Deficiency of CBL and CBLB ubiquitin ligases leads to hyper T follicular helper cell responses and lupus by reducing BCL6 degradation.

Recent evidence reveals hyper T follicular helper (Tfh) cell responses in systemic lupus erythematosus (SLE); however, molecular mechanisms responsible for hyper Tfh cell responses and whether they cause SLE are unclear. We found that SLE patients downregulated both ubiquitin ligases, casitas B-lineage lymphoma (CBL) and CBLB (CBLs), in CD4+ T cells. T cell-specific CBLs-deficient mice developed hyper Tfh cell responses and SLE, whereas blockade of Tfh cell development in the mutant mice was sufficient to prevent SLE. ICOS was upregulated in SLE Tfh cells, whose signaling increased BCL6 by attenuating BCL6 degradation via chaperone-mediated autophagy (CMA). Conversely, CBLs restrained BCL6 expression by ubiquitinating ICOS. Blockade of BCL6 degradation was sufficient to enhance Tfh cell responses. Thus, the compromised expression of CBLs is a prevalent risk trait shared by SLE patients and causative to hyper Tfh cell responses and SLE. The ICOS-CBLs axis may be a target to treat SLE.

Spontaneous development of an autoimmune hepatitis - primary biliary cholangitis overlap syndrome in dnTGFßRII Aire-/- mice.

Autoimmune regulator (Aire) and TGF-ß signaling play important roles in central tolerance and peripheral tolerance, respectively, by eliminating or suppressing the activity of autoreactive T cells. We previously demonstrated that dnTGFßRII mice develop a defect in peripheral tolerance and a primary biliary cholangitis (PBC)-like disease. We hypothesized that by introducing the Aire gene to this model, we would observe a more severe PBC phenotype. Interestingly, however, we demonstrated that, while dnTGFßRII Aire-/- mice do manifest key histological and serological features of autoimmune cholangitis, they also develop mild to moderate interface hepatitis and show high levels of alanine transaminase (ALT) and antinuclear antibodies (ANA), characteristics of autoimmune hepatitis (AIH). To further understand this unique phenotype, we performed RNA sequencing (RNA-seq) and flow cytometry to explore the functional pathways and immune cell pathways in the liver of dnTGFßRII Aire-/- mice. Our data revealed enrichments of programmed cell death pathways and predominant CD8+ T cell infiltrates. Depleting CD8+ T cells using an anti-CD8α antibody significantly alleviated hepatic inflammation and prolonged the life span of these mice. Finally, RNA-seq data indicated the clonal expansion of hepatic CD8+ T cells. In conclusion, these mice developed an autoreactive CD8+ T-cell-mediated autoimmune cholangitis with concurrent hepatitis that exhibited key histological and serological features of the AIH-PBC overlap syndrome, representing a novel model for the study of tolerance and autoimmune liver disease. © 2023 The Pathological Society of Great Britain and Ireland.

CenterNet-Saccade: Enhancing Sonar Object Detection with Lightweight Global Feature Extraction.

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring.

High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.

FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.

Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment.

Autonomous driving systems are crucial complicated cyber-physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability of the reward-function settings in the face of complex environments and the influence of uncontrolled uncertainties. Therefore, a formal security reinforcement learning method is proposed. First, we propose an environmental modeling approach based on the influence of nondeterministic environmental factors, which enables the precise quantification of environmental issues. Second, we use the environment model to formalize the reward machine's structure, which is used to guide the reward-function setting in reinforcement learning. Third, we generate a control barrier function to ensure a safer state behavior policy for reinforcement learning. Finally, we verify the method's effectiveness in intelligent driving using overtaking and lane-changing scenarios.

Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios.

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

Cost-effective improvement of the performance of AWG-based FBG wavelength interrogation via a cascaded neural network.

Fiber Bragg grating (FBG) sensors have been widely applied in various applications, especially for structural health monitoring. Low cost, wide range, and low error are necessary for an excellent performance FBG sensor signal demodulation system. Yet the improvement of performance is commonly accompanied by costly and complex systems. A high-performance, low-cost wavelength interrogation method for FBG sensors was introduced in this paper. The information from the FBG sensor signal was extracted by the array waveguide grating (AWG) and fed into the proposed cascaded neural network. The proposed network was constructed by cascading a convolutional neural network and a residual backpropagation neural network. We demonstrate that our network yields a vastly significant performance improvement in AWG-based wavelength interrogation over that given by other machine learning models and validate it in experiments. The proposed network cost-effectively widens the wavelength interrogation range of the demodulation system and optimizes the wavelength interrogation error substantially, also making the system scalable.

Fabry-Perot interferometric sensor demodulation system utilizing multi-peak wavelength tracking and neural network algorithm.

