1,178 research outputs found
Possible involvement of a calmodulin regulated Ca2+-ATPase in exocytosis performance in Paramecium tetraurelia cells
AbstractSurface membrane fractions from Paramecium tetraurelia cells contain a calmodulin-stimulated Ca2+-ATPase responding to low levels of free Ca2+ and with features characteristic of a membrane-bound ATPase responding to low levels of free Ca2+ and with features characteristics of a membrane-bound ATPase. Among the different strains analyzed this enzyme was practically absent selectively from the ‘non-discharge” mutant nd9—28°C (from J. Beisson); if cultured at a permissive temperature (18°C), this strain showed identical values of calmodulin-stimulated Ca2+-ATPase activity as wild-type cells (7S) or strains with mutations which do not affect exocytosis performance. We conclude that this calmodulin-stimulated Ca2+-activated ATPase might be a prerequisite for membrane fusion in the course of exocytosis performance
Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm
Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementatio
3-D electrical resistivity tomography using adaptive wavelet parameter grids
We present a novel adaptive model parametrization strategy for the 3-D electrical resistivity tomography problem and demonstrate its capabilities with a series of numerical examples. In contrast to traditional parametrization schemes, which are based on fixed disjoint blocks, we discretize the subsurface in terms of Haar wavelets and adaptively adjust the parametrization as the iterative inversion proceeds. This results in a favourable balance of cell sizes and parameter reliability, that is, in regions where the data constrain the subsurface properties well, our parametrization strategy leads to a fine grid, whereas poorly resolved areas are represented only by a few large blocks. This is documented with eigenvalue analyses and by computing model resolution matrices. During the initial iteration steps, only a few model parameters are involved, which reduces the risk that the regularization dominates the inversion. The algorithm also automatically accounts for non-linear effects caused by pronounced conductivity contrasts. Inside conductive features a finer grid is generated than inside more resistive structures. The automated parameter adaptation is computationally efficient, because the coarsening and refinement subroutines have a nearly linear numerical complexity with respect to the number of model parameters. Because our approach is not tightly coupled to electrical resistivity tomography, it should be straightforward to adapt it to other data type
Probabilistic climate change projections using neural networks
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding -1.2Wm-2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5K is assumed for climate sensitivit
Study in the impact of dispersant on paraffin crystallization using the method of dynamic scattering
Trends in marine dissolved oxygen: Implications for ocean circulation changes and the carbon budget
Recent measurements and model studies have consistently identified a decreasing trend in the concentration of dissolved O2 in the ocean over the last several decades. This trend has important implications for our understanding of anthropogenic climate change. First, the observed oceanic oxygen changes may be a signal of the beginning of a reorganization of large-scale ocean circulation in response to anthropogenic radiative forcing. Second, the repartitioning of oxygen between the ocean and the atmosphere requires a revision of the current atmospheric carbon budget and the estimates of the terrestrial and oceanic carbon sinks as calculated by the Intergovernmental Panel on Climate Change (IPCC) from measurements of atmospheric O2/N2
Fatal scuba diving incident with massive gas embolism in cerebral and spinal arteries
CT and MRI have the potential to become useful adjuncts to forensic autopsy in the near future. The examination of fatal injuries facilitates a profound experience in the clinical-radiological examination of these cases; the more severe findings in corpses with autopsy verification can help one to understand the tiny signs seen in clinical cases of surviving victims. We present the case of a 44-year-old male diver who died from severe decompression sickness after rapid ascent from approximately 120m. Post-mortem CT and MRI studies of the brain and spinal cord revealed extensive gas inclusions in cerebral arteries, spinal arteries and cerebrospinal fluid (CSF) spaces, while the intracranial venous sinuses remained unaffected. These findings were confirmed at autopsy. Appropriate imaging techniques can help forensic pathologists to aim their autopsies at findings that might otherwise remain undetecte
FUM1—A Gene Required for Fumonisin Biosynthesis But Not for Maize Ear Rot and Ear Infection by Gibberella moniliformis in Field Tests
We have analyzed the role of fumonisins in infection of maize (Zea mays) by Gibberella moniliformis (anamorph Fusarium verticillioides) in field tests in Illinois and Iowa, United States. Fumonisin-nonproducing mutants were obtained by disrupting FUM1 (previouslyFUM5), the gene encoding a polyketide synthase required for fumonisin biosynthesis. Maize ear rot, ear infection, and fumonisin contamination were assessed by silk-channel injection in 1999 and 2000 and also by spray application onto maize silks, injection into maize stalks, and application with maize seeds at planting in 1999. Ear rot was evaluated by visual assessment of whole ears and by calculating percentage of symptomatic kernels by weight. Fumonisin levels in kernels were determined by high-performance liquid chromatography. The presence of applied strains in kernels was determined by analysis of recovered isolates for genetic markers and fumonisin production. Two independent fumonisin-nonproducing (fum1-3 and fum1-4) mutants were similar to their respective fumonisin-producing (FUM1-1) progenitor strains in ability to cause ear rot following silk-channel injection and also were similar in ability to infect maize ears following application by all four methods tested. This evidence confirms that fumonisins are not required for G. moniliformis to cause maize ear rot and ear infection
Making SPIFFI SPIFFIER: Upgrade of the SPIFFI instrument for use in ERIS and performance analysis from re-commissioning
SPIFFI is an AO-fed integral field spectrograph operating as part of SINFONI
on the VLT, which will be upgraded and reused as SPIFFIER in the new VLT
instrument ERIS. In January 2016, we used new technology developments to
perform an early upgrade to optical subsystems in the SPIFFI instrument so
ongoing scientific programs can make use of enhanced performance before ERIS
arrives in 2020. We report on the upgraded components and the performance of
SPIFFI after the upgrade, including gains in throughput and spatial and
spectral resolution. We show results from re-commissioning, highlighting the
potential for scientific programs to use the capabilities of the upgraded
SPIFFI. Finally, we discuss the additional upgrades for SPIFFIER which will be
implemented before it is integrated into ERIS.Comment: 20 pages, 12 figures. Proceedings from SPIE Astronomical Telescopes
and Instrumentation 201
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