Mathematical modelling of carbohydrate and protein metabolism in muscle.

We propose a mathematical model based on coupled ordinary differential equations (ODEs) for metabolite concentrations with the aim of investigating how modifications to the rates affects outputs from a regulatory network. Our aim is to model the relationships between energy metabolism and the biosynthesis of non-essential amino acids, such as serine. We consider a network of cytosolic glycolysis, the mitochondrial TCA cycle, and the associated serine synthesis pathway, with the aim of modelling the role of metabolic reprogramming as a mechanism to enhance protein synthesis and growth, particularly in skeletal muscle.

Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples.

This study demonstrates a discrimination of endometrial cancer (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed using four discriminant methods (LDA, QDA, kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM).

Prevalence, risk factors and genotype distribution of Toxoplasma gondii DNA in soil in China.

In the present study, we performed a cross-sectional survey to determine the occurrence and genotype distribution of T. gondii DNA in soil samples collected from different sources from six geographic regions in China. Between March 2015 and June 2017, 2100 soil samples were collected from schools, parks, farms and coastal beaches, and examined for T.

BAYESIAN ALIGNMENT OF SIMILARITY SHAPES.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical analysis.

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point.