Ultra-high-speed four-dimensional hyperspectral imaging.

We propose, to the best of our knowledge, a novel deep learning-enabled four-dimensional spectral imaging system composed of a reflective coded aperture snapshot spectral imaging system and a panchromatic camera. The system simultaneously captures a compressively coded hyperspectral measurement and a panchromatic measurement. The hyperspectral data cube is recovered by the U-net-3D network. The depth information of the scene is then acquired by estimating a disparity map between the hyperspectral data cube and the panchromatic measurement through stereo matching. This disparity map is used to align the hyperspectral data cube and the panchromatic measurement. A designed fusion network is used to improve the spatial reconstruction of the hyperspectral data cube by fusing aligned panchromatic measurements. The hardware prototype of the proposed system demonstrates high-speed four-dimensional spectral imaging that allows for simultaneously acquiring depth and spectral images with an 8 nm spectral resolution between 450 and 700 nm, 2.5 mm depth accuracy, and a 1.83 s reconstruction time.

An iterative BP-CNN decoder for optical fiber communication systems.

The conventional belief propagation (BP) of the low-density parity-check (LDPC) is designed based on additive white Gaussian noise (AWGN) close to the Shannon limit; however, the correlated noise due to chromatic dispersion or square-law detection results in a performance penalty in the intensity modulation and direct-detection (IM/DD) system. We propose an iterative BP cascaded convolution neural network (CNN) decoder to mitigate the correlated channel noise. We use a model of correlated Gaussian noise to verify that the noise correlation can be identified by the CNN and the decoding performance is improved by the iterative processing. We successfully demonstrate the proposed method in a 50-Gb/s 4-ary pulse amplitude modulation (PAM-4) IM/DD system. The simulation results show that the proposed decoder can achieve a BER performance improvement which is robust to transmission distance and launch optical power. The experimental results show that the iterative BP-CNN decoder outperforms the standard BP decoder by 1.2 dB in received optical power over 25-km SSMF.

Digital radio-over-fiber system based on differential pulse code modulation and space division multiplexing.

In this paper, we present and experimentally demonstrate a digital-radio-over-fiber (D-RoF) architecture based on differential pulse code modulation (DPCM) and space division multiplexing (SDM). At low quantization resolution, DPCM can effectively reduce quantization noise and obtain significant signal-to-quantization noise ratio (SQNR) gain. We experimentally study the 7-core and 8-core multicore fiber transmission of 64-ary quadrature amplitude modulation (64QAM) orthogonal frequency division multiplexing (OFDM) signals with a bandwidth of 100 MHz in a fiber-wireless hybrid transmission link. Compared to PCM-based D-RoF, the error vector magnitude (EVM) performance in the DPCM-based D-RoF is effectively improved when the quantization bits (QBs) are 3-5 bits. In particular, when the QB is 3 bits, the EVM of the DPCM-based D-RoF is 6.5% and 7% lower than that of the PCM-based system in 7-core- and 8-core-multicore fiber-wireless hybrid transmission links, respectively.

Simultaneous illumination and imaging based on a single multimode fiber.

Due to the small core diameter, a single-core multimode fiber (MMF) has been extensively investigated for endoscopic imaging. However, an extra light path is always utilized for illumination in MMF imaging system, which takes more space and is inapplicable in practical endoscopy imaging. In order to make the imaging system more practical and compact, we proposed a dual-function MMF imaging system, which can simultaneously transmit the illumination light and the images through the same imaging fiber. Meanwhile, a new deep learning-based encoder-decoder network with full-connected (FC) layers was designed for image reconstruction. We conducted an experiment of transmitting images via a 1.6 m long MMF to verify the effectiveness of the dual-function MMF imaging system. The experimental results show that the proposed network achieves the best reconstruction performance compared with the other four networks on different datasets. Besides, it is worth mentioning that the cropped speckle patterns can still be used to reconstruct the original images, which helps to reduce the computing complexity significantly. We also demonstrated the ability of cross-domain generalization of the proposed network. The proposed system shows the potential for more compact endoscopic imaging without external illumination.

Intelligent design of the chiral metasurfaces for flexible targets: combining a deep neural network with a policy proximal optimization algorithm.

