Humoral immune responses to microbial polysaccharide surface antigens can prevent bacterial infection but are typically strain specific and fail to mediate broad protection against different serotypes. Here we describe a panel of affinity-matured monoclonal human antibodies from peripheral blood immunoglobulin M-positive (IgM) and IgA memory B cells and clonally related intestinal plasmablasts, directed against the lipopolysaccharide (LPS) O-antigen of Klebsiella pneumoniae, an opportunistic pathogen and major cause of antibiotic-resistant nosocomial infections. The antibodies showed distinct patterns of in vivo cross-specificity and protection against different clinically relevant K. pneumoniae serotypes. However, cross-specificity was not limited to K. pneumoniae, as K. pneumoniae-specific antibodies recognized diverse intestinal microbes and neutralized not only K. pneumoniae LPS but also non-K. pneumoniae LPS. Our data suggest that the recognition of minimal glycan epitopes abundantly expressed on microbial surfaces might serve as an efficient humoral immunological mechanism to control invading pathogens and the large diversity of the human microbiota with a limited set of cross-specific antibodies.
The front cover artwork is provided by the groups of Dr. Christoph Scheurer, Prof. Axel Haase, Prof. Steffen J. Glaser, Prof. Markus Schwaiger and Dr. Franz Schilling from Technical University of Munich. The image shows deuterated zymonic acid, its pH‐sensitive 13C NMR resonances and examples of how this sensor can be used to non‐invasively image pH both in vitro and in vivo. Read the full text of the article at 10.1002/cphc.201700779.
The solution of (generalized) eigenvalue problems for symmetric or Hermitian matrices is a common subtask of many numerical calculations in electronic structure theory or materials science. Depending on the scientific problem, solving the eigenvalue problem can easily amount to a sizeable fraction of the whole numerical calculation, and quite often is even the dominant part by far. For researchers in the field of computational materials science, an efficient and scalable solution of the eigenvalue problem is thus of major importance. The ELPA-library (Eigenvalue SoLvers for Petaflop-Applications) is a well-established dense direct eigenvalue solver library, which has proven to be very efficient and scalable up to very large core counts. It is in a wide-spread production use on a large variety of HPC systems worldwide, and is applied by many codes in the field of materials science. In this paper, we describe the latest optimizations of the ELPAlibrary for new HPC architectures of the Intel Skylake processor family with an AVX-512 SIMD instruction set, or for HPC systems accelerated with recent GPUs. Apart from those direct hardware-related optimizations, we also describe a complete redesign of the API in a modern modular way, which, apart from a much simpler and more flexible usability, leads to a new path to access system-specific performance optimizations. In order to ensure optimal performance for a particular scientific setting or a specific HPC system, the new API allows the user to influence in straightforward way the internal details of the algorithms and of performance-critical parameters used in the ELPA-library. On top of that, we introduced an autotuning functionality, which allows for finding the best settings in a self-contained automated way, without the need of much user effort. In situations where many eigenvalue problems with similar settings have to be solved consecutively, the autotuning process of the ELPA-library can be done "on-the-fly", without the need of preceding the simulation with an "artificial" autotuning step. Practical applications from materials science which rely on reaching a numerical convergence limit by so-called self-consistency iterations can profit from the on-the-fly autotuning. On some examples of scientific interest, simulated with the FHI-aims [17] application, the advantages of the latest optimizations of the ELPA-library are demonstrated.
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