189 research outputs found
Multi view based imaging genetics analysis on Parkinson disease
No full textLongitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson’s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases
Correction to: Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning
No full textThis is a correction to: William J Scotton, Cameron Shand, Emily Todd, Martina Bocchetta, David M Cash, Lawren VandeVrede, Hilary Heuer, PROSPECT Consortium, 4RTNI Consortium, Alexandra L Young, Neil Oxtoby, Daniel C Alexander, James B Rowe, Huw R Morris, Adam L Boxer, Jonathan D Rohrer, Peter A Wijeratne, Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning, Brain Communications, Volume 5, Issue 2, 2023, https://doi.org/10.1093/braincomms/fcad04
Opportunities and Barriers for Adoption of a Decision-Support Tool for Alzheimer's Disease
No full textClinical
decision-support tools (DSTs)
represent a valuable resource in healthcare. However, lack of Human Factors considerations and early design research has often limited their successful adoption. To complement previous technically focused work, we studied adoption opportunities of a future DST built on a predictive model of
Alzheimer’s Disease (AD)
progression. Our aim is two-fold: exploring adoption opportunities for DSTs in AD clinical care, and testing a novel combination of methods to support this process. We focused on understanding current clinical needs and practices, and the potential for such a tool to be integrated into the setting, prior to its development. Our user-centred approach was based on field observations and semi-structured interviews, analysed through workflow analysis, user profiles, and a design-reality gap model. The first two are common practice, whilst the latter provided added value in highlighting specific adoption needs. We identified the likely early adopters of the tool as being both psychiatrists and neurologists based in research-oriented clinical settings. We defined ten key requirements for the translation and adoption of DSTs for AD around IT, user, and contextual factors. Future works can use and build on these requirements to stand a greater chance to get adopted in the clinical setting
Novel precision biomarker models applied to Alzheimer’s disease
No full textBiomarkers are essential for diagnosing, monitoring, and developing treatments for Alzheimer's disease (AD). However, conventional biomarkers for tau pathology (PET), brain atrophy (MRI), and amyloid beta peptides often rely on simple, predefined ratios that limit precision, stability, and statistical power, hindering clinical trial efficiency and research progress.
This thesis addresses these challenges by introducing and validating BioDisCVR, a novel, modality-agnostic framework for the data-driven discovery of optimised biomarkers. The work first establishes the methodological fragility of the gold-standard Standardised Uptake Value Ratio (SUVR) in tau PET, exposing the instability of commonly used reference regions and the detrimental effects of certain processing techniques. It further reveals the suboptimal performance of traditional volumetric measures for tracking longitudinal change.
One central innovation is the Composite Value Ratio (CVR), a data-driven biomarker construct where both numerator and denominator are optimised to maximise statistical power. Applied to tau PET and structural MRI, CVR demonstrates transformative improvements over established methods, with the potential to reduce clinical trial sample sizes by over 79%, and improve detection of pathological changes. The framework is extended to proteomics with a weighted CVR (wCVR), providing novel insights into the pathogenic contributions of different amyloid beta peptides. This culminates in theta, a parameter-free multidimensional ratio that achieved outstanding classification of familial AD mutations (AUC > 0.99), dramatically outperforming all conventional peptide ratios.
Overall, this thesis delivers a new paradigm for biomarker discovery. It provides a validated framework and a suite of statistically superior biomarkers to accelerate clinical trials, enhance disease monitoring, and advance the fundamental understanding of Alzheimer's disease
"Keeping it real": A quantum trajectory approach to realistic measurement of solid-state quantum systems
No full textTo obtain information about a system of interest a measurement has to be made. In experiments that probe the quantum nature of our world, the system itself is, in general, necessarily affected by the act of measurement. If the system is weakly coupled to its bath and the dynamics are such that information concerning the system is spread throughout the many degrees of freedom of the bath, and the bath is being measured then a stochastic master equation for the conditioned state of the system can be found. This is termed a quantum trajectory equation. Realistic detectors are not perfect. Information is lost in the conversion to a signal that the observer can use. This loss may occur in the detector itself, in the circuit containing the detector (described by a response time and electronic noise) or at the circuit output (electronic output noise). In order to obtain a true quantum trajectory for the experiment, the observer must condition the state of the quantum system on results that are available in the laboratory rather than on the microscopic events considered previously in quantum trajectories. A method for treating this was first proposed by Warszawski, Wiseman and Mabuchi [Phys. Rev. A 65, 023802 (2002)], in which the quantum system is embedded within a supersystem that also contains the state of the detector. They applied their theory to photodetectors of various sorts. Warszawski has also done the preliminary work on applying this theory to detecting the state of a pair of quantum dots using a SET (single-electron transistor) [MSc. Thesis, Griffith University (2001)]. The resulting theory is termed 'realistic' quantum trajectory theory.
In this thesis, the approach of Warszawski, et al.is applied to various solidstate readout devices. These include the SET, the QPC (quantum point contact), and the RF-QPC (radio-frequency QPC). Numerically obtained realistic quantum trajectories for the QPC agree with heuristic results. In particular, in certain limits, the realistic quantum trajectories can take on the appearance of ideal quantum trajectories. This thesis also resolves a problem in solid-state continuous quantum measurement theory by deriving a quantum trajectory model for a SET-monitored charge qubit, that guarantees physically meaningful qubit states. The particular limit necessary to achieve this is discussed, and the SET measurement quality is analysed using techniques borrowed from quantum optics. Conditions for which the SET can approach operation at the limit allowed by quantum mechanics are given. This is also done for the QPC, for which the results agree with previous work.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of ScienceFull Tex
Enhanced compositional sensitivity in atomic force microscopy by the excitation of the first two flexural modes
No full textThe authors demonstrate that the compositional sensitivity of an atomic force microscope is enhanced by the simultaneous excitation of its first two flexural eigenmodes. The coupling of those modes by the nonlinear probe-surface interactions enables to map compositional changes in several conjugated molecular materials with a phase shift sensitivity that is about one order of magnitude higher than the one achieved in amplitude modulation atomic force microscopy.The authors thank Fabio Biscarini, Neil Oxtoby, and Concepció Rovira for providing the conjugated molecules. This work was financially supported by the European Commission (FORCETOOL, NMP4-CT-2004-013684).Peer reviewe
Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.
No full textNeurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology
Weakly dense subsets of the measure algebra
No full textPT: J; CR: CARLSON T, THEOREM LIFTING CICHON J, 1985, P AM MATH SOC, V94, P142 FREMLIN D, 1977, 2 THEOREMS MOKOBODZK FREMLIN DH, 1984, MATHEMATIKA, V31, P323 GOFFMAN C, 1953, REAL FUNCTIONS HALMOS PR, 1950, MEASURE THEORY HODEL, 1984, HDB SET THEORETIC TO JECH TJ, 1978, SET THEORY KUNEN K, 1980, SET THEORY MAGIDOR M, 1977, ISRAEL J MATH, V28, P1 MAHARAM D, 1942, P NATL ACAD SCI USA, V28, P108 OXTOBY JC, 1971, MEASURE CATEGORY RUDIN W, 1983, AM MATH MON, V90, P41 SIKORSKI R, 1964, BOOLEAN ALGEBRAS VANDOUWEN E, IN PRESS HDB BOOLEAN; NR: 15; TC: 3; J9: PROC AMER MATH SOC; PG: 9; GA: AR774Source type: Electronic(1
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