Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.

Alzheimer’s disease (AD), the most common type of dementia, is an ageing-related progressive neurodegenerative brain disorder. Extracellular neuritic plaques composed of misfolded amyloid β (Aβ) proteins and intracellular neurofibrillary tangles formed by hyperphosphorylated tau protein are the two classical characteristics of AD. Aβ and tau pathologies induce neurite atrophy and neuronal apoptosis, leading to cognitive, language, and behavioral deficits. For decades, researchers have made great efforts to explore the pathogens and therapeutics of AD; however, its intrinsic mechanism remains unclear and there are still no well-established strategies to restore or even prevent this disease. Therefore, it would be beneficial for the establishment of novel therapeutic strategy to determine the intrinsic molecular mechanism that is interrelated with the initiation and progression of AD. A variety of evidence indicates that neuroinflammation plays a crucial role in the pathogenesis of AD. Nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing protein 3 (NLRP3) is a key inflammasome sensor of cellular stress and infection that is involved in the innate immune system. In response to a wide range of stimuli like Aβ, NLRP3 assembles apoptosis-associated speck-like protein (ASC) and procaspase-1 into an inflammasome complex to induce the caspase-1 mediated secretion of interleukin (IL)-1β/IL-18 in M1 polarized microglia, triggering the pathophysiological changes and cognitive decline of AD. Therefore, targeting NLRP3 inflammasome seems an efficient path for AD treatment via regulating brain immune microenvironment. Furthermore, accumulating evidence indicates that traditional Chinese medicine (TCM) exerts beneficial effects on AD via NLRP3 inflammasome inactivation. In this review, we summarize current reports on the role and activated mechanisms of the NLRP3 inflammasome in the pathogenesis of AD. We also review the natural products for attenuating neuroinflammation by targeting NLRP3 inflammasome activation, which provides useful clues for developing novel AD treatments.

Constructing two-dimensional (2-D) Golay complementary array sets (GCASs) with flexible array sizes become an important topic due to their practical application for omnidirectional precoding in massive multi-input multi-output (MIMO) systems. In this letter, we propose two direct constructions of GCASs from 2-D extended generalized Boolean functions (EGBFs). Compared with the previous works, the presented constructions lead to more flexible array sizes for a fixed set size, which may give an extra benefit in accommodating more URA configurations. Simulation results indicate that the proposed GCASs can be employed as precoding matrices to achieve omnidirectional transmission, and behave a superior bit error rate (BER) performance over other schemes.