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

Contributions of cochlea-scaled entropy and consonant-vowel boundaries to prediction of speech intelligibility in noise

published_or_final_versio

Comprehensive analysis and optimal design of top-emitting organic light-emitting devices

We present an accurate analysis of light emission in top-emitting organic light-emitting devices (TOLEDs) by explicitly considering the Purcell effect. TOLEDs are optimized separately for maximum zero-degree luminance, maximum electroluminescence (EL) efficiency, and wide viewing angle with high EL efficiency. For fluorescent material with an internal quantum efficiency ( int 0) of 0.25, the maximum zero-degree luminance and EL efficiency can be achieved by locating the emitters around the first antinode of the microcavity while for phosphorescent material with int 0 =1.0, the maximum zero-degree luminance and EL efficiency are around the second antinode. Through relaxing the efficiency by 10%-20%, the angular intensity distribution can be even better than the Lambertian distribution; meanwhile, the color shows only a small variation over an angle range of 150°. Our results, which are in good agreement with experiments, show that the Purcell effect on TOLED performances is significant and should be carefully examined in studying TOLEDs. © 2007 American Institute of Physics.published_or_final_versio

Recurrent neural network language model training with noise contrastive estimation for speech recognition

In recent years recurrent neural network language models (RNNLMs) have been successfully applied to a range of tasks including speech recognition. However, an important issue that limits the quantity of data used, and their possible application areas, is the computational cost in training. A significant part of this cost is associated with the softmax function at the output layer, as this requires a normalization term to be explicitly calculated. This impacts both the training and testing speed, especially when a large output vocabulary is used. To address this problem, noise contrastive estimation (NCE), is used in RNNLM training in this paper. It does not require the above normalization during both training and testing and is insensitive to the output layer size. On a large vocabulary conversational telephone speech recognition task, a doubling in training speed and 56 time speed up in test time evaluation were obtained.Xie Chen is supported by Toshiba Research Europe Ltd, Cambridge Research Lab. The research leading to these results was also supported by EPSRC grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation (BOLT) and RATS programs. The paper does not necessarily reflect the position or the policy of US Government and no official endorsement should be inferred. The authos also would like to thanks Ashish Vaswani from USC for suggestions and discussion on training of NNLMs with NCE.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2015.717900

Exercise-Induced Changes in Exhaled NO Differentiates Asthma With or Without Fixed Airway Obstruction From COPD With Dynamic Hyperinflation.

Asthmatic patients with fixed airway obstruction (FAO) and patients with chronic obstructive pulmonary disease (COPD) share similarities in terms of irreversible pulmonary function impairment. Exhaled nitric oxide (eNO) has been documented as a marker of airway inflammation in asthma, but not in COPD. To examine whether the basal eNO level and the change after exercise may differentiate asthmatics with FAO from COPD, 27 normal subjects, 60 stable asthmatics, and 62 stable COPD patients were studied. Asthmatics with FAO (n = 29) were defined as showing a postbronchodilator FEV(1)/forced vital capacity (FVC) ≤70% and FEV(1) less than 80% predicted after inhaled salbutamol (400 μg). COPD with dynamic hyperinflation (n = 31) was defined as a decrease in inspiratory capacity (ΔIC%) after a 6 minute walk test (6MWT). Basal levels of eNO were significantly higher in asthmatics and COPD patients compared to normal subjects. The changes in eNO after 6MWT were negatively correlated with the percent change in IC (r = −0.380, n = 29, P = 0.042) in asthmatics with FAO. Their levels of basal eNO correlated with the maximum mid-expiratory flow (MMEF % predicted) before and after 6MWT. In COPD patients with air-trapping, the percent change of eNO was positively correlated to ΔIC% (rs = 0.404, n = 31, P = 0.024). We conclude that asthma with FAO may represent residual inflammation in the airways, while dynamic hyperinflation in COPD may retain NO in the distal airspace. eNO changes after 6MWT may differentiate the subgroups of asthma or COPD patients and will help toward delivery of individualized therapy for airflow obstruction

Improving the training and evaluation efficiency of recurrent neural network language models

Recurrent neural network language models (RNNLMs) are becoming increasingly popular for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) output layer (F-RNNLMs) can be trained efficiently using a GPU giving a large reduction in training time over conventional class-based models (C-RNNLMs) on a standard CPU. However, since test-time RNNLM evaluation is often performed entirely on a CPU, standard F-RNNLMs are inefficient since the entire output layer needs to be calculated for normalisation. In this paper, it is demonstrated that C-RNNLMs can be efficiently trained on a GPU, using our spliced sentence bunch technique which allows good CPU test-time performance (42x speedup over F-RNNLM). Furthermore, the performance of different classing approaches is investigated. We also examine the use of variance regularisation of the softmax denominator for F-RNNLMs and show that it allows F-RNNLMs to be efficiently used in test (56x speedup on CPU). Finally the use of two GPUs for F-RNNLM training using pipelining is described and shown to give a reduction in training time over a single GPU by a factor of 1.6.Xie Chen is supported by Toshiba Research Europe Ltd, Cambridge Research Lab. The research leading to these results was also supported by EPSRC grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation (BOLT) and RATS programs. The paper does not necessarily reflect the position or the policy of US Government and no official endorsement should be inferred.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2015.717900

Highly efficient fluorescence of a fluorescing nanoparticle with a silver shell

Spontaneous emission (SE) rate and the fluorescence efficiency of a bare fluorescing nanoparticle and the nanoparticle with a silver nanoshell are analyzed rigorously by using a classical electromagnetic approach with the consideration of the nonlocal effect of the silver nanoshell. The dependences of the SE rate and the fluorescence efficiency on the core-shell structure are carefully studied and the physical interpretations of the results are addressed. The results show that the SE rate of a bare nanoparticle is much slower than that in the infinite medium by almost an order of magnitude and consequently the fluorescence efficiency is usually low. However, by encapsulating the nanoparticle with a silver shell, highly efficient fluorescence can be achieved as a result of a large Purcell enhancement and high out-coupling efficiency for a well-designed core-shell structure. We also show that a higher SE rate may not offer a larger fluorescence efficiency since the fluorescence efficiency not only depends on the internal quantum yield but also the out-coupling efficiency. © 2007 Optical Society of America.published_or_final_versio

Paraphrastic recurrent neural network language models

Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems. Linguistic factors influencing the realization of surface word sequences, for example, expressive richness, are only implicitly learned by RNNLMs. Observed sentences and their associated alternative paraphrases representing the same meaning are not explicitly related during training. In order to improve context coverage and generalization, paraphrastic RNNLMs are investigated in this paper. Multiple paraphrase variants were automatically generated and used in paraphrastic RNNLM training. Using a paraphrastic multi-level RNNLM modelling both word and phrase sequences, significant error rate reductions of 0.6% absolute and perplexity reduction of 10% relative were obtained over the baseline RNNLM on a large vocabulary conversational telephone speech recognition system trained on 2000 hours of audio and 545 million words of texts. The overall improvement over the baseline n-gram LM was increased from 8.4% to 11.6% relative.The research leading to these results was supported by EPSRC grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation (BOLT) and RATS programs. The paper does not necessarily reflect the position or the policy of US Government and no official endorsement should be inferred. Xie Chen is supported by Toshiba Research Europe Ltd, Cambridge Research Lab.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2015.717900

The Purcell effect of silver nanoshell on the fluorescence of nanoparticles

Proceedings of the Asia Optical Fiber Communication and Optoelectronics Conference, 2007, p. 81-83The Purcell effect on the spontaneously emission rate and fluorescence efficiency of nanoparticles with and without a silver nanoshell will be investigated which are important for nanoparticle applications in biomedical diagnostics, information storage and optoelectronics.published_or_final_versio

Two efficient lattice rescoring methods using recurrent neural network language models

An important part of the language modelling problem for automatic speech recognition (ASR) systems, and many other related applications, is to appropriately model long-distance context dependencies in natural languages. Hence, statistical language models (LMs) that can model longer span history contexts, for example, recurrent neural network language models (RNNLMs), have become increasingly popular for state-of-the-art ASR systems. As RNNLMs use a vector representation of complete history contexts, they are normally used to rescore N-best lists. Motivated by their intrinsic characteristics, two efficient lattice rescoring methods for RNNLMs are proposed in this paper. The first method uses an $\textit{n}$-gram style clustering of history contexts. The second approach directly exploits the distance measure between recurrent hidden history vectors. Both methods produced 1-best performance comparable to a 10 k-best rescoring baseline RNNLM system on two large vocabulary conversational telephone speech recognition tasks for US English and Mandarin Chinese. Consistent lattice size compression and recognition performance improvements after confusion network (CN) decoding were also obtained over the prefix tree structured N-best rescoring approach.This work was supported by EPSRC under Grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation and RATS programs. The work of X. Chen was supported by Toshiba Research Europe Ltd, Cambridge Research Lab.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TASLP.2016.255882