Reinforcement learning estimates muscle activations

A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in biomechanics, and hence can contribute to the development of the digital twin. Our preliminary results confirm the potential of RL algorithms to estimate the muscle activations of an athlete’s moves.Ein digitaler Zwilling des menschlichen neuromuskulären Systems kann die Vorhersage von Verletzungsrisiken und die Bewertung der Bereitschaft zur Rückkehr in den Sport erheblich verbessern. Algorithmen des bestärkenden Lernens (Reinforcement Learning, RL) lernen bereits physikalische Größen, die in der Biomechanik nicht messbar sind, und können daher zur Entwicklung des digitalen Zwillings beitragen. Unsere vorläufigen Ergebnisse bestätigen das Potenzial von RL-Algorithmen zur Schätzung der Muskelaktivierung bei den Bewegungen eines Sportlers

Parallelization of a Multigrid Algorithm on the KSR1

Here, we report our experiences in parallelizing a multigrid-like algorithm for the solution of the Poisson-problem on the KSR1. Keywords: Multigrid, domain decomposition, hierarchical transformation, parallel regions 1 Introduction Multigrid algorithms are known for their efficiency due to their asymptotically optimal convergence rate. For further acceleration it would be desirable to combine this property with the power of parallel processing. Unfortunately, this is not a straightforward task. Data dependencies in the smoothing iterations as well as in the coarse grid correction steps must be treated carefully. In the first section of this report we give a brief introduction to the employed multigrid method. For detailed information, we refer to [1]. In the next section, we present some details of the parallel implementation. At least, we present the results obtained on the KSR1. 2 The Hierarchical Transformation Multigrid Method The Hierarchical Transformation Multigrid Method (HT..

“Attention! A Door Could Open.”—Introducing Awareness Messages for Cyclists to Safely Evade Potential Hazards

Numerous statistics show that cyclists are often involved in road traffic accidents, often with serious outcomes. One potential hazard of cycling, especially in cities, is “dooring”—passing parked vehicles that still have occupants inside. These occupants could open the vehicle door unexpectedly in the cyclist’s path—requiring a quick evasive response by the cyclist to avoid a collision. Dooring can be very poorly anticipated; as a possible solution, we propose in this work a system that notifies the cyclist of opening doors based on a networked intelligent transportation infrastructure. In a user study with a bicycle simulator (N = 24), we examined the effects of three user interface designs compared to a baseline (no notifications) on cycling behavior (speed and lateral position), perceived safety, and ease of use. Awareness messages (either visual message, visual message + auditory icon, or visual + voice message) were displayed on a smart bicycle helmet at different times before passing a parked, still-occupied vehicle. Our participants found the notifications of potential hazards very easy to understand and appealing and felt that the alerts could help them navigate traffic more safely. Those concepts that (additionally) used auditory icons or voice messages were preferred. In addition, the lateral distance increased significantly when a potentially opening door was indicated. In these situations, cyclists were able to safely pass the parked vehicle without braking. In summary, we are convinced that notification systems, such as the one presented here, are an important component for increasing road safety, especially for vulnerable road users

Reinforcement learning estimates muscle activations

A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in biomechanics, and hence can contribute to the development of the digital twin. Our preliminary results confirm the potential of RL algorithms to estimate the muscle activations of an athlete’s moves.Ein digitaler Zwilling des menschlichen neuromuskulären Systems kann die Vorhersage von Verletzungsrisiken und die Bewertung der Bereitschaft zur Rückkehr in den Sport erheblich verbessern. Algorithmen des bestärkenden Lernens (Reinforcement Learning, RL) lernen bereits physikalische Größen, die in der Biomechanik nicht messbar sind, und können daher zur Entwicklung des digitalen Zwillings beitragen. Unsere vorläufigen Ergebnisse bestätigen das Potenzial von RL-Algorithmen zur Schätzung der Muskelaktivierung bei den Bewegungen eines Sportlers

