Pharmacokinetics of a syrup formulation of amoxycillin-potassium clavulanate in children

The pharmacokinetics of a syrup formulation consisting of four parts of amoxycillin and one part of potassium clavulanate (Augmentin) were studied in 11 paediatric patients, 3 to 14 years of age. Single oral doses of 25 mg of Augmentin per kg body weight (20 mg of amoxycillin per kg plus 5 mg of potassium clavulanate per kg, i.e. 1 mg of the syrup per kg) were administered on an empty stomach, and were well accepted and tolerated. Mean peak plasma concentrations 60-90 min after dosing were 7.2 mg/l for amoxycillin and 2-0 mg/l for clavulanic acid. Mean terminal phase plasma half-lifes were 1.4 and 1.0 h, respectively. It is concluded that 25-mg/kg doses of this syrup formulation of Augmentin administered three times daily should be adequate therapy for various childhood bacterial infection

Natural organic matter in sedimentary basins and its relation to arsenic in anoxic ground water: the example of West Bengal and its worldwide implications

In order to investigate the mechanism of As release to anoxic ground water in alluvial aquifers, the authors sampled ground waters from 3 piezometer nests, 79 shallow (80 m) wells, in an area 750 m by 450 m, just north of Barasat, near Kolkata (Calcutta), in southern West Bengal. High concentrations of As (200-1180 mug L-1) are accompanied by high concentrations of Fe (3-13.7 mgL(-1)) and PO4 (1-6.5 mg L-1). Ground water that is rich in Mn (1-5.3 mg L-1) contains <50 mug L-1 of As. The composition of shallow ground water varies at the 100-m scale laterally and the metre-scale vertically, with vertical gradients in As concentration reaching 200 mug L-1 m(-1). The As is supplied by reductive dissolution of FeOOH and release of the sorbed As to solution. The process is driven by natural organic matter in peaty strata both within the aquifer sands and in the overlying confining unit. In well waters, thermotolerant coliforms, a proxy for faecal contamination, are not present in high numbers (<10 cfu/100 ml in 85% of wells) showing that faecally-derived organic matter does not enter the aquifer, does not drive reduction of FeOOH, and so does not release As to ground water.Arsenic concentrations are high (much greater than50 mug L-1) where reduction of FeOOH is complete and its entire load of sorbed As is released to solution, at which point the aquifer sediments become grey in colour as FeOOH vanishes. Where reduction is incomplete, the sediments are brown in colour and resorption of As to residual FeOOH keeps As concentrations below 10 mug L-1 in the presence of dissolved Fe. Sorbed As released by reduction of Mn oxides does not increase As in ground water because the As resorbs to FeOOH. High concentrations of As are common in alluvial aquifers of the Bengal Basin arise because Himalayan erosion supplies immature sediments, with low surface-loadings of FeOOH on mineral grains, to a depositional environment that is rich in organic mater so that complete reduction of FeOOH is common. (C) 2004 Published by Elsevier Ltd

Machine learning methods for automatic silent speech recognition using a wearable graphene strain gauge sensor

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/

Att-TasNet: attending to encodings in time-domain audio speech separation of noisy, reverberant speech mixtures

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures

The University of Sheffield CHiME-7 UDASE challenge speech enhancement system

The CHiME-7 unsupervised domain adaptation speech enhancement (UDASE) challenge targets domain adaptation to unlabelled speech data. This paper describes the University of Sheffield team’s system submitted to the challenge. A generative adversarial network (GAN) methodology based on a conformer-based metric GAN (CMGAN) is employed as opposed to the unsupervised RemixIT strategy used in the CHiME-7 baseline system. The discriminator of the GAN is trained to predict the output score of a Deep Noise Suppression Mean Opinion Score (DNSMOS) metric. Additional data augmentation strategies are employed which provide the discriminator with historical training data outputs as well as more diverse training examples from an additional pseudo-generator. The proposed approach, denoted as CMGAN+/+, achieves significant improvement in DNSMOS evaluation metrics with the best proposed system achieving 3.51 OVR-MOS, a 24% improvement over the baseline

Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI)

Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI)

The eventization of leisure and the strange death of alternative Leeds

The communicative potential of city spaces as leisure spaces is a central assumption of political activism and the creation of alternative, counter-cultural and subcultural scenes. However, such potential for city spaces is limited by the gentrification, privatization and eventization of city centres in the wake of wider societal and cultural struggles over leisure, work and identity formation. In this paper, we present research on alternative scenes in the city of Leeds to argue that the eventization of the city centre has led to a marginalization and of alternative scenes on the fringes of the city. Such marginalization has not caused the death of alternative Leeds or political activism associated with those scenes—but it has changed the leisure spaces (physical, political and social) in which alternative scenes contest the mainstream

Material matters for learning in virtual networks : A case study of a professional learning programme hosted in a Google+ online community

Date of Acceptance: 13/07/2015Peer reviewedPublisher PD