Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

Detrended fluctuation analysis (DFA) and detrended moving average (DMA) are two scaling analysis methods designed to quantify correlations in noisy non-stationary signals. We systematically study the performance of different variants of the DMA method when applied to artificially generated long-range power-law correlated signals with an {\it a-priori} known scaling exponent $\alpha_{0}$ and compare them with the DFA method. We find that the scaling results obtained from different variants of the DMA method strongly depend on the type of the moving average filter. Further, we investigate the optimal scaling regime where the DFA and DMA methods accurately quantify the scaling exponent $\alpha_{0}$, and how this regime depends on the correlations in the signal. Finally, we develop a three-dimensional representation to determine how the stability of the scaling curves obtained from the DFA and DMA methods depends on the scale of analysis, the order of detrending, and the order of the moving average we use, as well as on the type of correlations in the signal.Comment: 15 pages, 16 figure

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

Measurement of lung volume and ventilation distribution with an ultrasonic flow meter in healthy infants

Small airway disease in infants is characterised by abnormal lung volume and uneven ventilation distribution. An inert tracer gas washin/washout technique using a pulsed ultrasonic flow meter is presented to measure functional residual capacity (FRC) and ventilation distribution in spontaneously breathing and unsedated infants

Identification of an excellent prognosis subset of human papillomavirus-associated oropharyngeal cancer patients by quantification of intratumoral CD103+ immune cell abundance

BACKGROUND: Accurate prognostic stratification of human papillomavirus-associated oropharyngeal cancers (HPV+OPSCC) is required to identify patients potentially suitable for treatment deintensification. We evaluated the prognostic significance of CD103, a surface marker associated with tissue-resident memory T cells (TRMs), in two independent cohorts of patients with HPV+OPSCC. PATIENTS AND METHODS: The abundance and distribution of CD103+ immune cells were quantified using immunohistochemistry in a cohort of 189 HPV+OPSCC patients treated with curative intent and correlated with outcome. Findings were then validated in an independent cohort comprising 177 HPV+OPSCCs using univariable and multivariable analysis. Intratumoral CD103+ immune cells were characterized by multispectral fluorescence immunohistochemistry and gene expression analysis. RESULTS: High intratumoral abundance of CD103+ immune cells using a  ≥30% cut-off was found in 19.8% of tumors in the training cohort of HPV+OPSCC patients and associated with excellent prognosis for overall survival (OS) with adjusted hazard ratio (HR) of 0.13 [95% confidence interval (CI) 0.02-0.94, P = 0.004]. In the independent cohort of HPV+OPSCCs, 20.4% had high intratumoral CD103+ abundance and an adjusted HR for OS of 0.16 (95% CI 0.02-1.22, P = 0.02). Five year OS of patients with high intratumoral CD103 was 100% across both cohorts. The C-statistic for the multivariate prognostic model with stage and age was significantly improved in both cohorts with the addition of intratumoral CD103+ cell abundance. On the basis of spatial location, co-expression of CD8 and CD69, and gene expression profiles, intratumoral CD103+ cells were consistent with TRMs. CONCLUSION: Quantification of intratumoral CD103+ immune cell abundance provides prognostic information beyond that provided by clinical parameters such as TNM-staging, identifying a population of low risk HPV+OPSCC patients who are good candidates for trials of deintensification strategies

Risk of severe asthma episodes predicted from fluctuation analysis of airway function

Asthma is an increasing health problem worldwide, but the long-term temporal pattern of clinical symptoms is not understood and predicting asthma episodes is not generally possible. We analyse the time series of peak expiratory flows, a standard measurement of airway function that has been assessed twice daily in a large asthmatic population during a long-term crossover clinical trial. Here we introduce an approach to predict the risk of worsening airflow obstruction by calculating the conditional probability that, given the current airway condition, a severe obstruction will occur within 30 days. We find that, compared with a placebo, a regular long-acting bronchodilator (salmeterol) that is widely used to improve asthma control decreases the risk of airway obstruction. Unexpectedly, however, a regular short-acting beta2-agonist bronchodilator (albuterol) increases this risk. Furthermore, we find that the time series of peak expiratory flows show long-range correlations that change significantly with disease severity, approaching a random process with increased variability in the most severe cases. Using a nonlinear stochastic model, we show that both the increased variability and the loss of correlations augment the risk of unstable airway function. The characterization of fluctuations in airway function provides a quantitative basis for objective risk prediction of asthma episodes and for evaluating the effectiveness of therapy