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Dysregulation of neuronal excitability underlies the pathogenesis of tauopathies, including frontotemporal dementia (FTD) with tau inclusions. A majority of FTD-causing tau mutations are located in the microtubule-binding domain, but how these mutations alter neuronal excitability is largely unknown. Here, using CRISPR/Cas9-based gene editing in human pluripotent stem cell (iPSC)-derived neurons and isogenic controls, we show that the FTD-causing V337M tau mutation impairs activity-dependent plasticity of the cytoskeleton in the axon initial segment (AIS). Extracellular recordings by multi-electrode arrays (MEAs) revealed that the V337M tau mutation in human neurons leads to an abnormal increase in neuronal activity in response to chronic depolarization. Stochastic optical reconstruction microscopy of human neurons with this mutation showed that AIS plasticity is impaired by the abnormal accumulation of end-binding protein 3 (EB3) in the AIS submembrane region. These findings expand our understanding of how FTD-causing tau mutations dysregulate components of the neuronal cytoskeleton, leading to network dysfunction.

Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution of the Intergovernmental Panel on Climate Change (IPCC) projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some differences. In addition, we discuss how the different reports included high-end projections. We then show how the AR5 projections (from 2007 onwards) compare against the observations and find that they are highly consistent with each other. Finally, we discuss how to further improve sea-level projections using high-resolution ocean modelling and recent vertical land motion estimates. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ-MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]