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Guzzo, A.

Publications and source records attributed to Guzzo, A..

2 recordsLinked to original sources

Examining the Atypical Structure Adopted by the 37-51 Segment of α-synuclein in the Early Stage of Aggregation

The aggregation of -synuclein into insoluble filaments is a hallmark of Parkinsons disease. Upon membrane binding, -synuclein adopts a helical conformation, whereas during neurodegeneration it misfolds and aggregates into {beta}-sheet-rich structures. However, the factors that trigger the misfolding and aggregation remain poorly understood. In this study, coarse-grained molecular dynamics simulations are used to characterize the structural ensembles of -synuclein monomers and dimers at atomic resolution during the early stage of aggregation, covering an effective timescale of 29.7 ms. The simulations reveal that a short sequence spanning residues 37-51 predominantly adopts three distinct conformations: {beta}-hairpin, braid, and random coil. Here, focus is placed on a braid conformation, which, to the best of our knowledge, has not been reported previously in either experimental or computational studies. Although we could not definitively rule out the possibility that the coarse-grained force field overestimates the stability of the braid, two all-atom simulations demonstrated that this structure is sterically feasible and metastable. Furthermore, in monomers, the braid appears to hinder the formation of long-range intramolecular interactions, leaving the N-terminus, NAC (non-amyloid-{beta} component) region, and C-terminus solvent-exposed and potentially facilitating fibril nucleation. In dimers, however, the braid appears to impede intermolecular contacts, especially homotypic intermolecular interactions, thereby slowing aggregation. Upon unfolding of the braid structure, intermolecular contacts, including homotypic interactions, increase noticeably.

biophysics↗

Following the Robot's Lead: Predicting Human and Robot Movement from EEG in a Motor Learning HRI Task

Human-robot interaction (HRI) offers unique opportunities to study the neuroscience of human motor control through controlled and reproducible sensory stimuli. In this study, we introduce an innovative neuroscience-HRI framework inspired by the Serial Reaction Time (SRT) task, that combines EEG with a task where a humanoid robot performs preprogrammed movement sequences that are mirrored by a human participant in real time. The use of a humanoid robot ensures precise and repeatable sensory-motor stimuli in the 3D peripersonal space of the participant, providing experimental conditions that may be challenging to replicate with traditional methods. Behavioral performance is assessed by measuring the temporal lag between human and robot movements, which decreases with training, reflecting motor sequence learning. Concurrently, EEG data from the human participant is analyzed to reveal neural correlates of learning and movement dynamics. Event-Related Spectral Perturbations (ERSP) in theta, mu, and beta frequency bands demonstrate distinct patterns associated with rest, fixation, and movement. Furthermore, the ERSP changes over successive trials reflect the progression of sequence learning, highlighting the relationship between neural oscillations and motor learning. A Markov-Switching Linear Regression model further decodes EEG signals to predict movement parameters including both human and robot position and velocity in a time-resolved manner. Our findings highlight the potential of HRI as a robust platform for neuroscience research and underscore the value of EEG-based neural decoding in understanding motor sequence learning. This work suggests further advances for integrating robotics into neuroscience and rehabilitation research.

neuroscience↗