A novel framework for parameter selection of the Autocorrelation Change detection method using 250m MODIS time-series data in the Gauteng province of South Africa

Human settlement expansion is one of the most prominent types of land cover change in South Africa. These changes typically occur in areas that are covered by natural vegetation. Methods that can rapidly indicate areas having a high probability of change are very valuable to analysts as this can be used to direct their attention to high probability change areas for further evaluation. MODIS time-series data (8-daily composite) at a resolution of 500 m has been proven to be an effective data source for detecting human settlements in South Africa and it was proposed in Kleynhans et al., 2012 that a Temporal Autocorrelation Change detection method (TACD) be used to detect the formation of new settlements in the Gauteng province of South Africa. In this paper, the TACD that was proposed by Kleynhans et al., 2012 is adapted to be usable with variable sampled temporal resolutions for 250m MODIS data by using a novel framework for parameter selection. The proposed method is applied to variably sampled 250m MODIS time-series data ranging from daily to semi-annually and a comparison of change detection accuracy vs. false alarm rate is done in each instance. Key results indicate that there is little difference in performance between daily sampled and 2-monthly sampled 250m MODIS time-series data for the use case evaluated in this paper

A novel framework for parameter selection of the autocorrelation change detection method using 250m MODIS time-series data in the Gauteng province of South Africa

Human settlement expansion is one of the most prominent types of land cover change in South Africa. These changes typically occur in areas that are covered by natural vegetation. Methods that can rapidly indicate areas having a high probability of change are very valuable to analysts as this can be used to direct their attention to high probability change areas for further evaluation. MODIS time-series data (8-daily composite) at a resolution of 500 m has been proven to be an effective data source for detecting human settlements in South Africa and it was proposed in Kleynhans et al., 2012 that a Temporal Autocorrelation Change detection method (TACD) be used to detect the formation of new settlements in the Gauteng province of South Africa. In this paper, the TACD that was proposed by Kleynhans et al., 2012 is adapted to be usable with variable sampled temporal resolutions for 250m MODIS data by using a novel framework for parameter selection. The proposed method is applied to variably sampled 250m MODIS time-series data ranging from daily to semi-annually and a comparison of change detection accuracy vs. false alarm rate is done in each instance. Key results indicate that there is little difference in performance between daily sampled and 2-monthly sampled 250m MODIS time-series data for the use case evaluated in this paper.http://www.sajg.org.za/index.php/sajgam2018Electrical, Electronic and Computer Engineerin

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Applying model parameters as a driving force to a deterministic nonlinear system to detect land cover change

In this paper, we propose a new method for extracting features from time-series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time-dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a Moderate Resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system, which appears predictable for the time series undergoing no change, while erratic for the time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large-amplitude pendulum, where the displacement angle is observed over time. If there has been no change in the land cover, the mean driving force will approximate zero, and hence the pendulum will behave as if in free motion under the influence of gravity only. If, however, there has been a change in the land cover, this will for a brief initial period introduce a nonzero mean driving force, which does work on the pendulum, changing its energy and future evolution, which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show a change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favorably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%).http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hj2017Electrical, Electronic and Computer Engineerin

Supplementary Material for: Characterisation of the Oxygenation Response to Inspired Oxygen Adjustments in Preterm Infants

<b><i>Background:</i></b> Oxygen saturation (SpO₂) targeting in the preterm infant may be improved with a better understanding of the SpO₂ responses to changes in inspired oxygen (FiO₂). <b><i>Objective:</i></b> We investigated the first-order FiO₂-SpO₂ relationship, aiming to quantify the parameters governing that relationship, the influences on these parameters and their variability. <b><i>Methods:</i></b> In recordings of FiO₂ and SpO₂ from preterm infants on continuous positive airway pressure and supplemental oxygen, we identified unique FiO₂ adjustments and mapped the subsequent SpO₂ responses. For responses identified as first-order, the delay, time constant and gain parameters were determined. Clinical and physiological predictors of these parameters were sought in regression analysis, and intra- and inter-subject variability was evaluated. <b><i>Results:</i></b> In 3,788 h of available data from 47 infants at 31 (28-33) post-menstrual weeks [median (interquartile range)], we identified 993 unique FiO₂ adjustments followed by a first-order SpO₂ response. All response parameters differed between FiO₂ increments and decrements, with increments having a shorter delay, longer time constant and higher gain [2.9 (1.7-4.8) vs. 1.3 (0.58-2.6), p < 0.05]. Gain was also higher in less mature infants and in the setting of recent SpO₂ instability, and was diminished with increasing severity of lung dysfunction. Intra-subject variability in all parameters was prominent. <b><i>Conclusions:</i></b> First-order SpO₂ responses show variable gain, influenced by the direction of FiO₂ adjustment and the severity of lung disease, as well as substantial intra-subject parameter variability. These findings should be taken into account in adjustment of FiO₂ for SpO₂ targeting in preterm infants