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This paper proposes a main-auxiliary cooperative receiving coil (MA-coil) with a lower space occupation rate and a simple control based on the time-sharing working principle, which can effectively improve the anti-misalignment capability of a dynamic wireless charging (DWC) system. First, the structure and circuit topology of the MA-coil are designed. The two auxiliary coils (A-coil) are connected in reverse series and symmetrically placed on both sides of the main coil (M-coil). Second, the output performance of the MA-coil in the y-direction is calculated based on the time-sharing working principle. The A-coil works by itself and enhances the output power when side shift occurs. Third, the most suitable ratio of coil width wM and wA is determined. The anti-misalignment performance and the effective side shift range are compared through simulation between the MA-coil in this case and the square coil. Finally, an experimental prototype is built to verify the feasibility of the proposed structure, and experimental results obtained from the prototype are basically consistent with the theoretical analysis. The anti-misalignment capability of the MA-coil is more than 20% higher than that of the square coil. [ABSTRACT FROM AUTHOR]

Metabolomics-based identification of bioactive metabolites holds promise for enhancing the diagnosis, prevention, and treatment of rheumatoid arthritis (RA). Targeting specific cell populations with metabolite dysregulation presents novel opportunities for modulating disease progression in RA. The alteration of endogenous ligands is dynamic and cell type specific. Each endogenous ligand contributes a unique mode of metabolic-sensing G protein-coupled receptor (GPCR) activation. Metabolite-sensing GPCR signals originating from the synovial joint and gut have a regulatory role in RA-related inflammatory immune responses. Persistent inflammation in damaged joints results in metabolic dysregulation of the synovial microenvironment, causing pathogenic alteration of cell activity in rheumatoid arthritis (RA). Recently, the role of metabolite and metabolite-sensing G protein-coupled receptors (GPCRs) in the RA-related inflammatory immune response (IIR) has become a focus of research attention. These GPCRs participate in the progression of RA by modulating immune cell activation, migration, and inflammatory responses. Here, we discuss recent evidence implicating metabolic dysregulation in RA pathogenesis, focusing on the connection between RA-related IIR and GPCR signals originating from the synovial joint and gut. Furthermore, we discuss future directions for targeting metabolite-sensing GPCRs for therapeutic benefit, emphasizing the importance of identifying endogenous ligands and investigating the various transduction mechanisms involved. [ABSTRACT FROM AUTHOR]

Geological hazards, especially landslides and mudslides, are frequent in Caoke County, Sichuan Province, China. In September 2022, the mechanical parameters of the soil were obtained through a basic investigation of the landslide characteristics of Ni changgou. Upon that, the finite element-discrete element method was used to reconstruct the three-dimensional numerical model of the landslide on the right bank of Ni changgou, and the initiation mechanism of rainfall on landslide and the formation of debris flow impact dam process were simulated. Furthermore, the pore pressure, stability coefficient as well as displacement of the landslide body were analyzed. It turned out that with the increase of rainfall intensity, the pore water pressure value also increases, where pore water pressure rises rapidly. the slope is close to the unstable edge, Eventually, it tends to one under rainfall conditions, and due to gravity, the slide of the landslide is induced. The duration of landslide movement is about 200 s, the maximum average velocity of the landslide reaches 4.85 m/s, and the average movement distance is close to 500 m. In addition, this method is applied to the Chutougou debris flow, and the corresponding hazard analysis is added which could better show the treatment and application of debris flow in actual engineering. [ABSTRACT FROM AUTHOR]

Classification algorithms integrated with convolutional neural networks (CNN) display high accuracies in synthetic aperture radar (SAR) image classification. However, their consideration of spatial information is not comprehensive and effective, which causes poor performance in edges and complex regions. This paper proposes a Markov random field (MRF)-based algorithm for SAR image classification which fully considers the spatial constraints between superpixel regions. Firstly, the initialization of region labels is obtained by the CNN. Secondly, a probability field is constructed to improve the distribution of spatial relationships between adjacent superpixels. Thirdly, a novel region-level MRF is employed to classify the superpixels, which combines the intensity field and probability field in one framework. In our algorithm, the generation of superpixels reduces the misclassification at the pixel level, and region-level misclassification is rectified by the improvement of spatial description. Experimental results on simulated and real SAR images confirm the efficacy of the proposed algorithm for classification. [ABSTRACT FROM AUTHOR]

Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal. Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation. Main results. The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard. Significance. The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Design and research of high misalignment tolerant magnetic couplers for dynamic wireless charging systems" published in the Journal of Power Electronics. The correction addresses an error in the affiliation of one of the authors. The correct affiliation is the College of Computer and Control Engineering, Northeast Forestry University, Harbin, China. The authors' backgrounds and research interests are also provided, including topics such as wireless charging systems, circuit topologies, magnetic coupler design, and sustainable energy. [Extracted from the article]

Based on the lack of sensors for high concentration methane in life, the present study introduces a new type of D-shaped photonic crystal fiber utilizing surface plasmon resonance effects, designed for the detection of high concentrations of methane. Differing from conventional D-type photonic crystal fiber, this model coats a gold film on an open-loop channel for excitation. Through the finite element method analysis, the simulation results demonstrate, this sensor exhibits an effective scope of 1.24–1.32 for the refractive index of analyte. The escalation of the analyte's refractive index leads to a gradual intensification of the loss peak within the spectrum. And then remains stable. The sensor demonstrates excellent performance at a refractive index of 1.32, delivering a maximum sensitivity of 22000 nm/RIU and a resolution of 4.55∙10−6 RIU. Achieving the methane concentration detection in the range of 27%–45% was made possible through the establishment of the relationship between refractive index and methane concentration. According to simulation results, within the concentration scope of 29%–39%, the maximum detection sensitivity for methane can reach 700 nm/%. The sensor has simple structure and excellent performance. We conducted an analysis of how structural parameters impact the resonance spectrum with the goal of optimization. The outstanding sensing performance makes it more competitive in the field of methane concentration detection, and it is not only used for methane detection. Based on the lack of sensors for high concentration methane in life, this paper proposes a new D-type photonic crystal fiber based on surface plasmon resonance effect for detecting high concentration methane. Through the finite element method analysis, the simulation results show that the sensor sensing range is 1.24–1.32. With the increase of the analyte RI, the loss peak of the loss spectrum gradually increases and stabilizes. The maximum spectral sensitivity of 22000 nm/RIU and the resolution of 4.55 × 10-6RIU can be obtained at 1.32. By establishing the relationship between methane concentration and refractive index, the detection of methane concentration in 27 %–45 % is realized. The simulation results show that the maximum detection sensitivity of methane is 700 nm/% in the concentration range of 29 %–39 %. The sensor has a simple structure and excellent performance. The outstanding sensing performance makes it more competitive in the field of methane concentration detection. Finally, by studying the influence of structural parameters on the resonance spectrum, the sensing performance can be optimized, and not only the detection of methane can be realized. [Display omitted] • T photonic crystal fiber has higher sensitivity in the lower refractive index scene. • By adjusting the structural parameters of the photonic crystal fiber, the sensing performance of the sensor is optimized. • The sensing characteristics of the sensor is determined that the sensor is not sensitive to the angle of the polishing plane. [ABSTRACT FROM AUTHOR]

Here, a fresh D-type photonic crystal fiber has been developed by us, relying on the surface plasmon resonance effect. Differing from conventional D-type photonic crystal fiber, this model coats gold film on an open-loop channel for excitation. Through the finite element method analysis, this sensor exhibits an effective scope of 1.20–1.30 for the refractive index of analyte. As the RI of the analyte rises, the loss peak within the spectrum gradually intensifies and then remains stable. At an RI value of 1.30, achieving a resolution of 9.52 × 10−6 RIU, this sensor exhibits excellent performance with a maximum sensitivity of 10,500 nm/RIU. Its simple structure adds to its overall appeal. The outstanding sensing performance demonstrates enhanced competitiveness in sectors with lower refractive indices. Farther, to enhance the sensor's sensing capabilities, we conducted an analysis of how structural parameters impact the resonance spectrum with the goal of optimization. We propose a photonic crystal fiber for low refractive index applications. The whole structure is composed of hexagonal air holes in the inner ring, hexagonal air holes in the outer ring, and other air holes evenly distributed in the outer ring. The simulation results show that the sensor has a maximum sensitivity of 10,500 nm/RIU at the analyte RI = 1.30. The control variables of the structural geometric parameters of the photonic crystal fiber are studied respectively to obtain the best sensing performance. In addition, the performance of previous work and our sensor is compared. These studies have shown that our sensors have the advantages of simple structure, large sensing range, and low application scenarios. [Display omitted] • The photonic crystal fiber based on surface plasmon resonance has high sensitivity in low refractive index scenarios. • By adjusting the structural parameters of the photonic crystal fiber, the sensing performance of the sensor is optimized. • The sensor is not sensitive to the angle of the polishing plane. [ABSTRACT FROM AUTHOR]

Background: Tumor necrosis factor (TNF)-α inhibitors represented by Etanercept (a fusion protein containing soluble TNF receptor II (sTNFRII) and the Fc segment of human IgG1) play a pivotal role in Rheumatoid arthritis (RA) treatment. However, long-term use increases the risk of infection and tumors for their systemic inhibition of TNF-α, which disrupts the regular physiological function of this molecular. Mesenchymal stem cells (MSCs)-based delivery system provides new options for RA treatment with their "homing" and immune-regulation capacities, whereas inflammatory environment (especially TNF-α) is not conducive to MSCs' therapeutic effects by inducing apoptosis/autophagy. Here, we constructed a strain of sTNFRII-Fc-expressing MSCs (sTNFRII-MSC), aiming to offset the deficiency of those two interventions. Methods: Constructed sTNFRII-Fc lentiviral vector was used to infect human umbilical cord-derived MSCs, and sTNFRII-MSC stable cell line was generated by monoclonal cultivation. In vitro and vivo characteristics of sTNFRII-MSC were assessed by coculture assay and an acute inflammatory model in NOD/SCID mice. The sTNFRII-MSC were transplanted into CIA model, pathological and immunological indicators were detected to evaluate the therapeutic effects of sTNFRII-MSC. The distribution of sTNFRII-MSC was determined by immunofluorescence assay. Apoptosis and autophagy were analyzed by flow cytometry, western blot and immunofluorescence. Results: sTNFRII-Fc secreted by sTNFRII-MSC present biological activity both in vitro and vivo. sTNFRII-MSC transplantation effectively alleviates mice collagen-induced arthritis (CIA) via migrating to affected area, protecting articular cartilage destruction, modulating immune balance and sTNFRII-MSC showed prolonged internal retention via resisting apoptosis/autophagy induced by TNF-α. Conclusion: sTNFRII-Fc modification protects MSCs against apoptosis/autophagy induced by TNF-α, in addition to releasing sTNFRII-Fc neutralizing TNF-α to block relevant immune-inflammation cascade, and thus exert better therapeutic effects in alleviating inflammatory arthritis. [ABSTRACT FROM AUTHOR]

Objective: Currently, BP measurement devices are mainly cuff-based which are not portable or convenient for users. To simplify the measurement of BP, this article proposed a new framework for noninvasive BP estimation using single-channel PPG signals. Methods: Various PPG features that may be related to BP were extracted and a filter-wrapper collaborated feature selection method was used for rejecting irrelevant and redundant features. The features that maximize the correlation with BP were finally selected as the BP-oriented IFS, and a new LASSO-LSTM model was designed to estimate BP from the IFS. Results: Experiments were conducted on a public dataset and a self-collected clinical dataset, respectively. Results demonstrated that the proposed method is superior to previously reported methods in the literature, giving a mean absolute error of 4.95 mmHg for SBP and 3.15 mmHg for DBP which complies with the standard of the AAMI. Conclusion: The proposed filter-wrapper collaborated feature selection method could effectively reject weak correlation and redundant features, and the designed LASSO-LSTM model is capable of learning complicated nonlinear relations between the selected IFS and BP. The proposed method shows improved accuracy of noninvasive BP estimation. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) image classification because of their powerful feature extraction ability and high performance. However, extracting discriminative features with limited training samples is still a challenge. Moreover, some samples may be image edge samples, which often contain multiple image categories, thus deteriorate classification accuracy. To address these issues, we propose a novel classification framework, named dense-connected global covariance network (DGCNet) with edge sample constraint (ESC). First, a dense-connected sub-network was designed, which can connect different convolutional layers of conventional CNN to strengthen feature propagation, encourage feature reuse, and alleviate gradient vanishing problem. Then, a global covariance pooling layer was introduced to fully exploit the second-order information of deep features and reduce the number of training parameters. Finally, an ESC strategy was integrated into DGCNet to further improve the classification performance by assigning a smaller weight to edge samples than non-edge samples during the training process. Experimental results on two datasets demonstrated that the proposed method achieves better classification results than several popular classification methods with limited training samples. [ABSTRACT FROM AUTHOR]

Recent studies have shown that changes in human facial skin color due to varying blood flows can be recorded on videos by consumer-level cameras and, interestingly, can be used to measure pulse rates in a noncontact manner. However, it remains unclear whether the video-based pulse rate detection is feasible when videos are recorded in unstable lighting conditions. To address unstable illumination, a novel algorithm named adaptive spatiotemporal homomorphic filtering (ASTHF) is proposed in this article, with a focus on the separation of pulse signals and illumination-induced noise. This method first employs the homomorphic filtering framework to make illumination-induced noise in a video linearly separable. Subsequently, it incorporates a spatiotemporal filter into the framework, aiming to remove illumination-induced noise from both the time domain and the space domain of the video. To adapt to diverse illumination in the real world, the filter parameters are determined adaptively according to the sparsity in frequency spectrum of pulse signals. ASTHF is tested on two data sets, including 406 video samples. The test results show that ASTHF has stronger robustness to unstable illumination than state-of-the-art methods, improving the Pearson’s correlation coefficient by 5% in a challenging scenario compared with the best contrasting method. The proposed method demonstrates promise to achieve noncontact pulse rate detection from facial videos involving unsteady illumination. [ABSTRACT FROM AUTHOR]

Objective: Modern consumer-level cameras can detect subtle changes in human facial skin color due to varying blood flow; they are beginning to be used as noncontact devices to detect pulse waves. Little, however, do we know about their capacity to perform pulse wave detection when the recorded faces are unstable. Methods: Here, we propose a novel method that can extract pulse waves from videos with drastic facial unsteadiness such as head twists and alternating expressions. The method first uses chrominance characteristics in multiple facial sub-regions to construct a raw pulse matrix. Subsequently, it employs a disturbance-adaptive orthogonal matching pursuit (DAOMP) algorithm to recover the underlying pulse matrix corrupted by facial unsteadiness. Results: To evaluate the efficacy of the method, we perform analyses on two datasets including 268 samples from 67 testing subjects. The results demonstrate that the proposed method outperforms state-of-the-art algorithms, especially in the terrain where drastic facial unsteadiness is present. Conclusion: The proposed framework shows promise to achieve videos-based noncontact pulse wave detection from both steady and unsteady faces recorded by consumer-level cameras. Significance: By employing the proposed method, disturbance robustness in noncontact pulse wave detection can be significantly improved. [ABSTRACT FROM AUTHOR]

Background: Left atrial enlargement (LAE) was reported to be associated with ischemic stroke and its recurrence. Limited data are available on the relationship of LAE and cardiogenic cerebral embolism (CCE). Our aim is to access the association of left atrial size and the recurrence of ischemic stroke in CCE. Methods: We prospectively included 303 CCE patients who underwent transthoracic echocardiography (TTE). Left atrial size was estimated with left atrial diameter (LAD), diameter/height (LAD/H), and left atrial diameter/body surface area (LAD/BSA). The endpoint was one‐year recurrent ischemic stroke. Cox proportional hazard models were performed to access the association between left atrial size and recurrent ischemic stroke. Results: During follow‐up, 27 patients suffered recurrent ischemic stroke. In multivariate COX regression models adjusted for confounders including age, gender, hypertension, diabetes, and history of stroke or transient ischemic attack (TIA), platelet count, fasting blood glucose (FBG), antithrombotic drugs at discharge, stroke volume, and cardiac output, LAD, LAD/H, and LAD/BSA all were independent risk factors of recurrent ischemic stroke [LAD: HR 1.065, 95% CI (1.006–1.128), p =.029; LAD/H: HR 1.157, 95% CI (1.066–1.255), p <.001; LAD/BSA: HR 1.128, 95% CI (1.059–1.202), p <.001]. Receiver‐operator characteristic curves showed that LAD/BSA had better predicting effect. The area under the curve (AUC) was 0.543 [95%CI (0.444–0.642), p =.461) for LAD, 0.626 [95%CI (0.530–0.723), p =.03] for LAD/H, and 0.655 [95%CI (0.558–0.752), p =.008] for LAD/BSA. Conclusion: LAE is an independent risk factor for one‐year recurrence of ischemic stroke in patients with CCE. [ABSTRACT FROM AUTHOR]

Macrophages maintain a dynamic balance in physiology. Various known or unknown microenvironmental signals influence the polarization, activation and death of macrophages, which creates an imbalance that leads to disease. Rheumatoid arthritis (RA) is characterized by the massive infiltration of a variety of chronic inflammatory cells in synovia. Abundant activated macrophages found in RA synovia are an early hallmark of RA, and the number of these macrophages can be decreased after effective treatment. In RA, the proportion of M1 (pro‐inflammatory macrophages) is higher than that of M2 (anti‐inflammatory macrophages). The increased pro‐inflammatory ability of macrophages is related to their excessive activation and proliferation as well as an enhanced anti‐apoptosis ability. At present, there are no clinical therapies specific to macrophages in RA. Understanding the mechanisms and functional consequences of the heterogeneity of macrophages will aid in confirming their potential role in inflammation development. This review will outline RA‐related macrophage properties (focus on polarization, metabolism and apoptosis) as well as the origin of macrophages. The molecular mechanisms that drive macrophage properties also be elucidated to identify novel therapeutic targets for RA and other autoimmune disease. [ABSTRACT FROM AUTHOR]

The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection. [ABSTRACT FROM AUTHOR]

• A novel region-level SAR image classification algorithm is proposed in this paper. • A RCC-MRF model which utilizes deep features and spatial constrains is proposed. • Experimental results demonstrate the effectiveness of the proposed algorithm. The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms. [ABSTRACT FROM AUTHOR]

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples. [ABSTRACT FROM AUTHOR]

In this letter, a pointwise approach using stereo-graph cuts (SGC) with spatio-temporal information is proposed for synthetic aperture radar (SAR) image change detection. The SGC is based on stereo-graph, which is designed to connect the local maximum pixels on two SAR images and can be used to capture the spatio-temporal information. With the support of stereo-graph, a novel SGC energy function is presented to quantify the spatio-temporal information and implicitly measure the difference information between the SAR images. Therefore, the change detection results can be obtained by minimizing the energy function with graph cuts technology. Experimental results on real SAR datasets confirm the validity of the proposed approach in which SGC and spatio-temporal information offer great contributions on improving the robustness and accuracy of detection. [ABSTRACT FROM AUTHOR]

Graphical abstract Figure. Mechanism of CP-25 combined with MTX/LEF to ameliorate AA rats In the one hand, CP-25 alone and in combination blocked the activation of P38, ERK, and JNK. CP-25 combined with MTX/LEF exerts synergistic effects to inhibit inflammation of AA rats by inhibiting the MAPK pathway. In the other hand, CP-25 inhibited GRK2 translocation in AA rats which further restored the inhibition of GRK2 on p-ERK. CP-25 synergized with MTX/LEF through GRK2-induced inhibition of p-ERK, thereby suppressing inflammation in AA rats. Highlights • CP-25 combined with low-dose MTX/LEF showed equivalent therapeutic effects of high-dose MTX/LEF. • CP-25 decreased the expression of p-ERK through the inhibition on GRK2 membrane expression. • CP-25 has potential as a useful drug for the combined treatment of patients with RA. Abstract Objective CP-25 attenuates arthritis progression in animal models by inhibiting G protein-coupled receptor kinase 2 (GRK2) membrane expression. This study compared groups treated with high-dose methotrexate (MTX)/leflunomide (LEF) and CP-25 combined with low-dose MTX/LEF in an adjuvant-induced arthritis (AA) rat model and investigated possible mechanisms. Methods AA was induced in rats via complete Freund's adjuvant. Experimental groups were divided into a normal group; vehicle group; monotherapy groups, including CP-25 (50 mg/kg), MTX (0.25, 0.5 mg/kg), and LEF (5, 10 mg/kg); and CP-25 (50 mg/kg)-combined MTX (0.25 mg/kg)/LEF (5 mg/kg) groups. We measured cytokine levels, phosphorylation and protein expression, and interactions between proteins. The role of GRK2 on phosphorylated extracellular signal-regulated kinase (p-ERK) was determined via GRK2-siRNA using a high content imaging system. Results Therapeutic effects, including pathology and cytokine balance, were equivalent between the CP-25-combination groups and the high-dose MTX/LEF groups. P38, ERK, and c-Jun N-terminal kinase (JNK) activation in AA fibroblast-like synoviocytes (FLS) was reduced in the treatment groups; GRK2 expression was only inhibited in the CP-25 group. Interactions between GRK2 and p-ERK decreased in the vehicle group and were restored in the CP-25 group. GRK2 membrane expression and p-ERK nuclear expression increased in FLS pre-treated with tumour necrosis factor alpha and stimulated with prostaglandin E2. Nuclear expression of p-ERK increased in GRK2-siRNA FLS. Conclusion Equivalent therapeutic effects were observed between CP-25-combination groups and high-dose MTX/LEF groups. CP-25 inhibited p-ERK by reducing the membrane expression of GRK2 in FLS from AA rats. [ABSTRACT FROM AUTHOR]

In this article, a novel pointwise approach is proposed for change detection in bi-temporal synthetic aperture radar (SAR) images using stereograph model. Due to the fact that SAR image suffers from the speckle noise, a pointwise approach based on a set of characteristic points only, not on the whole pixels, seems to be more efficient. Moreover, the correlations of neighbourhood points which have different locations in bi-temporal SAR images should be studied to repress the speckle in change detection. Therefore, the stereograph model, which extends the graph model to three-dimensional space, is designed to connect the local maximum pixels on bi-temporal SAR images and can be used to capture the multiple-span neighbourhood information from the edges. Furthermore, a specialized change measure function is presented to quantify the neighbourhood information from stereograph model, and thus, a novel nondense difference image (NDI) is generated. Finally, a traditional classification method is used to analyse the NDI into changed class and unchanged class. Experiments on real SAR images show that the proposed NDI can improve separability between changed and unchanged areas, and the final results possess high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images. [ABSTRACT FROM AUTHOR]

The convolutional neural network has been widely used in synthetic aperture radar (SAR) image classification, for it can learn discriminative features from massive amounts of data. However, it is short of distinctive learning mechanisms for different regions in SAR images. In this letter, a novel architecture called multi-depth convolutional neural network (Multi-depth CNN) is proposed which can select different levels of features for classification. Differing from classical convolutional neural network, Multi-depth CNN adopts a piecewise back-propagation method to optimize the network. Meanwhile, compared with classical convolutional neural network, the proposed network can reduce the training time effectively. Experimental results on two datasets demonstrate that the proposed network can achieve better classification accuracy compared with some state-of-art algorithms. [ABSTRACT FROM AUTHOR]

Natural organic matter influences the carbon nanotube transport in aqueous environments. The role of bound humic acid (HA) on carbon nanotubes and unbound HA in bulk solution in the aggregation and deposition of carboxylated multi-walled carbon nanotubes (C-MWNTs) was examined in NaCl and CaCl 2 electrolyte solution. Time-resolved dynamic light scattering and quartz crystal microbalance with dissipation monitoring were employed to investigate the C-MWNT aggregation and deposition kinetics, respectively. The critical coagulation concentration (CCC) of C-MWNTs is 30 mM in NaCl and 3 mM in CaCl 2 . The bound HA results in CCCs of 32 mM in NaCl and 2.9 mM in CaCl 2 . However, the existing unbound HA causes much slower aggregation in both NaCl and CaCl 2 electrolytes and results in CCCs of 86 mM in NaCl and 5.8 mM in CaCl 2 . The HA adsorption experiment confirms the additional adsorption of unbound HA in the presence of cations, which can increase the steric effect between C-MWNTs. The more negative charge of C-MWNTs in the presence of unbound HA also stabilizes the suspension. In contrast, the bound HA on C-MWNTs has a more remarkable effect on the deposition rate on the SiO 2 surface than the unbound HA. Bound HA changes the C-MWNT surface functional groups, leading to differences in the interaction between C-MWNTs and the SiO 2 surface. Hence, the C-MWNTs dispersed by their covalently bonded oxygen-containing groups on the carbon framework and dispersed by the bound HA show nearly the same aggregation rates but quite different deposition rates. The additional unbound HA adsorption does not change the surface functional groups or the changing trend of the CNT deposition rate. Distinguishing the role of bound and unbound HA in the aggregation and deposition of carbon nanomaterials is important to predict their transport in various natural waters. [ABSTRACT FROM AUTHOR]

The aggregation and deposition of carbon nanotubes (CNTs) determines their transport and fate in natural waters. Therefore, the aggregation kinetics of humic-acid treated multi-walled carbon nanotubes (HA-MWCNTs) was investigated by time-resolved dynamic light scattering in NaCl and CaCl 2 electrolyte solutions. Increased ionic strength induced HA-MWCNT aggregation due to the less negative zeta potential and the reduced electrostatic repulsion. The critical coagulation concentration (CCC) values of HA-MWCNTs were 80 mmol/L in NaCl and 1.3 mmol/L in CaCl 2 electrolyte, showing that Ca 2 + causes more serious aggregation than Na + . The aggregation behavior of HA-MWCNTs was consistent with Derjaguin–Landau–Verwey–Overbeek theory. The deposition kinetics of HA-MWCNTs was measured by the optical absorbance at 800 nm. The critical deposition concentrations for HA-MWCNT in NaCl and CaCl 2 solutions were close to the CCC values, therefore the rate of deposition cannot be increased by changing the ionic strength in the diffusion-limited aggregation regime. The deposition process was correlated to the aggregation since larger aggregates increased gravitational deposition and decreased random Brownian diffusion. HA-MWCNTs hydrodynamic diameters were evaluated at 5, 15 and 25°C. Higher temperature caused faster aggregation due to the reduced electrostatic repulsion and increased random Brownian motion and collision frequency. HA-MWCNTs aggregate faster at higher temperature in either NaCl or CaCl 2 electrolyte due to the decreased electrostatic repulsion and increased random Brownian motion. Our results suggest that CNT aggregation and deposition are two correlated processes governed by the electrolyte, and CNT transport is favored at low ionic strength and low temperature. [ABSTRACT FROM AUTHOR]

Inflammation, as a feature of rheumatoid arthritis (RA), leads to the activation of endothelial cells (ECs). Activated ECs induce atherosclerosis through an increased expression of leukocyte adhesion molecules. Endothelial dysfunction (ED) is recognized as a failure of endothelial repair mechanisms. It is also an early preclinical marker of atherosclerosis and is commonly found in RA patients. RA is now established as an independent cardiovascular risk factor, while mechanistic determinants of ED in RA are still poorly understood. An expanding body of study has shown that EC at a site of RA is both active participant and regulator of inflammatory process. Over the last decade, a role for endothelial dysfunction in RA associated with cardiovascular disease (CVD) has been hypothesized. At the same time, several maintenance drugs targeting this phenomenon have been tested, which has promising results. Assessment of endothelial function may be a useful tool to identify and monitor RA patients. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic autoimmune disease. Enhanced G protein coupled receptor kinase 2 (GRK2) translocation and prostaglandin E4 receptor (EP4) desensitization play a critical role in fibroblast-like synoviocytes (FLS) dysfunction. Paeoniflorin-6'O-benzene sulfonate (CP-25) exerts a protective effect in arthritis in the RA animal models. To demonstrate the role of Gβγ in EP4 desensitization and the mechanisms of CP-25 that protects FLS in RA, RA-FLS and adjuvant-induced arthritis (AA-FLS) were isolated from synovium of RA patients and AA rats. RA-FLS, AA-FLS and MH7A were treated with CP-25, Gβγ agonist and antagonist. The cell membrane expression of EP4, GRK2, and Gβγ were detected using western blot analysis. Co-immunoprecipitation (Co-IP) and immunofluorescence were adopted to detect the interactions of GRK2-Gβγ, GRK2-EP4, and EP4-Gβγ. Cell Counting Kit-8 and Transwell assay were used to analyze the proliferation and migration of the FLS. An increased membrane expression of GRK2 and Gβγ, enhanced GRK2-Gβγ interaction and decreased EP4 membrane expression in the RA synovial tissue were identified. In vitro, prostaglandin E2 (PGE2) enhanced the proliferation and migration of FLS. CP-25 exhibited an inhibition effect similar to Gβγ inhibitor, which downregulated GRK2-EP4 interaction, blocked the translocation of GRK2, and reversed EP4 desensitization, leading to the suppression of the proliferation and migration induced by PGE2. These results elucidated that an enhanced GRK2-Gβγ interaction was involved in the EP4 desensitization and dysfunction. CP-25 regulated EP4-GRK2-Gβγ signaling and re-sensitized EP4 by inhibiting GRK2-Gβγ interaction. The regulation of EP4-Gβγ-GRK2 signaling may be a novel potential therapeutic target in RA. [ABSTRACT FROM AUTHOR]

1. Entinostat, also known as SNDX-275 or MS-275, is a novel, potent, orally bioavailable, class I selective histone deacetylase inhibitor. Pre-clinical data has show that MS-275 can enhance the activity of lapatinib in HER2+ metastatic inflammatory and non-inflammatory breast cancer. This study examined whether oral administration of MS-275 to the rats with lapatinib led to any pharmacokinetic interactions. 2. To evaluate pharmacokinetic interaction of MS-275 and lapatinib in rat, a sensitive and simple LC-MS method was developed to simultaneously determine MS-275 and lapatinib in rat plasma with carbamazepine as internal standard (IS). Eighteen rats were divided randomly into three groups, lapatinib group (lapatinib 15 mg/kg, n = 8), MS-275 group (MS-275 15 mg/kg, n = 8) and co-administration group (MS-275 15 mg/kg and lapatinib 15 mg/kg, n = 8). 3. There was no statistical pharmacokinetics difference for MS-275 in MS-275 group and co-administration group; the lapatinib could not influence the pharmacokinetic profile of MS-275 in rats. However, there is a statistical pharmacokinetics difference between lapatinib in the lapatinib group and co-administration group, when co-oral administration MS-275 with lapatinib, AUC increased from 2375.5 to 9900.3 ng/mL h ( p < 0.05), Cmax increased from 538.0 to 2578.2 ng/mL ( p < 0.01), CL decreased from 6.2 to 1.7 L/h/kg ( p < 0.01). 4. These data indicate MS-275 could obviously influence the pharmacokinetic profile of lapatinib in rats, which might cause drug-drug interactions in humans when using lapatinib with MS-275. Further investigations should be carried out to elucidate the synergistic mechanisms between the two drugs. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new metric called despeckling structure loss (DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural features such as edges and textures. The ratio image of a synthetic aperture radar (SAR) image to its despeckled result can clearly indicate the loss of structural features caused by the despeckling process. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of structural features in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points. The DSL is shown to be a monotonic function of structure loss bounded between the best and worst cases, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. Experimental evaluations of the proposed metric have been carried out on simulated SAR images including one generated by SAR raw signal simulator. Despeckled results using five typical algorithms clearly demonstrate the efficiency of the DSL metric. In comparison, the commonly used despeckling metrics including the signal-to-mean-square error ratio, the edge correlation index, the Pratt’s figure of merit, the structural similarity and the equivalent number of looks of ratio images, fail to keep a consistency with the structure loss shown in despeckled results as well as ratio images. [Copyright &y& Elsevier]

A random beamforming (RBF) technique is proposed to achieve omnidirectional coverage for broadcast channels in multiple-antenna systems. In the proposed scheme, the time and frequency resources for communication are divided into many time–frequency blocks (TFBs). A random pattern is employed on each TFB, with the average power in all directions over many TFBs being equal. The design criteria and detailed design of the random pattern sequence are given. We propose a basic RBF scheme and an Alamouti-enhanced scheme, as well as the corresponding structures of the transmitter and the receiver. Simulations are carried out for an eight-element unitary linear array with half-waveform separation in a fast-fading channel, showing that the proposed scheme greatly outperforms the cyclic delay diversity scheme and is robust to calibration errors and failures of radio-frequency chains. [ABSTRACT FROM PUBLISHER]

Abstract: The solubility of levofloxacin in six solvents including ethanol, 1-propanol, 2-propanol, 1-butanol, acetic acid and ethyl acetate in the temperature range of 288.15–328.15K at atmospheric pressure was measured by a balance method. It is indicated that the solubility data of levofloxacin in selected solvents increase with increasing temperature. The experimental solubilities were correlated by the modified Apelblat equation which fits well with the experimental data. Further the molecular modeling studies indicate that the higher solubility of levofloxacin in acetic acid is due to the stronger hydrogen bonds between the solute and solvent molecules. The dissolution enthalpy, dissolution entropy and the molar Gibbs energy were predicted. Finally, the solubility data were correlated using two local composition models of NRTL 1 and UNIQUAC. [Copyright &y& Elsevier]

Andrographolide has a low aqueous solubility and oral bioavailability, which limits its clinical application. Reform the dosage forms of andrographolide to improve its aqueous solubility and oral bioavailability. The formulation, characterisation, stability, anti-inflammatory effect, pharmacokinetics and oral toxicity of andrographolide-loaded microemulsion, were studied. An formulation of O/W microemulsion consisting of an oil phase of isopropyl myristate, a surfactant phase of Tween 80, a co-surfactant of alcohol, and water was found to be ideal, with mean droplet size of 15.9 nm, a high capacity of solubilisation for andrographolide (8.02 mg mL−1). Such an andrographolide-loaded microemulsion is stable by monitoring the time, temperature and gravity-dependent change, and has a much better anti-inflammatory effect and a higher biological availability than andrographolide tablets. Besides, it also shows a very low acute oral toxicity. The andrographolide-loaded microemulsion is a promising dosage form of andrographolide. [ABSTRACT FROM AUTHOR]

Wavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved. [Copyright &y& Elsevier]

The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is characterized by the massive infiltration of various chronic inflammatory cells in synovia. In synovial fluid of patients with RA, M1 macrophages are dominant among all subtypes of macrophages, the mechanisms of macrophages polarization imbalance in RA has not been fully illuminated. The prostaglandin E2 (PGE2) augments M2 polarization in part via the cyclic adenosine monophosphate (cAMP)-cyclic AMP responsive element binding (CREB) signaling. However, previous study found constant stimulus of PGE2 on fibroblast-like synovial cells of adjuvant arthritis rats induced the decrease of cAMP, which is primarily caused by G protein-coupled receptor kinase 2 (GRK2)-induced EP4 over- desensitization. Whether GRK2 mediated-EP4 over-desensitization reduces the level of cAMP and inhibits M2 polarization in RA is unclear. Here we observed M1 macrophages were dominant in peritoneal macrophages (PMs), bone-marrow-derived macrophages (BMMs) and synovial macrophages of collagen-induced arthritis (CIA) mice. PGE2 stimulated M2 polarization via the EP4-cAMP-CREB in normal mice, while failed to promote M2 polarization in the PMs of CIA mice. Further, we found the EP4 over-desensitization stimulated by PGE2 induced abnormal PGE2-cAMP-CREB signaling as well as the imbalance of macrophage polarization. Targeted disruption of GRK2 in Raw264.7 (RAW) through GRK2 siRNA or CRISPR/Cas9 downregulated the M1 macrophage markers, upregulated the M2 macrophage markers and the EP4 membrane localization. The reduced M1/M2 ratio and increased p-CREB expression were observed in BMMs and PMs of GRK2+/− mice. This study highlighted a novel role of GRK2 in regulating macrophages function in RA and provided new idea for precision treatment of RA. [ABSTRACT FROM AUTHOR]

Background: Cancer is the leading cause of death among older adults. Although the integration of immunotherapy has revolutionized the therapeutic landscape of cancer, the complex interactions between age and immunotherapy efficacy remain incompletely defined. Here, we aimed to elucidate the relationship between aging and immunotherapy resistance. Methods: Flow cytometry was performed to evaluate the infiltration of immune cells in the tumor microenvironment (TME). In vivo T cell proliferation, cytotoxicity and migration assays were performed to evaluate the antitumor capacity of tumor antigen-specific CD8+ T cells in mice. Real-time quantitative PCR (qPCR) was used to investigate the expression of IFN-γ-associated gene and natural killer (NK)-associated chemokine. Adoptive NK cell transfer was adopted to evaluate the effects of NK cells from young mice in overcoming the immunotherapy resistance of aged mice. Results: We found that elderly patients with advanced non-small cell lung cancer (aNSCLC) aged ≥ 75 years exhibited poorer progression-free survival (PFS), overall survival (OS) and a lower clinical response rate after immunotherapy. Mechanistically, we showed that the infiltration of NK cells was significantly reduced in aged mice compared to younger mice. Furthermore, the aged NK cells could also suppress the activation of tumor antigen-specific CD8+ T cells by inhibiting the recruitment and activation of CD103+ dendritic cells (DCs). Adoptive transfer of NK cells from young mice to aged mice promoted TME remodeling, and reversed immunotherapy resistance. Conclusion: Our findings revealed the decreased sensitivity of elderly patients to immunotherapy, as well as in aged mice. This may be attributed to the reduction of NK cells in aged mice, which inhibits CD103+ DCs recruitment and its CD86 expression and ultimately leads to immunotherapy resistance. [ABSTRACT FROM AUTHOR]

Histone deacetylase 6 (HDAC6), as the key regulatory enzyme, plays an important role in the development of the nervous system. More and more studies indicate that HDAC6 has become a promising therapeutic target for CNS diseases. Herein we designed and synthesized a series of novel HDAC6 inhibitors with benzothiadiazinyl systems as cap groups and evaluated their activity in vitro and in vivo. Among them, compound 3 exhibited superior selective inhibitory activity against HDAC6 (IC50 = 5.1 nM, about 30‐fold selectivity over HDAC1). The results of docking showed that compound 3 can interact well with the key amino acid residues of HDAC6. Compound 3 showed lower cytotoxicity (20 μM to SH‐SY5Y cells, inhibition rate = 25.75%) and better neuroprotective activity against L‐glutamate‐induced SH‐SY5Y cell injury model in vitro. Meanwhile, compound 3 exhibited weak cardiotoxicity (10 μM hERG inhibition rate = 17.35%) and possess good druggability properties. Especially, compound 3 could significantly reduce cerebral infarction from 49.87% to 32.18%, and similar with butylphthalide in MCAO model, indicating potential clinical application prospects for alleviating ischemic stroke‐induced brain infarction. [ABSTRACT FROM AUTHOR]

Video magnification reveals important and informative subtle variations in the world. These signals are often combined with large motions which result in significant blurring artifacts and haloes when conventional video magnification approaches are used. To counter these issues, this paper presents an amplitude-based filtering algorithm that can magnify small changes in video in presence of large motions. We seek to understand the amplitude characteristic of small changes and large motions with the goal of extracting accurate signals for visualization. Based on spectrum amplitude filtering, the large motions can be removed while small changes can still be magnified by Eulerian approach. An advantage of this algorithm is that it can handle large motions, whether they are linear or nonlinear. Our experimental results show that the proposed method can amplify subtle variations in the presence of large motion, as well as significantly reduce artifacts. We demonstrate the presented algorithm by comparing to the state-of-the-art and provide subjective and objective evidence for the proposed method. [ABSTRACT FROM AUTHOR]

Inhaled atmospheric fine particulate matter (PM 2.5) includes soluble and insoluble fractions, and each fraction can interact with cells and cause adverse effects. PM 2.5 samples were collected in Jinan, China, and the soluble and insoluble fractions were separated. According to physiochemical characterization, the soluble fraction mainly contains water-soluble ions and organic acids, and the insoluble fraction mainly contains kaolinite, calcium carbonate and some organic carbon. The interaction between PM 2.5 and model cell membranes was examined with a quartz crystal microbalance with dissipation (QCM-D) to quantify PM 2.5 attachment on membranes and membrane disruption. The cytotoxicity of the total PM 2.5 and the soluble and insoluble fractions, was investigated. Negatively charged PM 2.5 can adhere to the positively charged membranes and disrupt them. PM 2.5 also adheres to negatively charged membranes but does not cause membrane rupture. Therefore, electrostatic repulsion does not prevent PM 2.5 attachment, but electrostatic attraction induces remarkable membrane rupture. The human lung epithelial cell line A549 was used for cytotoxicity assessment. The detected membrane leakage, cellular swelling and blebbing indicated a cell necrosis process. Moreover, the insoluble PM 2.5 fraction caused a higher cell mortality and more serious cell membrane damage than the soluble fraction. The levels of reactive oxygen species (ROS) enhanced by the two fractions were not significantly different. The findings provide more information to better understand the mechanism of PM 2.5 cytotoxicity and the effect of PM 2.5 solubility on cytotoxicity. Image 1 [ABSTRACT FROM AUTHOR]

