12,576 research outputs found

    Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

    Get PDF
    When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises—models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, ‘information-rich’ protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict—highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems

    Towards Inferring Network Properties from Epidemic Data

    Get PDF
    Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the high-dimensionality becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or individual level, e.g., recovery times. To understand if and how network inference is influenced by the type of data, we employed the widely-used MLE approach for population-level data and dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. In contrast, for real-world data, such as foot-and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential model mismatch and produces parameter estimates that are epidemiologically plausible, our results with the MLE method revealed several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network

    Mental illness in chronic kidney disease : prognosis, drug utilization, and treatment outcomes

    Get PDF
    Chronic kidney disease (CKD) is a common yet heterogeneous condition, covering a wide spectrum of disease severity ranging from mildly decreased kidney function to kidney failure. Patients with CKD can often encounter mental health conditions that are related to poor prognosis. Depression is the most studied mental illness, while previous research has primarily focused on patients with kidney failure undergoing dialysis. Antidepressant medications are the main pharmacological approach for treating depression, with selective serotonin reuptake inhibitors (SSRIs) being the most frequently prescribed type. Notably, antidepressants may have different risk-benefit profiles in CKD patients, for whom dose adjustments are recommended. However, the implementation of SSRI dose adjustment in routine care is largely unknown. Despite the widespread use, there remains uncertainty about the efficacy and safety of antidepressants in the CKD population due to limited clinical evidence, whereas a few observational studies have reported several adverse health outcomes associated with antidepressant use in patients with CKD. In addition, little is known about the burden of bipolar disorder and schizophrenia, less common but severe mental illnesses, in patients with CKD. This thesis aims to expand existing knowledge about the prevalence and impact of mental illnesses, as well as the utilization and safety of antidepressants in patients with CKD. Study I evaluated to what extent patients’ kidney function influences SSRI dosing in routine practice. We found that a lower estimated glomerular filtration rate (eGFR) was moderately associated with being prescribed SSRIs with a reduced initial or maintenance dose. Nonetheless, two-fifths of patients with severely decreased eGFR received SSRI prescriptions without proper dose reduction, potentially exposed to a higher risk of adverse drug reactions. Study II examined the association between an incident diagnosis of depression and adverse clinical outcomes in patients with non-dialysis CKD. We found significant associations between incident depression and hospitalization, CKD progression, major adverse cardiovascular events, and all-cause mortality in patients with non-dialysis CKD. The association with CKD progression became more evident one year after the depression diagnosis, while the associations with the other outcomes were more pronounced within the first year after diagnosis. Study III investigated the comparative safety of antidepressant treatment in patients with CKD and incident depression, using the target trial emulation framework. We found that compared with non-initiation, initiation of antidepressants was associated with a higher risk of short-term adverse events such as hip fracture and upper gastrointestinal bleeding, but was not associated with long-term outcomes, including all-cause mortality, major adverse cardiovascular events, CKD progression, and suicidal behavior. Selection of the appropriate type and dosage of antidepressants is crucial to improve treatment safety. Initiating mirtazapine versus SSRIs was associated with a lower risk of upper gastrointestinal bleeding but a higher risk of mortality. Initiating SSRIs with a reduced dose versus a standard dose was associated with lower risks of upper gastrointestinal bleeding and CKD progression but a higher risk of cardiac arrest. Study IV described the prevalence of three severe mental illnesses (i.e., depression, bipolar disorder, and schizophrenia) and examined their impact on clinical outcomes in patients with CKD. In a nationwide cohort of nephrologist-referred CKD patients, we estimated a prevalence of 5.4% for depression, 1.9% for bipolar disorder, and 0.5% for schizophrenia, amounting to an overall prevalence of 7% for any of these disorders, which was 60% higher than the general population. We found that each of the disorders was associated with a higher mortality rate and bipolar disorder was also associated with a faster eGFR decline. Nevertheless, CKD patients with bipolar disorder or schizophrenia exhibited a lower rate of initiating kidney replacement therapy, suggesting potential inequities in access to this life-sustaining treatment. In conclusion, the present thesis highlights the commonness and negative impact of mental illness in patients with CKD and provides real-world evidence on the prescribing and safety of antidepressants in the CKD population

    The Comparative Intercultural Sensitivity of American Faculty Teaching Abroad and Domestically : A Mixed-Methods Investigation Employing Participant-Generated Visuals

    Get PDF
    This thesis aimed to identify and compare the intercultural sensitivity, or IS, of tertiary American instructors teaching mono-national, non-American student populations abroad in the UAE and that of American tertiary instructors in multinational, non-American student populations domestically in the US. The study investigated the use of reflexive photography and photo-elicitation interviews methods as both data collection approaches and possible cultivators of IS, as well as any variation in findings between the two participant groups. The study employed a mixed-methods approach involving surveys and semi-structured photo-elicitation interviews following a four-week reflexive photography project. Qualitative data were analyzed through the lens of a developmental framework and inductively through thematic analysis to capture fuller images of participants’ environments. Both groups of participants self-report fairly high IS, with the US-based group’s sensitivity averaging higher than the UAE-based group. Both groups, on average, showed slightly increased IS quantitatively following the reflexive photography project and photo-elicitation interviews, with the UAE-based group experiencing a slightly greater increase. This research involves a small number of participants; findings should be considered for indicative purposes only. Participants’ IS, when observed through the theoretical lens, indicate more progressive sensitivity among US-based participants. Thematic analysis of interview data reflects distinct teaching contexts faced by each participant group, with five and six themes emerging from the UAE- and US-based groups, respectively. This research is the first to the best of the author’s knowledge to investigate the IS of tertiary American faculty teaching internationally diverse student populations domestically and is also the first to compare differences in IS between this group and America

    Knowledge-informed neuro-integrators for aggregation kinetics

    Get PDF
    We report a novel approach for the efficient computation of solutions of a broad class of large-scale systems of non-linear ordinary differential equations, describing aggregation kinetics. The method is based on a new take on the dimensionality reduction for this class of equations which can be naturally implemented by a cascade of small feed-forward artificial neural networks. We show that this cascade, of otherwise static models, is capable of predicting solutions of the original large-scale system over large intervals of time, using the information about the solution computed over much smaller intervals. The computational cost of the method depends very mildly on the temporalhorizon, which is a major improvement over the current state-of-the-art methods, whose complexity increases super-linearly with the system’s size and proportionally to the simulation time. In cases when prior information about the values of solutions over a relatively small interval of time is already available, the method’s computational complexity does not depend explicitly on the system’s size. The successful application of the new method is illustrated for spatially-homogeneous systems, with a source of monomers, for a number of the most representative reaction rates kernels

    Identifying the controls on nitrate and metabolic state within the Red River delta (Vietnam) with the use of stable isotopes

    Get PDF
    In many places around the world, anthropogenic activities have resulted in nitrate (NO3−) pollution and changes in the metabolic state of aquatic ecosystems. Here we combined stable isotope and physico-chemical monitoring to assess the sources of NO3− and the overall metabolic state within the Red River delta, Vietnam. River water stable isotope compositions (δ18O-H2O) ranged between −11.2 and −2.7 ‰, δ18O-NO3− between −7.1 and + 29.7 ‰ and δ15N-NO3− between −3.9 and + 14.0 ‰. We identified the dominant NO3− sources as: 1) soil leachate, 2) domestic waste flushed from urban areas, and 3) NH4+ fertilizers washed from paddy fields. The relative impact of each source depends on geographical location within the delta and the time of year, due to dilution and concentration effects during wet and dry seasons. The primary NO3−source upstream is natural soil leachates, predominantly from tributaries connected to the Red River’s main stream. Within the middle-lower section of Red River delta, urban pollution from manure and septic waste reaches as high as 50 % of the total NO3− load during dry season. NO3− leached from fertilizers is also high at sites in the middle of the delta, related to agricultural activities. Dissolved oxygen isotope (δ18O-O2) values calculated from δ18O-H2O and δ18O-NO3− values indicate that the aquatic metabolism is net autotrophic (oxygen from primary production exceeds consumption by respiration), but high inputs of biodegradable organic matter from untreated domestic waste and high rates of sediment oxygen demand (SOD) and chemical oxygen demand (COD) have resulted in the whole river system becoming undersaturated in oxygen. High NO3− loads and low DO saturation are of critical concern and require mitigation practices to improve water quality for millions of people

    Robust interventions in network epidemiology

    Get PDF
    Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions. First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology. Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes. Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools

    AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

    Get PDF
    The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

    Don't waste your time measuring intelligence: Further evidence for the validity of a three-minute speeded reasoning test

    Get PDF
    The rise of large-scale collaborative panel studies has generated a need for fast, reliable, and valid assessments of cognitive abilities. In these studies, a detailed characterization of participants' cognitive abilities is often unnecessary, leading to the selection of tests based on convenience, duration, and feasibility. This often results in the use of abbreviated measures or proxies, potentially compromising their reliability and validity. Here we evaluate the mini-q (Baudson & Preckel, 2016), a three-minute speeded reasoning test, as a brief assessment of general cognitive abilities. The mini-q exhibited excellent reliability (0.96–0.99) and a substantial correlation with general cognitive abilities measured with a comprehensive test battery (r = 0.57; age-corrected r = 0.50), supporting its potential as a brief screening of cognitive abilities. Working memory capacity accounted for the majority (54%) of the association between test performance and general cognitive abilities, whereas individual differences in processing speed did not contribute to this relationship. Our results support the notion that the mini-q can be used as a brief, reliable, and valid assessment of general cognitive abilities. We therefore developed a computer-based version, ensuring its adaptability for large-scale panel studies. The paper- and computer-based versions demonstrated scalar measurement invariance and can therefore be used interchangeably. We provide norm data for young (18 to 30 years) and middle-aged (31 to 60 years) adults and provide recommendations for incorporating the mini-q in panel studies. Additionally, we address potential challenges stemming from language diversity, wide age ranges, and online testing in such studies
    • …
    corecore