Drug-induced agranulocytosis in Southern Chinese population: a twelve-year retrospective study

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Vitamin K intake reduces mortality in people with chronic kidney disease

Vitamin K intake reduces mortality in people with chronic kidney diseasepublished_or_final_versio

Online fault detection and isolation of nonlinear systems

This paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.published_or_final_versio

B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly than that for other recurrent neural networks. Moreover, an adaptive weight updating algorithm for the recurrent network is proposed. It can speed up the training process of the network greatly and its learning speed is more quickly than existing algorithms, e.g., back-propagation algorithm. Examples are presented comparing the adaptive weight updating algorithm and the constant learning rate method, and illustrating its application to modelling of nonlinear dynamic system.published_or_final_versio

The Efficacy and Clinical Safety of Various Analgesic Combinations for Post-Operative Pain after Third Molar Surgery: A Systematic Review and Meta-Analysis.

Objectives To run a systematic review and meta-analysis of randomized clinical trials aiming to answer the clinical question "which analgesic combination and dosage is potentially the most effective and safe for acute post-operative pain control after third molar surgery?". Materials and Methods A systematic search of computer databases and journals was performed. The search and the evaluations of articles were performed by 2 independent reviewers in 3 rounds. Randomized clinical trials related to analgesic combinations for acute post-operative pain control after lower third molar surgery that matched the selection criteria were evaluated to enter in the final review. Results Fourteen studies with 3521 subjects, with 10 groups (17 dosages) of analgesic combinations were included in the final review. The analgesic efficacy were presented by the objective pain measurements including sum of pain intensity at 6 hours (SPID6) and total pain relief at 6 hours (TOTPAR6). The SPID6 scores and TOTPAR6 scores of the reported analgesic combinations were ranged from 1.46 to 6.44 and 3.24 - 10.3, respectively. Ibuprofen 400mg with oxycodone HCL 5mg had superior efficacy (SPID6: 6.44, TOTPAR6: 9.31). Nausea was the most common adverse effect, with prevalence ranging from 0-55%. Ibuprofen 200mg with caffeine 100mg or 200mg had a reasonable analgesic effect with fewer side effects. Conclusion This systematic review and meta-analysis may help clinicians in their choices of prescribing an analgesic combination for acute post-operative pain control after lower third molar surgery. It was found in this systematic review Ibuprofen 400mg combined with oxycodone HCL 5mg has superior analgesic efficacy when compared to the other analgesic combinations included in this study.published_or_final_versio

Fault estimation for a class of nonlinear dynamical systems

In this paper, model based fault estimation for a class of nonlinear dynamical systems is investigated. The state of the system is assumed unavailable, and a nonlinear observer is used to estimate the state. In the observer, neurofuzzy network is used as the approximator to estimate faults. The network is trained on-line and the convergence of the proposed learning algorithm is established. Abrupt fault and incipient fault are analyzed in the paper and they can be estimated accurately using neurofuzzy network with the proposed learning algorithm.published_or_final_versio

Nonlinear observer design with unknown nonlinearity via B-spline network approach

A novel approach is proposed to the state estimation of a class of nonlinear systems which consist of known linear part and unknown nonlinear part. A linear observer is first designed then a nonlinear compensation term in the nonlinear observer is determined using the proposed “deconvolution method”. The B-spline neural network is used to model the estimated compensation term. Three simulation examples are given to compare the effectiveness of the proposed approach and some analytical approaches.published_or_final_versio

Comprehensive characterization of the patient-derived xenograft and the paralleled primary hepatocellular carcinoma cell line

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State estimation with measurement error compensation using neural network

For a system with redundant sensors, the estimated state from the Kalman filter is biased if sensor mounting error existed. To remove this bias, the mounting errors must be compensated first before using the Kalman filter. It is shown that only the projection part of the sensors errors in the measurement space needs to be compensated. If the state of a system is unavailable, a neurofuzzy network can be used to estimate the compensation term. This method is simpler, as it does not require a model for the errors as that proposed in [2]. A sub-optimal Kalman filter with measurement compensation that restrains each row of the Kalman gain matrix to be in the measurement space is also derived. An example is presented to illustrate the performance of the proposed methods.published_or_final_versio

Modelling of nonlinear stochastic dynamical systems using neurofuzzy networks

Though nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in practical application. It is well known that the training of recurrent networks using gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.published_or_final_versio