Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images. Different data augmentation methods are applied to optimize the DCNN performance. We also investigated the efficacy of different stain normalization methods as a pre-processing step. The proposed network architecture using a pre-trained Xception model yields 92.50% average classification accuracy.
Results are presented from a study of various sunspot contrast parameters in broadband red (672.3 nm) Cartesian full-disk digital images taken at the San Fernando Observatory (SFO) over eight years, 1997 -2004, of the twenty-third sunspot cycle. A subset of over 2700 red sunspots was analyzed and values of average and maximum sunspot contrast as well as maximum umbral contrast were compared to various sunspot parameters. Average and maximum sunspot contrasts were found to be significantly correlated with sunspot area (r s = − 0.623 and r s = − 0.714, respectively). Maximum umbral contrast was found to be significantly correlated with umbral area (r s = − 0.535). These results are in agreement with the works of numerous other authors. No significant dependence was detected between average contrast, maximum contrast, or maximum umbral contrast during the rising phase of the solar cycle (r s = 0.024, r s = 0.033, and r s = 0.064, respectively). During the decay phase, no significant correlation was found between average contrast or maximum contrast and time (r s = − 0.057 and r s = 0.009, respectively), with a weak dependence seen between maximum umbral contrast and cycle (r s = 0.102).
Hydrogen-and methyl-capped polyynes have been synthesized by irradiating pure liquid toluene with 35 femtosecond, 300 μJ laser pulses having a central wavelength of 800 nm, generated by a regeneratively amplified Ti:sapphire tabletop laser at a repetition rate of 1 kHz. Raman spectroscopy was used to confirm the presence of polyynes in the irradiated samples while highperformance liquid chromatography was used to separate hydrogen-capped polyynes up to C18H2 and methyl-capped polyynes up to HC14CH3. These represent the first such methyl-capped polyynes and the longest hydrogen capped chains synthesized to date by the ultrafast laser based method. Furthermore our results show that choice of the starting solvent molecule directly influences the end caps of the polyynes which can be produced.
Highly enhanced Raman scattering of graphene on a plasmonic nano-structure platform is demonstrated. The plasmonic platform consists of silver nano-structures in a periodic array on top of a gold mirror. The gold mirror is used to move the hot spot to the top surface of the silver nano-structures, where the graphene is located. Two different nano-structures, ring and crescent, are studied. The actual Raman intensity is enhanced by a factor of 890 for the G-peak of graphene on crescents as compared to graphene on a silicon dioxide surface. The highest enhancement is observed for the G-peak as compared to the 2D-peak. The results are quantitatively well-matched with a theoretical model using an overlap integral of incident electric field intensities with the corresponding intensities of Raman signals at the G- and 2D-peaks. The interaction of light with nano-structures is simulated using finite element method (FEM).
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