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Exercise-induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise-induced phenotypes. Using this network, we formulated a logic-based ordinary differential equation model predicting time-dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP-activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross-talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

Introduction: Accumulating evidence shows that human health and disease are closely related to the microbes in the human body. Methods: In this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe-disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe-disease relationships, disease similarity, and microbial similarity. Then, we adopted the improved GCN and the CSAE to extract neighbor relations in the adjacency matrix and novel feature representations in heterogeneous networks. After that, in order to estimate the likelihood of a potential microbe associated with a disease, we integrated these two types of representations to create unique eigenmatrices for diseases and microbes, respectively, and obtained predicted scores for potential microbe-disease associations by calculating the inner product of these two types of eigenmatrices. Results and discussion: Based on the baseline databases such as the HMDAD and the Disbiome, intensive experiments were conducted to evaluate the prediction ability of GCANCAE, and the experimental results demonstrated that GCANCAE achieved better performance than state-of-the-art competitive methods under the frameworks of both 2-fold and 5-fold CV. Furthermore, case studies of three categories of common diseases, such as asthma, irritable bowel syndrome (IBS), and type 2 diabetes (T2D), confirmed the efficiency of GCANCAE. [ABSTRACT FROM AUTHOR]

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large‐scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long‐range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long‐range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long‐range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long‐range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex. [ABSTRACT FROM AUTHOR]

Dustin Stokes book Thinking and Perceiving is a substantial achievement. In this comment, I discuss issues related to cognitive penetration. While I agree with Stokes' criticisms of Fodor and Pylyshyn's discussion of cognitive penetration with respect to the role of attention, I provide a supporting, but different argument against how they understand attention. I also emphasize that the common appeal to behavioural data in arguing for cognitive penetration is less effective than an argument that supplements behavioural data with computational models. I sketch how drawing on computational models strengthens the case for the cognitive penetration of attention by intention. This suggests that cognitive penetration is as common as intentional action. [ABSTRACT FROM AUTHOR]

Dopamine modulates working memory in the prefrontal cortex (PFC) and is crucial for obsessive–compulsive disorder (OCD). However, the mechanism is unclear. Here we establish a biophysical model of the effect of dopamine (DA) in PFC to explain the mechanism of how high dopamine concentrations induce persistent neuronal activities with the network plunging into a deep, stable attractor state. The state develops a defect in working memory and tends to obsession and compulsion. Weakening the reuptake of dopamine acts on synaptic plasticity according to Hebbian learning rules and reward learning, which in turn affects the strength of neuronal synaptic connections, resulting in the tendency of compulsion and learned obsession. In addition, we elucidate the potential mechanisms of dopamine antagonists in OCD, indicating that dopaminergic drugs might be available for treatment, even if the abnormality is a consequence of glutamate hypermetabolism rather than dopamine. The theory highlights the significance of early intervention and behavioural therapies for obsessive–compulsive disorder. It potentially offers new approaches to dopaminergic pharmacotherapy and psychotherapy for OCD patients. [ABSTRACT FROM AUTHOR]

There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson's disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson's disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson's disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( g snge ) and from GPe to STN ( g gesn ). And there is an approximate negative correlation between EI and g gesn . Increasing g snge and decreasing g gesn can improve the fidelity of thalamus information transmission and alleviate Parkinson's disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson's disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Airway distensibility is defined as the ease whereby airways are dilating in response to inflating lung pressure. If measured swiftly and accurately, airway distensibility would be a useful readout to parse the various elements contributing to airway wall stiffening, such as smooth muscle contraction, surface tension, and airway remodeling. The goal of the present study was to develop a method for measuring airway distensibility in mice. Lungs of BALB/c and C57BL/6 mice from either sex were subjected to stepwise changes in pressure. At each pressure step, an oscillometric perturbation was used to measure the impedance spectrum, on which the constant-phase model was fitted to deduce a surrogate for airway caliber called Newtonian conductance (GN). The change in GN over the change in pressure was subsequently used as an index of airway distensibility. An additional group of mice was infused with methacholine to confirm that smooth muscle contraction changes airway distensibility. GN increased with increasing steps in pressure, suggesting that the extent to which this occurs can be used as an index of airway distensibility. Airway distensibility was greater in BALB/c than C57BL/6 mice, and its variation by sex was mouse strain dependent, being greater in female than male in BALB/c mice with an inverse trend in C57BL/6 mice. Airway distensibility was also decreased by methacholine. This novel method swiftly measures airway distensibility in mice. Airway distensibility was also shown to vary with sex and mouse strain and to be sensitive to the contraction of smooth muscle. [ABSTRACT FROM AUTHOR]

Purpose: The electroretinogram (ERG) is the summed response from all levels of the retinal processing of light, and exhibits several profound nonlinearities in the underlying processing pathways. Accurate computational models of the ERG are important, both for understanding the multifold processes of light transduction to ecologically useful signals by the retina, and for their diagnostic capabilities for the identification and characterization of retinal disease mechanisms. There are, however, very few computational models of the ERG waveform, and none that account for the full extent of its features over time. Methods: This study takes the neuroanalytic approach to modeling the ERG waveform, defined as a computational model based on the main features of the transmitter kinetics of the retinal neurons. Results: The present neuroanalytic model of the human rod ERG is elaborated from the same general principles as that of Hood and Birch (Vis Neurosci 8(2):107–126, 1992), but incorporates the more recent understanding of the early nonlinear stages of ERG generation by Robson and Frishman (Prog Retinal Eye Res 39:1–22, 2014). As a result, it provides a substantially better match than previous models of rod responses in six different waveform features of the ERG flash intensity series on which the Hood and Birch model was based. Conclusion: The neuroanalytic approach extends previous models of the component waves of the ERG, and can be structured to provide an accurate characterization of the full timecourse of the ERG waveform. The approach thus holds promise for advancing the theoretical understanding of the retinal kinetics of the light response. [ABSTRACT FROM AUTHOR]

In this work, we couple a lumped‐parameter closed‐loop model of the cardiovascular system with a physiologically‐detailed mathematical description of the baroreflex afferent pathway. The model features a classical Hodgkin–Huxley current‐type model for the baroreflex afferent limb (primary neuron) and for the second‐order neuron in the central nervous system. The pulsatile arterial wall distension triggers a frequency‐modulated sequence of action potentials at the afferent neuron. This signal is then integrated at the brainstem neuron model. The efferent limb, representing the sympathetic and parasympathetic nervous system, is described as a transfer function acting on heart and blood vessel model parameters in order to control arterial pressure. Three in silico experiments are shown here: a step increase in the aortic pressure to evaluate the functionality of the reflex arch, a hemorrhagic episode and an infusion simulation. Through this model, it is possible to study the biophysical dynamics of the ionic currents proposed for the afferent limb components of the baroreflex during the cardiac cycle, and the way in which currents dynamics affect the cardiovascular function. Moreover, this system can be further developed to study in detail each baroreflex loop component, helping to unveil the mechanisms involved in the cardiovascular afferent information processing. [ABSTRACT FROM AUTHOR]

Amotivation is a typical feature in major depressive disorder (MDD), which produces reduced willingness to exert effort. The dorsolateral prefrontal cortex (DLPFC) is a crucial structure in goal-directed actions and therefore is a potential target in modulating effortful motivation. However, it remains unclear whether the intervention is effective for patients with MDD. We employed transcranial magnetic stimulation (TMS), computational modelling and event-related potentials (ERPs) to reveal the causal relationship between the left DLPFC and motivation for effortful rewards in MDD. Fifty patients underwent both active and sham TMS sessions, each followed by performing an Effort-Expenditure for Rewards Task, during which participants chose and implemented between low-effort/low-reward and high-effort/high-reward options. The patients showed increased willingness to exert effort for rewards during the DLPFC facilitated session, compared with the sham session. They also had a trend in larger P3 amplitude for motivated attention toward chosen options, larger CNV during preparing for effort exertion, and larger SPN during anticipating a high reward. Besides, while behavior indexes for effortful choices were negatively related to depression severity in the sham session, this correlation was weakened in the active stimulation session. These findings provide behavioral, computational, and neural evidence for the left DLPFC on effortful motivation for rewards. Facilitated DLPFC improves motor preparation and value anticipation after making decisions especially for highly effortful rewards in MDD. Facilitated DLPFC also has a potential function in enhancing motivated attention during cost-benefit trade-off. This neuromodulation effect provides a potential treatment for improving motivation in clinics. • TMS-activated DLPFC enhances MDD patients' motivational behavior to exert effort for rewards • TMS results in enhanced the P3, CNV, and SPN amplitudes • DLPFC plays a causal role in preparing for effort exertion and anticipating rewards in MDD [ABSTRACT FROM AUTHOR]

Systemic vocal fold dehydration is known to increase vocal fold stiffness, which has been hypothesized to have important effect on voice production. However, it remains unclear whether the dehydration-induced vocal fold stiffness changes can have a noticeable impact on phonation, particularly in normal phonation conditions. The goal of this study was to investigate the impact of vocal fold stiffness changes due to vocal fold systemic dehydration and its significance in daily communication. Parametric computational simulation using a three-dimensional vocal fold model, in which the vocal fold stiffness was varied as a function of systemic dehydration levels based on previously-reported experimental data. The results showed that systemic dehydration had significant effects on voice production only at high levels of dehydration, at which dehydration increased the phonation threshold pressure and fundamental frequency, and decreased glottal opening area, vocal intensity and glottal efficiency. The effect depended mainly on the overall dehydration level but was also slightly affected by the dehydration distribution and muscular control. However, for dehydration levels typical of normal phonation conditions, the effect was negligible. The results indicated that dehydration-induced vocal fold stiffness change likely is not an important mechanism through which vocal fold systemic dehydration affects voice production. Nevertheless, a large decrease in glottal efficiency implies a possible perceived increase of vocal effort under a realistic dehydration condition. [ABSTRACT FROM AUTHOR]

Direct reduced iron (DRI) and hot briquetted iron (HBI) are essential feedstocks for tramp element control in the electric arc furnace (EAF). Due to greenhouse gas (GHG) concerns related to CO2 emissions, hydrogen as a substitute for natural gas and a reductant in DRI production is being widely explored to reduce GHG emissions in ironmaking. This study examines the melting behavior of hydrogen DRI (H-DRI) pellets in the EAF containing low-carbon (0.1 wt.%) molten steel and molten slag. A computational heat transfer model was developed to predict the melting behavior of H-DRI pellets. To validate the model, a set of experimental laboratory simulations was conducted by immersing H-DRI in a molten steel bath and slag. The temperature history at the center of the pellet during melting and the shell thickness at different melting stages were utilized to validate the model. The simulation results agree with the experimental measurements of steel balls and H-DRI in different metallic molten steel and slag baths. [ABSTRACT FROM AUTHOR]

Coastal protection structures are essential defenses against wave energy, safeguarding coastal communities. This study aims to refine coastal protection strategies by employing a semicircular breakwater (SBW) model. Through a combination of physical and computational models, the hydrodynamic properties of the SBW under regular wave conditions were thoroughly examined. The primary objectives included delineating the hydrodynamic characteristics of SBWs, developing a computational model to validate experimental findings. Hydrodynamic characteristics of the SBW model were scrutinized across various wave conditions. Experimental testing in a wave flume covered a range of relative water depths (d/h) from 0.667 to 1.667, wave steepness (Hi/L) spanning 0.02 to 0.06 and wave periods ranging from 0.8 to 2.5 s. Notably, analysis of an emerged SBW with d/h = 0.667 revealed superior wave reflection, while an alternative submerged SBW with d/h = 1.000 showed the highest energy loss. These findings are further corroborated by the validation of computational models against experimental outcomes for d/h = 0.667, 1.000, 1.333 and 1.667. Moreover, the investigation of forces revealed an inverse correlation between horizontal forces and wave height, while vertical forces showed nuanced variations, including a slightly decreasing average vertical force with greater relative wave period (B/L) for different immersion scenarios. [ABSTRACT FROM AUTHOR]

Exposure to vibration can be harmful to the spinal column, which can cause injury to the spine and related tissues by jolting and compressing the spinal column's vertebrae. Long-term exposure to vibration can lead to problems with the spinal column, including stenosis, herniated discs, and degenerative disc disease (degeneration of the intervertebral discs). Vibration can exacerbate muscle strains, sprains, and pre-existing back problems. To date, experimental studies on vibrations have been limited to simple human dummies, which cannot determine its effects on exposed critical organs such as the human spine. However, with computational modelling, it is possible to not only study the effects of vibrations on the realistic spine geometry, but also allow the assignment of biofidelic material properties, loads and boundary conditions. This work aims to comprehensively review computational models studying the various segments of the spinal column and the effects of spinal vibrations. This study is anticipated to lead to a better understanding of the state-of-the-art spine modelling and computational complexities associated with spinal geometry development, material modelling, and finite-element modelling with vibrational loads, and also highlights the gaps and essential future research prospects. [ABSTRACT FROM AUTHOR]

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson’s disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients’ outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies. [ABSTRACT FROM AUTHOR]

Background: There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. Objective: We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. Methods: We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. Results: The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Conclusions: Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification. [ABSTRACT FROM AUTHOR]

AbstractAlternating electric fields (AEFs) at intermediate frequencies (100–300 kHz) and low intensities (1–3 V/cm) have shown promise as an effective approach for inhibiting cancer cell proliferation. However, a noticeable research gap exists in comparing the biophysical properties of invasive and non-invasive AEFs methods, and AEFs delivery strategies require further improvement. In this study, we constructed a realistic head model to simulate the effects of non-invasive and invasive AEFs on malignant gliomas. Additionally, a novel method was proposed involving the placement of a return electrode under the scalp. We simulated the electric field and temperature distributions in the brain tissue for each method. Our results underscore the advantages of invasive AEFs, showcasing their superior tumor-targeting abilities and reduced energy requirements. The analysis of brain tissue temperature changes reveals that non-invasive AEFs primarily generate heat at the scalp level, whereas invasive methods localize heat production within the tumor itself, thereby preserving surrounding healthy brain tissue. Our proposed invasive AEFs method also shows potential for selective electric field intervention. In conclusion, invasive AEFs demonstrate potential for precise and effective tumor treatment. Its enhanced targeting capabilities and limited impact on healthy tissue make it a promising avenue for further research in the realm of cancer treatment. [ABSTRACT FROM AUTHOR]

Research on working memory (WM) has followed two largely independent traditions: One concerned with memory for sequentially presented lists of discrete items, and the other with short-term maintenance of simultaneously presented arrays of objects with simple, continuously varying features. Here we present a formal model of WM, the interference model (IM), that explains benchmark findings from both traditions: The shape of the error distribution from continuous reproduction of visual features, and how it is affected by memory set size; the effects of serial position for sequentially presented items, the effect of output position, and the intrusion of nontargets as a function of their distance from the target in space and in time. We apply the model to two experiments combining features of popular paradigms from both traditions: Lists of colors (Experiment 1) or of nonwords (Experiment 2) are presented sequentially and tested through selection of the target from a set of candidates, ordered by their similarity. The core assumptions of the IM are: Contents are encoded into WM through temporary bindings to contexts that serve as retrieval cues to access the contents. Bindings have limited precision on the context and the content dimension. A subset of the memory set—usually one item and its context—is maintained in a focus of attention with high precision. Successive events in an episode are encoded with decreasing strength, generating a primacy gradient. With each encoded event, automatic updating of WM reduces the strength of preceding memories, creating a recency gradient and output interference. [ABSTRACT FROM AUTHOR]

Theories of category learning have typically focused on how the underlying category structure affects the category representations acquired by learners. However, there is limited research as to how other factors affect what representations are learned and utilized and how representations might change across the time course of learning. We used a novel "5/5" categorization task developed from the well-studied 5/4 task with the addition of one more stimulus to clarify an ambiguity in the 5/4 prototypes. We used multiple methods including computational modeling to identify whether participants categorized on the basis of exemplar or prototype representations. We found that, overall, for the stimuli we used (schematic robot-like stimuli), learning was best characterized by the use of prototypes. Most importantly, we found that relative use of prototype and exemplar strategies changed across learning, with use of exemplar representations decreasing and prototype representations increasing across blocks. [ABSTRACT FROM AUTHOR]

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies. [ABSTRACT FROM AUTHOR]

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications. [ABSTRACT FROM AUTHOR]

Staple yarn is the basic element of most textiles, and its strength affects the performance and processing efficiency of the final textile products. Meanwhile, its strength is the combination of the resistance to breakage and slip of fibers in the yarn. In this paper, a computational model is proposed to predict the strength of staple yarn. The resistance to breakage and slip of each fiber were first computed based on the random arrangement of the fibers and the idealized yarn structure, and the strength was computed by identifying fiber breakage or slip in the yarn breakage zone. The reliability and applicability of the model were verified on viscose and polyester ring-spun staple yarns. The results show that the mean error of prediction is mostly less than 5%, indicating that the model has good accuracy and applicability; and the correlation coefficient R is mostly greater than 0.95, indicating that the predicted strength is in good agreement with the tested strength. [ABSTRACT FROM AUTHOR]

Computational models provide an efficient paradigm for integrating and linking multiple spatial and temporal scales. However, these models are difficult to parameterize and match to experimental data. Recent advances in both data collection and model analyses have helped overcome this limitation. Here, we combine a multiscale, biventricular interaction model with mouse data before and after left ventricular (LV) ischemia. Sensitivity analyses are used to identify the most influential parameters on pressure and volume predictions. The subset of influential model parameters are calibrated to biventricular pressure-volume loop data (n = 3) at baseline. Each mouse underwent left anterior descending coronary artery ligation, during which changes in fractional shortening and RV pressure-volume dynamics were recorded. Using the calibrated model, we simulate acute LV ischemia and contrast outputs at baseline and in simulated ischemia. Our baseline simulations align with the LV and RV data, and our predictions during ischemia complement recorded RV data and prior studies on LV function during myocardial infarction. We show that a model with both biventricular mechanical interaction and systems-level cardiovascular dynamics can quantitatively reproduce in-vivo data and qualitatively match prior findings from animal studies on LV ischemia.

Controlling cell-substrate interactions via the microstructural characteristics of biomaterials offers an advantageous path for modulating cell dynamics, mechanosensing, and migration, as well as for designing immune-modulating implants, all without the drawbacks of chemical-based triggers. Specifically, recent in vivo studies have suggested that a porous implants microscale curvature landscape can significantly impact cell behavior and ultimately the immune response. To investigate such cell-substrate interactions, we utilized a 3D computational model incorporating the minimum necessary physics of cell migration and cell-substrate interactions needed to replicate known in vitro behaviors. This model specifically incorporates the effect of membrane tension, which was found to be necessary to replicate in vitro cell behavior on curved surfaces. Our simulated substrates represent two classes of porous materials recently used in implant studies, which have markedly different microscale curvature distributions and pore geometries. We found distinct differences between the overall migration behaviors, shapes, and actin polymerization dynamics of cells interacting with the two substrates. These differences were correlated to the shape energy of the cells as they interacted with the porous substrates, in effect interpreting substrate topography as an energetic landscape interrogated by cells. Our results demonstrate that microscale curvature directly influences cell shape and migration and, therefore, is likely to influence cell behavior. This supports further investigation of the relationship between the surface topography of implanted materials and the characteristic immune response, a complete understanding of which would broadly advance principles of biomaterial design.

Abstract Exercise‐induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise‐induced phenotypes. Using this network, we formulated a logic‐based ordinary differential equation model predicting time‐dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP‐activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross‐talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies. a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally. minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion. [ABSTRACT FROM AUTHOR]

ObjectiveWhile the vocal fold is in direct contact anteriorly with the thyroid cartilage, posteriorly the vocal fold connects to the thyroid cartilage through a soft tissue layer in the paraglottic space. Currently the paraglottic space is often neglected in computational models of phonation, in which a fixed boundary condition is often imposed on the lateral surface of the vocal fold. The goal of this study was to investigate the effect of the paraglottic space on voice production in an MRI-based vocal fold model, and how this effect may be counteracted by vocal fold stiffening due to laryngeal muscle activation.MethodsParametric simulation study using an MRI-based computational vocal fold model.ResultsThe results showed that the presence of the paraglottic space increased the mean and amplitude of the glottal area waveform, decreased the phonation frequency and closed quotient. For the particular vocal fold geometry used in this study, the presence of the paraglottic space also reduced the occurrence of irregular vocal fold vibration. These effects of the paraglottic space became smaller with increasing paraglottic space stiffness and to a lesser degree with vocal fold stiffening.ConclusionsThe results suggest that the paraglottic space may be neglected in qualitative evaluations of normal phonation, but needs to be included in simulations of pathological phonation or vocal fold posturing.

Neuronal cell dysfunction plays an important role in neurodegenerative diseases. Oxidative stress can disrupt the redox balance within neuronal cells and may cause neuronal nitric oxide synthase (nNOS) to uncouple, contributing to the neurodegenerative processes. Experimental studies and clinical trials using nNOS cofactor tetrahydrobiopterin (BH 4) and antioxidants in neuronal cell dysfunction have shown inconsistent results. A better mechanistic understanding of complex interactions of nNOS activity and oxidative stress in neuronal cell dysfunction is needed. In this study, we developed a computational model of neuronal cell using nNOS biochemical pathways to explore several key mechanisms that are known to influence neuronal cell redox homeostasis. We studied the effects of oxidative stress and BH 4 synthesis on nNOS nitric oxide production and biopterin ratio (BH 4 /total biopterin). Results showed that nNOS remained coupled and maintained nitric oxide production for oxidative stress levels less than 230 nM/s. The results showed that neuronal oxidative stress above 230 nM/s increased the degree of nNOS uncoupling and introduced instability in the nitric oxide production. The nitric oxide production did not change irrespective of initial biopterin ratio of 0.05–0.99 for a given oxidative stress. Oxidative stress resulted in significant reduction in BH 4 levels even when nitric oxide production was not affected. Enhancing BH 4 synthesis or supplementation improved nNOS coupling, however the degree of improvement was determined by the levels of oxidative stress and BH 4 synthesis. The results of our mechanistic analysis indicate that there is a potential for significant improvement in neuronal dysfunction by simultaneously increasing BH 4 levels and reducing cellular oxidative stress. [Display omitted] • Computational model of nNOS was developed to understand the role of oxidative stress. • nNOS remained coupled & maintained NO production for oxidative stress level <230 nM/s. • NO production decreased and caused nNOS to uncouple at high oxidative stress levels. • BH 4 deficiency contributed to uncoupling of nNOS and reduced NO production. • BH 4 supplementation/synthesis enhanced neuronal function by restoring nNOS coupling. [ABSTRACT FROM AUTHOR]

While it is well‐established that early detection and initiation of treatment of developmental dysplasia of the hip (DDH) is crucial to successful clinical outcomes, research on the mechanics of the hip joint during healthy and pathological hip development in infants is limited. Quantification of mechanical behavior in both the healthy and dysplastic developing joints may provide insight into the causes of DDH and facilitate innovation in treatment options. In this study, subject‐specific three‐dimensional finite element models of two pigs were developed: one healthy pig and one pig with induced dysplasia in the right hindlimb. The objectives of this study were: (1) to characterize mechanical behavior in the acetabular articular cartilage during a normal walking cycle by analyzing six metrics: contact pressure, contact area, strain energy density, von Mises stress, principal stress, and principal strain; and (2) to quantify the effect on joint mechanics of three anatomic abnormalities previously identified as related to DDH: variation in acetabular coverage, morphological changes in the femoral head, and changes in the articular cartilage. All metrics, except the contact area, were elevated in the dysplastic joint. Morphological changes in the femoral head were determined to be the most significant factors in elevating contact pressure in the articular cartilage, while the effects of acetabular coverage and changes in the articular cartilage were less significant. The quantification of the pathomechanics of DDH in this study can help identify key mechanical factors that restore normal hip development and can lead to mechanics‐driven treatment options. [ABSTRACT FROM AUTHOR]

Biological experiments targeting the mammalian primary visual cortex have shown that neuronal response to a preferred orientation grating is cross-inhibited by an orthogonal orientation mask grating. The plaid formed by the overlap of the two gratings not only causes a decrease in the neuronal firing rate but also shifts the gamma oscillation to a weaker oscillation at a higher frequency. The mechanism for the above phenomena is unclarified. In this paper, a large-scale cortical neuronal network model with biological details is constructed. In this model, two modes of connectivity that may contribute to cross-inhibition are considered: the thalamo-cortical feedforward pathway and the push-pull organization of cortical layer 4. Based on this model, the modulation of firing rate and gamma oscillation by a plaid stimulation are successfully reproduced, which is consistent with biological experiments and suggests that it is the thalamo-cortical feedforward pathway that leads to cross-inhibition. Furthermore, our analysis of the neuronal spike clusters and current fluctuations suggests that the push-pull organization leads to an increase in gamma frequency during the transition of visual stimuli from grating to plaid by modulating the source of synaptic inhibition in local neuronal populations. Such results will help to understand the visual processing under multi-input integration.

The increasing scarcity of water sources near urban areas, coupled with urbanization and population growth, necessitates the development of solutions that optimize water demand management, which entails a more accurate estimation of consumption patterns and implementing measures that promote the rational use of water. A stochastic residential water demand model was built based on the superposition of pulses of constant intensity and variable arrival time and duration applied to the microcomponent consumption at a 1-minute resolution, reflecting the aggregate system contributions to the total instantaneous demand. The model can support the planning of water supply systems (WSSs) and was calibrated using data from Brazilian institutes combined with a literature review of statistical data on users and end uses of water in Brazilian dwellings. Innovatively, it proposes using water demand forecasting to apply demand management measures evaluating the replacement of conventional devices for water-saving ones by analyzing hydrographs of 50–50,000 households, in addition to detailing the influence of the K2 peak factor. The results demonstrated a new water consumption profile with about a 40% reduction in water demand. A new equation for K2 is proposed as an alternative to estimate the demand for small populations fittingly. HIGHLIGHTS The model can support the planning of water supply systems (WSS).; From its use, it is possible to obtain essential characteristics for the dimensioning and operation of supply networks, such as maximum, average, and minimum flows, peak hours, per capita consumption, and consumption per appliance, among others.; A new formulation for the peak coefficient is available from the data used in the model.;

In this study, we introduce an asymmetry calculation model for evaluating the effectiveness of facial paralysis diagnosis and treatment, implemented through a Python and OpenCV algorithm. The algorithm is based on actual images from cases of facial paralysis treatments, and numerical experiments for image comparison and subsequent detailed statistical analysis are conducted. The research findings indicate that the calculated numerical indices provided by this model can relatively accurately assess the effectiveness of facial paralysis diagnosis and treatment on a graded scale. Consequently, in this study, we propose to combine overall facial and specific facial region motion disparity features for a comprehensive facial paralysis grading evaluation. This innovative approach provides a robust tool for the accurate assessment of facial paralysis treatment outcomes, offering significant support for clinical practices and treatment optimization. [ABSTRACT FROM AUTHOR]

Learning, an important activity for both human and animals, has long been a focal point of research. During the learning process, subjects assimilate not only their own information but also information from others, a phenomenon known as social learning. While numerous studies have explored the impact of social feedback as a reward/punishment during learning, few studies have investigated whether social feedback facilitates or inhibits the learning of environmental rewards/punishments. This study aims to test the effects of social feedback on economic feedback and its cognitive processes by using the Iowa Gambling Task (IGT). One hundred ninety-two participants were recruited and categorized into one non-social feedback group and four social feedback groups. Participants in the social feedback groups were informed that after the outcome of each choice, they would also receive feedback from an online peer. This peer was a fictitious entity, with variations in identity (novice or expert) and feedback type (random or effective). The Outcome-Representation Learning model (ORL model) was used to quantify the cognitive components of learning. Behavioral results showed that both the identity of the peer and the type of feedback provided significantly influenced the deck selection, with effective social feedback increasing the ratio of chosen good decks. Results in the ORL model showed that the four social feedback groups exhibited lower learning rates for gain and loss compared to the nonsocial feedback group, which suggested, in the social feedback groups, the impact of the recent outcome on the update of value decreased. Parameters such as forgetfulness, win frequency, and deck perseverance in the expert-effective feedback group were significantly higher than those in the non-social feedback and expert-random feedback groups. These findings suggest that individuals proactively evaluate feedback providers and selectively adopt effective feedback to enhance learning. [ABSTRACT FROM AUTHOR]

We recently reported strong, replicable (i.e., replicated) evidence for lexically mediated compensation for coarticulation (LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support for interactive models of cognition that include top‐down feedback and is inconsistent with autonomous models that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter‐arguments against our interpretation; we respond to each of those arguments here and conclude that top‐down feedback provides the most parsimonious explanation of extant data. [ABSTRACT FROM AUTHOR]

The ventricular folds, located superiorly to the vocal folds, do not usually vibrate during normal phonations. It has been shown, however, that they do vibrate together with the vocal folds under special circumstances such as voice pathology and singing voice. Towards understanding the effect of the ventricular fold oscillations on the vocal fold oscillations, the present study developed a synthetic model that takes into account anatomical features of the human ventricular folds. The synthetic model is made of flexible silicone compounds with material properties comparable to those of human ventricular fold tissues. In our experiment, an air-flow was injected into the vocal and ventricular fold models. As the distance between the left and right ventricular folds was reduced, the ventricular folds started to co-vibrate with the vocal folds. Depending upon the distance, various oscillation patterns of the vocal-ventricular folds were observed, e.g. , synchronized dynamics with 1 : 1 or 1 : 2 frequency ratio and desynchronized chaotic dynamics. The observed chaotic dynamics might be related to voice pathology induced by the ventricular phonation. A computational model was further presented to elucidate the experimental findings. [ABSTRACT FROM AUTHOR]

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Deciding on a course of action requires both an accurate estimation of option values and the right amount of effort invested in deliberation to reach sufficient confidence in the final choice. In a previous study, we have provided evidence, across a series of judgment and choice tasks, for a dissociation between the ventromedial prefrontal cortex (vmPFC), which would represent option values, and the dorsomedial prefrontal cortex (dmPFC), which would represent the duration of deliberation. Here, we first replicate this dissociation and extend it to the case of an instrumental learning task, in which 24 human volunteers (13 women) choose between options associated with probabilistic gains and losses. According to fMRI data recorded during decision-making, vmPFC activity reflects the sum of option values generated by a reinforcement learning model and dmPFC activity the deliberation time. To further generalize the role of the dmPFC in mobilizing effort, we then analyze fMRI data recorded in the same participants while they prepare to perform motor and cognitive tasks (squeezing a handgrip or making numerical comparisons) to maximize gains or minimize losses. In both cases, dmPFC activity is associated with the output of an effort regulation model, and not with response time. Taken together, these results strengthen a general theory of behavioral control that implicates the vmPFC in the estimation of option values and the dmPFC in the energization of relevant motor and cognitive processes. [ABSTRACT FROM AUTHOR]

Biological experiments targeting the mammalian primary visual cortex have shown that neuronal response to a preferred orientation grating is cross-inhibited by an orthogonal orientation mask grating. The plaid formed by the overlap of the two gratings not only causes a decrease in the neuronal firing rate but also shifts the gamma oscillation to a weaker oscillation at a higher frequency. The mechanism for the above phenomena is unclarified. In this paper, a large-scale cortical neuronal network model with biological details is constructed. In this model, two modes of connectivity that may contribute to cross-inhibition are considered: the thalamo-cortical feedforward pathway and the push-pull organization of cortical layer 4. Based on this model, the modulation of firing rate and gamma oscillation by a plaid stimulation are successfully reproduced, which is consistent with biological experiments and suggests that it is the thalamo-cortical feedforward pathway that leads to cross-inhibition. Furthermore, our analysis of the neuronal spike clusters and current fluctuations suggests that the push-pull organization leads to an increase in gamma frequency during the transition of visual stimuli from grating to plaid by modulating the source of synaptic inhibition in local neuronal populations. Such results will help to understand the visual processing under multi-input integration. [ABSTRACT FROM AUTHOR]

Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion. [ABSTRACT FROM AUTHOR]

Recombination among different phages sometimes facilitates their ability to grow on new hosts. Protocols to direct the evolution of phage host range, as might be used in the application of phage therapy, would then benefit from including steps to enable recombination. Applying mathematical and computational models, in addition to experiments using phages T3 and T7, we consider ways that a protocol may influence recombination levels. We first address coinfection, which is the first step to enabling recombination. The multiplicity of infection (MOI, the ratio of phage to cell concentration) is insufficient for predicting (co)infection levels. The force of infection (the rate at which cells are infected) is also critical but is more challenging to measure. Using both a high force of infection and high MOI (>1) for the different phages ensures high levels of coinfection. We also apply a four-genetic-locus model to study protocol effects on recombinant levels. Recombinants accumulate over multiple generations of phage growth, less so if one phage outgrows the other. Supplementing the phage pool with the low-fitness phage recovers some of this 'lost' recombination. Overall, fine tuning of phage recombination rates will not be practical with wild phages, but qualitative enhancement can be attained with some basic procedures. [ABSTRACT FROM AUTHOR]

The timescales of the dynamics of a system depend on the combination of the timescales of its components and of its transmission delays between components. Here, we combine experimental stimulation data from 10 studies in macaque monkeys that reveal the timing of excitatory and inhibitory events in the basal ganglia circuit, to estimate its set of transmission delays. In doing so, we reveal possible inconsistencies in the existing data, calling for replications, and we propose two possible sets of transmission delays. We then integrate these delays in a model of the primate basal ganglia that does not rely on direct and indirect pathways' segregation and show that extrastriatal dopaminergic depletion in the external part of the globus pallidus and in the subthalamic nucleus is sufficient to generate β‐band oscillations (in the high part, 20–35 Hz, of the band). More specifically, we show that D2 and D5 dopamine receptors in these nuclei play opposing roles in the emergence of these oscillations, thereby explaining how completely deactivating D5 receptors in the subthalamic nucleus can, paradoxically, cancel oscillations. [ABSTRACT FROM AUTHOR]