Royal Society Open Science最新文献

We report a family of propeller-shaped polynuclear metal complexes whose overall chirality is dictated by a single stereogenic centre within their component amino alcohol-ligand. These topologically intriguing complexes are readily prepared in enantiomerically pure form and are shown here to catalyse the conjugate addition of barbituric acids and their derivatives to nitroalkenes, with a catalyst loading of 1 mol%. Although only low levels of enantioinduction are observed, control experiments indicate that the enantioselectivity is dictated by the overall topology of the complex and not governed by binding to the tetrametallic entity, heralding a potentially new mode of catalysis.

Language acquisition is multifaceted, relying on cognitive and social abilities in addition to language-specific skills. We hypothesized that executive function (EF) may assist language development by enabling children to revise misinterpretations during online processing, encode language input more accurately and/or learn non-canonical sentence structures like the passive better over time. One hundred and twenty Dutch preschoolers each completed three sessions of testing (pre-test, exposure and post-test). During pre-test and post-test, we measured their comprehension of passive sentences and performance in three EF tasks. In the exposure session, we tracked children's eye movements as they listened to passive (and other) sentences. Each child was also assessed for short-term memory and receptive language. Multiple regression evaluated the relationship between EF and online processing and longer-term learning. EF predicted online revision accuracy, while controlling for receptive language, prior passive knowledge and short-term memory, consistent with theories linking EF to the revision of misinterpretations. EF was also associated with longer-term learning, but the results could not disentangle EF from receptive language. These findings broadly support a role for EF in language acquisition, including a specific role in revision during sentence processing and potentially other roles that depend on reciprocal interaction between EF and receptive language.

The western honeybee, Apis mellifera, harbours a simple and distinct microbiota that has been linked to various positive outcomes for the host. Among these cited benefits is improved weight gain for bees that have been inoculated with their native microbes. This result has been challenged by recent studies which investigated the impact of the gut microbiota on behavioural maturation and associated physiological changes and revealed no effect of the gut microbiota on weight gain. Therefore, we re-examined the role of the microbiota in weight gain by comparing microbiota-deprived bees with those inoculated with gut homogenate or defined communities composed of isolates representing the major bacterial taxa inhabiting the bee gut. We observed no differences in weight gain of adult bees or of their gut tissues across these groups. Further analysis based on nurse/forager cuticular hydrocarbon status and bacterial composition also revealed no significant changes. These results suggest the need for more nuanced investigations aimed at exploring factors such as the conditions in the hive of origin, including larval nutrition.

We present a theory of the inner layer of the cerebellar cortex, the granular layer, where the main excitatory input to the cerebellum is received. We ask how input signals are converted into an internal code and what form that has. While there is a computational element, and the ideas are quantified with a computer simulation, the approach is primarily evidence-led and aimed at experimenters rather than the computational community. Network models are often simplified to provide a noiseless medium for sophisticated computations. We propose, with evidence, the reverse: physiology is highly adapted to provide a noiseless medium for straightforward computations. We find that input data are converted to a hyper low-resolution internal code. Information is coded in the joint activity of large cell groups and therefore has minimum spatial dimensions-the dimensions of a code group. The conversion exploits statistical effects of random sampling. Code group dimensions are an effect of topography, cell morphologies and granular layer architecture. The activity of a code group is the smallest unit of information but not the smallest unit of code-the same information is coded in any random sample of signals. Code in this form is unexpectedly wasteful-there is a huge sacrifice of resolution-but may be a solution to fundamental problems involved in the biological representation of information.

A number of studies have linked autistic traits to difficulties in learning from social (versus non-social) stimuli. However, these stimuli are often difficult to match on low-level visual properties, which is especially important given the impact of autistic traits on sensory processing. Additionally, studies often fail to account for dissociable aspects of the learning process in the specification of model parameters (learning rates and reward sensitivity). Here, we investigate whether learning deficits in individuals with high autistic traits exhibit deficits when learning from facial point-light displays (PLDs) depicting emotional expressions. Social and non-social stimuli were created from random arrangements of the same number of point-lights and carefully matched on low-level visual properties. Neurotypical participants (N = 63) were assessed using the autism spectrum quotient (AQ) and completed a total of 96 trials in a reinforcement learning task. Although linear multi-level modelling did not indicate learning deficits, pre-registered computational modelling using a Rescorla-Wagner framework revealed that higher autistic traits were associated with reduced reward sensitivity in the win domain, demonstrating an attenuated response to received feedback during learning. These findings suggest that autistic traits can significantly impact learning from PLD feedback beyond a general deficit in learning rates.

Face mask wearing is a protective health behaviour that helps mitigate the spread of infectious diseases such as influenza and COVID-19. Understanding predictors of face mask wearing can help refine public health messaging and policy in future pandemics. Government mandates influence face mask wearing, but how mandates change predictors of face mask wearing has not been explored. We investigate how mandates changed predictors of face mask wearing and general protective behaviours within Australia during the COVID-19 pandemic using cross-sectional survey data. We compared four machine learning models to predict face mask wearing and general protective behaviours before and after mandates started in Australia; ensemble, tree-based models (XGBoost and random forests) performed best. Other than state, common predictors before and after mandates included age, survey week, average number of contacts, wellbeing, and perception of illness threat. Predictors that only appeared in the top ten before mandates included trust in government, and employment status; and after mandates were willingness to isolate. These distinct predictors are possible targets for future public health messaging at different stages of a new pandemic.

People lead increasingly sedentary lifestyles and spend extended periods sitting in slouched and head-forward positions, which can lead to health issues. People are so accustomed to seeing slouched posture that they may perceive it as normal and fail to notice their own slouched posture. We aim to investigate this possibility using the visual adaptation paradigm, which has provided insights into the perception of body size and shape in the context of exposure to thin bodies in the media. The experiment was conducted in three phases. First, participants established the posture they perceived as normal by manipulating body stimuli shown in profile view. In the second phase, the adaptation phase, participants viewed bodies with extremely upright or slouched postures before establishing their perceived normal posture again in the third phase. Perceived normal posture differed significantly before versus after adaptation, demonstrating a visual aftereffect. However, this only applied if test and adaptation bodies were presented in the same orientation, suggesting that our representation of posture is retina-centred rather than object-centred. This result reduces the likelihood that visual adaptation influences the increase in slouched posture in the population. These results contribute to understanding visual influences on people's perception of body posture.

The effects of iconicity and autistic traits on novel word learning were investigated through an online experiment involving 1481 healthy adult participants aged between 18 and 40 years from four countries: Brazil (N = 261), India (N = 416), Japan (N = 493) and the USA (N = 311). Participants completed a bouba-kiki-based word learning task, viewing novel images paired with either iconic names (congruent condition) or arbitrary names (incongruent condition). Word recognition was assessed using a three-alternative forced-choice procedure, and autistic traits were measured with the autism spectrum quotient (AQ). Results showed a significant benefit of iconicity across all countries, with better performance in the congruent condition. While a linear mixed model revealed no significant effect of AQ on bouba-kiki scores overall, a country-specific analysis found a weak but significant positive correlation between AQ scores and bouba-kiki performance in Japanese participants. This country-specific finding should be interpreted cautiously and warrants further exploration. Overall, the findings demonstrate the robustness and universality of the bouba-kiki effect on word learning across both Western and Eastern cultures. However, the relationship between autistic traits and iconicity was not consistent across all countries and may depend on cultural factors. Further research is needed to explore this in more detail.

We evaluated what guidance exists in the literature to improve the transparency of studies that make secondary use of health data. To find peer-reviewed papers, we searched PubMed and Google Scholar. To find institutional documents, we used our personal expertise to draft a list of health organizations and searched their websites. We quantitatively and qualitatively coded different types of research transparency: registration, methods reporting, results reporting, data sharing and code sharing. We found 56 documents that provide recommendations to improve the transparency of studies making secondary use of health data, mainly in relation to study registration (n = 27) and/or methods reporting (n = 39). Only three documents made recommendations on data sharing or code sharing. Recommendations for study registration and methods reporting mainly came in the form of structured documents like registration templates and reporting guidelines. Aside from the recommendations aimed directly at researchers, we also found recommendations aimed at the wider research community, typically on how to improve research infrastructure. Limitations or challenges of improving transparency were rarely mentioned, highlighting the need for more nuance in providing transparency guidance for studies that make secondary use of health data.

Recent high-throughput experiments unveil substantial electrophysiological diversity among uncoupled healthy myocytes under identical conditions. To quantify inter-cell variability, the values of a subset of the parameters in a well-regarded mathematical model of the action potential of rabbit ventricular myocytes are estimated from fluorescence voltage measurements of a large number of cells. Statistical inference yields a population of nearly 1200 cell-specific model variants that, on a population-level replicate experimentally measured biomarker ranges and distributions, and in contrast to earlier studies, also match experimental biomarker values on a cell-by-cell basis. This model population may be regarded as a random sample from the phenotype of healthy rabbit ventricular myocytes. Univariate and bivariate joint marginal distributions of the estimated parameters are presented, and the parameter dependencies of several commonly used electrophysiological biomarkers are determined. Parameter values are weakly correlated, while summary metrics such as the action potential duration are not strongly dependent on any single electrophysiological characteristic of the myocyte. Our results demonstrate the feasibility of accurately and efficiently fitting entire action potential waveforms at scale.