ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms

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.

Synchronized and Desynchronized Dynamics Observed from Physical Models of the Vocal and Ventricular Folds

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.

Brain stimulation over the left DLPFC enhances motivation for effortful rewards in patients with major depressive disorder

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.

Modeling and simulation for prediction of multiple sclerosis progression

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.

A Quick Method to Assess Airway Distensibility in Mice

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.

Coarse-grained molecular dynamics model to evaluate the mechanical properties of bacterial cellulose-hemicellulose composites

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.

The Role of Attention in Category Representation

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.

Circuits in the motor cortex explain oscillatory responses to transcranial magnetic stimulation

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.

Neural Fluctuation Contrast as a Code for Complex Sounds: The Role and Control of Peripheral Nonlinearities

The nonlinearities of the inner ear are often considered to be obstacles that the central nervous system has to overcome to decode neural responses to sounds. This review describes how peripheral nonlinearities, such as saturation of the inner-hair-cell response and of the IHC-auditory-nerve synapse, are instead beneficial to the neural encoding of complex sounds such as speech. These nonlinearities set up contrast in the depth of neural-fluctuations in auditory-nerve responses along the tonotopic axis, referred to here as neural fluctuation contrast (NFC). Physiological support for the NFC coding hypothesis is reviewed, and predictions of several psychophysical phenomena, including masked detection and speech intelligibility, are presented. Lastly, a framework based on the NFC code for understanding how the medial olivocochlear (MOC) efferent system contributes to the coding of complex sounds is presented. By modulating cochlear gain control in response to both sound energy and fluctuations in neural responses, the MOC system is hypothesized to function not as a simple feedback gain-control device, but rather as a mechanism for enhancing NFC along the tonotopic axis, enabling robust encoding of complex sounds across a wide range of sound levels and in the presence of background noise. Effects of sensorineural hearing loss on the NFC code and on the MOC feedback system are presented and discussed.

In silico study of combination thrombolytic therapy with alteplase and mutant pro-urokinase for fibrinolysis in ischemic stroke

The synergistic advantage of combining tissue plasminogen activator (tPA) with pro-urokinase (proUK) for thrombolysis has been demonstrated in several in vitro experiments, and a single site proUK mutant (m-proUK) has been developed for better stability in plasma. Based on these studies, combination thrombolytic therapy with intravenous tPA and m-proUK has been suggested as a promising treatment for patients with ischemic stroke. This paper evaluates the efficacy and safety of the dual therapy by computational simulations of pharmacokinetics and pharmacodynamics coupled with a local fibrinolysis model. Seven dose regimens are simulated and compared with the standard intravenous tPA monotherapy. Our simulation results provide more insights into the complementary reaction mechanisms of tPA and m-proUK during clot lysis and demonstrate that the dual therapy can achieve a similar recanalization time (about 50 min) to tPA monotherapy, while keeping the circulating fibrinogen level within a normal range. Specifically, our results show that for all dual therapies with a 5 mg tPA bolus, the plasma concentration of fibrinogen remains stable at around 7.5 μM after a slow depletion over 50 min, whereas a rapid depletion of circulating fibrinogen (to 5 μM) is observed with the standard tPA therapy, indicating the potential advantage of dual therapy in reducing the risk of intracranial hemorrhage. Through simulations of varying dose combinations, it has been found that increasing tPA bolus can significantly affect fibrinogen level but only moderately improves recanalization time. Conversely, m-proUK doses and infusion duration exhibit a mild impact on fibrinogen level but significantly affect recanalization time. Therefore, future optimization of dose regimen should focus on limiting the tPA bolus while adjusting m-proUK dosage and infusion rate. Such adjustments could potentially maximize the therapeutic advantages of this combination therapy for ischemic stroke treatment.

Computational model to address lens-based acoustic field aperture in the in vitro ultrasonic cell stimulation

Low-Intensity Pulsed Ultrasound Stimulation (LIPUS) is a therapeutic modality used for bone tissue regeneration and healing. Its clinical efficacy is still debated, as the underlying physical phenomena remain poorly understood. The interaction between ultrasonic waves and cells, likely to trigger mechanotransduction inducing bone regeneration, is at the center of scientific concerns on the subject. In order to get new insights into these phenomena, the development of in vitro experiments is a key step but special attentions should be paid concerning to the actual acoustic area covered that has to be sufficiently large and homogeneous. To address this issue, an acoustic lens can be placed on the transducer to improve the homogeneity of the acoustic field over the entire cell culture area. A computational model is developed to test several shapes and heights of acoustic lenses and compare their effectiveness in order to find a compromise between the surface covered, the homogeneity of the intensity distribution and the acoustic pressure loss. All the lenses studied improve the enlargement of the field and its homogeneity but they all generate pressure acoustic loss. The best performing lens in terms of field homogeneity is the one that minimizes pressure acoustic loss but covers only 22% of the target surface. The best enlargement (68% of the surface covered) is obtained for a lens that produces a field that is 4 times less homogeneous and 3 times less efficient in terms of pressure acoustic loss. As no one lens is ideal, the choice of the lens should be the result of a compromise taking into account the prioritization of criteria.

Mechanical analysis of radial performance in biodegradable polymeric vascular stents manufactured using micro-injection molding

Micro-injection molding (MiM) is a promising technique for manufacturing biodegradable polymeric vascular stents (BPVSs) at scale, in which a trapezoidal strut cross section is needed to ensure high-quality de-molding. However, there is a lack of research on the influence of the strut cross-sectional shape on its mechanical properties, posing a challenge in determining the key geometries of the strut when using MiM to produce BPVSs. Hence, this work has investigated the relationships between the geometry parameters, including the de-molding angle, and the radial support property of BPVSs using the finite element method. The results reveal that the radial stiffness of BPVSs is significantly affected by the de-molding angle, which can be counteracted by adjusting strut height, bending radius, and strut thickness. Stress distribution analysis underscores the crucial role of the curved portion of the support ring during compression, with the inner side of the curved region experiencing stress concentration. A mathematical model has been established to describe the relationships between the geometry parameters and the radial support property of the BPVSs. Notably, the radius of the neutral layer emerges as a key determinant of radial stiffness. This study is expected to serve as a guideline for the development of BPVSs that can be manufactured using MiM.

Bayesian uncertainty quantification for anaerobic digestion models

Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian procedure, called VarBUQ, ensuring a correct tradeoff between flexibility and computational cost. A benchmark against three existing methods (Fisher's information, bootstrapping and Beale's criteria) was conducted using synthetic data. This Bayesian procedure offered a good compromise between fitting ability and confidence estimation, while the other methods proved to be repeatedly overconfident. The method's performances notably benefitted from inductive bias brought by the prior distribution, although it requires careful construction. This article advocates for more systematic consideration of uncertainty for anaerobic digestion models and showcases a new, computationally efficient Bayesian method. To facilitate future implementations, a Python package called 'aduq' is made available.

A biophysically comprehensive model of urothelial afferent neurons: implications for sensory signalling in urinary bladder

The urothelium is the innermost layer of the bladder wall; it plays a pivotal role in bladder sensory transduction by responding to chemical and mechanical stimuli. The urothelium also acts as a physical barrier between urine and the outer layers of the bladder wall. There is intricate sensory communication between the layers of the bladder wall and the neurons that supply the bladder, which eventually translates into the regulation of mechanical activity. In response to natural stimuli, urothelial cells release substances such as ATP, nitric oxide (NO), substance P, acetylcholine (ACh), and adenosine. These act on adjacent urothelial cells, myofibroblasts, and urothelial afferent neurons (UAN), controlling the contractile activity of the bladder. There is rising evidence on the importance of urothelial sensory signalling, yet a comprehensive understanding of the functioning of the urothelium-afferent neurons and the factors that govern it remains elusive to date. Until now, the biophysical studies done on UAN have been unable to provide adequate information on the ion channel composition of the neuron, which is paramount to understanding the electrical functioning of the UAN and, by extension, afferent signalling. To this end, we have attempted to model UAN to decipher the ionic mechanisms underlying the excitability of the UAN. In contrast to previous models, our model was built and validated using morphological and biophysical properties consistent with experimental findings for the UAN. The model included all the channels thus far known to be expressed in UAN, including; voltage-gated sodium and potassium channels, N, L, T, P/Q, R-type calcium channels, large-conductance calcium-dependent potassium (BK) channels, small conductance calcium-dependent (SK) channels, Hyperpolarisation activated cation (HCN) channels, transient receptor potential melastatin (TRPM8), transient receptor potential vanilloid (TRPV1) channel, calcium-activated chloride(CaCC) channels, and internal calcium dynamics. Our UAN model a) was constrained as far as possible by experimental data from the literature for the channels and the spiking activity, b) was validated by reproducing the experimental responses to current-clamp and voltage-clamp protocols c) was used as a base for modelling the non-urothelial afferent neurons (NUAN). Using our models, we also gained insights into the variations in ion channels between UAN and NUAN neurons.

Grid codes underlie multiple cognitive maps in the human brain

Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes-empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.

Enhancing Associative Learning in Rats With a Computationally Designed Training Protocol

Background

Learning requires the activation of protein kinases with distinct temporal dynamics. In Aplysia, nonassociative learning can be enhanced by a computationally designed learning protocol with intertrial intervals (ITIs) that maximize the interaction between fast-activated PKA (protein kinase A) and slow-activated ERK (extracellular signal-regulated kinase). Whether a similar strategy can enhance associative learning in mammals is unknown.

Methods

We simulated 1000 training protocols with varying ITIs to predict an optimal protocol based on empirical data for PKA and ERK dynamics in rat hippocampus. Adult male rats received the optimal protocol or control protocols in auditory fear conditioning and fear extinction experiments. Immunohistochemistry was performed to evaluate pCREB (phosphorylated cAMP response element binding)\protein levels in brain regions that have been implicated in fear acquisition.

Results

Rats exposed to the optimal conditioning protocol with irregular ITIs exhibited impaired extinction memory acquisition within the session using a standard footshock intensity, and stronger fear memory retrieval and spontaneous recovery with a weaker footshock intensity, compared with rats that received massed or spaced conditioning protocols with fixed ITIs. Rats exposed to the optimal extinction protocol displayed improved extinction of contextual fear memory and reduced spontaneous recovery compared with rats that received standard extinction protocols. Moreover, the optimal conditioning protocol increased pCREB levels in the dentate gyrus of the dorsal hippocampus, suggesting enhanced induction of long-term potentiation.

Conclusions

These findings demonstrate that a computational model-driven behavioral intervention can enhance associative learning in mammals and may provide insight into strategies to improve cognition in humans.

A primer on modelling pancreatic islets: from models of coupled β-cells to multicellular islet models

Pancreatic islets are mini-organs composed of hundreds or thousands of ɑ, β and δ-cells, which, respectively, secrete glucagon, insulin and somatostatin, key hormones for the regulation of blood glucose. In pancreatic islets, hormone secretion is tightly regulated by both internal and external mechanisms, including electrical communication and paracrine signaling between islet cells. Given its complexity, the experimental study of pancreatic islets has been complemented with computational modeling as a tool to gain a better understanding about how all the mechanisms involved at different levels of organization interact. In this review, we describe how multicellular models of pancreatic cells have evolved from the early models of electrically coupled β-cells to models in which experimentally derived architectures and both electrical and paracrine signals have been considered.

Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model

Emerging computational tools such as healthcare digital twin modeling are enabling the creation of patient-specific surgical planning, including microwave ablation to treat primary and secondary liver cancers. Healthcare digital twins (DTs) are anatomically one-to-one biophysical models constructed from structural, functional, and biomarker-based imaging data to simulate patient-specific therapies and guide clinical decision-making. In microwave ablation (MWA), tissue-specific factors including tissue perfusion, hepatic steatosis, and fibrosis affect therapeutic extent, but current thermal dosing guidelines do not account for these parameters. This study establishes an MR imaging framework to construct three-dimensional biophysical digital twins to predict ablation delivery in livers with 5 levels of fat content in the presence of a tumor. Four microwave antenna placement strategies were considered, and simulated microwave ablations were then performed using 915 MHz and 2450 MHz antennae in Tumor Naïve DTs (control), and Tumor Informed DTs at five grades of steatosis. Across the range of fatty liver steatosis grades, fat content was found to significantly increase ablation volumes by approximately 29-l42% in the Tumor Naïve and 55-60% in the Tumor Informed DTs in 915 MHz and 2450 MHz antenna simulations. The presence of tumor did not significantly affect ablation volumes within the same steatosis grade in 915 MHz simulations, but did significantly increase ablation volumes within mild-, moderate-, and high-fat steatosis grades in 2450 MHz simulations. An analysis of signed distance to agreement for placement strategies suggests that accounting for patient-specific tumor tissue properties significantly impacts ablation forecasting for the preoperative evaluation of ablation zone coverage.

A computational analysis of transcranial magnetic stimulation in patients with cranial defects and skull plate implants

We utilized computational analysis to investigate the impact of skull defects and skull implants on the TMS-induced EF. Our findings revealed a noteworthy alteration in the induced EF when acute skull defects were present. When high-conductivity titanium plates were used, we observed a pronounced increase in the peak EF, accompanied by a shift in the induced EF from the center towards both ends of the implant. These findings underscore the importance of carefully considering skull defects and implant materials during TMS.

Making time and space for calcium control of neuron activity

Calcium directly controls or indirectly regulates numerous functions that are critical for neuronal network activity. Intracellular calcium concentration is tightly regulated by numerous molecular mechanisms because spatial domains and temporal dynamics (not just peak amplitude) are critical for calcium control of synaptic plasticity and ion channel activation, which in turn determine neuron spiking activity. The computational models investigating calcium control are valuable because experiments achieving high spatial and temporal resolution simultaneously are technically unfeasible. Simulations of calcium nanodomains reveal that specific calcium sources can couple to specific calcium targets, providing a mechanism to determine the direction of synaptic plasticity. Cooperativity of calcium domains opposes specificity, suggesting that the dendritic branch might be the preferred computational unit of the neuron.

Regional brain strain dependance on direction of head rotation

Brain injuries in automated vehicles during crash events are likely to include mechanisms of head impact in non-standard positions and postures (i.e., occupants not facing forward in an upright position). Federal regulations currently focus on impact conditions in primary planes of motion, such as frontal or rear impacts (sagittal plane of motion) or side impact (coronal plane of motion) and do not account for out of position occupants or non-standard postures. The objective of the present study was to develop and use the anatomically accurate brain finite element model to parametrically determine the injury metrics under different vectors with head rotation. A custom developed brain finite element model with anatomical accuracy and several anatomical regions defined was used to evaluate whole-brain strain as well as regional brain strain. Cumulative Strain Damage Measure (CSDM) at a threshold of 20% strain and the 95th percentile of the maximum principal strain (MPS95) were calculated for the whole brain and each brain region under multiple rotational directions. The model was exposed to a sinusoidal angular acceleration pulse of 5000 rad per second squared (rad/s2-) over 12.5 ms. The same pulse was used in the primary axes of motion and (lateral bending, flexion, extension, axial rotation) and combined axes representing oblique flexion and oblique extension. Whole brain CSDM20 was highest for lateral bending. Whole brain MPS95 was highest for axial rotation. The rCSDM20 was more susceptible to impact direction, with several brain regions having substantial accumulation of strain for oblique flexion and lateral bending. Comparatively, rMPS95 was more consistent across all rotation directions. The present study quantified the regional brain strain response under multiple rotational vectors identifying a high amount of variability in the accumulation of strain (i.e., CSDM20) in the hypothalamus, hippocampus, and midbrain specifically. While there was a high amount of variability in the accumulation of strain for multiple regions, the maximum strain measured (i.e., MPS95) in the regions was more consistent.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.

Results

Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.

Conclusion

We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.

Statement of Significance

This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.

Air kerma rate from radionuclides distributed in forest ecosystem

This study evaluates the air kerma rate in radioactively contaminated forests. The air kerma rates created by plane sources of monoenergetic photons in the energy range 0.02-3 MeV located at different depths in soil up to 50 g cm-2 and at different heights in the forest medium from 0.05 to 50 m were calculated using numeric solution of the transport (Boltzmann) equation. To simplify the practical use of the results obtained by solving the Boltzmann equation, the study additionally includes approximation formulae for calculating air kerma rate separately from contaminated soil, crowns and trunks of trees in a forest ecosystem for 20 radionuclides - fission products that significantly contribute to the external dose. Biomaterial of trunks and crowns was modeled as uniformly distributed in corresponding layers and homogeneously mixed with air. Different distributions of radionuclides in soil were considered including plane source located at different depths, exponential and uniform distribution. Based on the results, the effect of forest biomass presence as an absorbing and scattering medium on the air kerma rate at 1 m above soil was evaluated. The estimated relative difference in air kerma rate at 1 m above soil in the forest medium and in free air for monoenergetic photon sources with energies 0.1 MeV, 0.66 MeV and 3 MeV did not correlate significantly with the energy of photons. Its maximum value in forest medium with biomass density of 5 kg m-3 was 15-20% for the source at soil depth ∼0.3 g cm-2, decreasing to less than 5% when it is at soil depth greater than 7 g cm-2. An example calculation of the air kerma rate dynamics is presented for the initial period after radioactive fallout considering weathering processes (rainfall and wind action) that contribute to the transfer of activity from the canopies to the forest floor. The differences in air kerma rate values, as an integral characteristic of the gamma radiation field from a radioactive cloud in the forest and in the open area, were evaluated.

Biventricular Interaction During Acute Left Ventricular Ischemia in Mice: A Combined In-Vivo and In-Silico Approach

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.

Conscious and unconscious memory and eye movements in context-guided visual search: A computational and experimental reassessment of Ramey, Yonelinas, and Henderson (2019)

Are eye movements unconsciously guided towards target locations in familiar scenes? In a recent eyetracking study, Ramey, Yonelinas, and Henderson (2019) measured eye-movement efficiency (scanpath ratio) and memory judgments when participants searched for targets in repeated and novel scenes. When trials judged new with high confidence were selected, scanpath ratio was lower for old scenes (misses) than for new scenes (correct rejections). In addition, familiarity as measured by recognition confidence did not significantly predict scanpath ratio. Ramey et al. attributed these results to unconscious learning guiding eye movements. In a re-assessment of Ramey et al.'s data, we show that their findings can be accounted for by a single-system computational model in which eye movements and memory judgments are driven by a common latent memory representation. In particular, (a) the scanpath ratio difference between high-confidence misses and correct rejections is a consequence of regression to the mean, while (b) the null correlation between familiarity and scanpath ratio, partly a natural consequence of the low reliability of the scanpath ratio measure, is also reproduced by the model. Two pre-registered experiments confirm a novel prediction of the alternative single-system model. This work offers a parsimonious account of Ramey et al.'s findings without recourse to unconscious guidance of eye movements.

An efficient reduced-lattice model of IP3R for probing Ca2+ dynamics

Numerous cellular processes are regulated by Ca2+ signals, and the endoplasmic reticulum (ER) membrane's inositol triphosphate receptor (IP3R) is critical for modulating intracellular Ca2+ dynamics. The IP3Rs are seen to be clustered in a variety of cell types. The combination of IP3Rs clustering and IP3Rs-mediated Ca2+-induced Ca2+ release results in the hierarchical organization of the Ca2+ signals, which challenges the numerical simulation given the multiple spatial and temporal scales that must be covered. The previous methods rather ignore the spatial feature of IP3Rs or fail to coordinate the conflicts between the real biological relevance and the computational cost. In this work, a general and efficient reduced-lattice model is presented for the simulation of IP3Rs-mediated multiscale Ca2+ dynamics. The model highlights biological details that make up the majority of the calcium events, including IP3Rs clustering and calcium domains, and it reduces the complexity by approximating the minor details. The model's extensibility provides fresh insights into the function of IP3Rs in producing global Ca2+ events and supports the research under more physiological circumstances. Our work contributes to a novel toolkit for modeling multiscale Ca2+ dynamics and advances knowledge of Ca2+ signals.

Bone density and proximal support effects on dental implant stability - Finite element analysis and in vitro experiments

OBJECTIVES

The application of dental implants presents the occurrence of implant failures associated with bone proximal support. This study aims to assess implant behavior, in particular implant stability and strain distribution in the bone at different bone densities, and the effect of proximal bone support.

MATERIAL AND METHODS

Three bone densities (D20, D15, and D10) were considered in the experimental in vitro study, represented by solid rigid polyurethane foam and two conditions of bone support in the proximal region. A finite element model was developed and validated experimentally and a Branemark model at a 3:1 scale was implanted in the experiments; the model was loaded and extracted.

RESULTS

The results of the experimental models validate the finite element models with a correlation R2 equal to 0.899 and NMSE of 7%. The implant extraction tests for the effect of bone properties in the maximum load were 2832 N for D20 and 792 N for D10. The effect of proximal bone support changes the implant stability was observed experimentally; at 1 mm less bone support decreases by 20% of stability and at 2 mm by 58% for D15 density.

CONCLUSIONS

Bone properties and bone quantity are important for the initial stability of the implant. A bone volume fraction of less than 24 g/cm3 exhibits poor behavior and is not indicated for implantation. Proximal bone support reduces the primary stability of the implant and the effect is critical in lower bone density.

Cooperativity and oscillations: Regulatory mechanisms of K-Ras nanoclusters

K-Ras nanoclusters (NCs) concentrate all required molecules belonging to the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) pathway in a small area where signaling events take place, increasing efficiency and specificity of signaling. Such nanostructures are characterized by controlled sizes and lifetimes distributions, but there is a poor understanding of the mechanisms involved in their dynamics of growth/decay. Here, a minimum computational model is presented to analyze the behavior of K-Ras NCs as cooperative dynamic structures that self-regulate their growth and decay according to their size. Indeed, the proposed model reveals that the growth and the local production of a K-Ras nanocluster depend positively on its actual size, whilst its lifetime is inversely proportional to the root of its size. The cooperative binding between the structural constituents of the NC (K-Ras proteins) induces oscillations in the size distributions of K-Ras NCs allowing them to range within controlled values, regulating the growth/decay dynamics of these NCs. Thereby, the size of a K-Ras NC is proposed as a key factor to regulate cell signaling, opening a range of possibilities to develop strategies for use in chronic diseases and cancer.

Validating a model to detect infant crying from naturalistic audio

Human infant crying evolved as a signal to elicit parental care and actively influences caregiving behaviors as well as infant-caregiver interactions. Automated cry detection algorithms have become more popular in recent decades, and while some models exist, they have not been evaluated thoroughly on daylong naturalistic audio recordings. Here, we validate a novel deep learning cry detection model by testing it in assessment scenarios important to developmental researchers. We also evaluate the deep learning model's performance relative to LENA's cry classifier, one of the most commonly used commercial software systems for quantifying child crying. Broadly, we found that both deep learning and LENA model outputs showed convergent validity with human annotations of infant crying. However, the deep learning model had substantially higher accuracy metrics (recall, F1, kappa) and stronger correlations with human annotations at all timescales tested (24 h, 1 h, and 5 min) relative to LENA. On average, LENA underestimated infant crying by 50 min every 24 h relative to human annotations and the deep learning model. Additionally, daily infant crying times detected by both automated models were lower than parent-report estimates in the literature. We provide recommendations and solutions for leveraging automated algorithms to detect infant crying in the home and make our training data and model code open source and publicly available.

