The <SPP> Green function and SU(3) breaking in Kl3 decays

Using the 1=/N-C expansion scheme and truncating the hadronic spectrum to the lowest-lying resonances, we match a meromorphic approximation to the Green function onto QCD by imposing the correct large-momentum falloff, both off- shell and on the relevant hadron mass shells. In this way we determine a number of chiral low-energy constants of O(p(6)), in particular the ones governing SU(3) breaking in the K-l3 vector form factor at zero momentum transfer. The main result of our matching procedure is that the known loop contributions largely dominate the corrections of O(p(6)) to f(+)(0). We discuss the implications of our final value f(+)(K0 pi-) (0) = 0.984 +/- 0.012 for the extraction of V-us from K-l3 decays

Quantum Zeno Effect and Light-Dark Periods for a Single Atom

The quantum Zeno effect (QZE) predicts a slow-down of the time development of a system under rapidly repeated ideal measurements, and experimentally this was tested for an ensemble of atoms using short laser pulses for non-selective state measurements. Here we consider such pulses for selective measurements on a single system. Each probe pulse will cause a burst of fluorescence or no fluorescence. If the probe pulses were strictly ideal measurements, the QZE would predict periods of fluorescence bursts alternating with periods of no fluorescence (light and dark periods) which would become longer and longer with increasing frequency of the measurements. The non-ideal character of the measurements is taken into account by incorporating the laser pulses in the interaction, and this is used to determine the corrections to the ideal case. In the limit, when the time between the laser pulses goes to zero, no freezing occurs but instead we show convergence to the familiar macroscopic light and dark periods of the continuously driven Dehmelt system. An experiment of this type should be feasible for a single atom or ion in a trapComment: 16 pages, LaTeX, a4.sty; to appear in J. Phys.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of the Southeast Atmosphere Study 2013

Natural emissions of ozone-and-aerosol-precursor gases such as isoprene and monoterpenes are high in the southeastern US. In addition, anthropogenic emissions are significant in the southeastern US and summertime photochemistry is rapid. The NOAA-led SENEX (Southeast Nexus) aircraft campaign was one of the major components of the Southeast Atmosphere Study (SAS) and was focused on studying the interactions between biogenic and anthropogenic emissions to form secondary pollutants. During SENEX, the NOAA WP-3D aircraft conducted 20 research flights between 27 May and 10 July 2013 based out of Smyrna, TN. Here we describe the experimental approach, the science goals and early results of the NOAA SENEX campaign. The aircraft, its capabilities and standard measurements are described. The instrument payload is summarized including detection limits, accuracy, precision and time resolutions for all gas-and-aerosol phase instruments. The inter-comparisons of compounds measured with multiple instruments on the NOAA WP-3D are presented and were all within the stated uncertainties, except two of the three NO2 measurements. The SENEX flights included day- and nighttime flights in the southeastern US as well as flights over areas with intense shale gas extraction (Marcellus, Fayetteville and Haynesville shale). We present one example flight on 16 June 2013, which was a daytime flight over the Atlanta region, where several crosswind transects of plumes from the city and nearby point sources, such as power plants, paper mills and landfills, were flown. The area around Atlanta has large biogenic isoprene emissions, which provided an excellent case for studying the interactions between biogenic and anthropogenic emissions. In this example flight, chemistry in and outside the Atlanta plumes was observed for several hours after emission. The analysis of this flight showcases the strategies implemented to answer some of the main SENEX science questions.</p