Development and validation of a qPCR-based method for quantifying Shiga toxin-encoding and other lambdoid bacteriophages

P>To address whether seasonal variability exists among Shiga toxin-encoding bacteriophage (Stx phage) numbers on a cattle farm, conventional plaque assay was performed on water samples collected over a 17 month period. Distinct seasonal variation in bacteriophage numbers was evident, peaking between June and August. Removal of cattle from the pasture precipitated a reduction in bacteriophage numbers, and during the winter months, no bacteriophage infecting Escherichia coli were detected, a surprising occurrence considering that 1031 tailed-bacteriophages are estimated to populate the globe. To address this discrepancy a culture-independent method based on quantitative PCR was developed. Primers targeting the Q gene and stx genes were designed that accurately and discriminately quantified artificial mixed lambdoid bacteriophage populations. Application of these primer sets to water samples possessing no detectable phages by plaque assay, demonstrated that the number of lambdoid bacteriophage ranged from 4.7 x 104 to 6.5 x 106 ml-1, with one in 103 free lambdoid bacteriophages carrying a Shiga toxin operon (stx). Specific molecular biological tools and discriminatory gene targets have enabled virus populations in the natural environment to be enumerated and similar strategies could replace existing propagation-dependent techniques, which grossly underestimate the abundance of viral entities.

Is prospective memory enhanced by cue-action semantic relatedness and enactment at encoding?

Benefits and costs on prospective memory performance, of enactment at encoding and a semantic association between a cue-action word pair, were investigated in two experiments. Findings revealed superior performance for both younger and older adults following enactment, in contrast to verbal encoding, and when cue-action semantic relatedness was high. Although younger adults outperformed older adults, age did not moderate benefits of cue-action relatedness or enactment. Findings from a second experiment revealed that the inclusion of an instruction to perform a prospective memory task led to increments in response latency to items from the ongoing activity in which that task was embedded, relative to latencies when the ongoing task only was performed. However, this task interference ‘cost’ did not differ as a function of either cue-action relatedness or enactment. We argue that the high number of cue-action pairs employed here influenced meta-cognitive consciousness, hence determining attention allocation, in all experimental conditions.

On the universal TRO of a JC*-triple, ideals and tensor products

In a development from material introduced in recent work, we discuss the interconnections between ternary rings of operators (TROs) and right C*-algebras generated by JC*-triples, deducing that every JC*-triple possesses a largest universally reversible ideal, that the universal TRO commutes with appropriate tensor products and establishing a reversibility criterion for type I JW*-triples.

Characterization of the complex locus of bean encoding polygalacturonase-inhibiting proteins reveals subfunctionalization for defense against fungi and insects.

Polygalacturonase-inhibiting proteins (PGIPs) are extracellular plant inhibitors of fungal endopolygalacturonases (PGs) that belong to the superfamily of Leu-rich repeat proteins. We have characterized the full complement of pgip genes in the bean (Phaseolus vulgaris) genotype BAT93. This comprises four clustered members that span a 50-kb region and, based on their similarity, form two pairs (Pvpgip1/Pvpgip2 and Pvpgip3/Pvpgip4). Characterization of the encoded products revealed both partial redundancy and subfunctionalization against fungal-derived PGs. Notably, the pair PvPGIP3/PvPGIP4 also inhibited PGs of two mirid bugs (Lygus rugulipennis and Adelphocoris lineolatus). Characterization of Pvpgip genes of Pinto bean showed variations limited to single synonymous substitutions or small deletions. A three-amino acid deletion encompassing a residue previously identified as crucial for recognition of PG of Fusarium moniliforme was responsible for the inability of BAT93 PvPGIP2 to inhibit this enzyme. Consistent with the large variations observed in the promoter sequences, reverse transcription-PCR expression analysis revealed that the different family members differentially respond to elicitors, wounding, and salicylic acid. We conclude that both biochemical and regulatory redundancy and subfunctionalization of pgip genes are important for the adaptation of plants to pathogenic fungi and phytophagous insects.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

Role of the Skyrme tensor force in heavy-ion fusion

We make use of the Skyrme effective nuclear interaction within the time-dependent Hartree-Fock framework to assess the effect of inclusion of the tensor terms of the Skyrme interaction on the fusion window of the 16O–16O reaction. We find that the lower fusion threshold, around the barrier, is quite insensitive to these details of the force, but the higher threshold, above which the nuclei pass through each other, changes by several MeV between different tensor parametrisations. The results suggest that eventually fusion properties may become part of the evaluation or fitting process for effective nuclear interactions.

Unrestricted Skyrme-tensor time-dependent Hartree-Fock model and its application to the nuclear response from spherical to triaxial nuclei

The nuclear time-dependent Hartree-Fock model formulated in three-dimensional space, based on the full standard Skyrme energy density functional complemented with the tensor force, is presented. Full self-consistency is achieved by the model. The application to the isovector giant dipole resonance is discussed in the linear limit, ranging from spherical nuclei (16O and 120Sn) to systems displaying axial or triaxial deformation (24Mg, 28Si, 178Os, 190W and 238U). Particular attention is paid to the spin-dependent terms from the central sector of the functional, recently included together with the tensor. They turn out to be capable of producing a qualitative change on the strength distribution in this channel. The effect on the deformation properties is also discussed. The quantitative effects on the linear response are small and, overall, the giant dipole energy remains unaffected. Calculations are compared to predictions from the (quasi)-particle random-phase approximation and experimental data where available, finding good agreement

The tensor-kinetic field in nuclear collisions

The role of the tensor terms in the Skyrme interaction is studied for their effect in dynamic calculations where non-zero contributions to the mean-field may arise, even when the starting nucleus, or nuclei are even-even and have no active time-odd potentials in the ground state. We study collisions in the test-bed 16O-16O system, and give a qualitative analysis of the behaviour of the time-odd tensor-kinetic density, which only appears in the mean field Hamiltonian in the presence of the tensor force. We find an axial excitation of this density is induced by a collision.

The effect of the tensor force on the predicted stability of superheavy nuclei

The effect of the tensor component of the Skyrme effective nucleon-nucleon interaction on the single-particle structure in superheavy elements is studied. A selection of the available Skyrme forces has been chosen and their predictions for the proton and neutron shell closures investigated. The inclusion of the tensor term with realistic coupling strength parameters leads to a small increase in the spin-orbit splitting between the proton 2f7/2 and 2f5/2 partners, opening the Z=114 shell gap over a wide range of nuclei. The Z=126 shell gap, predicted by these models in the absence of the tensor term, is found to be stongly dependent on neutron number with a Z=138 gap opening for large neutron numbers, having a consequent implication for the synthesis of neutron-rich superheavy elements. The predicted neutron shell structures remain largely unchanged by inclusion of the tensor component.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets.

Second language influence on first language motion event encoding and categorization in Spanish-speaking children learning L2 English

Studies show cross-linguistic differences in motion event encoding, such that English speakers preferentially encode manner of motion more than Spanish speakers, who preferentially encode path of motion. Focusing on native Spanish speaking children (aged 5;00-9;00) learning L2 English, we studied path and manner verb preferences during descriptions of motion stimuli, and tested the linguistic relativity hypothesis by investigating categorization preferences in a non-verbal similarity judgement task of motion clip triads. Results revealed L2 influence on L1 motion event encoding, such that bilinguals used more manner verbs and fewer path verbs in their L1, under the influence of English. We found no effects of linguistic structure on non-verbal similarity judgements, and demonstrate for the first time effects of L2 on L1 lexicalization in child L2 learners in the domain of motion events. This pattern of verbal behaviour supports theories of bilingual semantic representation that postulate a merged lexico-semantic system in early bilinguals.

Optimized hash for network path encoding with minimized false positives

The Bloom filter is a space efficient randomized data structure for representing a set and supporting membership queries. Bloom filters intrinsically allow false positives. However, the space savings they offer outweigh the disadvantage if the false positive rates are kept sufficiently low. Inspired by the recent application of the Bloom filter in a novel multicast forwarding fabric, this paper proposes a variant of the Bloom filter, the optihash. The optihash introduces an optimization for the false positive rate at the stage of Bloom filter formation using the same amount of space at the cost of slightly more processing than the classic Bloom filter. Often Bloom filters are used in situations where a fixed amount of space is a primary constraint. We present the optihash as a good alternative to Bloom filters since the amount of space is the same and the improvements in false positives can justify the additional processing. Specifically, we show via simulations and numerical analysis that using the optihash the false positives occurrences can be reduced and controlled at a cost of small additional processing. The simulations are carried out for in-packet forwarding. In this framework, the Bloom filter is used as a compact link/route identifier and it is placed in the packet header to encode the route. At each node, the Bloom filter is queried for membership in order to make forwarding decisions. A false positive in the forwarding decision is translated into packets forwarded along an unintended outgoing link. By using the optihash, false positives can be reduced. The optimization processing is carried out in an entity termed the Topology Manger which is part of the control plane of the multicast forwarding fabric. This processing is only carried out on a per-session basis, not for every packet. The aim of this paper is to present the optihash and evaluate its false positive performances via simulations in order to measure the influence of different parameters on the false positive rate. The false positive rate for the optihash is then compared with the false positive probability of the classic Bloom filter.