factor 1 (HIF1) under hypoxia. Here we demonstrated a HIF1-mediated induction of protein phosphatase 1, regulatory subunit 3C gene (PPP1R3C) in human MCF7 cells under hypoxia. By mutation analysis we confirmed the presence of a functional hypoxia response element that is located 229bp upstream from the PPP1R3C gene. PPP1R3C induction correlates with a significant glycogen accumulation in MCF7 cells under hypoxia. Knockdown of either HIF1α or PPP1R3C attenuated hypoxia-induced glycogen accumulation significantly. Knockdown of HIF2α reduced hypoxia-induced glycogen accumulation slightly (but not significantly). Our results demonstrated that HIF1 promotes glycogen accumulation through regulating PPP1R3C expression under hypoxia, which revealed a novel metabolic adaptation of cells to hypoxia

A Survey on Extreme Multi-label Learning

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML.Comment: A preliminary versio