We report direct coupling of plasmonic and photonic nanowires using ultracompact near-field interaction. Photon-plasmon coupling efficiency up to 80% with coupling length down to the 200 nm level is achieved between individual Ag and ZnO nanowires. Hybrid nanophotonic components, including polarization splitters, Mach-Zehnder interferometers, and microring cavities, are fabricated out of coupled Ag and ZnO nanowires. These components offer relatively low loss with subwavelength confinement; a hybrid nanowire microcavity exhibits a Q-factor of 520.

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

Metallic and plasmonic nanolasers have attracted growing interest recently. Plasmonic lasers demonstrated so far operate in hybrid photon-plasmon modes in transverse dimensions, rendering it impossible to separate photonic from plasmonic components. Thus only the far-field photonic component can be measured and utilized directly. But spatially separated plasmon modes are highly desired for applications including high-efficiency coupling of single-photon emitters and ultrasensitivity optical sensing. Here, we report a nanowire (NW) laser that offers subdiffraction-limited beam size and spatially separated plasmon cavity modes. By near-field coupling a high-gain CdSe NW and a 100 nm diameter Ag NW, we demonstrate a hybrid photon-plasmon laser operating at 723 nm wavelength at room temperature, with a plasmon mode area of 0.008λ(2). This device simultaneously provides spatially separated photonic far-field output and highly localized coherent plasmon modes, which may open up new avenues in the fields of integrated nanophotonic circuits, biosensing, and quantum information processing.

No abstract

Traditional methods used for intelligent condition monitoring and diagnosis significantly depend on manual feature extraction and selection. To address this issue, a transfer learningconvolutional neural network (TLCNN) based on AlexNet is proposed for bearing fault diagnosis. Firstly, a 2D image representation method converts vibration signals to 2D timefrequency images. Secondly, the proposed TLCNN model extracts the features of the 2D time-frequency images and achieves the classification conditions of the bearing, which is faster to train and more accurate. Thirdly, t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize the feature learning process to demonstrate the feature learning ability of the proposed model. The experimental results verify that the proposed fault diagnosis model has higher accuracy and has much better robustness against noise than other deep learning and traditional methods.

The introduction of mammographic screening has considerably increased the detection rate of ductal carcinoma in situ (DCIS), which has a high probability of recurrence. We carried out a meta-analysis to evaluate the predictive factors including biomarkers, tumor characteristics, and modes of detection on the risk of local invasive recurrence (LIR) following DCIS. Searches were performed in PubMed and EMBASE up to 8 July 2014. Risk estimates (hazard ratios, odds ratios, and relative risks) and their 95% confidence intervals (CIs) were extracted to calculate the strength of the associations between predictive factors and the risk of LIR after treatment of DCIS. STATA 12.0 was used to combine results in this meta-analysis. A total of 18 articles were included in the analysis. Pooled risk estimates and 95% CIs were 1.36 (1.04–1.69) for the positive margin, 1.38 (1.12–1.63) for the nonscreening detection method, 1.04 (0.84–1.24) for high nuclear grade 1, 1.32 (0.98–1.66) for intermediate nuclear grade 2, 1.18 (0.98–1.37) for comedonecrosis, 1.00 (0.92–1.08) for large tumor size, 1.34 (0.82–1.87) for multifocality, 0.74 (0.36–1.12) for estrogen receptor-positive tumors, 0.89 (0.47–1.31) for progesterone receptor-positive tumors, and 1.25 (0.7–1.81) for HER2/neu-positive tumors. Positive margin and non-screening-detected cancers were associated with a higher risk of LIR following DCIS. These predictive factors, after further validation, could be considered to tailor treatment for individual patients.

We further develop a simple and compact technique for calculating the three flavor neutrino oscillation probabilities in uniform matter density. By performing additional rotations instead of implementing a perturbative expansion we significantly decrease the scale of the perturbing Hamiltonian and therefore improve the accuracy of zeroth order. We explore the relationship between implementing additional rotations and that of performing a perturbative expansion. Based on our analysis, independent of the size of the matter potential, we find that the first order perturbation expansion can be replaced by two additional rotations and a second order perturbative expansion can be replaced by one more rotation. Numerical tests have been applied and all the exceptional features of our analysis have been verified.

No abstract