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

Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) greater than 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right heart catheterization (RHC). Patient-specific cardiovascular systems-level computational models provide a potential non-invasive tool for determining additional indicators of disease severity. Using computational modelling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers, the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic and diastolic values) RHC data and a combination of static and continuous, time-series waveform data. We determine a subset of identifiable parameters for model calibration using sensitivity analyses and multi-start inference and perform posterior uncertainty quantification. Results show that additional waveform data enables accurate calibration of the right atrial reservoir and pump function across the PH cohort. Model outcomes, including stroke work and pulmonary resistance-compliance relations, reflect typical right heart dynamics in PH phenotypes. Lastly, we show that estimated parameters agree with previous, non-modelling studies, supporting this type of analysis in translational PH research.

The increasing demand for biomass for food, animal feed, fiber, and bioenergy requires optimization of soil productivity, while at the same time, protecting other soil functions such as nutrient cycling and buffering, carbon storage, habitat for biological activity, and water filter and storage. Therefore, one of the main challenges for sustainable agriculture is to produce high yields while maintaining all the other soil functions. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological, and chemical processes of soils that generate those functions. We developed a soil model to simulate the impact of various agricultural management options and climate change on soil functions by integrating the relevant processes mechanistically and in a systemic way. As a special feature, we include the dynamics of soil structure induced by tillage and biological activity, which is especially relevant in arable soils. The model operates on a 1D soil profile consisting of a number of discrete layers with dynamic thickness. We demonstrate the model performance by simulating crop growth, root growth, nutrient and water uptake, nitrogen cycling, soil organic matter turnover, microbial activity, water distribution, and soil structure dynamics in a long-term field experiment including different crops and different types and levels of fertilization. The model is able to capture essential features that are measured regularly including crop yield, soil organic carbon, and soil nitrogen. In this way, the plausibility of the implemented processes and their interactions is confirmed. Furthermore, we present the results of explorative simulations comparing scenarios with and without tillage events to analyze the effect of soil structure on soil functions. Since the model is process-based, we are confident that the model can also be used to predict quantities that have not been measured or to estimate the effect o

Learning from experience is a central mechanism underlying organizational capabilities. However, in examining how organizations learn from past experiences, much of the literature has focused on situations in which actors are facing a repeated event. We direct attention to a relatively underexamined question: when an organization experiences a largely idiosyncratic series of events, at what level of granularity should these events, and the associated actions and outcomes, be encoded? How does generalizing from experience impact the wisdom of future choices and what are the boundary conditions or factors that might mitigate the degree of desired generalization? To address these questions, we develop a computational model that incorporates how characteristics of opportunities (e.g., acquisition candidates, new investments, product development) might be encoded so that experiential learning is possible even when the organization’s experience is a series of unique events. Our results highlight the power of learning through generalization in a world of novelty as well as the features of the problem environment that reduce this “power.”

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.

Current computational models of cognitive control exhibit notable limitations. In machine learning, artificial agents are now capable of performing complex tasks but often ignore critical constraints such as resource limitations and how long it takes for the agent to make decisions and act. Conversely, cognitive control models in psychology are limited in their ability to tackle complex tasks (e.g., play video games) or generalize across a battery of simple cognitive tests. Here we introduce CogPonder, a flexible, differentiable, cognitive control framework that is inspired by the Test-Operate-Test-Exit (TOTE) architecture in psychology and the PonderNet framework in machine learning. CogPonder functionally decouples the act of control from the controlled processes by introducing a controller that acts as a wrapper around any end-to-end deep learning model and decides when to terminate processing and output a response, thus producing both a response and response time. Our experiments show that CogPonder effectively learns from data to generate behavior that closely resembles human responses and response times in two classic cognitive tasks. This work demonstrates the value of this new computational framework and offers promising new research prospects for both psychological and computer sciences.

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms

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

INTRODUCTION: Erectile dysfunction (ED) affects to some degree approximately 52% of the male population aged 40-70 years. Many men do not respond to, or are precluded from using, pharmaceutical treatments for ED and are therefore advised to consider penile prostheses. Different types of penile prosthesis are available, such as inflatable penile prostheses (IPPs). IPPs consist of a pair of inflatable cylinders inserted into the corpora cavernosa (CC). During inflation/deflation of these cylinders, the CC and other surrounding tissues such as the tunica albuginea (TA) are highly impacted. Therefore, it is critical to understand the mechanics of penile tissues for successful implantation of IPPs and to reduce tissue damage induced by IPPs. OBJECTIVES: We explored the importance of the biomechanics of penile tissues for successful IPP function and reviewed and summarized the most significant studies on penile biomechanics that have been reported to date. METHODS: We performed an extensive literature review of publications on penile biomechanics and IPP implantation. RESULTS: Indenters have been used to characterize the mechanical behavior of whole penile tissue; however, this technique applied only local deformation, which limited insights into individual tissue components. Although one reported study addressed the mechanical behavior of TA, this investigation did not consider anisotropy, and there is a notable absence of biomechanical studies on CC and CS. This lack of understanding of penile tissue biomechanics has resulted in computational models that use linear-elastic materials, despite soft tissues generally exhibiting hyperelastic behavior. Furthermore, available benchtop/synthetic models do not have tissue properties matched to those of the human penis, limiting the scope of these models for use as preclinical testbeds for IPP testing. CONCLUSION: Improved understanding of penile tissue biomechanics would assist the development of realistic benchtop/synthetic, Medical Instruments & Bio-Inspired Technology

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., CC BY 4.0Copyright 2023 Elsevier B.V.Corresponding author: Vipul Nair.This work has been partially carried out at the Machine Learning Genoa (MaLGa) center, Università di Genova (IT). It has been partially supported by AFOSR, grant n. FA8655-20-1-7035, and research collaboration between University of Skövde and Istituto Italiano di Tecnologia, Genoa.

Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., CC BY 4.0Copyright 2023 Elsevier B.V.Corresponding author: Vipul Nair.This work has been partially carried out at the Machine Learning Genoa (MaLGa) center, Università di Genova (IT). It has been partially supported by AFOSR, grant n. FA8655-20-1-7035, and research collaboration between University of Skövde and Istituto Italiano di Tecnologia, Genoa.

Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., CC BY 4.0Copyright 2023 Elsevier B.V.Corresponding author: Vipul Nair.This work has been partially carried out at the Machine Learning Genoa (MaLGa) center, Università di Genova (IT). It has been partially supported by AFOSR, grant n. FA8655-20-1-7035, and research collaboration between University of Skövde and Istituto Italiano di Tecnologia, Genoa.

Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., CC BY 4.0Copyright 2023 Elsevier B.V.Corresponding author: Vipul Nair.This work has been partially carried out at the Machine Learning Genoa (MaLGa) center, Università di Genova (IT). It has been partially supported by AFOSR, grant n. FA8655-20-1-7035, and research collaboration between University of Skövde and Istituto Italiano di Tecnologia, Genoa.

Overview: In the recent years, a bi-directional association between Alzheimer’s Disease (AD) and epilepsy has been observed, with AD-like cognitive impairments often presenting in epilepsy patients, and high rates of epileptic seizures seen in a sub-set of AD patients. These seizure-prone AD patients reportedly show accelerated cognitive decline and more aggressive disease progression compared to those without seizures. The mechanism and primary mode of action of this association remains unknown, although a synergistic interaction has been proposed. The general aim of this doctoral research was to investigate the electrical and molecular properties of the above-mentioned pathophenotypes and elucidate the mechanisms underlying the potential synergy between AD-like amyloid pathology and epileptiform activity, and their role in accelerated cognitive decline. Introduction and literature review: The introductory sections of Chapter 1 provide an overview of the current literature on epilepsy and Alzheimer’s Disease, focusing on pathophysiological mechanisms commonly implicated in both syndromes. The subsequent sections discuss several benchmark studies that first reported on the increased co-occurrence of seizures among AD patients, followed by a critical review of the most prominent as well as recently emerged hypotheses that aim to provide mechanistic insight into the nature of the proposed bi-directional association between AD and acquired epilepsy. The concluding sections provide a gentle introduction into the emerging field of network medicine, systems- based analysis, interrogation methods of high-throughput biological data and the general framework of computational models and methodology that was implemented throughout this work. Experimental chapters: The first and second experimental chapters aim to characterize the molecular signature of a brain affected by amyloid pathology and seizures. Utilizing proteomic and metabolomic data from two collaborative studies as

Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.

Current computational models of cognitive control exhibit notable limitations. In machine learning, artificial agents are now capable of performing complex tasks but often ignore critical constraints such as resource limitations and how long it takes for the agent to make decisions and act. Conversely, cognitive control models in psychology are limited in their ability to tackle complex tasks (e.g., play video games) or generalize across a battery of simple cognitive tests. Here we introduce CogPonder, a flexible, differentiable, cognitive control framework that is inspired by the Test-Operate-Test-Exit (TOTE) architecture in psychology and the PonderNet framework in machine learning. CogPonder functionally decouples the act of control from the controlled processes by introducing a controller that acts as a wrapper around any end-to-end deep learning model and decides when to terminate processing and output a response, thus producing both a response and response time. Our experiments show that CogPonder effectively learns from data to generate behavior that closely resembles human responses and response times in two classic cognitive tasks. This work demonstrates the value of this new computational framework and offers promising new research prospects for both psychological and computer sciences.

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., CC BY 4.0Copyright 2023 Elsevier B.V.Corresponding author: Vipul Nair.This work has been partially carried out at the Machine Learning Genoa (MaLGa) center, Università di Genova (IT). It has been partially supported by AFOSR, grant n. FA8655-20-1-7035, and research collaboration between University of Skövde and Istituto Italiano di Tecnologia, Genoa.

Cognitive control is essential to human cognitive functioning as it allows us to adapt and respond to a wide range of situations and environments. The possibility to enhance cognitive control in a way that transfers to real life situations could greatly benefit individuals and society. However, the lack of a formal, quantitative definition of cognitive control has limited progress in developing effective cognitive control training programs. To address this issue, the first part of the thesis focuses on gaining clarity on what cognitive control is and how to measure it. This is accomplished through a large-scale text analysis that integrates cognitive control tasks and related constructs into a cohesive knowledge graph. This knowledge graph provides a more quantitative definition of cognitive control based on previous research, which can be used to guide future research. The second part of the thesis aims at furthering a computational understanding of cognitive control, in particular to study what features of the task (i.e., the environment) and what features of the cognitive system (i.e., the agent) determine cognitive control, its functioning, and generalization. The thesis first presents CogEnv, a virtual cognitive assessment environment where artificial agents (e.g., reinforcement learning agents) can be directly compared to humans in a variety of cognitive tests. It then presents CogPonder, a novel computational method for general cognitive control that is relevant for research on both humans and artificial agents. The proposed framework is a flexible, differentiable end-to-end deep learning model that separates the act of control from the controlled act, and can be trained to perform the same cognitive tests that are used in cognitive psychology to assess humans. Together, the proposed cognitive environment and agent architecture offer unique new opportunities to enable and accelerate the study of human and artificial agents in an interoperable framework. Resea

Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.

Protein folding and protein maturation largely occur in the controlled environment of the Endoplasmic Reticulum (ER). Perturbation to the correct functioning of this organelle leads to altered proteostasis and accumulation of misfolded proteins in the ER lumen. This condition is commonly known as ER stress and is appearing as an important contributor in the pathogenesis of several human diseases. Monitoring of the quality control processes is mediated by the Unfolded Protein Response (UPR). This response consists in a complex network of signalling pathways that aim to restore protein folding and ER homeostasis. Conditions in which UPR is not able to overcome ER stress lead to a switch of the UPR signalling program from an adaptive to a pro-apoptotic one, revealing a key role of UPR in modulating cell fate decisions. Because of its high complexity and its involvement in the regulation of different cellular outcomes, UPR has been the centre of the development of computational models, which tried to better dissect the role of UPR or of its specific components in several contexts. In this review, we go through the existing mathematical models of UPR. We emphasize how their study contributed to an improved characterization of the role of this intricate response in the modulation of cellular functions., info:eu-repo/semantics/published

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions., Funding agency:AFOSR FA8655-20-1-7035

Control of the timing of behavior is thought to require the basal ganglia (BG) and BG pathologies impair performance in timing tasks. Temporal interval discrimination depends on the ramping activity of medium spiny neurons (MSN) in the main BG input structure, the striatum, but the underlying mechanisms driving this activity are unclear. Here, we combine an MSN dynamical network model with an action selection system applied to an interval discrimination task. We find that when network parameters are appropriate for the striatum so that slowly fluctuating marginally stable dynamics are intrinsically generated, up and down ramping populations naturally emerge which enable significantly above chance task performance. We show that emergent population activity is in very good agreement with empirical studies and discuss how MSN network dysfunction in disease may alter temporal perception., source:https://www.frontiersin.org/articles/10.3389/fncom.2022.902741/full

Various subtypes of inhibitory interneurons contact one another to organize cortical networks. Most cortical inhibitory interneurons express 1 of 3 genes: parvalbumin (PV), somatostatin (SOM), or vasoactive intestinal polypeptide (VIP). This diversity of inhibition allows the flexible regulation of neuronal responses within and between cortical areas. However, the exact roles of these interneuron subtypes and of excitatory pyramidal (Pyr) neurons in regulating neuronal network activity and establishing perception (via interactions between feedforward sensory and feedback attentional signals) remain largely unknown. To explore the regulatory roles of distinct neuronal types in cortical computation, we developed a computational microcircuit model with biologically plausible visual cortex layers 2/3 that combined Pyr neurons and the 3 inhibitory interneuron subtypes to generate network activity. In simulations with our model, inhibitory signals from PV and SOM neurons preferentially induced neuronal firing at gamma (30–80 Hz) and beta (20–30 Hz) frequencies, respectively, in agreement with observed physiological results. Furthermore, our model indicated that rapid inhibition from VIP to SOM subtypes underlies marked attentional modulation for low-gamma frequency (30–50 Hz) in Pyr neuron responses. Our results suggest the distinct but cooperative roles of inhibitory interneuron subtypes in the establishment of visual perception., source:https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhac355/6706754?login=true

Photonic crystal (PhC) hydrogels are a unique class of material that has tremendous promise as biomedical sensors. The underlying crystal structure allows for simple analysis of microstructural properties by assessing the diffraction pattern generated following laser illumination. The hydrogel medium provides elasticity, regenerability, and potential functionalization. Combining these two properties, photonic crystal hydrogels have the potential for sensing physical forces and chemical reagents using a low-cost, reusable platform. The development of biomedical sensors using this material is limited due to the lack of a method to accurately predict the diffraction pattern generated. To overcome this, a computational model was developed specifically for PhCs and validated against existing analytical models and an existing electromagnetic scattering model in the literature. Assessment of its accuracy in comparison to existing analytical equations and a more generalized multiparticle scattering model in the literature, CELES, found clear alignment. Another challenge is the lack of a technique to assess the specific positions of each particle in the crystal structure non-destructively. To overcome this, a novel fabrication approach was created using fluorescent particles, allowing subsequent confocal fluorescence microscopy and analyses to extract per-particle position information. This technique was used to directly compare experimental, computational, and analytical results within a single sample. To demonstrate a novel biomedical application of this material, ultrasound detection was chosen since it would be able to leverage the elastomeric structure of the PhC hydrogel as well as the ability to optically measure small changes in crystal microstructure. The sensitivity, frequency bandwidth, and limit of detection of fabricated PhC hydrogels were assessed using three ultrasound transducers. All transducers created a measurable optical response, with the limit of detect

Proper segregation of chromosomes during mitosis depends on "amphitelic attachments"-load-bearing connections of sister kinetochores to the opposite spindle poles via bundles of microtubules, termed as the "K-fibers." Current models of spindle assembly assume that K-fibers arise largely from stochastic capture of microtubules, which occurs at random times and locations and independently at sister kinetochores. We test this assumption by following the movements of all kinetochores in human cells and determine that most amphitelic attachments form synchronously at a specific stage of spindle assembly and within a spatially distinct domain. This biorientation domain is enriched in bundles of antiparallel microtubules, and perturbation of microtubule bundling changes the temporal and spatial dynamics of amphitelic attachment formation. Structural analyses indicate that interactions of kinetochores with microtubule bundles are mediated by non-centrosomal short microtubules that emanate from most kinetochores during early prometaphase. Computational analyses suggest that momentous molecular motor-driven interactions with antiparallel bundles rapidly convert these short microtubules into nascent K-fibers. Thus, load-bearing connections to the opposite spindle poles form simultaneously on sister kinetochores. In contrast to the uncoordinated sequential attachments of sister kinetochores expected in stochastic models of spindle assembly, our model envisions the formation of amphitelic attachments as a deterministic process in which the chromosomes connect with the spindle poles synchronously at a specific stage of spindle assembly and at a defined location determined by the spindle architecture. Experimental analyses of changes in the kinetochore behavior in cells with perturbed activity of molecular motors CenpE and dynein confirm the predictive power of the model.

Fibrin deformation and interaction of fibrin with other blood components play critical roles in hemostasis and thrombosis. In this review, computational and mathematical biomechanical models of fibrin network deformation and contraction at different spatio-temporal scales as well as challenges in developing and calibrating multiscale models are discussed. There are long standing challenges. For instance, applicability of models to identify and test potential mechanisms of the biomechanical processes mediating interactions between platelets and fiber networks in blood clot stretching and contraction needs to be examined carefully. How the structural and mechanical properties of major blood clot components influences biomechanical responses of the entire clot subjected to external forces, such as blood flow or vessel wall deformations needs to be investigated thoroughly.

Microgravity is a hostile environment for the cardiovascular system. It triggers a cascade of adaptations that lead to problems on return to gravity. With the relatively low number of astronauts and the current duration of their spaceflights, some unknowns remain regarding the exact risks of future interplanetary missions. In addition, the current countermeasures are suboptimal and there is a high inter-subject variability that is still poorly understood. It will thus be necessary to closely monitor the cardiovascular health of the future interplanetary crews with the constraints of such expeditions, i.e. limited mass and volume, absence of resupply, delay in the communications, etc. Accordingly, the main objective of this research is to evaluate the possibility to use wearable devices based on the micro-vibrations generated by the cardiovascular activity – measured on the chest (SCG) or at the center of mass (BCG) – to bring information about the cardiovascular status of astronauts.Firstly, the results from a study simulating microgravity by 60-day exposure to head-down tilt bed rest are reported. In particular, the fine adaptations of the cardiovascular system and the impact of a countermeasure based on artificial gravity are studied using 4D flow cardiac MRI. No significant effect of this countermeasure is found in the tested setup. During the head-down tilt phase, the cardiac output decreased because of a smaller blood flow towards the lower body. In parallel, the aortic pulse wave velocity increased. However, all these changes were reversible and seemed to indicate only functional adaptations. It is noteworthy that BCG and SCG have the potential to follow the trends in cardiac output and pulse wave velocity, which will soon be tested using these MRI data as a reference.Secondly, the results from another 60-day head-down tilt bed rest study are reported. The aim is to evaluate the possibility for SCG and BCG to report the longitudinal cardiovascular changes occu, La micropesanteur représente un environnement hostile pour le système cardiovasculaire. Elle entraîne une cascade d’adaptations menant à des problèmes lors du retour à la pesanteur. Étant donnés le nombre relativement faible d’astronautes et la durée actuelle de leurs vols, certaines inconnues demeurent en ce qui concerne les risques exacts des futures missions interplanétaires. Par ailleurs, les contremesures actuellement employées sont sous-optimales et l’on observe une forte variabilité inter-sujet qui est encore mal comprise. Il sera donc nécessaire de suivre de près la santé cardiovasculaire des futures équipages interplanétaires, avec toutes les contraintes de telles expéditions, c’est-à-dire une masse et un volume limités, une absence de ravitaillement, un délai dans les communications, etc. Dans ce contexte, l’objectif principal de cette recherche est d’évaluer la possibilité d’utiliser un système portable basé sur les micro-vibrations générées par l’activité cardiovasculaire – mesurées au niveau de la poitrine (SCG) ou bien du centre de masse (BCG) – afin d’apporter des informations sur l’état cardiovasculaire des astronautes.Premièrement, les résultats d’une étude de simulation de la micropesanteur de 60 jours par alitement tête-en-bas sont rapportés. En particulier, les adaptations fines du système cardiovasculaire, ainsi que l’impact d’une contremesure basée sur la gravité artificielle, sont étudiés en utilisant des protocoles d’IRM cardiaque avec flux 4D. Aucun effet significatif de cette contremesure n’a été identifié dans la configuration testée. Au cours de la phase tête-en-bas, le débit cardiaque a diminué en raison d’une baisse du flux vers le bas du corps. En parallèle la vitesse d’onde de pouls a augmenté. Néanmoins, ces changements se sont avérés réversibles et semblaient n’indiquer que des adaptations fonctionnelles. Il est intéressant de noter que le BCG et le SCG ont le potentiel de suivre ce type de tendance en termes de débit cardiaque et d, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

