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New insights into the association between arthritis and overactive bladder in NHANES 2005–2020
Psychological capital and farmer families’ quality of life under the climate variability conditions
How COVID vaccination keeps a ‘breakthrough’ infection in check
Inverse determination of the thermal contact conductance for an interface between a Co28Cr6Mo hip stem and a PMMA-based bone cement
Abstract Traditional mechanical methods for implant and bone cement removal during total hip arthroplasty (THA) revision surgeries typically lead to surrounding tissue damage and increased risk of femoral fractures. Transcutaneous induction heating is a promising new removal approach as it causes softening of the thermoplastic bone cement, and thus prevents damage to the surrounding tissue during removal and increases stability post-revision. However, precise knowledge of the heat transfer between implant and bone cement is necessary to minimize the risk of thermal damage to surrounding tissues. In this context, knowledge of the thermal contact conductance (TCC) at the interface of Co28Cr6Mo hip stems and PMMA-based bone cement is a key issue. The present study addresses the challenge of measuring TCC by proposing an inverse method of determination using infrared thermography measurements of the heating process and a finite element simulation with a variable parameter for the TCC. Results indicate TCC values of 3,125 ± 275 Wm− 2K− 1 for dry interfaces and 5,100 ± 300 Wm− 2K− 1 for wet interfaces. The influence of heat conduction on bone cement surface temperature is significant, impacting the measured surface temperatures by 15–19% for wet and 23–30% for dry interfaces. These findings are crucial for the design of heating procedures and minimization of thermal damage during induction heating assisted THA revisions.
Author Correction: Optimization of thermosonication conditions for critical quality parameters of watermelon juice using response surface methodology
Introducing AVAC as an ultra-sensitive platform with broad dynamical range for high-throughput multiplexed biomarker detection using digital counting of plasmonic nanoparticles
Effects of shapes and kinematics of hovering flapping wings on aerodynamic forces and vortex structures
Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks
Abstract Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. Here, focussing on purely excitatory networks of small to intermediate size and continuous node dynamics, we provide a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model in conjunction with known ground truth structural connectivity, we reconstruct the system’s connectivity matrix using a variety of algorithms. We show that, in sparse non-linear networks with delays, combining a lagged-cross-correlation (LCC) approach with a recently published derivative-based covariance analysis method provides the most reliable estimation of the known ground truth connectivity matrix. We outline how the parameters of the Hopf model, including those controlling the bifurcation, noise, and delay distribution, affect this result. We also show that in linear networks, LCC has comparable performance to a method based on transfer entropy, at a drastically lower computational cost. We highlight that LCC works best for small sparse networks, and show how performance decreases in larger and less sparse networks. Applying the method to linear dynamics without time delays, we find that it does not outperform derivative-based methods. We comment on this finding in light of recent theoretical results for such systems. Employing the Hopf model, we then use the estimated structural connectivity matrix as the basis for a forward simulation of the system dynamics, in order to recreate the observed node activity patterns. We show that, under certain conditions, the best method, LCC, results in higher trace-to-trace correlations than derivative-based methods for sparse noise-driven systems. Finally, we apply the LCC method to empirical biological data. Choosing a suitable threshold for binarization, we reconstruct the structural connectivity of a subset of the nervous system of the nematode C. elegans. We show that the computationally simple LCC method performs better than another recently published, computationally more expensive reservoir computing-based method. We apply different methods to this dataset and find that they all lead to similar performances. Our results show that a comparatively simple method can be used to reliably estimate directed effective connectivity in sparse neural systems in the presence of spatio-temporal delays and noise. We provide concrete suggestions for the estimation of effective connectivity in a scenario common in biological research, where only neuronal activity of a small set of neurons, but not connectivity or single-neuron and synapse dynamics, are known.