The role of Acinetobacter sp. in biological phosphorus removal from forest industry wastewaters

Yhteenveto: Acinetobacter sp. metsäteollisuuden jätevesien biologisessa fosforinpoistossa

Electrical and magnetic properties of pure and Li intercalated Bi1.7-xPbxV8O16 phases

Hollandite oxides of the type Bi1.7-xPbxV8O16 have been synthesized. The electrical resistivity studies show that the conductivity improves upon Pb substitution. The feasibility of Li intercalation in the system has been established. Magnetic susceptibility studies on the pure and Li intercalated phases show that except for pure Bi1.7V8O16, all phases exhibit Pauli paramagnetism. No superconductivity is encountered down to 12 K in any of the phases. (C) 1998 Elsevier Science B.V. All rights reserved.

Fast-Group-Decodable STBCs via Codes over GF(4)

In this paper we construct low decoding complexity STBCs by using the Pauli matrices as linear dispersion matrices. In this case the Hurwitz-Radon orthogonality condition is shown to be easily checked by transferring the problem to $\mathbb{F}_4$ domain. The problem of constructing low decoding complexity STBCs is shown to be equivalent to finding certain codes over $\mathbb{F}_4$. It is shown that almost all known low complexity STBCs can be obtained by this approach. New codes are given that have the least known decoding complexity in particular ranges of rate.

Low temperature synthesis, structure and properties of alkali-doped La2NiO4, LaNiO3 and LaNi0.85Cu0.15O3 from alkali hydroxide fluxes

We report a low-temperature synthesis of La1.95Na0.05NiO4 from NaOH flux, La0.97K0.03NiO3 and La0.95K0.05Ni0.85Cu0.15O3 phases from KOH flux at 400 degreesC. Alkali-doped LaNiO3 can be prepared in KOH, but not in NaOH flux and La2NiO4 can be prepared in NaOH, but not in KOH flux. The flux-grown oxides were characterized by powder X-ray Rietveld profile analysis and electron microscopy. Sodium doped La2NiO4 crystallizes in orthorhombic structure and potassium doped LaNiO3-phases crystallizes in rhombohedral structure. La1.95Na0.05NiO4 is weakly paramagnetic and semiconducting while La0.97K0.03NiO3 and La0.95K0.05Ni0.85Cu0.15O3 show Pauli paramagnetic and metallic behavior. (C) 2002 Editions scientifiques et medicales Elsevier SAS. All rights reserved.

Nature and Properties of a Repulsive Fermi Gas in the Upper Branch of the Energy Spectrum

We generalize the Nozieres-Schmitt-Rink method to study the repulsive Fermi gas in the absence of molecule formation, i.e., in the so-called ``upper branch.'' We find that the system remains stable except close to resonance at sufficiently low temperatures. With increasing scattering length, the energy density of the system attains a maximum at a positive scattering length before resonance. This is shown to arise from Pauli blocking which causes the bound states of fermion pairs of different momenta to disappear at different scattering lengths. At the point of maximum energy, the compressibility of the system is substantially reduced, leading to a sizable uniform density core in a trapped gas. The change in spin susceptibility with increasing scattering length is moderate and does not indicate any magnetic instability. These features should also manifest in Fermi gases with unequal masses and/or spin populations.

Algorithmic approach to simulate Hamiltonian dynamics and an NMR simulation of quantum state transfer

We propose an iterative algorithm to simulate the dynamics generated by any n-qubit Hamiltonian. The simulation entails decomposing the unitary time evolution operator U (unitary) into a product of different time-step unitaries. The algorithm product-decomposes U in a chosen operator basis by identifying a certain symmetry of U that is intimately related to the number of gates in the decomposition. We illustrate the algorithm by first obtaining a polynomial decomposition in the Pauli basis of the n-qubit quantum state transfer unitary by Di Franco et al. [Phys. Rev. Lett. 101, 230502 (2008)] that transports quantum information from one end of a spin chain to the other, and then implement it in nuclear magnetic resonance to demonstrate that the decomposition is experimentally viable. We further experimentally test the resilience of the state transfer to static errors in the coupling parameters of the simulated Hamiltonian. This is done by decomposing and simulating the corresponding imperfect unitaries.

