Rise-and-fall dynamics reveal a molecular and cellular vulnerability axis in prion-like <i>α</i> -synuclein propagation

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-31

Abstract The spread of misfolded proteins through neuronal circuits is a defining feature of neurodegenerative disease, yet the dynamics underlying this process remain poorly understood. Most studies rely on sparsely sampled datasets that capture spatial patterns of pathology but not their temporal evolution. Here, we analyze longitudinal histopathology measurements of α -synuclein pathology across hundreds of brain regions in a mouse model of Parkinson’s disease. Network-based dynamical modeling shows that regional pathology does not simply accumulate but instead follows rise-and-fall trajectories across the brain. The inferred parameter landscape reveals a one-dimensional vulnerability axis along which regions with stronger fall dynamics have greater monoaminergic neuronal composition and higher expression of proteostatic and metabolic genes. This vulnerability axis replicates in an independent histopathological dataset, indicating that its dominant transcriptomic structure is preserved. Together, these results suggest that regional vulnerability collapses onto a low-dimensional molecular and cellular axis defined by rise-and-fall dynamics.

Topological and morphological signatures of disorder in a self-assembled, soft matter sponge network

ArXiv.org · 2026-05-13

Many soft matter systems exhibit ordered, polycontinuous network morphologies, such as the cubic (double) gyroid or diamond, as well as disordered network morphologies known generically as ``random sponges". While presumed to share similar local packing geometry, the structural relationship between these ordered and disordered network morphologies has remained obscure. We use slice and view scanning electron microscopy to analyze and compare multi-scale morphological features of an ordered double-gyroid morphology to the amorphous sponge morphology formed in the same block copolymer sample. We find that node valence of the minority component network of the sponge is mostly gyroidal (trivalent), with a small fraction of diamond-like (tetravalent) connections. We analyze mesoatoms -- space-filling volumes occupied by chains around each network node -- finding significant differences in shape and size between ordered and amorphous regions. Local block thickness and inter-domain curvature within mesoatomic units of the disordered sponge exhibits a surprisingly similar degree of dispersity to the ordered double-gyroid. The mean differences in local packing geometry derive from topological distinction: loops of the minority networks of the ordered double-gyroid are intercatenated, while loops of the disordered sponge are not. In this way, the sponge may be viewed as disordered variant of a single-gyroidal morphology. We exploit these topological differences to demarcate the boundary region between ordered and disordered networks and highlight modulations of the mesoatom motifs at the boundary. These observations point to new questions about potential metastability of disordered networks and their possible role as kinetic precursors to long-range ordered network morphologies.

Staying connected: How close friendships supported emotional well-being during the COVID-19 pandemic.

Emotion · 2026-03-19

= 205; 10,088 observations), we examined how close friendship networks related to emotional well-being during the early months of the pandemic (May-October 2020). Leveraging prepandemic social network data and 28 days of ecological momentary assessments of affect and social interactions, we found that students with more close college friends reported higher positive affect and lower negative affect in daily life, even while physically separated from those friends. These individuals were buffered from the emotional toll of pandemic-related stressors, a pattern not explained by personality, interaction frequency, or living conditions. Rather, participants with more close friends experienced higher quality online interactions. Additionally, personal disclosures, whether in-person or online, were consistently associated with greater feelings of closeness. Notably, individuals with fewer close friends showed the largest boost in closeness following partner disclosures, suggesting that emotional sharing may play a compensatory role for those with limited social ties. These findings illustrate how friendships can continue to shape affective experiences from afar and highlight disclosure as a key mechanism through which closeness and its emotional benefits can be cultivated. Integrating social network structure, daily affect, and interaction-level processes, this work advances affective science by providing evidence of how the social regulation of emotion extends beyond physical proximity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Linear control theory for jammed particle systems

arXiv (Cornell University) · 2026-03-06

Amorphous particulate matter constitutes a wide range of natural and synthetic materials. Despite this ubiquity, the way in which these systems' disordered microstructure couples to their often subtle and complex dynamical behavior is not yet fully understood, with profound consequences for phenomena ranging from landscape evolution to cellular unjamming during tumor metastasis. With this paper, we introduce tools from linear control theory that quantify system response to external input, and demonstrate their utility in elucidating the dynamics of jammed amorphous materials under stress. Our results indicate that average controllability, the response of a system to perturbation, strongly correlates with particle rearrangement in systems subject to quasistatic shear, implying that average controllability is an accurate predictor of rearrangement dynamics in certain contexts. Moreover, we show that the time scale over which average controllability is calculated can be tuned to optimize its predictive capacity for particle rearrangement. Values of the optimal time scale provide physical insight into the system; namely, that multiple rearranging particles participate on average in vibrational eigenmodes of lower and lower energy as the system is sheared until the rearrangement event. Broadly, our study demonstrates that linear control theory is a promising mathematical framework for predicting and designing mechanical response in disordered media.

Sex Differences in Fall Frequency, Risk Factors, and Outcomes in Parkinson's Disease: A Cross‐Sectional Analysis

Movement Disorders Clinical Practice · 2026-05-07

BACKGROUND: Female sex is an independent fall risk factor in Parkinson's disease (PD), yet sex-specific fall patterns remain unclear. OBJECTIVES: To compare sex-specific fall risk and outcomes across PD, prodromal alpha-synucleinopathy (PAS), and healthy controls (HC); estimate fall frequency across PD progression; and assess how sex modifies fall risk and outcomes. METHODS: Fall outcomes were analyzed in the Parkinson's Progression Markers Initiative. Yearly rates of rare and frequent falls were estimated by time since diagnosis. PD participants were classified as never, rare, or frequent fallers. Clinical measures included motor, cognitive, behavioral, sleep, and autonomic domains. Outcomes included injuries and healthcare utilization. Regression models adjusted for age, sex, and disease duration with Benjamini-Hochberg correction. RESULTS: Among 3100 participants (937 PD, 1926 PAS, 237 HC; 6977 visits), PD participants had higher odds of falling than PAS (OR 1.66, 95% CI 1.46-1.87) and HC (OR 4.03, 95% CI 3.14-5.23). In PD and PAS, females had higher odds of injuries (OR 1.50, 95% CI 1.20-1.88) and fractures (OR 1.62, 95% CI 1.15-2.29), including hip (OR 2.30, 95% CI 1.09-4.91) and upper-extremity fractures (OR 2.67, 95% CI 1.51-4.85). Within PD, falls increased with disease duration and were higher in females (7 years: 42% vs. 32%; 14 years: 88% vs. 61%) despite milder clinical profiles. Across the Neuronal Synuclein Disease-Integrated Staging System (NSD-ISS) stages, fall occurrence was higher in females. CONCLUSION: Falls increase with disease duration and NSD-ISS stage. Female PD participants are at greater risk despite milder symptoms, supporting sex-specific prevention strategies.

Linear control theory for jammed particle systems

ArXiv.org · 2026-03-06

Amorphous particulate matter constitutes a wide range of natural and synthetic materials. Despite this ubiquity, the way in which these systems' disordered microstructure couples to their often subtle and complex dynamical behavior is not yet fully understood, with profound consequences for phenomena ranging from landscape evolution to cellular unjamming during tumor metastasis. With this paper, we introduce tools from linear control theory that quantify system response to external input, and demonstrate their utility in elucidating the dynamics of jammed amorphous materials under stress. Our results indicate that average controllability, the response of a system to perturbation, strongly correlates with particle rearrangement in systems subject to quasistatic shear, implying that average controllability is an accurate predictor of rearrangement dynamics in certain contexts. Moreover, we show that the time scale over which average controllability is calculated can be tuned to optimize its predictive capacity for particle rearrangement. Values of the optimal time scale provide physical insight into the system; namely, that multiple rearranging particles participate on average in vibrational eigenmodes of lower and lower energy as the system is sheared until the rearrangement event. Broadly, our study demonstrates that linear control theory is a promising mathematical framework for predicting and designing mechanical response in disordered media.

