Link and Network-wide Study of Incoherent GN/EGN Models

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy

Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies

Highly ordered protein patterns generated from self-assembly of mixed protein coated nanoparticles

The generation of protein patterns with controllable spacing in the nanometer to micrometer range is of great interest particularly for gaining a more fundamental understanding of the molecular mechanisms associated with protein-surface interactions. Information which can provide new insights into how proteins regulate cell shape and function have great interest in many areas including medical implants, tissue engineering, sustained drug delivery devices, biofilms, and biosensors. We present here a new but simple method for generating highly ordered protein patterns that can be applied over large areas (cm2) based on the self-assembly of mixed nanoparticles of different size, from very low volume fractions. The proof concept experiments initially involve separately coating the particles with a different type of protein. The particles are then mixed at variable concentrations at low total volume fraction. Experiments are performed with fluorescently labelled proteins. For example, FITC-labelled lysozyme is adsorbed to 2µm carboxylated polystyrene particles and rhodamine-labelled BSA is adsorbed to 0.2µm aminated polystyrene particles both in PBS at pH=7.4. In separate experiments either both types of protein-coated particles are mixed in solution, or one protein coated particle is mixed with an uncoated particle. The particle suspensions are drop-cast onto the centre of a rubber ring placed on a hydrophilic Si wafer substrate. A concave shaped layer of solvent is formed allowing for long range assembly of binary colloidal particles firstly through lateral capillary forces, and later by convective flow and entropic forces. The resultant patterns consist of highly ordered hexagonal arrays of large particles as a single layer encapsulated by crystals of the smaller particles, in which single or binary proteins patterns are generated. The coverage is simply controlled by calculating the area of particles needed to fill the rubber ring, and the spacing is controlled by the appropriate ratio of starting particles. The ordered protein patterns are shown using fluorescent microscopy combined with AFM and SEM analysis. The extent of protein adsorption to the particles is determined using XPS and zeta potential measurements. The method is very simple, fast and inexpensive and we demonstrate that the patterning of proteins covers the range for a few nanometers up to a few micrometers depending on the appropriate selection of particles

SSMS: A Split Step MultiBand Simulation Software

We introduce SSMS, a multiband optical fiber simulator entirely developed in MATLAB. SSMS solves the generalized nonlinear Schrödinger equation relying on the 4th order Runge-Kutta method in Interaction Picture (RK4IP) with adaptive step size approach and compare it with the widely used split-step Fourier method (SSFM). The simulator is validated considering S+C+L multiband transmission. Results show that the RK4IP method is approximately 10× faster than the traditional SSFM model for a similar level of accuracy

Highly ordered protein patterns from self-assembly of mixed colloids

Abstract not available

Reduced attachment of blood mononuclear and alphaM-beta2-expressing cells to surfaces coated with fish proteins

Abstract not available