Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Duality in multi-channel Luttinger Liquid with local scatterer

We have devised a general scheme that reveals multiple duality relations valid for all multi-channel Luttinger Liquids. The relations are universal and should be used for establishing phase diagrams and searching for new non-trivial phases in low-dimensional strongly correlated systems. The technique developed provides universal correspondence between scaling dimensions of local perturbations in different phases. These multiple relations between scaling dimensions lead to a connection between different inter-phase boundaries on the phase diagram. The dualities, in particular, constrain phase diagram and allow predictions of emergence and observation of new phases without explicit model-dependent calculations. As an example, we demonstrate the impossibility of non-trivial phase existence for fermions coupled to phonons in one dimension. © 2013 EPLA.

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

Manifold learning and the quantum Jensen-Shannon divergence kernel

The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

Support enhanced α-pinene isomerization over HPW/SBA-15

A family of mesoporous SBA-15 supported H3PW12O40 (HPW) catalysts were synthesized by wet-impregnation and compared with fumed silica analogues for the solventless isomerization of α-pinene under mild conditions. Structural and acidic properties of supported HPW materials were characterized by powder XRD, HRTEM, XPS, TGA, N2 porosimetry, DRIFTS, and ammonia and propylamine chemisorption and TPD. The high area, mesoporous SBA-15 architecture facilitates the formation of highly dispersed (isolated or low dimensional) HPW clusters and concomitant high acid site densities (up to 0.54 mmol g−1) relative to fumed silica wherein large HPW crystallites are formed even at low HPW loadings. α-Pinene exhibits a volcano dependence on HPW loading over the SBA-15 support due to competition between the number and accessibility of acid sites to the non-polar reactant, with the superior acid site accessibility for HPW/SBA-15 conferring a 10-fold rate enhancement with respect to HPW/fumed silica and pure HPW. Monocyclic limonene and terpinolene products are favoured over polycyclic camphene and β-pinene by weaker polyoxometallate analogues over SBA-15.

Downstream effects on human low density lipoprotein of homocysteine exported from endothelial cells in an in vitro system

A model system is presented using human umbilical vein endothelial cells (HUVECs) to investigate the role of homocysteine (Hcy) in atherosclerosis. HUVECs are shown to export Hcy at a rate determined by the flux through the methionine/Hcy pathway. Additional methionine increases intracellular methionine, decreases intracellular folate, and increases Hcy export, whereas additional folate inhibits export. An inverse relationship exists between intracellular folate and Hcy export. Hcy export may be regulated by intracellular S-adenosyl methionine rather than by Hcy. Human LDLs exposed to HUVECs exporting Hcy undergo time-related lipid oxidation, a process inhibited by the thiol trap dithionitrobenzoate. This is likely to be related to the generation of hydroxyl radicals, which we show are associated with Hcy export. Although Hcy is the major oxidant, cysteine also contributes, as shown by the effect of glutamate. Finally, the LDL oxidized in this system showed a time-dependent increase in uptake by human macrophages, implying an upregulation of the scavenger receptor. These results suggest that continuous export of Hcy from endothelial cells contributes to the generation of extracellular hydroxyl radicals, with associated oxidative modification of LDL and incorporation into macrophages, a key step in atherosclerosis. Factors that regulate intracellular Hcy metabolism modulate these effects. Copyright © 2005 by the American Society for Biochemistry and Molecular Biology, Inc.

A three dimensional surface measurement and reconstruction system for respiratory function analysis

