MALDI-TOF Baseline Drift Removal Using Stochastic Bernstein Approximation

Stochastic Bernstein (SB) approximation can tackle the problem of baseline drift correction of instrumentation data. This is demonstrated for spectral data: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) data. Two SB schemes for removing the baseline drift are presented: iterative and direct. Following an explanation of the origin of the MALDI-TOF baseline drift that sheds light on the inherent difficulty of its removal by chemical means, SB baseline drift removal is illustrated for both proteomics and genomics MALDI-TOF data sets. SB is an elegant signal processing method to obtain a numerically straightforward baseline shift removal method as it includes a free parameter sigma(x) that can be optimized for different baseline drift removal applications. Therefore, research that determines putative biomarkers from the spectral data might benefit from a sensitivity analysis to the underlying spectral measurement that is made possible by varying the SB free parameter. This can be manually tuned ( for constant sigma) or tuned with evolutionary computation ( for sigma( x)). Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved

Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging

Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images

Mass spectrometry analysis of amyloid formation mechanisms

More than 50 human conditions are characterised by the deposition of aberrantly aggregated proteins into amyloid fibrils. These diseases range from neurodegenerative diseases associated with aging to systemic conditions associated with medical interventions. The protein(s) involved in aggregation varies in each condition. Few of these diseases currently have therapeutics available and they represent a growing health burden to society. Increasing the understanding of the processes of amyloid protein aggregation and the mechanisms by which it can be inhibited is vital to developing strategies to combat these diseases. In this thesis, mass spectrometry and supporting biophysical and biochemical techniques were used to characterise the activity and interactions of two amyloid aggregation modulators, YDL085CA and the peptide QBP1, with amyloidogenic proteins. YDL085CA, a small highly charged protein orthologous to the known modifier of amyloid aggregation MOAG-4/SERF1, was found to have an extremely buffer dependent effect on the aggregation of the amyloid protein Aβ40. In ammonium bicarbonate it was shown to accelerate amyloid formation while in sodium phosphate it was shown to act as an inhibitor. This observation suggests a prominent role for ionic strength and specific ion effects. The activity of YDL085CA was shown, by crosslinking followed by mass spectrometric analysis, to be mediated by interactions with regions of α-helical secondary structure in the YDL085CA protein. QBP1, an 11-residue synthetic peptide, has been shown previously to inhibit the formation of amyloid fibrils by polyglutamine proteins, including ataxin-3. Native mass spectrometry studies on a range of ataxin-3 constructs revealed that QBP1 interacts with the monomeric ataxin-3. Unexpectedly, the site of interaction was localised not to the polyglutamine domain, but to a distal region lacking defined structure

Investigation of Heterogeneous Proteins and Protein Complexes with Native Ion Mobility-Mass Spectrometry and Theory

Native ion mobility-mass spectrometry (IM-MS) offers many advantages for the study of biomolecules and their complexes. High mass accuracy and sensitivity enable unambiguous determination of complex stoichiometries with respect to subunit composition as well as bound ligands. Ion mobility spectrometry adds an additional dimension of separation and can provide some structural information. Native IM-MS experiments are also fast with minimal sample requirements. Because of these reasons, native IM-MS has become an important tool in structural biology, able to investigate challenging samples that may not be amenable to study by other techniques. However, there are still some major challenges for using native IM-MS in the study of biomolecules. Heterogeneity—arising from the presence of multiple conformations, subunit compositions, ligands and small molecules, for example—results in complicated native mass spectra that can be difficult or even impossible to deconvolute and interpret. Characterizing the heterogeneity of these samples is desirable, as reports of lipids, small drugs, and metals being important for physiological structure and function continue to accumulate. Additionally, interpretation of structural information from IM data has remained largely qualitative, and more fundamental questions about this technique persist, including detailed understanding of the nature of gas-phase protein structure and behavior and how it might differ from solution-phase. Investigation into this aspect is required to make structural interpretation from native IM-MS data quantitative. In the first half of this dissertation, strategies to overcome the challenges of heterogeneity are explored, and computational methods are developed to solve the quantitation problem. With these methods, key features of gas-phase protein ion compaction are revealed, allowing more informed interpretation of structural details from this technique. The second half of this dissertation illustrates the wealth of information that can be accessed for challenging, heterogeneous biomolecules in native IM-MS experiments upon application of these computational methods. With results from both experiment and computation, oligomeric states of the membrane pore-forming protein toxin Cytolysin A are identified, and the composition and topology of multimeric β-crystallin protein complexes, which are implicated in cataract formation, are characterized. This dissertation includes previously published and unpublished co-authored material

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment

Computational Tools for the Processing and Analysis of Time-course Metabolomic Data

Modern, high-throughput techniques for the acquisition of metabolomic data, combined with an increase in computational power, have provided not only the need for, but also the means to develop and use, methods for the interpretation of large and complex datasets. This thesis investigates the methods by which pertinent information can be extracted from nontargeted metabolomic data and reviews the current state of chemometric methods. The analysis of real-world data and research questions relevant to the agri-food industry reveals several problems for which novel solutions are proposed. Three LC-MS datasets are studied: Medicago, Alopecurus and aged Beef, covering stress resistance, herbicide resistance and product misbranding. The new methods include preprocessing (batch correction, data-filtering), processing (clustering, classification) and visualisation and their use facilitated within a flexible data-to-results pipeline. The resulting software suite with a user-friendly graphical interface is presented, providing a pragmatic realisation of these methods in an easy to access workflow

Structure and Stability in Cluster Ions Produced from Binary Bulk Metallic Glass

Experimental knowledge of the short range order in metallic glass is primarily restricted to diffraction or transmission electron-based results, which provide information about average coordination numbers and bond lengths within a glass structure with little insight to local atomic configurations or structural stability. In the distantly related field of metal cluster physics, “magic numbers” in abundance in the mass spectra of ablated or evaporated metallic cluster ions provide information about topological and electronic cluster stability. Because of the importance of short range order to metallic glass, by producing cluster ions from bulk metallic glass (BMG) it should theoretically be possible to obtain experimental insight into which short- and medium-range structures may exhibit intrinsically greater stability, supplementing existing data and providing a basis for the stability of amorphous structure at specific alloy compositions. BMGs from five binary systems were examined, representing a broad range of possible topological and electronic/valence states: Ca66.4Al33.6, Mg70Zn30, Ni62Nb38, Cu46Zr54, Cu50Zr50, Cu64.5Zr35.5, and Pd82Si18. Time-of-flight mass spectrometry (ToF-SIMS) was used to produce cluster ions in both positive and negative polarity using 1 keV Cs+ or 1keV O2+ for sputter cleaning and 30 keV Bi3+ as a primary ablation ion. Laser-assisted atom probe tomography (La-APT) was also trialled for majority of alloys, however, the results obtained from ToF-SIMS were more illustrative, producing significantly larger cluster structures for analysis. The type of cluster ions produced using ToF-SIMS varied between alloy systems. Mass degeneracy obscured Ca66.4Al33.6 results, though significant Aln- anions were observed. Mg70Zn30 produced Mg-rich MgmZnn- anions observed at (m + n) = 9, 10, 19 and MgmZnnCs- anions at (m + n) = 9 and perhaps 19, consistent with intercrossed Zn-centred icosahedra. Ni62Nb38 showed high abundance of a potential Ni-centred icosahedral cluster cation, Ni7Nb6+. The abundance of Ni6-9Nbn+ cations supported electron shell closings influencing stability, and the high relative abundance of Ni6-9Nb6+ may indicate a chemical pathway to link Ni7Nb6 with Ni10Nb6, the presence of which could not be confirmed but which would represent either an efficiently packed Nb-centred cluster or the cluster-plus-glue-atom formula [Ni-Ni6Nb6](Ni3). Cu-Zr alloys only produced Cun+/-, Zrn+/-, and CuZrn+ metallic ions by ToF SIMS, providing little structural insight. Pd82Si18 provided a significant dataset of cluster ions in both positive and negative polarity, which pointed to the importance of the Si-centred trigonal prism (Pd6Si) and its face-capped prism Pd9Si. An anticipated Pd11Si3 Pd-centred cluster structure was also found to be significant as an independent structure and in relation to Pd9Si structural tessellation. Many of these proposed short- and medium-range structures could be correlated to local structures inherent to equilibrium crystalline phases. These results show that ToF-SIMS is capable of producing magic number cluster ions that are broadly consistent with both magic number literature and existing knowledge of short range order in metallic glass, providing a means to supplement existing experimental data