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This year's cover illustration shows an unexpected singular structure occurring in a flow seen in an ordinary household kitchen sink. When a vertical jet of liquid impinges on a flat surface it creates a ring discontinuity, the circular hydraulic jump, at a well-defined distance from the jet, where the depth of the fluid layer changes by an order of magnitude. By using a liquid more viscous than water, we have noticed [1] that the circular hydraulic jump may spontaneously deform to a polygonal structure (as shown in figure 1) breaking the axial symmetry. The corners can be very sharp and carry a large radial flux while the edges (sides) generate resistance on the stream. We describe this experiment in detail, and present a heuristic explanation of the rather aesthetically pleasing states. The explanation is admittedly not systematic, but illuminates the role of the internal eddy structure in the flow, and qualitatively agrees with the observed parameter dependence.The circular hydraulic jump is a familiar phenomenon, in which a flow experiences a strong surface deformation [2][3][4][5][6][7][8][9][10][11]. It is remarkable because it allows the observation of strongly nonlinear phenomena like shocks and wave breaking under laminar, stationary conditions. It is closely akin to flows occurring in channels, rivers, beaches and the atmosphere. Although these flows tend to have much larger spatial scales and thus be turbulent, the table-top circular jump geometry is expected to illuminate many important features of these flows.Following a classic idea by Lord Rayleigh [2], we have designed an experiment as shown schematically in figure 2(a). Close to the jet, the fluid layer is thin (height h in ) and the motion is rapid, whereas further away, the layer height has increased by an order of magnitude to h ext , and the fluid moves correspondingly more slowly. The transition between these two types of motion occurs within a surprisingly short distance: a millimeter or so. The horizontal surface is a disc of a large diameter (36 cm), made of glass so that the jump can be observed from below. The rim around the disc can be raised or lowered in order to control h ext . (Note that even when the rim height is zero, a jump still occurs. Raising the rim forces the jump to be stronger.) The fluid going over the rim is collected and recirculated; the flow rate Q from the jet is kept constant. The flow patterns are visualized using fine aluminium powders. It is a simple experiment, but particular care is needed to secure smoothness of the horizontal surface and the nozzle, which also needs to be long enough to avoid disturbances. We have described

A growing body of evidence suggests the involvement of connective tissue growth factor (CTGF) in the development and maintenance of fibrosis and excessive scarring. As the expression of this protein requires an intact actin cytoskeleton, disruption of the cytoskeleton represents an attractive strategy to decrease CTGF expression and, consequently, excessive scarring. The small heat-shock-related protein (HSP20), when phosphorylated by cyclic nucleotide signaling cascades, displaces phospho-cofilin from the 14-3-3 scaffolding protein leading to activation of cofilin as an actin-depolymerizing protein. In the present study, we evaluated the effect of AZX100, a phosphopeptide analogue of HSP20, on transforming growth factor-β-1 (TGF-β1)-induced CTGF and collagen expression in human keloid fibroblasts. We also examined the effect of AZX100 on scar formation in vivo in dermal wounds in a Siberian hamster model. AZX100 decreased the expression of CTGF and type I collagen induced by TGF-β1, endothelin, and lysophosphatidic acid. Treatment with AZX100 decreased stress fiber formation and altered the morphology of human dermal keloid fibroblasts. In vivo, AZX100 significantly improved collagen organization in a Siberian hamster scarring model. Taken together, these results suggest the potential use of AZX100 as a strategy to prevent excessive scarring and fibrotic disorders.

We present experimental evidence and theoretical arguments showing that the timeevolution of freely decaying 2-d turbulence is governed by a discrete time scale invariance rather than a continuous time scale invariance. Physically, this reflects that the timeevolution of the merging of vortices is not smooth but punctuated, leading to a prefered scale factor and as a consequence to log-periodic oscillations. From a thorough analysis of freely decaying 2-d turbulence experiments, we show that the number of vortices, their radius and separation display log-periodic oscillations as a function of time with an average log-frequency of ≈ 4 − 5 corresponding to a prefered scaling ratio of ≈ 1.2 − 1.3.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary center cohort, diagnostic phase IIb study ‘Consciousness in neurocritical care cohort study using EEG and fMRI’ (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC-patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks were assessed. Next, we used EEG and fMRI data at study enrollment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel), to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS), at time of study enrollment and at ICU-discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC-patients (mean age, 50.0 ± 18 years, 43% women), 51 (59%) were ≤ UWS and 36 (41%) were ≥ MCS at study enrollment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrollment and ICU-discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrollment and ICU-discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.