Lincoln County lore, from the Journal of Lieut. Theodore Talbot, U.S.A., on his journey through Lincoln County and along the Oregon coast in 1849

Gerald W. Williams Collectio

Imaging of opioid receptors in the central nervous system

In vivo functional imaging by means of positron emission tomography (PET) is the sole method for providing a quantitative measurement of μ-, κ and δ-opioid receptor-mediated signalling in the central nervous system. During the last two decades, measurements of changes to the regional brain opioidergic neuronal activation—mediated by endogenously produced opioid peptides, or exogenously administered opioid drugs—have been conducted in numerous chronic pain conditions, in epilepsy, as well as by stimulant- and opioidergic drugs. Although several PET-tracers have been used clinically for depiction and quantification of the opioid receptors changes, the underlying mechanisms for regulation of changes to the availability of opioid receptors are still unclear. After a presentation of the general signalling mechanisms of the opioid receptor system relevant for PET, a critical survey of the pharmacological properties of some currently available PET-tracers is presented. Clinical studies performed with different PET ligands are also reviewed and the compound-dependent findings are summarized. An outlook is given concluding with the tailoring of tracer properties, in order to facilitate for a selective addressment of dynamic changes to the availability of a single subclass, in combination with an optimization of the quantification framework are essentials for further progress in the field of in vivo opioid receptor imaging

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

A foundation for exact binarized morphological neural networks

International audienceTraining and running deep neural networks (NNs) often demands a lot of computation and energy-intensive specialized hardware (e.g. GPU, TPU...). One way to reduce the computation and power cost is to use binary weight NNs, but these are hard to train because the sign function has a non-smooth gradient. We present a model based on Mathematical Morphology (MM), which can binarize ConvNets without losing performance under certain conditions, but these conditions may not be easy to satisfy in real-world scenarios. To solve this, we propose two new approximation methods and develop a robust theoretical framework for ConvNets binarization using MM. We propose as well regularization losses to improve the optimization. We empirically show that our model can learn a complex morphological network, and explore its performance on a classification task. ∀I ∈ I(δ), χ(I)

Binary morphological neural network

International audienceIn the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks. However, they are not necessarily optimal. For example, mathematical morphology is known to be better suited to deal with binary images. In this work, we create a morphological neural network that handles binary inputs and outputs. We propose their construction inspired by CNNs to formulate layers adapted to such images by replacing convolutions with erosions and dilations. We give explainable theoretical results on whether or not the resulting learned networks are indeed morphological operators. We present promising experimental results designed to learn basic binary operators, and we have made our code publicly available online