Objective To identify plasma-based biomarkers for Parkinson's Disease (PD) risk. Methods In a discovery cohort of 152 PD patients, plasma levels of 96 proteins were measured by multiplex immunoassay; proteins associated with age at PD onset were identified by linear regression. Findings from discovery screening were then assessed in a second cohort of 187 PD patients, using a different technique. Finally, in a third cohort of at-risk, asymptomatic individuals enrolled in the Parkinson's Associated Risk Study (PARS, n=134), plasma levels of the top candidate biomarker were measured, and dopamine transporter (DAT) imaging performed, to evaluate the association of plasma protein levels with dopaminergic system integrity. Results One of the best candidate protein biomarkers to emerge from discovery screening was apolipoprotein A1 (ApoA1, p=0.001). Low levels of ApoA1 correlated with earlier PD onset, with a 26% decrease in risk of developing PD associated with each tertile increase in ApoA1 (Cox proportional hazards p<0.001, hazard ratio=0.742). The association between plasma ApoA1 levels and age at PD onset replicated in an independent cohort of PD patients (p<0.001). Finally, in the PARS cohort of high-risk, asymptomatic subjects, lower plasma levels of ApoA1 were associated with greater putaminal DAT deficit (p=0.037). Interpretation Lower ApoA1 levels correlate with dopaminergic system vulnerability in symptomatic PD patients and in asymptomatic individuals with physiological reductions in dopamine transporter density consistent with prodromal PD. Plasma ApoA1 may be a new biomarker for PD risk.

ObjectiveGlucocerebrosidase gene (GBA) variants that cause Gaucher disease are associated with Parkinson disease (PD) and dementia with Lewy bodies (DLB). To investigate the role of GBA variants in multiple system atrophy (MSA), we analyzed GBA variants in a large case–control series.MethodsWe sequenced coding regions and flanking splice sites of GBA in 969 MSA patients (574 Japanese, 223 European, and 172 North American) and 1509 control subjects (900 Japanese, 315 European, and 294 North American). We focused solely on Gaucher-disease-causing GBA variants.ResultsIn the Japanese series, we found nine carriers among the MSA patients (1.65%) and eight carriers among the control subjects (0.89%). In the European series, we found three carriers among the MSA patients (1.35%) and two carriers among the control subjects (0.63%). In the North American series, we found five carriers among the MSA patients (2.91%) and one carrier among the control subjects (0.34%). Subjecting each series to a Mantel–Haenszel analysis yielded a pooled odds ratio (OR) of 2.44 (95% confidence interval [CI], 1.14–5.21) and a P-value of 0.029 without evidence of significant heterogeneity. Logistic regression analysis yielded similar results, with an adjusted OR of 2.43 (95% CI 1.15–5.37) and a P-value of 0.022. Subtype analysis showed that Gaucher-disease-causing GBA variants are significantly associated with MSA cerebellar subtype (MSA-C) patients (P = 7.3 × 10−3).InterpretationThe findings indicate that, as in PD and DLB, Gaucher-disease-causing GBA variants are associated with MSA.

Why is it difficult to refold a previously folded sheet of paper? We show that even crease patterns with only one designed folding motion inevitably contain an exponential number of 'distractor' folding branches accessible from a bifurcation at the flat state. Consequently, refolding a sheet requires finding the ground state in a glassy energy landscape with an exponential number of other attractors of higher energy, much like in models of protein folding (Levinthal's paradox) and other NP-hard satisfiability (SAT) problems. As in these problems, we find that refolding a sheet requires actuation at multiple carefully chosen creases. We show that seeding successful folding in this way can be understood in terms of sub-patterns that fold when cut out ('folding islands'). Besides providing guidelines for the placement of active hinges in origami applications, our results point to fundamental limits on the programmability of energy landscapes in sheets. arXiv:1703.04161v1 [cond-mat.soft]

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Disordered mechanical systems, when strongly deformed, have complex configuration spaces with multiple stable states and pathways connecting them. The topology of such pathways determines which states are smoothly accessible from any part of configuration space. Controlling this topology would allow us to limit access to undesired states and select desired behaviors in metamaterials. Here, we show that the topology of such pathways, as captured by bifurcation diagrams, can be tuned using imperfections such as stiff hinges in elastic networks and creased thin sheets. We derive Linear Programming-like equations for designing desirable pathway topologies. These ideas are applied to eliminate the exponentially many ways of misfolding self-folding sheets by making some creases stiffer than others. Our approach allows robust folding by entire classes of external folding forces. Finally, we find that the bifurcation diagram makes pathways accessible only at specific folding speeds, enabling speed-dependent selection of different folded states.

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Mechanical metamaterials are usually designed to show desired responses to prescribed forces. In some applications, the desired force–response relationship is hard to specify exactly, but examples of forces and desired responses are easily available. Here, we propose a framework for supervised learning in thin, creased sheets that learn the desired force–response behavior by physically experiencing training examples and then, crucially, respond correctly (generalize) to previously unseen test forces. During training, we fold the sheet using training forces, prompting local crease stiffnesses to change in proportion to their experienced strain. We find that this learning process reshapes nonlinearities inherent in folding a sheet so as to show the correct response for previously unseen test forces. We show the relationship between training error, test error, and sheet size (model complexity) in learning sheets and compare them to counterparts in machine-learning algorithms. Our framework shows how the rugged energy landscape of disordered mechanical materials can be sculpted to show desired force–response behaviors by a local physical learning process.

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