In chronic obstructive pulmonary disease (COPD), loss of computed tomography (CT)-measured intercostal mass correlates with spirometric severity. Intercostal muscle ultrasound offers a repeatable and radiation-free alternative, however requires validation. We aimed to determine the reliability of parasternal intercostal muscle ultrasound, and the concurrent validity of parasternal ultrasound with clinicometric parameters. Twenty stable COPD patients underwent ultrasound measurement of thickness and echogenicity of 2^nd and 3^rd parasternal intercostal muscles, dominant pectoralis major and quadriceps, and diaphragm thickness; spirometry; and chest CT. Intra-rater intraclass correlation (ICC) for ultrasound intercostal thickness was 0.87–0.97 depending on site, with echogenicity ICC 0.63–0.91. Inter-rater ICC was fair to excellent. Ultrasound intercostal thickness moderately correlated with FEV₁% predicted (r = 0.33) and quadriceps thickness (r = 0.31). Echogenicity correlated negatively with FEV₁% predicted (r = −0.32). CT-measured lateral intercostal mass correlate negatively with parasternal ultrasound intercostal thickness. These data confirm ultrasound of parasternal intercostal musculature is reproducible. Lower intercostal muscle quantity and quality reflects greater COPD spirometric severity. This novel tool may have biomarker potential for both the systemic effects of COPD on muscle as well as local disruption of respiratory mechanics. The negative correlation between CT and ultrasound measurements may reflect complex site-dependent interactions between respiratory muscles and the chest wall.

Singapore is an intermediate tuberculosis (TB) incidence country, with a recent rise in TB incidence from 2008, after a fall in incidence since 1998. This study identified population characteristics that were associated with the recent increase in TB cases, and built a predictive model of TB risk in Singapore. Retrospective time series analysis was used to study TB notification data collected from 1995 to 2011 from the Singapore Tuberculosis Elimination Program (STEP) registry. A predictive model was developed based on the data collected from 1995 to 2010 and validated using the data collected in 2011. There was a significant difference in demographic characteristics between resident and non-resident TB cases. TB risk was higher in non-residents than in residents throughout the period. We found no significant association between demographic and macro-economic factors and annual incidence of TB with or without adjusting for the population-at-risk. Despite growing non-resident population, there was a significant decrease in the non-resident TB risk (p < 0.0001). However, there was no evidence of trend in the resident TB risk over this time period, though differences between different demographic groups were apparent with ethnic minorities experiencing higher incidence rates. The study found that despite an increasing size of non-resident population, TB risk among non-residents was decreasing at a rate of about 3% per year. There was an apparent seasonality in the TB reporting.

Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures.

In time series analysis, there is an extensive literature on hypothesis tests that attempt to distinguish a stationary time series from a non-stationary one. However, the binary distinction provided by a hypothesis test can be somewhat blunt when trying to determine the degree of non-stationarity of a time series. This article creates an index that estimates a degree of non-stationarity. This index might be used, for example, to classify or discriminate between series. Our index is based on measuring the roughness of a statistic estimated from the time series, which is calculated from the roughness penalty associated with a spline smoothing/penalized least-squares method. We further use a resampling technique to obtain a likely range of index values obtained from a single realization of a time series. We apply our method to ascertain and compare the non-stationarity index of the well-known earthquake and explosion data. © 2016 The Authors. Stat Published by John Wiley & Sons Ltd

This study builds on fundamental knowledge of granular failure dynamics to develop a statistical and machine learning approach for characterization of a landslide. We demonstrate our approach for a rockslide using surface displacement data from a ground based radar monitoring system. The algorithm has three key components: (i) identification of a regime change point $t_0$ marking the departure from statistical invariance of the global velocity field, (ii) characterization of the clustering pattern formed by the velocity time series at $t_0$ , and (iii) classification of velocity patterns for $t>t_0$ to deliver a measure of risk of failure from $t_0$ and estimates of the time of emergent and imminent risk of failure. Unlike the prevailing approach of analysing time series data from one or a few chosen locations, we make full use of data from all monitored points on the slope (here 1803). We do not make a priori assumptions on the monitored domain and base our characterization of the complex spatial patterns and associated dynamics only from the data. Our approach is informed by recent developments in the physics and micromechanics of failure in granular media and is configured to accommodate additional data on landslide triggers and other determinants of landslide risk readily.