Informing the Exposure Landscape: The Fate of Microplastics in a Large Pelagic In-Lake Mesocosm Experiment.

Understanding microplastic exposure and effects is critical to understanding risk. Here, we used large, in-lake closed-bottom mesocosms to investigate exposure and effects on pelagic freshwater ecosystems. This article provides details about the experimental design and results on the transport of microplastics and exposure to pelagic organisms. Our experiment included three polymers of microplastics (PE, PS, and PET) ranging in density and size. Nominal concentrations ranged from 0 to 29,240 microplastics per liter on a log scale. Mesocosms enclosed natural microbial, phytoplankton, and zooplankton communities and yellow perch (Perca flavescens). We quantified and characterized microplastics in the water column and in components of the food web (biofilm on the walls, zooplankton, and fish). The microplastics in the water stratified vertically according to size and density. After 10 weeks, about 1% of the microplastics added were in the water column, 0.4% attached to biofilm on the walls, 0.01% within zooplankton, and 0.0001% in fish. Visual observations suggest the remaining >98% were in a surface slick and on the bottom. Our study suggests organisms that feed at the surface and in the benthos are likely most at risk, and demonstrates the value of measuring exposure and transport to inform experimental designs and achieve target concentrations in different matrices within toxicity tests.

A Multicompartment Assessment of Microplastic Contamination in Semi-remote Boreal Lakes.

Microplastic contamination is ubiquitous across the globe, even in remote locations. Still, the sources and pathways of microplastics to such locations are largely unknown. To investigate microplastic contamination in a semi-remote location, we measured microplastic concentrations in nine oligotrophic lakes within and around the International Institute for Sustainable Development-Experimental Lakes Area in northwestern Ontario, Canada. Our first objective was to establish ambient concentrations of microplastics in bottom sediments, surface water, and atmospheric deposition in semi-remote boreal lakes. Across all lakes, mean shallow and deep sediment microplastic concentrations, near-surface water microplastic concentrations from in situ filtering, and dry atmospheric microplastic deposition rates were 551 ± 354 particles kg-1, 177 ± 103 particles kg-1, 0.2 ± 0.3 particles L-1, and 0.4 ± 0.2 particles m-2 day-1, respectively. Our second objective was to investigate whether microplastic contamination of these lakes is driven by point sources including local runoff and direct anthropogenic inputs or nonpoint sources such as atmospheric deposition. Lakes were selected based on three levels of anthropogenic activity-low, medium, and high-though activity levels were minimal across all study lakes compared with highly populated areas. Whereas a positive correlation would indicate that point sources were a likely pathway, we observed no relationship between the level of anthropogenic activity and microplastic contamination of surface water. Moreover, the composition of microplastics in surface water and atmospheric deposition were similar, comprising mostly polyester and acrylic fibers. Together, these results suggest that atmospheric deposition may be the main pathway of microplastics to these remote boreal lakes. Environ Toxicol Chem 2024;43:999-1011. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A City-Wide Emissions Inventory of Plastic Pollution.

A global agreement on plastic should have quantitative reduction targets for the emissions of plastic pollution and regular measurements to track success. Here, we present a framework for measuring plastic emissions, akin to greenhouse gas emissions, and demonstrate its utility by calculating a baseline measurement for the City of Toronto in Ontario, Canada. We identify relevant sources of plastic pollution in the city, calculate emissions for each source by multiplying activity data by emission factors for each source, and sum the emissions to obtain the total annual emissions of plastic pollution generated. Using Monte Carlo simulations, we estimate that 3,531 to 3,852 tonnes (T) of plastic pollution were emitted from Toronto in 2020. Littering is the largest source overall (3,099 T), and artificial turf is the largest source of microplastic (237 T). Quantifying source emissions can inform the most effective mitigation strategies to achieve reduction targets. We recommend this framework be scaled up and replicated in cities, states, provinces, and countries around the world to inform global reduction targets and measure progress toward reducing plastic pollution.

Reconstructing Cardiac Electrical Excitations from Optical Mapping Recordings.

The reconstruction of electrical excitation patterns through the unobserved depth of the tissue is essential to realizing the potential of computational models in cardiac medicine. We have utilized experimental optical-mapping recordings of cardiac electrical excitation on the epicardial and endocardial surfaces of a canine ventricle as observations directing a local ensemble transform Kalman Filter (LETKF) data assimilation scheme. We demonstrate that the inclusion of explicit information about the stimulation protocol can marginally improve the confidence of the ensemble reconstruction and the reliability of the assimilation over time. Likewise, we consider the efficacy of stochastic modeling additions to the assimilation scheme in the context of experimentally derived observation sets. Approximation error is addressed at both the observation and modeling stages, through the uncertainty of observations and the specification of the model used in the assimilation ensemble. We find that perturbative modifications to the observations have marginal to deleterious effects on the accuracy and robustness of the state reconstruction. Further, we find that incorporating additional information from the observations into the model itself (in the case of stimulus and stochastic currents) has a marginal improvement on the reconstruction accuracy over a fully autonomous model, while complicating the model itself and thus introducing potential for new types of model error. That the inclusion of explicit modeling information has negligible to negative effects on the reconstruction implies the need for new avenues for optimization of data assimilation schemes applied to cardiac electrical excitation.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-ß (TGFß) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFß dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFß dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Patient Characteristics in Oral Cancer Staging.

Background: Oral cancer is a largely preventable malignancy with many modifiable risk factors, such as tobacco use and proper oral hygiene. Early detection of oral cancer is an important goal for oral healthcare providers, as survival rates for oral cancers diagnosed at an advanced stage are less than half the rates for cancers diagnosed in early stages. As many patients are asymptomatic in early stages, it is crucial for oral healthcare providers to have a high index of suspicion while treating patients at risk for late diagnosis. Objectives: To identify characteristics associated with early vs. late stage diagnosis of oral cancer. Methods: We performed a retrospective chart review using the TriNetX database. We identified two cohorts of interest: patients with an initial diagnosis of stage 1 oral cancer, and patients with an initial diagnosis of stage 3 or 4 oral cancer. Statistical comparison of cohort characteristics was completed through the TriNetX statistical software platform. Results: We identified 386 patients diagnosed at stage 1 and 869 patients diagnosed at stage 3 or 4. We identified several characteristics not previously reported in the literature. Race, BMI between 20 and 29, malnurition, anemia were all associated with late stage diagnosis. Certain medications were also associated with late stage diagnosis, such as heparin derivatives and diclofenac. Our findings also reinforced prior research for characteristics such as nicotine use and ethnicity. Conclusion: Our findings offer new characteristics that may aid oral healthcare providers in detecting oral cancer at an early stage. Increasing provider awareness of factors that they may not have considered previously could increase the rates of early stage cancer detection, improving overall patient mortality and curative outcomes.

Btg2 mutation induces renal injury and impairs blood pressure control in female rats.

Hypertension (HTN) is a complex disease influenced by heritable genetic elements and environmental interactions. Dietary salt is among the most influential modifiable factors contributing to increased blood pressure (BP). It is well established that men and women develop BP impairment in different patterns and a recent emphasis has been placed on identifying mechanisms leading to the differences observed between the sexes in HTN development. The current work reported here builds on an extensive genetic mapping experiment that sought to identify genetic determinants of salt-sensitive (SS) HTN using the Dahl SS rat. BTG antiproliferation factor 2 (Btg2) was previously identified by our group as a candidate gene contributing to SS HTN in female rats. In the current study, Btg2 was mutated using transcription activator-like effector nuclease (TALEN)-targeted gene disruption on the SSBN congenic rat background. The Btg2 mutated rats exhibited impaired BP and proteinuria responses to a high-salt diet compared with wild-type rats. Differences in body weight, mutant pup viability, skeletal morphology, and adult nephron density suggest a potential role for Btg2 in developmental signaling pathways. Subsequent cell cycle gene expression assessment provides several additional signaling pathways that Btg2 may function through during salt handling in the kidney. The expression analysis also identified several potential upstream targets that can be explored to further isolate therapeutic approaches for SS HTN.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses.

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

Incorporating terrain specific beaching within a lagrangian transport plastics model for Lake Erie.

Mass estimates of plastic pollution in the Great Lakes based on surface samples differ by orders of magnitude from what is predicted by production and input rates. It has been theorized that a potential location of this missing plastic is on beaches and in nearshore water. We incorporate a terrain dependent beaching model to an existing hydrodynamic model for Lake Erie which includes three dimensional advection, turbulent mixing, density driven sinking, and deposition into the sediment. When examining parameter choices, in all simulations the majority of plastic in the lake is beached, potentially identifying a reservoir holding a large percentage of the lake's plastic which in previous studies has not been taken into account. The absolute amount of beached plastic is dependent on the parameter choices. We also find beached plastic does not accumulate homogeneously through the lake, with eastern regions of the lake, especially those downstream of population centers, most likely to be impacted. This effort constitutes a step towards identifying sinks of missing plastic in large bodies of water.

Projected land ice contributions to twenty-first-century sea level rise.

The land ice contribution to global mean sea level rise has not yet been predicted1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models2-8, but primarily used previous-generation scenarios9 and climate models10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Robust data assimilation with noise: Applications to cardiac dynamics.

Reconstructions of excitation patterns in cardiac tissue must contend with uncertainties due to model error, observation error, and hidden state variables. The accuracy of these state reconstructions may be improved by efforts to account for each of these sources of uncertainty, in particular, through the incorporation of uncertainty in model specification and model dynamics. To this end, we introduce stochastic modeling methods in the context of ensemble-based data assimilation and state reconstruction for cardiac dynamics in one- and three-dimensional cardiac systems. We propose two classes of methods, one following the canonical stochastic differential equation formalism, and another perturbing the ensemble evolution in the parameter space of the model, which are further characterized according to the details of the models used in the ensemble. The stochastic methods are applied to a simple model of cardiac dynamics with fast-slow time-scale separation, which permits tuning the form of effective stochastic assimilation schemes based on a similar separation of dynamical time scales. We find that the selection of slow or fast time scales in the formulation of stochastic forcing terms can be understood analogously to existing ensemble inflation techniques for accounting for finite-size effects in ensemble Kalman filter methods; however, like existing inflation methods, care must be taken in choosing relevant parameters to avoid over-driving the data assimilation process. In particular, we find that a combination of stochastic processes-analogously to the combination of additive and multiplicative inflation methods-yields improvements to the assimilation error and ensemble spread over these classical methods.

Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics.

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'.

Modeling the three-dimensional transport and distribution of multiple microplastic polymer types in Lake Erie.

We model three-dimensional motion of plastic pollution in Lake Erie due to advection, density-driven sinking, and turbulent mixing using a Lagrangian transport model to explore the distribution of plastic in the water column and sediment. Nine polymer types that make up over 75% of predicted worldwide plastic waste were modeled, and the model keeps track of particles that hit the bottom to represent deposition. Modeled spatial distributions are compared with samples from the surface to calibrate the model and derive estimates for the mass of plastic in the lake volume and the flux of plastic into the sediment. The mass estimate of 381 ± 204 metric tons is two orders of magnitude larger than previous surface estimates, though still a fraction of predicted yearly input. The results are a step towards closing the plastic mass balance in Lake Erie as well as understanding the transport of plastic into the sediment.

Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition.

Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-ß (TGFß) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFß-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept "synthetic" in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFß doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating "populations" of model parameters. We performed synthetic experiments for a wide range of TGFß doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform.

Formed in late 1999, the Rat Genome Database (RGD, https://rgd.mcw.edu) will be 20 in 2020, the Year of the Rat. Because the laboratory rat, Rattus norvegicus, has been used as a model for complex human diseases such as cardiovascular disease, diabetes, cancer, neurological disorders and arthritis, among others, for >150 years, RGD has always been disease-focused and committed to providing data and tools for researchers doing comparative genomics and translational studies. At its inception, before the sequencing of the rat genome, RGD started with only a few data types localized on genetic and radiation hybrid (RH) maps and offered only a few tools for querying and consolidating that data. Since that time, RGD has expanded to include a wealth of structured and standardized genetic, genomic, phenotypic, and disease-related data for eight species, and a suite of innovative tools for querying, analyzing and visualizing this data. This article provides an overview of recent substantial additions and improvements to RGD's data and tools that can assist researchers in finding and utilizing the data they need, whether their goal is to develop new precision models of disease or to more fully explore emerging details within a system or across multiple systems.

Rat Genome Databases, Repositories, and Tools.

Resources for rat researchers are extensive, including strain repositories and databases all around the world. The Rat Genome Database (RGD) serves as the primary rat data repository, providing both manual and computationally collected data from other databases.

Integrated curation and data mining for disease and phenotype models at the Rat Genome Database.

Rats have been used as research models in biomedical research for over 150 years. These disease models arise from naturally occurring mutations, selective breeding and, more recently, genome manipulation. Through the innovation of genome-editing technologies, genome-modified rats provide precision models of disease by disrupting or complementing targeted genes. To facilitate the use of these data produced from rat disease models, the Rat Genome Database (RGD) organizes rat strains and annotates these strains with disease and qualitative phenotype terms as well as quantitative phenotype measurements. From the curated quantitative data, the expected phenotype profile ranges were established through a meta-analysis pipeline using inbred rat strains in control conditions. The disease and qualitative phenotype annotations are propagated to their associated genes and alleles if applicable. Currently, RGD has curated nearly 1300 rat strains with disease/phenotype annotations and about 11% of them have known allele associations. All of the annotations (disease and phenotype) are integrated and displayed on the strain, gene and allele report pages. Finding disease and phenotype models at RGD can be done by searching for terms in the ontology browser, browsing the disease or phenotype ontology branches or entering keywords in the general search. Use cases are provided to show different targeted searches of rat strains at RGD.

A Primer for the Rat Genome Database (RGD).

The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and disease. The Rat Genome Database (RGD, http://rgd.mcw.edu ) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components for genes, quantitative trait loci, and strains. We present an overview of the database followed by specific examples that can be used to gain experience in employing RGD to explore the wealth of functional data available for the rat.

A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes.

The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200-400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.