Interpreting signals from astrophysical transient experiments

Time domain astronomy has come of age with astronomers now able to monitor the sky at high cadence both across the electromagnetic spectrum and using neutrinos and gravitational waves. The advent of new observing facilities permits new science, but the ever increasing throughput of facilities demands efficient communication of coincident detections and better subsequent coordination among the scientific community so as to turn detections into scientific discoveries. To discuss the revolution occurring in our ability to monitor the Universe and the challenges it brings, on 2012 April 25-26 a group of scientists from observational and theoretical teams studying transients met with representatives of the major international transient observing facilities at the Kavli Royal Society International Centre, UK. This immediately followed the Royal Society Discussion meeting "New windows on transients across the Universe" held in London. Here we present a summary of the Kavli meeting at which the participants discussed the science goals common to the transient astronomy community and analysed how to better meet the challenges ahead as ever more powerful observational facilities come on stream.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition

Merit, Speech Disability and Discrimination

There is a substantial body of evidence – going back over decades – which indicates that the employment sphere is difficult for those who suffer a speech disability. To a large extent, I argue, this is due to the setting of merit in terms of orality and aesthetic. It also relates to the low perception of competence of the speech disabled. I argue that to be effective against discrimination the notion of merit and its assessment requires focus. ‘Merit’ as a concept in discrimination law has had its critics, yet it remains important to investigate it as social construct in order to help understand discrimination and how to counter this. For example, in this article I look at an instance where the resetting of what was viewed as ‘meritorious’ in judicial recruitment successfully improved the diversity in lower judicial posts.

Further, given the relative failure of the employment tribunal system to improve the general position of those who are disabled, I look to alternative methods of countering disability discrimination. The suggestion provided is that an enforced ombudsman type approach capable of dealing with what may be the core issue around employment discrimination (‘merit’) would provide a better mechanism for handling the general situation of disability discrimination than the tribunal system.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

LYRIC/AEG-1 is targeted to different subcellular compartments by ubiquitinylation and intrinsic nuclear localization signals

PURPOSE: LYRIC/AEG-1 has been reported to influence breast cancer survival and metastases, and its altered expression has been found in a number of cancers. The cellular function of LYRIC/AEG-1 has previously been related to its subcellular distribution in cell lines. LYRIC/AEG-1 contains three uncharacterized nuclear localization signals (NLS), which may regulate its distribution and, ultimately, function in cells.

EXPERIMENTAL DESIGN: Immunohistochemistry of a human prostate tissue microarray composed of 179 prostate cancer and 24 benign samples was used to assess LYRIC/AEG-1 distribution. Green fluorescent protein-NLS fusion proteins and deletion constructs were used to show the ability of LYRIC/AEG-1 NLS to target green fluorescent protein from the cytoplasm to the nucleus. Immunoprecipitation and Western blotting were used to show posttranslational modification of LYRIC/AEG-1 NLS regions.

RESULTS: Using a prostate tissue microarray, significant changes in the distribution of LYRIC/AEG-1 were observed in prostate cancer as an increased cytoplasmic distribution in tumors compared with benign tissue. These differences were most marked in high grade and aggressive prostate cancers and were associated with decreased survival. The COOH-terminal extended NLS-3 (amino acids 546-582) is the predominant regulator of nuclear localization, whereas extended NLS-1 (amino acids 78-130) regulates its nucleolar localization. Within the extended NLS-2 region (amino acids 415-486), LYRIC/AEG-1 can be modified by ubiquitin almost exclusively within the cytoplasm.

CONCLUSIONS: Changes in LYRIC/AEG-1 subcellular distribution can predict Gleason grade and survival. Two lysine-rich regions (NLS-1 and NLS-3) can target LYRIC/AEG-1 to subcellular compartments whereas NLS-2 is modified by ubiquitin in the cytoplasm.

Integration of Indicator Alarm Signals for Ecosystem‐Based Fishery Management

We consider the problem of regulating the rate of harvesting a natural resource, taking account of the wider system represented by a set of ecological and economic indicators, given differing stakeholder priorities. This requires objective and transparent decision making to show how indicators impinge on the resulting regulation decision. We offer a new scheme for combining indicators, derived from assessing the suitability of lowering versus not lowering the harvest rate based on indicator values relative to their predefined reference levels. Using the practical example of fisheries management under an “ecosystem approach,” we demonstrate how different stakeholder views can be quantitatively represented by weighting sets applied to these comparisons. Using the scheme in an analysis of historical data from the Celtic Sea fisheries, we find great scope for negotiating agreement among disparate stakeholders.

Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.