Poly(ethylene terephthalate)/expanded graphite conductive composites were prepared by the melt-blending method. The relationships between the preparation methods, microstructures, and conductivity properties of the composites were studied with scanning electron microscopy and conductivity measurements. The results showed that the composites presented a low percolation threshold and strong anisotropic conductivity. The epoxy resin had a strong intercalation effect on the expanded graphite that led to the easy formation of the conductive network. With classical statistical percolation theory, the conductivity behaviors of the composites were investigated. The results indicated that the nonuniversal critical exponent should be attributed to the anisotropy of conductivity, the tunneling conduction, and the particular structure. In addition, preliminary studies on the crystallization and dynamic mechanical behavior of the composites were performed.

Abstract:Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images, with complex background variations are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method, which can significantly improve the matching performance of multi-temporal remote sensing images with complex background variations, is better than the state-of-the-art matching methods. In our experiments, the proposed method obtained a large number of evenly distributed matches (at least 10 times more than other methods) and achieved a high accuracy (less than 1 pixel in terms of root mean square error).

Inflammation is known as an important mechanism of cognitive dysfunction. Systemic immune inflammation index (SII) and system inflammation response index (SIRI) are two blood inflammatory markers, which are related to many chronic diseases including cognitive impairment. It is recognized that dietary inflammatory index (DII), which is used to estimate the overall inflammatory potential of diet, may be related to mild cognitive impairment (MCI) as well. This study aimed to explore the relationship between SII, SIRI and DII, as well as the role of these inflammatory indexes on MCI in elderly people. A total of 1050 participants from Beijing were included. Neuropsychological tests were used for cognitive evaluation. Energy-adjusted DII scores were calculated based on semi-quantitative food frequency questionnaire. Blood samples were tested for calculating SII and SIRI. Log-binomial regression models were used to estimate the correlation of indexes. After adjusting demographic characteristics, SII and SIRI in MCI individuals were higher than controls (p ≤ 0.001). DII, SII and SIRI had positive relationship with MoCA scores (p < 0.005). DII also correlated with SIRI in MCI (β = 0.11, p = 0.031). Higher DII and SIRI could definitely increase the risk of MCI, as well as DII and SII (p < 0.005). In conclusion, DII was positively correlated with blood inflammation. The elderly with higher level of DII and SIRI, or DII and SII could be considered as people with higher risk of developing MCI.

Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this area and the significant progress which has been achieved in the past decade. This paper presents an effective approach to extracting buildings from Unmanned Aerial Vehicle (UAV) images through the incorporation of superpixel segmentation and semantic recognition. A framework for building extraction is constructed by jointly using an improved Simple Linear Iterative Clustering (SLIC) algorithm and Multiscale Siamese Convolutional Networks (MSCNs). The SLIC algorithm, improved by additionally imposing a digital surface model for superpixel segmentation, namely 6D-SLIC, is suited for building boundary detection under building and image backgrounds with similar radiometric signatures. The proposed MSCNs, including a feature learning network and a binary decision network, are used to automatically learn a multiscale hierarchical feature representation and detect building objects under various complex backgrounds. In addition, a gamma-transform green leaf index is proposed to truncate vegetation superpixels for further processing to improve the robustness and efficiency of building detection, the Douglas–Peucker algorithm and iterative optimization are used to eliminate jagged details generated from small structures as a result of superpixel segmentation. In the experiments, the UAV datasets, including many buildings in urban and rural areas with irregular shapes and different heights and that are obscured by trees, are collected to evaluate the proposed method. The experimental results based on the qualitative and quantitative measures confirm the effectiveness and high accuracy of the proposed framework relative to the digitized results. The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).

Long noncoding RNAs (lncRNAs) are commonly associated with various biological functions, in which the function of lncRNA maternally expressed gene 3 (MEG3) has been identified in various cancers. Strikingly, an association between MEG3 with microRNAs (miRNAs), mRNAs, and proteins has been reported. This study investigates the role of MEG3 in vascular endothelial cell (VEC) senescence. Expression of Girdin and miR-128 was monitored in the blood vessel samples of young and old mice/healthy volunteers, along with the measurement of human umbilical vein endothelial cells (HUVECs). The relationship between MEG3/Girdin and miR-128 was determined and verified. Loss- and gain-of-function approaches were applied to analyze the regulatory effects of MEG3 on platelet phagocytosis and lipoprotein oxidation of HUVEC membrane. In addition, the effect of MEG3 on HUVEC senescence was evaluated by detection of the reactive oxygen species, telomerase activity, and telomere length. To further analyze the MEG3-mediated regulatory mechanism, miR-128 upregulation and inhibition were introduced into the HUVECs. Downregulated Girdin and upregulated miR-128 were found in the blood vessels of old individuals and old mice, as well as in senescent HUVECs. MEG3 downregulation was found to be capable of inhibiting Girdin but enhancing miR-128 expression. It was also indicated to inhibit platelet phagocytosis and reduce telomerase activity and telomere length, while enhancing lipoprotein oxidation and reactive oxygen species production, which ultimately contributed in preventing and protecting HUEVCs from senescence. These findings provide evidence supporting that MEG3 leads to miR-128 downregulation and Girdin upregulation, which promotes platelet phagocytosis, thus protecting VECs from senescence.