Study of axial strain induced torsion of single wall carbon nanotubes by 2D continuum anharmonic anisotropic elastic model

Recent molecular dynamic simulations have found chiral single wall carbon nanotubes (SWNTs) twist during stretching, which is similar to the motion of a screw. Obviously this phenomenon, as a type of curvature-chirality effect, can not be explained by usual isotropic elastic theory of SWNT. More interestingly, with larger axial strains (before buckling), the axial strain induced torsion (a-SIT) shows asymmetric behaviors for axial tensile and compressing strains, which suggests anharmonic elasticity of SWNTs plays an important role in real a-SIT responses. In order to study the a-SIT of chiral SWNTs with actual sizes, and avoid possible deviations of computer simulation results due to the finite-size effect, we propose a 2D analytical continuum model which can be used to describe the the SWNTs of arbitrary chiralities, curvatures, and lengths, with the concerning of anisotropic and anharmonic elasticity of SWNTs. This elastic energy of present model comes from the continuum limit of lattice energy based on Second Generation Reactive Empirical Bond Order potential (REBO-II), a well-established empirical potential for solid carbons. Our model has no adjustable parameters, except for those presented in REBO-II, and all the coefficients in the model can be calculated analytically. Using our method, we obtain a-SIT responses of chiral SWNTs with arbitrary radius, chiralities and lengthes. Our results are in reasonable agreement with recent molecular dynamic simulations. [Liang {\it et. al}, Phys. Rev. Lett, ${\bf 96}$, 165501 (2006).] Our approach can also be used to calculate other curvature-chirality dependent anharmonic mechanic responses of SWNTs.Comment: 14 pages, 2 figure

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017

Output entanglement and squeezing of two-mode fields generated by a single atom

A single four-level atom interacting with two-mode cavities is investigated. Under large detuning condition, we obtain the effective Hamiltonian which is unitary squeezing operator of two-mode fields. Employing the input-output theory, we find that the entanglement and squeezing of the output fields can be achieved. By analyzing the squeezing spectrum, we show that asymmetric detuning and asymmetric atomic initial state split the squeezing spectrum from one valley into two minimum values, and appropriate leakage of the cavity is needed for obtaining output entangled fields

Protein tyrosine kinase 6 is associated with nasopharyngeal carcinoma poor prognosis and metastasis

BACKGROUND: The aim of this study was to analyze the expression of protein tyrosine kinase 6 (PTK6) in nasopharyngeal carcinoma (NPC) samples, and to identify whether PTK6 can serve as a biomarker for the diagnosis and prognosis of NPC. METHODS: We used quantitative RT-PCR and Western blotting analysis to detect mRNA and protein expression of PTK6 in NPC cell lines and immortalized nasopharyngeal epithelial cell lines. 31 NPC and 16 non-tumorous nasopharyngeal mucosa biopsies were collected to detect the difference in the expression of mRNA level of PTK6 by quantitative RT-PCR. We also collected 178 NPC and 10 normal nasopharyngeal epithelial cases with clinical follow-up data to investigate the expression of PTK6 by immunohistochemistry staining (IHC). PTK6 overexpression on cell growth and colony formation ability were measured by the method of cell proliferation assay and colony formation assay. RESULTS: The expression of PTK6 was higher in most of NPC cell lines at both mRNA and protein levels than in immortalized nasopharyngeal epithelial cell lines (NPECs) induced by Bmi-1 (Bmi-1/NPEC1, and Bmi-1/NPEC2). The mRNA level of PTK6 was high in NPC biopsies compared to non-tumorous nasopharyngeal mucosa biopsies. IHC results showed the expression of PTK6 was significantly correlated to tumor size (P<0.001), clinical stage (P<0.001), and metastasis (P=0.016). The patients with high-expression of PTK6 had a significantly poor prognosis compared to those of low-expression (47.8% versus 80.0%, P<0.001), especially in the patients at the advanced stages (42.2% versus 79.1%, P<0.001). Multivariate analysis indicated that the level of PTK6 expression was an independent prognostic factor for the overall survival of patients with NPC (P <0.001). Overexpression of PTK6 in HNE1 cells enhanced the ability of cell proliferation and colony formation. CONCLUSIONS: Our results suggest that high-expression of PTK6 is an independent factor for NPC patients and it might serve as a potential prognostic biomarker for patients with NPC