Recently, deep reinforcement learning (DRL) for metasurface design has received increased attention for its excellent decision-making ability in complex problems. However, time-consuming numerical simulation has hindered the adoption of DRL-based design method. Here we apply the Deep learning-based virtual Environment Proximal Policy Optimization (DE-PPO) method to design the 3D chiral plasmonic metasurfaces for flexible targets and model the metasurface design process as a Markov decision process to help the training. A well trained DRL agent designs chiral metasurfaces that exhibit the optimal absolute circular dichroism value (typically, ∼ 0.4) at various target wavelengths such as 930 nm, 1000 nm, 1035 nm, and 1100 nm with great time efficiency. Besides, the training process of the PPO agent is exceptionally fast with the help of the deep neural network (DNN) auxiliary virtual environment. Also, this method changes all variable parameters of nanostructures simultaneously, reducing the size of the action vector and thus the output size of the DNN. Our proposed approach could find applications in efficient and intelligent design of nanophotonic devices.

Data-driven fiber model based on the deep neural network with multi-head attention mechanism.

In this paper, we put forward a data-driven fiber model based on the deep neural network with multi-head attention mechanism. This model, which predicts signal evolution through fiber transmission in optical fiber telecommunications, can have advantages in computation time without losing much accuracy compared with conventional split-step fourier method (SSFM). In contrast with other neural network based models, this model obtains a relatively good balance between prediction accuracy and distance generalization especially in cases where higher bit rate and more complicated modulation formats are adopted. By numerically demonstration, this model can have ability of predicting up to 16-QAM 160Gbps signals with any transmission distances ranging from 0 to 100 km under both circumstances of the signals without or with the noise.

Deep learning enabled reflective coded aperture snapshot spectral imaging.

Coded aperture snapshot spectral imaging (CASSI) can acquire rich spatial and spectral information at ultra-high speed, which shows extensive application prospects. CASSI innovatively employed the idea of compressive sensing to capture the spatial-spectral data cube using a monochromatic detector and used reconstruction algorithms to recover the desired spatial-spectral information. Based on the optical design, CASSI currently has two different implementations: single-disperser (SD) CASSI and dual-disperser (DD) CASSI. However, SD-CASSI has poor spatial resolution naturally while DD-CASSI increases size and cost because of the extra prism. In this work, we propose a deep learning-enabled reflective coded aperture snapshot spectral imaging (R-CASSI) system, which uses a mask and a beam splitter to receive the reflected light by utilizing the reflection of the mask. The optical path design of R-CASSI makes the optical system compact, using only one prism as two dispersers. Furthermore, an encoder-decoder structure with 3D convolution kernels is built for the reconstruction, dubbed U-net-3D. The designed U-net-3D network achieves both spatial and spectral consistency, leading to state-of-the-art reconstruction results. The real data is released and can serve as a benchmark dataset to test new reconstruction algorithms.

Physiological and proteomic analyses reveal the effects of exogenous nitrogen in diminishing Cd detoxification in Acacia auriculiformis.

Cadmium (Cd) has toxic effects on plants. Nitrogen (N), an essential element, is critical for plant growth, development and stress response. However, their combined effects on woody plants, especially in N-fixing tree species is still poorly understood. Our previous study revealed that the fast-growing Acacia auriculiformis showed strong Cd tolerance but the underlying mechanisms was not clear, which constrained its use in mine land reclamation. Herein, we investigated the physiological and proteomic changes in A. auriculiformis leaves to reveal the mechanisms of Cd tolerance and toxicity without N fertilizer (treatment Cd) and with excess N fertilizer (treatment CdN). Results showed that Cd tolerance in A. auriculiformis was closely associated with the coordinated gas exchange and antioxidant defense reactions under Cd treatment alone. Exogenous excessive N, however, inhibited plant growth, increased Cd concentrations, and weaken photosynthetic performance, thus, aggregated the toxicity under Cd stress. Furthermore, the aggregated Cd toxicity was attributed to the depression in the abundance of proteins, as well as their corresponding genes, involved in photosynthesis, energy metabolism (oxidative phosphorylation, carbon metabolism, etc.), defense and stress response (antioxidants, flavonoids, etc.), plant hormone signal transduction (MAPK, STN, etc.), and ABC transporters. Collectively, this study unveils a previously unknown physiological and proteomic network that explains N diminishes Cd detoxification in A. auriculiformis. It may be counterproductive to apply N fertilizer to fast-growing, N-fixing trees planted for phytoremediation of Cd-contaminated soils.

Genome-wide identification and analysis of DNA methyltransferase and demethylase gene families in Dendrobium officinale reveal their potential functions in polysaccharide accumulation.

BACKGROUND: DNA methylation is a conserved and important epigenetic modification involved in the regulation of numerous biological processes, including plant development, secondary metabolism, and response to stresses. However, no information is available regarding the identification of cytosine-5 DNA methyltransferase (C5-MTase) and DNA demethylase (dMTase) genes in the orchid Dendrobium officinale. RESULTS: In this study, we performed a genome-wide analysis of DoC5-MTase and DodMTase gene families in D. officinale. Integrated analysis of conserved motifs, gene structures and phylogenetic analysis showed that eight DoC5-MTases were divided into four subfamilies (DoCMT, DoDNMT, DoDRM, DoMET) while three DodMTases were divided into two subfamilies (DoDML3, DoROS1). Multiple cis-acting elements, especially stress-responsive and hormone-responsive ones, were found in the promoter region of DoC5-MTase and DodMTase genes. Furthermore, we investigated the expression profiles of DoC5-MTase and DodMTase in 10 different tissues, as well as their transcript abundance under abiotic stresses (cold and drought) and at the seedling stage, in protocorm-like bodies, shoots, and plantlets. Interestingly, most DoC5-MTases were downregulated whereas DodMTases were upregulated by cold stress. At the seedling stage, DoC5-MTase expression decreased as growth proceeded, but DodMTase expression increased. CONCLUSIONS: These results provide a basis for elucidating the role of DoC5-MTase and DodMTase in secondary metabolite production and responses to abiotic stresses in D. officinale.

PDL and CD insensitive low complexity equalizer for short reach coherent systems.

We propose a polarization dependent loss (PDL) and chromatic dispersion (CD) insensitive, low-complexity adaptive equalizer (AEQ) for short-reach coherent optical transmission systems. The AEQ contains a 1-tap butterfly finite impulse response (FIR) filter and two N-tap FIR filters. It first performs polarization demultiplexing using the 1-tap filter, of which the coefficients are obtained based on Stokes space. Then it mitigates the inter-symbol interference (ISI) using the two N-tap finite impulse response (FIR) filters and adjust the filter's coefficients by utilizing constant modulus algorithm (CMA). Through theoretical and experimental analysis, we verify that this proposed AEQ can perform robust polarization demultiplexing when PDL and CD exists. Besides, our proposed AEQ has faster convergence speed compared with recently proposed AEQs. In addition, it reduces the number of multipliers and thus reduce the computational complexity of conventional butterfly filter structure AEQ. And this proposed AEQ suffers little bit error ratio loss compared with the conventional AEQ. Due to the low-complexity and robustness to PDL and CD, this proposed AEQ is well-suited for future low-cost short-reach optical communication system.

Genome-Wide Identification and Analysis of the APETALA2 (AP2) Transcription Factor in Dendrobium officinale.

The APETALA2 (AP2) transcription factors (TFs) play crucial roles in regulating development in plants. However, a comprehensive analysis of the AP2 family members in a valuable Chinese herbal orchid, Dendrobium officinale, or in other orchids, is limited. In this study, the 14 DoAP2 TFs that were identified from the D. officinale genome and named DoAP2-1 to DoAP2-14 were divided into three clades: euAP2, euANT, and basalANT. The promoters of all DoAP2 genes contained cis-regulatory elements related to plant development and also responsive to plant hormones and stress. qRT-PCR analysis showed the abundant expression of DoAP2-2, DoAP2-5, DoAP2-7, DoAP2-8 and DoAP2-12 genes in protocorm-like bodies (PLBs), while DoAP2-3, DoAP2-4, DoAP2-6, DoAP2-9, DoAP2-10 and DoAP2-11 expression was strong in plantlets. In addition, the expression of some DoAP2 genes was down-regulated during flower development. These results suggest that DoAP2 genes may play roles in plant regeneration and flower development in D. officinale. Four DoAP2 genes (DoAP2-1 from euAP2, DoAP2-2 from euANT, and DoAP2-6 and DoAP2-11 from basal ANT) were selected for further analyses. The transcriptional activation of DoAP2-1, DoAP2-2, DoAP2-6 and DoAP2-11 proteins, which were localized in the nucleus of Arabidopsis thaliana mesophyll protoplasts, was further analyzed by a dual-luciferase reporter gene system in Nicotiana benthamiana leaves. Our data showed that pBD-DoAP2-1, pBD-DoAP2-2, pBD-DoAP2-6 and pBD-DoAP2-11 significantly repressed the expression of the LUC reporter compared with the negative control (pBD), suggesting that these DoAP2 proteins may act as transcriptional repressors in the nucleus of plant cells. Our findings on AP2 genes in D. officinale shed light on the function of AP2 genes in this orchid and other plant species.