Reinforcement learning estimates muscle activations

A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in biomechanics, and hence can contribute to the development of the digital twin. Our preliminary results confirm the potential of RL algorithms to estimate the muscle activations of an athlete’s moves.Ein digitaler Zwilling des menschlichen neuromuskulären Systems kann die Vorhersage von Verletzungsrisiken und die Bewertung der Bereitschaft zur Rückkehr in den Sport erheblich verbessern. Algorithmen des bestärkenden Lernens (Reinforcement Learning, RL) lernen bereits physikalische Größen, die in der Biomechanik nicht messbar sind, und können daher zur Entwicklung des digitalen Zwillings beitragen. Unsere vorläufigen Ergebnisse bestätigen das Potenzial von RL-Algorithmen zur Schätzung der Muskelaktivierung bei den Bewegungen eines Sportlers

Structural Basis for Parathyroid Hormone-related Protein Binding to the Parathyroid Hormone Receptor and Design of Conformation-selective Peptides*

Parathyroid hormone (PTH) and PTH-related protein (PTHrP) are two related peptides that control calcium/phosphate homeostasis and bone development, respectively, through activation of the PTH/PTHrP receptor (PTH1R), a class B G protein-coupled receptor. Both peptides hold clinical interest for their capacities to stimulate bone formation. PTH and PTHrP display different selectivity for two distinct PTH1R conformations, but how their binding to the receptor differs is unclear. The high resolution crystal structure of PTHrP bound to the extracellular domain (ECD) of PTH1R reveals that PTHrP binds as an amphipathic α-helix to the same hydrophobic groove in the ECD as occupied by PTH, but in contrast to a straight, continuous PTH helix, the PTHrP helix is gently curved and C-terminally “unwound.” The receptor accommodates the altered binding modes by shifting the side chain conformations of two residues within the binding groove: Leu-41 and Ile-115, the former acting as a rotamer toggle switch to accommodate PTH/PTHrP sequence divergence, and the latter adapting to the PTHrP curvature. Binding studies performed with PTH/PTHrP hybrid ligands having reciprocal exchanges of residues involved in different contacts confirmed functional consequences for the altered interactions and enabled the design of altered PTH and PTHrP peptides that adopt the ECD-binding mode of the opposite peptide. Hybrid peptides that bound the ECD poorly were selective for the G protein-coupled PTH1R conformation. These results establish a molecular model for better understanding of how two biologically distinct ligands can act through a single receptor and provide a template for designing better PTH/PTHrP therapeutics

Oxidized and synchrotron cleaved structures of the disulfide redox center in the N-terminal domain of Salmonella typhimurium AhpF

The flavoprotein component (AhpF) of Salmonella typhimurium alkyl hydroperoxide reductase contains an N-terminal domain (NTD) with two contiguous thioredoxin folds but only one redox-active disulfide (within the sequence -Cys129-His-Asn-Cys132-). This active site is responsible for mediating the transfer of electrons from the thioredoxin reductase-like segment of AhpF to AhpC, the peroxiredoxin component of the two-protein peroxidase system. The previously reported crystal structure of AhpF possessed a reduced NTD active site, although fully oxidized protein was used for crystallization. To further investigate this active site, we crystallized an isolated recombinant NTD (rNTD); using diffraction data sets collected first at our in-house X-ray source and subsequently at a synchrotron, we showed that the active site disulfide bond (Cys129–Cys132) is oxidized in the native crystals but becomes reduced during synchrotron data collection. The NTD disulfide bond is apparently particularly sensitive to radiation cleavage compared with other protein disulfides. The two data sets provide the first view of an oxidized (disulfide) form of NTD and show that the changes in conformation upon reduction of the disulfide are localized and small. Furthermore, we report the apparent pKa of the active site thiol to be ~5.1, a relatively low pKa given its redox potential (~265 mV) compared with most members of the thioredoxin family