Search Results
Impacts of large-scale climatic disturbances on the terrestrial carbon cycle
Background: The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO2 growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO2 increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98. Results: We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO2 flux. Conclusion: Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events.
Expression stability of commonly used reference genes in canine articular connective tissues
Background: The quantification of gene expression in tissue samples requires the use of reference genes to normalise transcript numbers between different samples. Reference gene stability may vary between different tissues, and between the same tissue in different disease states. We evaluated the stability of 9 reference genes commonly used in human gene expression studies. Realtime reverse transcription PCR and a mathematical algorithm were used to establish which reference genes were most stably expressed in normal and diseased canine articular tissues and two canine cell lines stimulated with lipolysaccaride (LPS). Results: The optimal reference genes for comparing gene expression data between normal and diseased infrapatella fat pad were RPL13A and YWHAZ (M = 0.56). The ideal reference genes for comparing normal and osteoarthritic (OA) cartilage were RPL13A and SDHA (M = 0.57). The best reference genes for comparing normal and ruptured canine cranial cruciate ligament were B2M and TBP (M = 0.59). The best reference genes for normalising gene expression data from normal and LPS stimulated cell lines were SDHA and YWHAZ (K6) or SDHA and HMBS (DH82), which had expression stability (M) values of 0.05 (K6) and 0.07 (DH82) respectively. The number of reference genes required to reduce pairwise variation (V) to <0.20 was 4 for cell lines, 5 for cartilage, 7 for cranial cruciate ligament and 8 for fat tissue. Reference gene stability was not related to the level of gene expression. Conclusion: The reference genes demonstrating the most stable expression within each different canine articular tissue were identified, but no single reference gene was identified as having stable expression in all different tissue types. This study underlines the necessity to select reference genes on the basis of tissue and disease specific expression profile evaluation and highlights the requirement for the identification of new reference genes with greater expression stability for use in canine articular tissue gene expression studies.
MAC and baseband processors for RF-MIMO WLAN
The article describes hardware solutions for the IEEE 802.11 medium access control (MAC) layer and IEEE 802.11a digital baseband in an RF-MIMO WLAN transceiver that performs the signal combining in the analogue domain. Architecture and implementation details of the MAC processor including a hardware accelerator and a 16-bit MAC-physical layer (PHY) interface are presented. The proposed hardware solution is tested and verified using a PHY link emulator. Architecture, design, implementation, and test of a reconfigurable digital baseband processor are described too. Description includes the baseband algorithms (the main blocks being MIMO channel estimation and Tx-Rx analogue beamforming), their FPGA-based implementation, baseband printed-circuit-board, and real-time tests.
The polycyclic aromatic hydrocarbon concentrations in soils in the Region of Valasske Mezirici, the Czech Republic
The polycyclic aromatic hydrocarbon (PAH) contamination of urban, agricultural and forest soil samples was investigated from samples obtained in the surroundings of Valasske Mezirici. Valasske Mezirici is a town located in the north-east mountainous part of the Czech Republic, where a coal tar refinery is situated. 16 PAHs listed in the US EPA were investigated. Organic oxidizable carbon was also observed in the forest soils. The PAH concentrations ranged from 0.86-10.84 (with one anomalous value of 35.14) and 7.66-79.39 mg/kg dm in the urban/agricultural and forest soils, respectively. While the PAH levels in the urban/agricultural soils are within the range typically found in industrialized areas, the forest soils showed elevated PAH concentrations compared to other forest soils in Western and Northern Europe. The PAH concentrations and their molecular distribution ratios were studied as functions of the sample location and the meteorological history. The soils from localities at higher altitudes above sea level have the highest PAH concentrations, and the PAH concentrations decrease with increasing distance from the town.
Terrestrial vegetation redistribution and carbon balance under climate change
Background Dynamic Global Vegetation Models (DGVMs) compute the terrestrial carbon balance as well as the transient spatial distribution of vegetation. We study two scenarios of moderate and strong climate change (2.9 K and 5.3 K temperature increase over present) to investigate the spatial redistribution of major vegetation types and their carbon balance in the year 2100. Results The world's land vegetation will be more deciduous than at present, and contain about 125 billion tons of additional carbon. While a recession of the boreal forest is simulated in some areas, along with a general expansion to the north, we do not observe a reported collapse of the central Amazonian rain forest. Rather, a decrease of biomass and a change of vegetation type occurs in its northeastern part. The ability of the terrestrial biosphere to sequester carbon from the atmosphere declines strongly in the second half of the 21st century. Conclusion Climate change will cause widespread shifts in the distribution of major vegetation functional types on all continents by the year 2100.
The DeoR-type transcriptional regulator SugR acts as a repressor for genes encoding the phosphoenolpyruvate:sugar phosphotransferase system (PTS) in Corynebacterium glutamicum
Background: The major uptake system responsible for the transport of fructose, glucose, and sucrose in Corynebacterium glutamicum ATCC 13032 is the phosphoenolpyruvate:sugar phosphotransferase system (PTS). The genes encoding PTS components, namely ptsI, ptsH, and ptsF belong to the fructose-PTS gene cluster, whereas ptsG and ptsS are located in two separate regions of the C. glutamicum genome. Due to the localization within and adjacent to the fructose-PTS gene cluster, two genes coding for DeoR-type transcriptional regulators, cg2118 and sugR, are putative candidates involved in the transcriptional regulation of the fructose-PTS cluster genes. Results: Four transcripts of the extended fructose-PTS gene cluster that comprise the genes sugRcg2116, ptsI, cg2118-fruK-ptsF, and ptsH, respectively, were characterized. In addition, it was shown that transcription of the fructose-PTS gene cluster is enhanced during growth on glucose or fructose when compared to acetate. Subsequently, the two genes sugR and cg2118 encoding for DeoR-type regulators were mutated and PTS gene transcription was found to be strongly enhanced in the presence of acetate only in the sugR deletion mutant. The SugR regulon was further characterized by microarray hybridizations using the sugR mutant and its parental strain, revealing that also the PTS genes ptsG and ptsS belong to this regulon. Binding of purified SugR repressor protein to a 21 bp sequence identified the SugR binding site as an AC-rich motif. The two experimentally identified SugR binding sites in the fructose-PTS gene cluster are located within or downstream of the mapped promoters, typical for transcriptional repressors. Effector studies using electrophoretic mobility shift assays (EMSA) revealed the fructose PTS-specific metabolite fructose-1-phosphate (F-1-P) as a highly efficient, negative effector of the SugR repressor, acting in the micromolar range. Beside F-1-P, other sugar-phosphates like fructose-1,6-bisphosphate (F-1,6- P) and glucose-6-phosphate (G-6-P) also negatively affect SugR-binding, but in millimolar concentrations.
Critical aspects of substrate nanopatterning for the ordered growth of GaN nanocolumns
Precise and reproducible surface nanopatterning is the key for a successful ordered growth of GaN nanocolumns. In this work, we point out the main technological issues related to the patterning process, mainly surface roughness and cleaning, and mask adhesion to the substrate. We found that each of these factors, process-related, has a dramatic impact on the subsequent selective growth of the columns inside the patterned holes. We compare the performance of e-beam lithography, colloidal lithography, and focused ion beam in the fabrication of hole-patterned masks for ordered columnar growth. These results are applicable to the ordered growth of nanocolumns of different materials.
Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study
Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.
Malignant transformation in a defined genetic background: Proteome changes displayed by 2D-PAGE
Background: Cancer arises from normal cells through the stepwise accumulation of genetic alterations. Cancer development can be studied by direct genetic manipulation within experimental models of tumorigenesis. Thereby, confusion by the genetic heterogeneity of patients can be circumvented. Moreover, identification of the critical changes that convert a pre-malignant cell into a metastatic, therapy resistant tumor cell, however, is one necessary step to develop effective and selective anti-cancer drugs. Thus, for the current study a cell culture model for malignant transformation was used: Primary human fibroblasts of the BJ strain were sequentially transduced with retroviral vectors encoding the genes for hTERT (cell line BJ-T), simian virus 40 early region (SV40 ER, cell line BJ-TE) and H-Ras V12 (cell line BJ-TER). Results: The stepwise malignant transformation of human fibroblasts was analyzed on the protein level by differential proteome analysis. We observed 39 regulated protein spots and therein identified 67 different proteins. The strongest change of spot patterns was detected due to integration of SV40 ER. Among the proteins being significantly regulated during the malignant transformation process well known proliferating cell nuclear antigen (PCNA) as well as the chaperones mitochondrial heat shock protein 75 kDa (TRAP-1) and heat shock protein HSP90 were identified. Moreover, we find out, that TRAP-1 is already up-regulated by means of SV40 ER expression instead of H-Ras V12. Furthermore Peroxiredoxin-6 (PRDX6), Annexin A2 (p36), Plasminogen activator inhibitor 2 (PAI-2) and Keratin type II cytoskeletal 7 (CK-7) were identified to be regulated. For some protein candidates we confirmed our 2D-PAGE results by Western Blot. Conclusion: These findings give further hints for intriguing interactions between the p16-RB pathway, the mitochondrial chaperone network and the cytoskeleton. In summary, using a cell culture model for malignant transformation analyzed with 2D-PAGE, proteome and cellular changes can be related to defined steps of tumorigenesis
Synthesis of carbon nanotubes with and without catalyst particles
The initial development of carbon nanotube synthesis revolved heavily around the use of 3d valence transition metals such as Fe, Ni, and Co. More recently, noble metals (e.g. Au) and poor metals (e.g. In, Pb) have been shown to also yield carbon nanotubes. In addition, various ceramics and semiconductors can serve as catalytic particles suitable for tube formation and in some cases hybrid metal/metal oxide systems are possible. All-carbon systems for carbon nanotube growth without any catalytic particles have also been demonstrated. These different growth systems are briefly examined in this article and serve to highlight the breadth of avenues available for carbon nanotube synthesis.