Interlayer tunnelling in Bi2Sr2CaCu2O8+d single crystals

We present measurements of the intrinsic quasi-particle conductivity along the c-axis of 2212-BSCCO single-crystal mesa structures in the superconducting and normal states. Direct measurement of the mesa temperature enables corrections to be made for self-heating and permits the acquisition of reliable I-V characteristics over a wide range of temperatures and voltages. Unlike a conventional superconductor, there is no evidence for any change in the quasiparticle conductivity at Tc, consistent with precursor pairing of electrons in the normal state. At low temperatures the initial low-voltage linear conductivity exhibits a T2 dependence, approaching a limiting value at zero temperature

Understanding the newly observed Y(4008) by Belle

Very recently a new enhancement around 4.05 GeV was observed by Belle experiment. In this short note, we discuss some possible assignments for this enhancement, i.e. $\psi(3S)$ and $D^*\bar{D}^*$ molecular state. In these two assignments, Y(4008) can decay into $J/\psi\pi^0\pi^0$ with comparable branching ratio with that of $Y(4008)\to J/\psi\pi^+\pi^-$. Thus one suggests high energy experimentalists to look for Y(4008) in $J/\psi\pi^0\pi^0$ channel. Furthermore one proposes further experiments to search missing channel $D\bar{D}$, $D\bar{D}^*+h.c.$ and especially $\chi_{cJ}\pi^+\pi^-\pi^0$ and $\eta_c\pi^+\pi^-\pi^0$, which will be helpful to distinguish $\psi(3S)$ and $D^*\bar{D}^*$ molecular state assignments for this new enhancement.Comment: 4 pages, 5 figures. Typos correcte

Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with arXiv:1612.0597

Many-body effects in tracer particle diffusion with applications for single-protein dynamics on DNA

30% of the DNA in E. coli bacteria is covered by proteins. Such high degree of crowding affect the dynamics of generic biological processes (e.g. gene regulation, DNA repair, protein diffusion etc.) in ways that are not yet fully understood. In this paper, we theoretically address the diffusion constant of a tracer particle in a one dimensional system surrounded by impenetrable crowder particles. While the tracer particle always stays on the lattice, crowder particles may unbind to a surrounding bulk and rebind at another or the same location. In this scenario we determine how the long time diffusion constant ${\cal D}$ (after many unbinding events) depends on (i) the unbinding rate of crowder particles $k_{\rm off}$, and (ii) crowder particle line density $\rho$, from simulations (Gillespie algorithm) and analytical calculations. For small $k_{\rm off}$, we find ${\cal D}\sim k_{\rm off}/\rho^2$ when crowder particles are immobile on the line, and ${\cal D}\sim \sqrt{D k_{\rm off}}/\rho$ when they are diffusing; $D$ is the free particle diffusion constant. For large $k_{\rm off}$, we find agreement with mean-field results which do not depend on $k_{\rm off}$. From literature values of $k_{\rm off}$ and $D$, we show that the small $k_{\rm off}$-limit is relevant for in vivo protein diffusion on a crowded DNA. Our results applies to single-molecule tracking experiments.Comment: 10 pages, 8 figure

Mostly Music: Sounds of China, Sep. 14, 2001

Betty Xiang, Wei Yanghttps://neiudc.neiu.edu/mostlymusic/1008/thumbnail.jp