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Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations

Background: Frontotemporal dementia (FTD) covers a spectrum of neurodegenerative disorders with various clinical and neuropathological subtypes. The two major pathological proteins accumulating in the brains of FTD patients, depending on their genetic background, are TDP-43 and tau. We aimed to evaluate whether total TDP-43 levels measured from the serum associate with the genotype or clinical phenotype of the FTD patients and whether serum TDP-43 provides prognostic or diagnostic value in the FTD spectrum disorders. Methods: The study cohort included 254 participants with a clinical diagnosis of FTD (including all major genotypes and clinical phenotypes) and 105 cognitively healthy controls. Serum total TDP-43 levels measured with a single-molecule array (Simoa) were compared within the FTD group according to the genotype, clinical phenotype, and predicted neuropathological subtype of the patients. We also evaluated the associations between the TDP-43 levels and disease severity or survival in FTD. Results: Total TDP-43 levels in the serum were significantly lower in the FTD group as compared to the healthy control group (275.3 pg/mL vs. 361.8 pg/mL, B = 0.181, 95%CI = 0.014–0.348, p = 0.034). The lowest TDP-43 levels were observed in the subgroup of FTD patients harboring predicted TDP-43 brain pathology (FTD-TDP, 241.4 pg/mL). The low levels in the FTD-TDP group were especially driven by C9orf72 repeat expansion carriers (169.2 pg/mL) and FTD patients with concomitant motoneuron disease (FTD-MND, 113.3 pg/mL), whereas GRN mutation carriers did not show decreased TDP-43 levels (328.6 pg/mL). Serum TDP-43 levels showed no correlation with disease severity nor progression in FTD. Conclusions: Our results indicate that the total levels of TDP-43 in the serum are decreased especially in FTD patients with the C9orf72 repeat expansion or FTD-MND phenotype, both subtypes strongly associated with TDP-43 type B brain pathology. Serum-based measurement of TDP-43 co

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely-used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at

Frontotemporal lobar degeneration (FTLD) comprises a heterogenous group of fatal neurodegenerative diseases and, to date, no validated diagnostic or prognostic biomarkers or effective disease-modifying therapies exist for the different clinical or genetic subtypes of FTLD. Current treatment strategies rely on the off-label use of medications for symptomatic treatment. Changes in several neurotransmitter systems including the glutamatergic, GABAergic, dopaminergic, and serotonergic systems have been reported in FTLD spectrum disease patients. Many FTLD-related clinical and neuropsychiatric symptoms such as aggressive and compulsive behaviour, agitation, as well as altered eating habits and hyperorality can be explained by disturbances in these neurotransmitter systems, suggesting that their targeting might possibly offer new therapeutic options for treating patients with FTLD. This review summarizes the present knowledge on neurotransmitter system deficits and synaptic dysfunction in model systems and patients harbouring the most common genetic causes of FTLD, the hexanucleotide repeat expansion in C9orf72 and mutations in the granulin (GRN) and microtubule-associated protein tau (MAPT) genes. We also describe the current pharmacological treatment options for FLTD that target different neurotransmitter systems.

Frontotemporal lobar degeneration (FTLD) comprises a heterogenous group of progressive neurodegenerative syndromes. To date, no validated biomarkers or effective disease-modifying therapies exist for the different clinical or genetic subtypes of FTLD. The most common genetic cause underlying FTLD and amyotrophic lateral sclerosis (ALS) is a hexanucleotide repeat expansion in the C9orf72 gene (C9-HRE). FTLD is accompanied by changes in several neurotransmitter systems, including the glutamatergic, GABAergic, dopaminergic, and serotonergic systems and many clinical symptoms can be explained by disturbances in these systems. Here, we aimed to elucidate the effects of the C9-HRE on synaptic function, molecular composition of synapses, and dendritic spine morphology. We overexpressed the pathological C9-HRE in cultured E18 mouse primary hippocampal neurons and characterized the pathological, morphological, and functional changes by biochemical methods, confocal microscopy, and live cell calcium imaging. The C9-HRE-expressing neurons were confirmed to display the pathological RNA foci and DPR proteins. C9-HRE expression led to significant changes in dendritic spine morphologies, as indicated by decreased number of mushroom-type spines and increased number of stubby and thin spines, as well as diminished neuronal branching. These morphological changes were accompanied by concomitantly enhanced susceptibility of the neurons to glutamate-induced excitotoxicity as well as augmented and prolonged responses to excitatory stimuli by glutamate and depolarizing potassium chloride as compared to control neurons. Mechanistically, the hyperexcitation phenotype in the C9-HRE-expressing neurons was found to be underlain by increased activity of extrasynaptic GluN2B-containing N-methyl-d-aspartate (NMDA) receptors. Our results are in accordance with the idea suggesting that C9-HRE is associated with enhanced excitotoxicity and synaptic dysfunction. Thus, therapeutic interventio

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely-used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at

This is the deliverable D-JRP21-WP1.13 "Project report 2nd year submitted" (12 MONTH REPORT Y4 (2021)) of the OHEJP BIOPIGEE project produced in WP1 T3 to inform about the progress in the project.