Stress-induced lipocalin-2 controls dendritic spine formation and neuronal activity in the amygdala.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Behavioural adaptation to psychological stress is dependent on neuronal plasticity and dysfunction at this cellular level may underlie the pathogenesis of affective disorders such as depression and post-traumatic stress disorder. Taking advantage of genome-wide microarray assay, we performed detailed studies of stress-affected transcripts in the amygdala - an area which forms part of the innate fear circuit in mammals. Having previously demonstrated the role of lipocalin-2 (Lcn-2) in promoting stress-induced changes in dendritic spine morphology/function and neuronal excitability in the mouse hippocampus, we show here that the Lcn-2 gene is one of the most highly upregulated transcripts detected by microarray analysis in the amygdala after acute restraint-induced psychological stress. This is associated with increased Lcn-2 protein synthesis, which is found on immunohistochemistry to be predominantly localised to neurons. Stress-naïve Lcn-2(-/-) mice show a higher spine density in the basolateral amygdala and a 2-fold higher rate of neuronal firing rate compared to wild-type mice. Unlike their wild-type counterparts, Lcn-2(-/-) mice did not show an increase in dendritic spine density in response to stress but did show a distinct pattern of spine morphology. Thus, amygdala-specific neuronal responses to Lcn-2 may represent a mechanism for behavioural adaptation to psychological stress.Marie Curie Excellence Grant from the European Commission.Medical Research Council Project GrantCOST Action ECMNe

Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern, following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types