Emergent dynamic chirality in a thermally driven artificial spin ratchet

Modern nanofabrication techniques have opened the possibility to create novel functional materials, whose properties transcend those of their constituent elements. In particular, tuning the magnetostatic interactions in geometrically frustrated arrangements of nanoelements called artificial spin ice1, 2 can lead to specific collective behaviour3, including emergent magnetic monopoles4, 5, charge screening6, 7 and transport8, 9, as well as magnonic response10, 11, 12. Here, we demonstrate a spin-ice-based active material in which energy is converted into unidirectional dynamics. Using X-ray photoemission electron microscopy we show that the collective rotation of the average magnetization proceeds in a unique sense during thermal relaxation. Our simulations demonstrate that this emergent chiral behaviour is driven by the topology of the magnetostatic field at the edges of the nanomagnet array, resulting in an asymmetric energy landscape. In addition, a bias field can be used to modify the sense of rotation of the average magnetization. This opens the possibility of implementing a magnetic Brownian ratchet13, 14, which may find applications in novel nanoscale devices, such as magnetic nanomotors, actuators, sensors or memory cells

Indigenous crop diversity maintained despite the introduction of major global crops in an African centre of agrobiodiversity

Societal Impact Statement The global success and expansion of a small pool of major crops, including rice, wheat and maize, risks homogenising global agriculture. Focusing on the agriculturally diverse Ethiopian Highlands, this study tested whether farm diversity tends to be lower among farmers who grow more introduced crops. Surprisingly, it was found that farmers have successfully integrated introduced crops, resulting in more diverse and heterogenous farms without negatively impacting indigenous crop diversity. This is encouraging because diverse farms, comprising indigenous agricultural systems supplemented by introduced crops, may help address global challenges such as food insecurity. Summary The global expansion of a handful of major crops risks eroding indigenous crop diversity and homogenising agroecosystems, with significant consequences for sustainable and resilient food systems. Here, we investigate the farm-scale impact of introduced crops on indigenous agroecosystems. We surveyed 1369 subsistence farms stratified across climate gradients in the Ethiopian Highlands, a hotspot of agrobiodiversity, to characterise the richness and cultivated area of the 83 edible crops they contained. We further categorise these crops as being indigenous to Ethiopia, or introduced across three different eras. We apply non-metric multidimensional scaling and mixed effects modelling to characterise agroecosystem composition across farms with different proportions of introduced crops. Crops from different periods do not differ significantly in frequency or abundance across farms. Among geographically matched pairs of farms, those with higher proportions of modern introduced crops had significantly higher overall crop richness. Furthermore, farms with a high proportion of modern introduced crops showed higher heterogeneity in crop composition. An analysis of socio-economic drivers indicated that poverty is negatively associated with the cultivated area of introduced crops. In our Ethiopian case study, global patterns of major crop expansion are not necessarily associated with agrobiodiversity loss at the farm scale or higher homogeneity across indigenous agricultural systems. Importantly, socioeconomic factors may influence farmers' propensity to adopt novel species, suggesting targets for agricultural extension policies. Given the rapid climatic, economic and demographic changes impacting global food systems and the threats to food security these entail, robust indigenous agricultural systems enriched with diverse introduced crops may help maintain resilience

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Regular testing of asymptomatic healthcare workers identifies cost-efficient SARS-CoV-2 preventive measures.

Protecting healthcare professionals is crucial in maintaining a functioning healthcare system. The risk of infection and optimal preventive strategies for healthcare workers during the COVID-19 pandemic remain poorly understood. Here we report the results of a cohort study that included pre- and asymptomatic healthcare workers. A weekly testing regime has been performed in this cohort since the beginning of the COVID-19 pandemic to identify infected healthcare workers. Based on these observations we have developed a mathematical model of SARS-CoV-2 transmission that integrates the sources of infection from inside and outside the hospital. The data were used to study how regular testing and a desynchronisation protocol are effective in preventing transmission of COVID-19 infection at work, and compared both strategies in terms of workforce availability and cost-effectiveness. We showed that case incidence among healthcare workers is higher than would be explained solely by community infection. Furthermore, while testing and desynchronisation protocols are both effective in preventing nosocomial transmission, regular testing maintains work productivity with implementation costs

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences