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Protocol for calculating binding free energy of RNA:RNA interactions through molecular dynamics simulations using adaptive biasing force technique
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Modeling of dimensions and sensing properties of gold gratings by spectroscopic ellipsometry and finite element method
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An alternative formulation of the quantum phase estimation using projection-based tensor decompositions
No full textImpact of the External Knee Flexion Moment on Patello-Femoral Loading Derived from in vivo Loads and Kinematics
No full textAnterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the invivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee.Six patients (65-80 years, 67-101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The
relationship between the EFM and the PF contact force was quantified using linear regression.Mean peak TF contact forces of 1.97 to 3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R^2 = 0.95 and 0.88 for sit-standsit and squat, respectively).The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living
Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges
No full textSimulation-based digital twins of bridges have the potential not only to serve as monitoring devices of the current state of the structure but also to generate new knowledge through physical predictions that allow for better-informed decisionmaking. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will show that quantifying the extent of the uncertainties introduced through the models that lead to such discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. The inclusion of an explicit bias term will be applied to a representative demonstrator case based on the thermal response of the Nibelungenbrücke of Worms. The findings from this work are englobed in the initiative SPP 100+, whose main aim is the extension of the service life of structures, especially through the implementation of digital twins
Understanding the Origins of Life – A Machine learning approach to estimate reaction mechanisms of biotic precursors
No full textUnderstanding the Origins of Life - A Machine learning approach to estimate reaction mechanisms of biotic precursors. Life as we know it is the result of billions of years of evolution; however, understanding how the very first organisms came into existence is a challenge that has yet to be solved. One theory states that components of these molecules may have formed in the interstellar medium (ISM) and been transported to Earth. The ISM, with its specific conditions, allows for molecular stability and the formation of biotic precursors that would otherwise be unlikely in Earth's prebiotic conditions. Understanding how these molecules formed in the ISM may be the key to determining how life began. The goal of this research is to develop a model for solving astrobiophysical problems by studying the formation mechanisms of biomolecules found in the ISM. Although such pathways have been studied individually, there has not yet been a comprehensive method to understand the complete reactions mechanisms. Several QM methods are available for finding transition states (TS) and energy barriers (E) of chemical reactions but are time-consuming and can hardly be applied to more complex systems. Our interest is to develop a machine learning approach to approximate TS, and E, requiring as input only estimates of geometry and energies of reactants and products
Chimeric U-Net – Modifying the standard U-Net towards Explainability
No full textHealthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net , encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under:
https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorc