Memory-less scattering imaging with ultrafast convolutional optical neural networks.

The optical memory effect in complex scattering media including turbid tissue and speckle layers has been a critical foundation for macroscopic and microscopic imaging methods. However, image reconstruction from strong scattering media without the optical memory effect has not been achieved. Here, we demonstrate image reconstruction through scattering layers where no optical memory effect exists, by developing a multistage convolutional optical neural network (ONN) integrated with multiple parallel kernels operating at the speed of light. Training this Fourier optics-based, parallel, one-step convolutional ONN with the strong scattering process for direct feature extraction, we achieve memory-less image reconstruction with a field of view enlarged by a factor up to 271. This device is dynamically reconfigurable for ultrafast multitask image reconstruction with a computational power of 1.57 peta-operations per second (POPS). Our achievement establishes an ultrafast and high energy-efficient optical machine learning platform for graphic processing.

From design to clinic: Engineered peptide nanomaterials for cancer immunotherapy.

Immunotherapy has revolutionized the field of cancer therapy. Nanomaterials can further improve the efficacy and safety of immunotherapy because of their tunability and multifunctionality. Owing to their natural biocompatibility, diverse designs, and dynamic self-assembly, peptide-based nanomaterials hold great potential as immunotherapeutic agents for many malignant cancers, with good immune response and safety. Over the past several decades, peptides have been developed as tumor antigens, effective antigen delivery carriers, and self-assembling adjuvants for cancer immunotherapy. In this review, we give a brief introduction to the use of peptide-based nanomaterials for cancer immunotherapy as antigens, carriers, and adjuvants, and to their current clinical applications. Overall, this review can facilitate further understanding of peptide-based nanomaterials for cancer immunotherapy and may pave the way for designing safe and efficient methods for future vaccines or immunotherapies.

Neuron-Inspired Steiner Tree Networks for 3D Low-Density Metastructures.

Three-dimensional (3D) micro-and nanostructures have played an important role in topological photonics, microfluidics, acoustic, and mechanical engineering. Incorporating biomimetic geometries into the design of metastructures has created low-density metamaterials with extraordinary physical and photonic properties. However, the use of surface-based biomimetic geometries restricts the freedom to tune the relative density, mechanical strength, and topological phase. The Steiner tree method inspired by the feature of the shortest connection distance in biological neural networks is applied, to create 3D metastructures and, through two-photon nanolithography, neuron-inspired 3D structures with nanoscale features are successfully achieved. Two solutions are presented to the 3D Steiner tree problem: the Steiner tree networks (STNs) and the twisted Steiner tree networks (T-STNs). STNs and T-STNs possess a lower density than surface-based metamaterials and that T-STNs have Young's modulus enhanced by 20% than the STNs. Through the analysis of the space groups and symmetries, a topological nontrivial Dirac-like conical dispersion in the T-STNs is predicted, and the results are based on calculations with true predictive power and readily realizable from microwave to optical frequencies. The neuron-inspired 3D metastructures opens a new space for designing low-density metamaterials and topological photonics with extraordinary properties triggered by a twisting degree-of-freedom.

Artificial neural networks enabled by nanophotonics.

The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, which is the study of the behaviour of light and the light-matter interaction at the nanometre scale, has unveiled new phenomena and led to new applications beyond the diffraction limit of light. These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs. In the present review, we summarise the recent progress in nanophotonics for emulating the structural, functional and biological features of ANNs, directly or indirectly.

Three-dimensional direct laser writing of biomimetic neuron structures.

Building biomimetic neuron structures that emulate the topological features of biological neural networks at multiple scales has been an active area in neuron cell culturing, neuron-chip interface and computer chip design. However, due to the fact that biological neural networks possess extraordinary connectivity and complexity from millimeter down to nanometer scale, with different dendritic branch angles, branch lengths, and branch diameters, previous methods to reproduce the topological features of biological neural networks are either limited to two dimensions or lack of fabrication resolution in building three-dimensional (3D) structures. Here we report on the generation of 3D biomimetic neuron structures at a micrometer scale, with high mechanical stability and controlled topologies by studying the effect of 3D direct laser writing (DLW) on the capillary force. This work provides an optical technology platform to replicate the topological features of biological neural networks and paves the avenue towards more applications of using 3D direct laser writing in engineered neural networks.