For FPI sensor demodulation systems to be used in actual engineering measurement, they must have high performance, low cost, stability, and scalability. Excellent performance, however, necessitates expensive equipment and advanced algorithms. This research provides a new absolute demodulation system for FPI sensors that is high-performance and cost-effective. The reflected light from the sensor was demultiplexed into distinct channels using an array waveguide grating (AWG), with the interference spectrum features change translated as the variation of the transmitted intensity in each AWG channel. This data was fed into an end-to-end neural network model, which was utilized to interrogate multiple interference peaks' absolute peak wavelengths simultaneously. This architecturally simple network model can achieve remarkable generalization capabilities without training large-scale datasets using an appropriate data augmentation strategy. Experiments show that in simultaneous multi-wavelength and cavity length interrogations, the proposed system has the precision of up to ± 14 pm and ± 0.07 µm, respectively. The interrogation resolution can theoretically reach the pm level benefit from the neural network method. Furthermore, the system's outstanding demodulation repeatability and suitability were demonstrated. The system is expected to provide a high-performance and cost-effective, reliable solution for practical engineering applications.

Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation.

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.

Discovery of Dipyridamole Analogues with Enhanced Metabolic Stability for the Treatment of Idiopathic Pulmonary Fibrosis.

Dipyridamole, apart from its well-known antiplatelet and phosphodiesterase inhibitory activities, is a promising old drug for the treatment of pulmonary fibrosis. However, dipyridamole shows poor pharmacokinetic properties with a half-life (T1/2) of 7 min in rat liver microsomes (RLM). To improve the metabolic stability of dipyridamole, a series of pyrimidopyrimidine derivatives have been designed with the assistance of molecular docking. Among all the twenty-four synthesized compounds, compound (S)-4h showed outstanding metabolic stability (T1/2 = 67 min) in RLM, with an IC50 of 332 nM against PDE5. Furthermore, some interesting structure-activity relationships (SAR) were explained with the assistance of molecular docking.

Discovery of effective phosphodiesterase 2 inhibitors with antioxidant activities for the treatment of Alzheimer's disease.

The multi-target-directed-ligand (MTDL) strategy has been widely applied in the discovery of novel drugs for the treatment of Alzheimer's disease (AD) because of the multifactorial pathological mechanisms of AD. Phosphodiesterase-2 (PDE2) has been identified to be a novel and promising target for AD. However, MTDL combining with the inhibitory activity against PDE2A and other anti-AD factors such as antioxidants has not been developed yet. Herein, a novel series of PDE2 inhibitors with antioxidant capacities were designed, synthesized, and evaluated. Most compounds showed remarkable inhibitory activities against PDE2A as well as antioxidant activities. Compound 6d was selected, which showed good IC50 of 6.1 nM against PDE2A, good antioxidant activity (ORAC (Trolox) = 8.4 eq.) and no cytotoxicity to SH-SY5Y cells. Molecular docking and dynamics simulations were applied for the rational design and explanation of structure-activity relationship (SAR) of lead compounds.

Design, synthesis and biological evaluation of novel pyrazolopyrimidone derivatives as potent PDE1 inhibitors.

Phosphodiesterase-1 (PDE1) is a promising drug target closely related to central and peripheral diseases. With the assistance of molecular docking and dynamics simulations, we designed and synthesized a novel series of pyrazolopyrimidone derivatives as effective and metabolically stable inhibitors against PDE1. Most compounds have good inhibitory activities against PDE1 at the concentration of 20 nM. Compound 2j with the IC50 of 21 nM against PDE1B, shows good metabolic stability in the rat liver microsomes (RLM) (t1/2 of 28.5 min), indicating that compound 2j can be used as a tool to explore the molecular recognition mechanism between inhibitors and the target protein PDE1.

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing.

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar.

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér-Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing.

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar.

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

Direction of Arrival Estimation for MIMO Radar via Unitary Nuclear Norm Minimization.

In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to double the virtual array aperture, and the real-valued data are obtained by utilizing unitary transformation. Then a real-valued block sparse model is established based on a novel over-complete dictionary, and a UNNM algorithm is formulated for recovering the block-sparse matrix. In addition, the real-valued NC-MUSIC spectrum is used to design a weight matrix for reweighting the nuclear norm minimization to achieve the enhanced sparsity of solutions. Finally, the DOA is estimated by searching the non-zero blocks of the recovered matrix. Because of using the noncircular properties of signals to extend the virtual array aperture and an additional real structure to suppress the noise, the proposed method provides better performance compared with the conventional sparse recovery based algorithms. Furthermore, the proposed method can handle the case of underdetermined DOA estimation. Simulation results show the effectiveness and advantages of the proposed method.

Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia From Chest X-Ray Images.

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

Efficient click-based interactive segmentation for medical image with improved Plain-ViT.

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88.11% and Number of Clicks(NoC) at 80% are 7.03 clicks.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning.

Objective: This study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). Methods: Currently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). Results: Cropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. Conclusion: This U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.