Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach.

BACKGROUND: During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. OBJECTIVE: The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. METHODS: We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. RESULTS: Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. CONCLUSIONS: Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations.

Understanding stigma of dementia during COVID-19: a scoping review.

Introduction: Stigma of dementia is one of the greatest challenges for people living with dementia. However, there is little research on the different types of stigma of dementia in the COVID-19 pandemic. The purpose of this scoping review is to synthesize the existing literature on dementia-related stigma (self, public, and structural stigma), during the pandemic. Methods: Guided by Arksey and O'Malley's scoping review framework and PRISMA guidelines, CINAHL, EMBASE, Google Scholar, Medline, PsycINFO, and Web of Science were searched for English language literature from January 2020 to June 2023. Inclusion criteria consisted of peer-reviewed, original research articles addressing stigma of dementia during the COVID-19 pandemic. Thematic analysis was used to analyze the data and steps were taken to ensure rigor. Results: Fifteen articles met our inclusion criteria. Four primary themes were identified including: 1) COVID-19 stereotypes and assumptions of dementia; 2) human rights issues and deprived dignity; 3) disparate access to health services and supports; and 4) cultural inequities and distrust. Discussion: The COVID-19 pandemic has contributed to the stigmatization of people living with dementia. Further research is needed to develop, implement, and evaluate interventions targeted towards the different types of dementia-related stigma (including self, public, and structural stigma). Moreover, our findings highlight the need for more collaborative research that prioritizes the lived experience and input of diverse people living with dementia. Research partnerships with diverse people living with dementia are vital to improving future pandemic planning. Only through evidence-informed research and lived experience can we begin to fully address the different types of dementia-related stigma and enhance the quality of life of people living with dementia.

Electronic and Structural Disorder of the Epitaxial La0.67Sr0.33MnO3 Surface.

Understanding and tuning epitaxial complex oxide films are crucial in controlling the behavior of devices and catalytic processes. Substrate-induced strain, doping, and layer growth are known to influence the electronic and magnetic properties of the bulk of the film. In this study, we demonstrate a clear distinction between the bulk and surface of thin films of La0.67Sr0.33MnO3 in terms of chemical composition, electronic disorder, and surface morphology. We use a combined experimental approach of X-ray-based characterization methods and scanning probe microscopy. Using X-ray diffraction and resonant X-ray reflectivity, we uncover surface nonstoichiometry in the strontium and lanthanum alongside an accumulation of oxygen vacancies. With scanning tunneling microscopy, we observed an electronic phase separation (EPS) on the surface related to this nonstoichiometry. The EPS is likely driving the temperature-dependent resistivity transition and is a cause of proposed mixed-phase ferromagnetic and paramagnetic states near room temperature in these thin films.

Using Twitter to Understand COVID-19 Vaccine-Related Ageism During the Pandemic.

BACKGROUND AND OBJECTIVES: During the rollout of coronavirus 2019 (COVID-19) vaccines, older adults in high-income countries were often prioritized for inoculation in efforts to reduce COVID-19-related mortality. However, this prioritization may have contributed to intergenerational tensions and ageism, particularly with the limited supply of COVID-19 vaccines. This study examines Twitter discourse to understand vaccine-related ageism during the COVID-19 pandemic to inform future vaccination policies and practices to reduce ageism. RESEARCH DESIGN AND METHODS: We collected 1,369 relevant tweets on Twitter using the Twint application in Python from December 8, 2020, to December 31, 2021. Tweets were analyzed using thematic analysis, and steps were taken to ensure rigor. RESULTS: Our research identified four main themes including (a) blame and hostility: "It's all their fault"; (b) incompetence and misinformation: "clueless boomer"; (c) ageist political slander; and (d) combatting ageism: advocacy and accessibility. DISCUSSION AND IMPLICATIONS: Our findings exposed issues of victim-blaming, hate speech, pejorative content, and ageist political slander that is deepening the divide of intergenerational conflict. Although a subset of tweets countered negative outcomes and demonstrated intergenerational solidarity, our findings suggest that ageism may have contributed to COVID-19 vaccine hesitancy among older adults. Consequently, urgent action is needed to counter vaccine misinformation, prohibit aggressive messaging, and promote intergenerational unity during the COVID-19 pandemic and beyond.

Stigma of dementia during the COVID-19 pandemic: a scoping review protocol.

