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Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

The Common Spatial Patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain-computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI.

A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated.

Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relaThey also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.We have carried out a theoretical investigation of hot electron power loss P, involving electron-acoustic phonon interaction, as a function of twist angle θ, electron temperature T e and electron density n s in twisted bilayer graphene. It is found that as θ decreases closer to magic angle θ m, P enhances strongly and θ acts as an important tunable parameter, apart from T e and n s. In the range of T e = 1-50 K, this enhancement is ∼250-450 times the P in monolayer graphene (MLG), which is manifestation of the great suppression of Fermi velocity v F* of electrons in moiré flat band. As θ increases away from θ m, the impact of θ on P decreases, tending to that of MLG at θ ∼ 3°. In the Bloch-Grüneisen (BG) regime, P ∼ T e4, n s-1/2 and v F*-2. In the higher temperature region (∼10-50 K), P ∼ T eδ , with δ ∼ 2.0, and the behavior is still super linear in T e, unlike the phonon limited linear-in-T (lattice temperature) resistivity ρ p. P is weakly, decreasing (increasing) with increasing n s at lower (higher) T e, as found in MLG. The energy relaxation time τ e is also discussed as a function of θ and T e. Expressing the power loss P = F e(T e) - F e(T), in the BG regime, we have obtained a simple and useful relation F e(T)μ p(T) = (ev s2/2) i.e. F e(T) = (n s e 2 v s2/2)ρ p, where μ p is the acoustic phonon limited mobility and v s is the acoustic phonon velocity. The ρ p estimated from this relation using our calculated F e(T) is nearly agreeing with the ρ p of Wu et al (2019 Phys. Rev. selleck inhibitor B 99 165112).We consider a non-chiral Luttinger liquid in the presence of a backscattering Hamiltonian which has an extended range. Right/left moving fermions at a given location can thus be converted as left/right moving fermions at a different location, within a specific range. We perform a momentum shell renormalization group treatment which gives the evolution of the relative degrees of freedom of this Hamiltonian contribution under the renormalization flow, and we study a few realistic examples of this extended backscattering Hamiltonian. We find that, for repulsive Coulomb interaction in the Luttinger liquid, any such Hamiltonian contribution evolves into a delta-like scalar potential upon renormalization to a zero temperature cutoff. On the opposite, for attractive couplings, the amplitude of this kinetic Hamiltonian is suppressed, rendering the junction fully transparent. As the renormalization procedure may have to be stopped because of experimental constraints such as finite temperature, we predict the actual spatial shape of the kinetic Hamiltonian at different stages of the renormalization procedure, as a function of the position and the Luttinger interaction parameter, and show that it undergoes structural changes. This renormalized kinetic Hamiltonian has thus to be used as an input for the perturbative calculation of the current, for which we provide analytic expressions in imaginary time. We discuss the experimental relevance of this work by looking at one-dimensional systems consisting of carbon nanotubes or semiconductor nanowires.