Application of superconducting coils to the NASA prototype magnetic balance

Application of superconducting coils to a general purpose magnetic balance was studied. The most suitable currently available superconducting cable for coils appears to be a bundle of many fine wires which are transposed and are mechanically confined. Sample coils were tested at central fields up to .5 Tesla, slewing rates up to 53 Tesla/ sec and frequencies up to 30 Hz. The ac losses were measured from helium boil-off and were approximately 20% higher than those calculated. Losses were dominated by hysteresis and a model for loss calculation which appears suitable for design purposes is presented along with computer listings. Combinations of two coils were also tested and interaction losses are reported. Two feasible geometries are also presented for prototype magnetic balance using superconductors

Some Additive Combinatorics Problems in Matrix Rings

We study the distribution of singular and unimodular matrices in sumsets in matrix rings over finite fields. We apply these results to estimate the largest prime divisor of the determinants in sumsets in matrix rings over the integers

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

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

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

Atmospheric sulfur cycling in the southeastern Pacific – longitudinal distribution, vertical profile, and diel variability observed during VOCALS-REx

Dimethylsulfide (DMS) emitted from the ocean is a biogenic precursor gas for sulfur dioxide (SO&lt;sub&gt;2&lt;/sub&gt;) and non-sea-salt sulfate aerosols (SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt;). During the VAMOS-Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) in 2008, multiple instrumented platforms were deployed in the Southeastern Pacific (SEP) off the coast of Chile and Peru to study the linkage between aerosols and stratocumulus clouds. We present here observations from the NOAA Ship &lt;i&gt;Ronald H. Brown&lt;/i&gt; and the NSF/NCAR C-130 aircraft along ~20° S from the coast (70° W) to a remote marine atmosphere (85° W). While SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt; and SO&lt;sub&gt;2&lt;/sub&gt; concentrations were distinctly elevated above background levels in the coastal marine boundary layer (MBL) due to anthropogenic influence (~800 and 80 pptv, respectively), their concentrations rapidly decreased west of 78° W (~100 and 25 pptv). In the remote region, entrainment from the free troposphere (FT) increased MBL SO&lt;sub&gt;2&lt;/sub&gt; burden at a rate of 0.05 &amp;plusmn; 0.02 μmoles m&lt;sup&gt;&amp;minus;2&lt;/sup&gt; day&lt;sup&gt;&amp;minus;1&lt;/sup&gt; and diluted MBL SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&lt;/sup&gt; burden at a rate of 0.5 &amp;plusmn; 0.3 μmoles m&lt;sup&gt;&amp;minus;2&lt;/sup&gt; day&lt;sup&gt;&amp;minus;1&lt;/sup&gt;, while the sea-to-air DMS flux (3.8 &amp;plusmn; 0.4 μmoles m&lt;sup&gt;&amp;minus;2&lt;/sup&gt; day&lt;sup&gt;&amp;minus;1&lt;/sup&gt;) remained the predominant source of sulfur mass to the MBL. In-cloud oxidation was found to be the most important mechanism for SO&lt;sub&gt;2&lt;/sub&gt; removal and in situ SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt; production. Surface SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt; concentration in the remote MBL displayed pronounced diel variability, increasing rapidly in the first few hours after sunset and decaying for the rest of the day. We theorize that the increase in SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt; was due to nighttime recoupling of the MBL that mixed down cloud-processed air, while decoupling and sporadic precipitation scavenging were responsible for the daytime decline in SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2&amp;minus;&lt;/sup&gt;

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p

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

Overview of ACE-Asia spring 2001 investigations on aerosol-radiation interactions

In spring 2001 the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) made extensive measurements from land, ocean, air and space platforms. A primary objective was to quantify the interactions between aerosols and radiation. This talk presents illustrative results from each type of platform, with initial assessments of regional aerosol radiative forcing obtained by combining satellite and suborbital results

Epistasis in a Model of Molecular Signal Transduction

Biological functions typically involve complex interacting molecular networks, with numerous feedback and regulation loops. How the properties of the system are affected when one, or several of its parts are modified is a question of fundamental interest, with numerous implications for the way we study and understand biological processes and treat diseases. This question can be rephrased in terms of relating genotypes to phenotypes: to what extent does the effect of a genetic variation at one locus depend on genetic variation at all other loci? Systematic quantitative measurements of epistasis – the deviation from additivity in the effect of alleles at different loci – on a given quantitative trait remain a major challenge. Here, we take a complementary approach of studying theoretically the effect of varying multiple parameters in a validated model of molecular signal transduction. To connect with the genotype/phenotype mapping we interpret parameters of the model as different loci with discrete choices of these parameters as alleles, which allows us to systematically examine the dependence of the signaling output – a quantitative trait – on the set of possible allelic combinations. We show quite generally that quantitative traits behave approximately additively (weak epistasis) when alleles correspond to small changes of parameters; epistasis appears as a result of large differences between alleles. When epistasis is relatively strong, it is concentrated in a sparse subset of loci and in low order (e.g. pair-wise) interactions. We find that focusing on interaction between loci that exhibit strong additive effects is an efficient way of identifying most of the epistasis. Our model study defines a theoretical framework for interpretation of experimental data and provides statistical predictions for the structure of genetic interaction expected for moderately complex biological circuits