@article{12682, abstract = {Since the seminal studies by Osborne Reynolds in the nineteenth century, pipe flow has served as a primary prototype for investigating the transition to turbulence in wall-bounded flows. Despite the apparent simplicity of this flow, various facets of this problem have occupied researchers for more than a century. Here we review insights from three distinct perspectives: (a) stability and susceptibility of laminar flow, (b) phase transition and spatiotemporal dynamics, and (c) dynamical systems analysis of the Navier—Stokes equations. We show how these perspectives have led to a profound understanding of the onset of turbulence in pipe flow. Outstanding open points, applications to flows of complex fluids, and similarities with other wall-bounded flows are discussed.}, author = {Avila, Marc and Barkley, Dwight and Hof, Björn}, issn = {0066-4189}, journal = {Annual Review of Fluid Mechanics}, pages = {575--602}, publisher = {Annual Reviews}, title = {{Transition to turbulence in pipe flow}}, doi = {10.1146/annurev-fluid-120720-025957}, volume = {55}, year = {2023}, } @article{12708, abstract = {Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement is an action over a system that limits its units’ translational and rotational degrees of freedom, thus also influencing the system's phase space probability density; it can function as either a catalyst or inhibitor of self-organisation. Confinement can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework and perspective for future research, we examine the role of confinement in the self-organisation of soft-matter systems and identify overarching scientific challenges that need to be addressed to harness its full scientific and technological potential in soft matter and related fields. By drawing analogies with other disciplines, this framework will accelerate a common deeper understanding of self-organisation and trigger the development of innovative strategies to steer it using confinement, with impact on, e.g., the design of smarter materials, tissue engineering for biomedicine and in guiding active matter.}, author = {Araújo, Nuno A.M. and Janssen, Liesbeth M.C. and Barois, Thomas and Boffetta, Guido and Cohen, Itai and Corbetta, Alessandro and Dauchot, Olivier and Dijkstra, Marjolein and Durham, William M. and Dussutour, Audrey and Garnier, Simon and Gelderblom, Hanneke and Golestanian, Ramin and Isa, Lucio and Koenderink, Gijsje H. and Löwen, Hartmut and Metzler, Ralf and Polin, Marco and Royall, C. Patrick and Šarić, Anđela and Sengupta, Anupam and Sykes, Cécile and Trianni, Vito and Tuval, Idan and Vogel, Nicolas and Yeomans, Julia M. and Zuriguel, Iker and Marin, Alvaro and Volpe, Giorgio}, issn = {1744-6848}, journal = {Soft Matter}, pages = {1695--1704}, publisher = {Royal Society of Chemistry}, title = {{Steering self-organisation through confinement}}, doi = {10.1039/d2sm01562e}, volume = {19}, year = {2023}, } @article{12702, abstract = {Hydrocarbon mixtures are extremely abundant in the Universe, and diamond formation from them can play a crucial role in shaping the interior structure and evolution of planets. With first-principles accuracy, we first estimate the melting line of diamond, and then reveal the nature of chemical bonding in hydrocarbons at extreme conditions. We finally establish the pressure-temperature phase boundary where it is thermodynamically possible for diamond to form from hydrocarbon mixtures with different atomic fractions of carbon. Notably, here we show a depletion zone at pressures above 200 GPa and temperatures below 3000 K-3500 K where diamond formation is thermodynamically favorable regardless of the carbon atomic fraction, due to a phase separation mechanism. The cooler condition of the interior of Neptune compared to Uranus means that the former is much more likely to contain the depletion zone. Our findings can help explain the dichotomy of the two ice giants manifested by the low luminosity of Uranus, and lead to a better understanding of (exo-)planetary formation and evolution.}, author = {Cheng, Bingqing and Hamel, Sebastien and Bethkenhagen, Mandy}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Thermodynamics of diamond formation from hydrocarbon mixtures in planets}}, doi = {10.1038/s41467-023-36841-1}, volume = {14}, year = {2023}, } @article{12719, abstract = {Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.}, author = {Bernabeu, Elena and Mccartney, Daniel L. and Gadd, Danni A. and Hillary, Robert F. and Lu, Ake T. and Murphy, Lee and Wrobel, Nicola and Campbell, Archie and Harris, Sarah E. and Liewald, David and Hayward, Caroline and Sudlow, Cathie and Cox, Simon R. and Evans, Kathryn L. and Horvath, Steve and Mcintosh, Andrew M. and Robinson, Matthew Richard and Vallejos, Catalina A. and Marioni, Riccardo E.}, issn = {1756-994X}, journal = {Genome Medicine}, publisher = {Springer Nature}, title = {{Refining epigenetic prediction of chronological and biological age}}, doi = {10.1186/s13073-023-01161-y}, volume = {15}, year = {2023}, } @article{12704, abstract = {Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in conjunction with adversarial robot learning, are capable of making adversarial training suitable for real-world robot applications. We evaluate three different robot learning tasks ranging from autonomous driving in a high-fidelity environment amenable to sim-to-real deployment to mobile robot navigation and gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative impact on the nominal accuracy caused by adversarial training still outweighs the improved robustness by an order of magnitude. We conclude that although progress is happening, further advances in robust learning methods are necessary before they can benefit robot learning tasks in practice.}, author = {Lechner, Mathias and Amini, Alexander and Rus, Daniela and Henzinger, Thomas A}, issn = {2377-3766}, journal = {IEEE Robotics and Automation Letters}, number = {3}, pages = {1595--1602}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Revisiting the adversarial robustness-accuracy tradeoff in robot learning}}, doi = {10.1109/LRA.2023.3240930}, volume = {8}, year = {2023}, }