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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]

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]

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]

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]

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]

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]

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]

This article discusses the use of spaceborne polarimetric L-band and C-band synthetic aperture radar (SAR) data for sea-ice detection and classification. The benefits of combining L-band with C-band polarimetric quantities for supervised sea-ice classification in the Eastern Weddell Sea, Antarctica, are investigated. In the experiments, we compared the performance of a maximum likelihood (ML) classifier when used with the combined preferred polarimetric parameters and the individual ones, respectively. The relation between the classification accuracy and the preferred number of polarimetric parameters for classification was examined as well as whether principal component analysis (PCA) and locally linear embedding (LLE) can be used to reduce the dimensionality of the parameter sets. Combining dual-frequency polarimetric quantities improves classification accuracy compared to using individual single-frequency polarimetric quantities. By increasing the dimensionality of the preferred polarimetric parameter sets, the classification using high dimensionality can either result in improvements over the smaller subsets or result in no significant differences. Therefore, using all available polarimetric quantities over the study region is recommended. Further, data fusion with a PCA-based approach is found to be beneficial for sea-ice detection and classification, and poor results have been produced with an LLE-based approach. [ABSTRACT FROM PUBLISHER]

Context: The difference between the chemical polarities of the two categories of active chemical constituents in Chinese angelica volatile oil (CAVO) and Chinese angelica water extract (CAWE) greatly limit the development and clinical application of Chinese angelica preparation. Object: The aim of this study is to design and prepare a 'whole Chinese angelica' microemulsion (WCAM) that contains both CAVO and CAWE and at the same time to evaluate it in vivo and in vitro. Materials and methods: CAVO and CAWE extracted from Chinese angelica were used as the oil and aqueous phases, respectively, to prepare the WCAM; its physicochemical property was observed, and its drug potency and oral bioavailability were evaluated. Results: The formula of the WCAM was optimized as Tween-80:ethanol:CAVO:CAWE = 3:3:1:40. The droplet size of the WCAM was 72.64 nm and the WCAM was proved to be physicochemically stable when it was kept at 0 °C, 4 °C, 25 °C and 40 °C for 3 months. The WCAM could markedly prolong blood clotting time, decrease whole blood viscosity and whole blood reduced viscosity at different shear rates, and improve hemorheological parameters. The results of the pharmacokinetic evaluation show that the AUC0-7 of the WCAM was 4510.66 and was about 4.41-fold compared to that of danggui concentrated pills (an existing Chinese angelica pharmaceutical preparation). Conclusion: It can be concluded, that the WCAM is a promising Chinese angelica preparation that has great prospects in the treatment of dysmenorrhea and irregular menstruation. [ABSTRACT FROM AUTHOR]

Agood drawingof a graphGis a drawing where the edges are non-self-intersecting and each of the two edges have at most one point in common, which is either a common end vertex or a crossing. Thecrossing numberof a graphGis the minimum number of pairwise intersections of edges in a good drawing ofGin the plane. Then-dimensional augmented cube AQn, proposed by S.A. Choudum and V. Sunitha [Augmented cubes, Networks 40 (2002), pp. 71–84], is an important interconnection network with good topological properties and applications. In this paper, we obtain an upper bound on the crossing number ofAQnless than. [ABSTRACT FROM PUBLISHER]

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]