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Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.
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Aprendizado Profundo , Imageamento Tridimensional , Neurônios , Neurônios/citologia , Imageamento Tridimensional/métodos , Software , Animais , Processamento de Imagem Assistida por Computador/métodos , Encéfalo/citologia , HumanosRESUMO
In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human-robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes.
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Paclitaxel, a potent anti-tumor drug widely recognized for its therapeutic efficacy, has faced limitations in clinical application due to its poor solubility. The use of Cremophor EL (CrEL) as a cosolvent in paclitaxel injections has been associated with hypersensitivity reactions in some patients. To overcome these challenges, we have developed a novel conjugate by linking a neuropilin-1 targeting peptide, RPPR, to paclitaxel, resulting in PTX-RPPR. This innovative approach has significantly enhanced the solubility of paclitaxel, achieving a 3.8â¯mg/mL concentration, a remarkable 90-fold increase over the native drug. PTX-RPPR has shown potent anti-tumor activity, inhibiting tumor cell proliferation with an IC50 ranging from 0.26 to 1.64⯵M and effectively suppressing migration, invasion, and angiogenesis at a concentration of 75â¯nM. Notably, in a 4T1 mammary carcinoma model, PTX-RPPR administered at a dose of 0.7 µmol/kg exhibited tumor growth inhibition comparable to that of paclitaxel at a higher dose of 3.5 µmol/kg, with superior efficacy in preventing lung metastasis. Furthermore, PTX-RPPR effectively reduced NRP-1 expression in both tumors and lungs post-treatment. In contrast to paclitaxel formulated with CrEL, PTX-RPPR did not induce IL-6 expression, suggesting a safer profile in terms of immunological response. Characterized by a particle size of 200â¯nm and a zeta potential of +30â¯mV, the nano-formulation of PTX-RPPR demonstrated remarkable stability over seven days. This study introduced PTX-RPPR as a promising peptide-drug conjugate that addresses the solubility and hypersensitivity issues associated with paclitaxel, offering a safer therapeutic strategy for cancer treatment.
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Proliferação de Células , Camundongos Endogâmicos BALB C , Neuropilina-1 , Paclitaxel , Paclitaxel/farmacologia , Neuropilina-1/metabolismo , Animais , Feminino , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Humanos , Camundongos , Antineoplásicos Fitogênicos/farmacologia , Peptídeos/farmacologia , Peptídeos/química , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/patologia , Movimento Celular/efeitos dos fármacos , Metástase Neoplásica/prevenção & controle , SolubilidadeRESUMO
The aim of individuals consuming health supplements is to attain a robust state through nutritional regulation. However, some unscrupulous manufacturers, motivated by profit, fraudulently incorporate drugs or unauthorized components with therapeutic effects into the product for instant product performance enhancement. The long-term use of these products may inadvertently inflict harm on human health and fail to promote nutritive healthcare. The illegal inclusion of these substances is prevalent in kidney-tonifying and sexuality-enhancing products. Developing effective analytical methods to identify these products and screen for illegal added ingredients can effectively prevent such products from reaching and remaining on the market. A target screening method for the detection and quantification of 90 phosphodiesterase type 5 inhibitors (PDE-5is) in 5 kinds of health products was developed and validated. The type of dietary supplements varied from tablets, capsules, and protein powder to wine and beverages. Sample preparation was completed with a one-step liquid phase extraction. The screening process of 90 PDE-5is was done efficiently within 25 min by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) using the dynamic multiple reaction monitoring (dMRM) technique. The LODs of 90 PDE-5is were detected at levels ranging from 25 to 85 ng/g or ng/mL. This novel targeting methodology was effective and can be applied to routine market supervision. Among 286 batches of samples, 8 batches were found to be positive. Three kinds of PDE-5is were first detected in healthy products. The screening method demonstrated herein will be a promising and powerful tool for rapid screening of PDE-5is.
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Suplementos Nutricionais , Espectrometria de Massa com Cromatografia Líquida , Inibidores da Fosfodiesterase 5 , Humanos , Cromatografia Líquida de Alta Pressão/métodos , Suplementos Nutricionais/análise , Espectrometria de Massa com Cromatografia Líquida/métodos , Inibidores da Fosfodiesterase 5/análise , Inibidores da Fosfodiesterase 5/química , Espectrometria de Massas em Tandem/métodosRESUMO
The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations.
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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.
