David Hughes

Mapping the Stability Diagram of a Quadrupole Mass Spectrometer with a Static Transverse Magnetic Field Applied

AbstractPrevious experimental and theoretical work identified that the application of a static magnetic (B) field can improve the resolution of a quadrupole mass spectrometer (QMS) and this simple method of performance enhancement offers advantages for field deployment. Presented here are further data showing the effect of the transverse magnetic field upon the QMS performance. For the first time, the asymmetry in QMS operation with Bx and By is considered and explained in terms of operation in the fourth quadrant of the stability diagram. The results may be explained by considering the additional Lorentz force (v x B) experienced by the ion trajectories in each case. Using our numerical approach, we model not only the individual ion trajectories for a transverse B field applied in x and y but also the mass spectra and the effect of the magnetic field upon the stability diagram. Our theoretical findings, confirmed by experiment, show an improvement in resolution and ion transmission by application of magnetic field for certain operating conditions. Figureᅟ

Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

There is an emerging need in clinical research to accurately predict patients’ disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a five-year follow-up period.

Personalized risk-based screening for diabetic retinopathy: a multivariate approach vs. the use of stratification rules.

To evaluate our proposed multivariate approach to identify patients who will develop sight‐threatening diabetic retinopathy (STDR) within a 1‐year screen interval, and explore the impact of simple stratification rules on prediction.

Dynamic classification using credible intervals in longitudinal discriminant analysis

Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarkers longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patients status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods.