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.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Association of genetic variants in the adiponectin encoding gene (ADIPOQ) with type 2 diabetes in Japanese Brazilians

Aim: The objective of this study is to assess the contribution of ADIPOQ variants to type 2 diabetes in Japanese Brazilians. Methods: We genotyped 200 patients with diabetes mellitus (100 male and 100 female, aged 55.0 years [47.5-64.0 years]) and 200 control subjects with normal glucose tolerant (NGT) (72 male and 128 female, aged 52.0 years [43.5-64.5 years]). Results: Whereas each polymorphism studied (T45G, G276T, and A349G) was not significantly associated with type 2 diabetes mellitus, the haplotype GGA was overrepresented in our diabetic population (9.3% against 3.1% in NGT individuals, P=.0003). Also, this haplotype was associated with decreased levels of adiponectin. We also identified three mutations in exon 3: I164T, R221S, and H241P, but, owing to the low frequencies of them, associations with type 2 diabetes could not be evaluated. The subjects carrying the R221S mutation had plasma adiponectin levels lower than those without the mutation (2.10 mu g/ml [1.35-2.55 mu g/ml] vs. 6.68 mu g/ml [3.90-11.23 mu g/ml], P=.015). Similarly, the I164T mutation carriers had mean plasma adiponectin levels lower than those noncarriers (3.73 mu g/ml [3.10-4.35 mu g/ml] vs. 6.68 mu g/ml [3.90-11.23 mu g/ml]), but this difference was not significant (P=.17). Conclusions: We identified in the ADIPOQ gene a risk haplotype for type 2 diabetes in the Japanese Brazilian population. (C) 2010 Elsevier Inc. All rights reserved.

Boosting systemic and secreted antibody responses in mice orally immunized with recombinant Bacillus subtilis strains following parenteral priming with a DNA vaccine encoding the enterotoxigenic Escherichia coli (ETEC) CFA/I fimbriae B subunit

Recombinant Bacillus subtilis strains, either spores or vegetative cells, may be employed as safe and low cost orally delivered live vaccine vehicles. In this study, we report the use of an orally delivered B. subtilis vaccine strain to boost systemic and secreted antibody responses in mice i.m. primed with a DNA vaccine encoding the structural subunit (CfaB) of the CFA/I fimbriae encoded by enterotoxigenic Escherichia coli (ETEC), an important etiological agent of diarrhea among travelers and children living in endemic regions. DBA/2 female mice submitted to the prime-boost immunization regimen developed synergic serum (IgG) and mucosal (IgA) antibody responses to the target CfaB antigen. Moreover, in contrast to mice immunized only with one vaccine formulation, sera harvested from prime-boosted vaccinated individuals inhibited adhesion of ETEC cells to human red blood cells. Additionally, vaccinated dams conferred full passive protection to suckling newborn mice challenged with a virulent ETEC strain. Taken together the present results further demonstrate the potential use of recombinant B. subtilis strains as an alternative live vaccine vehicle. (C) 2008 Elsevier Ltd. All rights reserved.

Immune Responses and Therapeutic Antitumor Effects of an Experimental DNA Vaccine Encoding Human Papillomavirus Type 16 Oncoproteins Genetically Fused to Herpesvirus Glycoprotein D

Recombinant adenovirus or DNA vaccines encoding herpes simplex virus type 1 (HSV-1) glycoprotein D (gD) genetically fused to human papillomavirus type 16 (HPV-16) oncoproteins (E5, E6, and E7) induce antigen-specific CD8(+) T-cell responses and confer preventive resistance to transplantable murine tumor cells (TC-1 cells). In the present report, we characterized some previously uncovered aspects concerning the induction of CD8(+) T-cell responses and the therapeutic anticancer effects achieved in C57BL/6 mice immunized with pgD-E7E6E5 previously challenged with TC-1 cells. Concerning the characterization of the immune responses elicited in mice vaccinated with pgD-E7E6E5, we determined the effect of the CD4(+) T-cell requirement, longevity, and dose-dependent activation on the E7-specific CD8(+) T-cell responses. In addition, we determined the priming/boosting properties of pgD-E7E6E5 when used in combination with a recombinant serotype 68 adenovirus (AdC68) vector encoding the same chimeric antigen. Mice challenged with TC-1 cells and then immunized with three doses of pgD-E7E6E5 elicited CD8(+) T-cell responses, measured by intracellular gamma interferon (IFN-gamma) and CD107a accumulation, to the three HPV-16 oncoproteins and displayed in vivo antigen-specific cytolytic activity, as demonstrated with carboxyfluorescein diacetate succinimidyl ester (CFSE)-labeled target cells pulsed with oligopeptides corresponding to the H-2D(b)-restricted immunodominant epitopes of the E7, E6, or E5 oncoprotein. Up to 70% of the mice challenged with 5 x 10(5) TC-1 cells and immunized with pgD-E7E6E5 controlled tumor development even after 3 days of tumor cell challenge. In addition, coadministration of pgD-E7E6E5 with DNA vectors encoding pGM-CSF or interleukin-12 (IL-12) enhanced the therapeutic antitumor effects for all mice challenged with TC-1 cells. In conclusion, the present results expand our previous knowledge on the immune modulation properties of the pgD-E7E6E5 vector and demonstrate, for the first time, the strong antitumor effects of the DNA vaccine, raising promising perspectives regarding the development of immunotherapeutic reagents for the control of HPV-16-associated tumors.

Detection of metallo-beta-lactamases-encoding genes in environmental isolates of Aeromonas hydrophila and Aeromonas jandaei

Aims: To determine the prevalence and expression of metallo-beta-lactamases (MBL)-encoding genes in Aeromonas species recovered from natural water reservoirs in southeastern Brazil. Methods and Results: Eighty-seven Aeromonas isolates belonging to Aeromonas hydrophila (n = 41) and Aer. jandaei (n = 46) species were tested for MBL production by the combined disk test using imipenem and meropenem disks as substrates and EDTA or thioglycolic acid as inhibitors. The presence of MBL genes was investigated by PCR and sequencing using new consensus primer pairs designed in this study. The cphA gene was found in 97.6% and 100% of Aer. hydrophila and Aer. jandaei isolates, respectively, whereas the acquired MBL genes bla(IMP), bla(VIM) and bla(SPM-1) were not detected. On the other hand, production of MBL activity was detectable in 87.8% and 10.9% of the cphA-positive Aer. hydrophila and Aer. jandaei isolates respectively. Conclusions: Our results indicate that cphA seems to be intrinsic in the environmental isolates of Aer. hydrophila and Aer. jandaei in southeastern Brazil, although, based on the combined disk test, not all of them are apparently able to express the enzymatic activity. Significance and Impact of the Study: These data confirm the presence of MBL-producing Aeromonas species in natural water reservoirs. Risk of water-borne diseases owing to domestic and industrial uses of freshwater should be re-examined from the increase of bacterial resistance point of view.

A DNA Vaccine Encoding the Enterohemorragic Escherichia coli Shiga-Like Toxin 2 A(2) and B Subunits Confers Protective Immunity to Shiga Toxin Challenge in the Murine Model

Production of verocytotoxin or Shiga-like toxin (Stx), particularly Stx2, is the basis of hemolytic uremic syndrome, a frequently lethal outcome for subjects infected with Stx2-producing enterohemorrhagic Escherichia coli (EHEC) strains. The toxin is formed by a single A subunit, which promotes protein synthesis inhibition in eukaryotic cells, and five B subunits, which bind to globotriaosylceramide at the surface of host cells. Host enzymes cleave the A subunit into the A(1) peptide, endowed with N-glycosidase activity to the 28S rRNA, and the A(2) peptide, which confers stability to the B pentamer. We report the construction of a DNA vaccine (pStx2 Delta AB) that expresses a nontoxic Stx2 mutated form consisting of the last 32 amino acids of the A(2) sequence and the complete B subunit as two nonfused polypeptides. Immunization trials carried out with the DNA vaccine in BALB/c mice, alone or in combination with another DNA vaccine encoding granulocyte-macrophage colony-stimulating factor, resulted in systemic Stx-specific antibody responses targeting both A and B subunits of the native Stx2. Moreover, anti-Stx2 antibodies raised in mice immunized with pStx2 Delta AB showed toxin neutralization activity in vitro and, more importantly, conferred partial protection to Stx2 challenge in vivo. The present vector represents the second DNA vaccine so far reported to induce protective immunity to Stx2 and may contribute, either alone or in combination with other procedures, to the development of prophylactic or therapeutic interventions aiming to ameliorate EHEC infection-associated sequelae.

Energy-momentum tensor in thermal strong-field QED with unstable vacuum

Themean value of the one-loop energy-momentum tensor in thermal QED with an electric-like background that creates particles from vacuum is calculated. The problem is essentially different from calculations of effective actions ( similar to the action of Heisenberg-Euler) in backgrounds that respect the stability of vacuum. The role of a constant electric background in the violation of both the stability of vacuum and the thermal character of particle distribution is investigated. Restrictions on the electric field and the duration over which one can neglect the back-reaction of created particles are established.

Abnormal Corpus Callosum Integrity in Bipolar Disorder: A Diffusion Tensor Imaging Study

Objective: Abnormalities in the anterior interhemispheric connections provided by the corpus callosum (CC) have long been implicated in bipolar disorder (BID). In this study, we used complementary diffusion tensor imaging methods to study the structural integrity of the CC and localization of potential abnormalities in BD. Methods: Subjects included 33 participants with BID and 40 healthy comparison participants. Fractional anisotropy (FA) measures were compared between groups with region of interest (ROD methods to investigate the anterior, middle, and posterior CC and voxel-based methods to further localize abnormalities. Results: In ROI-based analyses, FA was significantly decreased in the anterior and middle CC in the BID group (p <.05). Voxel-based analyses similarly localized group differences to the genu, rostral body, and anterior midbody of CC (p <.05, corrected). Conclusion: The findings demonstrate abnormalities in the structural integrity of the anterior CC in BID that might contribute to altered interhemispheric connectivity in this disorder.