Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.

Motivation: A wealth of protein–protein interaction (PPI) data has recently become available. These data are organized as PPI networks and an efficient and biologically meaningful method to compare such PPI networks is needed. As a first step, we would like to compare observed networks to established network models, under the aspect of small subgraph counts, as these are conjectured to relate to functional modules in the PPI network. We employ the software tool GraphCrunch with the Graphlet Degree Distribution Agreement (GDDA) score to examine the use of such counts for network comparison.Results: Our results show that the GDDA score has a pronounced dependency on the number of edges and vertices of the networks being considered. This should be taken into account when testing the fit of models. We provide a method for assessing the statistical significance of the fit between random graph models and biological networks based on non-parametric tests. Using this method we examine the fit of Erdös–Rényi (ER), ER with fixed degree distribution and geometric (3D) models to PPI networks. Under these rigorous tests none of these models fit to the PPI networks. The GDDA score is not stable in the region of graph density relevant to current PPI networks. We hypothesize that this score instability is due to the networks under consideration having a graph density in the threshold region for the appearance of small subgraphs. This is true for both geometric (3D) and ER random graph models. Such threshold behaviour may be linked to the robustness and efficiency properties of the PPI networks.Contact: tiago@stats.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

The concept of “new luxury” has challenged the conventional marketing of luxury goods as prestigious, leading to greater expansion of mass luxury meaning. This has become more evident since the outbreak of COVID-19, which has been a catalyst for consumption in the luxury market. This paper investigates the mass marketing of luxury goods and explores the essence of masstige luxury consumption since the outbreak of COVID-19. An interpretive approach was conducted based on semi-structured, in-depth interviews with 31 participants. It analyzes four themes of mass luxury: self as content, self as process, self as context, and self–other. We further argue that the mass consumption of luxury reduces cognitive dissonance, with the pandemic resolving the dark side of conventional luxury consumption. Our findings provide important insights for both scholars and practitioners in the development of a more holistic understanding of masstige in the post-COVID era.

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