• The ascending and descending phases of BOLD signal modulates face recognition. • Inter-regional phase lag distinguishes the face processing and frontoparietal systems. • Inter-regional phase lag in the infra-slow frequency range is essential for cognitive performance. Low frequency phase synchronization is an essential mechanism of information communication among brain regions. In the infra-slow frequency range (<0.1 Hz), inter-regional phase lag is of importance for brain function (e.g., anti-phase between the default mode network and task positive network). However, the role of phase lag in cognitive processing remains unclear. Based on the frequency tagging experimental paradigm and functional magnetic resonance imaging (fMRI) technique, we investigated inter-regional phase lag and phase coherence using a face recognition task (n = 30, 15 males/15 females). Phase coherence within the face processing system was significantly increased during task state, highlighting the importance of regular inter-regional phase relationship for face recognition. Moreover, results showed decreased phase lag within the core and extended face areas (face processing system) and increased phase lag between the face processing system and frontoparietal network, indicating a reorganization of inter-regional relationships of the two systems. Inter-regional phase lag was modulated by the task at ascending and descending phases of the fMRI signal, suggesting a phase-dependent inter-regional relationship. Furthermore, phase lags between visual cortex and amygdala and between visual cortex and motor area were positively related to reaction time, indicating better task performance depends on both rapid emotional detection pathway and visual-motor pathway. Overall, inter-regional phase synchronization in the infra-slow frequency range is of important for effective information communication and cognitive performance. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease (IBD) is a group of intestinal inflammatory diseases characterized by chronic, recurrent, remitting, or progressive inflammation, which causes the disturbance of the homeostasis between immune cells, such as macrophages, epithelial cells, and microorganisms. Intestinal macrophages (IMs) are the largest population of macrophages in the body, and the abnormal function of IMs is an important cause of IBD. Most IMs come from the replenishment of blood monocytes, while a small part come from embryos and can self-renew. Stimulated by the intestinal inflammatory microenvironment, monocyte-derived IMs can interact with intestinal epithelial cells, intestinal fibroblasts, and intestinal flora, resulting in the increased differentiation of proinflammatory phenotypes and the decreased differentiation of anti-inflammatory phenotypes, releasing a large number of proinflammatory factors and aggravating intestinal inflammation. Based on this mechanism, inhibiting the secretion of IMs' proinflammatory factors and enhancing the differentiation of anti-inflammatory phenotypes can help alleviate intestinal inflammation and promote tissue repair. At present, the clinical medication of IBD mainly includes 5-aminosalicylic acids (5-ASAs), glucocorticoid, immunosuppressants, and TNF-α inhibitors. The general principle of treatment is to control acute attacks, alleviate the condition, reduce recurrence, and prevent complications. Most classical IBD therapies affecting IMs function in a variety of ways, such as inhibiting the inflammatory signaling pathways and inducing IM2-type macrophage differentiation. This review explores the current understanding of the involvement of IMs in the pathogenesis of IBD and their prospects as therapeutic targets. [ABSTRACT FROM AUTHOR]

Objective: Previous studies reported a lower platelet count (PC) level and a higher mean platelet volume (MPV) level in severe ischemic stroke. We define MPV/PC as the platelet ratio (PR). This study investigates the relationship between PR and 90-day outcome of large artery atherosclerosis (LAA) stroke. Materials and Methods: A retrospective analysis was conducted on 338 LAA patients in two hospitals by looking up the electronic medical record (EMR). The 90-day functional outcome was divided into good and poor according to the modified Rankin Scale (mRS) score. Results: Multivariate logistic regression analysis of the First Affiliated Hospital of Wenzhou Medical University (Hospital 1) revealed that PR (OR1.083, 95%CI [1.044, 1.123], P < 0.001) and National Institutes of Health Stroke Scale (NIHSS) score were independent predictors of the poor outcome. Analysis from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University (Hospital 2) showed that PR (OR1.071, 95%CI [1.039, 1.104], P < 0.001), NIHSS score and age were independent adverse prognostic predictors. Furthermore, higher quartiles of PR presented an incremental OR value compared to the first quartile PR in multivariate regression. In Hospital 1, the cut-off value of PR to predict 90-day outcome was 39.23 (sensitivity: 74.24%, specificity: 55.17%, area under the curve (AUC): 0.694, P < 0.001). In Hospital 2, the optimal cut-off value of PR was 46.08 (sensitivity: 61.40%, specificity: 73.74%, AUC: 0.695, P < 0.001). Conclusions: Platelet ratio on admission is an independent predictor for 90-day outcome in LAA stroke patients and it has a good predictive effect. [ABSTRACT FROM AUTHOR]

Non-traditional stable isotopes, with no clear definition yet, generally refer to isotopes beyond C, H, O, N, and S with small variations in natural stable isotopic compositions, such as isotopes of transition and heavy metal elements. With the rapid development in the multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) technique, non-traditional stable isotopes have emerged as powerful tracers in geosciences, archaeology, anthropology, and environmental sciences. This article summarizes the recent advances in the analysis of non-traditional stable isotopes by MC-ICP-MS. We review recent efforts on improvement of the sensitivity of MC-ICP-MS and optimization of the sample preparation procedures. Special attention is paid to the “emerging” stable isotopes, e.g., vanadium (V), barium (Ba), potassium (K), platinum (Pt), palladium (Pd), silver (Ag), cerium (Ce), erbium (Er), and silicon (Si). We also introduce some novel aspects of the application of non-traditional stable isotopes, e.g., in nanotechnology and environmental health studies. Finally, we discuss current challenges and give our perspectives in this active research area. [ABSTRACT FROM AUTHOR]

In this paper, a novel frequency domain multi-user detector is proposed for a time division-code division multiple access (TD-CDMA) up-link. Unlike conventional frequency domain detectors, the proposed detector first transforms the system matrix of TD-CDMA systems into a circulant matrix by cyclic truncation. It subsequently uses a new method to convert the circulant matrix into a frequency domain block diagonalized matrix through discrete Fourier transforms and permutations. Therefore, the proposed detector can utilize the channel frequency domain coherence to further decrease its computational complexity with a controlled performance loss. Moreover, a novel approach is proposed to calculate the frequency domain correlation matrix and matched filter. With the help of this novel approach, the proposed detector expresses significant complexity advantage over other frequency domain detectors for a real TD-CDMA system in a short-time-dispersive channel. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is among the most common malignant tumours, and effective therapeutic strategies are still lacking. While Corynoxine (Cory) can induce autophagy in neuronal cells, it remains unclear whether Cory has anti-tumour activities against pancreatic cancer. Methods: Two pancreatic cancer cell lines, Patu-8988 and Panc-1, were used. Effects of Cory were evaluated by cell viability analysis, EdU staining, TUNEL assay, colony formation assay, and flow cytometry. Quantitative PCR and Western blot were performed to analyse mRNA and protein levels, respectively. In vivo anti-tumour efficacy of Cory was determined by a xenograft model. Results: Cory treatment inhibited cell proliferation, induced endoplasmic reticulum (ER) stress, and triggered apoptosis in the pancreatic cancer cell lines. CHOP knockdown-mediated inhibition of ER stress alleviated the Cory-induced apoptosis but showed a limited effect on cell viability. Cory induced cell death partially via promoting reactive oxygen species (ROS) production and activating p38 signalling. Pretreatment with ROS scavenger N-acetylcysteine and p38 inhibitor SB203580 relieved the Cory-induced inhibition on cell growth. Cory remarkably blocked pancreatic tumour growth in vivo. Conclusions: Cory exerts an anti-tumour effect on pancreatic cancer primarily via ROS-p38-mediated cytostatic effects. Cory may serve as a promising therapeutic agent for pancreatic cancer. [ABSTRACT FROM AUTHOR]

Background. Gliomas are primary malignant brain tumors. Despite recent advances in surgery and clinical neuro-oncology, the prognosis of patients with glioma is still poor. Therefore, there is an urgent need to find new therapeutic drugs. Methods. Here, we have studied the anticancer effect of maslinic acid in glioma and explored its potential molecular mechanism. CCK-8, Ki67 immunofluorescence, and colony formation tests are used to detect the proliferation of glioma cells. Transwell and migration experiments are used to detect the function of cell invasion and migration, and RNA-seq was performed to identify differentially expressed genes. Western blot analysis helps us identify important signaling pathways. Finally, the anticancer effect of maslinic acid was confirmed in vivo through tumor xenografting experiments. Results. Our experiments obtained high-throughput data on the treatment of maslinic acid in glioma. We found that maslinic acid significantly inhibits the proliferation, invasion, and migration of glioma cells and promotes the apoptosis of glioma cells via suppressing MAPK signaling. Conclusions. This is the first time to analyze the mechanism of maslinic acid against glioma based on transcription. Our experiments show that maslinic acid may be a useful natural product for the treatment of glioma. [ABSTRACT FROM AUTHOR]

Background and Purpose: Abnormal kynurenine (Kyn) metabolism has been closely linked to the pathogenesis of rheumatoid arthritis (RA). The aims of this study were to investigate the role of tryptophan 2,3‐dioxygenase 2 (TDO2), a rate‐limiting enzyme that converts tryptophan (Trp) to Kyn, in regulating fibroblast‐like synoviocyte (FLS)‐mediated synovial inflammation in autoimmune arthritis. Experimental Approach The expression of TDO2 was determined by immunohistochemistry, confocal laser scanning fluorescence microscopy, imaging flow cytometry and Western blot. TDO2 activity was tested by HPLC and colorimetric assay. TDO2 siRNA and TDO2 inhibitor 680C91 were used to inhibit TDO2 in AA‐FLS function in vitro. A rat model of adjuvant‐induced arthritis (AA) was used to evaluate the in vivo effect of allopurinol (Allo), a TDO2 inhibitor. Key Results: TDO2 expression was strongly increased in synovial tissue and FLS of RA and AA. Immune cells were found to express high amount of TDO2 proteins at the peak stage of AA. Pharmacological inhibition or knockdown of TDO2 in AA‐FLS resulted in a reduced proliferation, secretion, migration and invasion. Kyn restored the inhibitory effect of TDO2 inhibition on activation of AA‐FLS. Allo treatment ameliorated the arthritis severity and decreased the activity of TDO2. Conclusion and Implications: Our results suggest that elevated TDO2 expression may contribute to synovial inflammation and joint destruction during arthritis. Therefore, targeting TDO2 activity and the Kyn pathway of Trp degradation may represent a potential therapeutic strategy in RA. [ABSTRACT FROM AUTHOR]

Nanoplastics have attracted significant attention due to their harmful effects on the environment and organisms. However, accurately detecting and quantifying nanoplastics that are ingested by organisms poses a challenge. Conventional organic fluorescent-labeled nanoplastics have reduced labeling efficiency due to aggregation-caused quenching (ACQ), leading to artifacts caused by leakage of fluorescent molecules. To address these limitations, this study utilized AIE fluorescence for internally labeling polystyrene nanoparticles. AIE-active molecules do not emit fluorescence in dilute solutions, thus preventing accuracy issues associated with fluorescence leakage. Three model plastics (TPE@PS) of varying particle sizes were synthesized via the swelling method. The preparation parameters were optimized, followed by stability tests conducted on TPE@PS. Fluorescence microscopy imaging revealed significant uptake of TPE@PS by RAW246.7 cells. A successful standard curve of fluorescence intensity-concentration was established successfully and utilized to accurately quantify the amount of TPE@PS that was ingested by RAW246.7 cells. The results indicate a consistent increase with increasing concentration in TPE@PS uptake across all particle sizes during the fixed exposure duration of 48 h. Furthermore, RAW264.7 cells exhibited a higher propensity to uptake smaller TPE@PS particles. Flow cytometry was also employed to validated the suitability of TPE@PS as a model nanoplastic for tracing purposes. • AIE-labeled nanoplastics (TPE@PS) overcome drawbacks of conventional dyes. • TPE@PS accurately quantifies cellular uptake with minimal misclassification. • Fluorescence and flow cytometry experiments demonstrate a high level of agreement. • TPE@PS could clarify the pathways through which organisms internalise nanoplastics. [ABSTRACT FROM AUTHOR]

Recently, Convolutional Neural Network (CNN) has achieved some success in synthetic aperture radar (SAR) image classification. This outstanding performance mainly depends on a large number of training samples, but achieved satisfactory classification results with limited training samples remains a challenge. To address this problem, we propose a dual-scale Siamese densely connected network with Markov Random Fields (DS-SDCNet-MRF) for single-polarization SAR image classification. First, the Siamese densely connected network (SDCNet) is proposed to fully extract discriminative features under limited samples. Then, the proposed DS-SDCNet is constructed with two SDCNets of different scales to produce complementary classification results. Among them, the large-scale SDCNet has a better classification result in the homogeneous region, while the small-scale SDCNet tends to provide good detail preservation. Finally, an improved MRF model which combined the category probability information is proposed to further improve the classification performance. Experimental results on simulated and real single-polarization SAR data demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods with limited samples. [ABSTRACT FROM AUTHOR]

A novel cinnamic acid-based dipeptide is designed and prepared from cinnamic acid and glycylglycine. In aqueous solution, the molecules can self-assemble into vesicular structures, which were characterized in detail by transmission electron microscopy, scanning electron microscopy, and dynamic light scattering. Aromatic stacking and intermolecular hydrogen bonding interactions are regarded as the driving force in the formation of the stable supramolecular structure. Subsequently, the stimuli-responsive property of the vesicles to UV irradiation was also studied. The vesicles were also found to be able to efficiently carry hydrophobic drugs. We believe that this study may provide more references in the fields of body-friendly smart materials with the hope of in vivo applications. [ABSTRACT FROM AUTHOR]