An experiment was designed to determine the impact of the force requirements on the production of bimanual 1:2 coordination patterns requiring the same (symmetric) or different (asymmetric) forces when Lissajous displays and goal templates are provided. The Lissajous displays have been shown to minimize the influence of attentional and perceptual constraints allowing constraints related to neural crosstalk to be more clearly observed. Participants (N=20) were randomly assigned to a force condition in which the left or right limb was required to produce more force than the contralateral limb. In each condition participants were required to rhythmically coordinate the pattern of isometric forces in a 1:2 coordination pattern. Participant performed 13 practice trials and 1 test trial per force level. The results indicated that participants were able to effectively coordinate the 1:2 multi-frequency goal patterns under both symmetric and asymmetric force requirements. However, consistent distortions in the force and force velocity time series were observed for one limb that appeared to be associated with the production of force in the contralateral limb. Distortions in the force produced by the left limb occurred regardless of the force requirements of the task (symmetric, asymmetric) or whether the left or right limb had to produce more force than the contralateral limb. However, distinct distortions in the right limb occurred only when the left limb was required to produce 5 times more force than the right limb. These results are consistent with the notion that neural crosstalk can influence both limbs, but may manifest differently for each limb depending on the force requirements of the task.
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http://dx.doi.org/10.1016/j.humov.2016.12.007 | DOI Listing |
Development
January 2025
School of Science, Technische Universität Dresden, 01062 Dresden, Germany.
The elongation of tissues and organs is important for proper morphogenesis in animal development. In Drosophila ovaries, the elongation of egg chambers involves aligned Collagen IV fiber-like structures, a gradient of extracellular matrix stiffness and actin-based protrusion-driven collective cell migration, leading to the rotation of the egg chamber. Egg chamber elongation and rotation depend on the atypical cadherin Fat2.
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January 2025
Department of Biochemistry, Université de Genève, CH-1211 Genève, Switzerland.
regenerates one head when cut, but how forces shaping the head are coordinated remains unclear. Soft compression of 's head-regenerating tissues induces the formation of viable, two-headed animals. Compression creates new topological defects in the supracellular orientational order of muscular actin fibers, associated with additional heads.
View Article and Find Full Text PDFPlast Reconstr Surg Glob Open
January 2025
From the Department of Plastic and Reconstructive Surgery, Keio University School of Medicine, Tokyo, Japan.
Subcutaneous indwelling of nonabsorbable threads for tissue fixation and tension reduction is often used in plastic surgery and is applied in breast reduction surgery, umbilicoplasty, and in frontalis muscle lifting for congenital ptosis. However, in a few cases, exposure of the indwelling thread necessitates its removal. Exposure of the indwelling threads mostly occurs due to exposure of the knots.
View Article and Find Full Text PDFSimulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFCureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
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