A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data

This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples

Solvothermal Synthesis, Structure and Optical Property of Nanosized CoSb3 Skutterudite

Binary skutterudite CoSb3 nanoparticles were synthesized by solvothermal method. The nanostructuring of CoSb3 material was achieved by the inclusion of various kinds of additives. X-ray diffraction examination indicated the formation of the cubic phase of CoSb3. Structural analysis by transmission electron microscopy analysis further confirmed the formation of crystalline CoSb3 nanoparticles with high purity. With the assistance of additives, CoSb3 nanoparticles with size as small as 10 nm were obtained. The effect of the nanostructure of CoSb3 on the UV–visible absorption and luminescence was studied. The nanosized CoSb3 skutterudite may find application in developing thermoelectric devices with better efficiency

Solvothermal Synthesis, Structure and Optical Property of Nanosized CoSb3 Skutterudite

Binary skutterudite CoSb3 nanoparticles were synthesized by solvothermal method. The nanostructuring of CoSb3 material was achieved by the inclusion of various kinds of additives. X-ray diffraction examination indicated the formation of the cubic phase of CoSb3. Structural analysis by transmission electron microscopy analysis further confirmed the formation of crystalline CoSb3 nanoparticles with high purity. With the assistance of additives, CoSb3nanoparticles with size as small as 10 nm were obtained. The effect of the nanostructure of CoSb3on the UV–visible absorption and luminescence was studied. The nanosized CoSb3 skutterudite may find application in developing thermoelectric devices with better efficiency.

Generalized differential morphological profiles for remote sensing image classification

Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF "out-of-bag" error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional World View-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information

Thermal barrier coatings on polymer materials

Polyimide matrix composite (PIMC) has been widely used to replace metallic parts due to its low density and high strength. It is considered as an effective approach to improve thermal oxidation resistance, operation temperature and lifetime of PIMC by depositing a protection coating. The objective of the research was to fabricate a series of thermal barrier coatings (TBCs) on PIMC by a combined sol-gel/sealing treatment process and air plasma spraying (APS). By optimizing the experimental parameters, thermal shock resistance, thermal oxidation resistance and thermal ablation resistance of PIMC could be improved significantly. The ZrO2 sol was prepared by sol-gel process and the effects of the different organic additions on phase structure, crystallite size and crystal growth behavior of the ZrO2 nanocrystallite were investigated. The addition of HAc and DMF were beneficial to decrease the crystallite size and alter the activation energy for crystal growth, further inducing the crystallization of ZrO2 nanocrystallite at low temperature (300ºC) and the stability of tetragonal ZrO2 at 600ºC. Based on the optimized parameters of the sol preparation, the ZrO2/phosphates duplex coating was fabricated on PIMC via a combined sol-gel and sealing treatment process. The sealing mechanism of the phosphates in the duplex coating was primarily attributed to the adhesive binding of the phosphates and the chemical bonding between the sealant and the coating. It was demonstrated that the duplex coating exhibited excellent thermal shock resistance and no apparent delamination or spallation occurred. Relatively, the duplex coating with the thickness of 150 μm provided excellent thermal oxidation and thermal ablation resistance for the polymer substrate. However, the presence of cracks and delamination in the coatings provided the channels for oxygen diffusion, causing the final failure of the protection coating. Figure 4 – TBCs on CFPI The Zn/YSZ and Al/YSZ coating systems were successfully deposited on PIMC by APS. Metals with comparatively low melting point as the bond coats (Cu, Al, Zn) were beneficial to increase thermal shock resistance of the coating systems. In comparison with the Al/YSZ coating system, the Zn/YSZ coating exhibited the better thermal shock resistance, which was ascribable to the lower residual stress in the Zn layer after deposition and the lower thermal stress induced during thermal shock test. For these coatings, the increase in surface toughness of the substrate as well as the decrease in thickness of metal layer favored the improvement of thermal shock resistance of the coatings. With the temperature increases, thermal shock lifetime of the coatings decreased disastrously. However, the difference was that the slight increase of the thickness of YSZ layer favored the increase in thermal shock resistance of the Al/YSZ coatings, while for the Zn/YSZ coating systems the increase in the thickness of YSZ layer made thermal shock resistance weaken. Owing to the protection of Zn/YSZ and Al/YSZ coating systems, the time for 5 wt% weight loss of the sample was prolonged from 16 h to 50 h when oxidation at 400ºC; as the oxidation temperature increased to 450ºC, the time for 5wt% weight loss was extended from 5 h to 13 h. By depositing different coatings, the anti-ablation property of PIMC was significantly improved. During property testing, the formation of cracks and delamination in the coating and the occurrence of the spallation led to the failure of the coating systems, which was mainly due to the residual stress during the deposition process, thermal stress induced by the mismatch in thermal expansion coefficient and further oxidation of the substrate. Please click Additional Files below to see the full abstract

Impact of HBeAg on the maturation and function of dendritic cells

AbstractObjectivesHBV infection typically leads to chronic hepatitis in newborns and some adults with weakened immune systems. The mechanisms by which virus escapes immunity remain undefined. Regulatory dendritic cells (DCregs) contributing to immune escape have been described. We wondered whether or not HBeAg as an immunomodulatory protein could induce DCreg which might subsequently result into HBV persistence.MethodsThe immunophenotyping, T-cell activation and cytokine production were analyzed in HBeAg-treated DCs from normal or cyclophosphamide (Cy)-induced immunocompromised mice.ResultsHBeAg tended to promote bone marrow derived DCs (BMDCs) from Cy-treated mice into CD11bhighPIR-B+ regulatory DCs exhibiting the lowest T-cell stimulatory capacity and interleukin (IL)-12p70 production compared with controls. Neutralization of IL-10 significantly inhibited the regulatory effect of these DCs on T-cell stimulation of mature DCs. After lipopolysaccharides (LPS) stimulation, marked phosphorylation of Akt was detected in HBeAg treated DCs from immunocompromised mice. Blocking the PI3K-Akt pathway by LY294002 led to an enhancement of IL-12 production. PI3K signalling pathway appears to be involved in the decreased IL-12 secretion by HBeAg treated DCs.ConclusionsThese findings suggest that HBeAg may program the generation of a new DC subset with regulatory capacity under the condition of immunosuppression, which may presumably contribute to the persistent HBV infection

Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter

Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. In this paper, we propose a novel algorithm for hyperspectral (HS) image pansharpening with two stage guided filtering in PCA (principal component analysis) domain. In the first stage, we first downsample the high resolution RGB image to the same spatial resolution of original low-resolution HS image, and use guided filter to transfer the image details (e.g. edge) of the downsampled RGB image to the original HS image in the PCA domain In the second stage, we perform upsampling on the resulting HS image from the first stage by using original high-resolution RGB image and guided filter in PCA domain. This yields a clear improvement over an older approach with one stage guided filtering in PCA domain. Experimental results on fusion of a low spatial-resolution Thermal Infrared HS image and a high spatial-resolution visible RGB image from the 2014 IEEE GRSS Data Fusion Contest, are very encouraging

Robust joint sparsity model for hyperspectral image classification

Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model

Synthesis of N-(3-(4-[11C]methylpiperazin-1-yl)−1-(5-methylpyridin-2-yl)−1H-pyrazol-5-yl)pyrazolo[1,5-a]pyrimidine-3-carboxamide as a new potential PET agent for imaging of IRAK4 enzyme in neuroinflammation

The reference standard N-(3-(4-methylpiperazin-1-yl)−1-(5-methylpyridin-2-yl)−1H-pyrazol-5-yl)pyrazolo[1,5-a]pyrimidine-3-carboxamide (9) and its demethylated precursor N-(1-(5-methylpyridin-2-yl)−3-(piperazin-1-yl)−1H-pyrazol-5-yl)pyrazolo[1,5-α]pyrimidine-3-carboxamide (8) were synthesized from pyrazolo[1,5-a]pyrimidine-3-carboxylic acid and ethyl 2-cyanoacetate with overall chemical yield 13% in nine steps and 14% in eight steps, respectively. The target tracer N-(3-(4-[11C]methylpiperazin-1-yl)−1-(5-methylpyridin-2-yl)−1H-pyrazol-5-yl)pyrazolo[1,5-a]pyrimidine-3-carboxamide ([11C]9) was prepared from its precursor with [11C]CH3OTf through N-[11C]methylation and isolated by HPLC combined with SPE in 50–60% radiochemical yield, based on [11C]CO2 and decay corrected to EOB. The radiochemical purity was >99%, and the specific activity at EOB was 370–1110 GBq/μmol

A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for postapplications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where highdimensional data analysis is required