Biased amino acid composition in warm-blooded animals

Among eubacteria and archeabacteria, amino acid composition is correlated with habitat temperatures. In particular, species living at high temperatures have proteins enriched in the amino acids E-R-K and depleted in D-N-Q-T-S-H-A. Here, we show that this bias is a proteome-wide effect in prokaryotes, and that the same trend is observed in fully sequenced mammals and chicken compared to cold-blooded vertebrates (Reptilia, Amphibia and fish). Thus, warm-blooded vertebrates likely experienced genome-wide weak positive selection on amino acid composition to increase protein thermostability

Synthesis and reactivity of a nickel(ii) thioperoxide complex: demonstration of sulfide-mediated N2O reduction.

The thiohyponitrite ([SNNO]2-) complex, [K(18-crown-6)][L tBuNiII(κ2-SNNO)] (L tBu = {(2,6-iPr2C6H3)NC( t Bu)}2CH), extrudes N2 under mild heating to yield [K(18-crown-6)][L tBuNiII(η2-SO)] (1), along with minor products [K(18-crown-6)][L tBuNiII(η2-OSSO)] (2) and [K(18-crown-6)][L tBuNiII(η2-S2)] (3). Subsequent reaction of 1 with carbon monoxide (CO) results in the formation of [K(18-crown-6)][L tBuNiII(η2-SCO)] (4), [K(18-crown-6)][L tBuNiII(S,O:κ2-SCO2)] (5), [K(18-crown-6)][L tBuNiII(κ2-CO3)] (6), carbonyl sulfide (COS) (7), and [K(18-crown-6)][L tBuNiII(S2CO)] (8). To rationalize the formation of these products we propose that 1 first reacts with CO to form [K(18-crown-6)][L tBuNiII(S)] (I) and CO2, via O-atom abstraction. Subsequently, complex I reacts with CO or CO2 to form 4 and 5, respectively. Similarly, the formation of complex 6 and COS can be rationalized by the reaction of 1 with CO2 to form a putative Ni(ii) monothiopercarbonate, [K(18-crown-6)][L tBuNiII(κ2-SOCO2)] (11). The Ni(ii) monothiopercarbonate subsequently transfers a S-atom to CO to form COS and [K(18-crown-6)][L tBuNiII(κ2-CO3)] (6). Finally, the formation of 8 can be rationalized by the reaction of COS with I. Critically, the observation of complexes 4 and 5 in the reaction mixture reveals the stepwise conversion of [K(18-crown-6)][L tBuNiII(κ2-SNNO)] to 1 and then I, which represents the formal reduction of N2O by CO

Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

Quantum imaging by coherent enhancement

Conventional wisdom dictates that to image the position of fluorescent atoms or molecules, one should stimulate as much emission and collect as many photons as possible. That is, in this classical case, it has always been assumed that the coherence time of the system should be made short, and that the statistical scaling $\sim1/\sqrt{t}$ defines the resolution limit for imaging time $t$. However, here we show in contrast that given the same resources, a long coherence time permits a higher resolution image. In this quantum regime, we give a procedure for determining the position of a single two-level system, and demonstrate that the standard errors of our position estimates scale at the Heisenberg limit as $\sim 1/t$, a quadratic, and notably optimal, improvement over the classical case.Comment: 4 pages, 4 figue

Quantum Inference on Bayesian Networks

Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on $n$ variables with at most $m$ parents per node in time $\mathcal{O}(nmP(e)^{-1})$, depending critically on $P(e)$, the probability the evidence might occur in the first place. By implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking $\mathcal{O}(n2^mP(e)^{-\frac12})$ time per sample. We exploit the Bayesian network's graph structure to efficiently construct a quantum state, a q-sample, representing the intended classical distribution, and also to efficiently apply amplitude amplification, the source of our speedup. Thus, our speedup is notable as it is unrelativized -- we count primitive operations and require no blackbox oracle queries.Comment: 8 pages, 3 figures. Submitted to PR

Fixed-point quantum search with an optimal number of queries

Grover's quantum search and its generalization, quantum amplitude amplification, provide quadratic advantage over classical algorithms for a diverse set of tasks, but are tricky to use without knowing beforehand what fraction $\lambda$ of the initial state is comprised of the target states. In contrast, fixed-point search algorithms need only a reliable lower bound on this fraction, but, as a consequence, lose the very quadratic advantage that makes Grover's algorithm so appealing. Here we provide the first version of amplitude amplification that achieves fixed-point behavior without sacrificing the quantum speedup. Our result incorporates an adjustable bound on the failure probability, and, for a given number of oracle queries, guarantees that this bound is satisfied over the broadest possible range of $\lambda$.Comment: 4 pages plus references, 2 figure