Simultaneous presence of fhs and purT genes is disadvantageous for the fitness of Escherichia coli growth

In bacteria, alternate mechanisms are known to synthesize N-10-formyltetrahydrofolate (N10-formyl-THF) and formyl glycinamide ribotide (fGAR), which are important in purine biosynthesis. In one of the mechanisms, a direct transfer of one carbon unit from formate allows Fhs to convert tetrahydrofolate to N-10-formyl-THF, and PurT to convert glycinamide ribotide (GAR) to fGAR. Our bioinformatics analysis of fhs and purT genes (encoding Fhs and PurT) showed that in a majority of bacteria (similar to 94%), their presence was mutually exclusive. A large number of organisms possessing fhs lacked purT and vice versa. The phenomenon is so penetrating that even within a genus (Bacillus) if a species possessed fhs it lacked purT and vice versa. To investigate physiological importance of this phenomenon, we used Escherichia coli, which naturally lacks fhs (and possesses purT) as model. We generated strains, which possessed fhs and purT genes in singles or together. Deletion of purT from E. coli in the presence or absence of fhs did not confer a detectable growth disadvantage in pure cultures. However, growth competition assays revealed that the strains possessing either of the single genes outcompeted those possessing both the genes suggesting that mutual exclusion of purT and fhs in organisms confers fitness advantage in mixed cultures.

Simultaneous acquisition of three NMR spectra in a single experiment for rapid resonance assignments in metabolomics

NMR-based approach to metabolomics typically involves the collection of two-dimensional (2D) heteronuclear correlation spectra for identification and assignment of metabolites. In case of spectral overlap, a 3D spectrum becomes necessary, which is hampered by slow data acquisition for achieving sufficient resolution. We describe here a method to simultaneously acquire three spectra (one 3D and two 2D) in a single data set, which is based on a combination of different fast data acquisition techniques such as G-matrix Fourier transform (GFT) NMR spectroscopy, parallel data acquisition and non-uniform sampling. The following spectra are acquired simultaneously: (1) C-13 multiplicity edited GFT (3,2)D HSQC-TOCSY, (2) 2D H-1- H-1] TOCSY and (3) 2D C-13- H-1] HETCOR. The spectra are obtained at high resolution and provide high-dimensional spectral information for resolving ambiguities. While the GFT spectrum has been shown previously to provide good resolution, the editing of spin systems based on their CH multiplicities further resolves the ambiguities for resonance assignments. The experiment is demonstrated on a mixture of 21 metabolites commonly observed in metabolomics. The spectra were acquired at natural abundance of C-13. This is the first application of a combination of three fast NMR methods for small molecules and opens up new avenues for high-throughput approaches for NMR-based metabolomics.

Simultaneous nonvanishing of Dirichlet L-functions and twists of Hecke-Maass L-functions

We prove that given a Hecke-Maass form f for SL(2, Z) and a sufficiently large prime q, there exists a primitive Dirichlet character chi of conductor q such that the L-values L(1/2, f circle times chi) and L(1/2, chi) do not vanish.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

Simultaneous enhancement of electrical conductivity and thermopower in Bi2S3 under hydrostatic pressure

The inverse coupled dependence of electrical conductivity and thermopower on carrier concentration presents a big challenge in achieving a high figure of merit. However, the simultaneous enhancement of electrical conductivity and thermopower can be realized in practice by carefully engineering the electronic band structure. Here by taking the example of Bi2S3, we report a simultaneous increase in both electrical conductivity and thermopower under hydrostatic pressure. Application of hydrostatic pressure enables tuning of electronic structure in such a way that the conductivity effective mass decreases and the density of states effective mass increases. This dependence of effective masses leads to simultaneous enhancement in electrical conductivity and thermopower under n-type doping leading to a huge improvement in the power factor. Also lattice thermal conductivity exhibits very weak pressure dependence in the low pressure range. The large power factor together with low lattice thermal conductivity results in a high ZT value of 1.1 under n-type doping, which is nearly two times higher than the previously reported value. Hence, this pressure-tuned behaviour can enable the development of efficient thermoelectric devices in the moderate to high temperature range. We further demonstrate that similar enhancement can be observed by generating chemical pressure by doping Bi2S3 with smaller iso-electronic elements such as Sb at Bi sites, which can be achieved experimentally.

Simultaneous tunability of the electronic and phononic gaps in SnS2 under normal compressive strain

Controlled variation of the electronic properties of. two-dimensional (2D) materials by applying strain has emerged as a promising way to design materials for customized applications. Using density functional theory (DFT) calculations, we show that while the electronic structure and indirect band gap of SnS2 do not change significantly with the number of layers, they can be reversibly tuned by applying biaxial tensile (BT), biaxial compressive (BC), and normal compressive (NC) strains. Mono to multilayered SnS2 exhibit a reversible semiconductor to metal (S-M) transition with applied strain. For bilayer (2L) SnS2, the S-Mtransition occurs at the strain values of 17%,-26%, and -24% under BT, BC, and NC strains, respectively. Due to weaker interlayer coupling, the critical strain value required to achieve the S-Mtransition in SnS2 under NC strain is much higher than for MoS2. From a stability viewpoint, SnS2 becomes unstable at very low strain values on applying BC (-6.5%) and BT strains (4.9%), while it is stable even up to the transition point (-24%) in the case of NC strain. In addition to the reversible tuning of the electronic properties of SnS2, we also show tunability in the phononic band gap of SnS2, which increases with applied NC strain. This gap increases three times faster than for MoS2. This simultaneous tunability of SnS2 at the electronic and phononic levels with strain, makes it a potential candidate in field effect transistors (FETs) and sensors as well as frequency filter applications.