65 resultados para phospholipase A2


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Climate change is projected to impact forest ecosystems, including biodiversity and Net Primary Productivity (NPP). National level carbon forest sector mitigation potential estimates are available for India; however impacts of projected climate change are not included in the mitigation potential estimates. Change in NPP (in gC/m(2)/yr) is taken to represent the impacts of climate change. Long term impacts of climate change (2085) on the NPP of Indian forests are available; however no such regional estimates are available for short and medium terms. The present study based on GCM climatology scenarios projects the short, medium and long term impacts of climate change on forest ecosystems especially on NPP using BIOME4 vegetation model. We estimate that under A2 scenario by the year 2030 the NPP changes by (-5) to 40% across different agro-ecological zones (AEZ). By 2050 it increases by 15% to 59% and by 2070 it increases by 34 to 84%. However, under B2 scenario it increases only by 3 to 25%, 3.5 to 34% and (-2.5) to 38% respectively, in the same time periods. The cumulative mitigation potential is estimated to increase by up to 21% (by nearly 1 GtC) under A2 scenario between the years 2008 and 2108, whereas, under B2 the mitigation potential increases only by 14% (646 MtC). However, cumulative mitigation potential estimates obtained from IBIS-a dynamic global vegetation model suggest much smaller gains, where mitigation potential increases by only 6% and 5% during the period 2008 to 2108.

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We examine the potential for adaptation to climate change in Indian forests, and derive the macroeconomic implications of forest impacts and adaptation in India. The study is conducted by integrating results from the dynamic global vegetation model IBIS and the computable general equilibrium model GRACE-IN, which estimates macroeconomic implications for six zones of India. By comparing a reference scenario without climate change with a climate impact scenario based on the IPCC A2-scenario, we find major variations in the pattern of change across zones. Biomass stock increases in all zones but the Central zone. The increase in biomass growth is smaller, and declines in one more zone, South zone, despite higher stock. In the four zones with increases in biomass growth, harvest increases by only approximately 1/3 of the change in biomass growth. This is due to two market effects of increased biomass growth. One is that an increase in biomass growth encourages more harvest given other things being equal. The other is that more harvest leads to higher supply of timber, which lowers market prices. As a result, also the rent on forested land decreases. The lower prices and rent discourage more harvest even though they may induce higher demand, which increases the pressure on harvest. In a less perfect world than the model describes these two effects may contribute to an increase in the risk of deforestation because of higher biomass growth. Furthermore, higher harvest demands more labor and capital input in the forestry sector. Given total supply of labor and capital, this increases the cost of production in all the other sectors, although very little indeed. Forestry dependent communities with declining biomass growth may, however, experience local unemployment as a result.

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Design and operational details for a self-supported polymer electrolyte fuel cell (PEFC) system with anodic dead-end fuel supply and internally humidified cathodic oxidant flow are described. During the PEFC operation, nitrogen and water back diffuse across the Nafion membrane from the cathode to the anode and accumulate in the anode flow channels affecting stack performance. The accumulated inert species are flushed from the stack by purging the fuel cell stack with a timer-activated purge valve to address the aforesaid problem. To minimize the system complexity, stack is designed in such a way that all the inert species accumulate in only one cell called the purge cell. A pulsed purge sequence comprises opening the valve for purge duration followed by purge-valve closing for the hold period and repeating the sequence in cycles. Since self-humidification is inadequate to keep the membrane wet, the anodic dead-end-operated PEFC stack with composite membrane comprising perflourosulphonic acid (Nafion) and silica is employed for keeping the membrane humidified even while operating the stack with dry hydrogen and internally humidified air.

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The effect of docosahexaenoic acid (DHA) on the diacylglycerol kinase (DG kinase) activity in rat brain membranes was investigated. DHA at 500 mu M concentration, stimulated the enzyme activity by about 2 fold. This effect was concentration-and time-dependent and was observed after very short periods of incubation (one min). DHA stimulation of DG kinase was observed only with rat brain membranes, and not with rat brain cytosol or rat liver membranes. Treating the rat brain membranes with phospholipase A(2) which released free fatty acids including DHA, significantly stimulated the DG kinase activity. It is concluded that DHA through its stimulatory effect on DG kinase may regulate the signalling events in growth-related situations in the brain such as synaptogenesis.

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Coarse BO2·xH2O (2 < x < 80) gels, free of anion contaminants react with A(OH)2 under refluxing conditions at 70�100°C giving rise to crystallites of single phased, nanometer size powders of ABO3 perovskites (A = Ba, Sr, Ca, Mg, Pb; B = Zr, Ti, Sn). Solid solutions of perovskites could be prepared from compositionally modified gels or mixtures of A(OH)2. Donor doped perovskites could also be prepared from the same method so that the products after processing are often semiconducting. Faster interfacial diffusion of A2+ ions into the gel generates the crystalline regions whose composition is controllable by the A/B ratio as well as the A(OH)2 concentration.

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We investigated the role of cAMP/cGMP, protein kinases and intracellular calcium ( [Ca2+](i)) in pentoxifylline-stimulated hamster sperm capacitation and the acrosome reaction (AR) in vitro. Treatment with pentoxifylline [0.45 mM) initially increased sperm cAMP values 2.8-fold, compared with untreated controls (396 +/- 9.2 versus 141 +/- 6.0 fmoles/10(6) spermatozoa; mean +/- SEM, n = 6) after 15 min, although by 3 h, cAMP values were similar (503-531 fmoles/10(6) spermatozoal, cGMP values (similar to 27 fmoles/10(6) spermatozoa) were the same in treated and control spermatozoa. Both sperm capacitation and the AR, determined from the absence of an acrosomal cap, were stimulated by pentoxifylline; these were almost completely inhibited by a Cl-/HCO(3)(-)antiporter inhibitor (4,4-diisothiocyanato-stilbene-2,2 disulphonic acid; 1 mM) defined from the degree of sperm motility and by a protein kinase A inhibitor (H89; 10 mu M) A protein kinase G inhibitor (staurosporine, 1 nM) did not affect pentoxifylline-stimulated capacitation but inhibited the AR by 50%. A protein tyrosine kinase inhibitor (tyrphostin A-47, 0.1 mM) had no effect on either pentoxifylline-stimulated capacitation or AR, A phospholipase A(2) inhibitor (aristolochic acid, 0.4 mM) markedly inhibited the pentoxifylline-stimulated AR but not capacitation. When intracellular sperm calcium [Ca2+](i) was measured using fura-2-AM, there was an early rise 271 nM at 0.5 hi in pentoxifylline(-treated spermatozoa; this appeared to be due to intracellular mobilization rather than to uptake. In the absence of extracellular Ca2+, sperm motility was maintained in the presence of pentoxifylline, but capacitation did not occur; spermatozoa exhibited a low level of hyperactivated motility and had a poor rate of AR(20.5 +/- 2.3%). These results suggest that: (i) the pentoxifylline-stimulated early onset of sperm capacitation may be mediated by an early rise in cAMP and [Ca2+/-](i) and involves protein kinase A activity; and (ii) pentoxifylline-stimulated AR may require phospholipase A;A(2) and protein kinase C activity.

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The crystal structures of the solid solutions of Bi3-xLaxTiNbO9 (0 less than or equal to x less than or equal to 1) have been analyzed by powder X-ray diffraction with supporting evidence from selected area electron diffraction (SAD). The structure of the starting member (x = 0) is verified to be in the orthorhombic space group A2(1) am while the end member (x = 1) is determined to crystallize in the centrosymmetric orthorhombic space group Pmcb. The structure of x = 1 phase is solved by ab initio powder diffraction. The intermediate compositions belong to the space group A2(1) am as confirmed by Rietveld refinements. Rietveld refinements on all the compositions reveal that the La3+ ion is disordered only in the A site and not in the [Bi2O2](2+) layer. The tilt in the Ti/NbO6 octahedra decreases with increasing x. (C) 2003 Elsevier B.V. All rights reserved.

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A Space-Time Block Code (STBC) in K symbols (variables) is called g-group decodable STBC if its maximum-likelihood decoding metric can be written as a sum of g terms such that each term is a function of a subset of the K variables and each variable appears in only one term. In this paper we provide a general structure of the weight matrices of multi-group decodable codes using Clifford algebras. Without assuming that the number of variables in each group to be the same, a method of explicitly constructing the weight matrices of full-diversity, delay-optimal g-group decodable codes is presented for arbitrary number of antennas. For the special case of Nt=2a we construct two subclass of codes: (i) A class of 2a-group decodable codes with rate a2(a−1), which is, equivalently, a class of Single-Symbol Decodable codes, (ii) A class of (2a−2)-group decodable with rate (a−1)2(a−2), i.e., a class of Double-Symbol Decodable codes. Simulation results show that the DSD codes of this paper perform better than previously known Quasi-Orthogonal Designs.

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Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

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A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R (H) in a river basin at monthly scale. Uncertainty in the future projections of R (H) is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R (H) are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978-2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978-2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R (H) is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

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Plant oils are stored in oleosomes or oil bodies, which are surrounded by a monolayer of phospholipids embedded with oleosin proteins that stabilize the structure. Recently, a structural protein, Oleosin3 (OLE3), was shown to exhibit both monoacylglycerol acyltransferase and phospholipase A(2) activities. The regulation of these distinct dual activities in a single protein is unclear. Here, we report that a serine/threonine/tyrosine protein kinase phosphorylates oleosin. Using bimolecular fluorescence complementation analysis, we demonstrate that this kinase interacts with OLE3 and that the fluorescence was associated with chloroplasts. Oleosin-green fluorescent protein fusion protein was exclusively associated with the chloroplasts. Phosphorylated OLE3 exhibited reduced monoacylglycerol acyltransferase and increased phospholipase A(2) activities. Moreover, phosphatidylcholine and diacylglycerol activated oleosin phosphorylation, whereas lysophosphatidylcholine, oleic acid, and Ca2+ inhibited phosphorylation. In addition, recombinant peanut (Arachis hypogaea) kinase was determined to predominantly phosphorylate serine residues, specifically serine-18 in OLE3. Phosphorylation levels of OLE3 during seed germination were determined to be higher than in developing peanut seeds. These findings provide direct evidence for the in vivo substrate selectivity of the dual-specificity kinase and demonstrate that the bifunctional activities of oleosin are regulated by phosphorylation.

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Hydrogenperoxide (H2O2) is generated in mitochondria in aerobic cells as a minor product of electron transport, is inhibited selectively by phenolic acids (in animals) or salicylhydroxamate (in plants) and is regulated by hormones and environmental conditions. Failure to detect this activity is due to presence of H2O2-consuming reactions or inhibitors present in the reaction mixture. H2O2 has a role in metabolic regulation and signal transduction reactions. A number of enzymes and cellular activities are modified, mostly by oxidizing the protein-thiol groups, on adding H2O2 in mM concentrations. On complexing with vanadate, also occurring in traces, H2O2 forms diperoxovanadate (DPV), stable at physiological pH and resistant to degradation by catalase. DPV was found to substitute for H2O2 at concentrations orders of magnitude lower, and in presence of catalase, as a substrate for user reaction, horseradish peroxidase (HRP), and in inactivating glyceraldehyde-3-phosphate dehydrogenase. superoxide dismutase (SOD)-sensitive oxidation of NADH was found to operate as peroxovanadate cycle using traces of DPV and decameric vanadate (V-10) and reduces O-2 to peroxide (DPV in presence of free vanadate). This offers a model for respiratory burst. Diperoxovanadate reproduces several actions of H2O2 at low concentrations: enhances protein tyrosine phosphorylation, activates phospholipase D, produces smooth muscle contraction, and accelerates stress induced premature senescence (SIPS) and rounding in fibroblasts. Peroxovanadates can be useful tools in the studies on H2O2 in cellular activities and regulation.

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Impact of global warming on daily rainfall is examined using atmospheric variables from five General Circulation Models (GCMs) and a stochastic downscaling model. Daily rainfall at eleven raingauges over Malaprabha catchment of India and National Center for Environmental Prediction (NCEP) reanalysis data at grid points over the catchment for a continuous time period 1971-2000 (current climate) are used to calibrate the downscaling model. The downscaled rainfall simulations obtained using GCM atmospheric variables corresponding to the IPCC-SRES (Intergovernmental Panel for Climate Change - Special Report on Emission Scenarios) A2 emission scenario for the same period are used to validate the results. Following this, future downscaled rainfall projections are constructed and examined for two 20 year time slices viz. 2055 (i.e. 2046-2065) and 2090 (i.e. 2081-2100). The model results show reasonable skill in simulating the rainfall over the study region for the current climate. The downscaled rainfall projections indicate no significant changes in the rainfall regime in this catchment in the future. More specifically, 2% decrease by 2055 and 5% decrease by 2090 in monsoon (HAS) rainfall compared to the current climate (1971-2000) under global warming conditions are noticed. Also, pre-monsoon (JFMAM) and post-monsoon (OND) rainfall is projected to increase respectively, by 2% in 2055 and 6% in 2090 and, 2% in 2055 and 12% in 2090, over the region. On annual basis slight decreases of 1% and 2% are noted for 2055 and 2090, respectively.

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Climate change would significantly affect many hydrologic systems, which in turn would affect the water availability, runoff, and the flow in rivers. This study evaluates the impacts of possible future climate change scenarios on the hydrology of the catchment area of the TungaBhadra River, upstream of the Tungabhadra dam. The Hydrologic Engineering Center's Hydrologic Modeling System version 3.4 (HEC-HMS 3.4) is used for the hydrological modelling of the study area. Linear-regression-based Statistical DownScaling Model version 4.2 (SDSM 4.2) is used to downscale the daily maximum and minimum temperature, and daily precipitation in the four sub-basins of the study area. The large-scale climate variables for the A2 and B2 scenarios obtained from the Hadley Centre Coupled Model version 3 are used. After model calibration and testing of the downscaling procedure, the hydrological model is run for the three future periods: 20112040, 20412070, and 20712099. The impacts of climate change on the basin hydrology are assessed by comparing the present and future streamflow and the evapotranspiration estimates. Results of the water balance study suggest increasing precipitation and runoff and decreasing actual evapotranspiration losses over the sub-basins in the study area.