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Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances. [ABSTRACT FROM AUTHOR]

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats. [ABSTRACT FROM AUTHOR]

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones. [ABSTRACT FROM AUTHOR]

Cyclodextrins (CDs) are used as drug delivery agents. In this study, we examined whether CDs have an inflammatory effect on endothelial cells. First, we found that β-CD promoted cell proliferation in bovine aortic endothelial cells and elevated nitric oxide (NO) production through dephosphorylation of threonine-495 (T-495) in endothelial nitric oxide synthetase (eNOS). Dephosphorylation of T-495 is known to activate eNOS. Phosphorylation of T-495 was found to be catalyzed by protein kinase Cε (PKCε). We then found that β-CD inhibits binding of PKCε to diacylglycerol (DAG) via formation of a β-CD-DAG complex, indicating that β-CD inactivates PKCε. Furthermore, β-CD controls activation of PKCε by reducing the recruitment of PKCε into the plasma membrane. Finally, β-CD inhibits expression of intercellular and vascular cell adhesion molecule-1 by increasing NO via control of PKCε/eNOS and suppression of THP-1 cell adhesion to endothelial cells. These findings imply that β-CD plays an important role in anti-inflammatory processes. [ABSTRACT FROM AUTHOR]

Developmentally regulated GTP-binding protein 2 (DRG2) was first identified in the central nervous system of mice. However, the physiological function of DRG2 in the brain remains largely unknown. Here, we demonstrated that knocking out DRG2 impairs the function of dopamine neurons in mice. DRG2 was strongly expressed in the neurons of the dopaminergic system such as those in the striatum (Str), ventral tegmental area (VTA), and substantia nigra (SN), and on neuronal cell bodies in high-density regions such as the hippocampus (HIP), cerebellum, and cerebral cortex in the mouse brain. DRG2 knockout (KO) mice displayed defects in motor function in motor coordination and rotarod tests and increased anxiety. However, unexpectedly, DRG2 depletion did not affect the dopamine (DA) neuron population in the SN, Str, or VTA region or dopamine synthesis in the Str region. We further demonstrated that dopamine release was significantly diminished in the Str region of DRG2 KO mice and that treatment of DRG2 KO mice with l-3,4-dihydroxyphenylalanine (L-DOPA), a dopamine precursor, rescued the behavioral motor deficiency in DRG2 KO mice as observed with the rotarod test. This is the first report to identify DRG2 as a key regulator of dopamine release from dopamine neurons in the mouse brain. [ABSTRACT FROM AUTHOR]

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.

Developmentally regulated GTP-binding protein 2 (DRG2) was first identified in the central nervous system of mice. However, the physiological function of DRG2 in the brain remains largely unknown. Here, we demonstrated that knocking out DRG2 impairs the function of dopamine neurons in mice. DRG2 was strongly expressed in the neurons of the dopaminergic system such as those in the striatum (Str), ventral tegmental area (VTA), and substantia nigra (SN), and on neuronal cell bodies in high-density regions such as the hippocampus (HIP), cerebellum, and cerebral cortex in the mouse brain. DRG2 knockout (KO) mice displayed defects in motor function in motor coordination and rotarod tests and increased anxiety. However, unexpectedly, DRG2 depletion did not affect the dopamine (DA) neuron population in the SN, Str, or VTA region or dopamine synthesis in the Str region. We further demonstrated that dopamine release was significantly diminished in the Str region of DRG2 KO mice and that treatment of DRG2 KO mice with l-3,4-dihydroxyphenylalanine (L-DOPA), a dopamine precursor, rescued the behavioral motor deficiency in DRG2 KO mice as observed with the rotarod test. This is the first report to identify DRG2 as a key regulator of dopamine release from dopamine neurons in the mouse brain.