Scaling law in target-hunting processes

We study the hunting process for a target, in which the hunter tracks the goal by smelling odors it emits. The odor intensity is supposed to decrease with the distance it diffuses. The Monte Carlo experiment is carried out on a 2-dimensional square lattice. Having no idea of the location of the target, the hunter determines its moves only by random attempts in each direction. By sorting the searching time in each simulation and introducing a variable $x$ to reflect the sequence of searching time, we obtain a curve with a wide plateau, indicating a most probable time of successfully finding out the target. The simulations reveal a scaling law for the searching time versus the distance to the position of the target. The scaling exponent depends on the sensitivity of the hunter. Our model may be a prototype in studying such the searching processes as various foods-foraging behavior of the wild animals.Comment: 7 figure

Anomalous escape governed by thermal 1/f noise

We present an analytic study for subdiffusive escape of overdamped particles out of a cusp-shaped parabolic potential well which are driven by thermal, fractional Gaussian noise with a $1/\omega^{1-\alpha}$ power spectrum. This long-standing challenge becomes mathematically tractable by use of a generalized Langevin dynamics via its corresponding non-Markovian, time-convolutionless master equation: We find that the escape is governed asymptotically by a power law whose exponent depends exponentially on the ratio of barrier height and temperature. This result is in distinct contrast to a description with a corresponding subdiffusive fractional Fokker-Planck approach; thus providing experimentalists an amenable testbed to differentiate between the two escape scenarios

In-orbit Vignetting Calibrations of XMM-Newton Telescopes

We describe measurements of the mirror vignetting in the XMM-Newton Observatory made in-orbit, using observations of SNR G21.5-09 and SNR 3C58 with the EPIC imaging cameras. The instrument features that complicate these measurements are briefly described. We show the spatial and energy dependences of measured vignetting, outlining assumptions made in deriving the eventual agreement between simulation and measurement. Alternate methods to confirm these are described, including an assessment of source elongation with off-axis angle, the surface brightness distribution of the diffuse X-ray background, and the consistency of Coma cluster emission at different position angles. A synthesis of these measurements leads to a change in the XMM calibration data base, for the optical axis of two of the three telescopes, by in excess of 1 arcminute. This has a small but measureable effect on the assumed spectral responses of the cameras for on-axis targets.Comment: Accepted by Experimental Astronomy. 26 pages, 18 figure

Use of soil moisture information in yield models

There are no author-identified significant results in this report

Unusual Response to a Localized Perturbation in a Generalized Elastic Model

The generalized elastic model encompasses several physical systems such as polymers, membranes, single file systems, fluctuating surfaces and rough interfaces. We consider the case of an applied localized potential, namely an external force acting only on a single (tagged) probe, leaving the rest of the system unaffected. We derive the fractional Langevin equation for the tagged probe, as well as for a generic (untagged) probe, where the force is not directly applied. Within the framework of the fluctuation-dissipation relations, we discuss the unexpected physical scenarios arising when the force is constant and time periodic, whether or not the hydrodynamic interactions are included in the model. For short times, in case of the constant force, we show that the average drift is linear in time for long range hydrodynamic interactions and behaves ballistically or exponentially for local hydrodynamic interactions. Moreover, it can be opposite to the direction of external disturbance for some values of the model's parameters. When the force is time periodic, the effects are macroscopic: the system splits into two distinct spatial regions whose size is proportional to the value of the applied frequency. These two regions are characterized by different amplitudes and phase shifts in the response dynamics

Discovery of superstrong, fading, iron line emission and double-peaked Balmer lines of the galaxy SDSSJ0952+2143 - the light echo of a huge flare

We report the discovery of superstrong, fading, high-ionization iron line emission in the galaxy SDSSJ095209.56+214313.3 (SDSSJ0952+2143 hereafter), which must have been caused by an X-ray outburst of large amplitude. SDSSJ0952+2143 is unique in its strong multiwavelength variability; such a broadband emission-line and continuum response has not been observed before. The strong iron line emission is accompanied by unusual Balmer line emission with a broad base, narrow core and double-peaked narrow horns, and strong HeII emission. These lines, while strong in the SDSS spectrum taken in 2005, have faded away significantly in new spectra taken in December 2007. Comparison of SDSS, 2MASS, GALEX and follow-up GROND photometry reveals variability in the NUV, optical and NIR band. Taken together, these unusual observations can be explained by a giant outburst in the EUV--X-ray band, detected even in the optical and NIR. The intense and variable iron, Helium and Balmer lines represent the ``light echo'' of the flare, as it traveled through circumnuclear material. The outburst may have been caused by the tidal disruption of a star by a supermassive black hole. Spectroscopic surveys such as SDSS are well suited to detect emission-line light echoes of such rare flare events. Reverberation-mapping of these light echoes can then be used as a new and efficient probe of the physical conditions in the circumnuclear material in non-active or active galaxies.Comment: ApJ Letters, 678, L13 (May 1 issue); incl. 4 colour figures. This and related papers on tidal disruption flares also available at http://www.xray.mpe.mpg.de/~skomossa

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org/ webcite

Dynamic regulation of adipose tissue metabolism in the domestic broiler chicken – an alternative model for studies of human obesity

Background The domestic chicken is an attractive, but underutilized, animal model for studies of adipose tissue biology, metabolism and obesity: 1.) like humans, chickens rely on liver rather than adipose tissue for the majority of de novo lipogenesis; 2.) quantitative trait loci (QTLs) linked to fatness in chickens contain genes implicated in human susceptibility to obesity and diabetes; 3.) chickens are naturally hyperglycemic and insulin resistant; and 4.) a broad selection of genetic models exhibiting a range of fatness are available. To date, however, little is known about regulation of adipose metabolism in this model organism. Materials and methods Affymetrix arrays were used to profile gene expression in abdominal adipose tissue from broiler chickens fed ad libitum or fasted for five hours and from three distinct genetic lines with low (Fayoumi and Leghorn) or high (broiler) levels of adiposity. QPCR was used to validate microarray results for select genes. Western blotting was used to assay levels of signaling proteins. Tissue levels of beta-hydroxybutyrate were measured as an index of fatty acid oxidation using a colorimetric assay. Multiple testing was controlled using q-value. Mixed linear model and multivariate clustering analysis were implemented in SAS. The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 (http://david.abcc.ncifcrf.gov/ webcite) was used for Gene Ontology (GO) and KEGG pathway enrichment analyses. Results A total of 1780 genes were differentially expressed in fasted vs. ad libitum fed (p\u3c0.05) tissue after correction for multiple testing. Gene Ontology and pathway analyses, combined with Western blot validation, indicated significant effects on a broad selection of pathways related to metabolism, stress signaling and adipogenesis. In particular, fasting upregulated rate-limiting genes in both the mitochondrial and peroxisomal pathways of beta-oxidation. Enhanced fatty acid oxidation in white adipose tissue was further suggested by a significant increase in tissue content of the ketone beta-hydroxybutyrate. Expression profiles suggested that, despite the relatively brief duration of feed withdrawal, fasting suppressed adipogenesis; expression of key genes in multiple steps of adipogenesis, including lineage commitment from mesenchymal stem cells, were significantly down-regulated in fasted vs. fed adipose tissue. Interestingly, fasting increased expression of several inflammatory adipokines and components of the toll-like receptor 4 signaling pathway. Microarray analysis of Fayoumi, Leghorn and broiler adipose tissue revealed that genetic leanness shared molecular signatures with the effects of fasting. In supervised clustering analysis, fasted broiler chickens clustered with lean Fayoumi and Leghorn lines rather than with the fed broiler group, suggesting that fasting manipulated expression profiles to resemble those of the lean phenotype. Conclusions Collectively, these data suggest that leanness in chickens is associated with increased fat utilization which, given the similarities between avian and human adipose tissue with regard to lipid metabolism, may have relevance for humans. The paradoxical increase in some inflammatory markers with an acute fast suggests that the dynamic relationship between inflammation and adipose metabolism may differ from what is observed in obesity. These results highlight chicken as a useful model in which to study the interrelationships between food intake, adipose development, metabolism, and cell stress