Mutual information eigenlips for audio-visual speech recognition

This paper proposes an application of information theoretic approach for finding the most informative subset of eigenfeatures to be used for audio-visual speech recognition tasks. The state-of-the-art visual feature extraction methods in the area of speechreading rely on either pixel or geometric based methods or their combination. However, there is no common rule defining how these features have to be selected with respect to the chosen set of audio cues and how well they represent the classes of the uttered speech. Our main objective is to exploit the complementarity of audio and visual sources and select meaningful visual descriptors by the means of mutual information. We focus on the principal components projections of the mouth region images and apply the proposed method such that only those cues having the highest mutual information with word classes are retained. The algorithm is tested by performing various speech recognition experiments on a chosen audio-visual dataset. The obtained recognition rates are compared to those acquired using a conventional principal component analysis and promising results are shown

Impact of Sample Sizes on Information Theoretic Measures for Audio-Visual Signal Processing

In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects statistics of audio and video signals does not exist since one does not know the most appropriate features to be extracted in order to analyze their correlation. Previous approaches assumed simple parametric and non-parametric models for the joint distribution for capturing the complex signal relationships. The main problem in using the standard information theoretic quantities, such as entropy and mutual information, is the accurate estimation of the probability density function from a limited number of data. The main idea is to project the data into a statistically sufficient low-dimensional subspace, suitable for density estimation. Then using a simple parametric model based on assumption of Gaussianity, mutual information is estimated and applied as a measure of correspondence. We exploit how the choice of sample size affects the reliability of the correspondence measure (mutual information) between selected features of the two modalities, audio and video

Automatic extraction of geometric lip features with application to multi-modal speaker identification

In this paper we consider the problem of automatic extraction of the geometric lip features for the purposes of multi-modal speaker identification. The use of visual information from the mouth region can be of great importance for improving the speaker identification system performance in noisy conditions. We propose a novel method for automated lip features extraction that utilizes color space transformation and a fuzzy-based c-means clustering technique. Using the obtained visual cues closed-set audio-visual speaker identification experiments are performed on the CUAVE database, [1] showing promising results

Selected derivatives of erythromycin B- in silico and anti-malarial studies

Erythromycin A is an established anti-bacterial agent against Gram-positive bacteria, but it is unstable to acid. This led to an evaluation of erythromycin B and its derivatives because these have improved acid stability. These compounds were investigated for their anti-malarial activities, by their in silico molecular docking into segments of the exit tunnel of the apicoplast ribosome from Plasmodium falciparum. This is believed to be the target of the erythromycin A derivative, azithromycin, which has mild anti-malarial activity. The erythromycin B derivatives were evaluated on the multi-drug (chloroquine, pyrimethamine, and sulfadoxine)-resistant strain K1 of P. falciparum for asexual growth inhibition on asynchronous culture. The erythromycin B derivatives were identified as active in vitro inhibitors of asexual growth of P. falciparum with low micro-molar IC50 values after a 72 h cycle. 5-Desosaminyl erythronolide B ethyl succinate showed low IC50 of 68.6 µM, d-erythromycin B 86.8 µM, and erythromycin B 9-oxime 146.0 µM on the multi-drug-resistant K1 of P. falciparum. Based on the molecular docking, it seems that a small number of favourable interactions or the presence of unfavourable interactions of investigated derivatives of erythromycin B with in silico constructed segment from the exit tunnel from the apicoplast of P. falciparum is the reason for their weak in vitro anti-malarial activities

N-Myc-induced metabolic rewiring creates novel therapeutic vulnerabilities in neuroblastoma

N-Myc is a transcription factor that is aberrantly expressed in many tumor types and is often correlated with poor patient prognosis. Recently, several lines of evidence pointed to the fact that oncogenic activation of Myc family proteins is concomitant with reprogramming of tumor cells to cope with an enhanced need for metabolites during cell growth. These adaptions are driven by the ability of Myc proteins to act as transcriptional amplifiers in a tissue-of-origin specific manner. Here, we describe the effects of N-Myc overexpression on metabolic reprogramming in neuroblastoma cells. Ectopic expression of N-Myc induced a glycolytic switch that was concomitant with enhanced sensitivity towards 2-deoxyglucose, an inhibitor of glycolysis. Moreover, global metabolic profiling revealed extensive alterations in the cellular metabolome resulting from overexpression of N-Myc. Limited supply with either of the two main carbon sources, glucose or glutamine, resulted in distinct shifts in steady-state metabolite levels and significant changes in glutathione metabolism. Interestingly, interference with glutamine-glutamate conversion preferentially blocked proliferation of N-Myc overexpressing cells, when glutamine levels were reduced. Thus, our study uncovered N-Myc induction and nutrient levels as important metabolic master switches in neuroblastoma cells and identified critical nodes that restrict tumor cell proliferation

A conceptual framework for modelling the role of livestock systems in sustainable diets and a sustainable planet

The role of livestock in sustainable food systems and sustainable diets is a complex issue. It should be assessed in terms of its impacts on environmental, economic, and social sustainability, as well as the levels of animal performance, the human food supply, and the human food production system. However, such nuanced analyses are made difficult by the lack of multi-metric, multi-domain modelling frameworks and a lack of data on regional variation in livestock production. This paper proposes a conceptual biophysical modelling framework that could be used as a pathway to address existing methodology gaps and improve sustainability analyses across multiple levels. Realising this modelling framework requires clear, transparent, and enforceable frameworks for multi-scale sustainability assessments, as well as long-term investment into region-specific data collection, particularly from under-represented regions. To ensure representativeness and broader utility, this framework must also be able to model variation in both production systems and consumer dietary patterns, and the feedback loops between producer/consumer decisions and on-farm production. Beyond the level of science, this will also require concerted effort by the various actors in the livestock and food-chain sectors such as governmental bodies, the food production industry and local communities. Once realised, this framework could be used to assess trade-offs between potential food-system changes and to ensure that decisions are being made from a big picture, net-benefit perspective, while exploring methods for building flexible, diverse food systems that are sustainable across multiple scales

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

<p>Abstract</p> <p>Background</p> <p>Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity.</p> <p>Results</p> <p>We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time.</p> <p>Conclusions</p> <p>CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.</p