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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]

It is a means of injury assessment to detect respiratory rate using an unmanned aerial camera. However, the existing video- based respiratory rate detection algorithms are only applicable to fixed cameras. On the basis of spatial phase based respiratory signal extraction technology, a non-contact measurement method of human respiratory rate with videos recorded by unmanned aerial vehicle (UAV) is proposed. The complex steerable pyramid is used to extract the spatial phase of each frame image, and the phase sequence is obtained in chronological order. Secondly, the empirical mode decomposition (EMD) is used to decompose multiple modal components from the phase sequence, and the frequency variability analysis model is designed to select the components with stable frequencies, or the target respiratory signal. The peak value detection method is used to detect the human respiratory rate. The experimental results show that the average accuracy of the method can reach more than 98%, which is superior to the existing detection methods. [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]

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]

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]

It has been proven that intra-articular injection of mesenchymal stromal cells (MSCs) can alleviate cartilage damage in osteoarthritis (OA) by differentiating into chondrocytes and protecting inherent cartilage. However, the mechanism by which the OA articular microenvironment affects MSCs' therapeutic efficiency is yet to be fully elucidated. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor involved in various cellular processes, such as osteogenesis and immune regulation. Tryptophan (Trp) metabolites, most of which are endogenous ligand for AHR, are abnormally increased in synovial fluid (SF) of OA and rheumatoid arthritis (RA) patients. In this study, the effects of kynurenine (KYN), one of the most important metabolites of Trp, were evaluated on the chondrogenic and chondroprotective effects of human umbilical cord-derived mesenchymal stromal cells (hUC-MSCs). hUC-MSCs were cultured in conditioned medium containing different proportions of OA/RA SF, or stimulated with KYN directly, and then, AHR activation, proliferation, and chondrogenesis of hUC-MSCs were measured. Moreover, the chondroprotective efficiency of short hairpin-AHR-UC-MSC (shAHR-UC-MSC) was determined in a rat surgical OA model (right hind joint). OA SF could activate AHR signaling in hUC-MSCs in a concentration-dependent manner and inhibit the chondrogenic differentiation and proliferation ability of hUC-MSCs. Similar results were observed in hUC-MSCs stimulated with KYN in vitro. Notably, shAHR-UC-MSC exhibited superior therapeutic efficiency in OA rat upon intra-articular injection. Taken together, this study indicates that OA articular microenvironment is not conducive to the therapeutic effect of hUC-MSCs, which is related to the activation of the AHR pathway by tryptophan metabolites, and thus impairs the chondrogenic and chondroprotective effects of hUC-MSCs. AHR might be a promising modification target for further improving the therapeutic efficacy of hUC-MSCs on treatment of cartilage-related diseases such as OA. [ABSTRACT FROM AUTHOR]

During the development of urban underground space in Chengdu, China, it was found that glauberite rocks in the Guankou formation of the Cretaceous system had extensive dissolution and exerted a serious impact on the safety of the project. At present, research on the transport and mechanical properties of glauberite under hydrostatic conditions in this area is limited. In this paper, the effects of hydrostatic dissolution and seepage on the transport and mechanical properties of glauberite are studied. The results show that the concentration of aqueous solution increases step by step with the increase in the dissolution time of glauberite in still water, indicating that the forward movement of the dissolution front of glauberite rock is not a continuous process and has intermittent characteristics. The dissolution and infiltration of mirabilite seriously deteriorates the physical structure of mirabilite, resulting in a decrease in elastic modulus and an increase in axial deformation, and a decrease in mechanical properties. There are a dissolution layer, dissolution front and undissolved area in the karst infiltration of calcium and mirabilite in the hydrostatic solution, so the mechanism of the calcium and mirabilite karst dissolution process is determined. Because of the dissolution characteristics of mirabilite, underground space development should prevent the uneven settlement of mirabilite, ground collapse, pit and chamber water gushing, corrosive groundwater and other geological problems. [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]

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]

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]

Atrial fibrillation (AF) is the most common arrhythmia, but an estimated 30% of patients with AF are unaware of their conditions. The purpose of this work is to design a model for AF screening from facial videos, with a focus on addressing typical motion disturbances in our real life, such as head movements and expression changes. This model detects a pulse signal from the skin color changes in a facial video by a convolution neural network, incorporating a phase-driven attention mechanism to suppress motion signals in the space domain. It then encodes the pulse signal into discriminative features for AF classification by a coding neural network, using a de-noise coding strategy to improve the robustness of the features to motion signals in the time domain. The proposed model was tested on a dataset containing 1200 samples of 100 AF patients and 100 non-AF subjects. Experimental results demonstrated that VidAF had significant robustness to facial motions, predicting clean pulse signals with the mean absolute error of inter-pulse intervals less than 100 milliseconds. Besides, the model achieved promising performance in AF identification, showing an accuracy of more than 90% in multiple challenging scenarios. VidAF provides a more convenient and cost-effective approach for opportunistic AF screening in the community. [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]

Tryptophan 2,3-dioxygenase (TDO2) was an initial rate-limiting enzyme of the kynurenine (Kyn) pathway in tryptophan (Trp) metabolism. We undertook this study to determine a comprehensive analysis of TDO2 expression in immune cells and assess the characterization of immune cell phenotype in TDO2 knockout mice. The expression of TDO2 in various tissues of DBA/1 mice was detected by quantitative real-time PCR (qPCR) and immunohistochemistry. Both flow cytometry and immunofluorescence were used to analyze the expression of TDO2 in immune cells. Furthermore, TDO2 knockout (KO) mice were generated by CRISPR/Cas9 technology to detect immune cell phenotype. TDO2 protein level in liver was tested by western blot. High-performance liquid chromatography was used to detect the level of Trp and Kyn. Flow cytometry was used to test the proportions of splenic lymphocyte subsets in wild-type (WT) and TDO2 KO mice. We found that TDO2 was expressed in various tissues and immune cells, and TDO2 staining was mainly observed in the cytoplasm of cells. There was no difference in the development of immune cells between TDO2 KO mice and WT mice, including T cells, B cells, memory B cells, plasma cells, dendritic cells, and natural killer cells. Interestingly, the reduced M1/M2 ratio was observed in the peritoneal macrophages of TDO2 KO mice. Taken together, these findings enriched the known expression profile of TDO2, especially its expression in immune cells. Our study suggested that TDO2-mediated Trp-Kyn metabolism pathway might be involved in the immune response. [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]

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]

Ambient particulate matter pollution is one of the leading causes of global disease burden. Epidemiological studies have revealed the connections between particulate exposure and cardiovascular and respiratory diseases. However, until now, the real species of ambient ultrafine particles (UFPs) in humans are still scarcely known. Here we report the discovery and characterization of exogenous nanoparticles (NPs) in human serum and pleural effusion (PE) samples collected from non-occupational subjects in a typical polluted region. We show the wide presence of NPs in human serum and PE samples with extreme diversity in chemical species, concentration, and morphology. Through chemical multi-fingerprinting (including elemental fingerprints, high-resolution structural fingerprints, and stable iron isotopic fingerprints) of NPs, we identify the sources of the NPs to be abiogenic, particularly, combustion-derived particulate emission. Our results provide evidence for the translocation of ambient UFPs into the human circulatory system, and also provide information for understanding their systemic health effects. Exposure to ambient particulate matter is one of the leading global health risks. Here, the authors reveal, by means of chemical multi-fingerprinting, the presence of exogenous ultrafine particles with diverse species and morphology in non-occupational human serum and pleural effusion. [ABSTRACT FROM AUTHOR]

This paper proposes an outliers-robust constant false-alarm rate (OR-CFAR) detector of Gaussian clutter based on the truncated-maximum-likelihood estimator (TMLE) in SAR imagery. The proposed method aims at elevating the detection performance in multiple-target environment, where the sea clutter samples are often contaminated by the interfering target pixels, the azimuth ambiguities, and the breakwater. As a consequence, the parameters used for statistical modeling are over-estimated, resulting in a degradation of the CFAR detection rate. Inspired by the traditional two-parameter CFAR (TP-CFAR) detector of Gaussian clutter, OR-CFAR designs an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers from the clutter samples in the local reference window, and the probability density function (PDF) of the sea clutter can be accurately modeled through the newly raised TMLE. Furthermore, the optimal truncation depth used for clutter truncation and PDF modeling is evaluated and selected properly to get the best detection results. The OR-CFAR greatly enhances the CFAR detection rate in multiple-target environment, and it is computationally simple and efficient, which has a great application value. The Chinese Gaofen-3 SAR data are used for experiments to show the better detection performance of OR-CFAR. [ABSTRACT FROM AUTHOR]

Imaging photoplethysmography (IPPG) technique allows us to extract blood volume pulse (BVP) signals from face videos for measuring heart rate (HR), which is useful in applications such as neonatal monitoring, telemedicine and affective computing. Because the BVP signal is small, the HR estimation results are sensitive to face motion disturbance caused by spontaneous head movements and facial expressions of subjects. In this paper, we design a novel filtering method for refining the RGB signals with motion artifacts. Based on the observation that subtle color changes of face skin are smoother than large face motions at temporal scale, we use the three-order derivative of Gaussian filter to select subtle color changes under large motions. Our method is validated on both our self-collected dataset and public dataset MAHNOB-HCI containing face videos with head movements and facial expressions. By employing the proposed filtering method to pre-process the RGB signals before BVP signal extraction, a range of IPPG methods are improved to generate robust HR estimation results under realistic situations. [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]

The ability to measure heart rate (HR) from face videos is useful in applications such as neonatal monitoring, telemedicine and affective computing. In the realistic environments, subjects often have spontaneous head movements and facial expressions which severely degrade the performances of the current methods. We propose a novel patch-based fusion framework for estimating accurate HR from face videos in the presence of subjects' motions. The wavelet time–frequency analysis is applied on the raw blood volume pulse (BVP) signals for selecting less contaminated patches. Furthermore, a weighted fusion formula is constructed to obtain the final precise BVP signal, which is based on frequency and gradient information. Our method is validated on both our self-collected dataset and public dataset MAHNOB-HCI. Compared with the state of the art, experimental results show that the proposed method has an obvious superiority in the accuracy and robustness. [ABSTRACT FROM AUTHOR]

Aerosol particles are ubiquitous in the atmosphere and affect the quality of human life through their climatic and health effects. The formation and growth of aerosol particles involve extremely complex reactions and processes. Due to limited research tools, the sources and chemistry of aerosols are still not fully understood, and until now have normally been investigated by using chemical species of secondary aerosols (e.g., NH4+ , NO3- , SO42- , SOC) as tracers. Here we investigated the role of silicon (Si), an ubiquitous but relatively inert element, during the secondary aerosol formation process. We analyzed the correlation of Si in airborne fine particles (PM2.5) collected in Beijing – a typical pollution region – with the secondary chemical species and secondary particle precursors (e.g., SO2 and NOx). The total mass of Si in PM2.5 was found to be uncorrelated with the secondary aerosol formation process, which suggested that Si is a new conservative tracer for the amount of primary materials in PM2.5 and can be used to estimate the relative amount of secondary and primary compounds in PM2.5. This finding enables the accurate estimation of secondary aerosol contribution to PM2.5 by using Si as a single tracer rather than the commonly used multiple chemical tracers. In addition, we show that the correlation analysis of secondary aerosols with the Si isotopic composition of PM2.5 can further reveal the sources of the precursors of secondary aerosols. Therefore, Si may provide a new tool for aerosol chemistry studies. [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 (CNN)-based pixel-wise synthetic aperture radar (SAR) data classification does not take fully use the spatial neighborhood information due to the fact that the impact of neighborhood pixels is not taken into consideration. The flaw of CNN-based classification method may lead to misclassification under some conditions. In this paper, we propose a novel adaptive neighborhood-based convolutional neural network (AN-CNN) for the single polarimetric synthetic aperture radar data classification. In the convolution layer, the neighborhood pixels are adaptively weighted based on their bilateral distance (spatial and feature distance) to the central pixel. In this way, different pixels have different impact on the classification result of the central pixel. The spatial distance-based weighting can reduce the misclassifications in the homogenous regions which are caused by speckle noise and the feature distance-based weighting is beneficial for the classification in the boundary regions. As a result, the misclassification is obviously reduced by the proposed AN-CNN which has a new cost function. Experimental results on simulated and real SAR data show that our proposed AN-CNN can notably improve the classification accuracy in both boundary regions and homogeneous regions compared with conventional CNN in different scenes especially when limited training samples are explored. [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]

Video magnification techniques are useful for visualizing small changes in videos. Current methods are mainly applied for two aspects: motion amplification and color amplification. For instance, Eulerian video magnification (EVM) has shown impressive results in the context of color of human face and subtle head motion caused by the influx of blood at each beat. Such visual results have possible applications in non-contact human physiological parameter measurement, such as heart rate estimation. Unfortunately, the linear EVM is sensitive to noise and frequencies of the changes should be customized, which generates a limitation of applications. This paper presents an advanced EVM for magnifying the signal amplitude in the presence of relatively high noise as well as unknown the frequencies of changes in video. Principal component analysis (PCA) is performed to decompose the frames and the component whose spatial variation best matches small changes to be magnified. The advantage of PCA-based method is that it can select the subtle signals with a denoising process like spatial filtering. Experimental results show that the PCA-based EVM can support larger amplification factors for small changes visualization as well as less noise and artifacts. [ABSTRACT FROM AUTHOR]

Abstract: Traditional task‐evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low‐frequency steady‐state brain response (lfSSBR) reflects frequency‐tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra‐slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain–behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency‐specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks. [ABSTRACT FROM AUTHOR]

Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data. [ABSTRACT FROM PUBLISHER]

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]

We developed a serum metabolomic method by gas chromatography-mass spectrometry (GC-MS) to evaluate the effect of alprazolam in rats. The GC-MS with HP-5MS (0.25 μm × 30 m × 0.25 mm) mass was conducted in electron impact ionization (EI) mode with electron energy of 70 eV, and full-scan mode with m/ z 50-550. The rats were randomly divided to four groups, three alprazolam-treated groups and a control group. The alprazolam-treated rats were given 5, 10 or 20 mg/kg (low, medium, high) of alprazolam by intragastric administration each day for 14 days. The serum samples were corrected on the seventh and fourteenth days for metabolomic study. The blood was collected for biochemical tests. Then liver and brain were rapidly isolated and immersed for pathological study. Compared with the control group, on the seventh and fourteen days, the levels of d-glucose, 9,12-octadecadienoic acid, butanoic acid, l-proline, d-mannose and malic acid had changed, indicating that alprazolam induced energy metabolism, fatty acid metabolism and amino acid metabolism perturbations in rats. There was no significant difference for alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, urea and uric acid between controls and alprazolam groups. According to the pathological results, alprazolam is not hepatotoxic. Metabolomics could distinguish different alprazolam doses in rats. [ABSTRACT FROM AUTHOR]

Radar backscatter variations that occur because of incidence angle effects constrain the application of Scanning Synthetic Aperture Radar (ScanSAR) data for sea ice monitoring and observations. In this paper, a class-based correction is proposed for normalizing each class in ScanSAR data to a nominal incidence angle. Two tested sea ice synthetic aperture radar (SAR) data sets were acquired: a data set for the Gulf of Saint Lawrence, which was obtained by the RADARSAT-2 satellite, and a data set for the Bohai Sea, which was obtained by the ENVISAT Advanced Synthetic Aperture Radar. An unsupervised classification is performed on each image block prior to normalization, and the incidence angle range of each image block is approximately 5°. Because the distribution of the backscatter coefficients in the azimuth band is discrete and nonlinear, the class-based locally linear mapping (LLM) technique is implemented, based on the assumption that a small quantity of sorted backscatter coefficients is locally linear. This algorithm is a transplantable and easily applied method that requires limited ground data, and it is also a semiautomated technique because nearly all of its parameters can be adaptively determined during the image analysis. The results demonstrate that LLM-corrected ScanSAR images appear to have more detailed textures, and the natural signal variability in the radar data is preserved, which indicates that the LLM produces better results compared with the histogram-based-alike (HIST-alike) technique when correcting the incidence angle in the sea ice SAR data. The results of the data analysis in this paper show that the width of the azimuth band should be selected based on the extent of variation in the incidence angle, and the reference band can be calculated based on the maximum interclass distance principle. The intercomparisons also reveal that the proposed algorithm can improve the accuracy of supervised classifications. [ABSTRACT FROM AUTHOR]

This letter presents a novel method for ship classification that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features. The ratio of dimensions, which combines the 2-D and 3-D properties of scattering, is explored as an effective and credible means to describe the scattering features of ships. The proposed method consists of three main stages: 1) ship isolation from the sea; 2) parametric vector $(F)$ estimation; and 3) categorization using a support vector machine (SVM) classifier. To depict ship features more accurately and reduce feature redundancy, we propose employing peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts. The classification method is tested with RadarSat-2 images, and ground-truth information is supplied by an automatic identification system. The experimental results show that the proposed method can achieve satisfactory ship-classification performance compared with existing methods, with an overall accuracy exceeding 80%. [ABSTRACT FROM PUBLISHER]

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]

Purpose: To investigate the clinical character, diagnosis and treatment of chronic inflammatory demyelinating polyneuropathy accompanying myasthenia gravis so as to improve the understanding of such diseases. Materials and methods: A case of chronic inflammatory demyelinating polyneuropathy combined with myasthenia gravis were analyzed retrospectively with review of the literature. Results: This man was presented with chronic progressive sensory symptoms, flaccid tetraparesis, areflexia and protein-cell dissociation of cerebrospinal fluid. Nerve conduction study was indicative of demyelinating neuropathy. He was suspected as chronic inflammatory demyelinating polyneuropathy and treated with high-dose glucocorticoids. However, his condition worsened. Four months later, he was admitted and was diagnosed as combination of chronic inflammatory demyelinating polyneuropathy and myasthenia gravis. Good clinical results were observed after he was treated with pyridostigmine bromide, prednisone and mycophenolate mofetil. Conclusions: This case warns clinicians to be aware of these two diseases presenting in the same patient, and the possible implications on treatment choices. A common immunological abnormality might exist in this rare association, but it still remains unknown. [ABSTRACT FROM AUTHOR]