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Viruses are small parasites that can cause significant damage to their host organisms. Most viruses belong to the class of positive-sense single-stranded RNA (+ssRNA) viruses, which includes well-known viruses like SARS-CoV-2 and dengue. Recently, hidden protein-coding open reading frames (ORFs) have been discovered in the genomes of various viruses, including +ssRNA and ssDNA viruses. These hidden proteins have important biological functions and challenge previous assumptions about viral protein expression. The discovery of these hidden proteins expands our understanding of viral proteomes and could lead to new strategies for preventing and controlling viral infections. [Extracted from the article]

The hoist cage is used to lift miners in a coal mine's auxiliary shaft. Monitoring miners' unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners' statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light. [ABSTRACT FROM AUTHOR]

Taking E141A constant deceleration braking system in JKMD 4.5×4 mine hoist as research object, working principle of the constant deceleration braking system was analyzed. A simulation model of the constant deceleration braking system was built and reliability of the simulation model was verified by theoretical calculation. Typical faults such as spring stiffness decrease, friction factor of brake shoe decrease and brake leakage were simulated. The simulation results show that performance degradation of main components cannot immediately cause failure of the braking system, but can cause system performance degradation such as braking system pressure degradation, braker clearance smaller or bigger, etc. The braking system performance degradation with a certain degree will cause faults such as unsatisfactory braking deceleration, no braker clearance, too large braker clearance, too long idle motion time, etc.

Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings' operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples' feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset. [ABSTRACT FROM AUTHOR]

In this paper, a six-degree-of-freedom (6-DOF) comprehensive dynamic model of bearing-pedestal having waviness on its inner race and different sized surface damages has been presented to investigate the vibration response under time-varying speed conditions. It can provide a good solution to further analyze rotor systems with unfixed or elastic-supported pedestals, where vibrations in both horizontal and vertical directions are crucial. A signal processing method for fault feature extraction and enhancement of rolling element bearing in varying speed conditions is proposed. Vibration patterns with different raceway waviness and different sized surface damages are specifically studied. Characteristic frequencies are observed, which can be used for diagnosis of different sized waviness and faults under time-varying speed process. The present study will provide new insights into dynamic behavior of bearing system, [ABSTRACT FROM AUTHOR]

NONEXPRESSER OF PATHOGENESIS-RELATED GENES 1 (NPR1) is the master regulator of salicylic acid-mediated basal and systemic acquired resistance in plants. Here, we report that NPR1 plays a pivotal role in restricting compatible infection by turnip mosaic virus, a member of the largest plant RNA virus genus Potyvirus, and that such resistance is counteracted by NUCLEAR INCLUSION B (NIb), the viral RNA-dependent RNA polymerase. We demonstrate that NIb binds to the SUMO-interacting motif 3 (SIM3) of NPR1 to prevent SUMO3 interaction and sumoylation, while sumoylation of NIb by SUMO3 is not essential but can intensify the NIb–NPR1 interaction. We discover that the interaction also impedes the phosphorylation of NPR1 at Ser11/Ser15. Moreover, we show that targeting NPR1 SIM3 is a conserved ability of NIb from diverse potyviruses. These data reveal a molecular "arms race" by which potyviruses deploy NIb to suppress NPR1-mediated resistance through disrupting NPR1 sumoylation. Salicylic acid (SA) signaling pathway restricts the compatible infection of potyviruses. Here, Liu et al. show that potyviral NIb interacts with NPR1, the SA receptor in plants, preventing its sumoylation by SUMO3 and subsequent phosphorylation at Ser11/Ser15. This way, NPR1-mediated immunity is suppressed to promote virus infection. [ABSTRACT FROM AUTHOR]

Healthy agroecosystems are dependent on a complex web of factors and inter-species interactions. Flowers are hubs for pathogen transmission, including the horizontal or vertical transmission of plant-viruses and the horizontal transmission of bee-viruses. Pollination by the European honey bee (Apis mellifera) is critical for industrial fruit production, but bees can also vector viruses and other pathogens between individuals. Here, we utilized commercial honey bee pollination services in blueberry (Vaccinium corymbosum) farms for a metagenomics-based bee and plant virus monitoring system. Following RNA sequencing, viruses were identified by mapping reads to a reference sequence database through the bioinformatics portal Virtool. In total, 29 unique plant viral species were found at two blueberry farms in British Columbia (BC). Nine viruses were identified at one site in Ontario (ON), five of which were not identified in BC. Ilarviruses blueberry shock virus (BlShV) and prune dwarf virus (PDV) were the most frequently detected viruses in BC but absent in ON, while nepoviruses tomato ringspot virus and tobacco ringspot virus were common in ON but absent in BC. BlShV coat protein (CP) nucleotide sequences were nearly identical in all samples, while PDV CP sequences were more diverse, suggesting multiple strains of PDV circulating at this site. Ten bee-infecting viruses were identified, with black queen cell virus frequently detected in ON and BC. Area-wide bee-mediated pathogen monitoring can provide new insights into the diversity of viruses present in, and the health of, bee-pollination ecosystems. This approach can be limited by a short sampling season, biased towards pollen-transmitted viruses, and the plant material collected by bees can be very diverse. This can obscure the origin of some viruses, but bee-mediated virus monitoring can be an effective preliminary monitoring approach. [ABSTRACT FROM AUTHOR]

Viruses infect all cellular life forms and cause various diseases and significant economic losses worldwide. The majority of viruses are positive-sense RNA viruses. A common feature of infection by diverse RNA viruses is to induce the formation of altered membrane structures in infected host cells. Indeed, upon entry into host cells, plant-infecting RNA viruses target preferred organelles of the cellular endomembrane system and remodel organellar membranes to form organelle-like structures for virus genome replication, termed as the viral replication organelle (VRO) or the viral replication complex (VRC). Different viruses may recruit different host factors for membrane modifications. These membrane-enclosed virus-induced replication factories provide an optimum, protective microenvironment to concentrate viral and host components for robust viral replication. Although different viruses prefer specific organelles to build VROs, at least some of them have the ability to exploit alternative organellar membranes for replication. Besides being responsible for viral replication, VROs of some viruses can be mobile to reach plasmodesmata (PD) via the endomembrane system, as well as the cytoskeleton machinery. Viral movement protein (MP) and/or MP-associated viral movement complexes also exploit the endomembrane-cytoskeleton network for trafficking to PD where progeny viruses pass through the cell-wall barrier to enter neighboring cells. [ABSTRACT FROM AUTHOR]

Mitophagy that selectively eliminates damaged mitochondria is an essential mitochondrial quality control mechanism. Recently, mitophagy has been shown to be induced in host cells infected by a few animal viruses. Here, we report that southern rice black-streaked dwarf virus (SRBSDV), a plant nonenveloped double-stranded RNA virus, can also trigger mitophagy in its planthopper vector to prevent mitochondria-dependent apoptosis and promote persistent viral propagation. We find that the fibrillar structures constructed by the nonstructural protein P7-1 of SRBSDV directly target mitochondria via interaction with the mitophagy receptor BNIP3 (BCL2 interacting protein 3), and these mitochondria are then sequestered within autophagosomes to form mitophagosomes. Moreover, SRBSDV infection or P7-1 expression alone can promote BNIP3 dimerization on the mitochondria, and induce autophagy via the P7-1-ATG8 interaction. Furthermore, SRBSDV infection stimulates the phosphorylation of AMP-activated protein kinase (AMPK), resulting in BNIP3 phosphorylation via the AMPKα-BNIP3 interaction. Together, P7-1 induces BNIP3-mediated mitophagy by promoting the formation of phosphorylated BNIP3 dimers on the mitochondria. Silencing of ATG8, BNIP3, or AMPKα significantly reduces virus-induced mitophagy and viral propagation in insect vectors. These data suggest that in planthopper, SRBSDV-induced mitophagosomes are modified to accommodate virions and facilitate persistent viral propagation. In summary, our results demonstrate a previously unappreciated role of a viral protein in the induction of BNIP3-mediated mitophagy by bridging autophagosomes and mitochondria and reveal the functional importance of virus-induced mitophagy in maintaining persistent viral infection in insect vectors.Abbreviations: AMPK: AMP-activated protein kinase; ATG: autophagy related; BNIP3: BCL2 interacting protein 3; CASP3: caspase 3; dsRNA: double strand RNA; ER: endoplasmic reticulum; FITC: fluorescein isothiocyanate; FKBP8: FKBP prolyl isomerase 8; FUNDC1: FUN14 domain containing 1; GFP: green fluorescent protein; GST: glutathione S-transferase; padp: post-first access to diseased plants; Phos-tag: Phosphate-binding tag; PINK1: PTEN induced kinase 1; Sf9: Spodoptera frugiperda; SQSTM1: sequestosome 1; SRBSDV: southern rice black-streaked dwarf virus; STK11/LKB1: serine/threonine kinase 11; TOMM20: translocase of outer mitochondrial membrane 20; RBSDV: rice black-streaked dwarf virus; TUNEL: terminal deoxynucleotidyl dUTP nick end labeling; ULK1: unc-51 like autophagy activating kinase 1; VDAC1: voltage dependent anion channel 1. [ABSTRACT FROM AUTHOR]

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration signals into images for fault diagnosis research has been a practical diagnostic approach. The current paper presents a rolling bearing fault diagnosis method using symmetrized dot pattern (SDP) images and a deep residual network with convolutional block attention module (CBAM-DRN). The rolling bearing vibration signal is first visualized and transformed into an SDP image with distinct fault characteristics. Then, CBAM-DRN is utilized to derive characteristics directly and detect faults from the input SDP images. In order to prevent conventional time-frequency images from being limited by their inherent flaws and avoid missing the fault features, the SDP technique is employed to convert vibration signals into images for visualization. DRN enables adequate extraction of rolling bearing fault characteristics and prevents training difficulties and gradient vanishing in deep level networks. CBAM assists the diagnostic model in concentrating on the image's more distinctive parts and preventing the interference of non-featured parts. Finally, the method's validity was tested with a composite fault dataset of motor bearings containing multiple loads and fault diameters. The experimental results reflect that the presented approach can attain a diagnostic precision of over 99% and good stability and generalization. [ABSTRACT FROM AUTHOR]