PA-X is a novel protein encoded by PA mRNA and is found to decrease the pathogenicity of pandemic 1918 H1N1 virus in mice. However, the importance of PA-X proteins in current epidemiologically important influenza A virus strains is not known. In this study, we report on the pathogenicity and pathological effects of PA-X deficient 2009 pandemic H1N1 (pH1N1) and highly pathogenic avian influenza H5N1 viruses. We found that loss of PA-X expression in pH1N1 and H5N1 viruses increased viral replication and apoptosis in A549 cells and increased virulence and host inflammatory response in mice. In addition, PA-X deficient pH1N1 and H5N1 viruses up-regulated PA mRNA and protein synthesis and increased viral polymerase activity. Loss of PA-X was also accompanied by accelerated nuclear accumulation of PA protein and reduced suppression of PA on non-viral protein expression. Our study highlights the effects of PA-X on the moderation of viral pathogenesis and pathogenicity.
The PA-X protein, arising from ribosomal frameshift during PA translation, was recently discovered in influenza A virus (IAV). The C-terminal domain ‘X’ of PA-X proteins in IAVs can be classified as full-length (61 aa) or truncated (41 aa). In the main, avian influenza viruses express full-length PA-X proteins, whilst 2009 pandemic H1N1 (pH1N1) influenza viruses harbour truncated PA proteins. The truncated form lacks aa 232–252 of the full-length PA-X protein. The significance of PA-X length in virus function remains unclear. To address this issue, we constructed a set of contemporary influenza viruses (pH1N1, avian H5N1 and H9N2) with full and truncated PA-X by reverse genetics to compare their replication and host pathogenicity. All full-length PA-X viruses in human A549 cells conferred 10- to 100-fold increase in viral replication and 5–8 % increase in apoptosis relative to corresponding truncated PA-X viruses. Full-length PA-X viruses were more virulent and caused more severe inflammatory responses in mice. Furthermore, aa 233–252 at the C terminus of PA-X strongly suppressed co-transfected gene expression by ∼50 %, suggesting that these terminal 20 aa could play a role in enhancing viral replication and contribute to virulence.
In vitro studies have established the prevalent theory that the mitochondrial kinase PINK1 protects neurodegeneration by removing damaged mitochondria in Parkinson’s disease (PD). However, difficulty in detecting endogenous PINK1 protein in rodent brains and cell lines has prevented the rigorous investigation of the in vivo role of PINK1. Here we report that PINK1 kinase form is selectively expressed in the human and monkey brains. CRISPR/Cas9-mediated deficiency of PINK1 causes similar neurodegeneration in the brains of fetal and adult monkeys as well as cultured monkey neurons without affecting mitochondrial protein expression and morphology. Importantly, PINK1 mutations in the primate brain and human cells reduce protein phosphorylation that is important for neuronal function and survival. Our findings suggest that PINK1 kinase activity rather than its mitochondrial function is essential for the neuronal survival in the primate brains and that its kinase dysfunction could be involved in the pathogenesis of PD.
A dual-wavelength ratiometric electrochemiluminescence resonance energy transfer (ECL-RET) aptasensor based on the carbon nitride nanosheet (g-C 3 N 4 NS) and metal−organic frameworks (Ru@ MOFs) as energy donor−receptor pairs is first designed for the detection of the amyloid-β (Aβ) protein. The cathode ECL of g-C 3 N 4 NS gradually decreased, whereas the anode ECL from Ru@MOF pyramidally enhanced along with the increasing concentration of Aβ in a 0.1 M phosphatebuffered saline solution containing 0.1 M S 2 O 8 2− . Additionally, it is worth noting that 2-amino terephthalic acid from MOF not only can load abundant amounts of luminophor Ru(bpy) 3 2+ but also promote the conversion of more amounts of S 2 O 8 2− that served as a coreactant accelerator into SO 4•− , further enhancing the ECL signal of Ru@MOF. Besides, the ECL intensity from the g-C 3 N 4 NS had a tremendous spectrum overlap with the UV−vis spectrum of Ru@MOF, demonstrating the high-efficiency ECL-RET from g-C 3 N 4 NS to Ru@MOF. According to the ratio of ECL 460nm /ECL 620nm , the constructed aptasensor for the detection of Aβ showed a wide linear range from 10 −5 to 500 ng/mL and a low detection limit of 3.9 fg/mL (S/N = 3) with a correction coefficient of 0.9965. The obtained results certified that the dual-wavelength ratiometric ECL sensor could provide a reliable direction and have the potential for application in biosensing and clinical diagnosis fields.
Detecting safety helmet wearing in surveillance videos is an essential task for safety management, compliance with regulations, and reducing the death rate from construction industry accidents. However, it is much challenged by some factors like interocclusion, scale variances, perspective distortion, small object detection, and the carrier recognition of safety helmet. Traditional image‐based methods suffer from them. This article proposes a new methodology for detecting safety helmet wearing, which makes use of convolutional neural network‐based face detection and bounding‐box regression for safety helmet detection. On the one hand, the method can help to recognize the carrier of the safety helmet and detect a multiscale and small safety helmet. On the other hand, deep transfer learning based on DenseNet is introduced and applied using two different strategies, namely, object feature extractor and fine‐tuning for safety helmet recognition. To further improve the recognition accuracy, the network model with two peer DenseNet networks is trained by mutual distillation. Extensive analysis and experiments show that the novel methodology has considerable advantages in detecting safety helmet wearing compared to other state‐of‐the‐art models. The proposed model has achieved 96.2% recall, 96.2% precision, and 94.47% average detection accuracy. These results, precision‐recall (PR) curve, and receiver operating characteristic (ROC) curve demonstrate the feasibility of the new model.
Background. Reorganization in motor areas have been suggested after motor imagery training (MIT). However, motor imagery involves a large-scale brain network, in which many regions, andnot only the motor areas, potentially constitute the neural substrate for MIT. Objective. This study aimed to identify the targets for MIT in stroke rehabilitation from a voxel-based whole brain analysis of resting-state functional magnetic resonance imaging (fMRI). Methods. Thirty-four chronic stroke patients were recruited and randomly assigned to either an MIT group or a control group. The MIT group received a 4-week treatment of MIT plus conventional rehabilitation therapy (CRT), whereas the control group only received CRT. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale (FM-UL) and resting-state fMRI were collected. The fractional amplitude of low-frequency fluctuations (fALFF) in the slow-5 band (0.01-0.027 Hz) was calculated across the whole brain to identify brain areas with distinct changes between 2 groups. These brain areas were then targeted as seeds to perform seed-based functional connectivity (FC) analysis. Results. In comparison with the control group, the MIT group exhibited more improvements in FM-UL and increased slow-5 fALFF in the ipsilesional inferior parietal lobule (IPL). The change of the slow-5 oscillations in the ipsilesional IPL was positively correlated with the improvement of FM-UL. The MIT group also showed distinct alternations in FCs of the ipsilesional IPL, which were correlated with the improvement of FM-UL. Conclusions. The rehabilitation efficiency of MIT was associated with increased slow-5 oscillations and altered FC in the ipsilesional IPL. Clinical Trial Registration. http://www.chictr.org.cn . Unique Identifier. ChiCTR-TRC-08003005.
Automated object identification in three‐dimensional (3D) space is crucial for work zone safety, such as compliance with construction rules and preventing workplace injuries and deaths. However, it is greatly challenged by some factors like high‐quality detection, high‐quality instance segmentation, few engineering object datasets with masks, and accurate 3D object understanding due to scale variations and limited cues in the 3D world. Traditional hand‐crafted methods suffer from these challenges. Our key insight is to use 2D object detection, instance segmentation and camera vision to compute pseudo‐light detection and ranging (LiDAR) point cloud for 3D object identification. On the one hand, an enhanced feature pyramid network is proposed to extract more fine‐grained object features, and an improved cascade mask R‐CNN is applied to detect bounding boxes and masks for all 2D objects efficiently. Moreover, the AIM dataset for heavy equipment detection is augmented, and a new object class with the bounding box and mask is added. On the other hand, pseudo‐LiDAR point clouds of objects based on bounding boxes and masks are recovered from a monocular image by deep learning, automatic camera parameter estimation, vision‐based method, and space filter. Extensive experiments and analyses show that the new methodology can identify 3D objects and automatically analyze work zone safety. The proposed object detection model has achieved state‐of‐the‐art results on the AIM dataset and 97.2% in mean average precision for the augmented dataset. The collision detection model using pseudo‐LiDAR point cloud has obtained 95.99% in accuracy. The new model will serve as a baseline to support 3D object identification research for other 3D tasks.
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