The Self-Association of Graphane Is Driven by London Dispersion and Enhanced Orbital Interactions

We investigated the nature of the cohesive energy between graphane sheets via multiple CH center dot center dot center dot HC interactions, using density functional theory (DFT) including dispersion correction (Grimmes D3 approach) computations of n]graphane sigma dimers (n = 6-73). For comparison, we also evaluated the binding between graphene sheets that display prototypical pi/pi interactions. The results were analyzed using the block-localized wave function (BLW) method, which is a variant of ab initio valence bond (VB) theory. BLW interprets the intermolecular interactions in terms of frozen interaction energy (Delta E-F) composed of electrostatic and Pauli repulsion interactions, polarization (Delta E-pol), charge-transfer interaction (Delta E-CT), and dispersion effects (Delta E-disp). The BLW analysis reveals that the cohesive energy between graphane sheets is dominated by two stabilizing effects, namely intermolecular London dispersion and two-way charge transfer energy due to the sigma CH -> sigma*(HC) interactions. The shift of the electron density around the nonpolar covalent C-H bonds involved in the intermolecular interaction decreases the C-H bond lengths uniformly by 0.001 angstrom. The Delta E-CT term, which accounts for similar to 15% of the total binding energy, results in the accumulation of electron density in the interface area between two layers. This accumulated electron density thus acts as an electronic glue for the graphane layers and constitutes an important driving force in the self-association and stability of graphane under ambient conditions. Similarly, the double faced adhesive tape style of charge transfer interactions was also observed among graphene sheets in which it accounts for similar to 18% of the total binding energy. The binding energy between graphane sheets is additive and can be expressed as a sum of CH center dot center dot center dot HC interactions, or as a function of the number of C-H bonds.

NMR relaxation studies in doped poly-3-methylthiophene

NMR relaxation rates (1/T-1), magnetic susceptibility, and electrical conductivity studies in doped poly-3-methylthiophene are reported in this paper. The magnetic susceptibility data show the contributions from both Pauli and Curie spins, with the size of the Pauli term depending strongly on the doping level. Proton and fluorine NMR relaxation rates have been studied as a function of temperature (3-300 K) and field (for protons at 0.9, 9.0, 16.4, and 23.4 T, and for fluorine at 9.0 T). The temperature dependence of T-1 is classified into three regimes: (a) For T < (g mu(B) B/2k(B)), the relaxation mechanism follows a modified Korringa relation due to electron-electron interactions and disorder. H-1-T-1 is due to the electron-nuclear dipolar interaction in addition to the contact term. (b) For the intermediate temperature range (g mu(B) B/2k(B)) < T < T-BPP (the temperature where the contribution from the reorientation motion to the T-1 is insignificant) the relaxation mechanism is via spin diffusion to the paramagnetic centers. (c) In the high-temperature regime and at low Larmor frequency the relaxation follows the modified Bloembergen, Purcell, and Pound model. T-1 data analysis has been carried out in light of these models depending upon the temperature and frequency range of study. Fluorine relaxation data have been analyzed and attributed to the PF6 reorientation. The cross relaxation among the H-1 and F-19 nuclei has been observed in the entire temperature range suggesting the role of magnetic dipolar interaction modulated by the reorientation of the symmetric molecular subgroups. The data analysis shows that the enhancement in the Korringa ratio is greater in a less conducting sample. Intra-and interchain hopping of charge carriers is found to be a dominant relaxation mechanism at low temperature. Frequency dependence of T-1(-1) on temperature shows that at low temperature T < (g mu(B) B/2k(B))] the system shows three dimensions and changes to quasi one dimension at high temperature. Moreover, a good correlation between electrical conductivity, magnetic susceptibility, and NMR T-1 data has been observed.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.

First human hNT neurons patterned on parylene-C/silicon dioxide substrates: Combining an accessible cell line and robust patterning technology for the study of the pathological adult human brain

In this communication, we describe a new method which has enabled the first patterning of human neurons (derived from the human teratocarcinoma cell line (hNT)) on parylene-C/silicon dioxide substrates. We reveal the details of the nanofabrication processes, cell differentiation and culturing protocols necessary to successfully pattern hNT neurons which are each key aspects of this new method. The benefits in patterning human neurons on silicon chip using an accessible cell line and robust patterning technology are of widespread value. Thus, using a combined technology such as this will facilitate the detailed study of the pathological human brain at both the single cell and network level. © 2010 Elsevier B.V.

Isolating single primary rat hippocampal neurons and astrocytes on ultra-thin patterned parylene-C/silicon dioxide substrates

We report here the patterning of primary rat neurons and astrocytes from the postnatal hippocampus on ultra-thin parylene-C deposited on a silicon dioxide substrate, following observations of neuronal, astrocytic and nuclear coverage on strips of different lengths, widths and thicknesses. Neuronal and glial growth was characterized 'on', 'adjacent to' and 'away from' the parylene strips. In addition, the article reports how the same material combination can be used to isolate single cells along thin tracks of parylene-C. This is demonstrated with a series of high magnification images of the experimental observations for varying parylene strip widths and thicknesses. Thus, the findings demonstrate the possibility to culture cells on ultra-thin layers of parylene-C and localize single cells on thin strips. Such work is of interest and significance to the Neuroengineering and Multi-Electrode Array (MEA) communities, as it provides an alternative insulating material in the fabrication of embedded micro-electrodes, which can be used to facilitate single cell stimulation and recording in capacitive coupling mode. © 2010 Elsevier Ltd.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.