Parameter Estimation in a Crossbridge Muscle Model

2003 ◽  
Vol 125 (1) ◽  
pp. 132-140 ◽  
Author(s):  
David C. Lin ◽  
T. Richard Nichols
Keyword(s):  
Parameter Estimates ◽  
Model Parameters ◽  
Single Muscle ◽  
Rate Parameter ◽  
Attachment Rate ◽  
Detachment Rate ◽  
Wide Range ◽  
Distribution Moment ◽  
Force Velocity ◽  
Physiological Variations

Models of muscle crossbridge dynamics have great potential for understanding muscle contraction and having a wide range of application. However, the estimation of many model parameters, most of which are difficult to measure, limits their applicability. This study developed a method of estimating parameters in the Distribution Moment crossbridge model from measurements of force-length and force-velocity relationships in cat soleus single muscle fibers. Analysis of the parameter estimates showed that the detachment rate parameters had more uncertainty than the attachment rate parameter, which could reflect physiological variations in the contractile protein content and in the response of muscle to lengthenings.

scholarly journals Assessing model-based inferences in decision making with single-trial response time decomposition

2020 ◽  
Author(s):  
Gabriel Weindel ◽  
Royce anders ◽  
F.-Xavier Alario ◽  
Boris BURLE
Keyword(s):  
Decision Making ◽  
Decision Processes ◽  
Emg Activity ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Response Force ◽  
Model Based ◽  
Wide Range ◽  
Response Execution ◽  
Speed Accuracy

Decision-making models based on evidence accumulation processes (the most prolific one being the drift-diffusion model – DDM) are widely used to draw inferences about latent psychological processes from chronometric data. While the observed goodness of fit in a wide range of tasks supports the model’s validity, the derived interpretations have yet to be sufficiently cross-validated with other measures that also reflect cognitive processing. To do so, we recorded electromyographic (EMG) activity along with response times (RT), and used it to decompose every RT into two components: a pre-motor (PMT) and motor time (MT). These measures were mapped to the DDM's parameters, thus allowing a test, beyond quality of fit, of the validity of the model’s assumptions and their usual interpretation. In two perceptual decision tasks, performed within a canonical task setting, we manipulated stimulus contrast, speed-accuracy trade-off, and response force, and assessed their effects on PMT, MT, and RT. Contrary to common assumptions, these three factors consistently affected MT. DDM parameter estimates of non-decision processes are thought to include motor execution processes, and they were globally linked to the recorded response execution MT. However, when the assumption of independence between decision and non-decision processes was not met, in the fastest trials, the link was weaker. Overall, the results show a fair concordance between model-based and EMG-based decompositions of RTs, but also establish some limits on the interpretability of decision model parameters linked to response execution.

Effect of Macroporosity on Physical Property Estimates for Peat Soils

2021 ◽  
Author(s):  
Miaorun Wang ◽  
Haojie Liu ◽  
Bernd Lennartz
Keyword(s):  
Hydraulic Conductivity ◽  
Soil Properties ◽  
Total Porosity ◽  
Pedotransfer Functions ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Soil Water Retention ◽  
Peat Soils ◽  
Undisturbed Soil ◽  
Wide Range

<p>Hydrophysical soil properties play an important role in regulating the water balance of peatlands and are known to be a function of the status of peat degradation. The objective of this study was to revise multiple regression models (pedotransfer functions, PTFs) for the assessment of hydrophysical properties from readily available soil properties. We selected three study sites, each representing a different state of peat degradation (natural, degraded and extremely degraded). At each site, 72 undisturbed soil cores were collected. The saturated hydraulic conductivity (<em>K</em><sub>s</sub>), soil water retention curves, total porosity, macroporosity, bulk density (BD) and soil organic matter (SOM) content were determined for all sampling locations. The van Genuchten (VG) model parameters (<em>θ</em><sub>s</sub>, <em>α</em>, <em>n</em>) were optimized using the RETC software package. Macroporosity and the <em>K</em><sub>s</sub> were found to be highly correlated, but the obtained functions differ for differently degraded peatlands. The introduction of macroporosity into existing PTFs substantially improved the derivation of hydrophysical parameter values as compared to functions based on BD and SOM content alone. The obtained PTFs can be applied to a wide range of natural and degraded peat soils. We assume that the incorporation of macroposity helps to overcome effects possibly resulting from soil management. Our results suggest that the extra effort required to determine macroporosity is worth it, considering the quality of parameter estimates for hydraulic conductivity as well as the soil hydraulic VG model.</p>

scholarly journals Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package Ipcr

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 360-385
Author(s):  
Manuel Arnold ◽  
Andreas M. Brandmaier ◽  
Manuel C. Voelkle
Keyword(s):  
False Negative ◽  
R Package ◽  
Parametric Models ◽  
Alternative Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Modeling Framework ◽  
Data Set ◽  
Individual Parameter ◽  
Wide Range

Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation.

scholarly journals Event-scale power law recession analysis: quantifying methodological uncertainty

2017 ◽  
Vol 21 (1) ◽  
pp. 65-81 ◽  
Author(s):  
David N. Dralle ◽  
Nathaniel J. Karst ◽  
Kyriakos Charalampous ◽  
Andrew Veenstra ◽  
Sally E. Thompson
Keyword(s):  
Impact Parameter ◽  
Power Law ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Northern California ◽  
Event Scale ◽  
Methodological Choices ◽  
Daily Streamflow ◽  
Study Results ◽  
Wide Range

Abstract. The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely used power law recession model. Results are relevant to watersheds that are relatively steep, forested, and rain-dominated. The highly seasonal mediterranean climate of northern California and southern Oregon ensures study catchments explore a wide range of recession behaviors and wetness states, ideal for a sensitivity analysis. In such catchments, we show the following: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness of fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power-law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices. Considering study results, we recommend a combination of four key methodological decisions to maximize the quality of fitted recession curves, and to minimize bias in the related populations of fitted recession parameters.

scholarly journals Generating national projections of dementia cases for Ireland using a calibrated macro-simulation model

BMJ Open ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. e035463
Author(s):  
Tom Pierse ◽  
Fiona Keogh ◽  
Stephen O'Neill
Keyword(s):  
Large Scale ◽  
Census Data ◽  
Weighted Average ◽  
Random Parameter ◽  
Strong Relationship ◽  
Epidemiological Data ◽  
Search Pattern ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Wide Range

IntroductionEpidemiological data on dementia is not available in many European countries and regions due to the high cost and complexity of conducting large scale dementia screening studies. The available epidemiological studies identify potentially substantial variation in the prevalence of dementia over time and across Europe.MethodsIn this paper we generate simulations of the number of dementia cases in Ireland from 1991 to 2036 using a three-state Markov illness-death model. Parameters values are selected for each simulation from a range using a random parameter search pattern. We employ a novel calibration method which exploits the strong relationship between dementia, ageing and mortality. Simulation weights are generated based on differences between observed and modelled cohorts of older people and the reported number of deaths from dementia. Irish Census data from 1991 to 2016 and the number of recorded deaths due to dementia in 2018 are used as calibration points. A weighted average projection of the number of dementia cases is generated.ResultsWe estimate a weighted average number of cases of dementia in 2016 of 54 877 increasing to 98 946 in 2036; this estimate is substantially lower than the estimates generated using extrapolation methods. We show the wide range of possible outcomes given the range in the available parameter estimates and show that irrespective of whether the incidence rate of dementia is declining the number of cases of dementia is rapidly increasing due to population ageing.ConclusionPrevious studies have used parameter estimates from meta-analyses of the literature or from individual studies. In this paper we supplement these with a calibration approach using observed cause of death and population age structure data. These additional sources of data can be used to generate estimates of dementia prevalence in any country or region which has census data and data on deaths due to dementia.

scholarly journals Reconciling age-related changes in behavioural and neural indices of human perceptual decision making

2017 ◽  
Author(s):  
David P. McGovern ◽  
Aoife Hayes ◽  
Simon P. Kelly ◽  
Redmond O’Connell
Keyword(s):  
Decision Making ◽  
Change Detection ◽  
Computational Modelling ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Perceptual Decision Making ◽  
Neural Data ◽  
Motion Discrimination ◽  
Age Related ◽  
Wide Range

Ageing impacts on decision making behaviour across a wide range of cognitive tasks and scenarios. Computational modeling has proven highly valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations has yet to be tested. Here, we measured neural signatures of decision formation as younger and older participants performed motion discrimination and contrast-change detection tasks, and compared the dynamics of these signals to key parameter estimates from fits of a prominent accumulation-to-bound model (drift diffusion) to behavioural data. Our results indicate marked discrepancies between the age-related effects observed in the model output and the neural data. Most notably, while the model predicted a higher decision boundary in older age for both tasks, the neural data indicated no such differences. To reconcile the model and neural findings, we used our neurophysiological observations as a guide to constrain and adapt the model parameters. In addition to providing better fits to behaviour on both tasks, the resultant neurally-informed models furnished novel predictions regarding other features of the neural data which were empirically validated. These included a slower mean rate of evidence accumulation amongst older adults during motion discrimination and a beneficial reduction in between-trial variability in accumulation rates on the contrast-change detection task, which was linked to more consistent attentional engagement. Our findings serve to highlight how combining human brain signal measurements with computational modelling can yield unique insights into group differences in neural mechanisms for decision making.

