T-cell infiltration in the central nervous system and their association with brain calcification in Slc20a2-deficient mice

Primary familial brain calcification (PFBC) is a rare neurodegenerative and neuropsychiatric disorder characterized by bilateral symmetric intracranial calcification along the microvessels or inside neuronal cells in the basal ganglia, thalamus, and cerebellum. Slc20a2 homozygous (HO) knockout mice are the most commonly used model to simulate the brain calcification phenotype observed in human patients. However, the cellular and molecular mechanisms related to brain calcification, particularly at the early stage much prior to the emergence of brain calcification, remain largely unknown. In this study, we quantified the central nervous system (CNS)-infiltrating T-cells of different age groups of Slc20a2-HO and matched wild type mice and found CD45+CD3+ T-cells to be significantly increased in the brain parenchyma, even in the pre-calcification stage of 1-month-old -HO mice. The accumulation of the CD3+ T-cells appeared to be associated with the severity of brain calcification. Further immunophenotyping revealed that the two main subtypes that had increased in the brain were CD3+ CD4− CD8– and CD3+ CD4+ T-cells. The expression of endothelial cell (EC) adhesion molecules increased, while that of tight and adherents junction proteins decreased, providing the molecular precondition for T-cell recruitment to ECs and paracellular migration into the brain. The fusion of lymphocytes and EC membranes and transcellular migration of CD3-related gold particles were captured, suggesting enhancement of transcytosis in the brain ECs. Exogenous fluorescent tracers and endogenous IgG and albumin leakage also revealed an impairment of transcellular pathway in the ECs. FTY720 significantly alleviated brain calcification, probably by reducing T-cell infiltration, modulating neuroinflammation and ossification process, and enhancing the autophagy and phagocytosis of CNS-resident immune cells. This study clearly demonstrated CNS-infiltrating T-cells to be associated with the progression of brain calcification. Impairment of blood–brain barrier (BBB) permeability, which was closely related to T-cell invasion into the CNS, could be explained by the BBB alterations of an increase in the paracellular and transcellular pathways of brain ECs. FTY720 was found to be a potential drug to protect patients from PFBC-related lesions in the future

Visual Saliency Guided Foveated Video Compression

Video compression has become increasingly crucial as video resolution and bitrate have surged in recent years. However, most widely applied video compression methods do not fully exploit the characteristics of the Human Visual System (HVS) to reduce perceptual redundancy in videos. In this paper, we propose a novel video compression method that integrates visual saliency information with foveation to reduce perceptual redundancy. We present a new approach to subsample and restore the input image using saliency data, which allocates more space for salient regions and less for non-salient ones. We analyze the information entropy in video frames before and after applying our algorithm and demonstrate that the proposed method reduces redundancy. Through subjective and objective evaluations, we show that our method produces videos with superior perceptual visual quality. Moreover, our approach can be added to most existing video compression standards without altering their bitstream format

Development Method for the Driving Cycle of Electric Vehicles

With the development of electric vehicles, more attention has been paid to the role of the driving cycle in vehicle performance testing. At present, the K-means algorithm is often used in the development of driving cycles. However, it is sensitive to the outlier points and also difficult to determine the K value. To solve this problem, the hierarchical cluster method is applied in this study. First, the real-world driving data are collected and denoised through wavelet domain denoising. Then, the data are divided into micro-trips and the characteristic parameters are extracted. The hierarchical cluster method is adopted to classify the micro-trips into different categories. An appropriate number of micro-trips are selected from each group in proportion to each category to assemble the driving cycle. Finally, both the economic simulation and the statistical analysis prove the accuracy of the generated driving cycle and the feasibility of the development method proposed in this paper

Development Method for the Driving Cycle of Electric Vehicles

With the development of electric vehicles, more attention has been paid to the role of the driving cycle in vehicle performance testing. At present, the K-means algorithm is often used in the development of driving cycles. However, it is sensitive to the outlier points and also difficult to determine the K value. To solve this problem, the hierarchical cluster method is applied in this study. First, the real-world driving data are collected and denoised through wavelet domain denoising. Then, the data are divided into micro-trips and the characteristic parameters are extracted. The hierarchical cluster method is adopted to classify the micro-trips into different categories. An appropriate number of micro-trips are selected from each group in proportion to each category to assemble the driving cycle. Finally, both the economic simulation and the statistical analysis prove the accuracy of the generated driving cycle and the feasibility of the development method proposed in this paper

Insect-Microorganism Interaction Has Implicates on Insect Olfactory Systems

Olfaction plays an essential role in various insect behaviors, including habitat selection, access to food, avoidance of predators, inter-species communication, aggregation, and reproduction. The olfactory process involves integrating multiple signals from external conditions and internal physiological states, including living environments, age, physiological conditions, and circadian rhythms. As microorganisms and insects form tight interactions, the behaviors of insects are constantly challenged by versatile microorganisms via olfactory cues. To better understand the microbial influences on insect behaviors via olfactory cues, this paper summarizes three different ways in which microorganisms modulate insect behaviors. Here, we deciphered three interesting aspects of microorganisms-contributed olfaction: (1) How do volatiles emitted by microorganisms affect the behaviors of insects? (2) How do microorganisms reshape the behaviors of insects by inducing changes in the synthesis of host volatiles? (3) How do symbiotic microorganisms act on insects by modulating behaviors

Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks

Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the information overload resulting from the large number of network parameters. In this paper, we propose Frequency Regularization to restrict the non-zero elements of the network parameters in the frequency domain. The proposed approach operates at the tensor level, and can be applied to almost all network architectures. Specifically, the tensors of parameters are maintained in the frequency domain, where high-frequency components can be eliminated by zigzag setting tensor elements to zero. Then, the inverse discrete cosine transform (IDCT) is used to reconstruct the spatial tensors for matrix operations during network training. Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization. Comprehensive evaluations on various state-of-the-art network architectures, including LeNet, Alexnet, VGG, Resnet, ViT, UNet, GAN, and VAE, demonstrate the effectiveness of the proposed frequency regularization. For a very small accuracy decrease (less than 2%), a LeNet5 with 0.4M parameters can be represented by only 776 float16 numbers (over $1100\times$ reduction), and a UNet with 34M parameters can be represented by only 759 float16 numbers (over $80000\times$ reduction). In particular, the original size of the UNet model is reduced from 366 Mb to 4.5 Kb

Evaluating the Transcriptomic and Metabolic Profile of Mice Exposed to Source Drinking Water

Transcriptomic and metabonomic methods were used to investigate mice’s responses to drinking source water (DSW) exposure. After mice were fed with DSW for 90 days, hepatic transcriptome was characterized by microarray and serum metabonome were determined by ¹H nuclear magnetic resonance (NMR) spectroscopy. A total of 243 differentially expressed genes (DEGs) were identified, among which 141 genes were up-regulated and 102 genes were down-regulated. Metabonomics revealed significant changes in concentrations of creatine, pyruvate, glutamine, lysine, choline, acetate, lipids, taurine, and trimethylamine oxide. Four biological pathways were identified by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis where both gene expression and metabolite concentrations were altered in response to DSW exposure. These results highlight the significance of combined use of transcriptomic and metabonomic approaches in evaluating potential health risk induced by DSW contaminated with various hazardous materials