Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees-of-freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV). Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors

Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor

Quantitative phase imaging (QPI) is a label-free technique that provides optical path length information for transparent specimens, finding utility in biology, materials science, and engineering. Here, we present quantitative phase imaging of a 3D stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. Utilizing multiple spatially engineered diffractive layers trained through deep learning, this diffractive processor can transform the phase distributions of multiple 2D objects at various axial positions into intensity patterns, each encoded at a unique wavelength channel. These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor. Based on numerical simulations, we show that our diffractive processor could simultaneously achieve all-optical quantitative phase imaging across several distinct axial planes at the input by scanning the illumination wavelength. A proof-of-concept experiment with a 3D-fabricated diffractive processor further validated our approach, showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum. Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices

Role of Gastrointestinal Microbiota from Crucian Carp in Microbial Transformation and Estrogenicity Modification of Novel Plastic Additives

Ingestion is a major exposure route for hydrophobic organic pollutants in fish, but the microbial transformation and estrogenic modification of the novel plastic additives by the gut microbiota of fish remain obscure. Using an in vitro approach, we provide evidence that structure-related transformation of various plastic additives by the gastric and intestinal (GI) microbiota from crucian carp, with the degradation ratio of bisphenols and triphenyl phosphate faster than those of brominated compounds. The degradation kinetics for these pollutants could be limited by oxygen and cometabolic substrates (i.e., glucose). The fish GI microbiota could utilize the vast majority of carbon sources in a Biolog EcoPlate, suggesting their high metabolic potential and ability to transform various organic compounds. Unique microorganisms associated with transformation of the plastic additives including genera of Citrobacter, Klebsiella, and some unclassified genera in Enterobacteriaceae were identified by combining high-throughput genetic analyses and metagenomic analyses. Through identification of anaerobic transformation products by high-resolution mass spectrometry, alkyl-cleavage was found the common transformation mechanism, and hydrolysis was the major pathway for ester-containing pollutants. After anaerobic incubation, the estrogenic activities of triphenyl phosphate and bisphenols A, F, and AF declined, whereas that of bisphenol AP increased