The pharmacokinetic characteristics of per- and polyfluoroalkyl substances (PFAS) affect their distribution and bioaccumulation in biological systems. The enterohepatic circulation leads to reabsorption of certain chemicals from bile back into blood and the liver and thus influences their elimination, yet its influence on PFAS bioaccumulation remains unclear. We explored the role of enterohepatic circulation in PFAS bioaccumulation by examining tissue distribution of various PFAS in wild fish and a rat model. Computational models were used to determine the reabsorbed fractions of PFAS by calculating binding affinities of PFAS for key transporter proteins of enterohepatic circulation. The results indicated that higher concentrations were observed in blood, the liver, and bile compared to other tissues for some PFAS in fish. Furthermore, exposure to a PFAS mixture on the rat model showed that the reabsorption phenomenon appeared during 8-12 h for most long-chain PFAS. Molecular docking calculations suggest that PFAS can bind to key transporter proteins via electrostatic and hydrophobic interactions. Further regression analysis adds support to the hypothesis that binding affinity of the apical sodium-dependent bile acid transporter is the most important variable to predict the human half-lives of PFAS. This study demonstrated the critical role of enterohepatic circulation in reabsorption, distribution, and accumulation of PFAS., Funding agencies:National Natural Science Foundation of China (NSFC) 22136006 22193051 21777061 21806058 21906069 21477049 21507044 China Postdoctoral Science Foundation 2019M660185

Intracellular Ca2+ is an important regulator in cardiac electrophysiology and contraction under both physiology and pathophysiology. The regular excitation and effective contraction in healthy atrial myocytes are achieved by the well-organized transversal and axial tubular system (TATS, i.e., invaginations of cell membrane) that facilitate the coupling of key Ca2+-handling proteins, such as L-type Ca2+ channel (LCC) and Na+-Ca2+ exchanger (NCX) on the cell surface with ryanodine receptors (RyRs) on the sarcoplasmic reticulum (SR, intracellular Ca2+ storage). In atrial fibrillation (AF), the most common arrhythmia disease in the clinic, both ionic and ultrastructural remodeling have been associated with deranged Ca2+ signaling and electrophysiological instabilities through the altered channel and transporter expression and function, reduced density and regularity of the TATS, and subcellular re-distribution of Ca2+- handling proteins. However, due to the concurrent changes in TATS and Ca2+-handling protein expression and localization that occur in the disease, it is difficult to distinguish their individual contributions to the arrhythmogenic state.To address this, we developed a novel 3D human atrial myocyte model that couples electrophysiology and Ca2+ handling with variable TATS organization and density. We illustrate the construction of the model, which was extensively parameterized and validated against experimental data, and its use in examining TATS regulation of subcellular Ca2+ release. We then demonstrate the application of the model to investigate the isolated and interactive effects of changes in expression and localization of key Ca2+-handling proteins (i.e., NCX, RyR, and Calsequestrin, CSQ) and variable TATS density on Ca2+ abnormalities and Ca2+-driven membrane instabilities. We found that TATS loss, as seen in the disease, impairs NCX-mediated Ca2+ removal that increases intracellular Ca2+ concentration and thus elevated RyR open probability (PO). Co

The intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subject's ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between "retention" and "integration" modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate "stepping" and "ramping" activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.

Temporal preparation is the cognitive function that takes place when anticipating future events. This is commonly considered to involve a process that maximizes preparation at time points that yield a high hazard. However, despite their prominence in the literature, hazard-based theories fail to explain the full range of empirical preparation phenomena. Here, we present the formalized multiple trace theory of temporal preparation (f MTP), an integrative model which develops the alternative perspective that temporal preparation results from associative learning. f MTP builds on established computational principles from the domains of interval timing, motor planning, and associative memory. In f MTP, temporal preparation results from associative learning between a representation of time on the one hand and inhibitory and activating motor units on the other hand. Simulations demonstrate that f MTP can explain phenomena across a range of time scales, from sequential effects operating on a time scale of seconds to long-term memory effects occurring over weeks. We contrast f MTP with models that rely on the hazard function and show that f MTP’s learning mechanisms are essential to capture the full range of empirical effects. In a critical experiment using a Gaussian distribution of foreperiods, we show the data to be consistent with f MTP’s predictions and to deviate from the hazard function. Additionally, we demonstrate how changing f MTP’s parameters can account for participant-to-participant variations in preparation.

Les futures missions espacials de llarga durada requeriran un coneixement profund dels efectes de diferents nivells de gravetat al cos humà, en particular al sistema cardiovascular. Els models computacionals són una alternativa ràpida i barata als experiments de laboratori amb humans per a estudiar els efectes en diverses variables cardiovasculars. Aquest treball veu ambdós mètodes: utilitza un model computacional per a simular diferents condicions d’estrès ortostàtic i obtenir corbes de resposta en el rang entre 0 g i 1 g. A més, es veu un enfocament experimental de la resposta cardiovascular en diversos subjectes, comparant diferents instruments de mesura., Las futuras misiones espaciales de larga duración requerirán un conocimiento profundo de los efectos de diferentes niveles de gravedad al cuerpo humano, en particular al sistema cardiovascular. Los modelos computacionales son una alternativa rápida y barata a los experimentos de laboratorio con humanos para estudiar los efectos en varias variables cardiovasculares. Este trabajo ve ambos métodos: utiliza un modelo computacional para simular diferentes condiciones de estrés ortostático y obtener curvas de respuesta en el rango entre 0 g and 1 g. Además, se ve un enfoque experimental de la respuesta cardiovascular en varios sujetos, comparando diferentes instrumentos de medida., Future long duration space missions will require a deep knowledge of the effects of different levels of gravity on the human body, in particular the cardiovascular system. Computational models can be a faster, cheaper alternative to experiments with human subjects in studying the effects in different cardiovascular variables. This research effort implements both computational and experimental approaches. It uses a computational model to simulate different orthostatic stress conditions and generate dose-response curves of different cardiovascular variables in the range between 0 g and 1 g. Moreover, an experimental approach of the cardiovascular response in various subjects is also implemented, comparing different measurement instruments., Outgoing

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather ‘plastic’. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Purpose: Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity of the fetus. Computational anthropomorphic models have been widely used in patient-specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies. Acquisition and validation methods: Computed tomography (CT) images of abdominal and pelvic regions of 46 pregnant females were segmented by experienced medical physicists. The segmented tissues/organs include the body contour, skeleton, uterus, liver, kidney, intestine, stomach, lung, bladder, gall bladder, spleen, and pancreas for maternal body, and placenta, amniotic fluid, fetal body, fetal brain, and fetal skeleton. Nonuniform rational B-spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit values of each identified organs were gathered from CT images of pregnant patients and converted to tissue density. Organ volumes were further adjusted according to reference measurements for the developing fetus recommended by the World Health Organization (WHO) and International Commission on Radiological Protection. A series of anatomical parameters, including femur length, humerus length, biparietal diameter, abdominal circumference (FAC), and head circumference, were measured and compared with WHO recommendations. Data format and usage notes: The first fetal patient-specific model library was developed with the anatomical characteristics of each model derived from the corresponding patient whose gestational age varies between 8 and 35 weeks. Voxelized models are represented in the form of MCNP matrix input files representing the three-dimensional model of the fetus. The size distributions of each model are also provided in text files. All data are stored on Zenodo and

The pharmacokinetic characteristics of per- and polyfluoroalkyl substances (PFAS) affect their distribution and bioaccumulation in biological systems. The enterohepatic circulation leads to reabsorption of certain chemicals from bile back into blood and the liver and thus influences their elimination, yet its influence on PFAS bioaccumulation remains unclear. We explored the role of enterohepatic circulation in PFAS bioaccumulation by examining tissue distribution of various PFAS in wild fish and a rat model. Computational models were used to determine the reabsorbed fractions of PFAS by calculating binding affinities of PFAS for key transporter proteins of enterohepatic circulation. The results indicated that higher concentrations were observed in blood, the liver, and bile compared to other tissues for some PFAS in fish. Furthermore, exposure to a PFAS mixture on the rat model showed that the reabsorption phenomenon appeared during 8-12 h for most long-chain PFAS. Molecular docking calculations suggest that PFAS can bind to key transporter proteins via electrostatic and hydrophobic interactions. Further regression analysis adds support to the hypothesis that binding affinity of the apical sodium-dependent bile acid transporter is the most important variable to predict the human half-lives of PFAS. This study demonstrated the critical role of enterohepatic circulation in reabsorption, distribution, and accumulation of PFAS., Funding agencies:National Natural Science Foundation of China (NSFC) 22136006 22193051 21777061 21806058 21906069 21477049 21507044 China Postdoctoral Science Foundation 2019M660185

Control of the timing of behavior is thought to require the basal ganglia (BG) and BG pathologies impair performance in timing tasks. Temporal interval discrimination depends on the ramping activity of medium spiny neurons (MSN) in the main BG input structure, the striatum, but the underlying mechanisms driving this activity are unclear. Here, we combine an MSN dynamical network model with an action selection system applied to an interval discrimination task. We find that when network parameters are appropriate for the striatum so that slowly fluctuating marginally stable dynamics are intrinsically generated, up and down ramping populations naturally emerge which enable significantly above chance task performance. We show that emergent population activity is in very good agreement with empirical studies and discuss how MSN network dysfunction in disease may alter temporal perception., source:https://www.frontiersin.org/articles/10.3389/fncom.2022.902741/full

Various subtypes of inhibitory interneurons contact one another to organize cortical networks. Most cortical inhibitory interneurons express 1 of 3 genes: parvalbumin (PV), somatostatin (SOM), or vasoactive intestinal polypeptide (VIP). This diversity of inhibition allows the flexible regulation of neuronal responses within and between cortical areas. However, the exact roles of these interneuron subtypes and of excitatory pyramidal (Pyr) neurons in regulating neuronal network activity and establishing perception (via interactions between feedforward sensory and feedback attentional signals) remain largely unknown. To explore the regulatory roles of distinct neuronal types in cortical computation, we developed a computational microcircuit model with biologically plausible visual cortex layers 2/3 that combined Pyr neurons and the 3 inhibitory interneuron subtypes to generate network activity. In simulations with our model, inhibitory signals from PV and SOM neurons preferentially induced neuronal firing at gamma (30–80 Hz) and beta (20–30 Hz) frequencies, respectively, in agreement with observed physiological results. Furthermore, our model indicated that rapid inhibition from VIP to SOM subtypes underlies marked attentional modulation for low-gamma frequency (30–50 Hz) in Pyr neuron responses. Our results suggest the distinct but cooperative roles of inhibitory interneuron subtypes in the establishment of visual perception., source:https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhac355/6706754?login=true

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin-Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

Objective: Most methods for monitoring sweat gland activity use simple gravimetric methods, which merely measure the average sweat rate of multiple sweat glands over a region of skin. It would be extremely useful to have a method which could quantify individual gland activity in order to improve the treatment of conditions which use sweat tests as a diagnostic tool, such as hyperhidrosis, cystic fibrosis, and peripheral nerve degeneration. Methods: An optical method using an infrared camera to monitor the skin surface temperature was developed. A thermodynamics computer model was then implemented to utilize these skin temperature values along with other environmental parameters, such as ambient temperature and relative humidity, to calculate the sweat rates of individual glands using chemically stimulated and unstimulated sweating. The optical method was also used to monitor sweat pulsation patterns of individual sweat glands. Results: In this preliminary study, the feasibility of the optical approach was demonstrated by measuring sweat rates of individual glands at various bodily locations. Calculated values from this method agree with expected sweat rates given values found in literature. In addition, a lack of pulsatile sweat expulsion was observed during chemically stimulated sweating, and a potential explanation for this phenomenon was proposed. Conclusion: A simple, non-contact optical method to quantify sweat gland activity in-vivo was presented. Significance: This method allows researchers and clinicians to investigate several sweat glands simultaneously, which has the potential to provide more accurate diagnoses and treatment as well as increase the potential utility for wearable sweat sensors.

The pharmacokinetic characteristics of per- and polyfluoroalkyl substances (PFAS) affect their distribution and bioaccumulation in biological systems. The enterohepatic circulation leads to reabsorption of certain chemicals from bile back into blood and the liver and thus influences their elimination, yet its influence on PFAS bioaccumulation remains unclear. We explored the role of enterohepatic circulation in PFAS bioaccumulation by examining tissue distribution of various PFAS in wild fish and a rat model. Computational models were used to determine the reabsorbed fractions of PFAS by calculating binding affinities of PFAS for key transporter proteins of enterohepatic circulation. The results indicated that higher concentrations were observed in blood, the liver, and bile compared to other tissues for some PFAS in fish. Furthermore, exposure to a PFAS mixture on the rat model showed that the reabsorption phenomenon appeared during 8-12 h for most long-chain PFAS. Molecular docking calculations suggest that PFAS can bind to key transporter proteins via electrostatic and hydrophobic interactions. Further regression analysis adds support to the hypothesis that binding affinity of the apical sodium-dependent bile acid transporter is the most important variable to predict the human half-lives of PFAS. This study demonstrated the critical role of enterohepatic circulation in reabsorption, distribution, and accumulation of PFAS., Funding agencies:National Natural Science Foundation of China (NSFC) 22136006 22193051 21777061 21806058 21906069 21477049 21507044 China Postdoctoral Science Foundation 2019M660185

Purpose: Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity of the fetus. Computational anthropomorphic models have been widely used in patient-specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies. Acquisition and validation methods: Computed tomography (CT) images of abdominal and pelvic regions of 46 pregnant females were segmented by experienced medical physicists. The segmented tissues/organs include the body contour, skeleton, uterus, liver, kidney, intestine, stomach, lung, bladder, gall bladder, spleen, and pancreas for maternal body, and placenta, amniotic fluid, fetal body, fetal brain, and fetal skeleton. Nonuniform rational B-spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit values of each identified organs were gathered from CT images of pregnant patients and converted to tissue density. Organ volumes were further adjusted according to reference measurements for the developing fetus recommended by the World Health Organization (WHO) and International Commission on Radiological Protection. A series of anatomical parameters, including femur length, humerus length, biparietal diameter, abdominal circumference (FAC), and head circumference, were measured and compared with WHO recommendations. Data format and usage notes: The first fetal patient-specific model library was developed with the anatomical characteristics of each model derived from the corresponding patient whose gestational age varies between 8 and 35 weeks. Voxelized models are represented in the form of MCNP matrix input files representing the three-dimensional model of the fetus. The size distributions of each model are also provided in text files. All data are stored on Zenodo and

Ca2+ signalling plays an essential role in T cell activation, which is a key step to start an adaptive immune response. During the transition from a quiescent to a fully activated state, Ca2+ microdomains of reduced spatial and temporal extents develop in the junctions between the plasma membrane and the endoplasmic reticulum (ER). These microdomains rely on Ca2+ entry from the extracellular medium, via the ORAI1/STIM1/STIM2 system that mediates store operated Ca2+ entry Store operated calcium entry. The mechanism leading to local store depletion and subsequent Ca2+ entry depends on the activation state of the cells. The initial, smaller microdomains are triggered by D-myo-inositol 1,4,5-trisphosphate (IP3) signalling in response to T cell adhesion. T cell receptor (TCR)/CD3 stimulation then initiates nicotinic acid adenine dinucleotide phosphate signalling, which activates ryanodine receptors (RYR). We have recently developed a mathematical model to elucidate the spatiotemporal Ca2+ dynamics of the microdomains triggered by IP3 signalling in response to T cell adhesion (Gil et al. 2021). This reaction-diffusion model describes the evolution of the cytosolic and endoplasmic reticulum Ca2+ concentrations in a three-dimensional ER-PM junction and was solved using COMSOL Multiphysics. Modelling predicted that adhesion-dependent microdomains result from the concerted activity of IP3 receptors and pre-formed ORAI1-STIM2 complexes. In the present study, we extend this model to include the role of RYRs rapidly after TCR/CD3 stimulation. The involvement of STIM1, which has a lower KD for Ca2+ than STIM2, is also considered. Detailed 3D spatio-temporal simulations show that these Ca2+ microdomains rely on the concerted opening of ∼7 RYRs that are simultaneously active in response to the increase in NAADP induced by T cell stimulation. Opening of these RYRs provoke a local depletion of ER Ca2+ that triggers Ca2+ flux through the ORAI1 channels. Simulations predict that RYRs a, SCOPUS: ar.j, info:eu-repo/semantics/published

Purpose: Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. Methods: A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. Results: Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. Conclusion: The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.

Objective: Most methods for monitoring sweat gland activity use simple gravimetric methods, which merely measure the average sweat rate of multiple sweat glands over a region of skin. It would be extremely useful to have a method which could quantify individual gland activity in order to improve the treatment of conditions which use sweat tests as a diagnostic tool, such as hyperhidrosis, cystic fibrosis, and peripheral nerve degeneration. Methods: An optical method using an infrared camera to monitor the skin surface temperature was developed. A thermodynamics computer model was then implemented to utilize these skin temperature values along with other environmental parameters, such as ambient temperature and relative humidity, to calculate the sweat rates of individual glands using chemically stimulated and unstimulated sweating. The optical method was also used to monitor sweat pulsation patterns of individual sweat glands. Results: In this preliminary study, the feasibility of the optical approach was demonstrated by measuring sweat rates of individual glands at various bodily locations. Calculated values from this method agree with expected sweat rates given values found in literature. In addition, a lack of pulsatile sweat expulsion was observed during chemically stimulated sweating, and a potential explanation for this phenomenon was proposed. Conclusion: A simple, non-contact optical method to quantify sweat gland activity in-vivo was presented. Significance: This method allows researchers and clinicians to investigate several sweat glands simultaneously, which has the potential to provide more accurate diagnoses and treatment as well as increase the potential utility for wearable sweat sensors.

Aim: Micro-scale processes play a fundamental role in shaping groundwater ecosystems and yet they remain poorly understood and under-explained. Here, we aim to provide an overview of existing concepts, methods, and knowledge. We outline how increased understanding of micro-scale processes may lead to an improved appreciation of the range of ecosystems functions taking place at different scales.Main concepts covered: A reason why the groundwater ecosystems studies on the micro-scale are scarce lies in the fact that sampling is rarely carried out at the scale at which microorganisms and their grazers and predators, act. Within a larger scale framework in terms of geochemical features, supply with energy and nutrients, and exchange intensity and dynamics, the micro-scale adds heterogeneity which enables a wider range of redox reactions. Such processes are relied upon in bioremediation and we demonstrate that ecosystem modeling must consider the micro-scale.Main methods covered: We describe the scales at which field sampling and laboratory investigations usually occur, and explain how such results should be interpreted. An appropriate tool for deriving knowledge on the micro-scale will be models which have to be carefully validated wherever possible. Examples of micro-scale models are provided, both experimental and computational, that so far only cover prokaryotes but can be developed further to incorporate more elements of the food web.Conclusion/outlook: Models encompassing the micro-scale and whole food web processes, parameterized by experiments and investigations on the appropriate scale, may provide tools for testing concepts and improving our understanding of the subsurface ecosystems functioning.

The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what is distinctively human and what can be inferred from AI systems. The present article investigates to what extent recent developments in AI provide new elements to the debate and clarify the process of belief acquisition, consolidation, and recalibration. The article analyses and debates current issues and topics of investigation such as: different models to understand belief, the exploration of belief in an automated reasoning environment, the case of religious beliefs, and future directions of research.