Multiple channel crosstalk removal using limited connectivity neural networks

Limited connectivity neural network architectures are investigated for the removal of crosstalk in systems using mutually overlapping sub-channels for the communication of multiple signals, either analogue or digital. The crosstalk error is modelled such that a fixed proportion of the signals in adjacent channels is added to the main signal. Different types of neural networks, trained using gradient descent algorithms, are tested as to their suitability for reducing the errors caused by a combination of crosstalk and additional gaussian noise. In particular we propose a single layer limited connectivity neural network since it promises to be the most easily implemented in hardware. A variable gain neuron structure is described which can be used for both analogue and digital data

A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition

A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime. The GesRec system is introduced which provides a framework for data acquisition, training, recognition, and gesture-to-speech transcription in a Windows environment. A recognition accuracy of 92.5% was obtained for the hybrid system, compared to 89.6% for the neural network only and 82.7% for rules only. Training and recognition times are given for an able-bodied and a disabled user

Patient and family engagement in the ICU: report from the task force of the world federation of societies of intensive and critical care medicine

Patient and family engagement plays an important role in the intensive care unit (ICU), however the degree to which practices are being implemented globally is not known.To provide insights, a task force of the World Federation of Societies of Intensive and Critical Care Medicine conducted a cross-sectional survey.A total of 345 responses were received from 40 countries. Varying practices with respect to patient and family engagement were reported. Majority of those responding to individual survey questions (n=109, 61.2%) provided written materials on the ICU to family members. Just over half (n=184, 53.8%) of respondents identified that structured patient and family care conferences were held to review goals of care. Practices such as open visitation were reported by 39.6% (n=136), and family presence during resuscitation were reported to be fully (12%, n=41) or somewhat adopted (33%, n=113) by less than half of respondents. ICU diaries, music or pet therapy, or the use of a patient and family advisory group were reported by less than half of respondents.We document and hence provide successful implementation techniques, tactics, and strategies that could help clinicians to address barriers to implementing patient and family engagement in the ICU

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

Multiple channel crosstalk removal using limited connectivity neural networks

Limited connectivity neural network architectures are investigated for the removal of crosstalk in systems using mutually overlapping sub-channels for the communication of multiple signals, either analogue or digital. The crosstalk error is modelled such that a fixed proportion of the signals in adjacent channels is added to the main signal. Different types of neural networks, trained using gradient descent algorithms, are tested as to their suitability for reducing the errors caused by a combination of crosstalk and additional gaussian noise. In particular we propose a single layer limited connectivity neural network since it promises to be the most easily implemented in hardware. A variable gain neuron structure is described which can be used for both analogue and digital data

A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition

A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime. The GesRec system is introduced which provides a framework for data acquisition, training, recognition, and gesture-to-speech transcription in a Windows environment. A recognition accuracy of 92.5% was obtained for the hybrid system, compared to 89.6% for the neural network only and 82.7% for rules only. Training and recognition times are given for an able-bodied and a disabled user

Accurate Parameter Estimation for an Articulatory Speech Synthesizer with an Improved Neural Network Mapping

Neural network (NN) applications have recently been employed to extract the parameters of an articulatory speech synthesizer from a given speech signal. Results from these attempts showed that a single NN is insufficient to cover all of the possible configurations uniquely. Moreover, apart from their computational advantages, NN mapping is so far not superior to the other mapping techniques [1]. Thus there is a clear need to improve NN solution to the inverse problem. Results from our earlier experiments with an articulatory speech synthesizer have shown that the statistical characteristic of the articulatory target pattern vectors can be exploited for an improvement in the estimation performance of a Multi-Layer Perceptron (MLP) NN [2]. In this paper, the effect of the modification to the distribution characteristic of the acoustic input pattern vectors will be investigated. The theoretical background for the effect of the input distribution characteristics on neural learning has been detailed elsewhere [3]. Empirical results for a more correct estimation of articulatory speech synthesizerparameters through exploitingthe behavior of the Back Propagation (BP) algorithm are focused on here. 1