Testosterone administration modulates inequality aversion in healthy males: evidence from computational modeling

Fairness concerns play a prominent role in promoting cooperation in human societies. Social preferences involving fairness concern have been associated with individual testosterone levels. However, the causal effects of testosterone administration on fairness-related decision making remain to be elucidated. Here, we used a randomized, double-blind, between-participant design and administered testosterone or placebo gel to 120 healthy young men. Three hours after administration, participants performed a modified Dictator Game from behavioral economics, in which they were asked to choose one of two monetary allocations between themselves and anonymous partners. Participants were either in a position of advantageous inequality (i.e., endowed with more than others) or disadvantageous inequality (i.e., endowed with less than others). Computational modeling showed that inequality-related preferences explained behavior better than competing models. Importantly, compared with the placebo group, the testosterone group showed significantly reduced aversion to advantageous inequality but enhanced aversion to disadvantageous inequality. These findings suggest that testosterone facilitates decisions that prioritize selfish economic motives over fairness concerns, which in turn may boost status-enhancing behaviors.

A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis

Regular physical exercise and appropriate nutrition affect metabolic and hormonal responses and may reduce the risk of developing chronic non-communicable diseases such as high blood pressure, ischemic stroke, coronary heart disease, some types of cancer, and type 2 diabetes mellitus. Computational models describing the metabolic and hormonal changes due to the synergistic action of exercise and meal intake are, to date, scarce and mostly focussed on glucose absorption, ignoring the contribution of the other macronutrients. We here describe a model of nutrient intake, stomach emptying, and absorption of macronutrients in the gastrointestinal tract during and after the ingestion of a mixed meal, including the contribution of proteins and fats. We integrated this effort to our previous work in which we modeled the effects of a bout of physical exercise on metabolic homeostasis. We validated the computational model with reliable data from the literature. The simulations are overall physiologically consistent and helpful in describing the metabolic changes due to everyday life stimuli such as multiple mixed meals and variable periods of physical exercise over prolonged periods of time. This computational model may be used to design virtual cohorts of subjects differing in sex, age, height, weight, and fitness status, for specialized in silico challenge studies aimed at designing exercise and nutrition schemes to support health.

The defense of Shangri-La: Protecting isolated communities by periodic infection screening in the worst future pandemic

In the worst future pandemic, effective vaccines and medicines could be unavailable for a long time. In such circumstances, it is necessary to evaluate whether a periodic screening can protect isolated communities and critical facilities and avoid a complete shutdown. In this study, we introduced an epidemiological model that included the essential parameters of infection transmission and screening. With varying parameters, we studied the dynamics of viral infection in the isolated communities. In the scenario with a periodic infection screening once per 3 days and a viral basic reproduction number 3.0, >85% of the infection waves have a duration <7 days and the infection size in each of the waves is generally <4 individuals when the efficiency of infection discovery is 0.9 in the screening. When the period of screening was elongated to once per 7 days, the cases of infection dramatically increased to 5 folds of that mentioned previously. Further, with a weak discovery efficiency of 0.7 and the aforementioned low screening frequency, the spread of infection would be out of control. Our study suggests that frequent periodic screening is capable of controlling a future epidemic in isolated communities without other measures.

What we do (not) know about the mechanisms underlying adaptive speech perception: A computational framework and review

Speech from unfamiliar talkers can be difficult to comprehend initially. These difficulties tend to dissipate with exposure, sometimes within minutes or less. Adaptivity in response to unfamiliar input is now considered a fundamental property of speech perception, and research over the past two decades has made substantial progress in identifying its characteristics. The mechanisms underlying adaptive speech perception, however, remain unknown. Past work has attributed facilitatory effects of exposure to any one of three qualitatively different hypothesized mechanisms: (1) low-level, pre-linguistic, signal normalization, (2) changes in/selection of linguistic representations, or (3) changes in post-perceptual decision-making. Direct comparisons of these hypotheses, or combinations thereof, have been lacking. We describe a general computational framework for adaptive speech perception (ASP) that-for the first time-implements all three mechanisms. We demonstrate how the framework can be used to derive predictions for experiments on perception from the acoustic properties of the stimuli. Using this approach, we find that-at the level of data analysis presently employed by most studies in the field-the signature results of influential experimental paradigms do not distinguish between the three mechanisms. This highlights the need for a change in research practices, so that future experiments provide more informative results. We recommend specific changes to experimental paradigms and data analysis. All data and code for this study are shared via OSF, including the R markdown document that this article is generated from, and an R library that implements the models we present.

Validation of a novel finite-element model for evaluating patellofemoral forces and stress during squatting after posterior-stabilized total knee arthroplasty

INTRODUCTION

Several studies have documented the relationship between patellofemoral pain and patient dissatisfaction after total knee arthroplasty (TKA). However, few computer simulations have been designed to evaluate the patellofemoral joint during flexion. The aim of this study was to validate a new computational simulation, driven by forces and moments, and to analyze patellofemoral reaction forces and stress under squat loading conditions after TKA implantation.

HYPOTHESIS

This computational simulation of a squat using a model driven by forces and moments is comparable to in vitro and in silico data from the literature.

MATERIAL AND METHODS

We developed a finite element model of the lower limb after implantation of a fixed-bearing posterior-stabilized TKA. To simulate squat loading conditions when standing on both legs, an initial load of 130N was applied to the center of the femoral head. Quadriceps force, patellofemoral contact force and Von Mises stress on the patellar implant, tibiofemoral contact forces and pressure on the tibial insert, and post-cam contact force were evaluated from 0° to 100° of knee flexion.

