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Strong seasonal variability of hygric and thermal soil conditions are a defining environmental feature in Northern Australia. However, how such changes affect the soil–atmosphere exchange of nitrous oxide (N2O), nitric oxide (NO) and dinitrogen (N2) is still 5 not well explored. By incubating intact soil cores from four sites (3 savanna, 1 pasture) under controlled soil temperatures (ST) and soil moisture (SM) we investigated the release of the trace gas fluxes of N2O, NO and carbon dioxide (CO2). Furthermore, the release of N2 due to denitrification was measured using the helium gas flow soil core technique. Under dry pre-incubation conditions NO and N2O emission were very low (< 7.0± 5.0 μgNO-Nm−2 h−1; < 0.0± 1.4 μgN2O-Nm−2 h−1) or in case of N2O, even a net soil uptake was observed. Substantial NO (max: 306.5 μgNm−2 h−1) and relatively small N2O pulse emissions (max: 5.8±5.0 μgNm−2 h−1) were recorded following soil wetting, but these pulses were short-lived, lasting only up to 3 days. The total atmospheric loss of nitrogen was dominated by N2 emissions (82.4–99.3% of total N lost), although NO emissions contributed almost 43.2% at 50% SM and 30 °C ST. N2O emissions were systematically higher for 3 of 12 sample locations, which indicates substantial spatial variability at site level, but on average soils acted as weak N2O sources or even sinks. Emissions were controlled by SM and ST for N2O and CO2, ST and pH for NO, and SM and pH for N2.
Strong seasonal variability of hygric and thermal soil conditions are a defining environmental feature in northern Australia. However, how such changes affect the soil–atmosphere exchange of nitrous oxide (N2O), nitric oxide (NO) and dinitrogen (N2) is still not well explored. By incubating intact soil cores from four sites (three savanna, one pasture) under controlled soil temperatures (ST) and soil moisture (SM) we investigated the release of the trace gas fluxes of N2O, NO and carbon dioxide (CO2). Furthermore, the release of N2 due to denitrification was measured using the helium gas flow soil core technique. Under dry pre-incubation conditions NO and N2O emissions were very low (<7.0 ± 5.0 μg NO-N m−2 h−1; <0.0 ± 1.4 μg N2O-N m−2 h−1) or in the case of N2O, even a net soil uptake was observed. Substantial NO (max: 306.5 μg N m−2 h−1) and relatively small N2O pulse emissions (max: 5.8 ± 5.0 μg N m−2 h−1) were recorded following soil wetting, but these pulses were short lived, lasting only up to 3 days. The total atmospheric loss of nitrogen was generally dominated by N2 emissions (82.4–99.3% of total N lost), although NO emissions contributed almost 43.2% to the total atmospheric nitrogen loss at 50% SM and 30 °C ST incubation settings (the contribution of N2 at these soil conditions was only 53.2%). N2O emissions were systematically higher for 3 of 12 sample locations, which indicates substantial spatial variability at site level, but on average soils acted as weak N2O sources or even sinks. By using a conservative upscale approach we estimate total annual emissions from savanna soils to average 0.12 kg N ha−1 yr−1 (N2O), 0.68 kg N ha−1 yr−1 (NO) and 6.65 kg N ha−1 yr−1 (N2). The analysis of long-term SM and ST records makes it clear that extreme soil saturation that can lead to high N2O and N2 emissions only occurs a few days per year and thus has little impact on the annual total. The potential contribution of nitrogen released due to pulse events compared to the total annual emissions was found to be of importance for NO emissions (contribution to total: 5–22%), but not for N2O emissions. Our results indicate that the total gaseous release of nitrogen from these soils is low and clearly dominated by loss in the form of inert nitrogen. Effects of seasonally varying soil temperature and moisture were detected, but were found to be low due to the small amounts of available nitrogen in the soils (total nitrogen <0.1%).
Plattenepithelkarzinome des oberen Aerodigestivtraktes (SCCHN) sind Karzinome, die in den westlichen Industrienationen mit dem sechsten Rang in den Tumorstatistiken recht häufig sind. Ihre Inzidenz ist positiv mit einem erhöhten Tabak- und Alkoholkonsum und dem männlichen Geschlecht korreliert. Die Standardtherapie besteht in der Regel in einer operativen Sanierung und einer adjuvanten Radio- beziehungsweise Radio-Chemotherapie. Eine Induktionschemotherapie verspricht fortgeschrittene Primärtumoren und Tumoren vor einer operativen Intervention zu verkleinern. Jedoch besteht hierbei immer noch das Problem der hohen Rezidivfreundlichkeit dieser Tumoren, die wahrscheinlich mit der hohen Anzahl an G0-Phase-Zellen korreliert ist. Die vorliegende Arbeit hat die Frage untersucht, ob es möglich ist mit einer vorgeschalteten Stimulation durch Wachstumsfaktoren die Anzahl der G0-Phase-Zellen signifikant zu reduzieren und dadurch die gesamte Population der Tumorzellen sensibler für eine chemotherapeutische Intervention zu machen. Hierbei wurden besonders die Wachstumsfaktoren EGF und Serotonin als Stimulus verwendet. Die untersuchten Chemotherapeutika waren Mab425 und ZD1839, die die Signaltransduktion über einen EGFR-vermittelten Weg blockieren, sowie Docetaxel und Cisplatin, die als zytotoxische Substanzen wirksam sind. Die Untersuchungen wurden in vitro an drei Zelllinien durchgeführt. Detroit-562 und A431 bezogen wir von ATCC. Die Zelllinie UMSCC-10B wurde uns freundlicherweise von Thomas Carey von der University of Michigan zur Verfügung gestellt. Im Rahmen von Sondierungsversuchen stimulierten die Zellen mit IL-6, Serotonin, EGF und GCSF und fanden mit Hilfe der FACS-Analyse heraus, dass eine Kombination von EGF und Serotonin zur am stärksten ausgeprägten Stimulation der von uns untersuchten Zellen führte. In einem dreiphasigen Versuchsablauf wurden vier verschiedene Probengruppen untersucht. Zum einen eine Kontrollgruppe, die weder stimuliert noch chemotherapiert wurde. Eine Gruppe, die vor einer chemotherapeutischen Intervention mit Serotonin und EGF stimuliert wurde. Eine weitere Gruppe, die ausschließlich mit Serotonin und EGF stimuliert wurde und eine Gruppe, die nur eine Chemotherapie erhielt. Nach der Kultivierung der entsprechenden Proben, wurden die Zellen geerntet und zur Weiterverarbeitung vorbereitet. Es wurde mit Hilfe der Western-Blot-Analyse die Aktivität der Proteine untersucht, die an der über EGFR vermittelten Signaltransduktion beteiligt sind. Dazu wurden Antikörper gegen Proteine der Signaltransduktion eingesetzt, die dann mit Hilfe von zweiten Antikörpern erkannt wurden. Zum anderen wurden immunhistochemische Zellausstriche angefertigt mit einer Antikörperfärbung gegen das Kernprotein Ki-67, die uns zwischen proliferierenden und nichtproliferierenden Zellen unterscheiden ließ. Es erfolgte eine Auswertung der Gesamtzellanzahl bezogen auf die Reduktion der G0-Phase-Zellen. Die statistische Auswertung erfolgte mittels des Mann-Withney-U-Tests und des Wilcoxon- Tests. Hierbei lag das Signifikanzniveau bei 5%. Als Ergebnis kann man festhalten, dass eine signifikante Reduktion der Anzahl an G0-Phase-Zellen bei einer Vorstimulation und anschließender Chemotherapie durch Docetaxel oder Cisplatin zu beobachten war. Eine chemotherapeutische Intervention durch Mab425 und ZD1839 führte zwar zu einer Reduktion der Zellanzahl, aber nicht zu vergleichbar signifikanten Ergebnissen wie bei einer Intervention mit Docetaxel oder Cisplatin. Hieraus lässt sich der Schluss ableiten, dass eine chemotherapeutische Intervention auf Basis einer Blockade der durch EGFR vermittelten Signaltransduktion nicht ausreichend ist, es also einer zytotoxischen Komponente bedarf, um die Gesamtzellzahl signifikant zu reduzieren. Trotzdem nicht bei allen untersuchten Chemotherapeutika eine signifikante Reduktion der G0-Phase-Zellen beobachtet werden konnte, zeigten unsere Untersuchungen zum ersten Mal, dass durch eine Stimulation mit Wachstumsfaktoren vor einer chemotherapeutischen Intervention, die Sensibilität der Tumorzellen bei SCCHN für eine Chemotherapie durch eine Reduktion der G0-Phase-Zellen erhöht wird.
Increasing atmospheric CO2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO2-enrichment experiments in woody ecosystems that measured total NPP and biomass. CO2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m−2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO2 response of NPP (0.16 ± 0.03 kg C m−2 y−1) and the CO2-independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO2-independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO2 responses.
Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development.
The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used.
Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes.
Assessing the uncertainties of simulation results of ecological models is becoming of increasing importance, specifically if these models are used to estimate greenhouse gas emissions at site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure and (iv) accurateness of measurements (e.g. soil-atmosphere greenhouse gas exchange) which are used for model testing and development.
The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO), and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used.
Our approach showed that by means of the joined parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange.
In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of the calibration set (6 yr) and an independent validation set of 4 yr.
The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Residual mean squared error (RMSE) NO: 2.5 to 21.3 g N ha−1 d−1, N2O: 0.2 to 21.4 g N ha−1 d−1, CO2: 5.8 to 12.6 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes limited to accurately predict observed seasonal variations in fluxes.
Juvenile neuronal ceroid lipofuscinosis (JNCL or Batten disease) caused by mutations in the CLN3 gene is the most prevalent inherited neurodegenerative disease in childhood resulting in widespread central nervous system dysfunction and premature death. The consequences of CLN3 mutation on the progression of the disease, on neuronal transmission, and on central nervous network dysfunction are poorly understood. We used Cln3 knockout (Cln3Δex1-6) mice and found increased anxiety-related behavior and impaired aversive learning as well as markedly affected motor function including disordered coordination. Patch-clamp and loose-patch recordings revealed severely affected inhibitory and excitatory synaptic transmission in the amygdala, hippocampus, and cerebellar networks. Changes in presynaptic release properties may result from dysfunction of CLN3 protein. Furthermore, loss of calbindin, neuropeptide Y, parvalbumin, and GAD65-positive interneurons in central networks collectively support the hypothesis that degeneration of GABAergic interneurons may be the cause of supraspinal GABAergic disinhibition.
Tropical forest soils are a significant source for the greenhouse gas N2O as well as for NO, a precursor of tropospheric ozone. However, current estimates are uncertain due to the limited number of field measurements. Furthermore, there is considerable spatial and temporal variability of N2O and NO emissions due to the variation of environmental conditions such as soil properties, vegetation characteristics and meteorology. In this study we used a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from tropical highland forest (Nyungwe) soils in southwestern Rwanda. To extend the model inputs to regional scale, ForestDNDC-tropica was linked to an exceptionally large legacy soil dataset. There was agreement between N2O and NO measurements and the model predictions though the ForestDNDC-tropica resulted in considerable lower emissions for few sites. Low similarity was specifically found for acidic soil with high clay content and reduced metals, indicating that chemo-denitrification processes on acidic soils might be under-represented in the current ForestDNDC-tropica model. The results showed that soil bulk density and pH are the most influential factors driving spatial variations in soil N2O and NO emissions for tropical forest soils. The area investigated (1113 km2) was estimated to emit ca. 439 ± 50 t N2O-N yr−1 (2.8–5.5 kg N2O-N ha−1 yr−1) and 244 ± 16 t NO-N yr−1 (0.8–5.1 kg N ha−1 yr−1). Consistent with less detailed studies, we confirm that tropical highland rainforest soils are a major source of atmospheric N2O and NO.
Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. To investigate how soil properties affect N2O and NO emission, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90% water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 μg N m−2 h−1, while NO emission varied from 6.6 to 265 μg N m−2 h−1. Mean N2O emission at different moisture levels was 46.5 ± 11.1 (50% WFPS), 71.7 ± 11.5 (70% WFPS) and 98.8 ± 16.4 (90% WFPS) μg N m−2 h−1, while mean NO emission was 69.3 ± 9.3 (50% WFPS), 47.1 ± 5.8 (70% WFPS) and 36.1 ± 4.2 (90% WFPS) μg N m−2 h−1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a negative correlation (only N2O) with soil pH and a positive correlation with free iron. The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will not only benefit from better spatial explicit trace gas emission and basic soil property monitoring, but also by differentiating between biological and chemical pathways for N trace gas formation.
Spatial variations of nitrogen trace gas emissions from tropical mountain forests in Nyungwe, Rwanda
(2012)
Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. Therefore, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90 % water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 μg N m−2 h−1, while NO emission varied from 6.6 to 265 μg N m−2 h−1. Mean N2O emission at different moisture levels was 46.5 ± 11.1 (50 %WFPS), 71.7 ± 11.5 (70 %WFPS) and 98.8 ± 16.4 (90 %WFPS) μg N m−2 h−1, while mean NO emission was 69.3 ± 9.3 (50 %WFPS), 47.1 ± 5.8 (70 %WFPS) and 36.1 ± 4.2 (90 %WFPS) μg N m−2 h−1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a positive correlation with free iron and a negative correlation with soil pH (only N2O). The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will benefit from spatially explicit trace gas emission estimates linked to basic soil property data and differentiating between biological and chemical pathways for N trace gas formation.