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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.
Author Correction: Immunoinformatics approaches in developing a novel multi-epitope chimeric vaccine protective against Saprolegnia parasitica
The extracytoplasmic sigma factor σX supports biofilm formation and increases biocontrol efficacy in Bacillus velezensis 118
Author Correction: Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration
Prognosis and immune infiltration prediction in neuroblastoma based on neutrophil extracellular traps-related gene signature
Thousands of highly cited scientists have at least one retraction
The effects of amendments on cd and pb under different fertilizer application conditions
A nurse-led satellite clinic initiative for STI testing in an Australian correctional center: a qualitative study
Development and validation of a risk prediction model for caesarean delivery among multiparous women
Application of microfluidic technology and nanoencapsulation to amplify the antibacterial activity of clindamycin against a food born pathogen
US drug agency approves potent painkiller — the first non-opioid in decades
BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8
A fast quantum algorithm for solving partial differential equations
Mitigation potential of urban greening during heatwaves and stormwater events: a modeling study for Karlsruhe, Germany
Abstract Climate change is increasing the frequency and intensity of urban heat islands and stormwater flooding. In order to mitigate these threats cities are turning toward green infrastructure to restore the hydrologic cycle in a way that increases the ecosystem services provided by trees. Strategically designed green infrastructure can mitigate runoff volume by rainfall interception through tree canopies and redirect impervious runoff into bioswales that promote infiltration. In addition, urban greens mitigate extreme heat via evapotranspiration and shading. Here we applied the i-Tree HydroPlus model to the German city of Karlsruhe and its twenty-seven districts with varying initial conditions of tree cover to analyze the potential for both runoff and heat mitigation during dry and wet periods throughout a 5-year period. After analyzing initial tree cover and drainage conditions, we used the model to simulate a green infrastructure scenario for each district with restored hydrology and tree cover at 30%. Regarding trade-offs between runoff and heat mitigation, the results confirm that dry soils before storm events lead to greater runoff reduction by 10%, and wet soils prior to heatwaves resulted in a greater evaporative cooling. Compared to current conditions, the green infrastructure scenarios resulted in decreasing the number of extreme heat hours (Heat Index > 31 °C) per year on average by 64.5%, and to reduce runoff in average by 58% across all city districts. Thus, our simulation results show that investing into a greener infrastructure, has positive impacts on microclimate and hydrology. Finally, we discuss synergies and trade-offs of the investigated management options as well as the transferability of results to other cities.