Physiological Model Research Articles

Estimating the contribution of early and late noise in vision from psychophysical data.

Human performance in psychophysical detection and discrimination tasks is limited by inner noise. It is unclear to what extent this inner noise arises from early noise (e.g., in the photoreceptors) or from late noise (at or immediately prior to the decision stage, presumably in cortex). Very likely, the behaviorally limiting inner noise is a nontrivial combination of both early and late noise. Here we propose a method to quantify the contributions of early and late noise purely from psychophysical data. Our approach generalizes classical results for linear systemsby combining the theory of noise propagation through a nonlinear network with expressions to obtain a perceptual metric through a nonlinear network. We show that from threshold-only data, the relative contributions of early and late noise can only be disentangled when the experiments include substantial external noise. When full psychometric functions are available, early and late noise sources can be quantified even in the absence of external noise. Our psychophysical estimate of the magnitude of early noise-assuming a standard cascade of linear and nonlinear model stages-is substantially lower than the noise in cone photocurrents computed via an accurate model of retinal physiology, the ISETBio. This is consistent with the idea that one of the fundamental tasks of early vision is to reduce the comparatively large retinal noise.

Propagation Dynamic for a Physiological Age-Structured SIR Epidemic Model with Diffusion

Propagation Dynamic for a Physiological Age-Structured SIR Epidemic Model with Diffusion

VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics.

Complex systems, such as in brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often difficult. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external variables. Whereas this model aligns with Granger's statistical formalism for testing "causal relations", the connection between the two is not widely understood. Here, we bridge this gap by providing fundamental equations, user-friendly code, and demonstrations using simulated and real-world data from neuroscience, physiology, sociology, and economics. Our examples illustrate how the model avoids spurious correlation by factoring out external influences from internal dynamics, leading to more parsimonious explanations of these systems. For instance, in neural recordings we find that prolonged response of the brain can be explained as a short exogenous effect, followed by prolonged internal recurrent activity. In recordings of human physiology, we find that the model recovers established effects such as eye movements affecting pupil size and a bidirectional interaction of respiration and heart rate. We also provide methods for enhancing model efficiency, such as L2 regularization for limited data and basis functions to cope with extended delays. Additionally, we analyze model performance under various scenarios where model assumptions are violated. MATLAB, Python, and R code are provided for easy adoption: https://github.com/lcparra/varx.

Automated Electrochemical Oxygen Sensing Using a 3D-Printed Microfluidic Lab-on-a-Chip System

Dissolved oxygen is crucial for metabolism, growth, and other complex physiological and pathological processes; however, standard physiological models (such as organ-on-chip systems) often use ambient oxygen levels, which do not...

DigiLoCS: A leap forward in predictive organ-on-chip simulations.

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the conventional model and ii)explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Accurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

Ultra‐high temporal resolution 4D angiography using arterial spin labeling with subspace reconstruction

AbstractPurposeTo achieve ultra‐high temporal resolution non‐contrast 4D angiography with improved spatiotemporal fidelity.MethodsContinuous data acquisition using 3D golden‐angle sampling following an arterial spin labeling preparation allows for flexibly reconstructing 4D dynamic angiograms at arbitrary temporal resolutions. However, k‐space data is often temporally “binned” before image reconstruction, negatively affecting spatiotemporal fidelity and limiting temporal resolution. In this work, a subspace was extracted by linearly compressing a dictionary constructed from simulated curves of an angiographic kinetic model. The subspace was used to represent and reconstruct the voxelwise signal timecourse at the same temporal resolution as the data acquisition without temporal binning. Physiological parameters were estimated from the resulting images using a Bayesian fitting approach. A group of eight healthy subjects were scanned and the in vivo results reconstructed by different methods were compared. Because of the difficulty of obtaining ground truth 4D angiograms with ultra‐high temporal resolution, the in vivo results were cross‐validated with numerical simulations.ResultsThe proposed method enables 4D time‐resolved angiography with much higher temporal resolution (14.7 ms) than previously reported (˜50 ms) while maintaining high spatial resolution (1.1 mm isotropic). Blood flow dynamics were depicted in greater detail, thin vessel visibility was improved, and the estimated physiological parameters also exhibited more realistic spatial patterns with the proposed method.ConclusionIncorporating a subspace compressed kinetic model into the reconstruction of 4D ASL angiograms notably improved the temporal resolution and spatiotemporal fidelity, which was subsequently beneficial for accurate physiological modeling.

