R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution.

High-resolution microscopy hyperspectral (HS) images can provide highly detailed spatial and spectral information, enabling the identification and analysis of biological tissues at a microscale level. Recently, significant efforts have been devoted to enhancing the resolution of HS images by leveraging high spatial resolution multispectral (MS) images. However, the inherent hardware constraints lead to a significant distribution gap between HS and MS images, posing challenges for image super-resolution within biomedical domains. This discrepancy may arise from various factors, including variations in camera imaging principles (e.g., snapshot and push-broom imaging), shooting positions, and the presence of noise interference. To address these challenges, we introduced a unique unsupervised super-resolution framework named R2D2-GAN. This framework utilizes a generative adversarial network (GAN) to efficiently merge the two data modalities and improve the resolution of microscopy HS images. Traditionally, supervised approaches have relied on intuitive and sensitive loss functions, such as mean squared error (MSE). Our method, trained in a real-world unsupervised setting, benefits from exploiting consistent information across the two modalities. It employs a game-theoretic strategy and dynamic adversarial loss, rather than relying solely on fixed training strategies for reconstruction loss. Furthermore, we have augmented our proposed model with a central consistency regularization (CCR) module, aiming to further enhance the robustness of the R2D2-GAN. Our experimental results show that the proposed method is accurate and robust for super-resolution images. We specifically tested our proposed method on both a real and a synthetic dataset, obtaining promising results in comparison to other state-of-the-art methods. Our code and datasets are accessible through Multimedia Content.

Differential distribution of characteristic constituents in peel and pulp of Aurantii Fructus Immaturus (Citrus aurantium L.) using MALDI mass spectrometry imaging.

Aurantii Fructus Immaturus (AFI) was structurally divided into two parts named "peel" and "pulp". The exocarp and mesocarp of materials named "peel". The endocarp separated into multiple compartments and the cystic hair attached to it named "pulp". In order to explore the distribution and content of constituents in AFI, an efficient method to explore the distribution of constituents was developed based on matrix assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). After simple processing, thirty-two constituents with distinct localization in the mass range of 101-1200 Da were identified by MALDI-FTICR-MSI. In addition, the identified four flavnoids (poncirin, sinensetin, 3,5,6,7,8,3',4'-heptemthoxyflavone, and tangeritin) were analyzed for differences between using LC-MS. Quantitative analysis results supported the quantitative results from MALDI-FT-ICR-MSI. The results implied that different parts had different constituents in AFI, and demonstrated MALDI-MSI have high potential in the direct analysis of constituents.

A Two-Stage Noise-Tolerant Paradigm for Label Corrupted Person Re-Identification.

Supervised person re-identification (Re-ID) approaches are sensitive to label corrupted data, which is inevitable and generally ignored in the field of person Re-ID. In this paper, we propose a two-stage noise-tolerant paradigm (TSNT) for labeling corrupted person Re-ID. Specifically, at stage one, we present a self-refining strategy to separately train each network in TSNT by concentrating more on pure samples. These pure samples are progressively refurbished via mining the consistency between annotations and predictions. To enhance the tolerance of TSNT to noisy labels, at stage two, we employ a co-training strategy to collaboratively supervise the learning of the two networks. Concretely, a rectified cross-entropy loss is proposed to learn the mutual information from the peer network by assigning large weights to the refurbished reliable samples. Moreover, a noise-robust triplet loss is formulated for further improving the robustness of TSNT by increasing inter-class distances and reducing intra-class distances in the label-corrupted dataset, where a constraint condition for reliability discrimination is carefully designed to select reliable triplets. Extensive experiments demonstrate the superiority of TSNT, for instance, on the Market1501 dataset, our paradigm achieves 90.3% rank-1 accuracy (6.2% improvement over the state-of-the-art method) under noise ratio 20%.

Brain Image Segmentation for Ultrascale Neuron Reconstruction via an Adaptive Dual-Task Learning Network.

Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution of background noise, posing an enormous challenge to neuron reconstruction from whole brain images. In this paper, we propose an adaptive dual-task learning network (ADTL-Net) to quickly and accurately extract neuronal structures from ultrascale brain images. Specifically, this framework includes an External Features Classifier (EFC) and a Parameter Adaptive Segmentation Decoder (PASD), which share the same Multi-Scale Feature Encoder (MSFE). MSFE introduces an attention module named Channel Space Fusion Module (CSFM) to extract structure and intensity distribution features of neurons at different scales for addressing the problem of anisotropy in 3D space. Then, EFC is designed to classify these feature maps based on external features, such as foreground intensity distributions and image smoothness, and select specific PASD parameters to decode them of different classes to obtain accurate segmentation results. PASD contains multiple sets of parameters trained by different representative complex signal-to-noise distribution image blocks to handle various images more robustly. Experimental results prove that compared with other advanced segmentation methods for neuron reconstruction, the proposed method achieves state-of-the-art results in the task of neuron reconstruction from ultrascale brain images, with an improvement of about 49% in speed and 12% in F1 score.

Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration.

Hyperspectral images (HSIs) are composed of hundreds of contiguous waveband images, offering a wealth of spatial and spectral information. However, the practical use of HSIs is often hindered by the presence of complicated noise caused by various factors such as non-uniform sensor response and dark current. Traditional methods for denoising HSIs rely on constrained optimization approaches, where selecting appropriate prior knowledge is critical for achieving satisfactory results. Nevertheless, these traditional algorithms are limited by hand-crafted priors, leaving room for improvement in their denoising performance. Recently, the supervised deep learning technique has emerged as a promising approach for HSI denoising. However, their requirement for paired training data and poor generalization ability on untrained noise distributions pose challenges in practical applications. In this paper, we design a novel algorithm by the synergism of optimization-based methods and deep learning techniques. Specifically, we introduce a plug-and-play Deep Low-rank Decomposition (DLD) model into the optimization framework. Furthermore, we propose an effective mechanism to incorporate traditional prior knowledge into the DLD model. Finally, we provide a detailed analysis of the optimization process and convergence of the proposed method. Empirical evaluations on various tasks, including hyperspectral image denoising and spectral compressive imaging, demonstrate the superiority of our approach over state-of-the-art methods.

CFANet: Context fusing attentional network for preoperative CT image segmentation in robotic surgery.

Accurate segmentation of CT images is crucial for clinical diagnosis and preoperative evaluation of robotic surgery, but challenges arise from fuzzy boundaries and small-sized targets. In response, a novel 2D segmentation network named Context Fusing Attentional Network (CFANet) is proposed. CFANet incorporates three key modules to address these challenges, namely pyramid fusing module (PFM), parallel dilated convolution module (PDCM) and scale attention module (SAM). Integration of these modules into the encoder-decoder structure enables effective utilization of multi-level and multi-scale features. Compared with advanced segmentation method, the Dice score improved by 2.14% on the dataset of liver tumor. This improvement is expected to have a positive impact on the preoperative evaluation of robotic surgery and to support clinical diagnosis, especially in early tumor detection.

Piperine improves levodopa availability in the 6-OHDA-lesioned rat model of Parkinson's disease by suppressing gut bacterial tyrosine decarboxylase.

AIM: Tyrosine decarboxylase (TDC) presented in the gut-associated strain Enterococcus faecalis can convert levodopa (L-dopa) into dopamine (DA), and its increased abundance would potentially minimize the availability and efficacy of L-dopa. However, the known human decarboxylase inhibitors are ineffective in this bacteria-mediated conversion. This study aims to investigate the inhibition of piperine (PIP) on L-dopa bacterial metabolism and evaluates the synergistic effect of PIP combined with L-dopa on Parkinson's disease (PD). METHODS: Metagenomics sequencing was adopted to determine the regulation of PIP on rat intestinal microbiota structure, especially on the relative abundance of E. faecalis. Then, the inhibitory effects of PIP on L-dopa conversion and TDC expression of E. faecalis were tested in vitro. We examined the synergetic effect of the combination of L-dopa and PIP on 6-hydroxydopamine (6-OHDA)-lesioned rats and tested the regulations of L-dopa bioavailability and brain DA level by pharmacokinetics study and MALDI-MS imaging. Finally, we evaluated the microbiota-dependent improvement effect of PIP on L-dopa availability using pseudo-germ-free and E. faecalis-transplanted rats. RESULTS: We found that PIP combined with L-dopa could better ameliorate the move disorders of 6-OHDA-lesioned rats by remarkably improving L-dopa availability and brain DA level than L-dopa alone, which was associated with the effect of PIP on suppressing the bacterial decarboxylation of L-dopa via effectively downregulating the abnormal high abundances of E. faecalis and TDC in 6-OHDA-lesioned rats. CONCLUSION: Oral administration of L-dopa combined with PIP can improve L-dopa availability and brain DA level in 6-OHDA-lesioned rats by suppressing intestinal bacterial TDC.

LSKANet: Long Strip Kernel Attention Network for Robotic Surgical Scene Segmentation.

Surgical scene segmentation is a critical task in Robotic-assisted surgery. However, the complexity of the surgical scene, which mainly includes local feature similarity (e.g., between different anatomical tissues), intraoperative complex artifacts, and indistinguishable boundaries, poses significant challenges to accurate segmentation. To tackle these problems, we propose the Long Strip Kernel Attention network (LSKANet), including two well-designed modules named Dual-block Large Kernel Attention module (DLKA) and Multiscale Affinity Feature Fusion module (MAFF), which can implement precise segmentation of surgical images. Specifically, by introducing strip convolutions with different topologies (cascaded and parallel) in two blocks and a large kernel design, DLKA can make full use of region- and strip-like surgical features and extract both visual and structural information to reduce the false segmentation caused by local feature similarity. In MAFF, affinity matrices calculated from multiscale feature maps are applied as feature fusion weights, which helps to address the interference of artifacts by suppressing the activations of irrelevant regions. Besides, the hybrid loss with Boundary Guided Head (BGH) is proposed to help the network segment indistinguishable boundaries effectively. We evaluate the proposed LSKANet on three datasets with different surgical scenes. The experimental results show that our method achieves new state-of-the-art results on all three datasets with improvements of 2.6%, 1.4%, and 3.4% mIoU, respectively. Furthermore, our method is compatible with different backbones and can significantly increase their segmentation accuracy. Code is available at https://github.com/YubinHan73/LSKANet.

Weakly Supervised Tracklet Association Learning With Video Labels for Person Re-Identification.

Supervised person re-identification (re-id) methods require expensive manual labeling costs. Although unsupervised re-id methods can reduce the requirement of the labeled datasets, the performance of these methods is lower than the supervised alternatives. Recently, some weakly supervised learning-based person re-id methods have been proposed, which is a balance between supervised and unsupervised learning. Nevertheless, most of these models require another auxiliary fully supervised datasets or ignore the interference of noisy tracklets. To address this problem, in this work, we formulate a weakly supervised tracklet association learning (WS-TAL) model only leveraging the video labels. Specifically, we first propose an intra-bag tracklet discrimination learning (ITDL) term. It can capture the associations between person identities and images by assigning pseudo labels to each person image in a bag. And then, the discriminative feature for each person is learned by utilizing the obtained associations after filtering the noisy tracklets. Based on that, a cross-bag tracklet association learning (CTAL) term is presented to explore the potential tracklet associations between bags by mining reliable positive tracklet pairs and hard negative pairs. Finally, these two complementary terms are jointly optimized to train our re-id model. Extensive experiments on the weakly labeled datasets demonstrate that WS-TAL achieves 88.1% and 90.3% rank-1 accuracy on the MARS and DukeMTMC-VideoReID datasets respectively. The performance of our model surpasses the state-of-the-art weakly supervised models by a large margin, even outperforms some fully supervised re-id models.

Deep learning in mesoscale brain image analysis: A review.

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

Nifuratel reduces Salmonella survival in macrophages by extracellular and intracellular antibacterial activity.

Salmonella are intracellular bacterial pathogens for which, as with many of the other Enterobacteriaceae, antibiotic resistance is becoming an increasing problem. New antibiotics are being sought as recommended by the World Health Organization and other international institutions. These must be able to penetrate macrophages, and infect the major host cells and the Salmonella-containing vacuole. This study reports screening a small library of Food and Drug Administration (FDA)-approved drugs for their antibacterial effect in macrophages infected with a rapid-multiplying mutant of Salmonella Enteritidis. The most effective drug that was least toxic for macrophages was Nifuratel, a nitrofuran antibiotic already in use for parasitic infections. In mice, it provided 60% protection after oral infection with a lethal S. Enteritidis dose with reduced bacterial numbers in the tissues. It was effective against different serovars, including multidrug-resistant strains of Salmonella Typhimurium, and in macrophages from different host species and against Listeria monocytogenes and Shigella flexneri. It reduced IL-10 and STAT3 production in infected macrophages which should increase the inflammatory response against Salmonella. IMPORTANCE Salmonella can keep long-term persistence in host's macrophages to evade cellular immune defense and antibiotic attack and exit in some condition and reinfect to cause salmonellosis again. In addition to multidrug resistance, this infection circle causes Salmonella clearance difficult in the host, and so there is a great need for new antibacterial agents that reduce intramacrophage Salmonella survival to block endogenous Salmonella reinfection.

Reinforcement Learning-Based Adaptive Optimal Control for Nonlinear Systems With Asymmetric Hysteresis.

This article investigates the adaptive optimal tracking problem for a class of nonlinear affine systems with asymmetric Prandtl-Ishlinskii (PI) hysteresis nonlinearities based on actor-critic (A-C) learning mechanisms. Considering the huge obstacles arising from the uncertainty of hysteresis nonlinearity in actuators, we develop a scheme for the conflict between the construction of Hamilton functions and hysteresis nonlinearity. The actuator hysteresis forces the input into a hysteresis delay, thus preventing the Hamilton function from getting the current moment's input instantly and thus making optimization impossible. In the first step, an inverse model is constructed to compensate for the hysteresis model with a shift factor. In the second step, we compensate for the control input by designing a feedback controller and incorporating the estimation and approximation errors into the Hamilton error. Optimal control, the other part of the actual control input, is obtained by taking partial derivatives of the Hamiltonian function after the nonlinearities have been circumvented. At the end, a simulation is given to validate the developed solution.

Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem.

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.

Discovery of novel benzylquinazoline molecules as p97/VCP inhibitors.

Introduction: Protein p97 is an extensively investigated AAA ATPase with various cellular activities, including cell cycle control, ubiquitin-proteasome system, autophagy, and NF-κB activation. Method: In this study, we designed, synthesized and evaluated eight novel DBeQanalogs as potential p97 inhibitors in vivo and in vitro. Results: In the p97 ATPase inhibition assay, compounds 6 and 7 showed higher potency than the known p97 inhibitors, DBeQ and CB-5083. Compounds 4-6 dramatically induced G0/G1 phase arrest in the HCT116 cells, and compound 7 arrested the cells in both G0/G1 and S phases. Western blots showed elevated levels of SQSTM/p62, ATF-4, and NF-κB in HCT116 cells with the treatment of compounds 4-7, confirming their role in inhibiting the p97 signaling pathway in cells. In addition, the IC50 of compounds 4-6 against HCT116, RPMI-8226, and s180 proliferation were 0.24-6.9 µM with comparable potency as DBeQ. However, compounds 4-6 displayed low toxicity against the normal human colon cell line. Thus, compounds 6 and 7 were proved to be potential p97 inhibitors with less cytotoxicity. In vivo studies using the s180 xenograft model have demonstrated that compound 6 inhibited tumor growth, led to a significant reduction of p97 concentration in the serum and tumor, and indicated non-toxicity on the body weight and organ-to-brain weight ratios except for the spleen at the dose of 90 µmol/kg/day for 10 days. Furthermore, the present study indicated that compound 6 may not induce s180 mice myelosuppression often observed in the p97 inhibitors. Conclusion: Compound 6 displayed high binding affinity to p97, great p97 ATPase inhibition, selective cytotoxicity, remarkable anti-tumor effect, and upregulated safety, which improved the clinical potential of p97 inhibitors.

Branch Aggregation Attention Network for Robotic Surgical Instrument Segmentation.

Surgical instrument segmentation is of great significance to robot-assisted surgery, but the noise caused by reflection, water mist, and motion blur during the surgery as well as the different forms of surgical instruments would greatly increase the difficulty of precise segmentation. A novel method called Branch Aggregation Attention network (BAANet) is proposed to address these challenges, which adopts a lightweight encoder and two designed modules, named Branch Balance Aggregation module (BBA) and Block Attention Fusion module (BAF), for efficient feature localization and denoising. By introducing the unique BBA module, features from multiple branches are balanced and optimized through a combination of addition and multiplication to complement strengths and effectively suppress noise. Furthermore, to fully integrate the contextual information and capture the region of interest, the BAF module is proposed in the decoder, which receives adjacent feature maps from the BBA module and localizes the surgical instruments from both global and local perspectives by utilizing a dual branch attention mechanism. According to the experimental results, the proposed method has the advantage of being lightweight while outperforming the second-best method by 4.03%, 1.53%, and 1.34% in mIoU scores on three challenging surgical instrument datasets, respectively, compared to the existing state-of-the-art methods. Code is available at https://github.com/SWT-1014/BAANet.

A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction.

In this article, we consider the problem of inferring the sign of a link based on known sign data in signed networks. Regarding this link sign prediction problem, signed directed graph neural networks (SDGNNs) provides the best prediction performance currently to the best of our knowledge. In this article, we propose a different link sign prediction architecture called subgraph encoding via linear optimization (SELO), which obtains overall leading prediction performances compared to the state-of-the-art algorithm SDGNN. The proposed model utilizes a subgraph encoding approach to learn edge embeddings for signed directed networks. In particular, a signed subgraph encoding approach is introduced to embed each subgraph into a likelihood matrix instead of the adjacency matrix through a linear optimization (LO) method. Comprehensive experiments are conducted on five real-world signed networks with area under curve (AUC), F1, micro-F1, and macro-F1 as the evaluation metrics. The experiment results show that the proposed SELO model outperforms existing baseline feature-based methods and embedding-based methods on all the five real-world networks and in all the four evaluation metrics.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

OTP-NMS: Toward Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection.

Pedestrian detection is still a challenging task for computer vision, especially in crowded scenes where the overlaps between pedestrians tend to be large. The non-maximum suppression (NMS) plays an important role in removing the redundant false positive detection proposals while retaining the true positive detection proposals. However, the highly overlapped results may be suppressed if the threshold of NMS is lower. Meanwhile, a higher threshold of NMS will introduce a larger number of false positive results. To solve this problem, we propose an optimal threshold prediction (OTP) based NMS method that predicts a suitable threshold of NMS for each human instance. First, a visibility estimation module is designed to obtain the visibility ratio. Then, we propose a threshold prediction subnet to determine the optimal threshold of NMS automatically according to the visibility ratio and classification score. Finally, we re-formulate the objective function of the subnet and utilize the reward-guided gradient estimation algorithm to update the subnet. Comprehensive experiments on CrowdHuman and CityPersons show the superior performance of the proposed method in pedestrian detection, especially in crowded scenes.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

Prior Image Guided Snapshot Compressive Spectral Imaging.

Spectral images with rich spatial and spectral information have wide usage, however, traditional spectral imaging techniques undeniably take a long time to capture scenes. We consider the computational imaging problem of the snapshot spectral spectrometer, i.e., the Coded Aperture Snapshot Spectral Imaging (CASSI) system. For the sake of a fast and generalized reconstruction algorithm, we propose a prior image guidance-based snapshot compressive imaging method. Typically, the prior image denotes the RGB measurement captured by the additional uncoded panchromatic camera of the dual-camera CASSI system. We argue that the RGB image as a prior image can provide valuable semantic information. More importantly, we design the Prior Image Semantic Similarity (PIDS) regularization term to enhance the reconstructed spectral image fidelity. In particular, the PIDS is formulated as the difference between the total variation of the prior image and the recovered spectral image. Then, we solve the PIDS regularized reconstruction problem by the Alternating Direction Method of Multipliers (ADMM) optimization algorithm. Comprehensive experiments on various datasets demonstrate the superior performance of our method.