A comparison of similar aerosol measurements made on the NASA P3-B, DC-8, and NSF C-130 aircraft during TRACE-P and ACE-Asia

Two major aircraft experiments occurred off the Pacific coast of Asia during spring 2001: the NASA sponsored Transport and Chemical Evolution over the Pacific (TRACE-P) and the National Science Foundation (NSF) sponsored Aerosol Characterization Experiment-Asia (ACE-Asia). Both experiments studied emissions from the Asian continent (biomass burning, urban/industrial pollution, and dust). TRACE-P focused on trace gases and aerosol during March/April and was based primarily in Hong Kong and Yokota Air Force Base, Japan, and involved two aircraft: the NASA DC-8 and the NASA P3-B. ACE-Asia focused on aerosol and radiation during April/May and was based in Iwakuni Marine Corps Air Station, Japan, and involved the NSF C-130. This paper compares aerosol measurements from these aircraft including aerosol concentrations, size distributions (and integral properties), chemistry, and optical properties. Best overall agreement (generally within RMS instrumental uncertainty) was for physical properties of the submircron aerosol, including condensation nuclei concentrations, scattering coefficients, and differential mobility analyzer and optical particle counter (OPC) accumulation mode size distributions. Larger differences (typically outside of the RMS uncertainty) were often observed for parameters related to the supermicron aerosols (total scattering and absorption coefficients, coarse mode Forward Scattering Spectrometer Probe and OPC size distributions/integral properties, and soluble chemical species usually associated with the largest particles, e.g., Na+, Cl−, Ca2+, and Mg2+), where aircraft sampling is more demanding. Some of the observed differences reflect different inlets (e.g., low-turbulence inlet enhancement of coarse mode aerosol), differences in sampling lines, and instrument configuration and design. Means and variances of comparable measurements for horizontal legs were calculated, and regression analyses were performed for each platform and allow for an assessment of instrument performance. These results provide a basis for integrating aerosol data from these aircraft platforms for both the TRACE-P and ACE-Asia experiments

A forward genetic screen identifies host factors that influence the lysis-lysogeny decision in phage lambda

The lysis‐lysogeny decision made by bacteriophage lambda is one of the classic problems of molecular biology. Shortly after infecting a cell, the virus can either go down the lytic pathway and make more viruses, or go down the lysogenic pathway and integrate itself into the host genome. While much is known about how this decision takes place, the extent to which host physiology influences this decision and the mechanisms by which this influence takes place has remained mysterious. To answer this question, we performed a forward genetic screen to systematically identify all of the genes in E. coli that influence the lysis‐lysogeny decision. Our results demonstrate previously unknown links between host physiology and viral decision making and shed new light on this classic system

Directional Velocity Measurements

Author Institution: Sandia National LaboratoriesAuthor Institution: Atomic Weapons Establishment (UK)Slides presented at the 4th Annual Photonic Doppler Velocimetry (PDV) Workshop held at the University of Texas, Austin, Texas, November 5-6, 2009

Vertically resolved aerosol optical properties over the ARM SGP site

We will present an overview of early airborne results obtained aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRP AS) Twin Otter aircraft during the Atmospheric Radiation Measurement (ARM) program aerosol intensive observation period in May 2003

Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

<p>Abstract</p> <p>Background</p> <p>In the present article, we propose a method for determining optimal metabolic pathways in terms of the level of concentration of the enzymes catalyzing various reactions in the entire metabolic network. The method, first of all, generates data on reaction fluxes in a pathway based on steady state condition. A set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients.</p> <p>Results</p> <p>The effectiveness of the present method is demonstrated on two synthetic systems existing in the literature, two pentose phosphate, two glycolytic pathways, core carbon metabolism and a large network of carotenoid biosynthesis pathway of various organisms belonging to different phylogeny. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. Biological relevance and validation of the results are provided. Finally, the impact of the method on metabolic engineering is explained with a few examples.</p> <p>Conclusions</p> <p>The method may be viewed as determining an optimal set of enzymes that is required to get an optimal metabolic pathway. Although it is a simple one, it has been able to identify a carotenoid biosynthesis pathway and the optimal pathway of core carbon metabolic network that is closer to some earlier investigations than that obtained by the extreme pathway analysis. Moreover, the present method has identified correctly optimal pathways for pentose phosphate and glycolytic pathways. It has been mentioned using some examples how the method can suitably be used in the context of metabolic engineering.</p

Production of highly-polarized positrons using polarized electrons at MeV energies

The Polarized Electrons for Polarized Positrons experiment at the injector of the Continuous Electron Beam Accelerator Facility has demonstrated for the first time the efficient transfer of polarization from electrons to positrons produced by the polarized bremsstrahlung radiation induced by a polarized electron beam in a high-$Z$ target. Positron polarization up to 82\% have been measured for an initial electron beam momentum of 8.19~MeV/$c$, limited only by the electron beam polarization. This technique extends polarized positron capabilities from GeV to MeV electron beams, and opens access to polarized positron beam physics to a wide community.Comment: 5 pages, 4 figure

Context-Specific Metabolic Networks Are Consistent with Experiments

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available

Prospecting environmental mycobacteria: combined molecular approaches reveal unprecedented diversity

Background: Environmental mycobacteria (EM) include species commonly found in various terrestrial and aquatic environments, encompassing animal and human pathogens in addition to saprophytes. Approximately 150 EM species can be separated into fast and slow growers based on sequence and copy number differences of their 16S rRNA genes. Cultivation methods are not appropriate for diversity studies; few studies have investigated EM diversity in soil despite their importance as potential reservoirs of pathogens and their hypothesized role in masking or blocking M. bovis BCG vaccine. Methods: We report here the development, optimization and validation of molecular assays targeting the 16S rRNA gene to assess diversity and prevalence of fast and slow growing EM in representative soils from semi tropical and temperate areas. New primer sets were designed also to target uniquely slow growing mycobacteria and used with PCR-DGGE, tag-encoded Titanium amplicon pyrosequencing and quantitative PCR. Results: PCR-DGGE and pyrosequencing provided a consensus of EM diversity; for example, a high abundance of pyrosequencing reads and DGGE bands corresponded to M. moriokaense, M. colombiense and M. riyadhense. As expected pyrosequencing provided more comprehensive information; additional prevalent species included M. chlorophenolicum, M. neglectum, M. gordonae, M. aemonae. Prevalence of the total Mycobacterium genus in the soil samples ranged from 2.3×107 to 2.7×108 gene targets g−1; slow growers prevalence from 2.9×105 to 1.2×107 cells g−1. Conclusions: This combined molecular approach enabled an unprecedented qualitative and quantitative assessment of EM across soil samples. Good concordance was found between methods and the bioinformatics analysis was validated by random resampling. Sequences from most pathogenic groups associated with slow growth were identified in extenso in all soils tested with a specific assay, allowing to unmask them from the Mycobacterium whole genus, in which, as minority members, they would have remained undetected

An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions