Behavioral response of dissimilatory perchlorate-reducing bacteria to different electron acceptors

The response behavior of three dissimilatory perchlorate-reducing bacteria to different electron acceptors (nitrate, chlorate, and perchlorate) was investigated with two different assays. The observed response was species-specific, dependent on the prior growth conditions, and was inhibited by oxygen. We observed attraction toward nitrate when Dechloromonas aromatica strain RCB and Azospira suillum strain PS were grown with nitrate. When D. aromatica and Dechloromonas agitata strain CKB were grown with perchlorate, both responded to nitrate, chlorate, and perchlorate. When A. suillum was grown with perchlorate, the organism responded to chlorate and perchlorate but not nitrate. A gene replacement mutant in the perchlorate reductase subunit (pcrA) of D. aromatica resulted in a loss of the attraction response toward perchlorate but had no impact on the nitrate response. Washed-cell suspension studies revealed that the perchlorate grown cells of D. aromatica reduced both perchlorate and nitrate, while A. suillum cells reduced perchlorate only. Based on these observations, energy taxis was proposed as the underlying mechanism for the responses to (per)chlorate by D. aromatica. To the best of our knowledge, this study represents the first investigation of the response behavior of perchlorate-reducing bacteria to environmental stimuli. It clearly demonstrates attraction toward chlorine oxyanions and the unique ability of these organisms to distinguish structurally analogous compounds, nitrate, chlorate, and perchlorate and respond accordingly

A Large Catalog of Homogeneous Ultra-Violet/Optical GRB Afterglows: Temporal and Spectral Evolution

We present the second Swift Ultra-Violet/Optical Telescope (UVOT) gamma-ray burst (GRB) afterglow catalog, greatly expanding on the first Swift UVOT GRB afterglow catalog. The second catalog is constructed from a database containing over 120,000 independent UVOT observations of 538 GRBs first detected by Swift, the High Energy Transient Explorer 2 (HETE2), the INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL), the Interplanetary Network (IPN), Fermi, and Astro-rivelatore Gamma a Immagini Leggero (AGILE). The catalog covers GRBs discovered from 2005 Jan 17 to 2010 Dec 25. Using photometric information in three UV bands, three optical bands, and a `white' or open filter, the data are optimally co-added to maximize the number of detections and normalized to one band to provide a detailed light curve. The catalog provides positional, temporal, and photometric information for each burst, as well as Swift Burst Alert Telescope (BAT) and X-Ray Telescope (XRT) GRB parameters. Temporal slopes are provided for each UVOT filter. The temporal slope per filter of almost half the GRBs are fit with a single power-law, but one to three breaks are required in the remaining bursts. Morphological comparisons with the X-ray reveal that approximately 75% of the UVOT light curves are similar to one of the four morphologies identified by Evans et al. (2009). The remaining approximately 25% have a newly identified morphology. For many bursts, redshift and extinction corrected UV/optical spectral slopes are also provided at 2000, 20,000, and 200,000 seconds

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior