Capecitabine treatment of HCT-15 colon cancer cells induces apoptosis via mitochondrial pathway

Purpose: To investigate the effect of capecitabine on apoptosis induction in HCT-15 colon carcinoma cells and investigate the underlying mechanism.Methods: Phase-contrast microscopy was used for the examination of morphological changes while flow cytometry was employed for the analysis of cell cycle distribution, induction of apoptosis, reactive oxygen species (ROS) production and expression of caspases. Western blot assay was used for the analysis of expression level of apoptosis-related and cell cycle regulatory proteins.Results: Capecitabine treatment caused changes in the morphological appearance of HCT-15 cells after 48 h. The viability of HCT-15 cells was reduced to 23 % on treatment with capecitabine (5 μM) compared to 98 % in the control cultures. Incubation with capecitabine increased the population of HCT- 15 cells in G0/G1 phase to 56.43 % compared to 41.67 % in the control. Capecitabine treatment of HCT-15 cells caused condensation of DNA and induced apoptosis in a concentration-dependent manner. At 5 μM concentration of capecitabine, apoptosis was induced in 45.74 % of the cells. Incubation of HCT-15 cells with capecitabine for 48 h led to a significant increase in the production of ROS. Translocation of Endo G and AIF from mitochondria to the nuclei increased significantly (p < 0.005) on treatment with 5 μM capecitabine. Capecitabine treatment also reduced the expression of cyclin E and Cdc25c and promoted the level of caspases, Bax, AIF, Endo G, p21, PARP and p-p53. The expression level of Bcl-2 decreased in HCT-15 cells on incubation with 5 μM concentration of capecitabine.Conclusion: Capecitabine treatment causes inhibition of colon cancer growth via the mitochondrial pathway of apoptosis. Thus, capecitabine may have therapeutic application in colon carcinoma treatment.Keywords: Capecitabine, 5-Fluorouracil, Translocation, Colon cancer, Colitis, Apoptosi

Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification

The representation power of convolutional neural network (CNN) models for hyperspectral image (HSI) analysis is in practice limited by the available amount of the labeled samples, which is often insufficient to sustain deep networks with many parameters. We propose a novel approach to boost the network representation power with a two-stream 2-D CNN architecture. The proposed method extracts simultaneously, the spectral features and local spatial and global spatial features, with two 2-D CNN networks and makes use of channel correlations to identify the most informative features. Moreover, we propose a layer-specific regularization and a smooth normalization fusion scheme to adaptively learn the fusion weights for the spectral-spatial features from the two parallel streams. An important asset of our model is the simultaneous training of the feature extraction, fusion, and classification processes with the same cost function. Experimental results on several hyperspectral data sets demonstrate the efficacy of the proposed method compared with the state-of-the-art methods in the field

Spectral feature fusion networks with dual attention for hyperspectral image classification

Recent progress in spectral classification is largely attributed to the use of convolutional neural networks (CNN). While a variety of successful architectures have been proposed, they all extract spectral features from various portions of adjacent spectral bands. In this paper, we take a different approach and develop a deep spectral feature fusion method, which extracts both local and interlocal spectral features, capturing thus also the correlations among non-adjacent bands. To our knowledge, this is the first reported deep spectral feature fusion method. Our model is a two-stream architecture, where an intergroup and a groupwise spectral classifiers operate in parallel. The interlocal spectral correlation feature extraction is achieved elegantly, by reshaping the input spectral vectors to form the socalled non-adjacent spectral matrices. We introduce the concept of groupwise band convolution to enable efficient extraction of discriminative local features with multiple kernels adopting to the local spectral content. Another important contribution of this work is a novel dual-channel attention mechanism to identify the most informative spectral features. The model is trained in an end-to-end fashion with a joint loss. Experimental results on real data sets demonstrate excellent performance compared to the current state-of-the-art

Analysis on Customer Satisfaction from the Perspective of Cross-border Network Retail Platform AliExpress

On the basis of reviewing the research on customer satisfaction of domestic and foreign scholars, this paper takes the AliExpress platform as an example, expounds the connotation of customer satisfaction from the perspective of cross-border network retail platform, and focuses on the AliExpress DSR service score, namely, product description, customer service and cross-border logistics to analyze customer satisfaction. It is designed to enable merchants to provide superior customer service, increase customer satisfaction, attract and retain customers

An efficient approach to acoustic emission source identification based on harmonic wavelet packet and hierarchy support vector machine

