Characterizing microplastic hazards: which concentration metrics and particle characteristics are most informative for understanding toxicity in aquatic organisms?

There is definitive evidence that microplastics, defined as plastic particles less than 5 mm in size, are ubiquitous in the environment and can cause harm to aquatic organisms. These findings have prompted legislators and environmental regulators to seek out strategies for managing risk. However, microplastics are also an incredibly diverse contaminant suite, comprising a complex mixture of physical and chemical characteristics (e.g., sizes, morphologies, polymer types, chemical additives, sorbed chemicals, and impurities), making it challenging to identify which particle characteristics might influence the associated hazards to aquatic life. In addition, there is a lack of consensus on how microplastic concentrations should be reported. This not only makes it difficult to compare concentrations across studies, but it also begs the question as to which concentration metric may be most informative for hazard characterization. Thus, an international panel of experts was convened to identify 1) which concentration metrics (e.g., mass or count per unit of volume or mass) are most informative for the development of health-based thresholds and risk assessment and 2) which microplastic characteristics best inform toxicological concerns. Based on existing knowledge, it is recommended that microplastic concentrations in toxicity tests are calculated from both mass and count at minimum, though ideally researchers should report additional metrics, such as volume and surface area, which may be more informative for specific toxicity mechanisms. Regarding particle characteristics, there is sufficient evidence to conclude that particle size is a critical determinant of toxicological outcomes, particularly for the mechanisms of food dilution and tissue translocation

Residual stress analysis and finite element modelling of repair-welded titanium sheets

An innovative FE modelling approach has been tested to investigate the effects of weld repair thin sheets of titanium alloy, taking into account pre-existing stress field in the components. In the case study analysed, the residual stress fields due to the original welds are introduced by means of a preliminary sequentially-coupled thermo-mechanical analysis and considered as pre-existing stress in the sheets for the subsequent weld simulation. Comparisons are presented between residual stress predictions and experimental measurements available from the literature with the aim of validating the numerical procedure. As a destructive sectioning technique was used in the reference experimental measurements, an investigation is also presented on the use of the element deactivation strategy when adopted to simulate material removal. Although the numerical tool is an approximate approach to simulate the actual material removal, the strategy appears to compute a physical strain relaxation and stress redistribution in the remaining part of the component. The weld repair modelling strategy and the element deactivation tool adopted to simulate the residual stress measurement technique are shown to predict residual stress trends which are very well correlated with experimental findings from the literature

Research recommendations to better understand the potential health impacts of microplastics to humans and aquatic ecosystems

To assess the potential risk of microplastic exposure to humans and aquatic ecosystems, reliable toxicity data is needed. This includes a more complete foundational understanding of microplastic toxicity and better characterization of the hazards they may present. To expand this understanding, an international group of experts was convened in 2020–2021 to identify critical thresholds at which microplastics found in drinking and ambient waters present a health risk to humans and aquatic organisms. However, their findings were limited by notable data gaps in the literature. Here, we identify those shortcomings and describe four categories of research recommendations needed to address them: 1) adequate particle characterization and selection for toxicity testing; 2) appropriate experimental study designs that allow for the derivation of dose-response curves; 3) establishment of adverse outcome pathways for microplastics; and 4) a clearer understanding of microplastic exposure, particularly for human health. By addressing these four data gaps, researchers will gain a better understanding of the key drivers of microplastic toxicity and the concentrations at which adverse effects may occur, allowing a better understanding of the potential risk that microplastics exposure might pose to human and aquatic ecosystems

The SNAPSHOT study protocol : SNAcking, Physical activity, Self-regulation, and Heart rate Over Time

Peer reviewedPublisher PD

Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain

Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain updates reward prediction through stimulus–reward experiences. It remains unknown, however, how sensory processing of reward-predicting stimuli contributes to the computation of reward prediction error. To elucidate this issue, we examined the relation between stimulus discriminability of the reward-predicting stimuli and the reward prediction error signal in the brain using functional magnetic resonance imaging (fMRI). Before main experiments, subjects learned an association between the orientation of a perceptually salient (high-contrast) Gabor patch and a juice reward. The subjects were then presented with lower-contrast Gabor patch stimuli to predict a reward. We calculated the correlation between fMRI signals and reward prediction error in two reinforcement learning models: a model including the modulation of reward prediction by stimulus discriminability and a model excluding this modulation. Results showed that fMRI signals in the midbrain are more highly correlated with reward prediction error in the model that includes stimulus discriminability than in the model that excludes stimulus discriminability. No regions showed higher correlation with the model that excludes stimulus discriminability. Moreover, results show that the difference in correlation between the two models was significant from the first session of the experiment, suggesting that the reward computation in the midbrain was modulated based on stimulus discriminability before learning a new contingency between perceptually ambiguous stimuli and a reward. These results suggest that the human reward system can incorporate the level of the stimulus discriminability flexibly into reward computations by modulating previously acquired reward values for a typical stimulus

Quantitative imaging of concentrated suspensions under flow

We review recent advances in imaging the flow of concentrated suspensions, focussing on the use of confocal microscopy to obtain time-resolved information on the single-particle level in these systems. After motivating the need for quantitative (confocal) imaging in suspension rheology, we briefly describe the particles, sample environments, microscopy tools and analysis algorithms needed to perform this kind of experiments. The second part of the review focusses on microscopic aspects of the flow of concentrated model hard-sphere-like suspensions, and the relation to non-linear rheological phenomena such as yielding, shear localization, wall slip and shear-induced ordering. Both Brownian and non-Brownian systems will be described. We show how quantitative imaging can improve our understanding of the connection between microscopic dynamics and bulk flow.Comment: Review on imaging hard-sphere suspensions, incl summary of methodology. Submitted for special volume 'High Solid Dispersions' ed. M. Cloitre, Vol. xx of 'Advances and Polymer Science' (Springer, Berlin, 2009); 22 pages, 16 fig

Recent ecological change in ancient lakes

Ancient lakes are among the best archivists of past environmental change, having experienced more than one full glacial cycle, a wide range of climatic conditions, tectonic events, and long association with human settlements. These lakes not only record long histories of environmental variation and human activity in their sediments, but also harbor very high levels of biodiversity and endemism. Yet, ancient lakes are faced with a familiar suite of anthropogenic threats, which may degrade the unusual properties that make them especially valuable to science and society. In all ancient lakes for which data exist, significant warming of surface waters has occurred, with a broad range of consequences. Eutrophication threatens both native species assemblages and regional economies reliant on clean surface water, fisheries, and tourism. Where sewage contributes nutrients and heavy metals, one can anticipate the occurrence of less understood emerging contaminants, such as pharmaceuticals, personal care products, and microplastics that negatively affect lake biota and water quality. Human populations continue to increase in most of the ancient lakes' watersheds, which will exacerbate these concerns. Further, human alterations of hydrology, including those produced through climate change, have altered lake levels. Co-occurring with these impacts have been intentional and unintentional species introductions, altering biodiversity. Given that the distinctive character of each ancient lake is strongly linked to age, there may be few options to remediate losses of species or other ecosystem damage associated with modern ecological change, heightening the imperative for understanding these systems

Functional MRI in Awake Unrestrained Dogs

Because of dogs' prolonged evolution with humans, many of the canine cognitive skills are thought to represent a selection of traits that make dogs particularly sensitive to human cues. But how does the dog mind actually work? To develop a methodology to answer this question, we trained two dogs to remain motionless for the duration required to collect quality fMRI images by using positive reinforcement without sedation or physical restraints. The task was designed to determine which brain circuits differentially respond to human hand signals denoting the presence or absence of a food reward. Head motion within trials was less than 1 mm. Consistent with prior reinforcement learning literature, we observed caudate activation in both dogs in response to the hand signal denoting reward versus no-reward