ETH Zurich

About: ETH Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Computer science. The organization has 48393 authors who have published 122408 publications receiving 5111383 citations. The organization is also known as: Swiss Federal Institute of Technology in Zurich & Eidgenössische Technische Hochschule Zürich.

Alien species in a warmer world: risks and opportunities

Abstract: Climate change and biological invasions are key processes affecting global biodiversity, yet their effects have usually been considered separately. Here, we emphasise that global warming has enabled alien species to expand into regions in which they previously could not survive and reproduce. Based on a review of climate-mediated biological invasions of plants, invertebrates, fishes and birds, we discuss the ways in which climate change influences biological invasions. We emphasise the role of alien species in a more dynamic context of shifting species' ranges and changing communities. Under these circumstances, management practices regarding the occurrence of 'new' species could range from complete eradication to tolerance and even consideration of the 'new' species as an enrichment of local biodiversity and key elements to maintain ecosystem services.

Trace element signature of subduction-zone fluids, melts and supercritical liquids at 120–180 km depth

Abstract: Fluids and melts liberated from subducting oceanic crust recycle lithophile elements back into the mantle wedge, facilitate melting and ultimately lead to prolific subduction-zone arc volcanism1,2 The nature and composition of the mobile phases generated in the subducting slab at high pressures have, however, remained largely unknown3,4,5,6,7 Here we report direct LA-ICPMS measurements of the composition of fluids and melts equilibrated with a basaltic eclogite at pressures equivalent to depths in the Earth of 120–180 km and temperatures of 700–1,200 °C The resultant liquid/mineral partition coefficients constrain the recycling rates of key elements The dichotomy of dehydration versus melting at 120 km depth is expressed through contrasting behaviour of many trace elements (U/Th, Sr, Ba, Be and the light rare-earth elements) At pressures equivalent to 180 km depth, however, a supercritical liquid with melt-like solubilities for the investigated trace elements is observed, even at low temperatures This mobilizes most of the key trace elements (except the heavy rare-earth elements, Y and Sc) and thus limits fluid-phase transfer of geochemical signatures in subduction zones to pressures less than 6 GPa

Egalitarianism in young children

Abstract: Human social interaction is strongly shaped by other-regarding preferences, that is, a concern for the welfare of others. These preferences are important for a unique aspect of human sociality-large scale cooperation with genetic strangers-but little is known about their developmental roots. Here we show that young children's other-regarding preferences assume a particular form, inequality aversion that develops strongly between the ages of 3 and 8. At age 3-4, the overwhelming majority of children behave selfishly, whereas most children at age 7-8 prefer resource allocations that remove advantageous or disadvantageous inequality. Moreover, inequality aversion is strongly shaped by parochialism, a preference for favouring the members of one's own social group. These results indicate that human egalitarianism and parochialism have deep developmental roots, and the simultaneous emergence of altruistic sharing and parochialism during childhood is intriguing in view of recent evolutionary theories which predict that the same evolutionary process jointly drives both human altruism and parochialism.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Machine Learning for Fluid Mechanics

Abstract: The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.