Robert Feulgen

Mycobacterium tuberculosis is a significant human pathogen, responsible for approximately 1.3 million deaths annually. The increase of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis is of particular concern as current first and even second line medications are less or no longer effective. Current antibacterial drugs, such as isoniazid and ethambutol, target various biosynthetic pathways within the complex cell wall of these bacteria, which is a key virulence factor. Its successful defence against the host immune system is due to a largely impermeable cell wall consisting of a multi-laminate mycolyl-arabinogalactan-peptidoglycan complex (mAGP) and a glycolipid layer embedded into the cell wall. Phosphatidylinositol mannosides (PIMs), lipomannan (LM) and lipoarabinomannan (LAM), all virulence factors, are included in this glycolipid layer. Previous studies in our group identified a highly conserved genetic locus involved in mycobacterial cell wall synthesis. Gene knockout studies in Corynebacterium glutamicum, a related model species tolerant of cell wall defects that kill mycobacteria, identified three proteins involved in the transport of mycolic acid intermediates across the cell wall. We have shown that the orthologs NCgl2764, NCgl2762 and NCgl2759 are involved in mycolic acid synthesis. The aim of this study was to determine the roles of NCgl2760 and NCgl2761, the two remaining genes within the putative complex. NCgl2761 was disrupted in C. glutamicum by homologous recombination and next the mutant was studied for cell wall defects. Total lipids, PIMs LM, LAM and mycolic acids were investigated by high performance thin layer chromatography (HPTLC), sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and gas chromatography-mass spectrometry (GC-MS). Delayed growth and a block in the path from acetyl trehalose monocorynomycolate (AcTMCM) to trehalose dicorynomycolate (TDCM) indicate their involvement in mycolate metabolism. These conclusions were congruent with those of the other three genes in the putative complex. RNA extraction and co-transcription with NCgl2760 were additionally investigated. The results do indeed show co-transcription of C. glutamicum NCgl2761 and C. glutamicum NCgl2760. This co-transcription was replicated in Mycobacterium smegmatis, between genes MSMEG_0315 and MSMEG_0317, the orthologs of NCgl2761 and NCgl2760. Deletion mutants of NCgl2760 using the same principles were completed. Initial growth experimentation showed no delayed growth, unlike the other four genes, so the following procedures were performed in triplicate. Complementation of ΔNCgl2760 was unsuccessful, consequently slice overlap extension (SOE) was attempted. This too failed. Nevertheless, experimental procedures continued and next showed absence of a block in the mycolic acid biosynthetic pathway. But, a Pierce™ silver stain showed a truncated, faster migrating LAM of a size measuring approximately 10kDa on a 15% SDS-PAGE gel. Both these findings are novel and suggest that a complex of at least four proteins mediates mycolic acid transport, and RNA studies raise the possibility of a link between the mycolic acid and LM/LAM biosynthesis pathways in mycobacteria and corynebacteria. All these studies contribute to our understanding of mycobacterial cell wall pathways which may offer attractive drug targets

Generalized Spatial and Spatiotemporal ARCH Models

In time-series analyses, particularly for finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in the conditional second moments. Only a few models have been proposed for modelling local clusters of increased risks. In this paper, we introduce a novel spatial GARCH process in a unified spatial and spatiotemporal GARCH framework, which also covers all previously proposed spatial ARCH models, exponential spatial GARCH, and time-series GARCH models. In contrast to previous spatiotemporal and time series models, this spatial GARCH allows for instantaneous spill-overs across all spatial units. For this common modelling framework, estimators are derived based on a non-linear least-squares approach. Eventually, the use of the model is demonstrated by a Monte Carlo simulation study and by an empirical example that focuses on real estate prices from 1995 to 2014 across the ZIP-Code areas of Berlin. A spatial autoregressive model is applied to the data to illustrate how locally varying model uncertainties (e.g., due to latent regressors) can be captured by the spatial GARCH-type models

A general framework for spatial GARCH models

In time-series analysis, particularly in finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in the conditional second moments. Only a few models have been proposed for modelling local clusters of increased risks. In this paper, we introduce a novel spatial GARCH process in a unified spatial and spatiotemporal GARCH framework, which also covers all previously proposed spatial ARCH models, exponential spatial GARCH, and time-series GARCH models. In contrast to previous spatiotemporal and time series models, this spatial GARCH allows for instantaneous spill-overs across all spatial units. For this common modelling framework, estimators are derived based on a non-linear least-squares approach. Eventually, the use of the model is demonstrated by a Monte Carlo simulation study and by an empirical example that focuses on real estate prices from 1995 to 2014 across the postal code areas of Berlin. A spatial autoregressive model is applied to the data to illustrate how locally varying model uncertainties (e.g., due to latent regressors) can be captured by the spatial GARCH-type models

Time-resolved transillumination of turbid media

The suitability and limits of time-resolved transillumination to determine inner details of biological tissues are investigated by phantom experiments. The achievable improvement is demonstrated by using different phantoms (absorbing objects embedded in a turbid medium). By means of line-scans across a sharp edge the spatial resolution and its dependence on temporal resolution can be determined. To demonstrate the physical resolution according to the Rayleigh-criterion, measurements were performed on blackened bead pairs. Investigations with partially transparent beads demonstrate the high sensitivity of time-resolving techniques with respect to variations in scattering or absorption coefficients

Selektionsuntersuchungen an Schleppnetzsteerten für den Plattfischfang in der Ostsee

The intensified trawl fishery for flounder and otherflatfishes along the German Baltic coast during summer months resulted in the problem of increased undersized bycatches and their discarding. Thereforeselectivity trials with a standard codend, a codend withenlarged meshes and a so-called multipanel codendwith partly transverse netting were investigated. Results show that improvements in selectivity are possible, and tests including UW-TV observations shouldbe continued

Object-based detection of linear kinematic features in sea ice

Source at: https://doi.org/10.3390/rs9050493 Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features

A method to improve high-resolution sea ice drift retrievals in the presence of deformation zones

Source at: http://doi.org/10.3390/rs9070718 Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions