RNA interference (RNAi) in plants can move from cell to cell, allowing for systemic spread of an antiviral immune response. How this cell-to-cell spread of silencing is regulated is currently unknown. Here, we describe that the C4 protein from can inhibit the intercellular spread of RNAi. Using this viral protein as a probe, we have identified the receptor-like kinase (RLK) BARELY ANY MERISTEM 1 (BAM1) as a positive regulator of the cell-to-cell movement of RNAi, and determined that BAM1 and its closest homolog, BAM2, play a redundant role in this process. C4 interacts with the intracellular domain of BAM1 and BAM2 at the plasma membrane and plasmodesmata, the cytoplasmic connections between plant cells, interfering with the function of these RLKs in the cell-to-cell spread of RNAi. Our results identify BAM1 as an element required for the cell-to-cell spread of RNAi and highlight that signaling components have been coopted to play multiple functions in plants.

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Novel features of RNA structure, recognition and discrimination have been recently elucidated through the solution structural characterization of RNA aptamers that bind cofactors, aminoglycoside antibiotics, amino acids and peptides with high affinity and specificity. This review presents the solution structures of RNA aptamer complexes with adenosine monophosphate, flavin mononucleotide, arginine/citrulline and tobramycin together with an example of hydrogen exchange measurements of the base-pair kinetics for the AMP-RNA aptamer complex. A comparative analysis of the structures of these RNA aptamer complexes yields the principles, patterns and diversity associated with RNA architecture, molecular recognition and adaptive binding associated with complex formation.

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Multimode fibres (MMF) are remarkable high-capacity information channels owing to the large number of transmitting fibre modes 1-3 , and have recently attracted significant renewed interest in applications such as optical communication, imaging, and optical trapping [4][5][6][7][8][9][10][11][12][13][14][15] . At the same time, the optical transmitting modes inside MMFs are highly sensitive to external perturbations and environmental changes, resulting in MMF transmission channels being highly variable and random [16][17][18] . This largely limits the practical application of MMFs and hinders the full exploitation of their information capacity. Despite great research efforts made to overcome the high variability and randomness inside MMFs, any geometric change to the MMF leads to completely different transmission matrices, which unavoidably fails at the information recovery. Here, we show the successful binary image transmission using deep learning through a single MMF, which is stationary or subject to dynamic shape variations. We found that a single convolutional neural network has excellent generalisation capability with various MMF transmission states. This deep neural network can be trained by multiple MMF transmission states to accurately predict unknown information at the other end of the MMF at any of these states, without knowing which state is present. Our results demonstrate that deep learning is a promising solution to address the variability and randomness challenge of MMF based information channels. This deep-learning approach is the starting point of developing future high-capacity MMF optical systems and devices, and is applicable to optical systems concerning other diffusing media.We use a simple digital micromirror device (DMD) based single MMF transmission system 19 to demonstrate the idea, as shown in Fig. 1(a). The DMD can be controlled to display any binary pattern by turning 'ON' and 'OFF' individual micrormirrors, simulating a 2D object imaging from the DMD through the MMF to the camera. The experimental setup details are given in Methods. As the first step to verify our idea, we use transmission matrix (TM) calculated output speckle images for deep neural network training and prediction 20 . Firstly we calculate the MMF TM using 8000 input-output pairs collected from the experimental system. The measured TM is verified experimentally to have an average accuracy of 98% for output speckle pattern calculation (details in Methods). We then use the processed binary images of handwritten digits downloaded from the MNIST handwritten digits database 21,22 as the DMD input pattern. The binary processing is to convert the handwritten digit into 36×36 pixel image format that directly matches the fibre mode count and eigenchannels for optimal delivery 19 (details in Methods). As shown in Fig. 1(b), the MMF output speckle images are calculated using TM and the binary handwritten digits. We calculate 7,040 speckle images corresponding to different input binary images for the digits '0-9' (704 speckle i...

Satellites have shown free-space quantum-communication ability; however, they are orbit-limited from full-time all-location coverage. Meanwhile, practical quantum networks require satellite constellations, which are complicated and expensive, whereas the airborne mobile quantum communication may be a practical alternative to offering full-time all-location multi-weather coverage in a cost-effective way. Here, we demonstrate the first mobile entanglement distribution based on drones, realizing multi-weather operation including daytime and rainy nights, with a Clauser-Horne-Shimony-Holt S-parameter measured to be 2.41 ± 0.14 and 2.49 ± 0.06, respectively. Such a system shows unparalleled mobility, flexibility and reconfigurability compared to the existing satellite and fiber-based quantum communication, and reveals its potential to establish a multinode quantum network, with a scalable design using symmetrical lens diameter and single-mode-fiber coupling. All key technologies have been developed to pack quantum nodes into lightweight mobile platforms for local-area coverage, and arouse further technical improvements to establish wide-area quantum networks with high-altitude mobile communication.

We have studied the time-resolved and the steady-state fluorescence of the DNA groove binders 4',6-diamidino-2-phenylindole (DAPI) and Hoechst 33258 with the double stranded DNAs poly(dA-dU) and poly(dI-dC) and their halogenated analogs, poly(dA-I5dU) and poly(dI-Br5dC). These studies were prompted by earlier observations that steady-state fluorescence of Hoechst 33258 is quenched on binding to halogenated DNAs (presumably due to an intermolecular heavy atom effect involving the halogen atom in the major groove), and recent studies which clearly point to a binding-site in the minor groove of DNA. Measurements of the time resolved fluorescence decay demonstrate that the fluorescence of Hoechst 33258 is quenched on binding to the halogenated DNAs, in agreement with previous observations. However, quenching studies carried out using the free halogenated bases IdUrd and BrdCyd in solution yielded bimolecular rate constants more than one order of magnitude larger than those expected for an intermolecular heavy atom effect. Moreover, the quenching of the Hoechst 33258 fluorescence was accompanied by an accelerated photochemical destruction of Hoechst 33258. We therefore conclude that the fluorescence quenching observed with halogenated DNAs is probably due to a photochemical reaction involving Hoechst 33258, rather than direct contact of Hoechst 33258 with the halogen substituents in the major groove of the DNA. The fluorescence decay measurements however, do provide clear evidence for at least two different modes of binding. Taking into account the alternating sequences used in this study and the possibility of two different conformations for bound dye, at least four different modes of binding are plausible. Our present data do not allow us to distinguish between these alternatives. The time-resolved fluorescence decays and fluorescence quantum yields of DAPI are not affected by the presence of the heavy atom substituents in the DNA major groove. Based on this observation and earlier reports that DAPI binds in one of the DNA grooves, we conclude that the high affinity sites for DAPI on DNA are located in the minor groove.

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