The inaugural Nano Research Young Innovators （NR45） Award in nanobiotechnology

It is our great pleasure to announce awardees of the inaugural 2018 Nano Research Young Innovators （NR45） in nanobiotechnology. Congratulations to all of the 45 outstanding young investigators under 45! They were selected through a competitive process by an award committee from our editorial board. Nano Research is launching the NR45 Award program to young researchers in various fields of nanoscience and nanotechnology, in recognition to their distinguished accomplishments and/or potential to make substantial contributions to their fields. The aim of Nano Research NR45 is to recognize the outstanding contributions of young scientists and together with the Nano Research Symposium integrated in the annual US-SINO Nano Forum provide a platform for communication, discussions and collaborations between scientists inter- nationally. For this inaugural year.

Deep learning in holography and coherent imaging

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance.Through data-driven approaches,these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography.These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.

Bright-field holography:cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram.However,unlike a conventional bright-field microscopy image,the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects.Here,we demonstrate that cross-modality deep learning using a generative adversarial network(GAN)can endow holographic images of a sample volume with bright-field microscopy contrast,combining the volumetric imaging capability of holography with the speckle-and artifact-free image contrast of incoherent bright-field microscopy.We illustrate the performance of this“bright-field holography”method through the snapshot imaging of bioaerosols distributed in 3D,matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope.This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging,and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram,benefiting from the wave-propagation framework of holography.

All-optical image classification through unknown random diffusers using a single-pixel diffractive network

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor.These methods demand relatively large-scale computing using deep neural networks running on digital computers.Here,we present an all-optical processor to directly classify unknown objects through unknown,random phase diffusers using broadband illumination detected with a single pixel.A set of transmissive diffractive layers,optimized using deep learning,forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive network.We numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers,never used during the training phase,and achieved a blind testing accuracy of 87.74±1.12%.We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits"0"and"1"through a random diffuser using terahertz waves and a 3D-printed diffractive network.This single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of interest.These results have various potential applications in,e.g.,biomedical imaging,security,robotics,and autonomous driving.

Fourier Imager Network(FIN):A deep neural network for hologram reconstruction with superior external generalization

Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging.However,the generalization of their image reconstruction performance to new types of samples never seen by the network remains a challenge.Here we introduce a deep learning framework,termed Fourier Imager Network(FIN),that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples,exhibiting unprecedented success in external generalization.FIN architecture is based on spatial Fourier transform modules that process the spatial frequencies of its inputs using learnable filters and a global receptive field.Compared with existing convolutional deep neural networks used for hologram reconstruction,FIN exhibits superior generalization to new types of samples,while also being much faster in its image inference speed,completing the hologram reconstruction task in~0.04 s per 1 mm^(2) of the sample area.We experimentally validated the performance of FIN by training it using human lung tissue samples and blindly testing it on human prostate,salivary gland tissue and Pap smear samples,proving its superior external generalization and image reconstruction speed.Beyond holographic microscopy and quantitative phase imaging,FIN and the underlying neural network architecture might open up various new opportunities to design broadly generalizable deep learning models in computational imaging and machine vision fields.

Recurrent neural network-based volumetric fluorescence microscopy

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life sciences.Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume.Through a recurrent convolutional neural network,which we term as Recurrent-MZ,2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field.Using experiments on C.elegans and nanobead samples,Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens,also providing a 30-fold reduction in the number of axial scans required to image the same sample volume.We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions,including e.g.,different sequences of input images,covering various axial permutations and unknown axial positioning errors.We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input,matching confocal microscopy images of the same sample volume.Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework,overcoming the limitations of current 3D scanning microscopy tools.

Critical role of immunogenic cell death in cancer therapy

In traditional cancer therapy, including chemo and radiation therapy, the goal is always to kill as much cancer cells as possible. However, the truth is that it is difficult to kill 100% of the cancer cells for these therapies and the remaining cells often leads to relapse. So the million-dollar question is, how to eliminate the majority of the remaining cancer cells so that the disease can be manageable in a clinical setting. Currently the best approach that is attracting much attention is cancer immune therapy and excit- ing developments in this field have led to cure in some cases that is unimaginable before [1]. There are two major parts to cancer immune therapy that need to work together to make the therapy successful. One is on the reversal of immune suppressive microen- vironment in cancer to allow the immune cells to target the tumor.