Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at MICCAI International Conference 202

Effect of Silicon Content on the Microstructure and Mechanical Properties of Ti-Si-B-C Nanocomposite Hard Coatings

The content of the individual elements in the nanocomposite hard multicomponent coatings is known to affect the phase formation during deposition and the properties of the coatings. Silicon is one such element, which has been shown to improve or alter the properties of the nanocomposite hard coating systems. In the present work, we investigated the effect of Si addition on the microstructure, phase, and mechanical behavior of Ti-Si-B-C nanocomposite hard films deposited by direct current (DC) magnetron sputtering. X-ray photoelectron spectroscopy (XPS) analyses reveal that, with the increase of Si content, SiB4 and TiSi2 softer phases (compared to the hard phases of TiB2 and B4C) are formed. The Ti-Si-B-C films deposited with 13.4 at. pct Si content show maximum hardness ~ 32.55 GPa and modulus ~ 381.6 GPa. With the increase of Si content in the films, the hardness and modulus are decreased. The H/E ratio, which is a measure of materials’ ability to take the strain prior to deformation, is also decreased with the increase of Si content, suggesting the lowering of toughness of the films with higher Si content

Chemistry by Number Theory

Aspects of elementary number theory pertaining to the golden ratio and the golden spiral are shown to be related to and therefore of importance in the simulation of chemical phenomena. Readily derived concepts include atomic structure, electronegativity, bond order, the theory of covalent interaction and aspects of molecular chirality. The physical interpretation of the results implicates the 4D structure of space-time as a fundamental consideration. The implied classical nature of 3D molecular structure identifies molecular mechanics as an ideal method for structure optimization, and it is shown that the parameters may be related to number theory. All results point at a 4D wave structure of electrostatic charge.Alexander von Humboldt Foundationhttp://www.springer.com/series/430hb2014ai201