The Artisse intrasaccular device for the treatment of cerebral aneurysms: initial experience from three Austrian neurovascular centers.

Background And Purpose: This study evaluates the early clinical performance of the new Artisse Intrasaccular Device (Artisse ISD), a self-expandable intrasaccular flow diverter, for treating wide-necked aneurysms (WNAs). We report initial safety and efficacy outcomes in the first cohort of patients treated with this novel device.

Methods: Prospective clinical and radiological data were collected for all patients treated with the Artisse ISD at three Austrian neurovascular centers from July 2023 to August 2024.

Brain damage caused by status epilepticus: A prospective MRI study.

Background: Status epilepticus (SE) is a severe neurological condition that might lead to long-term consequences such as neuronal death. This study investigated whether SE leads to brain volume loss by characterizing the dynamic of peri-ictal MRI abnormalities (PMA) through follow-up MRIs and assessing whether SE duration and specific outcome characteristics are associated with brain atrophy.

Methods: A prospective single-center cohort study enrolled 590 adult patients with definitive or possible SE.

Embolism propagation does not rely on pressure only: time-based shifts in xylem vulnerability curves of angiosperms determine the accuracy of the flow-centrifuge method.

Centrifuges provide a fast approach to quantify embolism resistance of xylem in vulnerability curves (VCs). Since embolism formation is assumingly driven by pressure only, spin time is not standardised for flow centrifuge experiments. Here, we explore to what extent embolism resistance could be spin-time dependent, and hypothesise that changes in hydraulic conductivity (Kh) would shift VCs towards higher water potential (Ψ) values over time.

Spatio-Temporal Convolutional Neural Network for Enhanced Inter Prediction in Video Coding.

This paper presents a convolutional neural network (CNN)-based enhancement to inter prediction in Versatile Video Coding (VVC). Our approach aims at improving the prediction signal of inter blocks with a residual CNN that incorporates spatial and temporal reference samples. It is motivated by the theoretical consideration that neural network-based methods have a higher degree of signal adaptivity than conventional signal processing methods and that spatially neighboring reference samples have the potential to improve the prediction signal by adapting it to the reconstructed signal in its immediate vicinity.