Machine Learning for Instance Segmentation

Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level. A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms. In this thesis, we develop new algorithms for this task. In the first part we provide, for the first time in this field, a method for training a neural network to predict optimal input data for a watershed algorithm. We demonstrate its superior performance compared to other segmentation methods of its category. In the second part, we develop an efficient watershed-based algorithm for weighted graph partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time. We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge. Our algorithm is currently used by the leaders of two connectomics challenges. Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision

The Solution Precursor Plasma Spraying Process for Making Zirconia Based Electrolytes

Ceramic layers, such as yttria-stabilized zirconia or scandia-stabilized zirconia, used for functional layers of solid oxide fuel cells, i.e. the gas tight oxygen ion conductive electrolyte or as ceramic component in the porous cermet anode, were obtained by the Solution Precursor Plasma Spray (SPPS) process. The influence of different solvent types on microstructure was analyzed by comparison of coatings sprayed with water-based solution to ethanol-based one. Use of solvent with low surface tension and low boiling point enhances splat formation, coating micro-structure and crystalline structure. Parameter adjustment to receive coatings from nitrate solutions with ethanol as solvent was carried out. Results of Raman spectroscopy indicate that an intermediate of both nitrates (zirconyl and scandium nitrate hydrate) was deposited

Aortic volume determines global end-diastolic volume measured by transpulmonary thermodilution

BACKGROUND: Global end-diastolic volume (GEDV) measured by transpulmonary thermodilution is regarded as indicator of cardiac preload. A bolus of cold saline injected in a central vein travels through the heart and lung, but also the aorta until detection in a femoral artery. While it is well accepted that injection in the inferior vena cava results in higher values, the impact of the aortic volume on GEDV is unknown. In this study, we hypothesized that a larger aortic volume directly translates to a numerically higher GEDV measurement. METHODS: We retrospectively analyzed data from 88 critically ill patients with thermodilution monitoring and who did require a contrast-enhanced thoraco-abdominal computed tomography scan. Aortic volumes derived from imaging were compared with GEDV measurements in temporal proximity. RESULTS: Median aortic volume was 194 ml (interquartile range 147 to 249 ml). Per milliliter increase of the aortic volume, we found a GEDV increase by 3.0 ml (95% CI 2.0 to 4.1 ml, p < 0.001). In case a femoral central venous line was used for saline bolus injection, GEDV raised additionally by 2.1 ml (95% CI 0.5 to 3.7 ml, p = 0.01) per ml volume of the vena cava inferior. Aortic volume explained 59.3% of the variance of thermodilution-derived GEDV. When aortic volume was included in multivariate regression, GEDV variance was unaffected by sex, age, body height, and weight. CONCLUSIONS: Our results suggest that the aortic volume is a substantial confounding variable for GEDV measurements performed with transpulmonary thermodilution. As the aorta is anatomically located after the heart, GEDV should not be considered to reflect cardiac preload. Guiding volume management by raw or indexed reference ranges of GEDV may be misleading

A Delocalized Proton-Binding Site within a Membrane Protein

AbstractThe role of protein-bound water molecules in protein function and catalysis is an emerging topic. Here, we studied the solvation of an excess proton by protein-bound water molecules and the contribution of the surrounding amino acid residues at the proton release site of the membrane protein bacteriorhodopsin. It hosts an excess proton within a protein-bound water cluster, which is hydrogen bonded to several surrounding amino acids. Indicative of delocalization is a broad continuum absorbance experimentally observed by time-resolved Fourier transform infrared spectroscopy. In combination with site-directed mutagenesis, the involvement of several amino acids (especially Glu-194 and Glu-204) in the delocalization was elaborated. Details regarding the contributions of the glutamates and water molecules to the delocalization mode in biomolecular simulations are controversial. We carried out quantum mechanics/molecular mechanics (QM/MM) self-consistent charge density functional tight-binding simulations for all amino acids that have been experimentally shown to be involved in solvation of the excess proton, and systematically investigated the influence of the quantum box size. We compared calculated theoretical infrared spectra with experimental ones as a measure for the correct description of excess proton delocalization. A continuum absorbance can only be observed for small quantum boxes containing few amino acids and/or water molecules. Larger quantum boxes, including all experimentally shown involved amino acids, resulted in narrow absorbance bands, indicating protonation of a single binding site in contradiction to experimental results. We conclude that small quantum boxes seem to reproduce representative extreme cases of proton delocalization modes: proton delocalization only on water molecules or only between Glu-194 and Glu-204. Extending the experimental spectral region to lower wave numbers, a water-delocalized proton reproduces the observed continuum absorbance better than a glutamate-shared delocalized proton. However, a full agreement between QM simulations and experimental results on the delocalized excess proton will require a larger quantum box as well as more sophisticated QM/MM methods

Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.Comment: This preprint is the unformatted version of a manuscript that has been published as article in the Journal of Chemical Information and Modeling and can be downloaded for private use only. Copyright with ACS, the journal and the author

Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding

Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. We here present a theoretical framework how to compute kinetics along separate paths and to combine the path-specific rates into global binding and unbinding rates for comparison with experiment. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [Nat. Commun. \textbf{11}, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems.Comment: This preprint is the unedited version of a manuscript that has been sent to a scientific journal for consideration as a publication and can be downloaded for private use only. Copyright with the journal and its publisher after publicatio

Investigation of rare protein conformational transitions via dissipation-corrected targeted molecular dynamics

To sample rare events, dissipation-corrected targeted molecular dynamics (dcTMD) applies a constant velocity constraint along a one-dimensional reaction coordinate $s$, which drives an atomistic system from an initial state into a target state. Employing a cumulant approximation of Jarzynski's identity, the free energy $\Delta G (s)$ is calculated from the mean external work and dissipated work of the process. By calculating the friction coefficient $\Gamma (s)$ from the dissipated work, in a second step the equilibrium dynamics of the process can be studied by propagating a Langevin equation. While so far dcTMD has been mostly applied to study the unbinding of protein-ligand complexes, here its applicability to rare conformational transitions within a protein and the prediction of their kinetics is investigated. As this typically requires the introduction of multiple collective variables $\{x_j\}= \vec{x}$, a theoretical framework is outlined to calculate the associated free energy $\Delta G (\vec{x})$ and friction \matrix{\Gamma}(\vec{x}) from dcTMD simulations along coordinate $s$. Adopting the $\alpha$-$\beta$ transition of alanine dipeptide as well as the open-closed transition of T4 lysozyme as representative examples, the virtues and shortcomings of dcTMD to predict protein conformational transitions and the related kinetics are studied.Comment: This preprint is the unedited version of a manuscript that has been sent to a scientific journal for consideration as a publication and can be downloaded for private use only. Copyright with the journal and its publisher after publicatio