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Ground-penetrating radar (GPR) is an established geophysical method to explore near-surface sedimentary environments. Interpreting GPR images is largely based on manual procedures following concepts known as GPR facies analysis. We have developed a novel strategy to distinguish GPR facies in a largely automated and more objective manner. First, we calculate 13 textural attributes to quantify GPR reflection characteristics. Then, this database is reduced using principal component analysis. Finally, we image the dominating principal components using composite imaging and classify them using standard clustering methods. The potential of this work-flow is evaluated using a 2D GPR field example collected across stratified glaciofluvial deposits. Our results demonstrate that the derived facies images are well correlated with the composition and the porosity of the sediments as known from independent borehole logs. Our analysis strategy eases and improves the interpretability of GPR data and will help in a variety of geologic and hydrological problems.
Topographic migration of 2D and 3D ground-penetrating radar data considering variable velocities
(2015)
We present a 2D/3D topographic migration scheme for ground-penetrating radar (GPR) data which is able to account for variable velocities by using the root mean square (rms) velocity approximation. We test our migration scheme using a synthetic 2D example and compare our migrated image to the results obtained using common GPR migration approaches. Furthermore, we apply it to 2D and 3D field data. These examples are recorded across common subsurface settings including surface topography and variations in the GPR subsurface velocity field caused by a shallow ground water table. In such field settings, our migration strategy provides well focused images of commonoffset GPR data without the need for a detailed interval velocity model. The synthetic and field examples demonstrate that our topographic migration scheme allows for accurate GPR imaging in the presence of variations in surface topography and subsurface velocity.
Ground-based magnetic surveying is a common geophysical method to explore near-surface environments in a non-destructive manner. In many typical applications (such as archaeological prospection), the resulting anomaly maps are often characterized by low signal-to-noise ratios and, thus, the suppression of noise is a key step in data processing. Here, we propose the steering kernel regression (SKR) method to denoise magnetic data sets. SKR has been recently developed to suppress random noise in images and video sequences. The core of the method is the steering kernel function which represents a robust estimate of local image structure. Using such a kernel within an iterative regression based denoising framework, helps to minimize image blurring and to preserve the underlying structures such as edges and corners. Because such filter characteristics are desirable for random noise attenuation in potential field data sets, we apply the SKR method for processing high-resolution ground-based magnetic data as they are typically collected in archaeological applications. We test and evaluate the SKR method using synthetic and field data examples and also compare it to more commonly employed denoising strategies relying, for example, on fixed filter masks (e.g., Gaussian filters). Our results show that the SKR method is successful in removing random and acquisition related noise present in our data. Concurrently, it preserves the local image structure including the amplitudes of anomalies. As demonstrated by derivative based transformations, the mentioned filter characteristics significantly impact subsequent processing steps and, therefore, result in an improved analysis and interpretation of magnetic data. Thus, the method can be considered as a promising and novel approach for denoising ground-based magnetic data.
A large-scale metabolic quantitative trait loci (mQTL) analysis was performed on the well-characterized Solanum pennellii introgression lines to investigate the genomic regions associated with secondary metabolism in tomato fruit pericarp. In total, 679 mQTLs were detected across the 76 introgression lines. Heritability analyses revealed that mQTLs of secondary metabolism were less affected by environment than mQTLs of primary metabolism. Network analysis allowed us to assess the interconnectivity of primary and secondary metabolism as well as to compare and contrast their respective associations with morphological traits. Additionally, we applied a recently established real-time quantitative PCR platform to gain insight into transcriptional control mechanisms of a subset of the mQTLs, including those for hydroxycinnamates, acyl-sugar, naringenin chalcone, and a range of glycoalkaloids. Intriguingly, many of these compounds displayed a dominant-negative mode of inheritance, which is contrary to the conventional wisdom that secondary metabolite contents decreased on domestication. We additionally performed an exemplary evaluation of two candidate genes for glycolalkaloid mQTLs via the use of virus-induced gene silencing. The combined data of this study were compared with previous results on primary metabolism obtained from the same material and to other studies of natural variance of secondary metabolism.
In roots of Arabidopsis (Arabidopsis thaliana), L-lactate is generated by the reduction of pyruvate via L-lactate dehydrogenase, but this enzyme does not efficiently catalyze the reverse reaction. Here, we identify the Arabidopsis glycolate oxidase (GOX) paralogs GOX1, GOX2, and GOX3 as putative L-lactate-metabolizing enzymes based on their homology to CYB2, the L-lactate cytochrome c oxidoreductase from the yeast Saccharomyces cerevisiae. We found that GOX3 uses L-lactate with a similar efficiency to glycolate; in contrast, the photorespiratory isoforms GOX1 and GOX2, which share similar enzymatic properties, use glycolate with much higher efficiencies than L-lactate. The key factor making GOX3 more efficient with L-lactate than GOX1 and GOX2 is a 5- to 10-fold lower Km for the substrate. Consequently, only GOX3 can efficiently metabolize L-lactate at low intracellular concentrations. Isotope tracer experiments as well as substrate toxicity tests using GOX3 loss-of-function and overexpressor plants indicate that L-lactate is metabolized in vivo by GOX3. Moreover, GOX3 rescues the lethal growth phenotype of a yeast strain lacking CYB2, which cannot grow on L-lactate as a sole carbon source. GOX3 is predominantly present in roots and mature to aging leaves but is largely absent from young photosynthetic leaves, indicating that it plays a role predominantly in heterotrophic rather than autotrophic tissues, at least under standard growth conditions. In roots of plants grown under normoxic conditions, loss of function of GOX3 induces metabolic rearrangements that mirror wild-type responses under hypoxia. Thus, we identified GOX3 as the enzyme that metabolizes L-lactate to pyruvate in vivo and hypothesize that it may ensure the sustainment of low levels of L-lactate after its formation under normoxia.
MYB transcription factors (TFs) are important regulators of flavonoid biosynthesis in plants. Here, we report MYB112 as a formerly unknown regulator of anthocyanin accumulation in Arabidopsis (Arabidopsis thaliana). Expression profiling after chemically induced overexpression of MYB112 identified 28 up-and 28 down-regulated genes 5 h after inducer treatment, including MYB7 and MYB32, which are both induced. In addition, upon extended induction, MYB112 also positively affects the expression of PRODUCTION OF ANTHOCYANIN PIGMENT1, a key TF of anthocyanin biosynthesis, but acts negatively toward MYB12 and MYB111, which both control flavonol biosynthesis. MYB112 binds to an 8-bp DNA fragment containing the core sequence (A/T/G)(A/C) CC(A/T)(A/G/T)(A/C)(T/C). By electrophoretic mobility shift assay and chromatin immunoprecipitation coupled to quantitative polymerase chain reaction, we show that MYB112 binds in vitro and in vivo to MYB7 and MYB32 promoters, revealing them as direct downstream target genes. We further show that MYB112 expression is up-regulated by salinity and high light stress, environmental parameters that both require the MYB112 TF for anthocyanin accumulation under these stresses. In contrast to several other MYB TFs affecting anthocyanin biosynthesis, MYB112 expression is not controlled by nitrogen limitation or an excess of carbon. Thus, MYB112 constitutes a regulator that promotes anthocyanin accumulation under abiotic stress conditions.
Abiotic stresses, such as salinity, cause global yield loss of all major crop plants. Factors and mechanisms that can aid in plant breeding for salt stress tolerance are therefore of great importance for food and feed production. Here, we identified a MYB-like transcription factor, Salt-Related MYB1 (SRM1), that negatively affects Arabidopsis (Arabidopsis thaliana) seed germination under saline conditions by regulating the levels of the stress hormone abscisic acid (ABA). Accordingly, several ABA biosynthesis and signaling genes act directly downstream of SRM1, including SALT TOLERANT1/NINE-CIS-EPOXYCAROTENOID DIOXYGENASE3, RESPONSIVE TO DESICCATION26, and Arabidopsis NAC DOMAIN CONTAINING PROTEIN19. Furthermore, SRM1 impacts vegetative growth and leaf shape. We show that SRM1 is an important transcriptional regulator that directly targets ABA biosynthesis and signaling-related genes and therefore may be regarded as an important regulator of ABA-mediated salt stress tolerance.