Time-resolved impulse response of the magnetoplasmon resonance in a two-dimensional electron gas

We have used optically excited ultrashort electrical pulses to measure the magnetoplasmon resonance of a two-dimensional electron gas formed in an AlGaAs/GaAs heterostructure at frequencies up to 200 gigahertz. This is accomplished by incorporating the sample into a guided wave probe operating in a pumped (^{3}He) system. We are able to detect the resonance by launching a stimulus pulse in the guide, and monitoring the system response in a time resolved pump-probe arrangement. Data obtained from measurements yield resonant frequencies that agree with the magnetoplasmon dispersion relation.Comment: 4 pages, 4 figure

Thermodynamic Signature of a Two-Dimensional Metal-Insulator Transition

We present a study of the compressibility, K, of a two-dimensional hole system which exhibits a metal-insulator phase transition at zero magnetic field. It has been observed that dK/dp changes sign at the critical density for the metal-insulator transition. Measurements also indicate that the insulating phase is incompressible for all values of B. Finally, we show how the phase transition evolves as the magnetic field is varied and construct a phase diagram in the density-magnetic field plane for this system.Comment: 4 pages, 4 figures, submitted to Physical Review Letters; version 1 is identical to version 2 but didn't compile properl

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship

Efficacy of naproxen with or without esomeprazole for pain and inflammation in patients after bilateral third molar extractions : a double blinded crossover study

Using a double-blinded randomized crossover design, this study aimed to evaluate acute postoperative pain management, swelling and trismus in 46 volunteers undergoing extractions of the two lower third molars, in similar positions, at two different appointments who consumed a tablet of either NE (naproxen 500 mg + esomepraz ole 20 mg) or only naproxen (500 mg) every 12 hours for 4 days. Parameters were analyzed: self-reported pain intensity using a visual analog scale (VAS) pre- and postoperative mouth opening; incidence, type and severity of adverse reactions; total quantity consumed of rescue medication; and pre- and postoperative swelling. Female volunteers reported significantly more postoperative pain at 1, 1.5, 2, 3 and 4hrs after surgery while also taking their first rescue medication at a time significantly earlier when consuming NE when compared to naproxen (3.7hrs and 6.7hrs). Conversely, no differences were found between each drug group in males. In conclusion, throughout the entire study, pain was mild after using either drug in both men and women with pain scores on average well below 40mm (VAS), although in women naproxen improved acute postoperative pain management when compared to NE

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Current research in biotechnology: Exploring the biotech forefront

Biotechnology is an evolving research field that covers a broad range of topics. Here we aimed to evaluate the latest research literature, to identify prominent research themes, major contributors in terms of institutions, countries/regions, and journals. The Web of Science Core Collection online database was searched to retrieve biotechnology articles published since 2017. In total, 12,351 publications were identified and analyzed. Over 8500 institutions contributed to these biotechnology publications, with the top 5 most productive ones scattered over France, China, the United States of America, Spain, and Brazil. Over 140 countries/regions contributed to the biotechnology research literature, led by the United States of America, China, Germany, Brazil, and India. Journal of Bioscience and Bioengineering was the most productive journal in terms of number of publications. Metabolic engineering was among the most prevalent biotechnology study themes, and Escherichia coli and Saccharomyces cerevisiae were frequently used in biotechnology investigations, including the biosynthesis of useful biomolecules, such as myo-inositol (vitamin B8), monoterpenes, adipic acid, astaxanthin, and ethanol. Nanoparticles and nanotechnology were identified too as emerging biotechnology research themes of great significance. Biotechnology continues to evolve and will remain a major driver of societal innovation and development

Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins

<p>Abstract</p> <p>Background</p> <p>Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.</p> <p>Results</p> <p>We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information.</p> <p>Conclusion</p> <p>While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at <url>http://phos3d.mpimp-golm.mpg.de</url>.</p

Magnetoresistance of one-dimensional subbands in tunnel-coupled double quantum wires

We study the low-temperature in-plane magnetoresistance of tunnel-coupled quasi-one-dimensional quantum wires. The wires are defined by two pairs of mutually aligned split gates on opposite sides of a < 1 micron thick AlGaAs/GaAs double quantum well heterostructure, allowing independent control of their widths. In the ballistic regime, when both wires are defined and the field is perpendicular to the current, a large resistance peak at ~6 Tesla is observed with a strong gate voltage dependence. The data is consistent with a counting model whereby the number of subbands crossing the Fermi level changes with field due to the formation of an anticrossing in each pair of 1D subbands

Systems biology approaches applied to regenerative medicine

Systems biology is the creation of theoretical and mathematical models for the study of biological systems, as an engine for hypothesis generation and to provide context to experimental data. It is underpinned by the collection and analysis of complex datasets from different biological systems, including global gene, RNA, protein and metabolite profiles. Regenerative medicine seeks to replace or repair tissues with compromised function (for example, through injury, deficiency or pathology), in order to improve their functionality. In this paper, we will address the application of systems biology approaches to the study of regenerative medicine, with a particular focus on approaches to study modifications to the genome, transcripts and small RNAs, proteins and metabolites