The cold dark-matter model successfully explains both the emergence and evolution of cosmic structures on large scales and, when we include a cosmological constant, the properties of the homogeneous and isotropic Universe. However, the cold dark-matter model faces persistent challenges on the scales of galaxies. Indeed, N-body simulations predict some galaxy properties that are at odds with the observations. These discrepancies are primarily related to the dark-matter distribution in the innermost regions of the halos of galaxies and to the dynamical properties of dwarf galaxies. They may have three different origins: (1) the baryonic physics affecting galaxy formation is still poorly understood and it is thus not properly included in the model; (2) the actual properties of dark matter differs from those of the conventional cold dark matter; (3) the theory of gravity departs from General Relativity. Solving these discrepancies is a rapidly evolving research field. We illustrate some of the solutions proposed within the cold dark-matter model, and solutions when including warm dark matter, self-interacting dark matter, axion-like particles, or fuzzy dark matter. We also illustrate some modifications of the theory of gravity: Modified Newtonian Dynamics (MOND), MOdified Gravity (MOG), and f(R) gravity.

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Frequent itemset mining assists the data mining practitioner in searching for strongly associated items (and transactions) in large transaction databases. Since the number of frequent itemsets is usually extremely large and unmanageable for a human user, recent works have sought to define condensed representations of them, e.g. closed or maximal frequent itemsets. We argue that not only these methods often still fall short in sufficiently reducing of the output size, but they also output many redundant itemsets. In this paper we propose a philosophically new approach that resolves both these issues in a computationally tractable way. We present and empirically validate a statistically founded approach called MINI, to compress the set of frequent itemsets down to a list of informative and non-redundant itemsets.

SARS‐CoV‐2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti‐virals. Within the international Covid19‐NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80% of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR‐detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure‐based drug design against the SCoV2 proteome.