Empirical modeling and prediction of neuronal dynamics

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.

Influence of a changing wave climate on the quality and morphometry of the stalked barnacle Pollicipes pollicipes (Gmelin, 1789), along the coasts of NW Iberia

Wave climate is shifting over the last decades along the Atlantic coasts of Europe ultimately driven by large-scale patterns of atmospheric variability forced by anthropogenic global warming. Changes in wave height and surf zone orbital currents are hypothesized to drive marked shifts in the shape of intertidal organisms such as the stalked barnacle Pollicipes pollicipes, whose quality and market price are known to decrease non linearly with the peduncle length: width ratio S. This study evaluates wave trends in NW Iberian Peninsula, using the Spanish Port System 2006-2020 SIMAR wave hindcast. On the other hand, trends in stalked barnacle morphology and quality are estimated from 26 sites at the management regions of Baiona and A Guarda between 2011 and 2020. Results show evidence of temporal changes in barnacle quality and, especially, morphometry caused by simultaneous shifts in winter wave induced orbital currents. Because of the non linear relationship between S and the high quality threshold, large increases in S are usually translated to small reductions in quality. However, we identified a tipping point around S = 2.4 that if surpassed can lead to great drops in barnacle quality. In addition, changes in wave forcing will have different effects at each extraction site, as trends in wave climate are decoupled from barnacle morphometry at steeper sites sheltered from the predominant wave direction. In conclusion, this knowledge could be applied to develop site specific barnacle harvesting strategies based on annual wave climate forecasts.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11160-024-09838-2.

Establishing Quartz Crystal Microbalance with Dissipation (QCM-D) Coupled with Spectroscopic Ellipsometry (SE) as an Advantageous Technique for the Characterization of Ultra-Thin Film Hydrogels

Despite the considerable significance of utilizing ultra-thin film (utf) hydrogels as multipurpose platforms for biomedical applications, there is still an important lack of adequate characterization techniques suitable for such materials. In this Perspective, the use of quartz crystal microbalance with dissipation (QCM-D) coupled with spectral ellipsometry (SE) is presented as a potential tool for the complete characterization of utf-hydrogels due to its nanometric sensitivity and high versatility. Herein, the fundaments for utf-hydrogel characterization are settled down as far as the QCM-D/SE response is explored under a wide range of different in operando wet working conditions measurements such as temperature or liquid media, among others. Therefore, the design of measuring protocols capable to perform is proposed and compiled, for the first time, complete and precise characterization of the cross-link density, thickness variations (swelling ratio determination), stability analyses, and mechanical studies (including the simultaneous generation of stress-strain curves and the evaluation of the viscoelastic; leading to the final determination of the Poisson's ratio) under different in operando conditions. Finally, the future challenges and implications for the general characterization of soft-thin films are discussed.

Optimal dispersal and diffusion-enhanced robustness in two-patch metapopulations: origin's saddle-source nature matters

A two-patch logistic metapopulation model is investigated both analytically and numerically focusing on the impact of dispersal on population dynamics. First, the dependence of the global dynamics on the stability type of the full extinction equilibrium point is tackled. Then, the behaviour of the total population with respect to the dispersal is studied analytically. Our findings demonstrate that diffusion plays a crucial role in the preservation of both subpopulations and the full metapopulation under the presence of stochastic perturbations. At low diffusion, the origin is a repulsor, causing the orbits to flow nearly parallel to the axes, risking stochastic extinctions. Higher diffusion turns the repeller into a saddle point. Orbits then quickly converge to the saddle's unstable manifold, reducing extinction chances. This change in the vector field enhances metapopulation robustness. On the other hand, the well-known fact that asymmetric conditions on the patches is beneficial for the total population is further investigated. This phenomenon has been studied in previous works for large enough or small enough values of the dispersal. In this work, we complete the theory for all values of the dispersal. In particular, we derive analytically a formula for the optimal value of the dispersal that maximizes the total population.

Mass transfer vectors for nitric oxide removal through biological treatments

The reduction of nitric oxide (NO) emissions to atmosphere has been recently addressed using biological technologies. However, NO removal through bioprocesses is quite challenging due to the low solubility of NO in water. Therefore, the abatement of NO emissions might be improved by adding a chelating agent or a mass transfer vector (MTV) to increase the solubility of this pollutant into the aqueous phase where the bioprocess takes place. This research seeks to assess the performance of different non-aqueous phase liquids (NAPs): n-hexadecane (HEX), diethyl sebacate (DSE), 1,1,1,3,5,5,5-heptamethyl-trisiloxane (HTX), 2,2,4,4,6,8,8-heptamethylnonane (HNO), and high temperature silicone oil (SO) in chemical absorption-biological reduction (CABR) integrated systems. The results showed that HNO and HTX had the maximum gas-liquid mass transfer capacity, being 0.32 mol NO/kmol NAP and 0.29 mol NO/kmol NAP, respectively. When an aqueous phase was added to the system, the mass transfer gas-liquid of NO was increased, with HTX reaching a removal efficiency of 82 ± 3% NO with water, and 88 ± 6% with a phosphate buffer solution. All NAPs were tested for short-term toxicity assessment and resulted neither toxic nor inhibitory for the biological activity (denitrification). DSE was found to be biodegradable, which could limit its applicability in biological processes for gas treatment. Finally, in the CABR system tests, it was shown that NO elimination improved in a short time (30 min) when the three mass transfer vectors (HEX, HTX, HNO) were added to enriched denitrifying bacteria.

Dynamic sampling rate: harnessing frame coherence in graphics applications for energy-efficient GPUs

In real-time rendering, a 3D scene is modelled with meshes of triangles that the GPU projects to the screen. They are discretized by sampling each triangle at regular space intervals to generate fragments which are then added texture and lighting effects by a shader program. Realistic scenes require detailed geometric models, complex shaders, high-resolution displays and high screen refreshing rates, which all come at a great compute time and energy cost. This cost is often dominated by the fragment shader, which runs for each sampled fragment. Conventional GPUs sample the triangles once per pixel; however, there are many screen regions containing low variation that produce identical fragments and could be sampled at lower than pixel-rate with no loss in quality. Additionally, as temporal frame coherence makes consecutive frames very similar, such variations are usually maintained from frame to frame. This work proposes Dynamic Sampling Rate (DSR), a novel hardware mechanism to reduce redundancy and improve the energy efficiency in graphics applications. DSR analyzes the spatial frequencies of the scene once it has been rendered. Then, it leverages the temporal coherence in consecutive frames to decide, for each region of the screen, the lowest sampling rate to employ in the next frame that maintains image quality. We evaluate the performance of a state-of-the-art mobile GPU architecture extended with DSR for a wide variety of applications. Experimental results show that DSR is able to remove most of the redundancy inherent in the color computations at fragment granularity, which brings average speedups of 1.68x and energy savings of 40%.

Computer simulations on oxidative stress-induced reactions in SARS-CoV-2 spike glycoprotein: a multi-scale approach

Oxidative stress, which occurs when an organism is exposed to an adverse stimulus that results in a misbalance of antioxidant and pro-oxidants species, is the common denominator of diseases considered as a risk factor for SARS-CoV-2 lethality. Indeed, reactive oxygen species caused by oxidative stress have been related to many virus pathogenicity. In this work, simulations have been performed on the receptor binding domain of SARS-CoV-2 spike glycoprotein to study what residues are more susceptible to be attacked by ·OH, which is one of the most reactive radicals associated to oxidative stress. The results indicate that isoleucine (ILE) probably plays a crucial role in modification processes driven by radicals. Accordingly, QM/MM-MD simulations have been conducted to study both the ·OH-mediated hydrogen abstraction of ILE residues and the induced modification of the resulting ILE radical through hydroxylation or nitrosylation reactions. All in all, in silico studies show the importance of the chemical environment triggered by oxidative stress on the modifications of the virus, which is expected to help for foreseeing the identification or development of antioxidants as therapeutic drugs.