INTRODUCTION: Dementia-related stigma reduces the quality of life of people living with dementia and their care partners. However, there is a dearth of literature synthesising knowledge on stigma of dementia during the COVID-19 pandemic. This scoping review protocol outlines a methodology that will be used to understand the impact of stigma on people living with dementia during the pandemic. Addressing dementia-related stigma is critical to promoting timely dementia diagnoses and enhancing the quality of life for people living with dementia and their care partners. METHODS AND ANALYSIS: This review will follow the Arksey and O'Malley methodological framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist. The review will focus on English-language, peer-reviewed literature published between 13 January 2020 and 30 June 2023. Stigma will be broadly defined according to pre-established components (stereotypes, prejudice and discrimination). We will search six databases including CINAHL, EMBASE, Google Scholar, Medline, PsycINFO and Web of Science. We will also hand-search the reference lists of relevant articles to identify additional manuscripts. Two reviewers will develop the data extraction table, as well as independently conduct the data screening. Any disagreements will be resolved through open discussion between the two researchers, and if necessary, by consulting the full team to achieve consensus. Data synthesis will be conducted using an inductive thematic analysis approach. ETHICS AND DISSEMINATION: This review will be the first to explore the impact of dementia-related stigma during the COVID-19 pandemic. An advisory panel including a person living with dementia and a care partner will be consulted to inform our review's findings and support the data dissemination process. The results of this scoping review will be shared and disseminated through publication in a peer-reviewed journal, presentations at academic conferences, a community workshop and webinars with various stakeholders.

The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review.

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach.

BACKGROUND: Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficial in reducing this burden and improving the quality of life for affected individuals. To this end, the abundance of data exemplified by X (formerly Twitter) presents an invaluable resource for discerning insights into individuals' mental states and enabling timely detection of these transitory depressive feelings. OBJECTIVE: The objective of this study was to automate the detection of momentary depressive feelings in posts using contextual language approaches. METHODS: First, we identified terms expressing momentary depressive feelings and depression, scaled their relevance to depression, and constructed a lexicon. Then, we scraped posts using this lexicon and labeled them manually. Finally, we assessed the performance of the Bidirectional Encoder Representations From Transformers (BERT), A Lite BERT (ALBERT), Robustly Optimized BERT Approach (RoBERTa), Distilled BERT (DistilBERT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and machine learning (ML) algorithms in detecting momentary depressive feelings in posts. RESULTS: This study demonstrates a notable distinction in performance between binary classification, aimed at identifying posts conveying depressive sentiments and multilabel classification, designed to categorize such posts across multiple emotional nuances. Specifically, binary classification emerges as the more adept approach in this context, outperforming multilabel classification. This outcome stems from several critical factors that underscore the nuanced nature of depressive expressions within social media. Our results show that when using binary classification, BERT and DistilBERT (pretrained transfer learning algorithms) may outperform traditional ML algorithms. Particularly, DistilBERT achieved the best performance in terms of area under the curve (96.71%), accuracy (97.4%), sensitivity (97.57%), specificity (97.22%), precision (97.30%), and F1-score (97.44%). DistilBERT obtained an area under the curve nearly 12% points higher than that of the best-performing traditional ML algorithm, convolutional neural network. This study showed that transfer learning algorithms are highly effective in extracting knowledge from posts, detecting momentary depressive feelings, and highlighting their superiority in contextual analysis. CONCLUSIONS: Our findings suggest that contextual language approaches-particularly those rooted in transfer learning-are reliable approaches to automate the early detection of momentary depressive feelings and can be used to develop social media monitoring tools for identifying individuals who may be at risk of depression. The implications are far-reaching because these approaches stand poised to inform the creation of social media monitoring tools and are pivotal for identifying individuals susceptible to depression. By intervening proactively, these tools possess the potential to slow the progression of depressive feelings, effectively mitigating the societal load of depression and fostering improved mental health. In addition to highlighting the capabilities of automated sentiment analysis, this study illuminates its pivotal role in advancing global public health.

Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review.

BACKGROUND: Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. METHODS: A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. RESULTS: Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. CONCLUSION: The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.

Examining the Twitter Discourse on Dementia During Alzheimer's Awareness Month in Canada: Infodemiology Study.

BACKGROUND: Twitter has become a primary platform for public health campaigns, ranging from mental health awareness week to diabetes awareness month. However, there is a paucity of knowledge about how Twitter is being used during health campaigns, especially for Alzheimer's Awareness Month. OBJECTIVE: The purpose of our study was to examine dementia discourse during Canada's Alzheimer's Awareness Month in January to inform future awareness campaigns. METHODS: We collected 1289 relevant tweets using the Twint application in Python from January 1 to January 31, 2022. Thematic analysis was used to analyze the data. RESULTS: Guided by our analysis, 4 primary themes were identified: dementia education and advocacy, fundraising and promotion, experiences of dementia, and opportunities for future actions. CONCLUSIONS: Although our study identified many educational, promotional, and fundraising tweets to support dementia awareness, we also found numerous tweets with cursory messaging (ie, simply referencing January as Alzheimer's Awareness Month in Canada). While these tweets promoted general awareness, they also highlight an opportunity for targeted educational content to counter stigmatizing messages and misinformation about dementia. In addition, awareness strategies partnering with diverse stakeholders (such as celebrities, social media influencers, and people living with dementia and their care partners) may play a pivotal role in fostering dementia dialogue and education. Further research is needed to develop, implement, and evaluate dementia awareness strategies on Twitter. Increased knowledge, partnerships, and research are essential to enhancing dementia awareness during Canada's Alzheimer's Awareness Month and beyond.

Examining the Impact of COVID-19 on People With Dementia From the Perspective of Family and Friends: Thematic Analysis of Tweets.

BACKGROUND: The COVID-19 pandemic is taking a serious toll on people with dementia. Given the rapidly evolving COVID-19 context, policymakers and practitioners require timely, evidence-informed research to address the changing needs and challenges of people with dementia and their family care partners. OBJECTIVE: Using Twitter data, the objective of this study was to examine the COVID-19 impact on people with dementia from the perspective of their family members and friends. METHODS: Using the Twint application in Python, we collected 6243 relevant tweets over a 15-month time frame. The tweets were divided among 11 coders and analyzed using a 6-step thematic analysis process. RESULTS: Based on our analysis, 3 main themes were identified: (1) frustration and structural inequities (eg, denied dignity and inadequate supports), (2) despair due to loss (eg, isolation, decline, and death), and (3) resiliency, survival, and hope for the future. CONCLUSIONS: As the COVID-19 pandemic persists and new variants emerge, people with dementia and their family care partners are facing complex challenges that require timely interventions. More specifically, tackling COVID-19 challenges requires revisiting pandemic policies and protocols to ensure equitable access to health and support services, recognizing the essential role of family care partners, and providing financial assistance and resources to help support people with dementia in the pandemic. Revaluating COVID-19 policies is critical to mitigating the pandemic's impact on people with dementia and their family care partners.

Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study.

BACKGROUND: During the pandemic, there has been significant social media attention focused on the increased COVID-19 risks and impacts for people with dementia and their care partners. However, these messages can perpetuate misconceptions, false information, and stigma. OBJECTIVE: This study used Twitter data to understand stigma against people with dementia propagated during the COVID-19 pandemic. METHODS: We collected 1743 stigma-related tweets using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. RESULTS: Based on our analysis, 4 main themes were identified: (1) ageism and devaluing the lives of people with dementia, (2) misinformation and false beliefs about dementia and COVID-19, (3) dementia used as an insult for political ridicule, and (4) challenging stigma against dementia. Social media has been used to spread stigma, but it can also be used to challenge negative beliefs, stereotypes, and false information. CONCLUSIONS: Dementia education and awareness campaigns are urgently needed on social media to address COVID-19-related stigma. When stigmatizing discourse on dementia is widely shared and consumed amongst the public, it has public health implications. How we talk about dementia shapes how policymakers, clinicians, and the public value the lives of people with dementia. Stigma perpetuates misinformation, pejorative language, and patronizing attitudes that can lead to discriminatory actions, such as the limited provision of lifesaving supports and health services for people with dementia during the pandemic. COVID-19 policies and public health messages should focus on precautions and preventive measures rather than labeling specific population groups.

Using Twitter to Understand the COVID-19 Experiences of People With Dementia: Infodemiology Study.

BACKGROUND: The COVID-19 pandemic is affecting people with dementia in numerous ways. Nevertheless, there is a paucity of research on the COVID-19 impact on people with dementia and their care partners. OBJECTIVE: Using Twitter, the purpose of this study is to understand the experiences of COVID-19 for people with dementia and their care partners. METHODS: We collected tweets on COVID-19 and dementia using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. RESULTS: From the 5063 tweets analyzed with line-by-line coding, we identified 4 main themes including (1) separation and loss; (2) COVID-19 confusion, despair, and abandonment; (3) stress and exhaustion exacerbation; and (4) unpaid sacrifices by formal care providers. CONCLUSIONS: There is an imminent need for governments to rethink using a one-size-fits-all response to COVID-19 policy and use a collaborative approach to support people with dementia. Collaboration and more evidence-informed research are essential to reducing COVID-19 mortality and improving the quality of life for people with dementia and their care partners.

Inherent structure landscape of hard spheres confined to narrow cylindrical channels.

The inherent structure landscape for a system of hard spheres confined to a hard cylindrical channel, such that spheres can only contact their first and second neighbors, is studied using an analytical model that extends previous results [Phys. Rev. Lett. 115, 025702 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.025702] to provide a comprehensive picture of jammed packings over a range of packing densities. In the model, a packing is described as an arrangement of k helical sections, separated by defects, that have alternating helical twist directions and where all spheres satisfy local jamming constraints. The structure of each helical section is determined by a single helical twist angle, and a jammed packing is obtained by minimizing the length of the channel per particle with respect to the k helical section angles. An analysis of a small system of N=20 spheres shows that the basins on the inherent structure landscape associated with these helical arrangements split into a number of distinct jammed states separated by low barriers giving rise to a degree of hierarchical organization. The model accurately predicts the geometric properties of packings generated using the Lubachevsky and Stillinger compression scheme (N=10^{4}) and provides insight into the nature of the probability distribution of helical section lengths.

Chaste: Cancer, Heart and Soft Tissue Environment.

Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology ('Cardiac Chaste'), discrete cell-based modelling of soft tissues ('Cell-based Chaste'), and modelling of ventilation in lungs ('Lung Chaste'). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framework for cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community. Chaste is designed to be modular and extensible, providing libraries for common scientific computing infrastructure such as linear algebra operations, finite element meshes, and ordinary and partial differential equation solvers. This infrastructure is used by libraries for specific applications, such as continuum mechanics, cardiac models, and cell-based models. The software engineering techniques used to develop Chaste are intended to ensure code quality, re-usability and reliability. Primary applications of the software include cardiac and respiratory physiology, cancer and developmental biology.

Scoring algorithms for a computer-based cognitive screening tool: An illustrative example of overfitting machine learning approaches and the impact on estimates of classification accuracy.

Computerized cognitive screening tools, such as the self-administered Computerized Assessment of Memory Cognitive Impairment (CAMCI), require little training and ensure standardized administration and could be an ideal test for primary care settings. We conducted a secondary analysis of a data set including 887 older adults (M age = 72.7 years, SD = 7.1 years; 32.1% male; M years education = 13.4, SD = 2.7 years) with CAMCI scores and independent diagnoses of mild cognitive impairment (MCI). A study by the CAMCI developers used a portion of this data set with a machine learning decision tree model and suggested that the CAMCI had high classification accuracy for MCI (sensitivity = 0.86, specificity = 0.94). We found similar support for accuracy (sensitivity = 0.94, specificity = 0.94) by overfitting a decision tree model, but we found evidence of lower accuracy in a cross-validation sample (sensitivity = 0.62, specificity = 0.66). A logistic regression model, however, discriminated modestly in both training (sensitivity = 0.72, specificity = 0.80) and cross-validation data sets (sensitivity = 0.69, specificity = 0.74). Evidence for strong accuracy when overfitting a decision tree model and substantially reduced accuracy in cross-validation samples was replicated across 500 bootstrapped samples. In contrast, the evidence for accuracy of the logistic regression model was similar in the training and cross-validation samples. The logistic regression model produced accuracy estimates consistent with other published CAMCI studies, suggesting evidence for classification accuracy of the CAMCI for MCI is likely modest. This case study illustrates the general need for cross-validation and careful evaluation of the generalizability of machine learning models. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The effect of soft repulsive interactions on the diffusion of particles in quasi-one-dimensional channels: A hopping time approach.

Fluids confined to quasi-one-dimensional channels exhibit a dynamic crossover from single file diffusion to normal diffusion as the channel becomes wide enough for particles to hop past each other. In the crossover regime, where hopping events are rare, the diffusion coefficient in the long time limit can be related to a hopping time that measures the average time it takes for a particle to escape the local cage formed by its neighbors. In this work, we show that a transition state theory (TST) that calculates the free energy barrier for two particles attempting to pass each other in the small system isobaric ensemble is able to quantitatively predict the hopping time in a system of two-dimensional soft repulsive disks [U(rij)=(σ/rij)α] confined to a hard walled channel over a range of channel radii and degrees of particle softness measured in terms of 1/α. The free energy barrier exhibits a maximum at intermediate values of α that moves to smaller values of 1/α (harder particles) as the channel becomes narrower. However, the presence of the maximum is only observed in the hopping times for wide channels because the interaction potential dependence of the kinetic prefactor plays an increasingly important role for narrower channels. We also begin to explore how our TST approach can be used to optimize and control dynamics in confined quasi-one-dimensional fluids.

A transition state theory for calculating hopping times and diffusion in highly confined fluids.

Monte Carlo simulation is used to study the dynamical crossover from single file diffusion to normal diffusion in fluids confined to narrow channels. We show that the long time diffusion coefficients for a series of systems involving hard and soft interaction potentials can be described in terms of a hopping time that measures the time it takes for a particle to escape the cage formed by its neighbors in the pore. Free energy barriers for the particle hopping process are calculated and used to show that transition state theory effectively describes the hopping time for all the systems studied over a range of pore radii. Our work suggests that the combination of hopping times and transition state theory offers a useful and general framework to describe the dynamics of highly confined, single file fluids.

Cellular cardiac electrophysiology modeling with Chaste and CellML.

Chaste is an open-source C++ library for computational biology that has well-developed cardiac electrophysiology tissue simulation support. In this paper, we introduce the features available for performing cardiac electrophysiology action potential simulations using a wide range of models from the Physiome repository. The mathematics of the models are described in CellML, with units for all quantities. The primary idea is that the model is defined in one place (the CellML file), and all model code is auto-generated at compile or run time; it never has to be manually edited. We use ontological annotation to identify model variables describing certain biological quantities (membrane voltage, capacitance, etc.) to allow us to import any relevant CellML models into the Chaste framework in consistent units and to interact with them via consistent interfaces. This approach provides a great deal of flexibility for analysing different models of the same system. Chaste provides a wide choice of numerical methods for solving the ordinary differential equations that describe the models. Fixed-timestep explicit and implicit solvers are provided, as discussed in previous work. Here we introduce the Rush-Larsen and Generalized Rush-Larsen integration techniques, made available via symbolic manipulation of the model equations, which are automatically rearranged into the forms required by these approaches. We have also integrated the CVODE solvers, a 'gold standard' for stiff systems, and we have developed support for symbolic computation of the Jacobian matrix, yielding further increases in the performance and accuracy of CVODE. We discuss some of the technical details of this work and compare the performance of the available numerical methods. Finally, we discuss how this is generalized in our functional curation framework, which uses a domain-specific language for defining complex experiments as a basis for comparison of model behavior.

Single file and normal dual mode diffusion in highly confined hard sphere mixtures under flow.

We use Monte Carlo simulations to study the dual-mode diffusion regime of binary and tertiary mixtures of hard spheres confined in narrow cylindrical pores under the influence of an imposed flow. The flow is introduced to the dynamics by adding a small bias directed along the long axis of the pore to the random displacement of each Monte Carlo move. As a result, the motion of the particles in all the components is dominated by a drift velocity that causes the mean squared displacements to increase quadratically in the long time limit. However, an analysis of the mean squared displacements at intermediate time scales shows that components of the mixture above and below their passing thresholds still exhibit behaviors consistent with normal and single-file diffusion, respectively. The difference between the mean squared displacements of the various components is shown to go though a maximum, suggesting there may be an optimal pore diameter for the separation of mixtures exhibiting dual-mode diffusion.

The secrets to the success of the Rush-Larsen method and its generalizations.

One of the most popular methods for solving the ordinary differential equations (ODEs) that describe the dynamic behavior of myocardial cell models is known as the Rush-Larsen (RL) method. Its popularity stems from its improved stability over integrators such as the forward Euler (FE) method along with its easy implementation. The RL method partitions the ODEs into two sets: one for the gating variables, which are treated by an exponential integrator, and another for the remaining equations, which are treated by the FE method. The success of the RL method can be understood in terms of its relatively good stability when treating the gating variables. However, this feature would not be expected to be of benefit on cell models for which the stiffness is not captured by the gating equations. We demonstrate that this is indeed the case on a number of stiff cell models. We further propose a new partitioned method based on the combination of a first-order generalization of the RL method with the FE method. This new method leads to simulations of stiff cell models that are often one or two orders of magnitude faster than the original RL method.