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Biomolecular NMR spectroscopy requires large magnetic field strengths for high spectral resolution. Today’s highest fields comprise proton Larmor frequencies of 1.2 GHz and even larger field strengths are to be expected in the future. In protein triple resonance experiments, various carbon bandwidths need to be excited by selective pulses including the large aliphatic chemical shift range. When the spectrometer field strength is increased, the length of these pulses has to be decreased by the same factor, resulting in higher rf-amplitudes being necessary in order to cover the required frequency region. Currently available band-selective pulses like Q3/Q5 excite a narrow bandwidth compared to the necessary rf-amplitude. Because the maximum rf-power allowed in probeheads is limited, none of the selective universal rotation pulses reported so far is able to cover the full $$^{13}$$ 13 C aliphatic region on 1.2 GHz spectrometers. In this work, we present band-selective 90° and 180° universal rotation pulses (SURBOP90 and SURBOP180) that have a higher ratio of selective bandwidth to maximum rf-amplitude than standard pulses. Simulations show that these pulses perform better than standard pulses, e. g. Q3/Q5, especially when rf-inhomogeneity is taken into account. The theoretical and experimental performance is demonstrated in offset profiles and by implementing the SURBOP pulses in an HNCACB experiment at 1.2 GHz.

Accurate localization is an essential technology for flexible automation. Industrial applications require mobile platforms to be precisely localized in complex environments, often subject to continuous changes and reconfiguration. Most of the approaches use precomputed maps both for localization and for interfacing robots with workers and operators. This results in increased deployment time and costs as mapping experts are required to setup the robotic systems in factory facilities. Moreover, such maps need to be updated whenever significant changes in the environment occur in order to be usable within commanding tools. To overcome those limitations, in this work we present a robust and highly accurate method for long-term LiDAR-based indoor localization that uses CAD-based architectural floor plans. The system leverages a combination of graph-based mapping techniques and Bayes filtering to maintain a sparse and up-to-date globally consistent map that represents the latest configuration of the environment. This map is aligned to the CAD drawing using prior constraints and is exploited for relative localization, thus allowing the robot to estimate its current pose with respect to the global reference frame of the floor plan. Furthermore, the map helps in limiting the disturbances caused by structures and clutter not represented in the drawing. Several long-term experiments in changing real-world environments show that our system outperforms common state-of-the-art localization methods in terms of accuracy and robustness while remaining memory and computationally efficient.

Quantum chemical methods for calculating paramagnetic NMR observables are becoming increasingly accessible and are being included in the inorganic chemistry practice. Here, we test the performance of these methods in the prediction of proton hyperfine shifts of two archetypical high-spin pentacoordinate nickel(II) complexes (NiSAL-MeDPT and NiSAL-HDPT), which, for a variety of reasons, turned out to be perfectly suited to challenge the predictions to the finest level of detail. For NiSAL-MeDPT, new NMR experiments yield an assignment that perfectly matches the calculations. The slightly different hyperfine shifts from the two “halves” of the molecules related by a pseudo-C2 axis, which are experimentally divided into two well-defined spin systems, are also straightforwardly distinguished by the calculations. In the case of NiSAL-HDPT, for which no X-ray structure is available, the quality of the calculations allowed us to refine its structure using as a starting template the structure of NiSAL-MeDPT., State-of-the-art quantum chemical methods and paramagnetism-tailored NMR experiments provide a deep insight on the relation between the spectra and the electronic structure for two paramagnetic pentacoordinate nickel(II) complexes.

International audience; Lansoprazole (LPZ) is an acid pump inhibitor, which readily degrades upon acidic or basic conditions and under heating. We investigated here LPZ stability upon incorporation in particles made of cyclodextrin metal-organic frameworks (CD-MOFs). LPZ loaded CD-MOFs were successfully synthesized, reaching high LPZ payloads of 23.2 ± 2.1 wt%, which correspond to a molar ratio of 1:1 between LPZ and γ-CD. The homogeneity of LPZ loaded CD-MOFs in terms of component distribution was confirmed by elemental mapping by STEM-EDX. Both CTAB, the surfactant used in the CD-MOFs synthesis, and LPZ compete for their inclusion in the CD cavities. CTAB allowed obtaining regular cubic particles of around 5 µm with 15 wt% residual CTAB amounts. When LPZ was incorporated, the residual CTAB amount was less than 0.1 wt%, suggesting a higher affinity of LPZ for the CDs than CTAB. These findings were confirmed by molecular simulations. Vibrational circular dichroism studies confirmed the LPZ incorporation inside the CDs. Solid-state NMR showed that LPZ was located in the CDs and that it remained intact even after three years storage. Remarkably, the CD-MOFs matrix protected the drug upon thermal decomposition. This study highlights the interest of CD-MOFs for the incorporation and protection of LPZ.

Nuclear magnetic resonance (NMR) spectroscopy is a wide-spread analytical technique which is used in a large range of different fields, such as quality control, food analysis, material science and structural biology. In the widest sense, NMR is an analytical technique to determine the structure of molecules. At the time of writing this manuscript, commercial NMR spectrometers with a proton resonance frequency ⩾900 MHz are only available from Bruker. In 2019, Bruker installed the first 1.1 GHz (25.8 T) NMR spectrometer at the St. Jude Children Research Hospital in Memphis, Tennessee, followed by the installation of the first 1.2 GHz (28.2 T) NMR spectrometer at the University of Florence in Italy in 2020. These were the first commercial NMR spectrometers operating at magnetic fields in excess of what can be achieved with conventional low temperature superconductors, and which depend on high temperature superconductors to generate the required magnetic field. In this paper, the requirements on commercial NMR magnets are discussed and the history of high-field NMR magnets is reviewed. Bruker’s R&D program for 1.1 and 1.2 GHz NMR magnets and spectrometers will be described, and some of the key properties of these first commercial NMR magnets with high-temperature superconductors are reported.

Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In this paper, we propose a novel framework to robustly solve the active SLAM problem, in scenarios in which some prior information about the environment is available in the form of a topo-metric graph. This information is typically available or can be easily developed in industrial environments, but it is usually affected by uncertainties. In particular, the distinguishing features of our approach are: the inclusion of prior information for solving the active SLAM problem; the exploitation of this information to pursue active loop closure; the on-line correction of the inconsistencies in the provided data. We present some experiments, that are performed in different simulated environments: the results suggest that our method improves on state-of-the-art approaches, as it is able to deal with a wide variety of possibly large uncertainties.

Nucleophosmin (NPM1) is an abundant nucleolar protein that aids in the maturation of pre-ribosomal particles and participates in oncogenic stress responses through its interaction with the Alternative Reading Frame tumor suppressor (p14ARF). NPM1 mediates multiple mechanisms of phase separation which contribute to the liquid-like properties of nucleoli. However, the effects of phase separation on the structure and dynamics of NPM1 are poorly understood. Here we show that NPM1 undergoes phase separation with p14ARF in vitro, forming condensates that immobilize both proteins. We probed the structure and dynamics of NPM1 within the condensed phase using solid-state NMR spectroscopy. Our results demonstrate that within the condensed phase, the NPM1 oligomerization domain forms an immobile scaffold, while the central intrinsically disordered region and the C-terminal nucleic acid binding domain exhibit relative mobility.

Intrinsically disordered proteins (IDPs) as well as intrinsically disordered regions (IDRs) of complex protein machineries have recently been recognized as key players in many cellular functions. NMR represents a unique tool to access atomic resolution structural and dynamic information on highly flexible IDPs/IDRs. Improvements in instrumental sensitivity made heteronuclear direct detection possible for biomolecular NMR applications. The CON experiment has become one of the most useful NMR experiments to get a snapshot of an IDP/IDR in conditions approaching physiological ones. The availability of NMR spectrometers equipped with multiple receivers now enables the acquisition of several experiments simultaneously instead of one after the other. Here, we propose several variants of the CON experiment in which, during the recovery delay, a second two-dimensional experiment is acquired, either based on 1H detection (CON//HN) or on 15N detection (CON//btNH, CON//(H)CAN). The possibility to collect simultaneous snapshots of an IDP/IDR through different two-dimensional spectra provides a novel tool to follow chemical reactions, such as the occurrence of posttranslational modifications, as well as to study samples of limited lifetime such as cell lysates or whole cells.

Modern automation demands mobile robots to be robustly localized in complex scenarios. Current localization systems typically use maps that require to be built and interpreted by experienced operators, increasing deployment costs as well as reducing the adaptability of robots to rearrangements in the environment. In contrast, architectural floor plans can be easily understood by non-expert users and typically represent only the non-rearrangeable parts of buildings. In this paper we propose a system for robot localization in architectural CAD drawings. Our method employs a simultaneous localization and mapping approach to online augment the floor plan with a map represented as a pose-graph with LiDAR measurements. Whenever the environment is accurately mapped in the vicinity of the robot, we use the graph to perform relative localization. We thoroughly evaluate our system in challenging real-world scenarios. Experiments demonstrate that our method is able to robustly track the robot pose even when the floor plan shows major discrepancies from the real-world. We show that our system achieves sub-centimeter accuracy and is suitable for real-time application.

In the past, there has been a tremendous amount of progress in the area of autonomous robot navigation, and a large variety of robots have been developed that demonstrated robust navigation capabilities indoors, in nonurban outdoor environments, or on roads; relatively few approaches have focused on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system, including a simultaneous localization and mapping module for dealing with huge maps of city centers, a planning component for inferring feasible paths, taking into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and the means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than 3-km-long route through the city center of Freiburg, Germany.

The ability to reliably detect vegetation is an important requirement for outdoor navigation with mobile robots as it enables the robot to navigate more efficiently and safely. In this paper, we present an approach to detect flat vegetation, such as grass, which cannot be identified using range measurements. This type of vegetation is typically found in structured outdoor environments such as parks or campus sites. Our approach classifies the terrain in the vicinity of the robot based on laser scans and makes use of the fact that plants exhibit specific reflection properties. It uses a support vector machine to learn a classifier for distinguishing vegetation from streets based on laser reflectivity, measured distance, and the incidence angle. In addition, it employs a vibration-based classifier to acquire training data in a self-supervised way and thus reduces manual work. Our approach has been evaluated extensively in real world experiments using several mobile robots. We furthermore evaluated it with different types of sensors and in the context of mapping, autonomous navigation, and exploration experiments. In addition, we compared it to an approach based on linear discriminant analysis. In our real world experiments, our approach yields a classification accuracy close to 100%.

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms.

To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency. Artificial Intelligence & Integrated Computer Systems

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

Carbon-13 direct-detection NMR is becoming increasingly used to complement 1H-detected experiments in biomolecular applications. Here we describe the novel aspects that were addressed to convert 13C direct-detection into a useful routinely applicable tool, a selection of the most useful 13C direct-detection protonless NMR experiments and how they can be exploited for the study of proteins. © 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part A 32A: 183–200, 2008.

Thanks to recent improvements in NMR instrumentation, pulse sequence design, and sample preparation, a panoply of new NMR tools has become available for atomic resolution characterization of intrinsically disordered proteins (IDPs) that are optimized for the particular chemical and spectroscopic properties of these molecules. A wide range of NMR observables can now be measured on increasingly complex IDPs that report on their structural and dynamic properties in isolation, as part of a larger complex, or even inside an entire living cell. Herein we present basic NMR concepts, as well as optimised tools available for the study of IDPs in solution. In particular, the following sections are discussed hereafter: a short introduction to NMR spectroscopy and instrumentation (Sect. 3.1), the effect of order and disorder on NMR observables (Sect. 3.2), particular challenges and bottlenecks for NMR studies of IDPs (Sect. 3.3), 2D HN and CON NMR experiments: the fingerprint of an IDP (Sect. 3.4), tools for overcoming major bottlenecks of IDP NMR studies (Sect. 3.5), 13C detected experiments (Sect. 3.6), from 2D to 3D: from simple snapshots to site-resolved characterization of IDPs (Sect. 3.7), sequential NMR assignment: 3D experiments (Sect. 3.8), high-dimensional NMR experiments (nD, with n>3) (Sect. 3.9) and conclusions and perspectives (Sect. 3.10).

Carbon-13 direct detection NMR methods are feasible thanks to the improvements in probehead technology and to the development of new NMR experiments. We present here a complete set of experiments, based on C′ direct detection, developed to perform protein complete assignment of backbone and side-chains (except for aromatic rings). This strategy offers alternative solutions for demanding situations (paramagnetic and/or large molecules) and can be useful in general in conjunction with conventional experiments.

NMR-based binding and functional screening performed with FAXS (fluorine chemical shift anisotropy and exchange for screening) and 3-FABS (three fluorine atoms for biochemical screening) represents a potential alternative approach to high-throughput screening for the identification of novel potential drug candidates. The major limitation of this method in its current status is its intrinsic low sensitivity that limits the number of tested compounds. One approach for overcoming this problem is the use of a cryogenically cooled (19)F probe that reduces the thermal noise in the receiver circuitry. Sensitivity improvement in the two screening techniques achieved with the novel cryogenic (19)F probe technology permits an increased throughput, detection of weaker binders and inhibitors (relevant in a fragment-based lead discovery program), detection of slow binders, and reduction in protein and substrate consumption. These aspects are analyzed with theoretical simulations and experimental quantitative performance evaluation. Application of 3-FABS combined with the cryogenic (19)F probe technology to rapid screening at very low enzyme concentrations and the current detection limits reached with this approach are also presented.

(13)C-(13)C NOESY experiments were performed under long mixing time conditions on reduced human superoxide dismutase (32 kDa, (15)N, (13C) and 70% (2)H labeled). (13)C-(13)C couplings were successfully eliminated through post-processing of in-phase-anti-phase (IPAP) data. It appears that at mixing time tau(m) of 3.0 s the spin diffusion mechanism allows the detection of 96% of the two-bond correlations involving C' and C(beta). The interpretation was confirmed by simulations. This approach broadens the range of applicability of (13)C-(13)C NOESY spectroscopy.

In this report, the use of 13C direct detection has been pursued in 2D experiments (13C-13C COSY, 13C-13C COCAMQ, 13C-13C NOESY) to detect broad lines in nuclear magnetic resonance spectra of paramagnetic metalloproteins. The sample is a monomeric oxidized copper, zinc superoxide dismutase. Thanks to direct detection probeheads, cryogenic technology, and implementation of 13C band-selective homodecoupling, many broadened signals were detected. Proton signals for the same residues escaped detection in 1H and 1H-15N HSQC experiments because of the broadening. Only the 13C signals which experience large contact coupling escaped detection, i.e., the 13C nuclei of the metal coordinated histidines. Otherwise, nuclei as close to copper(II) as 4 A can be detected. Paramagnetic-based restraints can in principle be used for solution structure determination of paramagnetic metalloproteins and in copper(II) proteins in particular. The present study is significant also for the study of large diamagnetic proteins for which proton relaxation makes proton-based spectroscopy not adequate.