Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

A new variational radial basis function approximation for inference in multivariate diffusions

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

Molecular basis for reduced estrone sulfate transport and altered modulator sensitivity of transmembrane helix (TM) 6 and TM17 mutants of multidrug resistance protein 1 (ABCC1)

Multidrug resistance protein 1 (MRP1) confers drug resistance and also mediates cellular efflux of many organic anions. MRP1 also transports glutathione (GSH); furthermore, this tripeptide stimulates transport of several substrates, including estrone 3-sulfate. We have previously shown that mutations of Lys(332) in transmembrane helix (TM) 6 and Trp(1246) in TM17 cause different substrate-selective losses in MRP1 transport activity. Here we have extended our characterization of mutants K332L and W1246C to further define the different roles these two residues play in determining the substrate and inhibitor specificity of MRP1. Thus, we have shown that TM17-Trp(1246) is crucial for conferring drug resistance and for binding and transport of methotrexate, estradiol glucuronide, and estrone 3-sulfate, as well as for binding of the tricyclic isoxazole inhibitor N-[3-(9-chloro-3-methyl-4-oxo-4H-isoxazolo-[4,3-c]quinolin-5-yl)-cyclohexylmethyl]-benzamide (LY465803). In contrast, TM6-Lys(332) is important for enabling GSH and GSH-containing compounds to serve as substrates (e.g., leukotriene C(4)) or modulators (e.g., S-decyl-GSH, GSH disulfide) of MRP1 and, further, for enabling GSH (or S-methyl-GSH) to enhance the transport of estrone 3-sulfate and increase the inhibitory potency of LY465803. On the other hand, both mutants are as sensitive as wild-type MRP1 to the non-GSH-containing inhibitors (E)-3-[[[3-[2-(7-chloro-2-quinolinyl)ethenyl]phenyl][[3-(dimethylamino)-3-oxopropyl]thio]methyl]thio]-propanoic acid (MK571), 1-[2-hydroxy-3-propyl-4-[4-(1H-tetrazol-5-yl)butoxy]phenyl]-ethanone (LY171883), and highly potent 6-[4'-carboxyphenylthio]-5[S]-hydroxy-7[E], 11[Z]14[Z]-eicosatetrenoic acid (BAY u9773). Finally, the differing abilities of the cysteinyl leukotriene derivatives leukotriene C(4), D(4), and F(4) to inhibit estradiol glucuronide transport by wild-type and K332L mutant MRP1 provide further evidence that TM6-Lys(332) is involved in the recognition of the gamma-Glu portion of substrates and modulators containing GSH or GSH-like moieties.

A basis function approach to Bayesian inference in diffusion processes

In this paper, we present a framework for Bayesian inference in continuous-time diffusion processes. The new method is directly related to the recently proposed variational Gaussian Process approximation (VGPA) approach to Bayesian smoothing of partially observed diffusions. By adopting a basis function expansion (BF-VGPA), both the time-dependent control parameters of the approximate GP process and its moment equations are projected onto a lower-dimensional subspace. This allows us both to reduce the computational complexity and to eliminate the time discretisation used in the previous algorithm. The new algorithm is tested on an Ornstein-Uhlenbeck process. Our preliminary results show that BF-VGPA algorithm provides a reasonably accurate state estimation using a small number of basis functions.

Synthesis of sulforaphane and inhibition of cytochrome P450 enzymes as a basis for antigenotoxicity

Many dietary factors have been associated with a decreased risk of developing cancer. One potential mechanism by which these factors, chemopreventors, protect against cancer may be via alteration of carcinogen metabolism. The broccoli constituent sulforaphane (1-isothiocyanate-4-methylsulinylbutane) (CH3-S0-(CH2)4-NCS) has been isolated as a potential inducer of phase II detoxification enzymes and also protects rodents against 9,10-dimethyl-1,2-benz[aJanthracene-induced mammary tumours. The ability of sulforaphane to also modulate phase I activation enzymes (cytochrome P450) (CYP450) was studied here. Sulforaphane was synthesised with an overall yield of 15%, essentially via 1-methylsulfinylphthalimidobutane, which was oxidised to the sulfoxide moiety. Deprotective removal of phthalimide yielded the amine, which was converted into sulforaphane by reaction with N,N'-thionocarbonyldiimidazole. Purity (95 %) was checked by 1H-NMR,13C-NMR and infrared and mass spectrometry.Sulforaphane was a competitive inhibitor of CYP2E1 in acetone-induced Sprague-Dawley rat microsomes (Ki 37.9 ± 4.5μM), as measured by the p-nitrophenol hydroxylase assay. Ethoxyresorufin deethylase activity (EROD), a measurement of CYP1A activity, was also inhibited by sulforaphane (100μM) but was not competitive, and a preincubation time-dependence was observed. In view of these results, the capacity of sulforaphane to inhibit N-nitrosodimethylamine (NDMA)-induced genotoxicity (CYP2E1-mediated) was studied using mouse liver activation systems. Sulforaphane (>0.8μM) inhibited the mutagenicity of NDMA (4.4 mg/plate) in Salmonella typhimurium strain TA100 after pre-incubation for 45 min with acetone-induced liver 9000 g supernatants from Balb/c mice. Unscheduled DNA synthesis induced by NDMA (33μ5 M) in mouse hepatocytes was also reduced by sulforaphane in a concentration-dependent manner (0.064-20μM). Sulforaphane was not genotoxic itself in any of these systems and cytotoxic only at high concentrations (>0.5 mM and > 40μM respectively). The ability of sulforaphane to modulate the orthologous human enzymes was studied using a human epithelial liver cell line (THLE) expressing individual human CYP450 isoenzymes. Using the Comet assay (a measurement of DNA strand breakage under alkaline conditions), NDMA (0.01-1μg/ml) and IQ (0.1-10μg/ml) were used to produce strand breaks in T5-2E1 cells (expressing human CYP2E1) and T5-1A2 cells (expressing human CYP1A2) respectively, however no response was observed in T5-neo cells (without CYP450 cDNA transfection). Sulforaphane inhibited both NDMA and IQ-induced DNA strand breakage in a concentration-dependent manner (0.1-10μM).The inhibition of metabolic activation as a basis for the antigenotoxic action of sulforaphane in these systems (bacteria, rodent hepatocytes and human cells) is further supported by the lack of this chemopreventor to influence NaN3 mutagenicity in S. typhimurium and H202-induced DNA strand breakage in T5-neo cells. These findings suggest that inhibition of CYP2E1 and CYP1A by sulforaphane may contribute to its chemoprotective potential.

Non-curliation of Escherichia coli O78:K80 isolates associated with IS1 insertion in csgB and reduced persistence in poultry infection

The elaboration of curli fimbriae by Escherichia coli is associated with the development of a lacy colony morphology when grown on colonisation factor antigen agar at 25 degrees C. Avian colisepticaemia E. coli isolates screened for curliation by this culture technique showed lacy and smooth colonial morphologies and the genetic basis of the non-curliated smooth colonial phenotype was analysed. Two smooth E. coli O78:K80 isolates possessed about 40 copies of the IS1 element within their respective genomes of which one copy insertionally inactivated the csgB gene, the nucleator gene for curli fibril formation. One of these two isolates also possessed a defective rpoS gene which is a known regulator of curli expression. In the day-old chick model, both smooth isolates were as invasive as a known virulent O78:K80 isolate as determined by extent of liver and spleen colonisation post oral inoculation but were less persistent in terms of caecal colonisation.