Modeling deep ocean convection: Large eddy simulation in comparison with laboratory experiments

A large-eddy simulation model (LES) has been applied to study deep convective processes in a stratified ocean driven by the energetic cooling at the ocean surface. Closely related to a recent laboratory experiment, the numerical experiment deals with the inverted problem of the growth of a convective mixed layer driven by a localized source of bottom heating in a rotating, stably stratified fluid. In general, good agreement is found between numerical and laboratory results. After onset of the heating a well-mixed layer forms above the heated circular surface. Although small-scale turbulence quantities like rms velocities and length scale can be best described by the nonrotating turbulent velocity and length scales, they are also found to differ significantly from a nonrotating control run, which indicates that rotation affects but does not control the turbulence. Due to the horizontal radial temperature gradient between the mixed layer and the ambient fluid a rim current develops around the periphery of the heated surface. Its near-surface maximum can be well described by a simple thermal wind law. The strong counterrotating current also observed in the laboratory at greater heights above the surface is found to be mainly driven by surface friction and should not be observed in the ocean. As time progresses, the rim current becomes unstable, eventually generating a field of baroclinic eddies that stop the mixed layer growth by causing some horizontal exchange between the convective layer and its cooler surrounding. The wavelength of the instabilities slowly increases with time and is clearly related to the local Rossby radius

Beyond convergence rates: Exact recovery with Tikhonov regularization with sparsity constraints

The Tikhonov regularization of linear ill-posed problems with an $\ell^1$ penalty is considered. We recall results for linear convergence rates and results on exact recovery of the support. Moreover, we derive conditions for exact support recovery which are especially applicable in the case of ill-posed problems, where other conditions, e.g. based on the so-called coherence or the restricted isometry property are usually not applicable. The obtained results also show that the regularized solutions do not only converge in the $\ell^1$-norm but also in the vector space $\ell^0$ (when considered as the strict inductive limit of the spaces $\R^n$ as $n$ tends to infinity). Additionally, the relations between different conditions for exact support recovery and linear convergence rates are investigated. With an imaging example from digital holography the applicability of the obtained results is illustrated, i.e. that one may check a priori if the experimental setup guarantees exact recovery with Tikhonov regularization with sparsity constraints

Deep neural network or dermatologist?

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification

Exploration of Long-Chain Vitamin E Metabolites for the Discovery of a Highly Potent, Orally Effective, and Metabolically Stable 5-LOX Inhibitor that Limits Inflammation.

Endogenous long-chain metabolites of vitamin E (LCMs) mediate immune functions by targeting 5-lipoxygenase (5-LOX) and increasing the systemic concentrations of resolvin E3, a specialized proresolving lipid mediator. SAR studies on semisynthesized analogues highlight α-amplexichromanol (27a), which allosterically inhibits 5-LOX, being considerably more potent than endogenous LCMs in human primary immune cells and blood. Other enzymes within lipid mediator biosynthesis were not substantially inhibited, except for microsomal prostaglandin E2 synthase-1. Compound 27a is metabolized by sulfation and β-oxidation in human liver-on-chips and exhibits superior metabolic stability in mice over LCMs. Pharmacokinetic studies show distribution of 27a from plasma to the inflamed peritoneal cavity and lung. In parallel, 5-LOX-derived leukotriene levels decrease, and the inflammatory reaction is suppressed in reconstructed human epidermis, murine peritonitis, and experimental asthma in mice. Our study highlights 27a as an orally active, LCM-inspired drug candidate that limits inflammation with superior potency and metabolic stability to the endogenous lead

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Largeeddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue