Genome-wide analysis of the switchgrass YABBY family and functional characterization of PvYABBY14 in response to ABA and GA stress in Arabidopsis.

BACKGROUND: The small YABBY plant-specific transcription factor has a prominent role in regulating plant growth progress and responding to abiotic stress. RESULTS: Here, a total of 16 PvYABBYs from switchgrass (Panicum virgatum L.) were identified and classified into four distinct subgroups. Proteins within the same subgroup exhibited similar conserved motifs and gene structures. Synteny analyses indicated that segmental duplication contributed to the expansion of the YABBY gene family in switchgrass and that complex duplication events occurred in rice, maize, soybean, and sorghum. Promoter regions of PvYABBY genes contained numerous cis-elements related to stress responsiveness and plant hormones. Expression profile analysis indicated higher expression levels of many PvYABBY genes during inflorescence development and seed maturation, with lower expression levels during root growth. Real-time quantitative PCR analysis demonstrated the sensitivity of multiple YABBY genes to PEG, NaCl, ABA, and GA treatments. The overexpression of PvYABBY14 in Arabidopsis resulted in increased root length after treatment with GA and ABA compared to wild-type plants. CONCLUSIONS: Taken together, our study provides the first genome-wide overview of the YABBY transcription factor family, laying the groundwork for understanding the molecular basis and regulatory mechanisms of PvYABBY14 in response to ABA and GA responses in switchgrass.

Scatter correction for cone-beam CT via scatter kernel superposition-inspired convolutional neural network.

Objective.X-ray scatter leads to signal bias and degrades the image quality in Computed Tomography imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate x-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter x-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels.Approach.To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process.Main Results.By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects.Significance.The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

Masked Joint Bilateral Filtering via Deep Image Prior for Digital X-Ray Image Denoising.

Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be understood. Therefore, existing deep learning methods have been sparingly applied to clinical tasks. To this end, we integrate known filtering operators into deep learning and propose a novel Masked Joint Bilateral Filtering (MJBF) via deep image prior for digital X-ray image denoising. Specifically, MJBF consists of a deep image prior generator and an iterative filtering block. The deep image prior generator produces plentiful image priors by a multi-scale fusion network. The generated image priors serve as the guidance for the iterative filtering block, which is utilized for the actual edge-preserving denoising. The iterative filtering block contains three trainable Joint Bilateral Filters (JBFs), each with only 18 trainable parameters. Moreover, a masking strategy is introduced to reduce redundancy and improve the understanding of the proposed network. Experimental results on the ChestX-ray14 dataset and real data show that the proposed MJBF has achieved superior performance in terms of noise suppression and edge preservation. Tests on the portability of the proposed method demonstrate that this denoising modality is simple yet effective, and could have a clinical impact on medical imaging in the future.

Study on the Hydrophobic Modification of MTES/NH3 Vapor Surface Treatment for SiO2 Broadband Anti-Reflection Coating.

In this article, ammonia and methyltriethoxysilane (MTES) were chosen as vapor phase modifiers for the base-catalyzed SiO2 film. The surface of the film became more dense because of the hydroxyl condensation under the catalyst of ammonia, while the introduction of methyl groups by MTES of vapor treatment hindered the condensation to avoid over-change in film thickness. The hydrophobic of film was improved while the surface roughness of the film increased after treatment. The treated double-layer broadband anti-reflection (AR) coating retains high optical properties with the transmittance of 99.61%, 98.85%, and 99.16% at 355 nm, 532 nm, and 1064 nm, respectively. After exposing to the high humidity condition for 30 days, the broadband AR coating after treatment shows good optical durability, and the transmittance at 355 nm only drops by 0.12%. This vapor surface treatment can find potential application in high-power laser systems and solar cells.

QTL Mapping and Data Mining to Identify Genes Associated With the Sinorhizobium fredii HH103 T3SS Effector NopD in Soybean.

In some legume-rhizobium symbioses, host specificity is influenced by rhizobial type III effectors-nodulation outer proteins (Nops). However, the genes encoding host proteins that interact with Nops remain unknown. In this study, we aimed to identify candidate soybean genes associated with NopD, one of the type III effectors of Sinorhizobium fredii HH103. The results showed that the expression pattern of NopD was analyzed in rhizobia induced by genistein. We also found NopD can be induced by TtsI, and NopD as a toxic effector can induce tobacco leaf death. In 10 soybean germplasms, NopD played a positively effect on nodule number (NN) and nodule dry weight (NDW) in nine germplasms, but not in Kenjian28. Significant phenotype of NN and NDW were identified between Dongnong594 and Charleston, Suinong14 and ZYD00006, respectively. To map the quantitative trait locus (QTL) associated with NopD, a recombinant inbred line (RIL) population derived from the cross between Dongnong594 and Charleston, and chromosome segment substitution lines (CSSLs) derived from Suinong14 and ZYD00006 were used. Two overlapping conditional QTL associated with NopD on chromosome 19 were identified. Two candidate genes were identified in the confident region of QTL, we found that NopD could influence the expression of Glyma.19g068600 (FBD/LRR) and expression of Glyma.19g069200 (PP2C) after HH103 infection. Haplotype analysis showed that different types of Glyma.19g069200 haplotypes could cause significant nodule phenotypic differences, but Glyma.19g068600 (FBD/LRR) was not. These results suggest that NopD promotes S. fredii HH103 infection via directly or indirectly regulating Glyma.19g068600 and Glyma.19g069200 expression during the establishment of symbiosis between rhizobia and soybean plants.

RNA Sequencing-Associated Study Identifies GmDRR1 as Positively Regulating the Establishment of Symbiosis in Soybean.

In soybean (Glycine max)-rhizobium interactions, the type III secretion system (T3SS) of rhizobium plays a key role in regulating host specificity. However, the lack of information on the role of T3SS in signaling networks limits our understanding of symbiosis. Here, we conducted an RNA sequencing analysis of three soybean chromosome segment substituted lines, one female parent and two derived lines with different chromosome-substituted segments of wild soybean and opposite nodulation patterns. By analyzing chromosome-linked differentially expressed genes in the substituted segments and quantitative trait loci (QTL)-assisted selection in the substituted-segment region, genes that may respond to type III effectors to mediate plant immunity-related signaling were identified. To narrow down the number of candidate genes, QTL assistant was used to identify the candidate region consistent with the substituted segments. Furthermore, one candidate gene, GmDRR1, was identified in the substituted segment. To investigate the role of GmDRR1 in symbiosis establishment, GmDRR1-overexpression and RNA interference soybean lines were constructed. The nodule number increased in the former compared with wild-type soybean. Additionally, the T3SS-regulated effectors appeared to interact with the GmDDR1 signaling pathway. This finding will allow the detection of T3SS-regulated effectors involved in legume-rhizobium interactions.

QTL analysis of nodule traits and the identification of loci interacting with the type III secretion system in soybean.

Symbiotic nitrogen fixation is the main source of nitrogen for soybean growth. Since the genotypes of rhizobia and soybean germplasms vary, the nitrogen-fixing ability of soybean after inoculation also varies. A few studies have reported that quantitative trait loci (QTLs) control biological nitrogen fixation traits, even soybean which is an important crop. The present study reported that the Sinorhizobium fredii HH103 gene rhcJ belongs to the tts (type III secretion) cluster and that the mutant HH103ΩrhcJ can clearly decrease the number of nodules in American soybeans. However, few QTLs of nodule traits have been identified. This study used a soybean (Glycine max (L.) Merr.) 'Charleston' × 'Dongnong 594' (C × D, n = 150) recombinant inbred line (RIL). Nodule traits were analysed in the RIL population after inoculation with S. fredii HH103 and the mutant HH103ΩrhcJ. Plants were grown in a greenhouse with a 16-h light cycle at 26 °C and an 8-h dark cycle at 18 °C. Then, 4 weeks after inoculation, plants were harvested for evaluation of nodule traits. Through QTL mapping, 16 QTLs were detected on 8 chromosomes. Quantitative PCR (qRT-PCR) and RNA-seq analysis determined that the genes Glyma.04g060600, Glyma.18g159800 and Glyma.13g252600 might interact with rhcJ.

Identification of Soybean Genes Whose Expression is Affected by the Ensifer fredii HH103 Effector Protein NopP.

In some legume⁻rhizobium symbioses, host specificity is influenced by rhizobial nodulation outer proteins (Nops). However, the genes encoding host proteins that interact with Nops remain unknown. We generated an Ensifer fredii HH103 NopP mutant (HH103ΩNopP), and analyzed the nodule number (NN) and nodule dry weight (NDW) of 10 soybean germplasms inoculated with the wild-type E. fredii HH103 or the mutant strain. An analysis of recombinant inbred lines (RILs) revealed the quantitative trait loci (QTLs) associated with NopP interactions. A soybean genomic region containing two overlapping QTLs was analyzed in greater detail. A transcriptome analysis and qRT-PCR assay were used to identify candidate genes encoding proteins that interact with NopP. In some germplasms, NopP positively and negatively affected the NN and NDW, while NopP had different effects on NN and NDW in other germplasms. The QTL region in chromosome 12 was further analyzed. The expression patterns of candidate genes Glyma.12g031200 and Glyma.12g073000 were determined by qRT-PCR, and were confirmed to be influenced by NopP.