87 research outputs found

    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

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    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

    Predicting freshwater habitat integrity using land-use surrogates

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    Freshwater biodiversity is globally threatened due to human disturbances, but freshwater ecosystems have been accorded lessprotection than their terrestrial and marine counterparts. Few criteria exist for assessing the habitat integrity of rivers and data used for such assessments are generally of limited geographical coverage. Here, we use a fine-scale dataset describing river integrity in north-western South Africa to explore the extent to which measures of freshwater habitat integrity can be predicted from remotely sensed data, which are readily available in many parts of the world. A spatial statistical model was built using broad land-cover variables to predict the habitat integrity (subdivided into riparian and instream integrity) of rivers.We also explored the importance of the spatial scale. Results showed that riparian and, to a lesser degree, instream habitat integrity of river systems could be predicted with reasonable accuracy. The total area under natural vegetation was the most significant predictor of riparian integrity, which is best predicted by land-use activities at catchment level, rather than more locally. Our GIS-based model thus provides a fine-scale approach to assessing river habitat integrity as a supplement to landscape-level conservation plans for river systems, and represents a significant contribution towards the monitoring componentof the River Health Programme (RHP), which reports on the state of rivers in South Africa

    Leukocyte Count and Coronary Artery Disease Events in People With Human Immunodeficiency Virus: A Longitudinal Study

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    BACKGROUND: People with human immunodeficiency virus (HIV; PWH) have increased cardiovascular risk. Higher leukocyte count has been associated with coronary artery disease (CAD) events in the general population. It is unknown whether the leukocyte-CAD association also applies to PWH. METHODS: In a case-control study nested within the Swiss HIV Cohort Study, we obtained uni- and multivariable odds ratios (OR) for CAD events, based on traditional and HIV-related CAD risk factors, leukocyte count, and confounders previously associated with leukocyte count. RESULTS: We included 536 cases with a first CAD event (2000-2021; median age, 56 years; 87% male; 84% with suppressed HIV RNA) and 1464 event-free controls. Cases had higher latest leukocyte count before CAD event than controls (median [interquartile range], 6495 [5300-7995] vs 5900 [4910-7200]; P 11 000/µL) was uncommon (4.3% vs 2.1%; P = .01). In the highest versus lowest leukocyte quintile at latest time point before CAD event, participants had univariable CAD-OR = 2.27 (95% confidence interval, 1.63-3.15) and multivariable adjusted CAD-OR = 1.59 (1.09-2.30). For comparison, univariable CAD-OR for dyslipidemia, diabetes, and recent abacavir exposure were 1.58 (1.29-1.93), 2.19 (1.59-3.03), and 1.73 (1.37-2.17), respectively. Smoking and, to a lesser degree, alcohol and ethnicity attenuated the leukocyte-CAD association. Leukocytes measured up to 8 years before the event were significantly associated with CAD events. CONCLUSIONS: PWH in Switzerland with higher leukocyte counts have an independently increased risk of CAD events, to a degree similar to traditional and HIV-related risk factors

    Distinct serum biosignatures are associated with different tuberculosis treatment outcomes.

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    Biomarkers for TB treatment response and outcome are needed. This study characterize changes in immune profiles during TB treatment, define biosignatures associated with treatment outcomes, and explore the feasibility of predictive models for relapse. Seventy-two markers were measured by multiplex cytokine array in serum samples from 78 cured, 12 relapsed and 15 failed treatment patients from South Africa before and during therapy for pulmonary TB. Promising biosignatures were evaluated in a second cohort from Uganda/Brazil consisting of 17 relapse and 23 cured patients. Thirty markers changed significantly with different response patterns during TB treatment in cured patients. The serum biosignature distinguished cured from relapse patients and a combination of two clinical (time to positivity in liquid culture and BMI) and four immunological parameters (TNF-?, sIL-6R, IL-12p40 and IP-10) at diagnosis predicted relapse with a 75% sensitivity (95%CI 0.38-1) and 85% specificity (95%CI 0.75-0.93). This biosignature was validated in an independent Uganda/Brazil cohort correctly classifying relapse patients with 83% (95%CI 0.58-1) sensitivity and 61% (95%CI 0.39-0.83) specificity. A characteristic biosignature with value as predictor of TB relapse was identified. The repeatability and robustness of these biomarkers require further validation in well-characterized cohorts
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