bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling
Authors: Cao Shulin , Aboelkassem Yasser , Wang Ariel , Valdez-Jasso Daniela , Saucerman Jeffrey J. , Omens Jeffrey H. , McCulloch Andrew D. 
Published: May 2020

Cardiac myocytes transduce changes in mechanical loading into cellular responses via interacting cell signalling pathways. We previously reported a logic-based ordinary differential equation model of the myocyte mechanosignalling network that correctly predicts 78% of independent experimental results not used to formulate the original model. Here, we use Monte Carlo and polynomial chaos expansion simulations to examine the effects of uncertainty in parameter values, model logic and experimental validation data on the assessed accuracy of that model. The prediction accuracy of the model was robust to parameter changes over a wide range being least sensitive to uncertainty in time constants and most affected by uncertainty in reaction weights. Quantifying epistemic uncertainty in the reaction logic of the model showed that while replacing ‘OR’ with ‘AND’ reactions greatly reduced model accuracy, replacing ‘AND’ with ‘OR’ reactions was more likely to maintain or even improve accuracy. Finally, data uncertainty had a modest effect on assessment of model accuracy.
This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Reducing complexity and unidentifiability when modelling human atrial cells
Authors: Houston C. , Marchand B. , Engelbert L. , Cantwell C. D. 
Published: May 2020

Mathematical models of a cellular action potential (AP) in cardiac modelling have become increasingly complex, particularly in gating kinetics, which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalized medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the AP. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed.
This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity
Authors: Coveney Sam , Corrado Cesare , Roney Caroline H. , O’Hare Daniel , Williams Steven E. , O’Neill Mark D. , Niederer Steven A. , Clayton Richard H. , Oakley Jeremy E. , Wilkinson Richard D. 
Published: May 2020

In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds.
This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model
Authors: van Osta Nick , Lyon Aurore , Kirkels Feddo , Koopsen Tijmen , van Loon Tim , Cramer Maarten J. , Teske Arco J. , Delhaas Tammo , Huberts Wouter , Lumens Joost 
Published: May 2020

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters (n = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area.This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics
Authors: Hoffman Matthew J. , Cherry Elizabeth M. 
Published: May 2020

Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm’s sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble.
This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Creation and application of virtual patient cohorts of heart models
Authors: Niederer S. A. , Aboelkassem Y. , Cantwell C. D. , Corrado C. , Coveney S. , Cherry E. M. , Delhaas T. , Fenton F. H. , Panfilov A. V. , Pathmanathan P. , Plank G. , Riabiz M. , Roney C. H. , dos Santos R. W. , Wang L. 
Published: May 2020

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models.This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Hard to catch: Experimental evidence supports evasive mimicry
Authors: Páez V Erika , Valkonen Janne K. , Willmott Keith R. , Matos-Maraví Pável , Elias Marianne , Mappes Johanna 
Published: May 2020

ABSTRACTMost research on aposematism has focused on chemically defended prey, but signalling difficulty of capture remains poorly explored. Similarly to classical Batesian and Müllerian mimicry related to distastefulness, such “evasive aposematism” may also lead to convergence in warning colours, known as evasive mimicry. A prime candidate group for evasive mimicry areAdelphabutterflies, which are agile insects and show remarkable colour pattern convergence. We tested the ability of naïve blue tits to learn to avoid and generaliseAdelphawing patterns associated with difficulty of capture, and compared their response to that of birds that learned to associate the same wing patterns with distastefulness. Birds learned to avoid all wing patterns tested, but learning was faster with evasive prey compared with distasteful prey. Birds generalised their learned avoidance from evasive models to imperfect mimics if the mimic shared colours with the model. Despite imperfect mimics gaining protection from bird’s generalisation, perfect mimics always had the best fitness, supporting selection for accurate mimicry. Faster avoidance learning and broader generalisation of evasive prey suggest that being hard to catch may deter predators at least as effectively as distastefulness. Our results provide empirical evidence for a potentially widespread alternative scenario, evasive mimicry, for the evolution of similar aposematic colour patterns.
Physical constrains and functional plasticity of cellulases: Linear scaling relationships for a heterogeneous enzyme reaction
Authors: Kari Jeppe , Molina Gustavo A. , Schaller Kay S. , Christensen Stefan J. , Schiano-di-Cola Corinna , Badino Silke F. , Sørensen Trine H. , Røjel Nanna , Keller Malene B. , Kolaczkowski Bartlomiej M. , Olsen Johan P. , Krogh Kristian B. R. M. , Jensen Kenneth , Cavaleiro Ana Mafalda , Peters Günther H. J. , Spodsberg Nikolaj , Borch Kim , Westh Peter 
Published: May 2020

AbstractEnzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. We have produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the strength of substrate binding and the activation barrier. This common scaling occurred despite the investigated enzymes were structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection have condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising forin silicoselection and design within this industrially important group of enzymes. On a more general note, the LFER may identify a link to inorganic heterogeneous catalysis, and hence open for the implementation of approaches from this field within interfacial enzymology.
Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran
Authors: Goshayeshi Ladan , Rad Mina Akbari , Bergquist Robert , Allahyari Abolghasem ,   members of the MUMS Covid-19 Research Team, Hoseini Benyamin 
Published: May 2020

AbstractBackgroundCoronavirus Disease 2019 (Covid-19) is expanding worldwide. The characteristics of this infection in patients varies from country to country. To move forward, clinical data on infected patients are needed. Here, we report a comparison between fatalities and recovery of patients with severe Covid-19, based on demographic and clinical characteristics.MethodsBetween 5 March and 12 May 2020 in Mashhad, Iran, 1,278 of 4,000 suspected Covid-19 patients were confirmed positive by real-time reverse-transcriptase–polymerase-chain-reaction assay of upper respiratory specimens. We compared the demographic, exposure history and clinical symptoms of 925 survivors and 353 fatal cases with confirmed disease.ResultsMean (SD) age for all confirmed patients was 56.9 (18.7) years, 67.1 (15.9) years in fatal cases and 53.0 (18.3) years in survivors. Multivariable logistic regression analysis showed that the outcome of patients was associated with age (OR = 1.049, P = 0.0001, 95% CI = 1.040-1.057). Despite a high burden of Covid-19 infections in the 30-39 and 40-49 year age groups, most of these (89.6% and 87.2%, respectively) recovered. The median (IQR) duration of hospitalization was 9.0 (6.0-14.0) days. The most prevalent co-morbidities were cardiovascular disorders (21%) and diabetes (16.3%). Dyspnoea (72.7%), cough (68.1%) and fever (63.8%) were the most frequent clinical symptoms. Healthcare workers, of whom two (3%) died, comprised 5.2% of infected-cases. Combination antiviral and antibiotic therapy was used in 43.0% of cases.ConclusionsThe characteristics of severe Covid-19 varied substantially between fatal cases and survivors, with diabetes and cardiovascular disorders the most prevalent co-morbidities. In contrast to other studies, there were a higher number of fatalities in younger patients in our settings.
A Rapid Review and Meta-Analysis of the Asymptomatic Proportion of PCR-Confirmed SARS-CoV-2 Infections in Community Settings
Authors: Beale Sarah , Hayward Andrew , Shallcross Laura , Aldridge Robert W , Fragaszy Ellen 
Published: May 2020

AbstractBackgroundUp to 80% of active SARS-CoV-2 infections are proposed to be asymptomatic based on cross-sectional studies. However, accurate estimates of the asymptomatic proportion require systematic detection and follow-up to differentiate between truly asymptomatic and pre-symptomatic cases. We conducted a rapid review and meta-analysis of the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections based on methodologically-appropriate studies in community settings.MethodsWe searched Medline and EMBASE for peer-reviewed articles, and BioRxiv and MedRxiv for pre-prints published before 25/08/2020. We included studies based in community settings that involved systematic PCR testing on participants and follow-up symptom monitoring regardless of symptom status. We extracted data on study characteristics, frequencies of PCR-confirmed infections by symptom status, and (if available) cycle threshold/genome copy number values and/or duration of viral shedding by symptom status, and age of asymptomatic versus (pre)symptomatic cases. We computed estimates of the asymptomatic proportion and 95% confidence intervals for each study and overall using random effect meta-analysis.FindingsWe screened 1138 studies and included 21. The pooled asymptomatic proportion of SARS-CoV-2 infections was 23% (95% CI 16%-30%). When stratified by testing context, the asymptomatic proportion ranged from 6% (95% CI 0-17%) for household contacts to 47% (95% CI 21-75%) for non-outbreak point prevalence surveys with follow-up symptom monitoring. Estimates of viral load and duration of viral shedding appeared to be similar for asymptomatic and symptomatic cases based on available data, though detailed reporting of viral load and natural history of viral shedding by symptom status were limited. Evidence into the relationship between age and symptom status was inconclusive.ConclusionAsymptomatic viral shedding comprises a substantial minority of SARS-CoV-2 infections when estimated using methodologically-appropriate studies. Further investigation into variation in the asymptomatic proportion by testing context, the degree and duration of infectiousness for asymptomatic infections, and demographic predictors of symptom status are warranted.
Developmental demands contribute to early neuromuscular degeneration in CMT2D mice
Authors: Sleigh James N. , Mech Aleksandra M. , Schiavo Giampietro 
Published: May 2020

AbstractDominantly inherited, missense mutations in the widely expressed housekeeping gene,GARS1, cause Charcot-Marie-Tooth type 2D (CMT2D), a peripheral neuropathy characterised by muscle weakness and wasting in limb extremities. Mice modelling CMT2D display early and selective neuromuscular junction (NMJ) pathology, epitomised by disturbed maturation and neurotransmission, leading to denervation. Indeed, the NMJ disruption has been reported in several different muscles; however, a systematic comparison of neuromuscular synapses from distinct body locations has yet to be performed. We therefore analysed NMJ development and degeneration across five different wholemount muscles to identify key synaptic features contributing to the distinct pattern of neurodegeneration in CMT2D mice. Denervation was found to occur along a distal-to-proximal gradient, providing a cellular explanation for the greater weakness observed in mutantGarshindlimbs compared to forelimbs. Nonetheless, muscles from similar locations and innervated by axons of equivalent length showed significant differences in neuropathology, suggestive of additional factors impacting on site-specific neuromuscular degeneration. Defective NMJ development preceded and associated with degeneration, but was not linked to a delay of wild-type NMJ maturation processes. Correlation analyses indicate that muscle fibre type nor synaptic architecture explain the differential denervation of CMT2D NMJs, rather it is the extent of post-natal synaptic growth that predisposes to neurodegeneration. Together, this work improves our understanding of the mechanisms driving synaptic vulnerability in CMT2D and hints at pertinent pathogenic pathways.
MitoWave: Spatio-temporal analysis of mitochondrial membrane potential fluctuations during ischemia-reperfusion
Authors: Ashok D. , O’Rourke B. 
Published: May 2020

AbstractMitochondria exhibit non-stationary unstable membrane potential (ΔΨm) when subjected to stress, such as during Ischemia/Reperfusion (I/R). Understanding the mechanism of ΔΨminstability involves characterizing and quantifying this phenomenon in response to I/R stress in an unbiased and reproducible manner. We designed a simple ImageJ-MATLAB-based workflow called ‘MitoWave’ to unravel dynamic mitochondrial ΔΨmchanges that occur during ischemia and reperfusion. MitoWave employs MATLAB’s wavelet transform toolbox.In-vitroIschemia was effected by placing a glass coverslip for 60 minutes on a monolayer of neonatal mouse ventricular myocytes (NMVMs). Removal of the coverslip allowed for reperfusion. ΔΨmresponse to I/R was recorded on a confocal microscope using TMRM as the indicator. As proof-of-principle, we used MitoWave analysis on ten invitro I/R experiments. Visual observations corroborated quantitative MitoWave analysis results in classifying the ten I/R experiments into five outcomes that were observed based on the oscillatory state of ΔΨmthroughout the reperfusion time period. Statistical analysis of the distribution of oscillating mitochondrial clusters during reperfusion shows significant differences between five different outcomes (p< 0.001). Features such as time-points of ΔΨmdepolarization during I/R, area of mitochondrial clusters and time-resolved frequency components during reperfusion were determined per cell and per mitochondrial cluster. We found that mitochondria from NMVMs subjected to I/R oscillate in the frequency range of 8.6-45mHz, with a mean of 8.73±4.35mHz. Oscillating clusters had smaller areas ranging from 49.78±40.64 μm2while non-oscillating clusters had larger areas 65.97±42.07μm2. A negative correlation between frequency and mitochondrial cluster area was seen. We also observed that late ΔΨmloss during ischemia correlated with early ΔΨmstabilization after oscillation on reperfusion. Thus, MitoWave analysis provides a way to quantify complex time-resolved mitochondrial behavior. It provides an easy to follow workflow to automate microscopy analysis and allows for unbiased, reproducible quantitation of complex nonstationary cellular phenomena.Statement of SignificanceUnderstanding mitochondrial instability in Ischemia Reperfusion injury is key to determining efficacy of interventions. The MitoWave analysis is a powerful yet simple tool that enables even beginner MATALAB-Image J users to automate analysis of time-series from microscopy data. While we used it to detect ΔΨmchanges during I/R, it can be adapted to detect any such spatio-temporal changes. It standardizes the quantitative analysis of complex biological signals, opens the door to in-depth screening of the genes, proteins and mechanisms underlying metabolic recovery after ischemia-reperfusion.
Democratising “Microscopi”: a 3D printed automated XYZT fluorescence imaging system for teaching, outreach and fieldwork
Authors: Wincott Matthew , Jefferson Andrew , Dobbie Ian M. , Booth Martin J. , Davis Ilan , Parton Richard M. 
Published: May 2020

ABSTRACTCommercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing “Microscopi”, an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens.HighlightsPortable, low cost, self-build from 3D printed and commodity componentsMultimodal imaging: bright field, dark field, pseudo-phase and fluorescenceAutomated XYZt imaging from a tablet or smartphone via a simple GUIWide ranging applications in teaching, outreach and fieldworkOpen source hardware and software design, allowing user modification