Topological and morphological signatures of disorder in a self-assembled, soft matter sponge network

arXiv (Cornell University) · 2026-05-13

Many soft matter systems exhibit ordered, polycontinuous network morphologies, such as the cubic (double) gyroid or diamond, as well as disordered network morphologies known generically as ``random sponges". While presumed to share similar local packing geometry, the structural relationship between these ordered and disordered network morphologies has remained obscure. We use slice and view scanning electron microscopy to analyze and compare multi-scale morphological features of an ordered double-gyroid morphology to the amorphous sponge morphology formed in the same block copolymer sample. We find that node valence of the minority component network of the sponge is mostly gyroidal (trivalent), with a small fraction of diamond-like (tetravalent) connections. We analyze mesoatoms -- space-filling volumes occupied by chains around each network node -- finding significant differences in shape and size between ordered and amorphous regions. Local block thickness and inter-domain curvature within mesoatomic units of the disordered sponge exhibits a surprisingly similar degree of dispersity to the ordered double-gyroid. The mean differences in local packing geometry derive from topological distinction: loops of the minority networks of the ordered double-gyroid are intercatenated, while loops of the disordered sponge are not. In this way, the sponge may be viewed as disordered variant of a single-gyroidal morphology. We exploit these topological differences to demarcate the boundary region between ordered and disordered networks and highlight modulations of the mesoatom motifs at the boundary. These observations point to new questions about potential metastability of disordered networks and their possible role as kinetic precursors to long-range ordered network morphologies.

Novel tools for comparing the architecture of psychopathology between neurogenetic disorders: An application to X- versus Y-chromosome aneuploidy effects in males

Psychological Medicine · 2025-01-01 · 2 citations

BACKGROUND: Psychiatric symptoms are typically highly inter-correlated at the group level. Collectively, these correlations define the architecture of psychopathology - informing taxonomic and mechanistic models in psychiatry. However, to date, it remains unclear if this architecture differs between etiologically distinct subgroups, despite the core relevance of this understanding for personalized medicine. Here, we introduce a new analytic pipeline to probe group differences in the psychopathology architecture - demonstrated through the comparison of two distinct neurogenetic disorders. METHODS: = 134 age-matched XY) to characterize the structure of correlations among 53 diverse measures of psychopathology in XXY/KS and XYY syndrome - enabling us to compare the effects of X- versus Y-chromosome dosage on the architecture of psychopathology at multiple, distinctly informative levels. RESULTS: Behavior correlation matrices describe the architecture of psychopathology in each syndrome. A comparison of matrix rows reveals that social problems and externalizing symptoms are most differentially coupled to other aspects of psychopathology in XXY/KS versus XYY. Clustering the difference between matrices captures coordinated group differences in pairwise coupling between measures of psychopathology: XXY/KS shows greater coherence among externalizing, internalizing, and autism-related features, while XYY syndrome shows greater coherence in dissociality and early neurodevelopmental impairment. CONCLUSIONS: These methods offer new insights into X- and Y-chromosome dosage effects on behavior, and our shared code can now be applied to other clinical groups of interest - helping to hone mechanistic models and inform the tailoring of care.

Mechanical prions as self-assembling microstructures

Newton · 2025-05-08 · 2 citations

tabletop model capable of undergoing prion- like conformational change

Iterative Compositional Data Generation for Robot Control

ArXiv.org · 2025-12-11

Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of tasks that arise in multi-object, multi-robot, and multi-environment settings. While recent generative models can synthesize useful data for individual tasks, they do not exploit the compositional structure of robotic domains and struggle to generalize to unseen task combinations. We propose a semantic compositional diffusion transformer that factorizes transitions into robot-, object-, obstacle-, and objective-specific components and learns their interactions through attention. Once trained on a limited subset of tasks, we show that our model can zero-shot generate high-quality transitions from which we can learn control policies for unseen task combinations. Then, we introduce an iterative self-improvement procedure in which synthetic data is validated via offline reinforcement learning and incorporated into subsequent training rounds. Our approach substantially improves zero-shot performance over monolithic and hard-coded compositional baselines, ultimately solving nearly all held-out tasks and demonstrating the emergence of meaningful compositional structure in the learned representations.