Characterization of Laser-Resistant Port Wine Stain Blood Vessels Using In Vivo Reflectance Confocal Microscopy.

Background and objectivesPort wine stain (PWS) is a congenital vascular malformation of the human skin. Laser is the treatment of choice for PWS. Laser-resistant PWS is one crucial factor accounting for inadequate treatment outcome, which needs to be fully characterized. This study aims to quantitatively characterize the morphology of laser-resistant PWS blood vessels in the upper papillary dermis using in vivo reflectance confocal microscopy (RCM).Study design/materials and methodsA total of 42 PWS subjects receiving laser treatment from August 2016 through July 2018 were enrolled into this study. Thirty-three subjects had facial PWS; nine had extremity PWS. All subject's PWS received multiplex 585/1,064 nm laser treatment. RCM images were taken before and after treatment. The density, diameter, blood flow, and depth of PWS blood vessels were analyzed.ResultsWe found 44.4% PWS on the extremities (four out of nine subjects) were laser-resistant, which was significantly higher (P < 0.001) when compared with those PWS on the face (15.2%, 5 out of 33 subjects). The laser-resistant facial PWS blood vessels had significantly higher blood flow (1.35 ± 0.26 U vs. 0.89 ± 0.22 U, P < 0.001), larger blood vessel diameters (109.60 ± 18.24 µm vs. 84.36 ± 24.04 µm, P = 0.033) and were located deeper in the skin (106.01 ± 13.87 µm vs. 87.82 ± 12.57 µm, P < 0.001) in the skin when compared with laser-responsive PWS on the face. The average PWS blood vessel density (17.01 ± 4.63/mm2 vs. 16.61 ± 4.44/mm2 , P = 0.857) was not correlated to the laser resistance.ConclusionsLaser-resistant PWS blood vessels had significantly higher blood flow, larger diameters, and were located deeper in the skin. RCM can be a valuable tool for a prognostic evaluation on laser-resistant lesions before treatment, thereby providing guidance for tailored laser treatment protocols, which may improve the therapeutic outcome. The limitations for this study include relative small sample size and acquisitions of different blood vessels before and after 2 months of treatment. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Detection of coherent magnons via ultrafast pump-probe reflectance spectroscopy in multiferroic Ba0.6Sr1.4Zn2Fe12O22

We report the detection of a magnetic resonance mode in multiferroic Ba0.6Sr1.4Zn2Fe12O22 using time domain pump-probe reflectance spectroscopy. Magnetic sublattice precession is coherently excited via picosecond thermal modification of the exchange energy. Importantly, this precession is recorded as a change in reflectance caused by the dynamic magnetoelectric effect. Thus, transient reflectance provides a sensitive probe of magnetization dynamics in materials with strong magnetoelectric coupling, such as multiferroics, revealing new possibilities for application in spintronics and ultrafast manipulation of magnetic moments.Comment: 4 figure

Perfect separation of intraband and interband excitations in PdCoO2_2

The temperature dependence of the optical properties of the delafossite PdCoO$_2$ has been measured in the a-b planes over a wide frequency range. The optical conductivity due to the free-carrier (intraband) response falls well below the interband transitions, allowing the plasma frequency to be determined from the $f$-sum rule. Drude-Lorentz fits to the complex optical conductivity yield estimates for the free-carrier plasma frequency and scattering rate. The in-plane plasma frequency has also been calculated using density functional theory. The experimentally-determined and calculated values for the plasma frequencies are all in good agreement; however, at low temperature the optically-determined scattering rate is much larger than the estimate for the transport scattering rate, indicating a strong frequency-dependent renormalization of the optical scattering rate. In addition to the expected in-plane infrared-active modes, two very strong features are observed that are attributed to the coupling of the in-plane carriers to the out-of-plane longitudinal optic modes.Comment: 7 pages with five figures and three tables; 4 pages of supplementary materia

Electronic and phonon excitations in {\alpha}-RuCl3_3

We report on THz, infrared reflectivity and transmission experiments for wave numbers from 10 to 8000 cm$^{-1}$ ($\sim$ 1 meV - 1 eV) and for temperatures from 5 to 295 K on the Kitaev candidate material {\alpha}-RuCl$_3$. As reported earlier, the compound under investigation passes through a first-order structural phase transition, from a monoclinic high-temperature to a rhombohedral low-temperature phase. The phase transition shows an extreme and unusual hysteretic behavior, which extends from 60 to 166 K. In passing this phase transition, in the complete frequency range investigated we found a significant reflectance change, which amounts almost a factor of two. We provide a broadband spectrum of dielectric constant, dielectric loss and optical conductivity from the THz to the mid infrared regime and study in detail the phonon response and the low-lying electronic density of states. We provide evidence for the onset of an optical energy gap, which is of order 200 meV, in good agreement with the gap derived from measurements of the DC electrical resistivity. Remarkably, the onset of the gap exhibits a strong blue shift on increasing temperatures.Comment: 18 pages, 7 figure

Anomalous Proximity Effect in Underdoped YBaCuO Josephson Junctions

Josephson junctions were photogenerated in underdoped thin films of the YBa$_2$Cu$_3$O$_{6+x}$ family using a near-field scanning optical microscope. The observation of the Josephson effect for separations as large as 100 nm between two wires indicates the existence of an anomalously large proximity effect and show that the underdoped insulating material in the gap of the junction is readily perturbed into the superconducting state. The critical current of the junctions was found to be consistent with the conventional Josephson relationship. This result constrains the applicability of SO(5) theory to explain the phase diagram of high critical temperature superconductors.Comment: 11 pages, 4 figure

Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Separating an image into reflectance and shading layers poses a challenge for learning approaches because no large corpus of precise and realistic ground truth decompositions exists. The Intrinsic Images in the Wild~(IIW) dataset provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images. A number of methods use IIW to learn statistical dependencies between the images and their reflectance layer. Although learning plays an important role for high performance, we show that a standard signal processing technique achieves performance on par with current state-of-the-art. We propose a loss function for CNN learning of dense reflectance predictions. Our results show a simple pixel-wise decision, without any context or prior knowledge, is sufficient to provide a strong baseline on IIW. This sets a competitive baseline which only two other approaches surpass. We then develop a joint bilateral filtering method that implements strong prior knowledge about reflectance constancy. This filtering operation can be applied to any intrinsic image algorithm and we improve several previous results achieving a new state-of-the-art on IIW. Our findings suggest that the effect of learning-based approaches may have been over-estimated so far. Explicit prior knowledge is still at least as important to obtain high performance in intrinsic image decompositions.Comment: CVPR 201