A putative E3 ubiquitin ligase substrate receptor degrades transcription factor SmNAC to enhance bacterial wilt resistance in eggplant.

Bacterial wilt caused by Ralstonia solanacearum is a severe soil-borne disease globally, limiting the production in Solanaceae plants. SmNAC negatively regulated eggplant resistance to Bacterial wilt (BW) though restraining salicylic acid (SA) biosynthesis. However, other mechanisms through which SmNAC regulates BW resistance remain unknown. Here, we identified an interaction factor, SmDDA1b, encoding a substrate receptor for E3 ubiquitin ligase, from the eggplant cDNA library using SmNAC as bait. SmDDA1b expression was promoted by R. solanacearum inoculation and exogenous SA treatment. The virus-induced gene silencing of the SmDDA1b suppressed the BW resistance of eggplants; SmDDA1b overexpression enhanced the BW resistance of tomato plants. SmDDA1b positively regulates BW resistance by inhibiting the spread of R. solanacearum within plants. The SA content and the SA biosynthesis gene ICS1 and signaling pathway genes decreased in the SmDDA1b-silenced plants but increased in SmDDA1b-overexpression plants. Moreover, SmDDB1 protein showed interaction with SmCUL4 and SmDDA1b and protein degradation experiments indicated that SmDDA1b reduced SmNAC protein levels through proteasome degradation. Furthermore, SmNAC could directly bind the SmDDA1b promoter and repress its transcription. Thus, SmDDA1b is a novel regulator functioning in BW resistance of solanaceous crops via the SmNAC-mediated SA pathway. Those results also revealed a negative feedback loop between SmDDA1b and SmNAC controlling BW resistance.

[Retracted] Chronic alcohol exposure exacerbates inflammation and triggers pancreatic acinar­to­ductal metaplasia through PI3K/Akt/IKK.

Following the publication of the above article, a concerned reader drew to the Editor's attention that there were a number of apparent anomalies associated with the western blots featured in Figs. 1C and E, 3A, C and E, 4A, C and E, 5B, 8A and C; moreover, the images shown for the immunohistochemical experiments in Fig. 8E contained groupings of cells that were markedly similar in appearance, comparing across the eight separate figure parts. After having conducted an internal investigation of the data in this paper, the Editor of International Journal of Molecular Medicine has judged that the potentially anomalous presentation of the western blotting data and the strikingly similar groupings of cells in Fig. 8E were too extensive that these features could have been attributed to pure coincidence. Therefore, the Editor has decided that this article should be retracted from the publication on the grounds of an overall lack of confidence in the data. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor sincerely apologizes to the readership for any incovenience caused, and we thank the reader for bringing this matter to our attention. [International Journal of Molecular Medicine 35: 653­663, 2015; DOI: 10.3892/ijmm.2014.2055].

Identification and characterization analysis of candidate genes controlling mushroom leaf development in Chinese kale by BSA-seq.

Mushroom leaves (MLs) are malformed leaves that develop from the leaf veins in some of Chinese kale genotypes. To study the genetic model and molecular mechanism of ML development in Chinese kale, the F2 segregation population was constructed by two inbred lines, genotype Boc52 with ML and genotype Boc55 with normal leaves (NL). In the present study, we have identified for the first time that the development of mushroom leaves may be affected by the change of adaxial-abaxial polarity of leaves. Examination of the phenotypes of F1 and F2 segregation populations suggested that ML development is controlled by two dominant major genes inherited independently. BSA-seq analysis showed that a major quantitative trait locus (QTL) qML4.1 that controls ML development is located within 7.4 Mb on chromosome kC4. The candidate region was further narrowed to 255 kb by linkage analysis combined with insertion/deletion (InDel) markers, and 37 genes were predicted in this region. According to the expression and annotation analysis, a B3 domain-containing transcription factor NGA1-like gene, BocNGA1, was identified as a key candidate gene for controlling ML development in Chinese kale. Fifteen single nucleotide polymorphisms (SNPs) were found in coding sequences and 21 SNPs and 3 InDels found in the promoter sequences of BocNGA1 from the genotype Boc52 with ML. The expression levels of BocNGA1 in ML genotypes are significantly lower than in the NL genotypes, which suggests that BocNGA1 may act as a negative regulator for ML genesis in Chinese kale. This study provides a new foundation for Chinese kale breeding and for the study of the molecular mechanism of plant leaf differentiation. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01364-6.

Learned Video Compression With Efficient Temporal Context Learning.

In contrast to image compression, the key of video compression is to efficiently exploit the temporal context for reducing the inter-frame redundancy. Existing learned video compression methods generally rely on utilizing short-term temporal correlations or image-oriented codecs, which prevents further improvement of the coding performance. This paper proposed a novel temporal context-based video compression network (TCVC-Net) for improving the performance of learned video compression. Specifically, a global temporal reference aggregation (GTRA) module is proposed to obtain an accurate temporal reference for motion-compensated prediction by aggregating long-term temporal context. Furthermore, in order to efficiently compress the motion vector and residue, a temporal conditional codec (TCC) is proposed to preserve structural and detailed information by exploiting the multi-frequency components in temporal context. Experimental results show that the proposed TCVC-Net outperforms public state-of-the-art methods in terms of both PSNR and MS-SSIM metrics.

An R-R-type MYB transcription factor promotes non-climacteric pepper fruit carotenoid pigment biosynthesis.

Carotenoids are major accessory pigments in the chloroplast, and they also act as phytohormones and volatile compound precursors to influence plant development and confer characteristic colours, affecting both the aesthetic and nutritional value of fruits. Carotenoid pigmentation in ripening fruits is highly dependent on developmental trajectories. Transcription factors incorporate developmental and phytohormone signalling to regulate the biosynthesis process. By contrast to the well-established pathways regulating ripening-related carotenoid biosynthesis in climacteric fruit, carotenoid regulation in non-climacteric fruit is poorly understood. Capsanthin is the primary carotenoid of non-climacteric pepper (Capsicum) fruit; its biosynthesis is tightly associated with fruit ripening, and it confers red pigmentation to the ripening fruit. In the present study, using a coexpression analysis, we identified an R-R-type MYB transcription factor, DIVARICATA1, and demonstrated its role in capsanthin biosynthesis. DIVARICATA1 encodes a nucleus-localised protein that functions primarily as a transcriptional activator. Functional analyses showed that DIVARICATA1 positively regulates carotenoid biosynthetic gene (CBG) transcript levels and capsanthin levels by directly binding to and activating CBG promoter transcription. Furthermore, an association analysis revealed a significant positive association between DIVARICATA1 transcription level and capsanthin content. ABA promotes capsanthin biosynthesis in a DIVARICATA1-dependent manner. Comparative transcriptomic analysis of DIVARICATA1 in Solanaceae plants showed that its function likely differs among species. Moreover, the pepper DIVARICATA1 gene could be regulated by the ripening regulator MADS-RIN. The present study illustrates the transcriptional regulation of capsanthin biosynthesis and offers a target for breeding peppers with high red colour intensity.

Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360 ° Omnidirectional Image.

Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality (VR) technology, 360 ° omnidirectional image has been widely used, but the SOD task in 360 ° omnidirectional image is seldom studied due to its severe distortions and complex scenes. In this article, we propose a multi-projection fusion and refinement network (MPFR-Net) to detect the salient objects in 360 ° omnidirectional image. Different from the existing methods, the equirectangular projection (EP) image and four corresponding cube-unfolding (CU) images are embedded into the network simultaneously as inputs, where the CU images not only provide supplementary information for EP image but also ensure the object integrity of cube-map projection. In order to make full use of these two projection modes, a dynamic weighting fusion (DWF) module is designed to adaptively integrate the features of different projections in a complementary and dynamic manner from the perspective of inter and intrafeatures. Furthermore, in order to fully explore the way of interaction between encoder and decoder features, a filtration and refinement (FR) module is designed to suppress the redundant information of the feature itself and between the features. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively. The code and results can be found from the link of https://rmcong.github.io/proj_MPFRNet.html.

Cytotoxic nortriterpenoids from the barks of Juglans cathayensis.

A chemical study of 90% ethanol extract of the barks of Juglans cathayensis resulted in the isolation of three new nortriterpenoids, jugcathenoids A-C (1-3). The structures of the new compounds were elucidated by spectroscopic analysis (NMR, IR, UV, and MS). The isolated nortriterpenoids were tested in vitro for cytotoxic activities against 6 pancreatic cell lines. As a result, compounds 1-3 exhibited some cytotoxic activities against all the tested tumor cell lines with IC50 values less than 50 µM.

Mesoscale Analysis of the Effect of Interfacial Transition Zone on the Compressive Damage of Concrete Based on Discrete Element Method.

The coarse aggregate-mortar interface transition zone (ITZ) has a great influence on the mechanical properties of concrete, which cannot be easily studied using laboratory tests in the mesoscale. In this paper, a series of axial compression tests were conducted using the discrete element method (DEM) on concrete specimens for four phases: coarse aggregates, mortars, aggregate-mortar interface transition zones, and voids. The effects of ITZ strength on macroscopic stress and microscopic cracks under different strength reduction factors were investigated through axial compression testing. With the increase in interface transition strength, the compressive strength of the concrete becomes stronger; moreover, the number of cracks decreases, and the anisotropy of contact orientation becomes weaker. Meanwhile, the direction of crack development and the damage mode of compressed concrete specimens were also dependent on the coarse aggregate-mortar interface strength coefficient.

BocODD1 and BocODD2 Regulate the Biosynthesis of Progoitrin Glucosinolate in Chinese Kale.

Progoitrin (2-hydroxy-3-butenyl glucosinolate, PRO) is the main source of bitterness of Brassica plants. Research on the biosynthesis of PRO glucosinolate can aid the understanding of the nutritional value in Brassica plants. In this study, four ODD genes likely involved in PRO biosynthesis were cloned from Chinese kale. These four genes, designated as BocODD1-4, shared 75-82% similarities with the ODD sequence of Arabidopsis. The sequences of these four BocODDs were analyzed, and BocODD1 and BocODD2 were chosen for further study. The gene BocODD1,2 showed the highest expression levels in the roots, followed by the leaves, flowers, and stems, which is in accordance with the trend of the PRO content in the same tissues. Both the expression levels of BocODD1,2 and the content of PRO were significantly induced by high- and low-temperature treatments. The function of BocODDs involved in PRO biosynthesis was identified. Compared with the wild type, the content of PRO was increased twofold in the over-expressing BocODD1 or BocODD2 plants. Meanwhile, the content of PRO was decreased in the BocODD1 or BocODD2 RNAi lines more than twofold compared to the wildtype plants. These results suggested that BocODD1 and BocODD2 may play important roles in the biosynthesis of PRO glucosinolate in Chinese kale.

A yes-associated protein 1- Notch1 receptor positive feedback loop promotes breast cancer lung metastasis by attenuating the bone morphogenetic protein 4-SMAD family member 1/5 signaling.

The Notch1 (Notch1 receptor) and yes-associated protein 1 (YAP1) signaling can regulate breast cancer metastasis. This study aimed at investigating whether and how these two signal pathways crosstalk to promote breast cancer lung metastasis. Here, we show that YAP1 expression was positively correlated with Notch1 in breast cancer according to bioinformatics and experimental validation. Mechanistically, YAP1 with TEA domain transcription factors (TEADs) enhanced Jagged1(JAG1)-Notch1 signaling. Meanwhile, Notch1 promoted YAP1 stability in breast cancer cells by inhibiting the ß-TrCP-mediated degradation, thereby, forming a YAP1- JAG1/Notch1 positive feedback loop in breast cancer. Furthermore, YAP1 enhanced the mammosphere formation and stemness of MDA-MB-231 cells by attenuating the inhibition of the BMP4-SMAD1/5 signaling. In vivo, the YAP1- JAG1/Notch1 positive feedback loop promoted the lung colonization of MDA-MB-231 cells. Our data for the first time indicate that the YAP1-Notch1 positive feedback loop promotes lung metastasis of breast cancer by modulating self-renewal and inhibiting the BMP4-SMAD1/5 signaling.

C2FNet: A Coarse-to-Fine Network for Multi-View 3D Point Cloud Generation.

Generation of a 3D model of an object from multiple views has a wide range of applications. Different parts of an object would be accurately captured by a particular view or a subset of views in the case of multiple views. In this paper, a novel coarse-to-fine network (C2FNet) is proposed for 3D point cloud generation from multiple views. C2FNet generates subsets of 3D points that are best captured by individual views with the support of other views in a coarse-to-fine way, and then fuses these subsets of 3D points to a whole point cloud. It consists of a coarse generation module where coarse point clouds are constructed from multiple views by exploring the cross-view spatial relations, and a fine generation module where the coarse point cloud features are refined under the guidance of global consistency in appearance and context. Extensive experiments on the benchmark datasets have demonstrated that the proposed method outperforms the state-of-the-art methods.

BAT: Block and token self-attention for speech emotion recognition.

Transformers have achieved great success in many artificial intelligence fields, such as computer vision (CV), audio processing and natural language processing (NLP). In speech emotion recognition (SER), transformer-based architectures usually compute attention in a token-by-token (frame-by-frame) manner, but this approach lacks adequate capacity to capture local emotion information and is easily affected by noise. This paper proposes a novel SER architecture, referred to as block and token self-attention (BAT), that splits a mixed spectrogram into blocks and computes self-attention by combining these blocks with tokens, which can alleviate the effect of local noise while capturing authentic sentiment expressions. Furthermore, we present a cross-block attention mechanism to facilitate information interaction among blocks while integrating a frequency compression and channel enhancement (FCCE) module to smooth the attention biases between blocks and tokens. BAT achieves 73.2% weighted accuracy (WA) and 75.2% unweighted accuracy (UA) on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, surpassing the results of previously developed state-of-the-art approaches with the same dataset partitioning operation. Further experimental results reveal that our proposed method is also well suited for cross-database and cross-domain tasks, achieving 89% WA and 87.4% UA on Emo-DB and producing a top-1 recognition accuracy of 88.32% with only 15.01 Mb of parameters on the CIFAR-10 image dataset under a scenario with no data augmentation or pretraining.

Genome-wide analysis of histone acetyltransferase and histone deacetylase families and their expression in fruit development and ripening stage of pepper (Capsicum annuum).

The fruit development and ripening process involve a series of changes regulated by fine-tune gene expression at the transcriptional level. Acetylation levels of histones on lysine residues are dynamically regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), which play an essential role in the control of gene expression. However, their role in regulating fruit development and ripening process, especially in pepper (Capsicum annuum), a typical non-climacteric fruit, remains to understand. Herein, we performed genome-wide analyses of the HDAC and HAT family in the pepper, including phylogenetic analysis, gene structure, encoding protein conserved domain, and expression assays. A total of 30 HAT and 15 HDAC were identified from the pepper genome and the number of gene differentiation among species. The sequence and phylogenetic analysis of CaHDACs and CaHATs compared with other plant HDAC and HAT proteins revealed gene conserved and potential genus-specialized genes. Furthermore, fruit developmental trajectory expression profiles showed that CaHDAC and CaHAT genes were differentially expressed, suggesting that some are functionally divergent. The integrative analysis allowed us to propose CaHDAC and CaHAT candidates to be regulating fruit development and ripening-related phytohormone metabolism and signaling, which also accompanied capsaicinoid and carotenoid biosynthesis. This study provides new insights into the role of histone modification mediate development and ripening in non-climacteric fruits.

The 3D architecture of the pepper genome and its relationship to function and evolution.

The organization of chromatin into self-interacting domains is universal among eukaryotic genomes, though how and why they form varies considerably. Here we report a chromosome-scale reference genome assembly of pepper (Capsicum annuum) and explore its 3D organization through integrating high-resolution Hi-C maps with epigenomic, transcriptomic, and genetic variation data. Chromatin folding domains in pepper are as prominent as TADs in mammals but exhibit unique characteristics. They tend to coincide with heterochromatic regions enriched with retrotransposons and are frequently embedded in loops, which may correlate with transcription factories. Their boundaries are hotspots for chromosome rearrangements but are otherwise depleted for genetic variation. While chromatin conformation broadly affects transcription variance, it does not predict differential gene expression between tissues. Our results suggest that pepper genome organization is explained by a model of heterochromatin-driven folding promoted by transcription factories and that such spatial architecture is under structural and functional constraints.

Disparity-Aware Reference Frame Generation Network for Multiview Video Coding.

Multiview video coding (MVC) aims to compress the multiview video through the elimination of video redundancies, where the quality of the reference frame directly affects the compression efficiency. In this paper, we propose a deep virtual reference frame generation method based on a disparity-aware reference frame generation network (DAG-Net) to transform the disparity relationship between different viewpoints and generate a more reliable reference frame. The proposed DAG-Net consists of a multi-level receptive field module, a disparity-aware alignment module, and a fusion reconstruction module. First, a multi-level receptive field module is designed to enlarge the receptive field, and extract the multi-scale deep features of the temporal and inter-view reference frames. Then, a disparity-aware alignment module is proposed to learn the disparity relationship, and perform disparity shift on the inter-view reference frame to align it with the temporal reference frame. Finally, a fusion reconstruction module is utilized to fuse the complementary information and generate a more reliable virtual reference frame. Experiments demonstrate that the proposed reference frame generation method achieves superior performance for multiview video coding.

Multiscale Temporal Self-Attention and Dynamical Graph Convolution Hybrid Network for EEG-Based Stereogram Recognition.

Stereopsis is the ability of human beings to get the 3D perception on real scenarios. The conventional stereopsis measurement is based on subjective judgment for stereograms, leading to be easily affected by personal consciousness. To alleviate the issue, in this paper, the EEG signals evoked by dynamic random dot stereograms (DRDS) are collected for stereogram recognition, which can help the ophthalmologists diagnose strabismus patients even without real-time communication. To classify the collected Electroencephalography (EEG) signals, a novel multi-scale temporal self-attention and dynamical graph convolution hybrid network (MTS-DGCHN) is proposed, including multi-scale temporal self-attention module, dynamical graph convolution module and classification module. Firstly, the multi-scale temporal self-attention module is employed to learn time continuity information, where the temporal self-attention block is designed to highlight the global importance of each time segments in one EEG trial, and the multi-scale convolution block is developed to further extract advanced temporal features in multiple receptive fields. Meanwhile, the dynamical graph convolution module is utilized to capture spatial functional relationships between different EEG electrodes, in which the adjacency matrix of each GCN layer is adaptively tuned to explore the optimal intrinsic relationship. Finally, the temporal and spatial features are fed into the classification module to obtain prediction results. Extensive experiments are conducted on collected datasets i.e., SRDA and SRDB, and the results demonstrate the proposed MTS-DGCHN achieves outstanding classification performance compared with the other methods. The datasets are available at https://github.com/YANGeeg/TJU-SRD-datasets and the code is at https://github.com/YANGeeg/MTS-DGCHN.

The Capsicum MYB31 regulates capsaicinoid biosynthesis in the pepper pericarp.

Pepper (Capsicum) are consumed worldwide as vegetables and food additives due to their pungent taste. Capsaicinoids are the bioactive compounds that confer the desired pungency to pepper fruits. Capsaicinoid biosynthesis was thought to occur exclusively in fruit placenta. Recently, biosynthesis in the pericarp of extremely pungent varieties was discovered, however, the mechanism of capsaicinoid biosynthesis regulation in the pericarp remains largely unknown. Here, the capsaicinoid contents of placenta and pericarp were analyzed. The results indicated that the Capsicum chinense pericarp accumulated a vast amount of capsaicinoids. Expression of the master regulator MYB31 and capsaicinoid biosynthesis genes (CBGs) were significantly upregulated in the pericarp in C. chinense accessions compared to accessions in other tested species. Moreover, in fruit of extremely-pungent 'Trinidad Moruga Scorpion' (C. chinense) and low-pungent '59' inbred line (C. annuum), the capsaicinoid accumulation patterns in the pericarp were consistent with expression levels of CBGs and MYB31. Silencing MYB31 in 'Trinidad Moruga Scorpion' pericarp leads to a significantly decreased CBGs transcription level and capsaicinoids content. Taken together, our results provide insights into the molecular mechanism arising from the expression of MYB31 in the pericarp that results in exceedingly hot peppers.

VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment.

Guided by the free-energy principle, generative adversarial networks (GAN)-based no-reference image quality assessment (NR-IQA) methods have improved the image quality prediction accuracy. However, the GAN cannot well handle the restoration task for the free-energy principle-guided NR-IQA methods, especially for the severely destroyed images, which results in that the quality reconstruction relationship between the distorted image and its restored image cannot be accurately built. To address this problem, a visual compensation restoration network (VCRNet)-based NR-IQA method is proposed, which uses a non-adversarial model to efficiently handle the distorted image restoration task. The proposed VCRNet consists of a visual restoration network and a quality estimation network. To accurately build the quality reconstruction relationship between the distorted image and its restored image, a visual compensation module, an optimized asymmetric residual block, and an error map-based mixed loss function, are proposed for increasing the restoration capability of the visual restoration network. For further addressing the NR-IQA problem of severely destroyed images, the multi-level restoration features which are obtained from the visual restoration network are used for the image quality estimation. To prove the effectiveness of the proposed VCRNet, seven representative IQA databases are used, and experimental results show that the proposed VCRNet achieves the state-of-the-art image quality prediction accuracy. The implementation of the proposed VCRNet has been released at https://github.com/NUIST-Videocoding/VCRNet.

Advanced Dropout: A Model-Free Methodology for Bayesian Dropout Optimization.

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate overfitting and improve the performance of DNNs. The advanced dropout technique applies a model-free and easily implemented distribution with parametric prior, and adaptively adjusts dropout rate. Specifically, the distribution parameters are optimized by stochastic gradient variational Bayes in order to carry out an end-to-end training. We evaluate the effectiveness of the advanced dropout against nine dropout techniques on seven computer vision datasets (five small-scale datasets and two large-scale datasets) with various base models. The advanced dropout outperforms all the referred techniques on all the datasets. We further compare the effectiveness ratios and find that advanced dropout achieves the highest one on most cases. Next, we conduct a set of analysis of dropout rate characteristics, including convergence of the adaptive dropout rate, the learned distributions of dropout masks, and a comparison with dropout rate generation without an explicit distribution. In addition, the ability of overfitting prevention is evaluated and confirmed. Finally, we extend the application of the advanced dropout to uncertainty inference, network pruning, text classification, and regression. The proposed advanced dropout is also superior to the corresponding referred methods. Codes are available at https://github.com/PRIS-CV/AdvancedDropout.

Multi-Modality MR Image Synthesis via Confidence-Guided Aggregation and Cross-Modality Refinement.

Magnetic resonance imaging (MRI) can provide multi-modality MR images by setting task-specific scan parameters, and has been widely used in various disease diagnosis and planned treatments. However, in practical clinical applications, it is often difficult to obtain multi-modality MR images simultaneously due to patient discomfort, and scanning costs, etc. Therefore, how to effectively utilize the existing modality images to synthesize missing modality image has become a hot research topic. In this paper, we propose a novel confidence-guided aggregation and cross-modality refinement network (CACR-Net) for multi-modality MR image synthesis, which effectively utilizes complementary and correlative information of multiple modalities to synthesize high-quality target-modality images. Specifically, to effectively utilize the complementary modality-specific characteristics, a confidence-guided aggregation module is proposed to adaptively aggregate the multiple target-modality images generated from multiple source-modality images by using the corresponding confidence maps. Based on the aggregated target-modality image, a cross-modality refinement module is presented to further refine the target-modality image by mining correlative information among the multiple source-modality images and aggregated target-modality image. By training the proposed CACR-Net in an end-to-end manner, high-quality and sharp target-modality MR images are effectively synthesized. Experimental results on the widely used benchmark demonstrate that the proposed method outperforms state-of-the-art methods.