Adhesion-induced phase separation of multiple species of membrane junctions

A theory is presented for the membrane junction separation induced by the adhesion between two biomimetic membranes that contain two different types of anchored junctions (receptor/ligand complexes). The analysis shows that several mechanisms contribute to the membrane junction separation. These mechanisms include (i) the height difference between type-1 and type-2 junctions is the main factor which drives the junction separation, (ii) when type-1 and type-2 junctions have different rigidities against stretch and compression, the ``softer'' junctions are the ``favored'' species, and the aggregation of the softer junction can occur, (iii) the elasticity of the membranes mediates a non-local interaction between the junctions, (iv) the thermally activated shape fluctuations of the membranes also contribute to the junction separation by inducing another non-local interaction between the junctions and renormalizing the binding energy of the junctions. The combined effect of these mechanisms is that when junction separation occurs, the system separates into two domains with different relative and total junction densities.Comment: 23 pages, 6 figure

A Stable and Accurate Marker-less Augmented Reality Registration Method

Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper,we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the VisualSimultaneous Localization and Mapping (V-SLAM) framework,our method adds a three-dimensional dense cloud processingstep to the state-of-the-art ORB-SLAM in order to deal withmainly the point cloud fusion and the object recognition. Ouralgorithm for the object recognition process acts as a stabilizer toimprove the registration accuracy during the model to the scenetransformation process. This has been achieved by integrating theHough voting algorithm with the Iterative Closest Points(ICP)method. Our proposed AR framework also further increasesthe registration accuracy with the use of integrated cameraposes on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of marker-less augmented reality applications

Heavy Quarks on Anisotropic Lattices: The Charmonium Spectrum

We present results for the mass spectrum of $c{\bar c}$ mesons simulated on anisotropic lattices where the temporal spacing $a_t$ is only half of the spatial spacing $a_s$. The lattice QCD action is the Wilson gauge action plus the clover-improved Wilson fermion action. The two clover coefficients on an anisotropic lattice are estimated using mean links in Landau gauge. The bare velocity of light $\nu_t$ has been tuned to keep the anisotropic, heavy-quark Wilson action relativistic. Local meson operators and three box sources are used in obtaining clear statistics for the lowest lying and first excited charmonium states of $^1S_0$, $^3S_1$, $^1P_1$, $^3P_0$ and $^3P_1$. The continuum limit is discussed by extrapolating from quenched simulations at four lattice spacings in the range 0.1 - 0.3 fm. Results are compared with the observed values in nature and other lattice approaches. Finite volume effects and dispersion relations are checked.Comment: 36 pages, 6 figur

Author Correction: Systematic evaluation of 2′-Fluoro modified chimeric antisense oligonucleotide-mediated exon skipping in vitro

Correction to: Scientific Reports https://doi.org/10.1038/s41598-019-42523-0, published online 15 April 201

Numerical study of O(a) improved Wilson quark action on anisotropic lattice

The $O(a)$ improved Wilson quark action on the anisotropic lattice is investigated. We carry out numerical simulations in the quenched approximation at three values of lattice spacing ($a_{\sigma}^{-1}=1$--2 GeV) with the anisotropy $\xi=a_{\sigma}/a_{\tau}=4$, where $a_{\sigma}$ and $a_{\tau}$ are the spatial and the temporal lattice spacings, respectively. The bare anisotropy $\gamma_F$ in the quark field action is numerically tuned by the dispersion relation of mesons so that the renormalized fermionic anisotropy coincides with that of gauge field. This calibration of bare anisotropy is performed to the level of 1 % statistical accuracy in the quark mass region below the charm quark mass. The systematic uncertainty in the calibration is estimated by comparing the results from different types of dispersion relations, which results in 3 % on our coarsest lattice and tends to vanish in the continuum limit. In the chiral limit, there is an additional systematic uncertainty of 1 % from the chiral extrapolation. Taking the central value $\gamma_F=\gamma_F^*$ from the result of the calibration, we compute the light hadron spectrum. Our hadron spectrum is consistent with the result by UKQCD Collaboration on the isotropic lattice. We also study the response of the hadron spectrum to the change of anisotropic parameter, $\gamma_F \to \gamma_F^* + \delta\gamma_F$. We find that the change of $\gamma_F$ by 2 % induces a change of 1 % in the spectrum for physical quark masses. Thus the systematic uncertainty on the anisotropic lattice, as well as the statistical one, is under control.Comment: 27 pages, 25 eps figures, LaTe

De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images

'Spillout' effect in gold nanoclusters embedded in c-Al2O3(0001) matrix

Gold nanoclusters are grown by 1.8 MeV Au^\sup{2+} implantation on c-Al\sub{2}O\sub{3}(0001)substrate and subsequent air annealing at temperatures 1273K. Post-annealed samples show plasmon resonance in the optical (561-579 nm) region for average cluster sizes ~1.72-2.4 nm. A redshift of the plasmon peak with decreasing cluster size in the post-annealed samples is assigned to the 'spillout' effect (reduction of electron density) for clusters with ~157-427 number of Au atoms fully embedded in crystalline dielectric matrix with increased polarizability in the embedded system.Comment: 14 Pages (figures included); Accepted in Chem. Phys. Lett (In Press

Effective theory of the Delta(1232) in Compton scattering off the nucleon

We formulate a new power-counting scheme for a chiral effective field theory of nucleons, pions, and Deltas. This extends chiral perturbation theory into the Delta-resonance region. We calculate nucleon Compton scattering up to next-to-leading order in this theory. The resultant description of existing $\gamma$p cross section data is very good for photon energies up to about 300 MeV. We also find reasonable numbers for the spin-independent polarizabilities $\alpha_p$ and $\beta_p$.Comment: 29 pp, 9 figs. Minor revisions. To be published in PR

Nematic Films and Radially Anisotropic Delaunay Surfaces

We develop a theory of axisymmetric surfaces minimizing a combination of surface tension and nematic elastic energies which may be suitable for describing simple film and bubble shapes. As a function of the elastic constant and the applied tension on the bubbles, we find the analogues of the unduloid, sphere, and nodoid in addition to other new surfaces.Comment: 15 pages, 18 figure

Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement

Recent studies on observed wind variability have revealed a decline (termed “stilling”) of near-surface wind speed during the last 30–50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014–2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer