Spermidine protects from age-related synaptic alterations at hippocampal mossy fiber-CA3 synapses

Aging is associated with functional alterations of synapses thought to contribute to age-dependent memory impairment (AMI). While therapeutic avenues to protect from AMI are largely elusive, supplementation of spermidine, a polyamine normally declining with age, has been shown to restore defective proteostasis and to protect from AMI in Drosophila. Here we demonstrate that dietary spermidine protects from age-related synaptic alterations at hippocampal mossy fiber (MF)-CA3 synapses and prevents the aging-induced loss of neuronal mitochondria. Dietary spermidine rescued age-dependent decreases in synaptic vesicle density and largely restored defective presynaptic MF-CA3 long-term potentiation (LTP) at MF-CA3 synapses (MF-CA3) in aged animals. In contrast, spermidine failed to protect CA3-CA1 hippocampal synapses characterized by postsynaptic LTP from age-related changes in function and morphology. Our data demonstrate that dietary spermidine attenuates age-associated deterioration of MF-CA3 synaptic transmission and plasticity. These findings provide a physiological and molecular basis for the future therapeutic usage of spermidine

Perspectives on multiscale modelling and experiments to accelerate materials development for fusion

Prediction of material performance in fusion reactor environments relies on computational modelling, and will continue to do so until the first generation of fusion power plants come on line and allow long-term behaviour to be observed. In the meantime, the modelling is supported by experiments that attempt to replicate some aspects of the eventual operational conditions. In 2019, a group of leading experts met under the umbrella of the IEA to discuss the current position and ongoing challenges in modelling of fusion materials and how advanced experimental characterisation is aiding model improvement. This review draws from the discussions held during that workshop. Topics covering modelling of irradiation-induced defect production and fundamental properties, gas behaviour, clustering and segregation, defect evolution and interactions are discussed, as well as new and novel multiscale simulation approaches, and the latest efforts to link modelling to experiments through advanced observation and characterisation techniques.MRG, SLD, and DRM acknowledge funding by the RCUK Energy Programme [grant number EP/T012250/1]. Part of this work has been carried out within the framework of the EUROFusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant Agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. JRT acknowledges funding from the US Department of Energy (DOE) through grant DE-SC0017899. ZB, LY,BDW, and SJZ acknowledge funding through the US DOE Fusion Energy Sciences grant DE-SC0006661ZB, LY and BDW also were partially supported from the US DOE Office of Science, Office of Fusion Energy Sciences and Office of Advanced Scientific Computing Research through the Scientific Discovery through Advanced Computing (SciDAC) project on Plasma-Surface Interactions. JMa acknowledges support from the US-DOEs Office of Fusion Energy Sciences (US-DOE), project DE-SC0019157. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the US Department of Energy (DOE) under contract DE-AC05-76RL01830. YO and YZ were supported as part of the Energy Dissipation to Defect Evolution (EDDE), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under contract number DE-AC05-00OR22725. TS and TT are supported by JSPS KAKENHI Grant Number 19K05338

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. We present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives