Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v3): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames. (v4): Abstract fixes.

Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v2): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames.

Computational modelling of dynamic recrystallisation of Ni-based superalloy during linear friction welding

Linear friction welding (LFW) is an advanced joining technology used for manufacturing and repairing complex assemblies like blade integrated disks (blisks) of aeroengines. This paper presents an integrated multiphysics computational modelling for predicting the thermomechanical-microstructural processes of IN718 alloy (at the component-scale) during LFW. Johnson–Mehl–Avrami-Kolmogorov (JMAK) model was implemented for predicting the dynamic recrystallisation of γ grain, which was coupled with thermomechanical modelling of the LFW process. The computational modelling results of this paper agree well with experimental results from the literature in terms of γ grain size and weld temperature. Twenty different LFW process parameter configurations were systematically analysed in the computations by using the integrated model. It was found that friction pressure was the most influential process parameter, which significantly affected the dynamic recrystallisation of γ grains and weld temperature during LFW. The integrated multiphysics computational modelling was employed to find the appropriate process window of IN718 LFW.

A Review on Damage and Rupture Modelling for Soft Tissues

Computational modelling of damage and rupture of non-connective and connective soft tissues due to pathological and supra-physiological mechanisms is vital in the fundamental understanding of failures. Recent advancements in soft tissue damage models play an essential role in developing artificial tissues, medical devices/implants, and surgical intervention practices. The current article reviews the recently developed damage models and rupture models that considered the microstructure of the tissues. Earlier review works presented damage and rupture separately, wherein this work reviews both damage and rupture in soft tissues. Wherein the present article provides a detailed review of various models on the damage evolution and tear in soft tissues focusing on key conceptual ideas, advantages, limitations, and challenges. Some key challenges of damage and rupture models are outlined in the article, which helps extend the present damage and rupture models to various soft tissues.

Critical-like bistable dynamics in the resting-state human brain

Brain activity exhibits scale-free avalanche dynamics and power-law long-range temporal correlations (LRTCs) across the nervous system. This has been thought to reflect "brain criticality", i.e., brains operating near a critical phase transition between disorder and excessive order. Neuronal activity is, however, metabolically costly and may be constrained by activity-limiting mechanisms and resource depletion, which could make the phase transition discontinuous and bistable. Observations of bistability in awake human brain activity have nonetheless remained scarce and its functional significance unclear. First, using computational modelling where bistable synchronization dynamics emerged through local positive feedback, we found bistability to occur exclusively in a regime of critical-like dynamics. We then assessed bistability in vivo with resting-state magnetoencephalography and stereo-encephalography. Bistability was a robust characteristic of cortical oscillations throughout frequency bands from δ (3–7 Hz) to high-γ (100–225 Hz). As predicted by modelling, bistability and LRTCs were positively correlated. Importantly, while moderate levels of bistability were positively correlated with executive functioning, excessive bistability was associated with epileptic pathophysiology and predictive of local epileptogenicity. Critical bistability is thus a salient feature of spontaneous human brain dynamics in awake resting-state and is both functionally and clinically significant. These findings expand the framework of brain criticality and show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.

Combining ultra-high-field fMRI adaptation and computational modelling to estimate neuronal frequency selectivity in human auditory cortex

Frequency selectivity is a ubiquitous property of auditory neurons. Measuring it in human auditory cortex may be crucial for understanding common auditory deficits, but current non-invasive neuroimaging techniques can only measure the aggregate response of large populations of cells, thereby overestimating tuning width. Here we attempted to estimate neuronal frequency tuning in human auditory cortex using a combination of fMRI-adaptation paradigm at 7T and computational modelling. We measured the BOLD response in the auditory cortex of eleven participants to a high frequency (3.8 kHz) probe presented alone or preceded by adaptors at different frequencies (0.5 to 3.8 kHz). From these data, we derived both the response tuning curves (the BOLD response to adaptors alone as a function of adaptor frequency) and adaptation tuning curves (the degree of response suppression to the probe as a function of adaptor frequency, assumed to reflect neuronal tuning) in primary and secondary auditory cortical areas, delineated in each participant. Results suggested the existence of both frequency-independent and frequency-specific adaptation components, with the latter being more frequency-tuned than response tuning curves. Using a computational model of neuronal adaptation and BOLD non-linearity in topographically-organized cortex, we demonstrate both that the frequency-specific adaptation component overestimates the underlying neuronal frequency tuning and that frequency-specific and frequency-independent adaptation component cannot easily be disentangled from the adaptation tuning curve. By fitting our model directly to the response and adaptation tuning curves, we derive a range of plausible values for neuronal frequency tuning. Our results suggest that fMRI adaptation is suitable for measuring neuronal frequency tuning properties in human auditory cortex, provided population effects and the non-linearity of BOLD response are taken into account.

Time-sensitive Prefrontal Involvement in Associating Confidence With Task Performance Illustrates Metacognitive Introspection in Monkeys

Metacognition refers to the ability to be aware of one’s own cognition. Ample evidence indicates that metacognition in the human primate is highly dissociable from cognition, specialized across domains, and subserved by distinct neural substrates. However, these aspects remain relatively understudied in macaque monkeys. In the present study, we investigated the functionality of macaque metacognition by combining a confidence proxy, hierarchical Bayesian meta-d′ computational modelling, and a single-pulse transcranial magnetic stimulation technique. We found that Brodmann area 46d (BA46d) played a critical role in supporting metacognition independent of task performance; we also found that the critical role in of this region in meta-calculation was time-sensitive. Additionally, we report that macaque metacognition is highly domain-specific with respect to memory and perception decisions. These findings carry implications for our understanding of metacognitive introspection within the primate lineage.