Neural Analyses Validate and Emphasise the Role of Progesterone Receptor in Breast Cancer Progression and Prognosis

Oestrogen receptor (ER) expression is routinely measured in breast cancer management, but the clinical merits of measuring progesterone receptor (PR) expression have remained controversial. Hence the major objective here was to assess the potential of PR as a predictor of response to endocrine therapy. We report analyses of the relative importance of ER and PR for predicting prognosis using robust multilayer perceptron artificial neural networks. Receptor determinations use immunohistochemical (IHC) methods or radioactive ligand binding assays (LBA). In view of the heterogeneity of intratumoral receptor distribution, we examined the relative merits of the IHC and LBA methods. Our analyses reveal a more significant correlation of IHC-determined PR than ER with both nodal status and 5-year disease-free survival (DFS). In LBA, PR displayed higher correlation with survival and ER with nodal status. There was concordance of correlation of PR with DFS by both IHC and LBA. This study suggests a clear distinction between PR and ER, with PR displaying greater correlation than ER with disease progression and prognosis, and emphasises the marked superiority of the IHC method over LBA. These findings may be valuable in the management of patients with breast cancer

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Vehicle platoon systems are widely recognized as a key enabler to address mass-transport. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are two technologies that drive platooning. The inter-vehicle spacing and the collaboration velocity in the platoon are main important parameters that must be controlled. Recently, a new mass-transport system has been proposed, called the Tracked Electric Vehicles (TEV). In TEV, the inter-vehicular spacing is reduced to only a quarter of the regular car length and cars drive at 200km/h which enable mass transport at uniform speed. However, conventional radar based Adaptive Cruise Control (ACC) system fail to control each vehicle in these scenarios. Lately, Sliding Mode Control (SMC) has been applied to control platoons with communication technology but with low speed and without delay. This paper proposes a novel SMC design for TEV using global dynamic information with the communication delay. Also, graph theory has been employed to investigate different V2V communication topology structures. To address the issues of node vehicle stability and string stability, Lyapunov candidate function is chosen and developed for in-depth analysis. In addition, this paper, uses first-order vehicle models with different acceleration and deceleration parameters for simulation validations under communication delay. The results show that this novel SMC has a significant tolerance ability therefore meet the design requirements of TEV

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings

The Face of the Future: an animated virtual agent with life-like articulation

No abstract available

Automated Lip Synchronisation for Human-Computer Interaction and Special Effect Animation

The research presents MARTI (Man-machine Animation Real-Time Interface) for the realisation of automated special effect animation and human computer interaction. MARTI introduces novel research in a number of engineering disciplines, which include speech recognition, facial modelling, and computer animation. This interdisciplinary research utilises the latest, hybrid connectionist/hidden Markov model system, providing very accurate phone recognition and timing for speaker independent continuous speech, and expands' knowledge from the animation industry in the development of accurate facial models and automated animation. The system uses simple vocal soundtracks of human speakers to provide lip synchronisation of computer graphical facial models, to realise the first natural interface and animation system capable of high performance for real users and real-world applications

Speechreading-phone for the hearing impaired

No abstract available