Strong Electron-Phonon Interaction and Colossal Magnetoresistance in EuTiO3_3

At low temperatures, EuTiO$_3$ system has very large resistivities and exhibits colossal magnetoresistance. Based on a first principle calculation and the dynamical mean-field theory for small polaron we have calculated the transport properties of EuTiO$_3$. It is found that due to electron-phonon interaction the conduction band may form a tiny subband which is close to the Fermi level. The tiny subband is responsible for the large resistivity. Besides, EuTiO$_3$ is a weak antiferromagnetic material and its magnetization would slightly shift the subband via exchange interaction between conduction electrons and magnetic atoms. Since the subband is close to the Fermi level, a slight shift of its position gives colossal magnetoresistance.Comment: 6 pages, 5 figure

P53 tumour-suppressor gene mutations are mainly localised on exon 7 in human primary and metastatic prostate cancer.

Mutations in the p53 tumour-suppressor gene are among the most common genetic alterations in human cancers. In the present study we analysed the mutations in the p53 tumor-suppressor gene in 25 primary and 20 metastatic human prostate cancer specimens. DNA extracted from the paraffin-embedded sections was amplified by hot-start polymerase chain reaction, and p53 gene mutations in the conserved mid-region (exons 4-9) were examined using single-strand conformation polymorphism (SSCP) analysis and immunohistochemistry. In the present study, we used a novel hot-start PCR-SSCP technique using DNA Taq polymerase antibody, which eliminates primer-dimers and non-specific products. Because of this new technique, the results of PCR-SSCP showed very high resolution. Polymerase chain reaction products were sequenced directly for point mutations for the p53 gene. Mutations were found in 2 out of 25 primary prostate cancers (8%) and 4 out of 20 metastatic cancers (20%). Mutations were observed exclusively in exon 7 and not in exons 4, 5, 6, 8 or 9. Nuclear accumulation of p53 protein, determined by immunohistochemistry, correlated with the degree of metastasis in prostatic cancer

Large adiabatic temperature and magnetic entropy changes in EuTiO3

We have investigated the magnetocaloric effect in single and polycrystalline samples of quantum paraelectric EuTiO3 by magnetization and heat capacity measurements. Single crystalline EuTiO3 shows antiferromagnetic ordering due to Eu2+ magnetic moments below TN = 5.6 K. This compound shows a giant magnetocaloric effect around its Neel temperature. The isothermal magnetic entropy change is 49 Jkg-1K-1, the adiabatic temperature change is 21 K and the refrigeration capacity is 500 JKg-1 for a field change of 7 T at TN. The single crystal and polycrystalline samples show similar values of the magnetic entropy change and adiabatic temperature changes. The large magnetocaloric effect is due to suppression of the spin entropy associated with localized 4f moment of Eu2+ ions. The giant magnetocaloric effect together with negligible hysteresis, suggest that EuTiO3 could be a potential material for magnetic refrigeration below 20 K.Comment: 12 pages, 4 figure

Approximating Weighted Duo-Preservation in Comparative Genomics

Motivated by comparative genomics, Chen et al. [9] introduced the Maximum Duo-preservation String Mapping (MDSM) problem in which we are given two strings $s_1$ and $s_2$ from the same alphabet and the goal is to find a mapping $\pi$ between them so as to maximize the number of duos preserved. A duo is any two consecutive characters in a string and it is preserved in the mapping if its two consecutive characters in $s_1$ are mapped to same two consecutive characters in $s_2$. The MDSM problem is known to be NP-hard and there are approximation algorithms for this problem [3, 5, 13], but all of them consider only the "unweighted" version of the problem in the sense that a duo from $s_1$ is preserved by mapping to any same duo in $s_2$ regardless of their positions in the respective strings. However, it is well-desired in comparative genomics to find mappings that consider preserving duos that are "closer" to each other under some distance measure [19]. In this paper, we introduce a generalized version of the problem, called the Maximum-Weight Duo-preservation String Mapping (MWDSM) problem that captures both duos-preservation and duos-distance measures in the sense that mapping a duo from $s_1$ to each preserved duo in $s_2$ has a weight, indicating the "closeness" of the two duos. The objective of the MWDSM problem is to find a mapping so as to maximize the total weight of preserved duos. In this paper, we give a polynomial-time 6-approximation algorithm for this problem.Comment: Appeared in proceedings of the 23rd International Computing and Combinatorics Conference (COCOON 2017

Comprehensive Systematic Review of long-term opioids in women with chronic non cancer pain and associated reproductive dysfunction (hypothalmic-pituitary-gonadal axis disruption)

A comprehensive systematic literature review of reproductive side effects in women aged 18 to 55 years treated with opioids for 1 month or longer for chronic noncancer pain. A search of 7 databases including EMBASE and Medline was undertaken (October 2014 and a limited rerun April 2016). The search contained key words for opioids (generic and specific drug names) and side effects (generic and specific reproductive). Titles were screened using predefined criteria by a single reviewer and abstracts and full texts by 2 independent reviewers. A total of 10,684 articles were identified and 12 full texts (cohort [n 5 1], case–control [n 5 4], crosssectional [n 5 4], case series [n 5 1], and case report [n 5 2] with a maximum of 41 cases in 1 article) were included covering 3 different modes of administration: oral (n 5 6), intrathecal (n 5 5), and transdermal (n 5 1). Amenorrhoea occurred in 23% to 71% of those receiving oral or intrathecal opioids. Decreased libido was seen in 61% to 100%. Of the 10 studies that undertook hormonal assays, only 2 studies showed a statistically significant decrease in hormone levels. This review supports the view that there is a potential relationship between the use of long-term opioids in women and reproductive side effects. The evidence is however weak and the mode of administration, duration, type, and dose of opioid might influence associations. Although hormone levels were statistically significant in only 2 studies, women exhibited clinically important symptoms (decreased libido and altered menstrual cycle). Further investigation is required with larger cohorts and analysis of different delivery methods

Roles of Arabidopsis WRKY3 and WRKY4 Transcription Factors in Plant Responses to Pathogens

<p>Abstract</p> <p>Background</p> <p>Plant WRKY DNA-binding transcription factors are involved in plant responses to biotic and abiotic responses. It has been previously shown that <it>Arabidopsis WRKY3 </it>and <it>WRKY4</it>, which encode two structurally similar WRKY transcription factors, are induced by pathogen infection and salicylic acid (SA). However, the role of the two WRKY transcription factors in plant disease resistance has not been directly analyzed.</p> <p>Results</p> <p>Both WRKY3 and WRKY4 are nuclear-localized and specifically recognize the TTGACC W-box sequences <it>in vitro</it>. Expression of <it>WRKY3 </it>and <it>WRKY4 </it>was induced rapidly by stress conditions generated by liquid infiltration or spraying. Stress-induced expression of <it>WRKY4 </it>was further elevated by pathogen infection and SA treatment. To determine directly their role in plant disease resistance, we have isolated T-DNA insertion mutants and generated transgenic overexpression lines for <it>WRKY3 </it>and <it>WRKY4</it>. Both the loss-of-function mutants and transgenic overexpression lines were examined for responses to the biotrophic bacterial pathogen <it>Pseudomonas syringae </it>and the necrotrophic fungal pathogen <it>Botrytis cinerea</it>. The <it>wrky3 </it>and <it>wrky4 </it>single and double mutants exhibited more severe disease symptoms and support higher fungal growth than wild-type plants after <it>Botrytis </it>infection. Although disruption of <it>WRKY3 </it>and <it>WRKY4 </it>did not have a major effect on plant response to <it>P. syringae</it>, overexpression of <it>WRKY4 </it>greatly enhanced plant susceptibility to the bacterial pathogen and suppressed pathogen-induced <it>PR1 </it>gene expression.</p> <p>Conclusion</p> <p>The nuclear localization and sequence-specific DNA-binding activity support that WRKY3 and WRKY4 function as transcription factors. Functional analysis based on T-DNA insertion mutants and transgenic overexpression lines indicates that WRKY3 and WRKY4 have a positive role in plant resistance to necrotrophic pathogens and WRKY4 has a negative effect on plant resistance to biotrophic pathogens.</p

Multi-language neural network language models

Recently there has been a lot of interest in neural network based language models. These models typically consist of vocabulary dependent input and output layers and one or more vocabulary independent hidden layers. One standard issue with these approaches is that large quantities of training data are needed to ensure robust parameter estimates. This poses a significant problem when only limited data is available. One possible way to address this issue is augmentation: model-based, in the form of language model interpolation, and data-based, in the form of data augmentation. However, these approaches may not always be possible to use due to vocabulary dependent input and output layers. This seriously restricts the nature of the data possible to use in augmentation. This paper describes a general solution whereby only one or more vocabulary independent hidden layers are augmented. Such approach makes it possible to examine augmentation from previously impossible domains. Moreover, this approach paves a direct way for multi-task learning with these models. As a proof of the concept this paper examines the use of multilingual data for augmenting hidden layers of recurrent neural network language models. Experiments are conducted using a set of language packs released within IARPA Babel program

Frame Interpolation for Cloud-Based Mobile Video Streaming

© 2016 IEEE. Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance