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  • 1
    ISSN: 1365-3040
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: The influence of elevated [CO2] on the uptake and assimilation of nitrate and ammonium was investigated by growing tobacco plants in hydroponic culture with 2 mm nitrate or 1 mm ammonium nitrate and ambient or 800 p.p.m. [CO2]. Leaves and roots were harvested at several times during the diurnal cycle to investigate the levels of the transcripts for a high-affinity nitrate transporter (NRT2), nitrate reductase (NIA), cytosolic and plastidic glutamine synthetase (GLN1, GLN2), the activity of NIA and glutamine synthetase, the rate of 15N-nitrate and 15N-ammonium uptake, and the levels of nitrate, ammonium, amino acids, 2-oxoglutarate and carbohydrates. (i) In source leaves of plants growing on 2 mm nitrate in ambient [CO2], NIA transcript is high at the end of the night and NIA activity increases three-fold after illumination. The rate of nitrate reduction during the first part of the light period is two-fold higher than the rate of nitrate uptake and exceeds the rate of ammonium metabolism in the glutamate: oxoglutarate aminotransferase (GOGAT) pathway, resulting in a rapid decrease of nitrate and the accumulation of ammonium, glutamine and the photorespiratory intermediates glycine and serine. This imbalance is reversed later in the diurnal cycle. The level of the NIA transcript falls dramatically after illumination, and NIA activity and the rate of nitrate reduction decline during the second part of the light period and are low at night. NRT2 transcript increases during the day and remains high for the first part of the night and nitrate uptake remains high in the second part of the light period and decreases by only 30% at night. The nitrate absorbed at night is used to replenish the leaf nitrate pool. GLN2 transcript and glutamine synthetase activity rise to a maximum at the end of the day and decline only gradually after darkening, and ammonium and amino acids decrease during the night. (ii) In plants growing on ammonium nitrate, about 30% of the nitrogen is derived from ammonium. More ammonium accumulates in leaves during the day, and glutamine synthetase activity and glutamine levels remain high through the night. There is a corresponding 30% inhibition of nitrate uptake, a decrease of the absolute nitrate level, and a 15–30% decrease of NIA activity in the leaves and roots. The diurnal changes of leaf nitrate and the absolute level and diurnal changes of the NIA transcript are, however, similar to those in nitrate-grown plants. (iii) Plants growing on nitrate adjust to elevated [CO2] by a coordinate change in the diurnal regulation of NRT2 and NIA, which allows maximum rates of nitrate uptake and maximum NIA activity to be maintained for a larger part of the 24 h diurnal cycle. In contrast, tobacco growing on ammonium nitrate adjusts by selectively increasing the rate of ammonium uptake, and decreasing the expression of NRT2 and NIA and the rate of nitrate assimilation. In both conditions, the overall rate of inorganic nitrogen utilization is increased in elevated [CO2] due to higher rates of uptake and assimilation at the end of the day and during the night, and amino acids are maintained at levels that are comparable to or even higher than in ambient [CO2]. (iv) Comparison of the diurnal changes of transcripts, enzyme activities and metabolite pools across the four growth conditions reveals that these complex diurnal changes are due to transcriptional and post-transcriptional mechanisms, which act several steps and are triggered by various signals depending on the condition and organ. The results indicate that nitrate and ammonium uptake and root NIA activity may be regulated by the sugar supply, that ammonium uptake and assimilation inhibit nitrate uptake and root NIA activity, that the balance between the influx and utilization of nitrate plays a key role in the diurnal changes of the NIA transcript in leaves, that changes of glutamine do not play a key role in transcriptional regulation of NIA in leaves but instead inhibit NIA activity via uncharacterized post-transcriptional or post-translational mechanisms, and that high ammonium acts via uncharacterized post-transcriptional or post-translational mechanisms to stabilize glutamine synthetase activity during the night.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2016-08-21
    Description: Proteins are synthesized in cells by ribosomes and, in parallel, prepared for folding or targeting. While ribosomal protein synthesis is progressing, the nascent chain exposes amino-terminal signal sequences or transmembrane domains that mediate interactions with specific interaction partners, such as the signal recognition particle (SRP), the SecA–adenosine triphosphatase, or the trigger factor. These binding events can set the course for folding in the cytoplasm and translocation across or insertion into membranes. A distinction of the respective pathways depends largely on the hydrophobicity of the recognition sequence. Hydrophobic transmembrane domains stabilize SRP binding, whereas less hydrophobic signal sequences, typical for periplasmic and outer membrane proteins, stimulate SecA binding and disfavor SRP interactions. In this context, the formation of helical structures of signal peptides within the ribosome was considered to be an important factor. We applied dynamic nuclear polarization magic-angle spinning nuclear magnetic resonance to investigate the conformational states of the disulfide oxidoreductase A (DsbA) signal peptide stalled within the exit tunnel of the ribosome. Our results suggest that the nascent chain comprising the DsbA signal sequence adopts an extended structure in the ribosome with only minor populations of helical structure.
    Electronic ISSN: 2375-2548
    Topics: Natural Sciences in General
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  • 3
    Publication Date: 2019-05-01
    Description: Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.
    Print ISSN: 0004-6361
    Electronic ISSN: 1432-0746
    Topics: Physics
    Published by EDP Sciences
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  • 4
    Publication Date: 2018-03-01
    Description: Context. Future large-scale surveys with high-resolution imaging will provide us with approximately 105 new strong galaxy-scale lenses. These strong-lensing systems will be contained in large data amounts, however, which are beyond the capacity of human experts to visually classify in an unbiased way. Aims. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the strong-lensing challenge organized by the Bologna Lens Factory. It achieved first and third place, respectively, on the space-based data set and the ground-based data set. The goal was to find a fully automated lens finder for ground-based and space-based surveys that minimizes human inspection. Methods. We compared the results of our CNN architecture and three new variations (“invariant” “views” and “residual”) on the simulated data of the challenge. Each method was trained separately five times on 17 000 simulated images, cross-validated using 3000 images, and then applied to a test set with 100 000 images. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score, and the recall with no false positive (Recall0FP). Results. For ground-based data, our best method achieved an AUC score of 0.977 and a Recall0FP of 0.50. For space-based data, our best method achieved an AUC score of 0.940 and a Recall0FP of 0.32. Adding dihedral invariance to the CNN architecture diminished the overall score on space-based data, but achieved a higher no-contamination recall. We found that using committees of five CNNs produced the best recall at zero contamination and consistently scored better AUC than a single CNN. Conclusions. We found that for every variation of our CNN lensfinder, we achieved AUC scores close to 1 within 6%. A deeper network did not outperform simpler CNN models either. This indicates that more complex networks are not needed to model the simulated lenses. To verify this, more realistic lens simulations with more lens-like structures (spiral galaxies or ring galaxies) are needed to compare the performance of deeper and shallower networks.
    Print ISSN: 0004-6361
    Electronic ISSN: 1432-0746
    Topics: Physics
    Published by EDP Sciences
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