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Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency. 

Biomarkers play an important role in early detection and intervention in Alzheimer’s disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as a univariate biomarker to index cortical morphometry for tracking AD progression. The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that finds the most economical way between measures. To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an efficient algorithm to compute the WT map based on computational geometry. We apply the algorithm to analyze cortical shape difference between dementia due to AD and normal aging individuals. The experimental results reveal the effectiveness of our proposed method which yields better statistical performance than other competiting methods including the OT. 

Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD. 

Emerging applications of compact high-voltage SiC modules pose strong challenges in the module package insulation design. Such SiC module insulations are subjected to both high voltage DC and PWM excitations between different terminals during different switching intervals. High dV/dt strongly interferes with partial discharge (PD) testing as it is hard to distinguish PD pulses and PWM excitation induced interferences. This paper covers both the testing and modeling of PD phenomena in high-voltage power modules. A high dV/dt PD testing platform is proposed, which involves a Super-High-Frequency (SHF, >3GHz) down-mixing PD detection receiver and a high-voltage scalable square wave generator. The proposed method captures SHF PD signatures and determines PDIV for packaging insulation. Using this platform, this paper provides a group of PDIV comparisons of packaging insulation under DC and PWM waveforms and discloses discrepancies in these PDIV results with respect to their excitations. Based on these PD testing results, the paper further provides a model using space charge accumulation to explain the PD difference under DC and PWM waveforms. Both simulation and sample testing results are included in this paper to support this hypothesis. With this new model, the paper includes an updated insulation design procedure for high-voltage power modules. 

A highly chemoselective as well as enantioselective fluorescent probe has been discovered for the recognition of the acidic amino acids, including glutamic acid and aspartic acid. This study has established a novel amino acid recognition mechanism by an aldehyde-based fluorescent probe.