33 research outputs found
Phylogenetic distribution of direct pathways in Archaea and Bacteria.
No full text<p>Dark purple dots indicate clades in which the complete gene complement for direct reductive synthesis of glycine and serine is found, light purple dots indicate clades where a complete pathway is suspected. Red dots indicate clades in which direct reduction of is known to be active, but in a form that lacks synthesis of glycine and serine. See main text for further details. The unrooted phylogenetic tree was adapted, with modification, from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002455#pcbi.1002455-Puigbo1" target="_blank">[14]</a>. In that work, the tree was created from an analysis of 102 ‘nearly universal’ clusters of orthologous groups of proteins (COGs) (present in of species in the tree) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002455#pcbi.1002455-Puigbo1" target="_blank">[14]</a>.</p
Modeling the cooperative energy transfer dynamics of quantum cutting for solar cells
No full textCooperative energy transfer (ET) is a quantum cutting (or downconversion) process where a luminescent center splits its excited state energy in two by simultaneous transfer to two nearby acceptor centers, thus yielding two low-energy photons for each high-energy photon absorbed. It has the potential to greatly enhance the efficiency of phosphors for lighting or the UV/blue response of next generation photovoltaics. Many pairs of luminescent centers have been claimed to enable quantum cutting by cooperative ET. However, direct proof that the ET mechanism is cooperative is often lacking. Here we present a model that can be used to fit or predict the dynamics of cooperative ET in codoped crystals, as a function of the concentration of acceptor centers. It also yields an analytical expression for the efficiency of cooperative ET. Our model can be used to provide evidence for quantum cutting materials, quantify the ET parameter(s), and optimize the doping concentration
Optimal nonlinear filter to remove random impulses from Gaussian noise
No full textThis paper investigates the problem of removing random impulse noise from a white signal of Gaussian distribution. A nonlinear polynomial filter is used, whose coefficients are optimised using an exact least squares method. The method relies on exploiting the differing probability distributions of the impulsive noise and the Gaussian signal. The paper then looks at the effect of both the polynomial order and the normalised spike amplitude on the mean squared error and signal to noise ratio. The results are compared to the results found using a simple clipping filter. The results show that the optimal filter gives a much improved performance over the simple clipping filter in reducing the mean square error
Spectrum and context dependence of single-nucleotide substitutions.
No full text(A, B) Observed substitution rates relative to the rate expected if all substitutions were equally likely. (A) The six possible substitutions occurred at unequal rates (χ2 = 434, df = 5, p −15), with no difference between loaded and unloaded backgrounds (χ2 = 2.87, df = 11, p = 0.99). (B) Rates of substitution at the central nucleotide (highlighted in grey) for each 3-bp context, with standard error bars. Substitution rate was higher for G:C sites (paired t = 9.69, df = 15, p −7) and varied significantly with 3-bp context at both A:T sites (χ2 = 39.24, df = 15, p 2 = 25.24, df = 15, p = 0.047), but neither differed between loaded and unloaded backgrounds (χ2 = 10.56, df = 63, p = 1; values shown are pooled across backgrounds). (C) Comparison of substitution rate estimates among recent studies. Our estimate (“4”) is intermediate compared with the two backgrounds studied by [19] (“2” and “5”); “1” = [20], “3” = [21]. See S1 Data for plot data.</p
Indel and gene conversion rates are related to body mass.
No full textRates per haploid 2nd chromosome per generation versus dry mass of males relative to a standard genotype reared in the same vial [6]. Indel rate declined significantly with body mass (GLM: Z = −2.12, p = 0.034) and gene conversion rate increased significantly with body mass (quasi-Poisson GLM: t = 2.42, p = 0.021; GLMM: χ2 = 4.80, p = 0.029). Lines and shaded regions are predicted values and standard errors from (quasi) Poisson GLMs on 38 samples. Points are predicted values for each of the seven genetic backgrounds, with horizontal jitter added for clarity. Legend indicates the treatment alleles on each genetic background (wt = wild-type, i.e., the unloaded treatment). See S1 Data for plot data.</p
Substitution and indel rates are sensitive to local GC content.
No full textLines represent sliding window averages of GC content surrounding mutant relative to nonmutant sites (up to 500 bp on either side), with bootstrap standard errors. Bars depict the local regions where GC content best predicts the occurrence of mutations, based on the AIC (Akaike information criterion) values of logistic models, with the sign of the effect shown on each bar. The sliding window plot does not account for the spatial correlation in GC content, whereas the logistic model does, which can explain the apparent differences. See S1 Data for plot data.</p
