Role of Flavohemoglobins in the Development and Aflatoxin Biosynthesis of Aspergillus flavus.

Aspergillus flavus is notorious for contaminating food with its secondary metabolite-highly carcinogenic aflatoxins. In this study, we found that exogenous nitric oxide (NO) donor could influence aflatoxin production in A. flavus. Flavohemoglobins (FHbs) are vital functional units in maintaining nitric oxide (NO) homeostasis and are crucial for normal cell function. To investigate whether endogenous NO changes affect aflatoxin biosynthesis, two FHbs, FHbA and FHbB, were identified in this study. FHbA was confirmed as the main protein to maintain NO homeostasis, as its absence led to a significant increase in intracellular NO levels and heightened sensitivity to SNP stress. Dramatically, FHbA deletion retarded aflatoxin production. In addition, FHbA played important roles in mycelial growth, conidial germination, and sclerotial development, and response to oxidative stress and high-temperature stress. Although FHbB did not significantly impact the cellular NO level, it was also involved in sclerotial development, aflatoxin synthesis, and stress response. Our findings provide a new perspective for studying the regulatory mechanism of the development and secondary mechanism in A. flavus.

HacA, a key transcription factor for the unfolded protein response, is required for fungal development, aflatoxin biosynthesis and pathogenicity of Aspergillus flavus.

Aspergillus flavus is a fungus notorious for contaminating food and feed with aflatoxins. As a saprophytic fungus, it secretes large amounts of enzymes to access nutrients, making endoplasmic reticulum (ER) homeostasis important for protein folding and secretion. The role of HacA, a key transcription factor in the unfolded protein response pathway, remains poorly understood in A. flavus. In this study, the hacA gene in A. flavus was knockout. Results showed that the absence of hacA led to a decreased pathogenicity of the strain, as it failed to colonize intact maize kernels. This may be due to retarded vegetable growth, especially the abnormal development of swollen tips and shorter hyphal septa. Deletion of hacA also hindered conidiogenesis and sclerotial development. Notably, the mutant strain failed to produce aflatoxin B1. Moreover, compared to the wild type, the mutant strain showed increased sensitivity to ER stress inducer such as Dithiothreitol (DTT), and heat stress. It also displayed heightened sensitivity to other environmental stresses, including cell wall, osmotic, and pH stresses. Further transcriptomic analysis revealed the involvement of the hacA in numerous biological processes, including filamentous growth, asexual reproduction, mycotoxin biosynthetic process, signal transduction, budding cell apical bud growth, invasive filamentous growth, response to stimulus, and so on. Taken together, HacA plays a vital role in fungal development, pathogenicity and aflatoxins biosynthesis. This highlights the potential of targeting hacA as a novel approach for early prevention of A. flavus contamination.

Bayesian Hierarchical Graph Neural Networks With Uncertainty Feedback for Trustworthy Fault Diagnosis of Industrial Processes.

Deep learning (DL) methods have been widely applied to intelligent fault diagnosis of industrial processes and achieved state-of-the-art performance. However, fault diagnosis with point estimate may provide untrustworthy decisions. Recently, Bayesian inference shows to be a promising approach to trustworthy fault diagnosis by quantifying the uncertainty of the decisions with a DL model. The uncertainty information is not involved in the training process, which does not help the learning of highly uncertain samples and has little effect on improving the fault diagnosis performance. To address this challenge, we propose a Bayesian hierarchical graph neural network (BHGNN) with an uncertainty feedback mechanism, which formulates a trustworthy fault diagnosis on the Bayesian DL (BDL) framework. Specifically, BHGNN captures the epistemic uncertainty and aleatoric uncertainty via a variational dropout approach and utilizes the uncertainty information of each sample to adjust the strength of the temporal consistency (TC) constraint for robust feature learning. Meanwhile, the BHGNN method models the process data as a hierarchical graph (HG) by leveraging the interaction-aware module and physical topology knowledge of the industrial process, which integrates data with domain knowledge to learn fault representation. Moreover, the experiments on a three-phase flow facility (TFF) and secure water treatment (SWaT) show superior and competitive performance in fault diagnosis and verify the trustworthiness of the proposed method.

LHAR: Lightweight Human Activity Recognition on Knowledge Distillation.

Sensor-based Human Activity Recognition (HAR) is widely used in daily life and is the basic-level bridge to virtual healthcare in the metaverse. The current challenge is the low recognition accuracy for personalized users on smart wearable devices. The limited resource cannot support large deep learning models updated locally. Besides, integrating and transmitting sensor data to the cloud would reduce the efficiency. Considering the tradeoff between performance and complexity, we propose a Lightweight Human Activity Recognition (LHAR) framework. In LHAR, we combine the cross-people HAR task with the lightweight model task. LHAR framework is designed on the teacher-student architecture and the student network consists of multiple depthwise separable convolution layers to achieve fewer parameters. The dark knowledge distilled from the complex teacher model enhances the generalization ability of LHAR. To achieve effective knowledge distillation, we propose two optimization methods. Firstly, we train the teacher model by ensemble learning to promote teacher performance. Secondly, a multi-channel data augmentation method is proposed for the diversity of the dataset, which is a plug-in operation for the ensemble teacher model. In the experiments, we compare LHAR with state-of-art models in comparison evaluation, ablation study and the hyperparameter analysis, which proves the better performance of LHAR in efficiency and effectiveness.

Molecular Hydrogen Confers Resistance to Rice Stripe Virus.

Although molecular hydrogen (H2) has potential therapeutic effects in animals, whether or how this gas functions in plant disease resistance has not yet been elucidated. Here, after rice stripe virus (RSV) infection, H2 production was pronouncedly stimulated in Zhendao 88, a resistant rice variety, compared to that in a susceptible variety (Wuyujing No.3). External H2 supply remarkably reduced the disease symptoms and RSV coat protein (CP) levels, especially in Wuyujing No.3. The above responses were abolished by the pharmacological inhibition of H2 production. The transgenic Arabidopsis plants overexpressing a hydrogenase gene from Chlamydomonas reinhardtii also improved plant resistance. In the presence of H2, the transcription levels of salicylic acid (SA) synthetic genes were stimulated, and the activity of SA glucosyltransferases was suppressed, thus facilitating SA accumulation. Genetic evidence revealed that two SA synthetic mutants of Arabidopsis (sid2-2 and pad4) were more susceptible to RSV than the wild type (WT). The treatments with H2 failed to improve the resistance to RSV in two SA synthetic mutants. The above results indicated that H2 enhances rice resistance to RSV infection possibly through the SA-dependent pathway. This study might open a new window for applying the H2-based approach to improve plant disease resistance. IMPORTANCE Although molecular hydrogen has potential therapeutic effects in animals, whether or how this gas functions in plant disease resistance has not yet been elucidated. RSV was considered the most devastating plant virus in rice, since it could cause severe losses in field production. This disease was thus selected as a classical model to explore the interrelationship between molecular hydrogen and plant pathogen resistance. In this study, we discovered that both exogenous and endogenous H2 could enhance plant resistance against Rice stripe virus infection by regulating salicylic acid signaling. Compared with some frequently used agrochemicals, H2 is almost nontoxic. We hope that the findings presented here will serve as an opportunity for the scientific community to push hydrogen-based agriculture forward.

Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes.

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

Coarse-to-Fine: Progressive Knowledge Transfer-Based Multitask Convolutional Neural Network for Intelligent Large-Scale Fault Diagnosis.

In modern industry, large-scale fault diagnosis of complex systems is emerging and becoming increasingly important. Most deep learning-based methods perform well on small number of fault diagnosis, but cannot converge to satisfactory results when handling large-scale fault diagnosis because the huge number of fault types will lead to the problems of intra/inter-class distance unbalance and poor local minima in neural networks. To address the above problems, a progressive knowledge transfer-based multitask convolutional neural network (PKT-MCNN) is proposed. First, to construct the coarse-to-fine knowledge structure intelligently, a structure learning algorithm is proposed via clustering fault types in different coarse-grained nodes. Thus, the intra/inter-class distance unbalance problem can be mitigated by spreading similar tasks into different nodes. Then, an MCNN architecture is designed to learn the coarse and fine-grained task simultaneously and extract more general fault information, thereby pushing the algorithm away from poor local minima. Last but not least, a PKT algorithm is proposed, which can not only transfer the coarse-grained knowledge to the fine-grained task and further alleviate the intra/inter-class distance unbalance in feature space, but also regulate different learning stages by adjusting the attention weight to each task progressively. To verify the effectiveness of the proposed method, a dataset of a nuclear power system with 66 fault types was collected and analyzed. The results demonstrate that the proposed method can be a promising tool for large-scale fault diagnosis.

ARGONAUTE 2 increases rice susceptibility to rice black-streaked dwarf virus infection by epigenetically regulating HEXOKINASE 1 expression.

ARGONAUTE (AGO) proteins play crucial roles in plant defence against virus invasion. To date, the role of OsAGO2 in rice antiviral defence remains largely unknown. In this study, we determined that the expression of OsAGO2 in rice was induced upon rice black-streaked dwarf virus (RBSDV) infection. Using transgenic rice plants overexpressing OsAGO2 and Osago2 mutants generated through transposon-insertion or CRISPR/Cas9 technology, we found that overexpression of OsAGO2 enhanced rice susceptibility to RBSDV infection. Osago2 mutant lines exhibited strong resistance to RBSDV infection through the elicitation of an early defence response, including reprogramming defence gene expression and production of reactive oxygen species (ROS). Compared to Nipponbare control, the expression level of OsHXK1 (HEXOKINASE 1) increased significantly, and the methylation levels of its promoter decreased in the Osago2 mutant on RBSDV infection. The expression profile of OsHXK1 was the opposite to that of OsAGO2 during RBSDV infection. Overexpression of OsHXK1 in rice also induced ROS production and enhanced rice resistance to RBSDV infection. These results indicate that OsHXK1 controls ROS accumulation and is regulated by OsAGO2 through epigenetic regulation. It is noteworthy that the Osago2 mutant plants are also resistant to southern rice black-streaked dwarf virus infection, another member of the genus Fijivirus. Based on the results presented in this paper, we conclude that OsAGO2 modulates rice susceptibility to fijivirus infection by suppressing OsHXK1 expression, leading to the onset of ROS-mediated resistance. This discovery may benefit future rice breeding programmes for virus resistance.

Syngeneic bone marrow transplantation in combination with PI3K inhibitor reversed hyperglycemia in later-stage streptozotocin-induced diabetes.

BACKGROUND: Type 1 diabetes (T1D) is a multiple factor autoimmune disease characterized by T cell-mediated immune destruction of islet ß cells. Autologous hematopoietic stem cell transplantation (AHSCT) has been a novel strategy for patients with new-onset T1D, but not for those with a later diagnosis. Disturbance of regulatory T cells (Tregs) likely contributes to poor response after transplantation in later-stage T1D. Inhibition of phosphoinositide 3-kinases (PI3K)/Akt signaling maintains Tregs' homeostasis. METHODS: We built a later-stage streptozotocin (STZ)-induced T1D mouse model. Syngeneic bone marrow transplantation (syn-BMT) was performed 20 days after the onset of diabetes in combination with BKM120 (a PI3K inhibitor). Meanwhile, another group of STZ-diabetic mice were transplanted with bone marrow cells cocultured with BKM120 in vitro for 24 h. Fasting glucose and glucose tolerance were recorded during the entire experimental observation after syn-BMT. Samples were collected 126 days after syn-BMT. Hematoxylin and eosin (H&E) staining was used to detect the effect of PI3K inhibitor combined with syn-BMT on morphology of the T1D pancreas. CD4+CD25- T cells and CD4+CD25+ T cells were sorted by magnetic cell sorting (MACS), then fluorescence activated cell sorting (FACS) and quantitative real-time PCR (qPCR) were used to detect the effect of PI3K inhibitor on modulating immune disorder and restoring the function of Treg cells. RESULTS: Our investigation showed syn-BMT in combination with BKM120 effectively maintained normoglycemia in later-stage T1D. The disease remission effects may be induced by the rebalance of Th17/Tregs dysregulation and restoration of Tregs' immunosuppressive function by BKM120 after syn-BMT. CONCLUSIONS: These results may reveal important connections for PI3K/Akt inhibition and Tregs' homeostasis in T1D after transplantation. AHSCT combining immunoregulatory strategies such as PI3K inhibition may be a promising therapeutic approach in later-stage T1D.

Scale-invariant amplitude spectrum modulation for visual saliency detection.

Saliency detection is one of the key issues in simulating visual attention selection. Most attention models adopt the competitive structure to simulate the human visual system. Although these models provide remarkable results and convincing biological plausibility, they are still confronted with many difficulties in practical applications because of their extreme time cost and parameter sensitivity. Recently, a new saliency detection approach based on Fourier transform, as represented by spectral residual (SR) and phase Fourier transform (PFT), has been attracting much attention for its excellent accuracy and computational speed. All these models can be unified into one framework called amplitude spectrum modulation (ASM). The aim of this paper is to explore the intrinsic mechanism of ASM and develop an advanced ASM model. After analyzing SR and PFT, we give a mathematical description for the fundamental idea and the inherent limitations of the existing ASM models. A new saliency detective model, based on the scale-invariant ASM, scene and context-based modulation, and competitive structure, is also proposed breaking through the limitations of the traditional ASM models. Simulation results suggest that the proposed model is more accurate in predicting human eye fixation and is more robust against different types of stimulus when compared with competing models.

Spatio-temporal adaptation in the unsupervised development of networked visual neurons.

There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The "in-place" nature of the model is based on two biologically plausible learning rules, Hebbian rule and lateral inhibition. We present the mathematical demonstration that the temporal weights are derived from the delay in lateral inhibition. By training with the natural videos, this model can develop spatio-temporal features such as orientation selective cells, motion sensitive cells, and spatio-temporal complex cells. The unified nature of the adaptation scheme allows us to construct a multilayered and task-independent attention selection network which uses the same learning rule for edge, motion, and color detection, and we can use this network to engage in attention selection in both static and dynamic scenes.