Search

Advanced photosensitizers for high-performance fluorescence imaging-guided photothermal therapy demand excellent near-infrared (NIR) brightness [molar absorption coefficient (ε) × quantum yield (QY)] and exceptional photothermal performance [ε × photothermal conversion efficiency (PCE)]. However, integrating high brightness and potent photothermal performance within a single molecule faces a formidable challenge. This article proposes a method to address this issue by preparing J-aggregate nanoparticles (NPs) using molecules with high ε. J-aggregates effectively improve QY and induce molecular emission redshift, while high ε molecules play a crucial role in improving the brightness and photothermal performance. By optimizing the molecular structure based on the pyrrolopyrrole cyanine (PPCy), precise control over the QY and PCE of PPCy J-aggregates is achieved. Ultimately, PDDO NPs exhibiting superior brightness (ε × QY = 3.32 × 104M–1cm–1) and photothermal performance (ε × PCE = 1.21 × 105M–1cm–1) are identified as high-performance photosensitizers. Notably, each parameter represents one of the highest levels among the reported fluorescence or photothermal probes to date. The in vivo studies demonstrate that PDDO NPs possess exceptional NIR imaging capabilities and remarkable photothermal tumor inhibition rates. This study provides innovative insights into the development of high-performance multifunctional photosensitizers.

Deep convolutional neural networks (CNNs) have gained great success in various computer vision applications. State-of-the-art CNN models for large-scale applications are computation intensive and memory expensive and, hence, are mainly processed on high-performance processors like server CPUs and GPUs. However, there is an increasing demand of high-accuracy or real-time object detection tasks in large-scale clusters or embedded systems, which requires energy-efficient accelerators because of the green computation requirement or the limited battery restriction. Due to the advantages of energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this article, we present an in-depth analysis of computation complexity and the memory footprint of each CNN layer type. Then a scalable parallel framework is proposed that exploits four levels of parallelism in hardware acceleration. We further put forward a systematic design space exploration methodology to search for the optimal solution that maximizes accelerator throughput under the FPGA constraints such as on-chip memory, computational resources, external memory bandwidth, and clock frequency. Finally, we demonstrate the methodology by optimizing three representative CNNs (LeNet, AlexNet, and VGG-S) on a Xilinx VC709 board. The average performance of the three accelerators is 424.7, 445.6, and 473.4GOP/s under 100MHz working frequency, which outperforms the CPU and previous work significantly.