During the influenza pandemic or seasonal influenza outbreak, influenza infection can cause acute influenza-associated encephalopathy/encephalitis (IAE), even death. Patients with severe IAE will also have severe neurological sequelae. Neurologic disorders have been demonstrated in the mice treated with peripheral influenza viruses infection, whether neurotropic or non-neurotropic viruses. However, previous studies focused on the acute phase of infection, and rarely paid attention to a longer range of observations. Therefore, the long-term effect of non-neurotropic virus infection on the host is not very clear. In this study, adult mice were infected with influenza virus H1N1/PR8. Then, spontaneous behavior, body weight, expression of cytokines in brain, spatial learning ability and spatial memory ability were observed, until the complete recovery period. The results showed that cytokines in the brain were highly expressed in the convalescent phase (14 day post inoculation, dpi), especially BDNF, IBA1, CX3CL1 and CD200 were still highly expressed in the recovery phase (28 dpi). Otherwise the emotional and spatial memory ability of mice were impacted in the convalescent phase (14 dpi) and the recovery phase (28 dpi). In brief, BALB/c mice infected with non-neurotropic influenza virus H1N1, the weight and motor ability decreased in acute stage. During the recovery period, the body weight and activity ability were completely restored, whereas the emotion disordered, and the ability of spatial learning and memory were impacted in the infected mice. This long-term behavior impact may be the lag injury caused by non-neurotropic influenza infection.
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http://dx.doi.org/10.1016/j.bbrc.2020.12.092 | DOI Listing |
Front Biosci (Landmark Ed)
January 2025
Department of Zoology, College of Science, King Saud University, 11451 Riyadh, Saudi Arabia.
Background: We investigated chitosan's protective effects against tertiary butylhydroquinone (TBHQ)-induced toxicity in adult male rats, focusing on cognitive functions and oxidative stress in the brain, liver, and kidneys.
Methods: Rats were divided into four groups (n = 8/group): (1) Control, (2) Chitosan only, (3) TBHQ only, and (4) Chitosan + TBHQ.
Results: TBHQ exposure led to significant cognitive impairments and increased oxidative stress, marked by elevated malondialdehyde (MDA) and decreased superoxide dismutase (SOD) and glutathione (GSH) levels.
Sensors (Basel)
January 2025
School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China.
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.
To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.
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