A new approach for acoustic emission (AE) source type identification based on harmonic wavelet packet (HWPT) feature extraction and hierarchy support vector machine (H-SVM) classifier is proposed for solving the fatigue damage identification problem of helicopter moving component. In this approach, HWPT is employed to extract the energy feature of AE signals on different frequency bands, as well as to reduce the dimensionality of original data features. We trained the H-SVM classifier on a subset of the experimental data for known AE source type, and then tested on the remaining set of data. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The experimental results indicate that the proposed approach can implement AE source type identification effectively, and achieves better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification

Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine

Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN

Highly sensitive magnetite nano clusters for MR cell imaging

High sensitivity and suitable sizes are essential for magnetic iron oxide contrast agents for cell imaging. In this study, we have fabricated highly MR sensitive magnetite nanoclusters (MNCs) with tunable sizes. These clusters demonstrate high MR sensitivity. Especially, water suspensions of the MNCs with average size of 63 nm have transverse relaxivity as high as 630 s-1mM-1, which is among the most sensitive iron oxide contrast agents ever reported. Importantly, such MNCs have no adverse effects on cells (RAW 264.7). When used for cell imaging, they demonstrate much higher efficiency and sensitivity than those of SHU555A (Resovist), a commercially available contrast agent, both in vitro and in vivo, with detection limits of 3,000 and 10,000 labeled cells, respectively. The studied MNCs are sensitive for cell imaging and promising for MR cell tracking in clinics

Outstanding supercapacitive properties of Mn-doped TiO2 micro/nanostructure porous film prepared by anodization method.

Mn-doped TiO2 micro/nanostructure porous film was prepared by anodizing a Ti-Mn alloy. The film annealed at 300 °C yields the highest areal capacitance of 1451.3 mF/cm(2) at a current density of 3 mA/cm(2) when used as a high-performance supercapacitor electrode. Areal capacitance retention is 63.7% when the current density increases from 3 to 20 mA/cm(2), and the capacitance retention is 88.1% after 5,000 cycles. The superior areal capacitance of the porous film is derived from the brush-like metal substrate, which could greatly increase the contact area, improve the charge transport ability at the oxide layer/metal substrate interface, and thereby significantly enhance the electrochemical activities toward high performance energy storage. Additionally, the effects of manganese content and specific surface area of the porous film on the supercapacitive performance were also investigated in this work

Thermal ageing and its impact on charge trap density and breakdown strength in ldpe LDPE

Low-density polyethylene (LDPE) has been widely used as power cable insulation, because of its good electrical performance and stable chemical characteristics. However, in recent years, with the rise of large-capacity and long-distance HVDC transmission systems, the effect of space charge has a significant impact on the insulation selection and design. Furthermore, the change in the electrical performance of insulation after ageing is also required to be understood. It has been reported that ageing leads to an increase in charge trap density. The increase of trap density in LDPE makes the transport of charge carriers between traps easier. Consequently, the electrical breakdown strength will also be affected. This paper focuses on the LDPE films with different degrees of thermal ageing and studies its impact on charge trap density and change in electrical breakdown strength. The ageing degrees of sample were characterized using Fourier-Transform Infrared (FTIR). Space charge dynamics were measured using the pulsed electroacoustic (PEA) technique. In addition, electrical breakdown strength of the aged samples was measured and breakdown data were processed using the Weibull distribution. The change in characteristic breakdown strength is related to the change in charge trap density. The results suggest that the change in charge trap density of an insulating material can be used to characterize electrical performance of the material, therefore, the ageing status

Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation

To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size. Previous methods often adopt co-training using two convolutional networks with the same architecture but different initialization, which fails to capture the sufficiently diverse features. This motivates us to use tri-training and develop the triple-view encoder to utilize the encoders with different architectures to derive diverse features, and exploit the knowledge distillation skill to learn the complementary semantics among these encoders. Moreover, existing methods simply concatenate the features from both encoder and decoder, resulting in redundant features that require large memory cost. This inspires us to devise a dual-frequency decoder that selects those important features by projecting the features from the spatial domain to the frequency domain, where the dual-frequency channel attention mechanism is introduced to model the feature importance. Therefore, we propose a Triple-view Knowledge Distillation framework, termed TriKD, for semi-supervised semantic segmentation, including the triple-view encoder and the dual-frequency decoder. Extensive experiments were conducted on two benchmarks, \ie, Pascal VOC 2012 and Cityscapes, whose results verify the superiority of the proposed method with a good tradeoff between precision and inference speed