In vivo hyperphosphorylation of tau is associated with synaptic loss and behavioral abnormalities in the absence of tau seeds

AbstractTau pathology is a hallmark of several neurodegenerative diseases, including frontotemporal dementia and Alzheimer’s disease. However, the sequence of events and the form of tau that confers toxicity are still unclear, due in large part to the lack of physiological models of tauopathy initiation and progression in which to test hypotheses. We have developed a series of targeted mice expressing frontotemporal-dementia-causing mutations in the humanized MAPT gene to investigate the earliest stages of tauopathy. MAPTInt10+3G>A and MAPTS305N;Int10+3G>A lines show abundant hyperphosphorylated tau in the hippocampus and entorhinal cortex, but they do not develop seed-competent fibrillar structures. Accumulation of hyperphosphorylated tau was accompanied by neurite degeneration, loss of viable synapses and indicators of behavioral abnormalities. Our results demonstrate that neuronal toxicity can occur in the absence of fibrillar, higher-order structures and that tau hyperphosphorylation is probably involved in the earliest etiological events in tauopathies showing isoform ratio imbalance.

Persistent humid climate favored the Qin and Western Han Dynasties in China around 2,200 y ago

The Qin and Western Han dynasties (221 BCE to 24 CE) represent an era of societal prosperity in China. However, due to a lack of high-resolution paleoclimate records it is still unclear whether the agricultural boost documented for this period was associated with more favorable climatic conditions. Here, multiparameter analysis of annually resolved tree-ring records and process-based physiological modeling provide evidence of stable and consistently humid climatic conditions during 270 to 77 BCE in northern China. Precipitation in the Asian summer monsoon region during the Qin–Western Han Dynasties was ~18 to 34% higher compared to present-day conditions. In shifting agricultural and pastoral boundaries ~60 to 100 km northwestward, possibility up to 200 km at times, persistently wetter conditions arguably increased food production, contributing to the socioeconomic prosperity around 2,200 y ago. A gradual wetting trend in the western part of arid northwestern China since the 1980s resembles the historical climate analogue, suggesting that similar benefits for regional environmental and agricultural systems may reoccur under current climate change, at least in the near term.

Fatty acid profiles and tolerance to temperature extremes in Daphnia.

Identification of physiological processes setting thermal tolerance limits is essential to describe adaptive response to temperature changes. We use the North American Daphnia pulex complex, which makes a remarkable model for comparative physiology as it is composed of clones differing in heat tolerance, ploidies and with a wide geographic distribution. The fatty acid composition of 18 diploid and triploid D. pulex clones acclimated to 16°C and 24 °C was measured and compared to their tolerance to extreme high and low temperatures (CTmax and CTmin). Eicosapentaenoic acid relative content (EPA) showed a strong negative relationship with CTmax and a clear association with CTmin. Higher unsaturation and peroxidation index were associated with better cold tolerance, while saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) were associated with lower cold tolerance. Triploid Daphnia accumulated more eicosapentaenoic acid (EPA) and had lower CTmin than diploid clones (better cold tolerance). Triploid clones retained more omega-6 polyunsaturated fatty acids at high temperature. CTmax was positively correlated to CTmin, suggesting the existence of important constraints in temperature tolerance caused by fatty acid composition.

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.

Light-exercise-induced dopaminergic and noradrenergic stimulation in the dorsal hippocampus: Using a rat physiological exercise model.

Exercise activates the dorsal hippocampus that triggers synaptic and cellar plasticity and ultimately promotes memory formation. For decades, these benefits have been explored using demanding and stress-response-inducing exercise at moderate-to-vigorous intensities. In contrast, our translational research with animals and humans has focused on light-intensity exercise (light exercise) below the lactate threshold (LT), which almost anyone can safely perform with minimal stress. We found that even light exercise can stimulate hippocampal activity and enhance memory performance. Although the circuit mechanism of this boost remains unclear, arousal promotion even with light exercise implies the involvement of the ascending monoaminergic system that is essential to modulate hippocampal activity and impact memory. To test this hypothesis, we employed our physiological exercise model based on the LT of rats and immunohistochemically assessed the neuronal activation of the dorsal hippocampal sub-regions and brainstem monoaminergic neurons. Also, we monitored the extracellular concentration of monoamines in the dorsal hippocampus using invivo microdialysis. We found that even light exercise increased neuronal activity in the dorsal hippocampal sub-regions and elevated the extracellular concentrations of noradrenaline and dopamine. Furthermore, we found that tyrosine hydroxylase-positive neurons in the locus coeruleus (LC) and the ventral tegmental area (VTA) were activated even by light exercise and were both positively correlated with the dorsal hippocampal activation. In conclusion, our findings demonstrate that light exercise stimulates dorsal hippocampal neurons, which are associated with LC-noradrenergic and VTA-dopaminergic activation. This shed light on the circuit mechanisms responsible for hippocampal neural activation during exercise, consequently enhancing memory function.

Remote Sensing Technologies Quantify the Contribution of Ambient Air Pollution to Asthma Severity and Risk Factors in Greenness, Air Pollution, and Wildfire Ecological Settings: A Literature Review

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines.

Seeing through the gray box: an integrated approach to physiological modeling of phytoplankton stoichiometry

The ‘black boxes’ of ecological stoichiometry, planktonic microbes, have long been recognized to have considerable effects on global biogeochemical cycles. Significant progress has been made in studying these effects and expanding our understanding of microbial stoichiometry. However, the ‘black box’ has not been completely cracked open; there remain gaps in our knowledge of the fate of elements within the phytoplankton cell, and the effect of external processes on nutrient fluxes through their metabolism and into macromolecules and biomass - the eponymous ‘gray box’. In this review paper, we describe the development of an integrative modeling approach that involves a stoichiometrically explicit model of Macromolecular Allocation and Genome-scale Metabolic Analysis (MAGMA) to gain insights into the intra- and extracellular fluxes of nutrients using the cyanobacterium Parasynechococcus marenigrum WH8102 as a target model organism. We then describe an example of the genome-scale resources for P. marenigrum that can be used to build such an integrated modeling tool to see through the gray box of phytoplankton stoichiometry and improve our understanding of the effects of resource supplies and other environmental drivers, especially temperature, on C:N:P demand, acquisition, and allocation at the cellular level.

Sugarcane Yield Estimation Using UAV-Based RGB Images and Allometric Equations

Accurately estimating pre-harvest sugarcane yield has long been a challenge. There are many methods for predicting sugarcane yield, ranging from simple empirical equations to complex physiological models. This paper reports a study on a method for predicting sugarcane yield using allometric equations combined with UAV-based RGB images (UAVI). UAVI were used for the estimation of the sugarcane height, which is in the form of the sugarcane height model (HM). Sugarcane height is one of the key factors in calculating sugarcane yield in the allometric equation. The results showed that the HM could be used in the allometric equation. There was only a slight discrepancy compared to measurement with a tape measure in the field. In developing the allometric equation, the authors created regression models to estimate aboveground biomass weight in leaf bush (Wl) and millable stalk (Wms) based on sugarcane height (H) and diameter measured from the first segment of sugarcane aboveground (Dfs). The model estimated aboveground biomass weight sufficiently for all stages of cultivation. Based on this model, the authors developed two general equations: Y = 0.0842 H * Dfs0.9827; R2 = 0.93 (used for leaf bush), and Y = 0.1254 H * Dfs1.3926; R2 = 0.93 (used for millable stalk). The decision correlation coefficient (R2) was 80% reliable. The HM models were slightly different from field measurements with a tape measure. Also, there was a little inaccuracy in the root-mean-square error (RMSE) between the sugarcane heights analyzed from UAVI and field measurements using tape measure. The RMSE values, arranged from highest to lowest according to the sugarcane growth stage were: 0.35 m (tillering phase, TP), 0.25 m (grand growth phase, GP), and 0.24 m (ripening phase, RP). Using the HM value in the allometric equation effectively estimated the sugarcane yield at different growth stages. Sugarcane growth at TP and GP phases gave the highest sugarcane yield at 200 cm of the HM value, with total yields of 11.49 and 16.75 ton/hectare, respectively. Whereas, RP gave the highest sugarcane yield at 350 cm of the HM value (total yield at 42.97 ton/hectare). Overall, an accurate estimation of the aboveground biomass of leaf bush and millable stalk can be obtained using these equations. The authors believe that the research methods presented here can help sugarcane farmers to better estimate yield prior to harvest.

Analyzing the Brain’s Dynamic Response to Targeted Stimulation using Generative Modeling

Abstract Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain-stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss: (i) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics; and (ii) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Non-Newtonian fluid model analysis due to metachronal waves of cilia: A physiological mathematical model

The Phan-Thien-Tanner non-Newtonian model of fluid flow is considered to mathematically model the cilia governed flow inside human body. The collective action of cilia generates a rhythmic motion in form of metachronal wave travelling along the wall of channel. We used the necessary assumptions to non-dimensionalize this complex problem and exact solutions are obtained for the cilia governed flow problem. The graphical results are also incorporated to further discuss these solutions in detail. The wavy movements developed because of rhythmic actions of cilia are evident in pressure gradient graphs. Further, a parabolic flow pattern is also observed in this ciliated channel flow problem. Streamlines disclose the trapping phenomenon and the multiple pumping regions can be seen in intrigued pressure rise graphical solutions. Cilia-generated peristaltic flow is very important for mucus clearance in the respiratory tract, ovum transit in the reproductive system, nutrient digestion, fluid circulation in the brain, and pathogen removal in different tissues.

Hallmarks of female reproductive aging in physiologic aging mice.

The female reproductive axis is one of the first organ systems to age, which has consequences for fertility and overall health. Here, we provide a comprehensive overview of the biological process of female reproductive aging across reproductive organs, tissues and cells based on research with widely used physiologic aging mouse models, and describe the mechanisms that underpin these phenotypes. Overall, aging is associated with dysregulation of the hypothalamic-pituitary-ovarian axis, perturbations of the ovarian stroma, reduced egg quantity and quality, and altered uterine morphology and function that contributes to reduced capacity for fertilization and impaired embryo development. Ultimately, these age-related phenotypes contribute to altered pregnancy outcomes and adverse consequences in offspring. Conserved mechanisms of aging, as well as those unique to the reproductive system, underlie these phenotypes. The knowledge of such mechanisms will lead to development of therapeutics to extend female reproductive longevity and support endocrine function and overall health.

Summer School in Computational Physiology: A Collaborative Course in Modeling Excitable Tissues

ABSTRACT Computational modeling of physiology is a multiscale, multidisciplinary field requiring a diverse set of skills and backgrounds. The Simula Summer School in Computational Physiology aims to teach graduate students from around the world practical methods for multiscale modeling of excitable tissues (specifically, the heart and brain). This joint summer school is collaboratively based on the complementary expertise and shared educational goals of three institutions: Simula Research Laboratory, the University of Oslo, and the University of California San Diego. The summer school’s core goal is to promote successful research collaboration among the host institutions and to train excellent computational researchers. Keys to the success of this type of school include sustainable funding, close mentorship, and innovative, adaptive teaching tools. Using a combination of lectures, hands-on programming modules, and real-world projects in small groups with experienced scientific mentors, this unique program is an immersive way for young scientists to develop skills and network with future peers from around the world.

N2 exchanges in hyperbaric environments: towards a model based on physiological gas transport (O2 and CO2).

Decompression sickness can occur in divers even when recommended decompression procedures are followed. Furthermore, the physiological state of individuals can significantly affect bubbling variability. These informations highlight the need for personalized input to improve decompression in SCUBA diving. The main objective of this study is to propose a fundamental framework for a new approach to inert gas exchanges. A physiological model of oxygen delivery to organs and tissues has been built and adapted to nitrogen. The validation of the model was made by transferring the N2 to CO2. Under normobaric conditions (air breathing, oxygen breathing, and static apnea) and hyperbaric conditions, the O2 model replicates the reference physiological Po2 (Spearman correlation tests p<0.001). The inert gas models can simulate inert gas partial pressures under normobaric and hyperbaric conditions. However, the lack of reference values prevents direct validation of this new model. Therefore, the N2 model has been transferred to CO2. The resulting CO2 model has been validated by comparing it with physiological reference values (Spearman correlation tests p<0.01). The validity of the CO2 model constructed from the N2 model demonstrates the plausibility of this physiological model of inert gas exchanges. In the context of personalized decompression procedures, the proposed model is of significant interest as it enables the integration of physiological and morphological parameters (blood and respiratory flows, alveolo-capillary diffusion, respiratory and blood volumes, oxygen consumption rate, fat mass, etc.) into a model of nitrogen saturation/desaturation, in which oxygen and CO2 partial pressures can also be incorporated.

Monitoring the Degree of Gansu Zokor Damage in Chinese Pine by Hyperspectral Remote Sensing

Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R2) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control.