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In single-pixel imaging or computational ghost imaging, the measurement matrix has a great impact on the performance of the imaging system, because it involves modulation of the optical signal and image reconstruction. The measurement matrix reported in the existing literatures is first binarized and then loaded onto the digital micro-mirror device (DMD) for optical modulation, that is, each pixel can only be modulated into on-off states. In this paper, we propose a digital grayscale modulation method for more efficient compressive sampling. On the basis of this, we demonstrate a single photon compressive imaging system. A control and counting circuit, based on field-programmable gate array (FPGA), is developed to control DMD to conduct digital grayscale modulation and count single-photon pulse output from the photomultiplier tube (PMT) simultaneously. The experimental results show that the imaging reconstruction quality can be improved by increasing the sparsity ratio properly and compressive sampling ratio (SR) of these gray-scale matrices. However, when the compressive SR and sparsity ratio are increased appropriately to a certain value, the reconstruction quality is usually saturated, and the imaging reconstruction quality of the digital grayscale modulation is better than that of binary modulation.

Brassica juncea is one of important oilseed crops, and FA compositions determine quality of vegetable oil. Although fatty acid desaturases (FADs) are mainly responsible for modifying seed FA compositions, genome-wide analysis of FAD gene family in B. juncea (BjuFAD) is not reported. Here, we identified 57 BjuFAD genes in B. juncea genome using homology searches. These FAD genes were unevenly distributed in 16 chromosomes and 3 scaffolds. Phylogenetic analysis showed that BjuFAD genes were divided into seven subfamilies. Exon–intron organizations, intron patterns and MEME motifs were highly conserved within each of BjuFAD subfamilies. Moreover, subcellular locations of deduced BjuFAD proteins and cis-acting elements in BjuFAD promoters were predicted and analyzed using online software. In addition, 16 SSR loci were totally found in BjuFAD genes/promoters. This work provides a basis for further function study of BjuFAD genes in quality improvement of B. juncea seed oil and in plant development as well as stress response.

We propose a time-adaptive sampling method and demonstrate a sampling-time-adaptive single-photon compressive imaging system. In order to achieve self-adapting adjustment of sampling time, the theory of threshold of light intensity estimation accuracy is deduced. According to this threshold, a sampling control module, based on field-programmable gate array, is developed. Finally, the advantage of the time-adaptive sampling method is proved experimentally. Imaging performance experiments show that the time-adaptive sampling method can automatically adjust the sampling time for the change of light intensity of image object to obtain an image with better quality and avoid speculative selection of sampling time.

We demonstrate an iterative adaptive photon-counting compressive imaging system. The low-resolution image obtained from the previous sampling are used to calculate the wavelet coefficients. A method based on multiple micro-mirrors combination is proposed to conduct iterative adaptive sampling on the imaging regions corresponding to important children wavelet coefficients at all levels. In order to reconstruct a better quality image directly with appropriate compression ratio and time and avoid speculative selection of thresholds, an automatic threshold acquisition method based on wavelet entropy is proposed, which can automatically follows the change of the object image to obtain the threshold. The iterative adaptive compressive sensing measurement method based on wavelet entropy automatic threshold acquisition is applied to photon-counting compressive imaging system. Experimental results show that a high resolution image can be reconstructed with low compression ratio and short time in extremely low light environment, the quality of reconstructed images of iterative adaptive compressive sensing measurement is better than that of adaptive basis scan.

Sclerotinia sclerotiorum (Ss) is a devastating fungal pathogen that causes Sclerotinia stem rot in rapeseed (Brassica napus), and is also detrimental to mulberry and many other crops. A wild mulberry germplasm, Morus laevigata, showed high resistance to Ss, but the molecular basis for the resistance is largely unknown. Here, the transcriptome response characteristics of M. laevigata to Ss infection were revealed by RNA-seq. A total of 833 differentially expressed genes (DEGs) were detected after the Ss inoculation in the leaf of M. laevigata. After the GO terms and KEGG pathways enrichment analyses, 42 resistance-related genes were selected as core candidates from the upregulated DEGs. Their expression patterns were detected in the roots, stems, leaves, flowers, and fruits of M. laevigata. Most of them (30/42) were specifically or mainly expressed in flowers, which was consistent with the fact that Ss mainly infects plants through floral organs, and indicated that Ss-resistance genes could be induced by pathogen inoculation on ectopic organs. After the Ss inoculation, these candidate genes were also induced in the two susceptible varieties of mulberry, but the responses of most of them were much slower with lower extents. Based on the expression patterns and functional annotation of the 42 candidate genes, we cloned the full-length gDNA and cDNA sequences of the Ss-inducible chitinase gene set (MlChi family). Phylogenetic tree construction, protein interaction network prediction, and gene expression analysis revealed their special roles in response to Ss infection. In prokaryotic expression, their protein products were all in the form of an inclusion body. Our results will help in the understanding of the molecular basis of Ss-resistance in M. laevigata, and the isolated MlChi genes are candidates for the improvement in plant Ss-resistance via biotechnology.

Arabidopsis thaliana MYB43 (AtMYB43) is suggested to be involved in cell wall lignification. PtrMYB152, the Populus orthologue of AtMYB43, is a transcriptional activator of lignin biosynthesis and vessel wall deposition. In this research, MYB43 genes from Brassica napus (rapeseed) and its parental species B. rapa and B. oleracea were molecularly characterized, which were dominantly expressed in stem and other vascular organs and showed responsiveness to Sclerotinia sclerotiorum infection. The BnMYB43 family was silenced by RNAi, and the transgenic rapeseed lines showed retardation in growth and development with smaller organs, reduced lodging resistance, fewer silique number and lower yield potential. The thickness of the xylem layer decreased by 28%, the numbers of sclerenchymatous cells, vessels, interfascicular fibers, sieve tubes and pith cells in the whole cross section of the stem decreased by 28%, 59%, 48%, 34% and 21% in these lines, respectively. The contents of cellulose and lignin decreased by 17.49% and 16.21% respectively, while the pectin content increased by 71.92% in stems of RNAi lines. When inoculated with S. sclerotiorum, the lesion length was drastically decreased by 52.10% in the stems of transgenic plants compared with WT, implying great increase in disease resistance. Correspondingly, changes in the gene expression patterns of lignin biosynthesis, cellulose biosynthesis, pectin biosynthesis, cell cycle, SA- and JA-signals, and defensive pathways were in accordance with above phenotypic modifications. These results show that BnMYB43, being a growth-defense trade-off participant, positively regulates vascular lignification, plant morphology and yield potential, but negatively affects resistance to S. sclerotiorum. Moreover, this lignification activator influences cell biogenesis of both lignified and non-lignified tissues of the whole vascular organ.

We have developed a single photon compressive imaging system based on single photon counting technology and compressed sensing theory, using a photomultiplier tube (PMT) photon counting head as the bucket detector. This system can realize ultra-weak light imaging with the imaging area up to the entire digital micromirror device (DMD) working region. The measurement matrix in this system is required to be binary due to the two working states of the micromirror corresponding to two controlled elements. And it has a great impact on the performance of the imaging system, because it involves modulation of the optical signal and image reconstruction. Three kinds of binary matrix including sparse binary random matrix, m sequence matrix and true random number matrix are constructed. The properties of these matrices are analyzed theoretically with the uncertainty principle. The parameters of measurement matrix including sparsity ratio, compressive sampling ratio and reconstruction time are verified in the experimental system. The experimental results show that, the increase of sparsity ratio and compressive sampling ratio can improve the reconstruction quality. However, when the increase is up to a certain value, the reconstruction quality tends to be saturated. Compared to the other two types of measurement matrices, the m sequence matrix has better performance in image reconstruction.

We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.

We demonstrate a single photon compressive imaging system with the image plane up to the entire digital micro-mirror device (DMD) work area. A parallel light source is designed to reduce the influence of light scattering on imaging resolution and a photon counting photomultiplier tube (PMT) with a large photosensitive area is used to effectively collect light reflected from the full screen of DMD. A control and counting circuit, based on Field-Programmable Gate Array (FPGA), is developed to load binary random matrix into the DMD controller for each measurement, and to count single-photon pulse output from PMT simultaneously. To reduce imaging time and huge memory occupation for image reconstruction, a multiple micro-mirrors combination imaging method is proposed. The signal-to-noise ratio and detection limit of the imaging system is theoretically deduced. Theoretical analysis and experimental results show that micro-mirrors combination imaging method is more suitable for faster imaging in a weaker-light-level environment. In order to achieve high imaging quality, the size of the combined pixels and the average time of each measurement should be moderate, so that the impact of Poisson shot noise is minimized.