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There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

This paper investigates the lockdowns to contain the spread of the SARS-CoV-2 coronavirus in France, Germany, Italy, Spain, the UK and the US and also recent developments since these lockdowns have been relaxed. The analysis employs a two-stage SEIR model with different reproductive numbers pre- and post-lockdown. These parameters are estimated from data on the daily number of confirmed cases in a process that automatically detects the time at which the lockdown became effective. The model is evaluated by considering its predictive accuracy on current data and is then extended to a three-stage version to explore relaxations. The results show the extent to which each country was successful in reducing the reproductive number and demonstrate how the approach is able to model recent increases in the number of cases in all six countries, including the second peak in the US. The results also indicate that the current levels of relaxation in all five European countries could lead to significant second waves that last longer than the corresponding first waves. While there is uncertainty about the implications of these findings at this stage, they do suggest that a lot of vigilance is needed.

Multiphoton ionization rates for H2 immersed in an intense linearly polarized laser field are calculated using the recently developed R-matrix Floquet theory of molecular multiphoton processes. We assume that the H2 molecule is aligned along the laser polarization direction and we adopt the fixed-nuclei approximation, in which the motion of the target electrons is calculated in the laser field and in the field of the nuclei, which are assumed to be fixed in space. An accurate multi-state wavefunction is employed to calculate one-, two- and four-photon ionization rates for H2 at several internuclear separations over a range of frequencies and intensities. Analysis of the ionization rates reveals the important role played both by resonances corresponding to Rydberg bound states converging to the H2+ ion ground state and by doubly excited states converging to the H2+ ion first excited state. These resonances give rise to resonant enhanced multiphoton ionization peaks in many of the ionization rates studied in this paper, and their possible role in controlling the vibrational population of the final H2+ ion is discussed.

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