Understanding fluorescent amyloid biomarkers by computational chemistry

Protein misfolding diseases, including neurodegenerative disorders like Alzheimer’s disease, are characterized by the involvement of amyloid aggregation, which emphasizes the need for molecular biomarkers for effective disease diagnosis. The thesis addresses two aspects of biomarker development: firstly, the computation of vibrationally resolved spectra of small fluorescent dyes to detect amyloid aggregation, and secondly, the binding and unbinding processes of a novel ligand to the target protein. In relation to the first aspect, a hybrid model for vibrational line shapes of optical spectra, called VCI-in-IMDHO, is introduced. This model enables the treatment of selected modes using highly accurate and anharmonic vibrational wave function methods while treating the remaining modes using the approximate IMDHO model. This model reduces the computational cost and allows for the calculation of emission line shapes of organic dyes with anharmonicity in both involved electronic states. The interaction between the dyes and their environment is also explored to predict the photophysical properties of the oxazine molecules in the condensed phase. The position and the choice of the solvent molecule have a significant impact on the spectra of the studied systems as they altered the spectral band shape. However, further studies are necessary to confirm the findings. In addition to neurodegenerative diseases, the systemic amyloidoses represent another group of disorders caused by misfolded or misassembled proteins. In the cardiac domain, the accumulation of amyloid fibrils formed by the transthyretin (TTR) protein leads to cardiac dysfunction and restrictive cardiomyopathy. The investigation of binding and unbinding pathways between the TTR protein and its ligands is crucial for gaining a comprehensive understanding and enabling early detection of systemic amyloidoses and related disorders. Hence, exploring the different binding modes and the dissociation pathways of TTR-ligand complex is the primary objective of the second aspect of this thesis. The experimental study provides evidence of binding and X-ray crystallographic structure data on TTR complex formation with the fluorescent salicylic acid-based pyrene amyloid ligand (Py1SA). However, the electron density from X-ray diffraction did not allow confident placement of Py1SA, possibly due to partial ligand occupancy. Molecular dynamics and umbrella sampling approaches were used to determine the preferred orientation of the Py1SA ligand in the binding pocket, with a distinct preference for the binding modes with the salicylic acid group pointing into the pocket.Deutsche Forschungs-gemeinschaft (DFG)/Emmy Noether/KO 5423/1- 1/E

Bayesian Deep Net GLM and GLMM

Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The consideration of neural networks with random effects is not widely used in the literature, perhaps because of the computational challenges of incorporating subject specific parameters into already complex models. Efficient computational methods for high-dimensional Bayesian inference are developed using Gaussian variational approximation, with a parsimonious but flexible factor parametrization of the covariance matrix. We implement natural gradient methods for the optimization, exploiting the factor structure of the variational covariance matrix in computation of the natural gradient. Our flexible DFNN models and Bayesian inference approach lead to a regression and classification method that has a high prediction accuracy, and is able to quantify the prediction uncertainty in a principled and convenient way. We also describe how to perform variable selection in our deep learning method. The proposed methods are illustrated in a wide range of simulated and real-data examples, and the results compare favourably to a state of the art flexible regression and classification method in the statistical literature, the Bayesian additive regression trees (BART) method. User-friendly software packages in Matlab, R and Python implementing the proposed methods are available at https://github.com/VBayesLabComment: 35 pages, 7 figure, 10 table

Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

Tailored anharmonic-harmonic vibrational profiles for fluorescent biomarkers

We propose a hybrid anharmonic-harmonic scheme for vibrational broadenings, which embeds a reduced-space vibrational configuration interaction (VCI) anharmonic wave function treatment in the independent-mode displaced harmonic oscillator (IMDHO) model. The resulting systematically-improvable VCI-in-IMDHO model allows including the vibronic effects of all vibrational degrees of freedom, while focusing the effort on the important degrees of freedom with minimal extra computational effort compared to a reduced-space VCI treatment. We show for oligothiophene examples that the VCI-in-IMDHO approach can yield accurate vibrational profiles employing smaller vibrational spaces in the VCI part than the reduced-space VCI approach. By this, the VCI-in-IMDHO model enables accurate calculation of vibrational profiles of common fluorescent dyes with more than 100 vibrational degrees of freedom. We illustrate this for three examples of fluorescent biomarkers of current interest. These are the oligothiophene-based fluorescent dye called HS84, 1,4-diphenylbutadiene, and an anthracene diimide. For all examples, we assess the impact of the anharmonic treatment on the vibrational broadening, which we find to be more pronounced for the intensities than for the peak positions

A Passivity-based Control Combined with Sliding Mode Control for a DC-DC Boost Power Converter

In this paper, a passivity-based control combined with sliding mode control for a DC-DC boost power converter is proposed. Moreover, a passivity-based control for a DC-DC boost power converter is also proposed. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. Then, a passivity-based control is applied to this plant such that the capacitor voltage is equal to the desired voltage. Additionally, the sliding mode control law is chosen such that the derivative of Lyapunov function is negative semidefinite. Finally, a passivity-based control combined with sliding mode control law is applied to this plant such that the capacitor voltage is equal to the desired voltage. The simulation results of the passivity-based control, the sliding mode control and the passivity-based control combined with sliding mode control demonstrate the effectiveness and show that the capacitor voltage is kept at the desired voltage when the desired voltage, the input voltage E and the load resistor R are changed. The results show that compared with the passivity-based control, the passivity-based control combined with sliding mode control has better performance such as shorter settling time, 8.5 ms when R changes and it has smaller steady-state error, which is indicated by the value of integral absolute error (IAE), 0.0679 when the desired voltage changes. The paper has limitations such as the assumed circuit parameters

Random Effects Models with Deep Neural Network Basis Functions: Methodology and Computation

Deep neural networks (DNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a deep neural network. The consideration of neural networks with random effects seems little used in the literature, perhaps because of the computational challenges of incorporating subject specific parameters into already complex models. Efficient computational methods for Bayesian inference are developed based on Gaussian variational approximation methods. A parsimonious but flexible factor parametrization of the covariance matrix is used in the Gaussian variational approximation. We implement natural gradient methods for the optimization, exploiting the factor structure of the variational covariance matrix to perform fast matrix vector multiplications in iterative conjugate gradient linear solvers in natural gradient computations. The method can be implemented in high dimensions, and the use of the natural gradient allows faster and more stable convergence of the variational algorithm. In the case of random effects, we compute unbiased estimates of the gradient of the lower bound in the model with the random effects integrated out by making use of Fisher's identity. The proposed methods are illustrated in several examples for DNN random effects models and high-dimensional logistic regression with sparse signal shrinkage priors

Combinatorics of ϕ\phi-deformed stuffle Hopf algebras

In order to extend the Sch\"utzenberger's factorization to general perturbations, the combinatorial aspects of the Hopf algebra of the $\phi$-deformed stuffle product is developed systematically in a parallel way with those of the shuffle product

GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation

Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. In this work, we build a new dataset dubbed "Aquatic Animal Species." We also devise a novel GUided mixup augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL) that leverages the advantages of multiple view segmentation models to effectively segment aquatic animals and improves the training performance by synthesizing hard samples. Extensive experiments demonstrated the superiority of our proposed framework over existing state-of-the-art instance segmentation methods