Application analysis of clipping and digital resolution enhancer in high-speed direct-detection PAM4 transmission.

In this paper, the application of a clipping and digital resolution enhancer (DRE) is numerically investigated in 56-GBaud direct-detection four-ary pulse amplitude modulation (PAM4) transmission systems. The influence of the tap length and the digital-to-analog converter (DAC) samples per symbol on DRE gain is first studied. After optimizing clipping probability, sampling jitter is introduced. The combination of clipping and DRE can increase jitter tolerance by ∼1.7% sampling time for 4-bit DAC. Then, the required optical signal-to-noise ratio (OSNR) at the hard-decision forward error correction (HD-FEC) threshold of 3.8×10-3 is investigated in back-to-back case and 80-km fiber transmission. It is shown that the combination of clipping and DRE can reduce the required OSNR significantly for 3-bit and 4-bit DAC. Finally, the performance gain introduced by clipping and DRE is analyzed from a perspective of the transmitted average signal power and the received mean-squared quantization error.

Low-complexity storage-reduced digital spectrum-based soft-failure management with Welch's method.

In this paper, we propose a low-complexity storage-reduced digital spectrum-based soft failure management solution including soft failure detection (SFD), identification (SFI) and failure magnitude estimation (FME). Five soft failures are considered. Random fluctuation of key link parameters is introduced in simulations to investigate the robustness of the proposed solution. To reduce computational complexity and storage requirement for digital spectrum calculation, Welch's method is employed instead of applying fast Fourier transform on a long sampling sequence. A false positive rate below 1% and a false negative rate below 3% are achieved for SFD after extensive numerical simulations. Besides, SFI realizes an identification accuracy of 97.4%. The mean square errors of FME for different soft failures are all below 0.4. Finally, with a very small segment length of 25, 46.2% of computational complexity and 99.6% of storage can be reduced for digital spectrum calculation.

Snapshot multispectral endomicroscopy.

Multispectral endomicroscopy provides tissue functional information in addition to structural information for accurate disease diagnosis. In this Letter, we propose a snapshot multispectral endomicroscope that employs a fiber bundle to deliver an in-body tissue spatial-spectral datastream to an external compressive spectral imager. Equipped with an end-to-end deep-learning-based reconstruction algorithm, we are able to capture tissue multispectral data in video rates and reconstruct high-resolution multispectral images with up to 24 spectral channels in near-real time.

Understanding different regulatory mechanisms of proteinaceous and non-proteinaceous amino acid formation in tea (Camellia sinensis) provides new insights into the safe and effective alteration of tea flavor and function.

Amino acids are the main contributors to tea (Camellia sinensis) flavor and function. Tea leaves contain not only proteinaceous amino acids but also specialized non-proteinaceous amino acids such as L-theanine and γ-aminobutyric acid (GABA). Here, we review different regulatory mechanisms of proteinaceous and non-proteinaceous amino acid formation in tea. The key findings were: (1) High accumulations of proteinaceous amino acids mainly result from protein degradation, which occurs in each tea stage, including preharvest, postharvest, manufacturing, and deep processing; (2) L-Theanine is the most represented non-proteinaceous amino acid that contributes to tea taste and function. Its accumulation is influenced more by the variety than by exogenous factors; and (3) GABA is the second most represented non-proteinaceous amino acid that contributes to tea function. Its formation, and resulting accumulation, are responses to stress. The combination of anoxic stress and mechanical damage are essential for a high GABA accumulation. An understanding of the biosynthesis, metabolism, and regulatory mechanisms of the proteinaceous and non-proteinaceous amino acids during the whole process from raw materials to tea products is necessary to safely and effectively alter tea flavor and function.

Genome-Wide Identification and Expression Profile of TPS Gene Family in Dendrobium officinale and the Role of DoTPS10 in Linalool Biosynthesis.

Terpene synthase (TPS) is a critical enzyme responsible for the biosynthesis of terpenes, which possess diverse roles in plant growth and development. Although many terpenes have been reported in orchids, limited information is available regarding the genome-wide identification and characterization of the TPS family in the orchid, Dendrobium officinale. By integrating the D. officinale genome and transcriptional data, 34 TPS genes were found in D. officinale. These were divided into four subfamilies (TPS-a, TPS-b, TPS-c, and TPS-e/f). Distinct tempospatial expression profiles of DoTPS genes were observed in 10 organs of D. officinale. Most DoTPS genes were predominantly expressed in flowers, followed by roots and stems. Expression of the majority of DoTPS genes was enhanced following exposure to cold and osmotic stresses. Recombinant DoTPS10 protein, located in chloroplasts, uniquely converted geranyl diphosphate to linalool in vitro. The DoTPS10 gene, which resulted in linalool formation, was highly expressed during all flower developmental stages. Methyl jasmonate significantly up-regulated DoTPS10 expression and linalool accumulation. These results simultaneously provide valuable insight into understanding the roles of the TPS family and lay a basis for further studies on the regulation of terpenoid biosynthesis by DoTPS in D. officinale.

DoRWA3 from Dendrobium officinale Plays an Essential Role in Acetylation of Polysaccharides.

The acetylation or deacetylation of polysaccharides can influence their physical properties and biological activities. One main constituent of the edible medicinal orchid, Dendrobium officinale, is water-soluble polysaccharides (WSPs) with substituted O-acetyl groups. Both O-acetyl groups and WSPs show a similar trend in different organs, but the genes coding for enzymes that transfer acetyl groups to WSPs have not been identified. In this study, we report that REDUCED WALL ACETYLATION (RWA) proteins may act as acetyltransferases. Three DoRWA genes were identified, cloned, and sequenced. They were sensitive to abscisic acid (ABA), but there were no differences in germination rate and root length between wild type and 35S::DoRWA3 transgenic lines under ABA stress. Three DoRWA proteins were localized in the endoplasmic reticulum. DoRWA3 had relatively stronger transcript levels in organs where acetyl groups accumulated than DoRWA1 and DoRWA2, was co-expressed with polysaccharides synthetic genes, so it was considered as a candidate acetyltransferase gene. The level of acetylation of polysaccharides increased significantly in the seeds, leaves and stems of three 35S::DoRWA3 transgenic lines compared to wild type plants. These results indicate that DoRWA3 can transfer acetyl groups to polysaccharides and is a candidate protein to improve the biological activity of other edible and medicinal plants.

Functional Characterization of a Dendrobium officinale Geraniol Synthase DoGES1 Involved in Floral Scent Formation.

Floral scent is a key ornamental trait that determines the quality and commercial value of orchids. Geraniol, an important volatile monoterpene in orchids that attracts pollinators, is also involved in responses to stresses but the geraniol synthase (GES) responsible for its synthesis in the medicinal orchid Dendrobium officinale has not yet been identified. In this study, three potential geraniol synthases were mined from the D. officinale genome. DoGES1, which was localized in chloroplasts, was characterized as a geraniol synthase. DoGES1 was highly expressed in flowers, especially in petals. DoGES1 transcript levels were high in the budding stage of D. officinale flowers at 11:00 a.m. DoGES1 catalyzed geraniol in vitro, and transient expression of DoGES1 in Nicotiana benthamiana leaves resulted in the accumulation of geraniol in vivo. These findings on DoGES1 advance our understanding of geraniol biosynthesis in orchids, and lay the basis for genetic modification of floral scent in D. officinale or in other ornamental orchids.

Intelligent optical performance monitor using multi-task learning based artificial neural network.

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The results obtained from simulation and experiment of NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% for the three modulation formats under consideration. Furthermore, OSNR monitoring with mean-square error (MSE) of 0.12 dB and accuracy of 100% is achieved while regarding it as regression problem and classification problem, respectively. In this intelligent optical performance monitor, only a single MTL-ANN is deployed, which enables reduced-complexity optical performance monitor (OPM) devices for multi-parameters estimation in future heterogeneous optical network.

Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation.

A loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) is applied for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI). We conduct an experiment of polarization division multiplexing (PDM) coherent optical system with 5 km standard single mode fiber (SSMF) transmission to verify this monitor. A group of modulation schemes including nine modulation adaptive M-QAM formats are selected as the transmission signals. Instead of circular constellation, signals' amplitude histograms after constant module algorithm (CMA) based polarization de-multiplexing are selected as input features for our proposed monitor. The experimental results show that the MFI accuracy reaches 100% in the estimated OSNR range. Furthermore, when treated as regression problem and classification problem, OSNR estimation with a root mean-square error (RMSE) of 0.68 dB and an accuracy of 98.7% are achieved, respectively. Unlike loss weight fixed MTL-ANN, loss weight adaptive MTL-ANN could search the optimal loss weight ratio automatically for different link configurations. Besides that, the number of estimated parameters can be easily expanded, which is attractive for multiple parameters estimation in future heterogeneous optical networks.