Showing papers by "Free University of Berlin published in 2019"

Mapping the world’s free-flowing rivers

Abstract: Free-flowing rivers (FFRs) support diverse, complex and dynamic ecosystems globally, providing important societal and economic services. Infrastructure development threatens the ecosystem processes, biodiversity and services that these rivers support. Here we assess the connectivity status of 12 million kilometres of rivers globally and identify those that remain free-flowing in their entire length. Only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length and 23 per cent flow uninterrupted to the ocean. Very long FFRs are largely restricted to remote regions of the Arctic and of the Amazon and Congo basins. In densely populated areas only few very long rivers remain free-flowing, such as the Irrawaddy and Salween. Dams and reservoirs and their up- and downstream propagation of fragmentation and flow regulation are the leading contributors to the loss of river connectivity. By applying a new method to quantify riverine connectivity and map FFRs, we provide a foundation for concerted global and national strategies to maintain or restore them. A comprehensive assessment of the world’s rivers and their connectivity shows that only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length.

Permafrost is warming at a global scale

Abstract: Permafrost warming has the potential to amplify global climate change, because when frozen sediments thaw it unlocks soil organic carbon. Yet to date, no globally consistent assessment of permafrost temperature change has been compiled. Here we use a global data set of permafrost temperature time series from the Global Terrestrial Network for Permafrost to evaluate temperature change across permafrost regions for the period since the International Polar Year (2007–2009). During the reference decade between 2007 and 2016, ground temperature near the depth of zero annual amplitude in the continuous permafrost zone increased by 0.39 ± 0.15 °C. Over the same period, discontinuous permafrost warmed by 0.20 ± 0.10 °C. Permafrost in mountains warmed by 0.19 ± 0.05 °C and in Antarctica by 0.37 ± 0.10 °C. Globally, permafrost temperature increased by 0.29 ± 0.12 °C. The observed trend follows the Arctic amplification of air temperature increase in the Northern Hemisphere. In the discontinuous zone, however, ground warming occurred due to increased snow thickness while air temperature remained statistically unchanged.

Microplastics Can Change Soil Properties and Affect Plant Performance

Abstract: Microplastics can affect biophysical properties of the soil. However, little is known about the cascade of events in fundamental levels of terrestrial ecosystems, i.e., starting with the changes in soil abiotic properties and propagating across the various components of soil-plant interactions, including soil microbial communities and plant traits. We investigated here the effects of six different microplastics (polyester fibers, polyamide beads, and four fragment types: polyethylene, polyester terephthalate, polypropylene, and polystyrene) on a broad suite of proxies for soil health and performance of spring onion ( Allium fistulosum). Significant changes were observed in plant biomass, tissue elemental composition, root traits, and soil microbial activities. These plant and soil responses to microplastic exposure were used to propose a causal model for the mechanism of the effects. Impacts were dependent on particle type, i.e., microplastics with a shape similar to other natural soil particles elicited smaller differences from control. Changes in soil structure and water dynamics may explain the observed results in which polyester fibers and polyamide beads triggered the most pronounced impacts on plant traits and function. The findings reported here imply that the pervasive microplastic contamination in soil may have consequences for plant performance and thus for agroecosystems and terrestrial biodiversity.

A complete catalogue of high-quality topological materials

Abstract: Using a recently developed formalism called topological quantum chemistry, we perform a high-throughput search of 'high-quality' materials (for which the atomic positions and structure have been measured very accurately) in the Inorganic Crystal Structure Database in order to identify new topological phases. We develop codes to compute all characters of all symmetries of 26,938 stoichiometric materials, and find 3,307 topological insulators, 4,078 topological semimetals and no fragile phases. For these 7,385 materials we provide the electronic band structure, including some electronic properties (bandgap and number of electrons), symmetry indicators, and other topological information. Our results show that more than 27 per cent of all materials in nature are topological. We provide an open-source code that checks the topology of any material and allows other researchers to reproduce our results.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Maximized electron interactions at the magic angle in twisted bilayer graphene

Abstract: The electronic properties of heterostructures of atomically thin van der Waals crystals can be modified substantially by moire superlattice potentials from an interlayer twist between crystals1,2. Moire tuning of the band structure has led to the recent discovery of superconductivity3,4 and correlated insulating phases5 in twisted bilayer graphene (TBG) near the ‘magic angle’ of twist of about 1.1 degrees, with a phase diagram reminiscent of high-transition-temperature superconductors. Here we directly map the atomic-scale structural and electronic properties of TBG near the magic angle using scanning tunnelling microscopy and spectroscopy. We observe two distinct van Hove singularities (VHSs) in the local density of states around the magic angle, with an energy separation of 57 millielectronvolts that drops to 40 millielectronvolts with high electron/hole doping. Unexpectedly, the VHS energy separation continues to decrease with decreasing twist angle, with a lowest value of 7 to 13 millielectronvolts at a magic angle of 0.79 degrees. More crucial to the correlated behaviour of this material, we find that at the magic angle, the ratio of the Coulomb interaction to the bandwidth of each individual VHS (U/t) is maximized, which is optimal for electronic Cooper pairing mechanisms. When doped near the half-moire-band filling, a correlation-induced gap splits the conduction VHS with a maximum size of 6.5 millielectronvolts at 1.15 degrees, dropping to 4 millielectronvolts at 0.79 degrees. We capture the doping-dependent and angle-dependent spectroscopy results using a Hartree–Fock model, which allows us to extract the on-site and nearest-neighbour Coulomb interactions. This analysis yields a U/t of order unity indicating that magic-angle TBG is moderately correlated. In addition, scanning tunnelling spectroscopy maps reveal an energy- and doping-dependent three-fold rotational-symmetry breaking of the local density of states in TBG, with the strongest symmetry breaking near the Fermi level and further enhanced when doped to the correlated gap regime. This indicates the presence of a strong electronic nematic susceptibility or even nematic order in TBG in regions of the phase diagram where superconductivity is observed. Scanning tunnelling spectroscopy is used to map the atomic-scale electronic structure of magic-angle twisted bilayer graphene, finding multiple signatures of electron correlations and thus providing insight into the sought-after mechanism behind superconductivity in graphene.

The German Socio-Economic Panel (SOEP)

Abstract: In his 2015 Economic Sciences Nobel lecture, Angus Deaton emphasized key issues for understanding welfare-enhancing policies (see Deaton 2015): First, differences in resources across individuals should be measured not only at specific points in time but also across the life course. Second, to better assess socio-economic outcomes, direct economic measures of well-being should be linked with other measures of well-being developed by other social science branches, such as sociology, demography, and psychology. Third, data observations should be reconciled with lifecycle models to investigate the causal mechanisms behind socio-economic outcomes. The main goal of the German Socio-Economic Panel (SOEP) is in line with the views and visions expressed in Angus Deaton’s lecture: Established in 1984 and located at the German Institute for Economic Research (DIW Berlin), SOEP serves a global research community by providing representative longitudinal data of private households in Germany. The SOEP’s primary research interests and the original survey concept was rooted in the multidisciplinary Collaborative Research Center SfB3, “Microanalytic Foundations of Social Policy” at the Universities of

FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP

Abstract: We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .

Electronic correlations in twisted bilayer graphene near the magic angle

Abstract: Twisted bilayer graphene with a twist angle of around 1.1° features a pair of isolated flat electronic bands and forms a platform for investigating strongly correlated electrons. Here, we use scanning tunnelling microscopy to probe the local properties of highly tunable twisted bilayer graphene devices and show that the flat bands deform when aligned with the Fermi level. When the bands are half-filled, we observe the development of gaps originating from correlated insulating states. Near charge neutrality, we find a previously unidentified correlated regime featuring an enhanced splitting of the flat bands. We describe this within a microscopic model that predicts a strong tendency towards nematic ordering. Our results provide insights into symmetry-breaking correlation effects and highlight the importance of electronic interactions for all filling fractions in twisted bilayer graphene.

All Magic Angles in Twisted Bilayer Graphene are Topological

Abstract: We show that the electronic structure of the low-energy bands in the small angle-twisted bilayer graphene consists of a series of semimetallic and topological phases. In particular, we are able to prove, using an approximate low-energy particle-hole symmetry, that the gapped set of bands that exist around all magic angles have a nontrivial topology stabilized by a magnetic symmetry, provided band gaps appear at fillings of +/- 4 electrons per moire unit cell. The topological index is given as the winding number (a Z number) of the Wilson loop in the moire Brillouin zone. Furthermore, we also claim that, when the gapped bands are allowed to couple with higher-energy bands, the Z index collapses to a stable Z(2) index. The approximate, emergent particle-hole symmetry is essential to the topology of graphene: When strongly broken, nontopological phases can appear. Our Letter underpins topology as the crucial ingredient to the description of low-energy graphene. We provide a four-band short-range tight-binding model whose two lower bands have the same topology, symmetry, and flatness as those of the twisted bilayer graphene and which can be used as an effective low-energy model. We then perform large-scale (11000 atoms per unit cell, 40 days per k-point computing time) ab initio calculations of a series of small angles, from 3 degrees to 1 degrees, which show a more complex and somewhat different evolution of the symmetry of the low-energy bands than that of the theoretical moire model but which confirm the topological nature of the system.

Microplastic effects on plants.

Abstract: Microplastic effects in terrestrial ecosystems have recently moved into focus, after about a decade of research being limited to aquatic systems. While effects on soil physical properties and soil biota are starting to become apparent, there is not much information on the consequences for plant performance. We here propose and discuss mechanistic pathways through which microplastics could impact plant growth, either positively or negatively. These effects will vary as a function of plant species, and plastic type, and thus are likely to translate to changes in plant community composition and perhaps primary production. Our mechanistic framework serves to guide ongoing and future research on this important topic.

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Abstract: Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot," vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed Boltzmann generators, which are shown to generate unbiased one-shot equilibrium samples of representative condensed-matter systems and proteins. Boltzmann generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates.

The role of multiple global change factors in driving soil functions and microbial biodiversity.

Abstract: Soils underpin terrestrial ecosystem functions, but they face numerous anthropogenic pressures. Despite their crucial ecological role, we know little about how soils react to more than two environmental factors at a time. Here, we show experimentally that increasing the number of simultaneous global change factors (up to 10) caused increasing directional changes in soil properties, soil processes, and microbial communities, though there was greater uncertainty in predicting the magnitude of change. Our study provides a blueprint for addressing multifactor change with an efficient, broadly applicable experimental design for studying the impacts of global environmental change.

Twisted Bilayer Graphene: A Phonon-Driven Superconductor.

Abstract: We study the electron-phonon coupling in twisted bilayer graphene (TBG), which was recently experimentally observed to exhibit superconductivity around the magic twist angle $\ensuremath{\theta}\ensuremath{\approx}1.05\ifmmode^\circ\else\textdegree\fi{}$. We show that phonon-mediated electron attraction at the magic angle is strong enough to induce a conventional intervalley pairing between graphene valleys $K$ and ${K}^{\ensuremath{'}}$ with a superconducting critical temperature ${T}_{c}\ensuremath{\sim}1\text{ }\text{ }\mathrm{K}$, in agreement with the experiment. We predict that superconductivity can also be observed in TBG at many other angles $\ensuremath{\theta}$ and higher electron densities in higher moir\'e bands, which may also explain the possible granular superconductivity of highly oriented pyrolytic graphite. We support our conclusions by ab initio calculations.

The job demands-resources model: A meta-analytic review of longitudinal studies

Abstract: The job demands-resources (JD-R) model is an influential framework to understand how job characteristics foster employee well-being. Differing from the cross-sectional focus of most JD-R model revi...

A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic

Abstract: Global deep‐time plate motion models have traditionally followed a classical rigid plate approach, even though plate deformation is known to be significant. Here we present a global Mesozoic–Cenozoic deforming plate motion model that captures the progressive extension of all continental margins since the initiation of rifting within Pangea at ~240 Ma. The model also includes major failed continental rifts and compressional deformation along collision zones. The outlines and timing of regional deformation episodes are reconstructed from a wealth of published regional tectonic models and associated geological and geophysical data. We reconstruct absolute plate motions in a mantle reference frame with a joint global inversion using hot spot tracks for the last 80 million years and minimizing global trench migration velocities and net lithospheric rotation. In our optimized model, net rotation is consistently below 0.2°/Myr, and trench migration scatter is substantially reduced. Distributed plate deformation reaches a Mesozoic peak of 30 × 10^6 km^2 in the Late Jurassic (~160–155 Ma), driven by a vast network of rift systems. After a mid‐Cretaceous drop in deformation, it reaches a high of 48 x 10^6 km^2 in the Late Eocene (~35 Ma), driven by the progressive growth of plate collisions and the formation of new rift systems. About a third of the continental crustal area has been deformed since 240 Ma, partitioned roughly into 65% extension and 35% compression. This community plate model provides a framework for building detailed regional deforming plate networks and form a constraint for models of basin evolution and the plate‐mantle system.

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.

Abstract: Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show tha...

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

Abstract: There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with the desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as a descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure–activity relationships in all analysed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances in all experiments. The continuity of the descriptor space and the existence of the decoder that permits deducing a chemical structure from an embedding vector allow for exploration of the space and open up new opportunities for compound optimization and idea generation.

Higher-order topology, monopole nodal lines, and the origin of large fermi arcs in transition metal dichalcogenides XTe2 (X = Mo,W)

Abstract: In recent years, transition metal dichalcogenides (TMDs) have garnered great interest as topological materials. In particular, monolayers of centrosymmetric β-phase TMDs have been identified as 2D topological insulators (TIs), and bulk crystals of noncentrosymmetric γ-phase MoTe_{2} and WTe_{2} have been identified as type-II Weyl semimetals. However, angle-resolved photoemission spectroscopy and STM probes of these semimetals have revealed huge, arclike surface states that overwhelm, and are sometimes mistaken for, the much smaller topological surface Fermi arcs of bulk type-II Weyl points. In this Letter, we calculate the bulk and surface electronic structure of both β- and γ-MoTe_{2}. We find that β-MoTe_{2} is, in fact, a Z_{4}-nontrivial higher-order TI (HOTI) driven by double band inversion and exhibits the same surface features as γ-MoTe_{2} and γ-WTe_{2}. We discover that these surface states are not topologically trivial, as previously characterized by the research that differentiated them from the Weyl Fermi arcs but, rather, are the characteristic split and gapped fourfold Dirac surface states of a HOTI. In β-MoTe_{2}, this indicates that it would exhibit helical pairs of hinge states if it were bulk insulating, and in γ-MoTe_{2} and γ-WTe_{2}, these surface states represent vestiges of HOTI phases without inversion symmetry that are nearby in parameter space. Using nested Wilson loops and first-principles calculations, we explicitly demonstrate that, when the Weyl points in γ-MoTe_{2} are annihilated, which may be accomplished by symmetry-preserving strain or lattice distortion, γ-MoTe_{2} becomes a nonsymmetry-indicated, noncentrosymmetric HOTI. We also show that, when the effects of spin-orbit coupling are neglected, β-MoTe_{2} is a nodal-line semimetal with Z_{2}-nontrivial monopole nodal lines (MNLSM). This finding confirms that MNLSMs driven by double band inversion are the weak-spin-orbit coupling limit of HOTIs, implying that MNLSMs are higher-order topological semimetals with flat-band-like hinge states, which we find to originate from the corner modes of 2D "fragile" TIs.

Cascade of Phase Transitions and Dirac Revivals in Magic Angle Graphene

Abstract: Twisted bilayer graphene near the magic angle exhibits remarkably rich electron correlation physics, displaying insulating, magnetic, and superconducting phases. Here, using measurements of the local electronic compressibility, we reveal that these phases originate from a high-energy state with an unusual sequence of band populations. As carriers are added to the system, rather than filling all the four spin and valley flavors equally, we find that the population occurs through a sequence of sharp phase transitions, which appear as strong asymmetric jumps of the electronic compressibility near integer fillings of the moire lattice. At each transition, a single spin/valley flavor takes all the carriers from its partially filled peers, "resetting" them back to the vicinity of the charge neutrality point. As a result, the Dirac-like character observed near the charge neutrality reappears after each integer filling. Measurement of the in-plane magnetic field dependence of the chemical potential near filling factor one reveals a large spontaneous magnetization, further substantiating this picture of a cascade of symmetry breakings. The sequence of phase transitions and Dirac revivals is observed at temperatures well above the onset of the superconducting and correlated insulating states. This indicates that the state we reveal here, with its strongly broken electronic flavor symmetry and revived Dirac-like electronic character, is a key player in the physics of magic angle graphene, forming the parent state out of which the more fragile superconducting and correlated insulating ground states emerge.

Promoting the Transformation of Li2S2 to Li2S: Significantly Increasing Utilization of Active Materials for High‐Sulfur‐Loading Li–S Batteries

Abstract: Lithium-sulfur (Li-S) batteries with high sulfur loading are urgently required in order to take advantage of their high theoretical energy density. Ether-based Li-S batteries involve sophisticated multistep solid-liquid-solid-solid electrochemical reaction mechanisms. Recently, studies on Li-S batteries have widely focused on the initial solid (sulfur)-liquid (soluble polysulfide)-solid (Li2 S2 ) conversion reactions, which contribute to the first 50% of the theoretical capacity of the Li-S batteries. Nonetheless, the sluggish kinetics of the solid-solid conversion from solid-state intermediate product Li2 S2 to the final discharge product Li2 S (corresponding to the last 50% of the theoretical capacity) leads to the premature end of discharge, resulting in low discharge capacity output and low sulfur utilization. To tackle the aforementioned issue, a catalyst of amorphous cobalt sulfide (CoS3 ) is proposed to decrease the dissociation energy of Li2 S2 and propel the electrochemical transformation of Li2 S2 to Li2 S. The CoS3 catalyst plays a critical role in improving the sulfur utilization, especially in high-loading sulfur cathodes (3-10 mg cm-2 ). Accordingly, the Li2 S/Li2 S2 ratio in the discharge products increased to 5.60/1 from 1/1.63 with CoS3 catalyst, resulting in a sulfur utilization increase of 20% (335 mAh g-1 ) compared to the counterpart sulfur electrode without CoS3 .

Recurrence is required to capture the representational dynamics of the human visual system

Abstract: The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.

Complex hazard cascade culminating in the Anak Krakatau sector collapse.

Abstract: Flank instability and sector collapses, which pose major threats, are common on volcanic islands. On 22 Dec 2018, a sector collapse event occurred at Anak Krakatau volcano in the Sunda Strait, triggering a deadly tsunami. Here we use multiparametric ground-based and space-borne data to show that prior to its collapse, the volcano exhibited an elevated state of activity, including precursory thermal anomalies, an increase in the island’s surface area, and a gradual seaward motion of its southwestern flank on a dipping decollement. Two minutes after a small earthquake, seismic signals characterize the collapse of the volcano’s flank at 13:55 UTC. This sector collapse decapitated the cone-shaped edifice and triggered a tsunami that caused 430 fatalities. We discuss the nature of the precursor processes underpinning the collapse that culminated in a complex hazard cascade with important implications for the early detection of potential flank instability at other volcanoes. On 22 December 2018, the western flank of Anak Krakatau collapsed into the sea of the Sunda Strait triggering a tsunami which killed approximately 430 people and displaced 33,000. Here, the authors show that Anak Krakatau exhibited an elevated state of activity several months prior to the collapse, including precursory thermal anomalies, an increase in the island’s surface area, and a gradual seaward motion of the southwestern flank.

Cytokinin action in response to abiotic and biotic stresses in plants.

Abstract: The phytohormone cytokinin was originally discovered as a regulator of cell division. Later, it was described to be involved in regulating numerous processes in plant growth and development including meristem activity, tissue patterning, and organ size. More recently, diverse functions for cytokinin in the response to abiotic and biotic stresses have been reported. Cytokinin is required for the defence against high light stress and to protect plants from a novel type of abiotic stress caused by an altered photoperiod. Additionally, cytokinin has a role in the response to temperature, drought, osmotic, salt, and nutrient stress. Similarly, the full response to certain plant pathogens and herbivores requires a functional cytokinin signalling pathway. Conversely, different types of stress impact cytokinin homeostasis. The diverse functions of cytokinin in responses to stress and crosstalk with other hormones are described. Its emerging roles as a priming agent and as a regulator of growth-defence trade-offs are discussed.

Global distribution of earthworm diversity

Abstract: Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.

Machine learning for molecular simulation

Abstract: Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

A Scientific Perspective on Microplastics in Nature and Society

Abstract: The best available evidence suggests that microplastics and nanoplastics do not pose a widespread risk to humans or the environment, except in small pockets. But that evidence is limited, and the situation could change if pollution continues at the current rate.