Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

Inspired by the mechanism of imaging and adaptation to luminosity in insect compound eyes (ICE), we propose an ICE-based adaptive reconstruction method (ARM-ICE), which can adjust the sampling vision field of image according to the environment light intensity. The target scene can be compressive, sampled independently with multichannel through ARM-ICE. Meanwhile, ARM-ICE can regulate the visual field of sampling to control imaging according to the environment light intensity. Based on the compressed sensing joint sparse model (JSM-1), we establish an information processing system of ARM-ICE. The simulation of a four-channel ARM-ICE system shows that the new method improves the peak signal-to-noise ratio (PSNR) and resolution of the reconstructed target scene under two different cases of light intensity. Furthermore, there is no distinct block effect in the result, and the edge of the reconstructed image is smoother than that obtained by the other two reconstruction methods in this work

Further characterization of the binding of heparin to granulocyte colony-stimulating factor: Importance of sulfate groups

Heparin mediates fundamental biological mechanisms through interaction with proteins. Previously, we have shown that standard heparin binds to granulocyte colony-stimulating factor (G-CSF) with an affinity of 4.8 x 10(5) M-1. To further study the structural features in heparin that are responsible for this interaction, we studied the bindings of G-CSF and N-desulfated and 2,3-O-desulfated heparin by CZE. Results showed that the N-desulfated heparin had a similar affinity for G-CSF ((5.4 +/- 0.9) x 10(5) M-1), but the 2,3-O-desulfated heparin had a 1000-fold lower affinity ((3.4 +/- 1.2) x 10(2) M-1) in comparison to standard heparin. The results showed that 2,3-O-sulfate groups are more important than N-sulfate groups in heparin-G-CSF interaction

FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

Fourier ptychographic microscopy (FPM), characterized by high-throughput computational imaging, theoretically provides a cunning solution to the trade-off between spatial resolution and field of view (FOV), which has a promising prospect in the application of digital pathology. However, block reconstruction and then stitching has currently become an unavoidable procedure due to vignetting effects. The stitched image tends to present color inconsistency in different image segments, or even stitching artifacts. In response, we reported a computational framework based on feature-domain backdiffraction to realize full-FOV, stitching-free FPM reconstruction. Different from conventional algorithms that establish the loss function in the image domain, our method formulates it in the feature domain, where effective information of images is extracted by a feature extractor to bypass the vignetting effect. The feature-domain error between predicted images based on estimation of model parameters and practically captured images is then digitally diffracted back through the optical system for complex amplitude reconstruction and aberration compensation. Through massive simulations and experiments, the method presents effective elimination of vignetting artifacts, and reduces the requirement of precise knowledge of illumination positions. We also found its great potential to recover the data with a lower overlapping rate of spectrum and to realize automatic blind-digital refocusing without a prior defocus distance