Articles you may be interested inEffect of intramolecular charge transfer on the two-photon absorption behavior of multibranched triphenylamine derivations Tunneling in jet-cooled 5-methyltropolone and 5-methyltropolone-OD. Coupling between internal rotation of methyl group and proton transfer Resonance Raman study of solvent dynamics on the spectral broadening and intramolecular charge transfer of a hemicyanine dye in aqueous solutionThe fluorescence excitation spectrum and the single vibronic level dispersed fluorescence spectra in the region of the S 0 S 1 transition were measured for jet-cooled 1-phenylpyrrole. The 0-0 band was observed at 35 493 cm Ϫ1 . Long and low-frequency progressions with somewhat irregular intensity distributions appeared on both spectra, and were assigned to torsional motion. The torsional energy levels in the S 0 and S 1 states were obtained up to 25 and 16 quanta, respectively. The torsional potentials in both states could be determined from the sufficient number of energy levels observed. In the S 0 state the most stable conformation was determined to be a twisted form with a dihedral angle of 38.7°, where the planar barrier height was calculated to be 457 cm Ϫ1 , and the perpendicular to be 748 cm Ϫ1 . On the other hand, it was discovered that 1-phenylpyrrole in the S 1 state also had a twisted form with a somewhat smaller dihedral angle of 19.8°, and that the barrier to planarity was 105 cm Ϫ1 and to perpendicularity, 1526 cm Ϫ1 . These facts indicated that the electronic excitation caused 1-phenylpyrrole to be rigid to twist. 1-Phenylpyrrole and its derivatives have been reported as a group of twisted intramolecular charge-transfer ͑TICT͒ molecules. No indication of TICT appeared on the shape of the S 1 -state torsional potential determined. The relation between torsional potential and TICT is discussed based on the results of this study.

Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.

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Background:Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity.Methods and Results:In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features.Conclusion:CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

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