RESULTS

Quadriceps force increased during flexion, up to 6 times the applied load. Von Mises stress on patellar implant increased up to 16MPa at 100° flexion. Tibiofemoral contact forces increased up to 415 N medially and 339 N laterally, with 64% distributed medially on the tibial insert. Post-cam contact started slightly before 70° of flexion.

DISCUSSION

In this simulation, tibiofemoral, patellofemoral and post-cam contact forces, and pressure distribution on the tibial insert were consistent with various published studies. This agreement suggests that computational simulation driven by forces and moments can reproduce squat loading conditions during knee flexion after TKA, without experimental kinematic data used to drive the simulation.

CONCLUSION

This study represents an initial step towards validating tibiofemoral and patellofemoral mechanical behavior under squat conditions, from this computational simulation driven by forces and moments. This model will help us better understand the influence of various implantation techniques on patellofemoral forces and stress during flexion.

LEVEL OF EVIDENCE

IV, biomechanical computational study.

Multicellular dynamics on structured surfaces: Stress concentration is a key to controlling complex microtissue morphology on engineered scaffolds

Tissue engineers have utilised a variety of three-dimensional (3D) scaffolds for controlling multicellular dynamics and the resulting tissue microstructures. In particular, cutting-edge microfabrication technologies, such as 3D bioprinting, provide increasingly complex structures. However, unpredictable microtissue detachment from scaffolds, which ruins desired tissue structures, is becoming an evident problem. To overcome this issue, we elucidated the mechanism underlying collective cellular detachment by combining a new computational simulation method with quantitative tissue-culture experiments. We first quantified the stochastic processes of cellular detachment shown by vascular smooth muscle cells on model curved scaffolds and found that microtissue morphologies vary drastically depending on cell contractility, substrate curvature, and cell-substrate adhesion strength. To explore this mechanism, we developed a new particle-based model that explicitly describes stochastic processes of multicellular dynamics, such as adhesion, rupture, and large deformation of microtissues on structured surfaces. Computational simulations using the developed model successfully reproduced characteristic detachment processes observed in experiments. Crucially, simulations revealed that cellular contractility-induced stress is locally concentrated at the cell-substrate interface, subsequently inducing a catastrophic process of collective cellular detachment, which can be suppressed by modulating cell contractility, substrate curvature, and cell-substrate adhesion. These results show that the developed computational method is useful for predicting engineered tissue dynamics as a platform for prediction-guided scaffold design. STATEMENT OF SIGNIFICANCE: Microfabrication technologies aiming to control multicellular dynamics by engineering 3D scaffolds are attracting increasing attention for modelling in cell biology and regenerative medicine. However, obtaining microtissues with the desired 3D structures is made considerably more difficult by microtissue detachments from scaffolds. This study reveals a key mechanism behind this detachment by developing a novel computational method for simulating multicellular dynamics on designed scaffolds. This method enabled us to predict microtissue dynamics on structured surfaces, based on cell mechanics, substrate geometry, and cell-substrate interaction. This study provides a platform for the physics-based design of micro-engineered scaffolds and thus contributes to prediction-guided biomaterials design in the future.

Astrocytic-based Controller Shifts Epileptic Activity to the Chaotic State

Introduction

Investigating an effective controller to shift hippocampal epileptic periodicity to normal chaotic behavior will be new hope for epilepsy treatment. Astrocytes nourish and protect neurons and maintain synaptic transmission and network activity. Therefore, this study explored the ameliorating effect of the astrocyte computational model on epileptic periodicity.

Methods

Modified Morris-Lecar equations were used to model the hippocampal CA3 network. Network inhibitory parameters were employed to generate oscillation-induced epileptiform periodicity. The astrocyte controller was based on a functional dynamic mathematical model of brain astrocytic cells.

Results

Results demonstrated that the synchronization of two neural networks shifted the brain's chaotic state to periodicity. Applying an astrocytic controller to the synchronized networks returned the system to the desynchronized chaotic state.

Conclusion

It is concluded that astrocytes are probably a good model for controlling epileptic periodicity. However, more research is needed to delineate this effect.

Highlights

Modeling of CA3 neurons reproduced synchronized periodic epileptiform discharges.An astrocyte mathematical model modulated neuronal network excitability.The astrocyte controller desynchronized neural network periodic oscillations.Application of the astrocyte model restored a chaotic desynchronized state.Results suggest astrocytes may control hypersynchronous epileptiform activity.

Plain Language Summary

This study looked at whether a mathematical model of brain cells called astrocytes could help control seizure activity. Seizures happen when groups of brain cells become overly active and synchronized. Normally, brain cell activity is chaotic and unsynchronized. The researchers modeled a small network of hippocampus brain cells using equations. We adjusted the model to create seizure-like periodic synchronized activity. Then we added a mathematical astrocyte model to try to disrupt this unwanted synchronization. Astrocytes are a type of glial cell in the brain. They help nourish neurons and regulate brain cell communication. The researchers modeled astrocyte activity using equations based on calcium levels. Calcium levels affect how astrocytes communicate with brain cells. When the researchers added the astrocyte model to the seizure-like network activity, it was able to restore chaotic unsynchronized activity. The astrocyte model accomplished this by affecting the excitability of the neuronal network. These results suggest astrocytes could potentially be used to control seizure activity. More research is needed to further test this astrocyte model. Currently, many seizure patients do not respond fully to medication. Astrocyte-based treatments could potentially provide an alternative approach. The findings are notable because they demonstrate a biologically-based method to restore normal chaotic brain activity. Most previous efforts have used electrical stimulation. An astrocyte-based approach could modulate communication between brain cells in a more natural way.

Ventricular fold oscillations lower the vocal pitch in rhesus macaques

We carried out ex vivo and in vivo experiments to explore the functional role of the ventricular folds in sound production in macaques. In the ex vivo experiments, 29 recordings out of 67 showed that the ventricular folds co-oscillated with the vocal folds. Transitions from normal vocal fold oscillations to vocal-ventricular fold co-oscillations as well as chaotic irregular oscillations were also observed. The in vivo experiments indicated that the vocal-ventricular fold co-oscillations were also observed in two macaque individuals. In both ex vivo and in vivo experiments, the vocal-ventricular fold co-oscillations significantly lowered the fundamental frequency. A mathematical model revealed that the lowering of the fundamental frequency was caused by a low oscillation frequency inherent in the ventricular folds, which entrained the vocal folds to their low-frequency oscillations. From a physiological standpoint, the macaques may utilize the ventricular fold oscillations more frequently than humans. The advantages as well as disadvantages of using the ventricular folds as an additional vocal repertory are discussed.

COVID-19-associated pulmonary aspergillosis in immunocompetent patients: a virtual patient cohort study

The opportunistic fungus Aspergillus fumigatus infects the lungs of immunocompromised hosts, including patients undergoing chemotherapy or organ transplantation. More recently however, immunocompetent patients with severe SARS-CoV2 have been reported to be affected by COVID-19 Associated Pulmonary Aspergillosis (CAPA), in the absence of the conventional risk factors for invasive aspergillosis. This paper explores the hypothesis that contributing causes are the destruction of the lung epithelium permitting colonization by opportunistic pathogens. At the same time, the exhaustion of the immune system, characterized by cytokine storms, apoptosis, and depletion of leukocytes may hinder the response to A. fumigatus infection. The combination of these factors may explain the onset of invasive aspergillosis in immunocompetent patients. We used a previously published computational model of the innate immune response to infection with Aspergillus fumigatus. Variation of model parameters was used to create a virtual patient population. A simulation study of this virtual patient population to test potential causes for co-infection in immunocompetent patients. The two most important factors determining the likelihood of CAPA were the inherent virulence of the fungus and the effectiveness of the neutrophil population, as measured by granule half-life and ability to kill fungal cells. Varying these parameters across the virtual patient population generated a realistic distribution of CAPA phenotypes observed in the literature. Computational models are an effective tool for hypothesis generation. Varying model parameters can be used to create a virtual patient population for identifying candidate mechanisms for phenomena observed in actual patient populations.

Incorporating models of subcortical processing improves the ability to predict EEG responses to natural speech

The goal of describing how the human brain responds to complex acoustic stimuli has driven auditory neuroscience research for decades. Often, a systems-based approach has been taken, in which neurophysiological responses are modeled based on features of the presented stimulus. This includes a wealth of work modeling electroencephalogram (EEG) responses to complex acoustic stimuli such as speech. Examples of the acoustic features used in such modeling include the amplitude envelope and spectrogram of speech. These models implicitly assume a direct mapping from stimulus representation to cortical activity. However, in reality, the representation of sound is transformed as it passes through early stages of the auditory pathway, such that inputs to the cortex are fundamentally different from the raw audio signal that was presented. Thus, it could be valuable to account for the transformations taking place in lower-order auditory areas, such as the auditory nerve, cochlear nucleus, and inferior colliculus (IC) when predicting cortical responses to complex sounds. Specifically, because IC responses are more similar to cortical inputs than acoustic features derived directly from the audio signal, we hypothesized that linear mappings (temporal response functions; TRFs) fit to the outputs of an IC model would better predict EEG responses to speech stimuli. To this end, we modeled responses to the acoustic stimuli as they passed through the auditory nerve, cochlear nucleus, and inferior colliculus before fitting a TRF to the output of the modeled IC responses. Results showed that using model-IC responses in traditional systems analyzes resulted in better predictions of EEG activity than using the envelope or spectrogram of a speech stimulus. Further, it was revealed that model-IC derived TRFs predict different aspects of the EEG than acoustic-feature TRFs, and combining both types of TRF models provides a more accurate prediction of the EEG response.

Hierarchical inference as a source of human biases

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty-namely, hierarchical inference-which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment's hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.

Uncertainty in learning and decision-making: Introduction to the special issue

Uncertainty is a fundamental aspect of the environment. This special issue presents interdisciplinary research on decision-making and learning under uncertainty. Thirty-one research and review papers report the findings of the behavioral, neural, and computational bases of coping with uncertainty, as well as changes of these mechanisms in development, aging, and psychopathology. Taken together, this special issue presents extant research, identifies gaps in our knowledge, and offers paths for future directions.

Network Hyperexcitability in Early Alzheimer's Disease: Is Functional Connectivity a Potential Biomarker?

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer's disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4-8 Hz and the 8-13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models

Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.

Mechanoelectric effects in healthy cardiac function and under Left Bundle Branch Block pathology

Mechanoelectric feedback (MEF) in the heart operates through several mechanisms which serve to regulate cardiac function. Stretch activated channels (SACs) in the myocyte membrane open in response to cell lengthening, while tension generation depends on stretch, shortening velocity, and calcium concentration. How all of these mechanisms interact and their effect on cardiac output is still not fully understood. We sought to gauge the acute importance of the different MEF mechanisms on heart function. An electromechanical computer model of a dog heart was constructed, using a biventricular geometry of 500K tetrahedral elements. To describe cellular behavior, we used a detailed ionic model to which a SAC model and an active tension model, dependent on stretch and shortening velocity and with calcium sensitivity, were added. Ventricular inflow and outflow were connected to the CircAdapt model of cardiovascular circulation. Pressure-volume loops and activation times were used for model validation. Simulations showed that SACs did not affect acute mechanical response, although if their trigger level was decreased sufficiently, they could cause premature excitations. The stretch dependence of tension had a modest effect in reducing the maximum stretch, and stroke volume, while shortening velocity had a much bigger effect on both. MEF served to reduce the heterogeneity in stretch while increasing tension heterogeneity. In the context of left bundle branch block, a decreased SAC trigger level could restore cardiac output by reducing the maximal stretch when compared to cardiac resynchronization therapy. MEF is an important aspect of cardiac function and could potentially mitigate activation problems.

Computational modeling of electromechanical coupling in human cardiomyocyte applied to study hypertrophic cardiomyopathy and its drug response

BACKGROUND AND OBJECTIVE

Knowledge of electromechanical coupling in cardiomyocyte and how it is influenced by various pathophysiological factors is fundamental to understanding the pathogenesis of myocardial disease and its response to medication, which is however hard to be thoroughly addressed by clinical/experimental studies due to technical limitations. At this point, computational modeling offers an alternative approach. The main objective of the study was to develop a computational model capable of simulating the process of electromechanical coupling and quantifying the roles of various factors in play in the human left ventricular cardiomyocyte.

METHODS

A new electrophysiological model was firstly built by combining several existing electrophysiological models and incorporating the mechanism of electrophysiological homeostasis, which was subsequently coupled to models representing the cross-bridge dynamics and active force generation during excitation-contraction coupling and the passive mechanical properties of cardiomyocyte to yield an integrative electromechanical model. Model parameters were calibrated or optimized based on a large amount of experimental data. The resulting model was applied to delineate the characteristics of electromechanical coupling and explore underlying determinant factors in hypertrophic cardiomyopathy (HCM) cardiomyocyte, as well as quantify their changes in response to different medications.

RESULTS

Model predictions captured the major electromechanical characteristics of cardiomyocyte under both normal physiological and HCM conditions. In comparison with normal cardiomyocyte, HCM cardiomyocyte suffered from systemic changes in both electrophysiological and mechanical variables. Numerical simulations of drug response revealed that Mavacamten and Metoprolol could both reduce the active contractility and alleviate calcium overload but had marked differential influences on many other electromechanical variables, which theoretically explained why the two drugs have differential therapeutic effects. In addition, our numerical experiments demonstrated the important role of compensatory ion transport in maintaining electrophysiological homeostasis and regulating cytoplasmic volume.

CONCLUSIONS

A sophisticated computational model has the advantage of providing quantitative and integrative insights for understanding the pathogenesis and drug responses of HCM or other myocardial diseases at the level of cardiomyocyte, and hence may contribute as a useful complement to clinical/experimental studies. The model may also be coupled to tissue- or organ-level models to strengthen the physiological implications of macro-scale numerical simulations.

Will the real resource theory please stand up! Vigilance is a renewable resource and should be modeled as such

The vigilance decrement or decline in signal detection performance with time on task is one of the most reliable findings in the cognitive neuroscience and psychology literatures. The majority of theories proposed to explain the decrement are limited cognitive or attention resource based theories; the central nervous system is a limited capacity processor. The decrement in performance is then due to resource reallocation (or misallocation), resource depletion or some combination of both mechanisms. The role of resource depletion, in particular, is hotly debated. However, this may be due to a lack of understanding of the renewable nature of the vigilance resources and how this renewal process impacts performance during vigilance tasks. In the present paper, a simple quantitative model of vigilance resource depletion and renewal is described and shown to generate performance data similar to results seen in both humans and spiders. This model clarifies the role resource depletion and resource renewal may play in vigilance in both people and other animals.

Modelling homeostatic plasticity in the auditory cortex results in neural signatures of tinnitus

Tinnitus is a clinical condition where a sound is perceived without an external sound source. Homeostatic plasticity (HSP), serving to increase neural activity as compensation for the reduced input to the auditory pathway after hearing loss, has been proposed as a mechanism underlying tinnitus. In support, animal models of tinnitus show evidence of increased neural activity after hearing loss, including increased spontaneous and sound-driven firing rate, as well as increased neural noise throughout the auditory processing pathway. Bridging these findings to human tinnitus, however, has proven to be challenging. Here we implement hearing loss-induced HSP in a Wilson-Cowan Cortical Model of the auditory cortex to predict how homeostatic principles operating at the microscale translate to the meso- to macroscale accessible through human neuroimaging. We observed HSP-induced response changes in the model that were previously proposed as neural signatures of tinnitus, but that have also been reported as correlates of hearing loss and hyperacusis. As expected, HSP increased spontaneous and sound-driven responsiveness in hearing-loss affected frequency channels of the model. We furthermore observed evidence of increased neural noise and the appearance of spatiotemporal modulations in neural activity, which we discuss in light of recent human neuroimaging findings. Our computational model makes quantitative predictions that require experimental validation, and may thereby serve as the basis of future human studies of hearing loss, tinnitus, and hyperacusis.

Development and Validation of an Active Muscle Simplified Finite Element Human Body Model in a Standing Posture

Active muscles play an important role in postural stabilization, and muscle-induced joint stiffening can alter the kinematic response of the human body, particularly that of the lower extremities, under dynamic loading conditions. There are few full-body human body finite element models with active muscles in a standing posture. Thus, the objective of this study was to develop and validate the M50-PS+Active model, an average-male simplified human body model in a standing posture with active musculature. The M50-PS+Active model was developed by incorporating 116 skeletal muscles, as one-dimensional beam elements with a Hill-type material model and closed-loop Proportional Integral Derivative (PID) controller muscle activation strategy, into the Global Human Body Models Consortium (GHBMC) simplified pedestrian model M50-PS. The M50-PS+Active model was first validated in a gravity standing test, showing the effectiveness of the active muscles in maintaining a standing posture under gravitational loading. The knee kinematics of the model were compared against volunteer kinematics in unsuited and suited step-down tests from NASA's active response gravity offload system (ARGOS) laboratory. The M50-PS+Active model showed good biofidelity with volunteer kinematics with an overall CORA score of 0.80, as compared to 0.64 (fair) in the passive M50-PS model. The M50-PS+Active model will serve as a useful tool to study the biomechanics of the human body in vehicle-pedestrian accidents, public transportation braking, and space missions piloted in a standing posture.