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Naturally occurring substances and their derivatives function as vital resources for pesticides that can be used in fields, such as insecticide production and fungicide development. As a botanical entity displaying multifaceted biological functions, wormwood has received thorough scrutiny across multiple sectors. The insect repellency potency combined with antibacterial and antifungal activities of wormwood position it as a potential candidate for prospective development into eco-friendly chemical pesticides. In this research, Wormwood essential oil was procured via ethanol water under ultrasonic scenarios and subsequently diluted with PEG 400 to formulate green chemical pesticides. The defensive efficacy of this green pesticide on plants was validated through 2 weeks of clustered plant growth experiments. Active constituents that exerted their effects were scrutinized by GC-MS. Furthermore, this green pesticide also displays efficacious effects on the prevention and management of aphids, exhibiting a dose-dependent relationship. 4-terpenol, eucalyptol, carvacrol, and L-borneol were identified by GC-MS as the predominant active constituents in this green chemical pesticide. Wormwood can be leveraged to develop green chemical pesticides, which can protect plants without contaminating the environment.
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Inseticidas , Óleos Voláteis , Inseticidas/química , Inseticidas/farmacologia , Animais , Óleos Voláteis/química , Óleos Voláteis/farmacologia , Cromatografia Gasosa-Espectrometria de Massas , Cimenos/química , Cimenos/farmacologia , Química Verde/métodos , Afídeos/efeitos dos fármacos , Eucaliptol/química , Eucaliptol/farmacologia , CanfanosRESUMO
After orthopedic surgeries, such as hip replacement, many patients are prone to developing deep vein thrombosis (DVT), which in severe cases can lead to fatal pulmonary embolism or major bleeding. Clinical intervention with high-dose anticoagulant therapy inevitably carries the risk of bleeding. Therefore, a targeted drug delivery system that adjusts local DVT lesions and potentially reduces drug dosage and toxic side effects important. In this study, we developed a targeted drug delivery platelet-derived nanoplatform (AMSNP@PM-rH/A) for DVT treatment that can simultaneously deliver a direct thrombin inhibitor (DTI) Recombinant Hirudin (rH), and the Factor Xa inhibitor Apixaban (A) by utilizing Aminated mesoporous silica nanoparticles (AMSNP). This formulation exhibits improved biocompatibility and blood half-life and can effectively eliminate deep vein thrombosis lesions and achieve therapeutic effects at half the dosage. Furthermore, we employed various visualization techniques to capture the targeted accumulation and release of a platelet membrane (PM) coating in deep vein thrombosis and explored its potential targeting mechanism.
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Plaquetas , Hirudinas , Piridonas , Trombose Venosa , Trombose Venosa/tratamento farmacológico , Plaquetas/efeitos dos fármacos , Plaquetas/metabolismo , Piridonas/química , Piridonas/uso terapêutico , Piridonas/farmacologia , Animais , Humanos , Hirudinas/química , Hirudinas/farmacologia , Pirazóis/química , Pirazóis/uso terapêutico , Pirazóis/farmacologia , Nanopartículas/química , Sistemas de Liberação de Medicamentos , Nanotecnologia/métodos , Masculino , Dióxido de Silício/química , Materiais Biomiméticos/química , Materiais Biomiméticos/farmacologia , Camundongos , Membrana Celular/metabolismo , Membrana Celular/efeitos dos fármacos , Proteínas Recombinantes/uso terapêutico , Inibidores do Fator Xa/uso terapêutico , Inibidores do Fator Xa/química , Inibidores do Fator Xa/farmacologiaRESUMO
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.
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Citrus , Frutas , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/métodos , Citrus/química , Frutas/química , Compostos Fitoquímicos/análise , Compostos Fitoquímicos/isolamento & purificação , Estrutura Molecular , Medicamentos de Ervas Chinesas/química , Flavonoides/análiseRESUMO
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.
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Algoritmos , Encéfalo , Neurônios , Encéfalo/diagnóstico por imagem , Humanos , Redes Neurais de Computação , Animais , Processamento de Imagem Assistida por Computador/métodos , Neuroimagem/métodosRESUMO
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%.
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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.
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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.
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Neoplasias Hepáticas , Procedimentos Cirúrgicos Robóticos , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/cirurgia , Compostos Radiofarmacêuticos , Tomografia Computadorizada por Raios X , Processamento de Imagem Assistida por ComputadorRESUMO
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.
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Alcaloides , Benzodioxóis , Microbioma Gastrointestinal , Doença de Parkinson , Piperidinas , Alcamidas Poli-Insaturadas , Humanos , Ratos , Animais , Levodopa/farmacologia , Doença de Parkinson/tratamento farmacológico , Oxidopamina/toxicidade , Tirosina Descarboxilase , Dopamina/metabolismo , Bactérias/metabolismo , Antiparkinsonianos/farmacologia , Antiparkinsonianos/uso terapêutico , Modelos Animais de DoençasRESUMO
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.
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Procedimentos Cirúrgicos Robóticos , Artefatos , Coluna Vertebral , Processamento de Imagem Assistida por ComputadorRESUMO
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.
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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.
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Aprendizado Profundo , Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Encéfalo/diagnóstico por imagem , MicroscopiaRESUMO
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.
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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.
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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.