Influence of drainage status on soil and water chemistry, litter decomposition and soil respiration in central Amazonian forests on sandy soils

Central Amazonian rainforest landscape supports a mosaic of tall terra firme rainforest and ecotone campinarana, riparian and campina forests, reflecting topography-induced variations in soil, nutrient and drainage conditions. Spatial and temporal variations in litter decomposition, soil and groundwater chemistry and soil CO2 respiration were studied in forests on sandy soils, whereas drought sensitivity of poorly-drained valley soils was investigated in an artificial drainage experiment. Slightly changes in litter decomposition or water chemistry were observed as a consequence of artificial drainage. Riparian plots did experience higher litter decomposition rates than campina forest. In response to a permanent lowering of the groundwater level from 0.1 m to 0.3 m depth in the drainage plot, topsoil carbon and nitrogen contents decreased substantially. Soil CO2 respiration decreased from 3.7±0.6 µmol m-2 s-1 before drainage to 2.5±0.2 and 0.8±0.1 µmol m-2 s-1 eight and 11 months after drainage, respectively. Soil respiration in the control plot remained constant at 3.7±0.6 µmol m-2 s-1. The above suggests that more frequent droughts may affect topsoil carbon and nitrogen content and soil respiration rates in the riparian ecosystem, and may induce a transition to less diverse campinarana or short-statured campina forest that covers areas with strongly-leached sandy soil

Significance of temperature and soil water content on soil respiration in three desert ecosystems in Northwest China

It is crucial to understand how abiotic factors influence soil respiration and to determine, in a quantitative manner, the site variation of abiotic regulators in desert ecosystems. In this study, soil respiration was measured using an automated CO2 efflux system (LI-COR 8100) in 2005 and 2006. Additionally, the effects of soil temperature, moisture and a short-term precipitation manipulation on the rate of soil respiration were examined in Haloxylon ammodendron, Anabasis aphylla and Halostachys caspica in three distinct desert ecosystems. The difference in soil respiration among sites was significant. Air temperature explained 35-65% of the seasonal changes in soil respiration when an exponential equation was used. The effect of temperature on soil respiration and temperature sensitivity was stronger at sites with higher soil moisture. Soil respiration was significantly positively correlated with soil moisture. Amounts of variation in soil respiration explained by temperature and gravimetric water content were 41-44% in H. ammodendron, 62-65% in A. aphylla and 67-84% in H. caspica sites. Artificial rainfall treatments of 5 mm, 2.5 mm and 0 mm (control) were conducted. Soil respiration increased in a small pulse following rainfall. Temperature dominantly influenced soil respiration and soil water content enhanced the response of respiration to temperature. (C) 2010 Elsevier Ltd. All rights reserved

Resuscitation after experimental cardiac arrest.

Thesis (M.A.)--Boston Universit

Assessing Respiration Rates and Nutrient Dynamics of Aritifical Reef Biofilms and Bacterioplankton in the Mississippi Sound

Artificial reefs are primarily used to provide a suitable habitat for target fish populations, but the structures can also improve water quality and benefit non-target organisms. Laboratory incubation experiments were conducted in the presence of biofilm on rubble and in its absence to examine bacterial growth, community respiration, and nutrient dynamics at four artificial reef habitats in the Mississippi Sound. Biofilm samples were also collected from settlement plates deployed at each site and were analyzed for 813C and 81sN stable isotope content. Respiration rates were always higher in the presence of biofilm but bacterial abundance often declined over time, and rates of decline were higher in the presence of biofilm. This suggests that heterotrophic activity was high but bacterial abundance was limited by some factor, such as grazing pressure. P04 and NH4 production were often observed in incubation experiments, and production rates were higher in the presence of biofilm, indicating that the benthic community supplements microbial water column nutrient regeneration. Respiration, P04 production, and NH4 production were higher in low profile reef incubations than high profile reef incubations when biofilm was present, which reflected the higher biofilm growth observed at low profile reefs. Seasonal effects were also observed. Respiration and nutrient production rates were positively correlated with temperature, and 813C and 81sN values were enriched during warmer seasons, all of which indicate higher benthic and water column productivity. Further studies are needed to compare productivity and nutrient regeneration at other artificial reefs and natural reefs

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Respiratory trigger signal generation by means of a stretchable sensor array

Respiratory monitoring is a clinical method which helps to examine the medical condition of patients. Patients diagnosed with types of respiratory distress are often supported through artificial respiration. To be able to adapt and synchronize airway pressures and flows to the patient’s own breathing for improved respiration efficiency, intelligent sensors are needed to detect the beginning and ending of the breathing cycle. An ultra-thin and stretchable 6x6 sensor array with skin-like properties is presented that is used to generate a trigger signal which is suitable to control and synchronize artificial respiration with the patient's own breathing. Stretchability of the sensor array is achieved by fs-laser structuring of the thin polyimide sensor substrate resulting in small sensor islands connected via slender meandering electrical leads. The resulting stretchable sensor grid is embedded in layers of PDMS whereby a skin-friendly sensor patch is created. To simulate respiration an externally ventilated dummy is used. The principle of trigger signal generation from multiple sensor signals is based on a self-developed algorithm that first evaluates the signal quality of each sensor based on adjustable parameters. Only the sensors selected as suitable are then used to calculate an averaged scaled signal, which is taken for trigger point detection. The best results were typically obtained when quality factures are set to a level where about half of the sensors are contributing to the trigger detection, leading to a trigger delay of about 80 ms relative to the pressure reference signal. It could also be shown that the algorithm can resume the trigger point detection within 2-3 seconds, after manually applying disturbances which could similarly occur in the clinical environment. The results show that the skin-friendly sensor patch provides suitable trigger signals for artificial respiration which are robust against drop out of single sensors, non-ideal sensor patch positioning on the thorax and mechanical irritations

Use of calcium carbide for artificial ripening of fruits : its application and hazards

A review of different articles related to artificial ripening was done. Focus was given on the hazards and applications of calcium carbide for artificial ripening, being a very common practice in Nepalese Market. Litterateurs showed many hazardous aspects of carbide use and also standard procedures of safety handling aspects. But being banned by regulation, due to its hazardous aspects and lack of proper handling methods among users, it was concluded that the use of calcium carbide is to be strictly monitored and controlled

Respiratory arrest by procaine in dogs hypoxic under ether anesthesia.

Thesis (M.A.)--Boston Universit

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented with a steadily evolving body of mechanistic theory provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates "readable" models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-east England. We find that the GEP retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components; the identification of a "general" terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data rich era, complementing more traditional modelling approaches