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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., Competing Interests: Declaration of competing interest There is no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2024 Orthopaedic Research Society.)

Men generally favor carbohydrate metabolism, while women lean towards lipid metabolism, resulting in significant sex-based differences in energy oxidation across various metabolic states such as fasting and feeding. These differences are influenced by body composition and inherent metabolic fluxes, including increased lipolysis rates in women. However, understanding how sex influences organ-specific metabolism and systemic manifestations remains incomplete. To address these gaps, we developed a sex-specific, whole-body metabolic model for feeding and fasting scenarios in healthy young adults. Our model integrates organ metabolism with whole-body responses to mixed meals, particularly high-carbohydrate and high-fat meals. Our predictions suggest that differences in liver and adipose tissue nutrient storage and oxidation patterns drive systemic metabolic disparities. We propose that sex differences in fasting hepatic glucose output may result from the different handling of free fatty acids, glycerol, and glycogen. We identified a metabolic pathway, possibly more prevalent in female livers, redirecting lipids towards carbohydrate metabolism to support hepatic glucose production. This mechanism is facilitated by the TG-FFA cycle between adipose tissue and the liver. Incorporating sex-specific data into multi-scale frameworks offers insights into how sex modulates human metabolism., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Several experimental studies have found that females have higher deposition of particles in the airways compared with males. This has implications for the delivery of aerosolized therapeutics and for understanding sex differences in respiratory system response to environmental exposures. This study evaluates several factors that potentially contribute to sex differences in particle deposition, using scale-specific structure-function models of 1D ventilation distribution, particle transport, and deposition. The impact of gravity, inhalation flow rate, and dead space are evaluated in 12 structure-based models (seven female; five male). Females were found to have significantly higher total, bronchial, and alveolar deposition than males across a particle size range from 0.01 to 10 . Results suggest that higher deposition fraction in females is due to higher alveolar deposition for smaller particle sizes, and higher bronchial deposition for larger particles. Females had higher alveolar deposition in the lower lobes, and slightly lower particle concentration in the left upper lobe. Males were found to be more sensitive to changes due to gravity, showing greater reduction in bronchial deposition fraction. Males were also more sensitive to change in inhalation flow rate, and to scaling of dead space due to the larger male baseline airway size. Predictions of sex differences in particle deposition - that are consistent with the literature - suggest that sex-based characteristics of lung and airway size interacting with particle size gives rise to differences in regional deposition.

People perform better collectively than individually, a phenomenon known as the collective benefit. To pursue the benefit, they may learn from previous behaviors, come to know whose initial opinion should be valued, and develop the inclination to take it as the collective one. Such learning may affect interpersonal brain communication. To test these hypotheses, this study recruited participant dyads to conduct a perceptual task on which they made individual decisions first and then the collective one. The enhanced interpersonal brain synchronization (IBS) between participants was explored when individual decisions were in disagreement vs. agreement. Computational modeling revealed that participant dyads developed the dyad inclination of taking the higher-able participants', not the lower-able ones' decisions as their collective ones. Brain analyses unveiled the enhanced IBS at frontopolar areas, premotor areas, supramarginal gyri, and right temporal-parietal junctions. The premotor IBS correlated negatively with dyad inclination and collective benefit in the absence of correction. The Granger causality analyses further supported the negative relation of dyad inclination with inter-brain communication. This study highlights that dyads learn to weigh individuals' decisions, resulting in dyad inclinations, and explores associated inter-brain communication, offering insights into the dynamics of collective decision-making., Competing Interests: Declaration of competing interest The authors declared that there were no conflicts of interest with respect to the authorship or the publication of this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

For conditions like osteoporosis, changes in bone pore geometry even when porosity is constant have been shown to correlate to increased fracture risk using techniques such as dual-energy x-ray absorptiometry (DXA) and computed tomography (CT). Additionally, studies have found that bone pore geometry can be characterized by ultrasound to determine fracture risk, since certain pore geometries can cause stress concentration which in turn will be a source for fracture. However, it is not yet fully understood if changes in pore geometry can be detected by ultrasound when the porosity is constant. Therefore, this study develops an unsupervised machine learning model classifying pore geometry between bioinspired and quadrilateral pore scaffolds with constant porosity using experimental ultrasound wave transmission data. Our results demonstrate that differences in pore geometry can be detected by ultrasound, even at constant porosity, and that these differences can be distinguished in an unsupervised manner with machine learning. For traumatic bone injuries and late-stage osteoporosis where fracture occurs, tissue scaffolds are used to aid the healing of fractures or bone loss. The scaffold design is optimized to match material properties closely with bone, and healing can be enhanced with ultrasound stimulation. In this study we predict the combined effects of ultrasound parameters, such as wave frequency and mode of displacement, and scaffold material properties on bone tissue growth. We therefore develop an unsupervised machine learning clustering model of bone tissue growth in the scaffolds using finite element analysis and bone growth algorithms evaluating effects of pore geometry, scaffold materials, ultrasound wave type and frequency, and mesenchymal stem cell distribution on bone tissue growth. The computational predictions of tissue growth agreed within 10% of comparable experimental studies. The data corresponding to pore geometry, mesenchymal stem cell distribution, and scaffold material demonstrate distinct clusters of total bone formation, while ultrasound frequency and mesenchymal stem cell distribution show distinct clusters in bone growth rate. These variables can be tuned to tailor the scaffold design and optimize the required amount and rate of bone growth to meet a patient's specific needs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) can markedly reduce muscle rigidity in people with Parkinson's disease (PD); however, the mechanisms mediating this effect are poorly understood. Computational modeling of DBS provides a method to estimate the relative contributions of neural pathway activations to changes in outcomes. In this study, we generated patient-specific biophysical models of GPi DBS (derived from individual 7T MRI) - including pallidal efferent, putamenal efferent and internal capsule pathways - to investigate how activation of neural pathways contributed to changes in forearm rigidity in PD. Ten individuals (17 arms) were tested off medication under four conditions: off stimulation, on clinically optimized stimulation, and on stimulation specifically targeting the dorsal GPi or ventral GPi. Quantitative measures of forearm rigidity, with and without a contralateral activation maneuver, were obtained using a robotic manipulandum. Clinically optimized GPi DBS settings significantly reduced forearm rigidity (p < 0.001), which aligned with GPi efferent fiber activation. The model demonstrated that GPi efferent axons could be activated at any location along the GPi dorsal-ventral axis. These results provide evidence that rigidity reduction produced by GPi DBS is mediated by preferential activation of GPi efferents to the thalamus, likely leading to a reduction in excitability of the muscle stretch reflex via overdriving pallidofugal output.

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., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

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., Competing Interests: Conflict of interestThe author declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

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., (© 2024. The Author(s).)

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., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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 (G N ). The change in G N 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. G N 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., (© 2024. The Author(s).)

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts ( Schistocerca americana ). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses., Competing Interests: YB, BR No competing interests declared, (© 2023, Bessonova and Raman.)

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet's focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation., Competing Interests: OC, JM, MK, PG No competing interests declared, JP Reviewing editor, eLife, (© 2023, Codol et al.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Zhang, Zhang, Zeng, Liu and Wang.)

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., (© 2024 John Wiley & Sons Ltd.)

Cellular electrophysiology is the foundation of many fields, from basic science in neurology, cardiology, oncology to safety critical applications for drug safety testing, clinical phenotyping, etc. Patch-clamp voltage clamp is the gold standard technique for studying cellular electrophysiology. Yet, the quality of these experiments is not always transparent, which may lead to erroneous conclusions for studies and applications. Here, we have developed a new computational approach that allows us to explain and predict the experimental artefacts in voltage-clamp experiments. The computational model captures the experimental procedure and its inadequacies, including: voltage offset, series resistance, membrane capacitance and (imperfect) amplifier compensations, such as series resistance compensation and supercharging. The computational model was validated through a series of electrical model cell experiments. Using this computational approach, the artefacts in voltage-clamp experiments of cardiac fast sodium current, one of the most challenging currents to voltage clamp, were able to be resolved and explained through coupling the observed current and the simulated membrane voltage, including some typically observed shifts and delays in the recorded currents. We further demonstrated that the typical way of averaging data for current-voltage relationships would lead to biases in the peak current and shifts in the peak voltage, and such biases can be in the same order of magnitude as those differences reported for disease-causing mutations. Therefore, the presented new computational pipeline will provide a new standard of assessing the voltage-clamp experiments and interpreting the experimental data, which may be able to rectify and provide a better understanding of ion channel mutations and other related applications., Competing Interests: Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Mechanical stress and fluid flow influence glioma cell phenotype in vitro, but measuring these quantities in vivo continues to be challenging. The purpose of this study was to predict these quantities in vivo, thus providing insight into glioma physiology and potential mechanical biomarkers that may improve glioma detection, diagnosis, and treatment. Image-based finite element models of human U251N orthotopic glioma in athymic rats were developed to predict structural stress and interstitial flow in and around each animal's tumor. In addition to accounting for structural stress caused by tumor growth, our approach has the advantage of capturing fluid pressure-induced structural stress, which was informed by in vivo interstitial fluid pressure (IFP) measurements. Because gliomas and the brain are soft, elevated IFP contributed substantially to tumor structural stress, even inverting this stress from compressive to tensile in the most compliant cases. The combination of tumor growth and elevated IFP resulted in a concentration of structural stress near the tumor boundary where it has the greatest potential to influence cell proliferation and invasion. MRI-derived anatomical geometries and tissue property distributions resulted in heterogeneous interstitial fluid flow with local maxima near cerebrospinal fluid spaces, which may promote tumor invasion and hinder drug delivery. In addition, predicted structural stress and interstitial flow varied markedly between irradiated and radiation-naïve animals. Our modeling suggests that relative to tumors in stiffer tissues, gliomas experience unusual mechanical conditions with potentially important biological (e.g., proliferation and invasion) and clinical consequences (e.g., drug delivery and treatment monitoring)., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Objective. Despite the widespread use and technical improvement of cochlear implant (CI) devices over past decades, further research into the bioelectric bases of CI stimulation is still needed. Various stimulation modes implemented by different CI manufacturers coexist, but their true clinical benefit remains unclear, probably due to the high inter-subject variability reported, which makes the prediction of CI outcomes and the optimal fitting of stimulation parameters challenging. A highly detailed full-head model that includes a cochlea and an electrode array is developed in this study to emulate intracochlear voltages and extracochlear current pathways through the head in CI stimulation. Approach. Simulations based on the finite element method were conducted under monopolar, bipolar, tripolar (TP), and partial TP modes, as well as for apical, medial, and basal electrodes. Variables simulated included: intracochlear voltages, electric field (EF) decay, electric potentials at the scalp and extracochlear currents through the head. To better understand CI side effects such as facial nerve stimulation, caused by spurious current leakage out from the cochlea, special emphasis is given to the analysis of the EF over the facial nerve. Main results. The model reasonably predicts EF magnitudes and trends previously reported in CI users. New relevant extracochlear current pathways through the head and brain tissues have been identified. Simulated results also show differences in the magnitude and distribution of the EF through different segments of the facial nerve upon different stimulation modes and electrodes, dependent on nerve and bone tissue conductivities. Significance. Full-head models prove useful tools to model intra and extracochlear EFs in CI stimulation. Our findings could prove useful in the design of future experimental studies to contrast FNS mechanisms upon stimulation of different electrodes and CI modes. The full-head model developed is freely available for the CI community for further research and use., (Creative Commons Attribution license.)

In recent years, nuclear mechanobiology gained a lot of attention for the study of cell responses to external cues like adhesive forces, applied compression, and/or shear-stresses. In details, the Lamin-A protein-as major constituent of the cell nucleus structure-plays a crucial role in the overall nucleus mechanobiological response. However, modeling and analysis of Lamin-A protein organization upon rapid compression conditions in microfluidics are still difficult to be performed. Here, we introduce the possibility to control an applied microfluidic compression on single cells, deforming them up to the nucleus level. In a wide range of stresses (~1-10 2  kPa) applied on healthy and cancer cells, we report increasing Lamin-A intensities which scale as a power law with the applied compression. Then, an increase up to two times of the nuclear viscosity is measured in healthy cells, due to the modified Lamin-A organization. This is ascribable to the increasing assembly of Lamin-A filament-like branches which increment both in number and elongation (up to branches four-time longer). Moreover, the solution of a computational model of differential equations is presented as a powerful tool for a single cell prediction of the Lamin-A assembly as a function of the applied compression., (© 2024 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Objective: 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., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

For convenience and experimental control, cognitive science has relied largely on images as stimuli rather than the real, tangible objects encountered in the real world. Recent evidence suggests that the cognitive processing of images may differ from real objects, especially in the processing of spatial locations and actions, thought to be mediated by the dorsal visual stream. Perceptual and semantic processing in the ventral visual stream, however, has been assumed to be largely unaffected by the realism of objects. Several studies have found that one key difference accounting for differences between real objects and images is actability; however, less research has investigated another potential difference - the three-dimensional nature of real objects as conveyed by cues like binocular disparity. To investigate the extent to which perception is affected by the realism of a stimulus, we compared viewpoint adaptation when stimuli (a face or a kettle) were 2D (flat images without binocular disparity) vs. 3D (i.e., real, tangible objects or stereoscopic images with binocular disparity). For both faces and kettles, adaptation to 3D stimuli induced stronger viewpoint aftereffects than adaptation to 2D images when the adapting orientation was rightward. A computational model suggested that the difference in aftereffects could be explained by broader viewpoint tuning for 3D compared to 2D stimuli. Overall, our finding narrowed the gap between understanding the neural processing of visual images and real-world objects by suggesting that compared to 2D images, real and simulated 3D objects evoke more broadly tuned neural representations, which may result in stronger viewpoint invariance., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Adeno-associated virus (AAV) is a clinically useful gene delivery vehicle for treating neurological diseases. To deliver AAV to focal targets, direct infusion into brain tissue by convection-enhanced delivery (CED) is often needed due to AAV's limited penetration across the blood-brain-barrier and its low diffusivity in tissue. In this study, computational models that predict the spatial distribution of AAV in brain tissue during CED were developed to guide future placement of infusion catheters in recurrent brain tumors following primary tumor resection. The brain was modeled as a porous medium, and material property fields that account for magnetic resonance imaging (MRI)-derived anatomical regions were interpolated and directly assigned to an unstructured finite element mesh. By eliminating the need to mesh complex surfaces between fluid regions and tissue, mesh preparation was expedited, increasing the model's clinical feasibility. The infusion model predicted preferential fluid diversion into open fluid regions such as the ventricles and subarachnoid space (SAS). Additionally, a sensitivity analysis of AAV delivery demonstrated that improved AAV distribution in the tumor was achieved at higher tumor hydraulic conductivity or lower tumor porosity. Depending on the tumor infusion site, the AAV distribution covered 3.67-70.25% of the tumor volume (using a 10% AAV concentration threshold), demonstrating the model's potential to inform the selection of infusion sites for maximal tumor coverage., (Copyright © 2024 by ASME.)

Artificial intelligence methods and techniques creatively support the processes of developing and improving methods for selecting shredders for the processing of polymer materials. This allows to optimize the fulfillment of selection criteria, which may include not only indicators related to shredding efficiency and recyclate quality but also energy consumption. The aim of this paper is to select methods of analysis based on artificial intelligence (AI) with independent rule extraction, i.e., data-based methods (machine learning-ML). This study took into account real data sets (feature matrix 1982 rows × 40 columns) describing the shredding process, including energy consumption used to optimize the parameters for the energy efficiency of the shredder. Each of the 1982 records in a .csv file (feature vector) has 40 numbers divided by commas. The data were divided into a learning set (70% of the data), a testing set (20% of the data), and a validation set (10% of the data). Cross-validation showed that the best model was LbfgsLogisticRegressionOva (0.9333). This promotes the development of the basis for an intelligent shredding methodology with a high level of innovation in the processing and recycling of polymer materials within the Industry 4.0 paradigm.

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.

Fairness is a fundamental value in human societies, with individuals concerned about unfairness both to themselves and to others. Nevertheless, an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes. To address this, we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral, cognitive, and neural patterns underlying unfairness to self and others. Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness. Moreover, self-unfairness consistently activates brain regions such as the anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex, spanning various spatial scales that encompass univariate activation, local multivariate patterns, and whole-brain multivariate patterns. These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making. Conversely, other-unfairness primarily engages the middle occipital gyrus. Collectively, our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness., (© 2024. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences.)

Introduction: Clinics increasingly require readily deployable tubular substitutes to restore the functionality of structures like ureters and blood vessels. Despite extensive exploration of various materials, both synthetic and biological, the optimal solution remains elusive. Drawing on abundant literature experiences, there is a pressing demand for a substitute that not only emulates native tissue by providing requisite signals and growth factors but also exhibits appropriate mechanical resilience and behaviour. Methods: This study aims to assess the potential of porcine ureters by characterizing their biomechanical properties in their native configuration through ring and membrane flexion tests. In order to assess the tissue morphology before and after mechanical tests and the eventual alteration of tissue microstructure that would be inserted in material constitutive description, histological staining was performed on samples. Corresponding computational analyses were performed to mimic the experimental campaign to identify the constitutive material parameters. Results: The absence of any damages to muscle and collagen fibres, which only compacted after mechanical tests, was demonstrated. The experimental tests (ring and membrane flexion tests) showed non-linearity for material and geometry and the viscoelastic behaviour of the native porcine ureter. Computational models were descriptive of the mechanical behaviour ureteral tissue, and the material model feasible. Discussion: This analysis will be useful for future comparison with decellularized tissue for the evaluation of the aggression of cell removal and its effect on microstructure. The computational model could lay the basis for a reliable tool for the prediction of solicitation in the case of tubular substitutions in subsequent simulations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Casarin, Toniolo, Todesco, Carniel, Astolfi, Morlacco and Moro.)

Alternating 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.

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2024 The Authors. Experimental Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Background: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic stresses. Therefore, it is crucial to identify TFs associated with abiotic stress response for breeding of abiotic stress tolerant crop cultivars., Methods: Based on a machine learning framework, a computational model was envisaged to predict TFs associated with abiotic stress response in plants. To numerically encode TF sequences, four distinct sequence derived features were generated. The prediction was performed using ten shallow learning and four deep learning algorithms. For prediction using more pertinent and informative features, feature selection techniques were also employed., Results: Using the features chosen by the light-gradient boosting machine-variable importance measure (LGBM-VIM), the LGBM achieved the highest cross-validation performance metrics (accuracy: 86.81%, auROC: 92.98%, and auPRC: 94.03%). Further evaluation of the proposed model (LGBM prediction method + LGBM-VIM selected features) was also done using an independent test dataset, where the accuracy, auROC and auPRC were observed 81.98%, 90.65% and 91.30%, respectively., Conclusions: To facilitate the adoption of the proposed strategy by users, the approach was implemented as a prediction server called ASPTF, accessible at https://iasri-sg.icar.gov.in/asptf/. The developed approach and the corresponding web application are anticipated to supplement experimental methods in the identification of transcription factors (TFs) responsive to abiotic stress in plants., Competing Interests: Declaration of competing interest All authors did not have any conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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.

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.

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.

We comment on the recently published study 'Modeling for predicting survival fraction of cells after ultra-high dose rate irradiation' by Shiraishi et al . While the general approach of the study may be appropriate, we wish to comment on its limitations and point out issues concerning their choice of the benchmarking and fitting data. The approach by the authors could become viable in an extended form once more comprehensive data is available., (© 2024 Institute of Physics and Engineering in Medicine.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Peng, Duan, Yang, Tang, Zhang, Zhang and Li.)

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., (Copyright © 2021 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 Cognitive Science Society LLC.)

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., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

The plant cell wall (PCW) inspires the preparation of fiber-based biomaterials, particularly emphasizing exploiting the intrinsic interactions within the load-bearing cellulose and hemicellulose network. Due to experimental difficulties in studying and interpreting the interaction between these polysaccharides, this research presents a numerical model based on coarse-grained molecular dynamics that evaluates the mechanical properties of fiber composites. To validate the model and explain the structural and mechanical role of hemicelluloses, bacterial cellulose (BC) was synthesized in the presence of different concentrations of xylan, arabinoxylan, xyloglucan, or glucomannan and subjected to nano- and macroscale structural and mechanical characterization. The data obtained were used to interpret the effects of each hemicellulose on the mechanics of the BC-hemicellulose composite based on the sensitivity of the model. The mechanical properties of the resulting simulated networks agreed well with the experimental observations of the BC-hemicellulose composites. Increased xylan and arabinoxylan contents increased the macroscale mechanical properties, fiber modulus (xylan), and fiber width (arabinoxylan). The addition of xyloglucan increased the mechanical properties of the composites in the elastic deformation phase, associated with an increase in the fiber modulus. Adding glucomannan to the culture medium decreased all the mechanical properties studied while the fiber width increased., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Piotr Mariusz Pieczywek reports financial support was provided by National Science Centre Poland. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Transcranial magnetic stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high-frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called 'I-waves', following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition, and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves that significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2023 Massachusetts Institute of Technology.)

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., (© 2024 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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., (© 2024 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

Background Aims: The appearance of genetically variant populations in human pluripotent stem cell (hPSC) cultures represents a concern for research and clinical applications. Genetic variations may alter hPSC differentiation potential or cause phenotype variation in differentiated cells. Further, variants may have properties such as proliferative rate, or response to the culture environment, that differ from wild-type cells. As such, understanding the behavior of these variants in culture, and any potential operational impact on manufacturing processes, will be necessary to control quality of putative hPSC-based products that include a proportion of variant threshold in their quality specification., Methods: Here we show a computational model that mathematically describes the growth dynamics between commonly occurring genetically variant hPSCs and their counterpart wild-type cells in culture., Results: We show that our model is capable of representing the growth behaviors of both wild-type and variant hPSCs in individual and co-culture systems., Conclusions: This representation allows us to identify three critical process parameters that drive critical quality attributes when genetically variant cells are present within the system: total culture density, proportion of variant cells within the culture system and variant cell overgrowth. Lastly, we used our model to predict how the variability of these parameters affects the prevalence of both populations in culture., Competing Interests: Declaration of Competing Interest The authors have no commercial, proprietary or financial interest in the products or companies described in this article., (Copyright © 2024 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.)

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.

This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape., (© The Author(s) 2024. Published by Oxford University Press.)