Toward Reproducible Enzyme Modeling with Isothermal Titration Calorimetry

To apply enzymes in technical processes, a detailed understanding of the molecular mechanisms is required. Kinetic and thermodynamic parameters of enzyme catalysis are crucial to plan, model, and implement biocatalytic processes more efficiently. While the kinetic parameters, Km and kcat, are often accessible by optical methods, the determination of thermodynamic parameters requires more sophisticated methods. Isothermal titration calorimetry (ITC) allows the label-free and highly sensitive analysis of kinetic and thermodynamic parameters of individual steps in the catalytic cycle of an enzyme reaction. However, since ITC is susceptible to interferences due to denaturation or agglomeration of the enzymes, the homogeneity of the enzyme sample must always be considered, and this can be accomplished by means of dynamic light scattering (DLS) analysis. The presented ITCdependent workflow was used to determine both the kinetic and the thermodynamic data for a cofactor-dependent enzyme. Using a standardized approach with the implementation of sample quality control by DLS, we obtained high-quality data suitable for the advanced modeling of the enzyme reaction mechanism. Specifically, we investigated stereoselective reactions catalyzed by the NADPH-dependent ketoreductase Gre2p under different reaction conditions. The results revealed that this enzyme operates with an ordered sequential mechanism in which the cofactor NADPH binds first, resulting in Gre2pholo, and that only Gre2pholo can then bind the substrate NDK. In addition, the enzyme was found to be affected by substrate or product inhibition depending on the reaction buffer. Data reproducibility, a mandatory prerequisite to achieve robust modeling, is ensured by specifying standard operating procedures, using programmed workflows for data analysis and storing all data in a F.A.I.R. (findable, accessible, interoperable, and reusable) repository. Our approach highlights the utility for combined binding and kinetic studies for such complex multisubstrate reactions. Because it is amenable to automation and scale-up for high-throughput, the combination of such diverse approaches will provide the high-quality data needed for the engineering of enzymes and biocatalytic processes through machine learning to accelerate future development of industrial biocatalysis

Thermodynamics of Strongly Correlated One-Dimensional Bose Gases

We investigate the thermodynamics of one-dimensional Bose gases in the strongly correlated regime. To this end, we prepare ensembles of independent 1D Bose gases in a two-dimensional optical lattice and perform high-resolution in situ imaging of the column-integrated density distribution. Using an inverse Abel transformation we derive effective one-dimensional line-density profiles and compare them to exact theoretical models. The high resolution allows for a direct thermometry of the trapped ensembles. The knowledge about the temperature enables us to extract thermodynamic equations of state such as the phase-space density, the entropy per particle and the local pair correlation function.Comment: 4 pages, 5 figure

Magnetic substructure in the northern Fermi Bubble revealed by polarized WMAP emission

We report a correspondence between giant, polarized microwave structures emerging north from the Galactic plane near the Galactic center and a number of GeV gamma-ray features, including the eastern edge of the recently-discovered northern Fermi Bubble. The polarized microwave features also correspond to structures seen in the all-sky 408 MHz total intensity data, including the Galactic center spur. The magnetic field structure revealed by the polarization data at 23 GHz suggests that neither the emission coincident with the Bubble edge nor the Galactic center spur are likely to be features of the local ISM. On the basis of the observed morphological correspondences, similar inferred spectra, and the similar energetics of all sources, we suggest a direct connection between the Galactic center spur and the northern Fermi Bubble.Comment: Accepted for publication in The Astrophysical Journal Letters after minor change

Wild at Heart:-The Particle Astrophysics of the Galactic Centre

We treat of the high-energy astrophysics of the inner ~200 pc of the Galaxy. Our modelling of this region shows that the supernovae exploding here every few thousand years inject enough power to i) sustain the steady-state, in situ population of cosmic rays (CRs) required to generate the region's non-thermal radio and TeV {\gamma}-ray emis-sion; ii) drive a powerful wind that advects non-thermal particles out of the inner GC; iii) supply the low-energy CRs whose Coulombic collisions sustain the temperature and ionization rate of the anomalously warm, envelope H2 detected throughout the Cen-tral Molecular Zone; iv) accelerate the primary electrons which provide the extended, non-thermal radio emission seen over ~150 pc scales above and below the plane (the Galactic centre lobe); and v) accelerate the primary protons and heavier ions which, advected to very large scales (up to ~10 kpc), generate the recently-identified WMAP haze and corresponding Fermi haze/bubbles. Our modelling bounds the average magnetic field amplitude in the inner few degrees of the Galaxy to the range 60 < B/microG < 400 (at 2 sigma confidence) and shows that even TeV CRs likely do not have time to penetrate into the cores of the region's dense molecular clouds before the wind removes them from the region. This latter finding apparently disfavours scenarios in which CRs - in this star-burst-like environment - act to substantially modify the conditions of star-formation. We speculate that the wind we identify plays a crucial role in advecting low-energy positrons from the Galactic nucleus into the bulge, thereby explaining the extended morphology of the 511 keV line emission. (abridged)Comment: One figure corrected. Accepted for publication in MNRAS. 29 pages, 14 figure

ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization

Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel modular architecture for long-term 6DoF VO that leverages temporal information and synergies between absolute pose estimates (from PoseNet-like modules) and relative pose estimates (from FlowNet-based modules) by combining both through recurrent layers. Experiments on known datasets and on our own Industry dataset show that our modular design outperforms state of the art in long-term navigation tasks.Comment: Conf. on Computer Vision and Pattern Recognition (CVPR): Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM 202

Imine Reductase Based All-Enzyme Hydrogel with Intrinsic Cofactor Regeneration for Flow Biocatalysis

All-enzyme hydrogels are biocatalytic materials, with which various enzymes can be immobilized in microreactors in a simple, mild, and efficient manner to be used for continuous flow processes. Here we present the construction and application of a cofactor regenerating hydrogel based on the imine reductase GF3546 from Streptomyces sp. combined with the cofactor regenerating glucose-1-dehydrogenase from Bacillus subtilis. The resulting hydrogel materials were characterized in terms of binding kinetics and viscoelastic properties. The materials were formed by rapid covalent crosslinking in less than 5 min, and they showed a typical mesh size of 67 ± 2 nm. The gels were applied for continuous flow biocatalysis. In a microfluidic reactor setup, the hydrogels showed excellent conversions of imines to amines for up to 40 h in continuous flow mode. Variation of flow rates led to a process where the gels showed a maximum space-time-yield of 150 g·(L·day)−1 at 100 μL/mi

Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments

The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regression (APR) methods learn a semantic understanding of the environment through neural networks. However, both fields face challenges caused by the environment such as motion blur, lighting changes, repetitive patterns, and feature-less structures. This study aims to address these challenges by incorporating additional information and regularizing the absolute pose using relative pose regression (RPR) methods. The optical flow between consecutive images is computed using the Lucas-Kanade algorithm, and the relative pose is predicted using an auxiliary small recurrent convolutional network. The fusion of absolute and relative poses is a complex task due to the mismatch between the global and local coordinate systems. State-of-the-art methods fusing absolute and relative poses use pose graph optimization (PGO) to regularize the absolute pose predictions using relative poses. In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction. We evaluate eight different recurrent units and construct a simulation environment to pre-train the APR and RPR networks for better generalized training. Additionally, we record a large database of different scenarios in a challenging large-scale indoor environment that mimics a warehouse with transportation robots. We conduct hyperparameter searches and experiments to show the effectiveness of our recurrent fusion method compared to PGO

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Recent methods directly regress the pose using convolutional and spatio-temporal networks. Absolute pose regression (APR) techniques predict the absolute camera pose from an image input in a known scene. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information of both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on PGO and attention networks. Auxiliary and Bayesian learning are integrated for the APR task. We show accuracy improvements for the RPR-aided APR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets, and record a novel industry dataset.Comment: Under revie

Molecular response of Deinococcus radiodurans to simulated microgravity explored by proteometabolomic approach

Regarding future space exploration missions and long-term exposure experiments, a detailed investigation of all factors present in the outer space environment and their effects on organisms of all life kingdoms is advantageous. Influenced by the multiple factors of outer space, the extremophilic bacterium Deinococcus radiodurans has been long-termly exposed outside the international Space Station in frames of the tanpopo orbital mission. the study presented here aims to elucidate molecular key components in D. radiodurans, which are responsible for recognition and adaptation to simulated microgravity. D. radiodurans cultures were grown for two days on plates in a fast-rotating 2-D clinostat to minimize sedimentation, thus simulating reduced gravity conditions. Subsequently, metabolites and proteins were extracted and measured with mass spectrometry-based techniques. our results emphasize the importance of certain signal transducer proteins, which showed higher abundances in cells grown under reduced gravity. these proteins activate a cellular signal cascade, which leads to differences in gene expressions. Proteins involved in stress response, repair mechanisms and proteins connected to the extracellular milieu and the cell envelope showed an increased abundance under simulated microgravity. focusing on the expression of these proteins might present a strategy of cells to adapt to microgravity conditions