Study of Iron Oxide Magnetic Nanoparticles in Cancer Cell Destruction and Cell Separation

Due to their adjustable physiochemical properties and proven biocompatibility, iron oxide (Fe3O4) magnetic nanoparticles are promising in drug delivery, magnetic resonance imaging and catalysis. In this thesis, we have utilized two types of iron oxide nanoparticles: i) superparamagnetic iron oxide nanoparticles (SPION) for targeted destruction of cancer cells, and ii) poly(N-isopropylacrylamide) (pNIPAM) coated magnetic particles (MNP) for multistage cell separation. SPION are generally considered as drug delivery vehicles for the enhanced permeability and retention (EPR) effect. SPION possess the intrinsic peroxidase-like activity as Horseradish peroxidase (HRP), which can generate reactive oxygen species (ROS) from H2O2 via Fenton’s reaction. ROS regulate cell signaling, but a significant ROS stress can disrupt the redox homeostasis of cancer cells leading to selective tumor cell toxicity and destruction. Hereby, we developed ROS-induced targeted cell destruction with SPION-GOx bioconjugates platform. GOx catalyzes glucose oxidation in cancer cells to produce H2O2. 24 h incubation with 10 μg/mL SPION-GOx on 4T1 cells resulted in almost zero cell viability. In vivo evaluation showed SPION-GOx led to a much slower tumor growth compared to control groups. Additionally, magnetic activated cell sorting (MACS) has become a common technique for the separation of target cell populations from biological suspensions. A major obstacle preventing current single stage MACS from achieving satisfying separation efficiency is the non-specific interactions between the cells and MNP. Thus, we designed a multistage separation platform similar to distillation concept in chemical engineering. The repeated capture-and-release separation process is enabled by attaching the temperature responsive polymer- pNIPAM to both MNP and target cells. We manipulate the reversible hydrophobic-hydrophilic interactions between such functionalized MNP and target cells through temperature cycling to capture and release target cells at a higher efficiency than non-target. After several temperature cycles, target cells are enriched in the product. Flow cytometry results suggest that A431 cells (target) could be effectively separated from HeLa cells (non-target) after three separation stages resulting in an enrichment factor of 3.69 when the starting ratio of target to non-target is 1:2

Intrinsic Spin Hall Conductivity of MoTe2 and WTe2 Semimetals

We report a comprehensive study on the intrinsic spin Hall conductivity (SHC) of semimetals MoTe2 and WTe2 by ab initio calculation. Large SHC and desirable spin Hall angles have been discovered, due to the strong spin orbit coupling effect and low charge conductivity in semimetals. Diverse anisotropic SHC values, attributed to the unusual reduced-symmetry crystalline structure, have been revealed. We report an effective method on SHC optimization by electron doping, and exhibit the mechanism of SHC variation respect to the energy shifting by the spin Berry curvature. Our work provides insights into the realization of strong spin Hall effects in 2D systems

LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.Comment: 28 pages, 14 figure

HAVE RATING AGENCIES BECOME MORE CONSERVATIVE? EVIDENCES

Beginning in 2008, rating agencies have loosen their rating criteria of Chinese corporate bond rating.  The change in rating standard remains statistically significant after considering the macroeconomic factors.  A lack of diversification in ratings and failure to rate through economic cycle are found. As for the factors that have impact on the rating, Bigger size, higher profitability and better solvency help increase the rating for a corporate bound issuer, while higher liquidity, and lower leverage do harm to the credit rating. Such discoveries are consistent with the consitions in US corporate debt market.  Our conclusion is robust after multicollinearity test and adding additional macroeconomic explanatory variables