In Vitro Clot Lysis as a Potential Indicator of Thrombus Resistance to Fibrinolysis – Study in Healthy Subjects and Correlation with Blood Fibrinolytic Parameters

1997 ◽  
Vol 77 (04) ◽  
pp. 725-729 ◽  
Author(s):  
Mario Colucci ◽  
Silvia Scopece ◽  
Antonio V Gelato ◽  
Donato Dimonte ◽  
Nicola Semeraro
Keyword(s):  
Plasminogen Activator ◽  
Clot Lysis ◽  
Individual Response ◽  
Plasminogen Activator Inhibitor 1 ◽  
Autologous Plasma ◽  
Wide Range ◽  
Blood Clots ◽  
Physiological Variations ◽  
Pai 1

SummaryUsing an in vitro model of clot lysis, the individual response to a pharmacological concentration of recombinant tissue plasminogen activator (rt-PA) and the influence on this response of the physiological variations of blood parameters known to interfere with the fibrinolytic/thrombolytic process were investigated in 103 healthy donors. 125I-fibrin labelled blood clots were submersed in autologous plasma, supplemented with 500 ng/ml of rt-PA or solvent, and the degree of lysis was determined after 3 h of incubation at 37° C. Baseline plasma levels of t-PA, plasminogen activator inhibitor 1 (PAI-1), plasminogen, α2-anti-plasmin, fibrinogen, lipoprotein (a), thrombomodulin and von Willebrand factor as well as platelet and leukocyte count and clot retraction were also determined in each donor. rt-PA-induced clot lysis varied over a wide range (28-75%) and was significantly related to endogenous t-PA, PAI-1, plasminogen (p <0.001) and age (p <0.01). Multivariate analysis indicated that both PAI-1 antigen and plasminogen independently predicted low response to rt-PA. Surprisingly, however, not only PAI-1 but also plasminogen was negatively correlated with rt-PA-ginduced clot lysis. The observation that neutralization of PAI-1 by specific antibodies, both in plasma and within the clot, did not potentiate clot lysis indicates that the inhibitor, including the platelet-derived form, is insufficient to attenuate the thrombolytic activity of a pharmacological concentration of rt-PA and that its elevation, similarly to the elevation of plasminogen, is not the cause of clot resistance but rather a coincident finding. It is concluded that the in vitro response of blood clots to rt-PA is poorly influenced by the physiological variations of the examined parameters and that factors other than those evaluated in this study interfere with clot dissolution by rt-PA. In vitro clot lysis test might help to identify patients who may be resistant to thrombolytic therapy.

scholarly journals Inferring Linkage Disequilibrium Between a Polymorphic Marker Locus and a Trait Locus in Natural Populations

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Z W Luo ◽  
S H Tao ◽  
Z-B Zeng
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Random Mating ◽  
Natural Populations ◽  
Polymorphic Marker ◽  
Marker Locus ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Wide Range ◽  
Trait Locus

Abstract Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.

scholarly journals Antifungal Activity of Propyl Disulfide from Neem (Azadirachta indica) in Vapor and Agar Diffusion Assays against Anthracnose Pathogens (Colletotrichum gloeosporioides and Colletotrichum acutatum) in Mango Fruit

2021 ◽  
Vol 9 (4) ◽  
pp. 839
Author(s):  
Muhammad Rafiullah Khan ◽  
Vanee Chonhenchob ◽  
Chongxing Huang ◽  
Panitee Suwanamornlert
Keyword(s):  
Antifungal Activity ◽  
Mycelial Growth ◽  
Colletotrichum Gloeosporioides ◽  
Colletotrichum Acutatum ◽  
Model Parameters ◽  
Pathogenicity Test ◽  
Agar Diffusion ◽  
Wide Range ◽  
Significant Difference ◽  
Diffusion Assay

Microorganisms causing anthracnose diseases have a medium to a high level of resistance to the existing fungicides. This study aimed to investigate neem plant extract (propyl disulfide, PD) as an alternative to the current fungicides against mango’s anthracnose. Microorganisms were isolated from decayed mango and identified as Colletotrichum gloeosporioides and Colletotrichum acutatum. Next, a pathogenicity test was conducted and after fulfilling Koch’s postulates, fungi were reisolated from these symptomatic fruits and we thus obtained pure cultures. Then, different concentrations of PD were used against these fungi in vapor and agar diffusion assays. Ethanol and distilled water were served as control treatments. PD significantly (p ≤ 0.05) inhibited more of the mycelial growth of these fungi than both controls. The antifungal activity of PD increased with increasing concentrations. The vapor diffusion assay was more effective in inhibiting the mycelial growth of these fungi than the agar diffusion assay. A good fit (R2, 0.950) of the experimental data in the Gompertz growth model and a significant difference in the model parameters, i.e., lag phase (λ), stationary phase (A) and mycelial growth rate, further showed the antifungal efficacy of PD. Therefore, PD could be the best antimicrobial compound against a wide range of microorganisms.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

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