Snow-vegetation-atmosphere interactions in alpine tundra

The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow-vegetation-atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land-atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined three years (2019&ndash;2021) of continuous eddy covariance flux measurements of net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site-typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian Pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a one-month delay in melt-out date, which falls on the 92nd-percentile in the distribution of yearly melt-out dates in the period 2001&ndash;2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to one month later than the other ecosystem types. While the summertime average Normalized Difference Vegetation Index (NDVI) was reduced considerably during the extreme snow year 2020, it reached the same maximum as in the other years for all but one the ecosystem type (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model as well as the high correlation of flux magnitudes (correlation coefficient r = 0.92 for NEE and r = 0.89 for ET) between both areas. The one-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021, and reduced the growing season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (&minus;31 to &minus;6 gC m&minus;2 yr&minus;1) to a source (34 to 20 gC m&minus;2 yr&minus;1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.</p

Comprehensive analysis of the ATM, CHEK2 and ERBB2 genes in relation to breast tumour characteristics and survival: a population-based case-control and follow-up study

BACKGROUND: Mutations in the ataxia-telangiectasia mutated (ATM) and checkpoint kinase 2 (CHEK2) genes and amplification of the v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2) gene have been suggested to have an important role in breast cancer aetiology. However, whether common variation in these genes has a role in the development of breast cancer or breast cancer survival in humans is still not clear. METHODS: We performed a comprehensive haplotype analysis of the ATM, CHEK2 and ERBB2 genes in a Swedish population-based study, which included 1,579 breast cancer cases and 1,516 controls. We followed the cases for 8.5 years, on average, and retrieved information on the date and cause of death during that period from the nationwide Swedish causes of death registry. We selected seven haplotype-tagging SNPs (tagSNPs) in the ATM gene, six tagSNPs in the CHEK2 gene and seven tagSNPs in the ERBB2 gene that predicted both haplotypic and single locus variations in the respective genes with R(2 )values ≥ 0.8. These tagSNPs were genotyped in the complete set of cases and controls. We computed expected haplotype dosages of the tagSNP haplotypes and included the dosages as explanatory variables in Cox proportional hazards or logistic regression models. RESULTS: We found no association between any genetic variation in the ATM, CHEK2 or ERBB2 genes and breast cancer survival or the risk of developing tumours with certain characteristics. CONCLUSION: Our results indicate that common variants in the ATM, CHEK2 or ERBB2 genes are not involved in modifying breast cancer survival or the risk of tumour-characteristic-defined breast cancer

The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS