6,882 research outputs found

    Chlorinated organic contaminants in breast milk of New Zealand women.

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    Breast milk samples from 38 women in New Zealand were analyzed for organochlorine pesticides, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs) as part of a World Health Organization collaborative study of breast-milk contaminants. The women were recruited from two urban areas (Auckland and Christchurch) and two rural areas (Northland and North Canterbury) in the North and South Islands of New Zealand. The best predictor of contaminant concentrations in breast milk was found to be the age of the mother. Regional differences were found for hexachlorobenzene, dieldrin, and pp-DDE, reflecting historical use patterns. Urban-rural differences were found for several PCBs, PCDDs, and PCDFs when contaminant concentrations were calculated on a whole-milk basis. However, these differences could be attributed to variation in breast-milk fat concentrations between urban and rural mothers. Urban mothers had about 50% more breast-milk fat than rural mothers. Evidence suggests that breast-milk consumption by babies is regulated by caloric intake. Almost all of the caloric content of milk is in the fat fraction. This suggests that breast-milk contaminant levels calculated on a whole-milk basis do not necessarily reflect the relative levels of exposure of infants to these contaminants. However, the factors that influence breast-milk fat concentration deserve further study

    Congener specific analysis of polychlorinated terphenyls

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    In order to identify and to quantify polychlorinated terphenyls (PCT) in environmental matrices, the chro- matographic behavior of coplanar and non-coplanar congeners was evaluated. A mixture of 16 single PCT congeners was used for method development. Four of these compounds were synthesized for the first time by SUZUKI-coupling reaction. These were p-PCT (2,200 ,6,600 -tetrachloro-, 20 ,3,300 ,4,400 ,50 ,-hexachloro-, 20 ,3,300 ,5,50 ,500 -hexachloro-) and m-PCT (2,200 ,3,300 ,5,500 -hexachloro-). They were characterized by NMR ( 1 H, 13 C) spectroscopy. By means of the new column chromatographic clean-up reported here, a good matrix removal and the separation of the coplanar PCT congeners from the non-coplanar ones was obtained. The recovery rates for all congeners were good for the PCT in different test matrices like fat, charcoal, and soil. The quality of the clean-up, the separation and the recovery rates were determined by GC/MS analysis. The method was applied for the first time to a real sample from a fire accident, where different PCT, obviously formed during the combustion process, were found. The conclusion is drawn that this method is suitable for the analysis of PCT in different environmental samples

    A blue light receptor that mediates RNA binding and translational regulation

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    Sensory photoreceptor proteins underpin light-dependent adaptations in nature and enable the optogenetic control of organismal behavior and physiology. We identified the bacterial light-oxygen-voltage (LOV) photoreceptor PAL that sequence-specifically binds short RNA stem loops with around 20 nM affinity in blue light and weaker than 1 µM in darkness. A crystal structure rationalizes the unusual receptor architecture of PAL with C-terminal LOV photosensor and N-terminal effector units. The light-activated PAL–RNA interaction can be harnessed to regulate gene expression at the RNA level as a function of light in both bacteria and mammalian cells. The present results elucidate a new signal-transduction paradigm in LOV receptors and conjoin RNA biology with optogenetic regulation, thereby paving the way toward hitherto inaccessible optoribogenetic modalities

    Production of FAME biodiesel in E. coli by direct methylation with an insect enzyme.

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    Most biodiesel currently in use consists of fatty acid methyl esters (FAMEs) produced by transesterification of plant oils with methanol. To reduce competition with food supplies, it would be desirable to directly produce biodiesel in microorganisms. To date, the most effective pathway for the production of biodiesel in bacteria yields fatty acid ethyl esters (FAEEs) at up to ~1.5 g/L. A much simpler route to biodiesel produces FAMEs by direct S-adenosyl-L-methionine (SAM) dependent methylation of free fatty acids, but FAME production by this route has been limited to only ~16 mg/L. Here we employ an alternative, broad spectrum methyltransferase, Drosophila melanogaster Juvenile Hormone Acid O-Methyltransferase (DmJHAMT). By introducing DmJHAMT in E. coli engineered to produce medium chain fatty acids and overproduce SAM, we obtain medium chain FAMEs at titers of 0.56 g/L, a 35-fold increase over titers previously achieved. Although considerable improvements will be needed for viable bacterial production of FAMEs and FAEEs for biofuels, it may be easier to optimize and transport the FAME production pathway to other microorganisms because it involves fewer enzymes

    Structural basis for CRISPR RNA-guided DNA recognition by Cascade

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    The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

    Energy dependent forward B \rightarrow J/ψJ/\psi measurements in p+p collisions at PHENIX

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    The heavy flavor studies at RHIC help improve the knowledge of the bottom/charm quark production and can test Quantum Chromodynamics (QCD). Compared to the LHC/Tevatron, the RHIC heavy flavor production originates from different partonic sub-processes and has a complementary kinematic coverage. The PHENIX forward rapidity silicon vertex detector (FVTX) provides precise determination of the event vertex, tracking and the Distance of Closest Approach (DCA) of charged tracks. This detector allows direct access to the BB meson production via measurements of non-prompt J/ψμ++μJ/\psi \to \mu^{+} + \mu^{-} within 1.2<y<2.21.2<|y|<2.2 rapidity in pp+pp collisions at s=\sqrt{s} = 510 and 200 GeV. Comparison among PHENIX measurements of the BJ/ψB \rightarrow J/\psi fraction with integrated J/ψJ/\psi pTp_{T} up to 5 GeV/c/c and higher energy results at the Tevatron and the LHC presents a smooth center of mass energy dependence from 0.2 to 13 TeV in pp+pp (pp+pˉ\bar{p}) collisions. The Next-To-Leading order Perturbative QCD (NLO pQCD) calculations are in reasonable agreement with the extracted total bbˉb\bar{b} cross section based on the BJ/ψB \rightarrow J/\psi fraction measurements at PHENIX.Comment: 6 pages, 4 figures, the XXV International Workshop on Deep-Inelastic Scattering and Related Subjects proceeding

    Policy Analysis Tool Applied to Colombian Needs: PATACON Model Description

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    In this document we lay out the microeconomic foundations of a dynamic stochastic general equilibrium model designed to forecast and to advice monetary policy authorities in Colombia. The model is called Policy Analysis Tool Applied to Colombian Needs (PATACON). In companion documents we present other aspects of the model and its platform, including the estimation of the parameters that affect the dynamics and the impulse responses functions.Monetary Policy, DSGE, Small open economy. Classification JEL: E32, E52, F41.
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