Genetic advances uncover mechanisms of chemotherapy-induced peripheral neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a common dose-limiting toxicity experienced in 30-40% of patients undergoing treatment with various chemotherapeutics, including taxanes, vinca alkaloids, epothilones, proteasome inhibitors, and thalidomide. Importantly, CIPN significantly affects a patient's quality of life. Recent genetic association studies are enhancing our understanding of CIPN pathophysiology and serve as a foundation for identification of genetic biomarkers to predict toxicity risk and for the development of novel strategies for prevention and treatment.

Automatic identification of epileptic electroencephalography signals using higher-order spectra

Epilepsy is a pathological condition characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behaviour of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on electroencephalography (EEG) recordings. The use of non-linear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal), and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model classifier and a support vector machine classifier. Results show that the classifiers were able to achieve 93.11 per cent and 92.67 per cent classification accuracy respectively, with selected HOS-based features. About 2 h of EEG recordings from ten patients were used in this study.

Enhanced biological phosphorus removal in the retrofitting from an anoxic selector to an anaerobic selector in a full-scale activated sludge process in Singapore

This paper presents the investigation results of retrofitting an anoxic selector to an anaerobic selector through stepwise reduction of air supply in a full-scale activated sludge process with a focus on enhanced biological phosphorus removal (EBPR). The process experienced gradual shift from a Ludzack-Ettinger (LE) to an anaerobic-anoxic-oxic (A(2)O) process and subsequently, an anaerobic-oxic (A/O) process. The major findings are: (i) the average influent-based PO(4) (3-)-P release in the anaerobic selector compartment was 16.3 mg P l(-1) and that in the secondary clarifier was 1.7 mg P l(-1). 75% of the SCOD and 93% of the acetic acid in the primary effluent were taken up in the anaerobic selector compartment, respectively; (ii) PO(4) (3-)-P uptake contributed by both aerobic and denitrifying phosphorus accumulating organisms (DPAOs) occurred mainly in the first and second aerobic lanes together with simultaneous nitrification and denitrification (SND) while there was not much contribution from the last aerobic lane; (iii) The average PO(4) (3-)-P concentration of the final effluent was 2.4 mg P l(-1) corresponding to a removal efficiency of 85%; (iv) the SVI was satisfactory after retrofitting; and (v) the increase of NH(4) (+)-N in the final effluent from the commencement to the completion of the retrofitting resulted in an approximate 40-50% reduction in oxygen demand and a significant aeration energy saving was achieved.

Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry

Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study

Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into these phenomena. The use of non-linear features motivated by the higher order spectra (HOS) had been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, the features are extracted from the power spectrum and the bispectrum. Their performance is studied by feeding them to a Gaussian mixture model (GMM) classifier. Results show that with selected HOS based features, we were able to achieve 93.11% compared to classification accuracy of 88.78% as that of features derived from PSD.