Sökresultat

From a finite-size scaling (FSS) theory of cumulants of the order parameter at phase-coexistence points, we reconstruct the scaling of the moments. Assuming that the cumulants allow a reconstruction of the free energy density no better than as an asymptotic expansion, we find that FSS for moments of low order is still complete. We suggest ways of using this theory for the analysis of numerical sim

The deformable templates method for track finding in high energy physics is reviewed and extended to handle multiple and secondary vertex positions. An automatized minimization method that handles different types of parametrizations is derived. It is based on the gradient descent method but modified with an explicit calculation of the natural metric. Also a simplified and more intuitive derivation

A novel algorithm for particle tracking is presented and evaluated. It is based on deformable templates that converge using a deterministic annealing algorithm. These deformable templates are initialized by Hough transforms. The algorithm, which effectively represents a merger between neuronic decision making and parameter fitting, naturally lends itself to parallel execution. Very good performanc

We present a method based on CRISPR/Cas9 excision and DNA combing to detect anti-microbial resistance (AMR) genes on bacterial plasmids. The assay is inexpensive, simple, fast, and also provides information on the number and size of plasmids in a sample. We demonstrate detection of the gene encoding for the New Delhi metallobeta-lactamase 1 (blaNDM-1) enzyme, known to make bacteria resistant to a

The growth of tissues and organs in plants is governed by the morphogen auxin coupled with the membrane protein PIN, which together generate patterns that guide development. Systems of this kind have been studied extensively in experiments and computational system biology models. This thesis builds on that work by introducing a stochastic version of these models to examine differences between stoc

Direct coupling analysis (DCA) models correlations in sets of related (homologous) protein sequences using a Potts-like spin model ansatz. From the couplings of the Potts model, derived by inverse statistical mechanics, residue-pair contacts in the 3D structure of the protein are predicted. In this thesis, this approach is applied to structures from the HP model on a square lattice. All HP sequenc

This thesis investigates the use of artificial neural networks for classifying Raman spectra of partially degraded cellulose samples by fungal species. A multilayer perceptron configuration of 4 hidden layers and 128 hidden nodes was able to classify a set of 60 samples with an overall prediction accuracy of 0.55. Results show that data resolution is an important factor when optimizing classifier

DNA barcodes carry coarse-grained genetic information of DNA sequences taken from a genome. Potential applications include bacteriology, medical diagnosis and taxonomy. However, the current state-of-the-art tools for extracting DNA molecules from cells provide only fragmented pieces of chromosomal DNA. As a consequence, also DNA barcodes are fragmented. This calls for the development of complement

During the last few years, crowding effects on the physics of proteins has become an increasingly popular topic of research. This is is because most biological processes involving proteins naturally take place in a crowded environment, e.g. in the cellular environment where macromolecules may occupy 30% of the volume. One such biological process would be the formation of amyloid aggregates, which

This thesis examines the state-of-the-art 2D super-resolution technique alternating descent conditional gradient (ADCG) method's ability to accurately localize fluorophores in diffraction-limited single molecule images (SMI) and analyze the impact of pre-processing and post-processing modules on ADCG's fluorophore localization. A synthetic dataset obtained from the 2013 Grand Challenge loc

Neurodegenerative diseases such as Parkinson’s can be treated with stem-cell derived specialized neurons. In order to achieve precise directed neural differentiation in vitro we need to understand the gene regulatory mechanisms behind in vivo neural tube patterning. We implement a 3D computational model of brain patterning to simulate this process. The mathematical model is set up by unifying two e

In the field of computational biology, fluorescence microscopy images often constitute the input source of information. The process of binarization of raw images to delineate interesting objects requires image segmentation into signal and background pixels. Several methods to perform image segmentation exist, the Otsu method being a popular unsupervised example. The Otsu method's lack of pro

Amino acids that are in close contact in a protein structure tend to co-evolve, which gives rise to sequence correlations. Direct coupling analysis (DCA) is a method for predicting such contacts directly from sequence correlations, without assuming any prior knowledge of structures. To this end, sequence correlations are modeled using an Ising-like ansatz, whose couplings are determined through an

Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field of Natural Language Processing (NLP) during the last years. In this project, CNNs are used on top of the Word2Vec word representation for a sentence classification task on medical research articles. Both individual networks for each category as well as a combined classification network are optimized

This paper proposes and investigates a Bayesian relation between optimal L2 regularization strengths and the number of training patterns and hidden nodes used for an artificial neural network. The results support the proposed dependence for number of training patterns, while the dependence on hidden architecture was less clear. Finally, applying different regularization strengths on different laye