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Studying placental functions is crucial for understanding pregnancy complications. However, imaging placenta is challenging due to its depth, volume, and motion distortions. In this study, we have developed an implantable placenta window in mice that enables high-resolution photoacoustic and fluorescence imaging of placental development throughout the pregnancy. The placenta window exhibits excellent transparency for light and sound. By combining the placenta window with ultrafast functional photoacoustic microscopy, we were able to investigate the placental development during the entire mouse pregnancy, providing unprecedented spatiotemporal details. Consequently, we examined the acute responses of the placenta to alcohol consumption and cardiac arrest, as well as chronic abnormalities in an inflammation model. We have also observed viral gene delivery at the single-cell level and chemical diffusion through the placenta by using fluorescence imaging. Our results demonstrate that intravital imaging through the placenta window can be a powerful tool for studying placenta functions and understanding the placental origins of adverse pregnancy outcomes.

 

Painless and long-term sustained delivery of ocular drugs is enabled through the innovation of silicon nanoneedles. 

High spatiotemporal resolution can offer high precision for vision applications, which is particularly useful to capture the nuances of visual features, such as for augmented reality. Unfortunately, capturing and processing high spatiotemporal visual frames generates energy-expensive memory traffic. On the other hand, low resolution frames can reduce pixel memory throughput, but reduce also the opportunities of high-precision visual sensing. However, our intuition is that not all parts of the scene need to be captured at a uniform resolution. Selectively and opportunistically reducing resolution for different regions of image frames can yield high-precision visual computing at energy-efficient memory data rates. To this end, we develop a visual sensing pipeline architecture that flexibly allows application developers to dynamically adapt the spatial resolution and update rate of different “rhythmic pixel regions” in the scene. We develop a system that ingests pixel streams from commercial image sensors with their standard raster-scan pixel read-out patterns, but only encodes relevant pixels prior to storing them in the memory. We also present streaming hardware to decode the stored rhythmic pixel region stream into traditional frame-based representations to feed into standard computer vision algorithms. We integrate our encoding and decoding hardware modules into existing video pipelines. On top of this, we develop runtime support allowing developers to flexibly specify the region labels. Evaluating our system on a Xilinx FPGA platform over three vision workloads shows 43 − 64% reduction in interface traffic and memory footprint, while providing controllable task accuracy.