Respiration is a complex activity. If the relationship between all neurological and skeletomuscular interactions was perfectly understood, an accurate dynamic model of the respiratory system could be developed and the interaction between different inputs and outputs could be investigated in a straightforward fashion. Unfortunately, this is not the case and does not appear to be viable at this time. In addition, the provision of appropriate sensor signals for such a model would be a considerable invasive task. Useful quantitative information with respect to respiratory performance can be gained from non-invasive monitoring of chest and abdomen motion. Currently available devices are not well suited in application for spirometric measurement for ambulatory monitoring. A sensor matrix measurement technique is investigated to identify suitable sensing elements with which to base an upper body surface measurement device that monitors respiration. This thesis is divided into two main areas of investigation; model based and geometrical based surface plethysmography. In the first instance, chapter 2 deals with an array of tactile sensors that are used as progression of existing and previously investigated volumetric measurement schemes based on models of respiration. Chapter 3 details a non-model based geometrical approach to surface (and hence volumetric) profile measurement. Later sections of the thesis concentrate upon the development of a functioning prototype sensor array. To broaden the application area the study has been conducted as it would be fore a generically configured sensor array. In experimental form the system performance on group estimation compares favourably with existing system on volumetric performance. In addition provides continuous transient measurement of respiratory motion within an acceptable accuracy using approximately 20 sensing elements. Because of the potential size and complexity of the system it is possible to deploy it as a fully mobile ambulatory monitoring device, which may be used outside of the laboratory. It provides a means by which to isolate coupled physiological functions and thus allows individual contributions to be analysed separately. Thus facilitating greater understanding of respiratory physiology and diagnostic capabilities. The outcome of the study is the basis for a three-dimensional surface contour sensing system that is suitable for respiratory function monitoring and has the prospect with future development to be incorporated into a garment based clinical tool.

A low-cost remote sensing system for agricultural applications

This research develops a low cost remote sensing system for use in agricultural applications. The important features of the system are that it monitors the near infrared and it incorporates position and attitude measuring equipment allowing for geo-rectified images to be produced without the use of ground control points. The equipment is designed to be hand held and hence requires no structural modification to the aircraft. The portable remote sensing system consists of an inertia measurement unit (IMU), which is accelerometer based, a low-cost GPS device and a small format false colour composite digital camera. The total cost of producing such a system is below GBP 3000, which is far cheaper than equivalent existing systems. The design of the portable remote sensing device has eliminated bore sight misalignment errors from the direct geo-referencing process. A new processing technique has been introduced for the data obtained from these low-cost devices, and it is found that using this technique the image can be matched (overlaid) onto Ordnance Survey Master Maps at an accuracy compatible with precision agriculture requirements. The direct geo-referencing has also been improved by introducing an algorithm capable of correcting oblique images directly. This algorithm alters the pixels value, hence it is advised that image analysis is performed before image georectification. The drawback of this research is that the low-cost GPS device experienced bad checksum errors, which resulted in missing data. The Wide Area Augmented System (WAAS) correction could not be employed because the satellites could not be locked onto whilst flying. The best GPS data were obtained from the Garmin eTrex (15 m kinematic and 2 m static) instruments which have a highsensitivity receiver with good lock on capability. The limitation of this GPS device is the inability to effectively receive the P-Code wavelength, which is needed to gain the best accuracy when undertaking differential GPS processing. Pairing the carrier phase L1 with the pseudorange C/A-Code received, in order to determine the image coordinates by the differential technique, is still under investigation. To improve the position accuracy, it is recommended that a GPS base station should be established near the survey area, instead of using a permanent GPS base station established by the Ordnance Survey.

Zero-dimensional noise:the best mask you never saw

The transmission of weak signals through the visual system is limited by internal noise. Its level can be estimated by adding external noise, which increases the variance within the detecting mechanism, causing masking. But experiments with white noise fail to meet three predictions: (a) noise has too small an influence on the slope of the psychometric function, (b) masking occurs even when the noise sample is identical in each two-alternative forced-choice (2AFC) interval, and (c) double-pass consistency is too low. We show that much of the energy of 2D white noise masks extends well beyond the pass-band of plausible detecting mechanisms and that this suppresses signal activity. These problems are avoided by restricting the external noise energy to the target mechanisms by introducing a pedestal with a mean contrast of 0% and independent contrast jitter in each 2AFC interval (termed zero-dimensional [0D] noise). We compared the jitter condition to masking from 2D white noise in double-pass masking and (novel) contrast matching experiments. Zero-dimensional noise produced the strongest masking, greatest double-pass consistency, and no suppression of perceived contrast, consistent with a noisy ideal observer. Deviations from this behavior for 2D white noise were explained by cross-channel suppression with no need to appeal to induced internal noise or uncertainty. We conclude that (a) results from previous experiments using white pixel noise should be re-evaluated and (b) 0D noise provides a cleaner method for investigating internal variability than pixel noise. Ironically then, the best external noise stimulus does not look noisy.

Fibre laser torsion sensor system using an excessively tilted fibre grating and low-cost time domain demodulation

In this paper, we report a simple fibre laser torsion sensor system using an intracavity tilted fibre grating as a torsion encoded loss filter. When the grating is subjected to twist, it induces loss to the cavity, thus affecting the laser oscillation build-up time. By measuring the build-up time, both twist direction and angle on the grating can be monitored. Using a low-cost photodiode and a two-channel digital oscilloscope, we have characterised the torsion sensing capability of this fibre laser system and obtained a torsion sensitivity of ~412µs/(rad/m) in the dynamic range from -150° to +150°.

Arbitrary real-time three-dimensional corporal object sensing and reconstruction scheme

A real-time three-dimensional (3D) object sensing and reconstruction scheme is presented that can be applied on any arbitrary corporeal shape. Operation is demonstrated on several calibrated objects. The system uses curvature sensors based upon in-line fiber Bragg gratings encapsulated in a low-temperature curing synthetic silicone. New methods to quantitatively evaluate the performance of a 3D object-sensing scheme are developed and appraised. It is shown that the sensing scheme yields a volumetric error of 1% to 9%, depending on the object.

Respiratory function monitoring using a real-time three-dimensional fiber-optic shaping sensing scheme based upon fiber Bragg gratings

An array of in-line curvature sensors on a garment is used to monitor the thoracic and abdominal movements of a human during respiration. The results are used to obtain volumetric changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The array of 40 in-line fiber Bragg gratings is used to produce 20 curvature sensors at different locations, each sensor consisting of two fiber Bragg gratings. The 20 curvature sensors and adjoining fiber are encapsulated into a low-temperature-cured synthetic silicone. The sensors are wavelength interrogated by a commercially available system from Moog Insensys, and the wavelength changes are calibrated to recover curvature. A three-dimensional algorithm is used to generate shape changes during respiration that allow the measurement of absolute volume changes at various sections of the torso. It is shown that the sensing scheme yields a volumetric error of 6%. Comparing the volume data obtained from the spirometer with the volume estimated with the synchronous data from the shape-sensing array yielded a correlation value 0.86 with a Pearson's correlation coefficient p <0.01.

Low-cost fully integrated fiber Bragg grating interrogation system

Fiber Bragg gratings can be used for monitoring different parameters in a wide variety of materials and constructions. The interrogation of fiber Bragg gratings traditionally consists of an expensive and spacious peak tracking or spectrum analyzing unit which needs to be deployed outside the monitored structure. We present a dynamic low-cost interrogation system for fiber Bragg gratings which can be integrated with the fiber itself, limiting the fragile optical in- and outcoupling interfaces and providing a compact, unobtrusive driving and read-out unit. The reported system is based on an embedded Vertical Cavity Surface Emitting Laser (VCSEL) which is tuned dynamically at 1 kHz and an embedded photodiode. Fiber coupling is provided through a dedicated 45° micromirror yielding a 90° in-the-plane coupling and limiting the total thickness of the fiber coupled optoelectronic package to 550 µm. The red-shift of the VCSEL wavelength is providing a full reconstruction of the spectrum with a range of 2.5 nm. A few-mode fiber with fiber Bragg gratings at 850 nm is used to prove the feasibility of this low-cost and ultra-compact interrogation approach.

A low-cost multimode fiber Bragg grating sensor system

A low-cost fiber optic sensor system based on multimode fiber and an LED light source is presented. A multimode fiber Bragg grating (MMFBG) element is used as a strain sensor. In a matched grating scheme, a MMFBG similar to the sensing one was used as a reference in the receiving unit. For detection of large wavelength shift we demonstrated the feasibility of MMFBG wavelength detection using a single mode fiber fused coupler edge filter. The high cost normally associated with wavelength interrogators for single mode fiber FBG sensors was overcome by the utilization of a low cost multimode fiber pigtailed LED light source. The multimode fiber sensing system has the potential of maintaining much of the advantages of its single mode FBG sensor system counterparts. The MMFBG sensing schemes could be used for short distance, high sensitivity, high speed, strain, temperature and acoustic sensing applications.