131 research outputs found
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Aesthetic complexity: practice and perception in art & design
My research investigates the aesthetics of visual complexity in the practice and perception of visual art and design. The aim is to understand visual complexity in terms of the relationship between the objective properties of images and subjective properties of perception. I take a computational and empirical approach to this subject, incorporating methods from information theory, computer graphics, complexity theory and experimental psychology. For testing, I create cellular automata programs to generate stimulus images, and borrow other types of visual material from students and professional artists, designers and craftspeople. Visual complexity is measured in two ways: Firstly, an objective measure of complexity is based on the compression of digital image files, which provides an information-based scale of order to randomness. Secondly, psychophysical techniques are employed to measure the subjective complexity of the images and other aesthetic judgements. Research in complex systems theory and experimental aesthetics suggests that we can expect an inverted ‘U’ correlation between the two measures of complexity
QSAR model development for early stage screening of monoclonal antibody therapeutics to facilitate rapid developability
PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a
biopharmaceutical perspective as they are highly target specific and well tolerated within the
human system. Nevertheless, several mAbs have been discontinued or withdrawn based either
on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of
drugs failing in clinical development mainly due to lack of efficacy and safety there arises an
urgent need for better understanding of biological activity, affinity, pharmacology, toxicity,
immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid
modelling framework was developed that enabled early stage screening of mAbs. The
applicability of the experimental methods was first tested on chemical compounds to assess the
assay quality following which they were used to assess potential off target adverse effects of
mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial
human skin explants based assay for safety and efficacy assessment of novel compounds and
drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune
related adverse effects of aggregated mAbs was studied where aggregation was induced using
a heat stress protocol. The aggregates were characterised by protein analysis techniques such
as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™
assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal,
EMBOSS Pepstat software as well as amino acid scales for different. Five independent and
novel X block datasets consisting of these descriptors were generated based on the
physicochemical, electronic, thermodynamic, electronic and topological properties of amino
acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study
describes the development of a hybrid QSAR based model with a structured workflow and clear
evaluation metrics, with several optimisation steps, that could be beneficial for broader and
more generic PLS modelling. Based on the results and observation from this study, it was
demonstrated incremental improvement via selection of datasets and variables help in further
optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity
as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models
generated for different applicability domains allow for rapid early stage screening and
developability. These models were validated with external test set comprising of proprietary
compounds from industrial partners, thus paving way for enhanced developability that tackles
manufacturing failures as well as attrition rates.European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions
grant agreemen
Database development and machine learning classification of medicinal chemicals and biomolecules
Ph.DDOCTOR OF PHILOSOPH
Database development and machine learning prediction of pharmaceutical agents
Ph.DDOCTOR OF PHILOSOPH
Development and Application of Pseudoreceptor Modeling
Quantitative Structure-Activity Relationship (QSAR) methods are a commonly used tool in the drug discovery process. These methods attempt to form a statistical model that relates descriptor properties of a ligand to the activity of that ligand compound towards a specific desired physiological response. QSAR methods are known as a ligand-based method, as they specifically use information from ligands and not protein structural data. However, a derivation of QSAR methods are pseudoreceptor methods. Pseudoreceptor methods go beyond standard QSAR by building a model representation of the protein pocket. However, the ability of pseudoreceptors to accurately replicate natural protein surfaces has not been studied. The goal of this thesis work is to investigate the necessary descriptors to map a protein binding pocket and a method to accurately recreate the 3-D spatial structure of the binding pocket. In addition, additional applications of existing pseudoreceptor methods are explored
Optical Gas Sensing: Media, Mechanisms and Applications
Optical gas sensing is one of the fastest developing research areas in laser spectroscopy. Continuous development of new coherent light sources operating especially in the Mid-IR spectral band (QCL—Quantum Cascade Lasers, ICL—Interband Cascade Lasers, OPO—Optical Parametric Oscillator, DFG—Difference Frequency Generation, optical frequency combs, etc.) stimulates new, sophisticated methods and technological solutions in this area. The development of clever techniques in gas detection based on new mechanisms of sensing (photoacoustic, photothermal, dispersion, etc.) supported by advanced applied electronics and huge progress in signal processing allows us to introduce more sensitive, broader-band and miniaturized optical sensors. Additionally, the substantial development of fast and sensitive photodetectors in MIR and FIR is of great support to progress in gas sensing. Recent material and technological progress in the development of hollow-core optical fibers allowing low-loss transmission of light in both Near- and Mid-IR has opened a new route for obtaining the low-volume, long optical paths that are so strongly required in laser-based gas sensors, leading to the development of a novel branch of laser-based gas detectors. This Special Issue summarizes the most recent progress in the development of optical sensors utilizing novel materials and laser-based gas sensing techniques
Virtual Screening of Multi-Target Agents by Combinatorial Machine Learning Methods
Ph.DDOCTOR OF PHILOSOPH
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