Mitochondrial dysfunction has been implicated in the etiology of monogenic Parkinson’s disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here, we comprehensively assessed the role of mitochondrial function-associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. We calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. We further reported that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomization, which showed that 14 of these mitochondrial function-associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy, but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD.

Using the memristive properties of vanadium dioxide, we experimentally demonstrate an adaptive filter by placing a memristor into an LC contour. This circuit reacts to the application of select frequency signals by sharpening the quality factor of its resonant response, and thus “learns” according to the input waveform. The proposed circuit employs only analog passive elements, and may find applications in biologically inspired processing and information storage. We also extend the learning-circuit framework mathematically to include memory-reactive elements, such as memcapacitors and meminductors, and show how this expands the functionality of adaptive memory filters.

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In this work, we experimentally and theoretically explore voltage controlled oscillations occurring in microbeams of vanadium dioxide. These oscillations are a result of the reversible insulator to metal phase transition in vanadium dioxide. Examining the structure of the observed oscillations in detail, we propose a modified percolative-avalanche model which allows for voltage-triggering. This model captures the periodicity and waveshape of the oscillations as well as several other key features. Importantly, our modeling shows that while temperature plays a critical role in the vanadium dioxide phase transition, electrically induced heating cannot act as the primary instigator of the oscillations in this configuration. This realization leads us to identify electric field as the most likely candidate for driving the phase transition.

In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons.