Multicellular tumor spheroid models (MCTS) are often coined as 3D in vitro models that can mimic the microenvironment of tissues. MCTS have gained increasing interest in the nano‐biotechnology field as they can provide easily accessible information on the performance of nanoparticles without using animal models. Considering that many countries have put restrictions on animals testing, which will only tighten in the future as seen by the recent developments in the Netherlands, 3D models will become an even more valuable tool. Here, an overview on MCTS is provided, focusing on their use in cancer research as most nanoparticles are tested in MCTS for treatment of primary tumors. Thereafter, various types of nanoparticles—from self‐assembled block copolymers to inorganic nanoparticles, are discussed. A range of physicochemical parameters including the size, shape, surface chemistry, ligands attachment, stability, and stiffness are found to influence nanoparticles in MCTS. Some of these studies are complemented by animal studies confirming that lessons from MCTS can in part predict the behaviour in vivo. In summary, MCTS are suitable models to gain additional information on nanoparticles. While not being able to replace in vivo studies, they can bridge the gap between traditional 2D in vitro studies and in vivo models. 相似文献
World Wide Web - Knowledge graph has gained significant popularity in recent years. As one of the W3C standards, SPARQL has become the de facto standard query language to retrieve the desired data... 相似文献
The convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity, which involve enormous communication bandwidth and storage resources requirement. The computation requirement can be addressed effectively to achieve high throughput by highly parallel compute paradigms of current CNNs accelerators. But the energy consumption still remains high as communication can be more expensive than computation, especially for low power embedded platform. To address this problem, this paper proposes a CNNs accelerator based on a novel storage and dataflow on multi-processor system on chip (MPSoC) platform. By minimizing data access and movement and maximizing data reuse, it can achieve the energy efficient CNNs inference acceleration. The optimization strategies mainly involve four aspects. Firstly, an external memory sharing architecture adopting two-dimensional array storage mode for CPU-FPGA collaborative processing is proposed to achieve high data throughput and low bandwidth requirement for off-chip data transmission. Secondly, the minimized data access and movement on chip are realized by designing a multi-level hierarchical storage architecture. Thirdly, a cyclic data shifting method is proposed to achieve maximized data reuse based on both spatial and temporal. In addition, a bit fusion method based on the 8-bit dynamic fixed-point quantization is adopted to achieve double throughput and computational efficiency of a single DSP. The accelerator proposed in this paper is implemented on Zynq UltraScale?+?MPSoC ZCU102 evaluation board. By running the benchmark network of VGG16 and Tiny-YOLO on the accelerator, the throughput and the energy efficiency are evaluated. Compared with the current typical accelerators, the proposed accelerator can increase system throughput by up to 41x, single DSP throughput by up to 7.63x, and system energy efficiency by up to 6.3x.
