Determining the safety, feasibility, and effects of anodal transcranial direct current stimulation on corticospinal excitability and quadriceps performance after anterior cruciate ligament reconstruction: a randomized crossover design.

Background: Alterations in corticospinal excitability (CSE) to the quadriceps persist after anterior cruciate ligament reconstruction (ACLR). Centrally targeted interventions, such as transcranial direct current stimulation (tDCS), may be necessary to increase CSE and quadriceps muscle strength. The purpose of this study was to determine (I) the feasibility and safety of a single session of tDCS and (II) the effects of a single session of tDCS on CSE and quadriceps muscle performance in participants after ACLR.

Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems.

The behaviors of many complex systems, from nanostructured materials to animal colonies, are governed by local events/rearrangements that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding their emergence and propagation in the system is often challenging. Common strategies consist, for example, in monitoring over time parameters (descriptors) that are designed ad hoc to analyze certain systems.

Synthetic data generation in motion analysis: A generative deep learning framework.

Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The objective of the current study is to introduce a data augmentation strategy that relies on a variational autoencoder to generate synthetic data of kinetic and kinematic variables.

Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects.

The application of machine learning in the field of motion capture research is growing rapidly. The purpose of the study is to implement a long-short term memory (LSTM) model able to predict sagittal plane hip joint moment (HJM) across three distinct cohorts (healthy controls, patients and post-operative patients) starting from 3D motion capture and force data. Statistical parametric mapping with paired samples -test was performed to compare machine learning and inverse dynamics HJM predicted values, with the latter used as gold standard.

Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables.

The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively refine three-site water models to fit macroscopic (average) thermodynamic properties, providing state-of-the-art three-site models that still present some deviations from the liquid water properties.

Hip joint contact forces are lower in people with femoroacetabular impingement syndrome during squat tasks.

It remains unknown if hip joint forces during squat tasks are altered in people with femoroacetabular impingement syndrome (FAIS). The aim of this study is to compare hip joint forces between people with FAIS and healthy controls during double leg squat and single leg squat tasks and within limbs during a single leg squat task in people with FAIS. Kinematic and kinetic data were collected in eight people with FAIS and eight healthy matched controls using 3D motion capture and force plates.

Automatic Optimization of Lipid Models in the Martini Force Field Using .

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ , an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF.

Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG.

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables.

Exploring the Potential of Benzene-1,3,5-tricarboxamide Supramolecular Polymers as Biomaterials.

The fast dynamics occurring in natural processes increases the difficulty of creating biomaterials capable of mimicking Nature. Within synthetic biomaterials, water-soluble supramolecular polymers show great potential in mimicking the dynamic behavior of these natural processes. In particular, benzene-1,3,5-tricaboxamide (BTA)-based supramolecular polymers have shown to be highly dynamic through the exchange of monomers within and between fibers, but their suitability as biomaterials has not been yet explored.