Molecular analysis of inorganic nitrogen assimilation in yeasts

Assimilation of nitrate and various other inorganic nitrogen compounds by different yeasts was investigated. Nitrate, nitrite, hydroxylamine, hydrazine, ammonium sulphate, urea and L-asparagine were tested as sole sources of nitrogen for the growth of Candida albicans, C. pelliculosa, Debaryomyces hansenii, Saccharomyces cerevisiae, C. tropicalis, and C. utilis. Ammonium sulphate and L-asparagine supported the growth of all the yeasts tested except D. hansenii while hydroxylamine and hydrazine failed to support the growth of any. Nitrate and nitrite were assimilated only by C. utilis. Nitrate utilization by C. utilis was also accompanied by the enzymatic activities of NAD(P)H: nitrate oxidoreductase (EC 1.6.6.2) and NAD(P)H: nitrite oxidoreductase (EC 1.6.6.4), but not reduced methyl viologen-or FAD-nitrate oxidoreductases (EC 1.7.99.4). It is demonstrated here that nitrate and nitrite reductase activities are responsible for the ability of C. utilis to assimilate primary nitrogen.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Data assimilation using ensemble transform Kalman filter (ETKF) in ROMS model for Indian Ocean

Study of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale.

Assimilation of endmember variability in spectral for urban land cover extraction mixture analysis

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class. (C) 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Purification and characterization of assimilatory nitrite reductase from Candida utilis

Nitrate assimilation in many plants, algae, yeasts and bacteria is mediated by two enzymes, nitrate reductase (EC 1.6.6.2) and nitrite reductase (EC 1.7.7.1). They catalyse the stepwise reduction of nitrate to nitrite and nitrite to ammonia respectively. The nitrite reductase from an industrially important yeast, Candida utilis, has been purified to homogeneity. Purified nitrite reductase is a heterodimer and the molecular masses of the two subunits are 58 and 66 kDa. The native enzyme exhibits a molecular mass of 126 kDa as analysed by gel filtration. The identify of the two subunits of nitrite reductase was confirmed by immunoblotting using antibody for Cucurbita pepo leaf nitrite reductase. The presence of two different sized transcripts coding for the two subunits was confirmed by (a) in vitro translation of mRNA from nitrate-induced C. utilis followed by immunoprecipitation of the in vitro translated products with heterologous nitrite reductase antibody and (b) Northern-blot analysis. The 66 kDa subunit is acidic in nature which is probably due to its phosphorylated status. The enzyme is stable over a range of temperatures. Both subunits can catalyse nitrite reduction, and the reconstituted enzyme, at a higher protein concentration, shows an activity similar to that of the purified enzyme. Each of these subunits has been shown to contain a few unique peptides in addition to a large number of common peptides. Reduced Methyl Viologen has been found to be as effective an electron donor as NADPH in the catalytic process, a phenomenon not commonly seen for nitrite reductases from other systems.

Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations

The peaking of most oil reserves and impending climate change are critically driving the adoption of solar photovoltaic's (PV) as a sustainable renewable and eco-friendly alternative. Ongoing material research has yet to find a breakthrough in significantly raising the conversion efficiency of commercial PV modules. The installation of PV systems for optimum yield is primarily dictated by its geographic location (latitude and available solar insolation) and installation design (tilt, orientation and altitude) to maximize solar exposure. However, once these parameters have been addressed appropriately, there are other depending factors that arise in determining the system performance (efficiency and output). Dust is the lesser acknowledged factor that significantly influences the performance of the PV installations. This paper provides an appraisal on the current status of research in studying the impact of dust on PV system performance and identifies challenges to further pertinent research. A framework to understand the various factors that govern the settling/assimilation of dust and likely mitigation measures have been discussed in this paper. (C) 2010 Elsevier Ltd. All rights reserved.

Particle filters for structural system identification using multiple test and sensor data: A combined computational and experimental study

The problem of structural system identification when measurements originate from multiple tests and multiple sensors is considered. An offline solution to this problem using bootstrap particle filtering is proposed. The central idea of the proposed method is the introduction of a dummy independent variable that allows for simultaneous assimilation of multiple measurements in a sequential manner. The method can treat linear/nonlinear structural models and allows for measurements on strains and displacements under static/dynamic loads. Illustrative examples consider measurement data from numerical models and also from laboratory experiments. The results from the proposed method are compared with those from a Kalman filter-based approach and the superior performance of the proposed method is demonstrated. Copyright (C) 2009 John Wiley & Sons, Ltd.

Production of biosurfactant “Biosur-Pm” by Pseudomonas maltophila CSV89: characterization and role in hydrocarbon uptake

Pseudomonas maltophilia CSV89, a soil bacterium, produces an extracellular biosurfactant, ''Biosur-Pm''. The partially purified product is nondialyzable and chemically composed of 50% protein and 12-15% sugar, which indicates the complex nature of Biosur-Pm. It reduces the surface tension of water from 73 to 53 x 10(-3) N m(-1) and has a critical micellar concentration of 80 mg/l. Compared to aliphatic hydrocarbons, Biosur-Pm shows good activity against aromatic hydrocarbons. The emulsion formed is stable and does not require any metal ions for emulsification. The kinetics of Biosur-Pm production suggest that its synthesis isa growth-associated and pH-dependent process. At pH 7.0, cells produced more Biosur-Pm with less cell surface hydrophobicity. At pH 8.0, however, the cells produced less Biosur-Pm with more cell surface hydrophobicity and showed a twofold higher affinity for aromatic hydrocarbons compared to the cells grown at pH 7.0. The Biosur-Pm showed a pH-dependent release, stimulated growth of the producer strain on mineral salts medium with 1-naphthoic acid when added externally, and facilitated the conversion of salicylate to catechol. All these results suggest that Biosur-Pm is probably a cell-wall component and helps in hydrocarbon assimilation/uptake.

In vitro and in vivo regulation of assimilatory nitrite reductase from Candida utilis

The nitrate assimilation pathway in Candida utilis, as in other assimilatory organisms, is mediated by two enzymes: nitrate reductase and nitrite reductase. Purified nitrite reductase has been shown to be a heterodimer consisting of 58- and 66-kDa subunits. In the present study, nitrite reductase was found to be capable of utilising both NADH and NADPH as electron donors. FAD, which is an essential coenzyme, stabilised the enzyme during the purification process. The enzyme was modified by cysteine modifiers, and the inactivation could be reversed by thiol reagents. One cysteine was demonstrated to be essential for the enzymatic activity. In vitro, the enzyme was inactivated by ammonium salts, the end product of the path way, proving that the enzyme is assimilatory in function. In vivo, the enzyme was induced by nitrate and repressed by ammonium ions. During induction and repression, the levels of nitrite reductase mRNA, protein, and enzyme activity were modulated together, which indicated that the primary level of regulation of this enzyme was at the transcriptional level. When the enzyme was incubated with ammonium salts in vitro or when the enzyme was assayed in cells grown with the same salts as the source of nitrogen, the residual enzymatic activities were similar. Thus, a study of the in vitro inactivation can give a clue to understanding the mechanism of in vivo regulation of nitrite reductase in Candida utilis.

Algal Photosynthetic Dynamics in Urban Lakes under Stress Conditions

Urban lakes form vital ecosystems supporting livelihood with social, economic and aesthetic benefits that are essential for quality life. This depends on the biotic and abiotic components in an ecosystem. The structure of an ecosystem forms a decisive factor in sustaining its functional abilities which include nutrient cycling, oxygen production, etc. A community assemblage of primary producers (algae) plays a crucial role in maintaining the balance as they form the base of energy pyramid in the ecosystem. Algae assimilate carbon in the environment via photosynthetic activities and releases oxygen for the next level of biotic elements in an ecosystem. Besides these, algal cells rich in protein serve as food and feed, used as manure and for production of biofuels. Understanding algal photosynthetic dynamics helps in assessing the level of dissolved oxygen (DO), food (fish, etc.), waste assimilation, etc. Algal chlorophyll content, algal biomass, primary productivity and algal photosynthetic quotient are some of the parameters that help in assessing the status of urban lakes. Chlorophyll content gives a measure of the growth, spread and quantity of algae. Unplanned rapid urbanization in Bangalore in recent times has resulted in either disappearance of lake ecosystems or deteriorated the lake water quality impairing the ecological processes. This paper computes algal growth, community structure, primary productivity and composition for three major lakes (T G Halli, Bellandur and Varthur lakes) under contrast levels of anthropogenic influences.

Assessment of treatment capabilities of Varthur Lake, Bangalore, India

Manmade waterbodies have traditionally been used for domestic and irrigation purposes. Unplanned urbanization and ad-hoc approaches have led to these waterbodies receiving untreated sewage. This enriches and eutrophies the waterbody. A physicochemical and biological analysis of sewage-fed Varthur Lake in Bangalore was carried out and its treatment capabilities in terms of BOD removal, nutrient assimilation and self-remediation were assessed. Anaerobic conditions (0 mg/L) prevail at the inlet which improves towards the outlets due to algal aeration. This removed > 50% BOD in the monsoon season but was inhibited by floating macrophytes in all other seasons. Alkalinity, TDS, conductivity and hardness values were higher when compared to earlier studies. This study shows the lake behaves as an anaerobic~aerobic lagoon with a residence time of 4.8 d treating the wastewater to a considerable extent. Further research is required to optimise the system performance.

TURBULENT PUMPING OF MAGNETIC FLUX REDUCES SOLAR CYCLE MEMORY AND THUS IMPACTS PREDICTABILITY OF THE SUN'S ACTIVITY

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. have shown that the dynamical memory of the solar dynamo mechanism governs predictability, and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum-allowing about five years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Evaluation of GODAS Using RAMA Mooring Observations from the Indian Ocean

We present a comparison of the Global Ocean Data Assimilation System (GODAS) five-day ocean analyses against in situ daily data from Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) moorings at locations 90 degrees E, 12 degrees N; 90 degrees E, 8 degrees N; 90 degrees E, 0 degrees N and 90 degrees E, 1.5 degrees S in the equatorial Indian Ocean and the Bay of Bengal during 2002-2008. We find that the GODAS temperature analysis does not adequately capture a prominent signal of Indian Ocean dipole mode of 2006 seen in the mooring data, particularly at 90 degrees E 0 degrees N and 90 degrees E 1.5 degrees S in the eastern India Ocean. The analysis, using simple statistics such as bias and root-mean-square deviation, indicates that standard GODAS temperature has definite biases and significant differences with observations on both subseasonal and seasonal scales. Subsurface salinity has serious deficiencies as well, but this may not be surprising considering the poorly constrained fresh water forcing, and possible model deficiencies in subsurface vertical mixing. GODAS reanalysis needs improvement to make it more useful for study of climate variability and for creating ocean initial conditions for prediction.

Bayesian parameter identification in dynamic state space models using modified measurement equations

When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending-torsion coupled, geometrically non-linear building frame under earthquake support motions. (C) 2015 Elsevier Ltd. All rights reserved.