Spatiotemporal assessment of deforestation and forest degradation indicates spillover effects from mining activities and related biodiversity offsets in Madagascar

Mining has severe environmental and social impacts. To compensate for the environmental damage caused at mining sites, mining companies are required to engage in biodiversity offsetting activities elsewhere. In forest landscapes, most offsetting policies focus on compensating for biodiversity loss from deforestation, while forest degradation is largely ignored – even though it contributes substantially to biodiversity loss. One reason for this is that forest degradation is challenging to assess and monitor. This study focuses on a large nickel and cobalt mine in Madagascar. By analysing remote sensing time series, we assess detailed annual forest change dynamics and distinguish different types of forest disturbance within and around the mining lease area and the two main associated biodiversity offset areas between 2006 and 2020. Our results show that deforestation rates within the two biodiversity offset areas are low (18 ha, or 0.4%; 164 ha, or 2.4%), suggesting that conservation measures are effective. However, this is not the case when looking at forest degradation. We found that substantial shares of forest within the two biodiversity offset areas are affected by degradation (545 ha, or 11.4%; 662 ha, or 9.7%). In the surrounding unprotected landscape, the rates of deforestation (451 ha, or 6.7%; 553 ha, or 4.9%) and forest degradation (2360 ha, or 34.8%; 5794 ha, or 51.1%) are much higher. The spatiotemporal pattern indicates spillover effects for both deforestation and forest degradation. Taken together, our findings show that restrictions on local communities’ access to forest resources within biodiversity offset areas affect the surrounding landscape and can cause substantial additional adverse environmental impacts there. We also demonstrate that forest degradation monitoring is feasible, and that forest degradation is widespread even though it is still largely ignored. These findings should be considered in future biodiversity offsetting policies and best practices

Cortisol levels and history of depression in acute coronary syndrome patients

Background Depressed mood following an acute coronary syndrome (ACS) is a risk factor for future cardiac morbidity. Hypothalamic-pituitary-adrenal (HPA) axis dysregulation is associated with depression, and may be a process through which depressive symptoms influence later cardiac health. Additionally, a history of depression predicts depressive symptoms in the weeks following ACS. The purpose of this study was to determine whether a history of depression and/or current depression are associated with the HPA axis dysregulation following ACS. Method A total of 152 cardiac patients completed a structured diagnostic interview, a standardized depression questionnaire and a cortisol profile over the day, 3 weeks after an ACS. Cortisol was analysed using: the cortisol awakening response (CAR), total cortisol output estimated using the area under the curve method, and the slope of cortisol decline over the day. Results Total cortisol output was positively associated with history of depression, after adjustment for age, gender, marital status, ethnicity, smoking status, body mass index (BMI), Global Registry of Acute Cardiac Events (GRACE) risk score, days in hospital, medication with statins and antiplatelet compounds, and current depression score. Men with clinically diagnosed depression after ACS showed a blunted CAR, but the CAR was not related to a history of depression. Conclusions Patients with a history of depression showed increased total cortisol output, but this is unlikely to be responsible for associations between depression after ACS and later cardiac morbidity. However, the blunted CAR in patients with severe depression following ACS indicates that HPA dysregulation is presen

Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

BACKGROUND To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI

Agricultural commercialization in borderlands: Capturing the transformation of a tropical forest frontier through participatory mapping

Forest-frontier landscapes in the humid tropics display distinct land use change dynamics compared to other world regions, providing useful examples of current global environmental and development challenges. In northwestern Laos, part of the former Golden Triangle region, investments in value chains for commercial crops—mainly to fulfill Chinese market demands—have triggered various land use changes and put increasing pressure on remaining biodiverse forest areas. Capturing the existing land use change trajectories is a key initial step toward further studies assessing land use change impacts. However, methodological challenges arise when conducting spatially-explicit change assessments in these regions, given the high temporal variability of land use at the plot level, compounded by the paucity of good quality satellite imagery. Thus, we applied a novel approach combining analysis of very high-resolution (VHR) satellite imagery with participatory mapping. This enabled joint collection of annual land use information for the last 17 years together with local land users, shedding light on temporally dense land system dynamics. For decades, the government of Laos has sought to halt shifting cultivation, labeling it environmentally degrading, and to reduce poverty through promotion of permanent commodity-oriented commercial agriculture. Among other things, this gave rise to a boom in banana and rubber investments in Luang Namtha province in order to satisfy growing Chinese demand for these commodities. The present paper investigates the impact of these cash crop booms on land use transitions and whether they reduced pressure on forest-frontier areas, as ostensibly desired by government authorities. Our study is among the first to demonstrate in a spatially-explicit manner that subsistence agriculture—in less than two decades—has virtually disappeared in northern Laos due to diverse cash-crop production and agricultural commercialization initiatives linked to Chinese investments. As subsistence-focused cultivation systems are being replaced by land uses solely aimed at commercial production for export, a telecoupled land system is being developed in northwestern Laos with potentially manifold impacts for sustainable development

Clinical and hemodynamic determinants of left ventricular dimensions

This study was designed to quantitate the influence of 20 clinical, hemodynamic, and volume determinants of left ventricular (LV) structure. Systemic hemodynamics, intravascular volume, and LV echocardiographic measurements were collected in a heterogeneous population of 171 patients. Stepwise multiple-regression analysis indicated that body weight and body-surface area were the most powerful determinants of LV chamber size, wall thickness, and muscle mass. Age, a pressure independent determinant of myocardial mass, had no influence on chamber size or LV function. Arterial pressure correlated best with the relative wall thickness and chamber volume. Intravascular volume was a major discriminator for chamber volume, LV mass, and velocity of circumferential fiber shortening. It is concluded that body weight, arterial pressure, intravascular volume, and age are each independent determinants of the LV dimension. Systolic pressure most closely correlated with relative wall thickness and thereby is the best predictor of degree of concentric LV hypertrophy

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated "sufficient image quality" and 117 images rated "insufficient image quality". The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists

Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

Objective: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). Methods: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. Results: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. Conclusions: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection

Remote sensing combined with social-ecological data: The importance of diverse land uses for ecosystem service provision in north-eastern Madagascar

Through ongoing deforestation in the tropics, forest-related ecosystem services are declining, while ecosystem services provided by agricultural land uses are on the increase. Land system science provides a framework for analysing the links between land use change and the resulting socio-environmental trade-offs. However, the evidence base to support the navigation of such trade-offs is often lacking, as information on land use cannot directly be obtained through remote sensing and census data is often unavailable at sufficient spatial resolution. The global biodiversity hotspot of north-eastern Madagascar exemplifies these challenges. Combining land use data obtained through remote sensing with social-ecological data from a regional level household survey, we attempt to make the links between land use and ecosystem service benefits explicit. Our study confirmed that remotely sensed information on landscapes reflects households’ involvement in rice production systems. We further characterized landscapes in terms of “ecosystem service bundles” linked to specific land uses, as well as in terms of ecosystem service benefits to households. The map of landscape types could help direct future conservation and development efforts towards places where there is potential for success

Low-dose CT from myocardial perfusion SPECT/CT allows the detection of anemia in preoperative patients

BACKGROUND To assess whether low-dose CT for attenuation correction of myocardial perfusion single-photon emission computed tomography (SPECT) allows for identification of anemic patients and grading anemia severity. METHODS AND RESULTS Patients who underwent a preoperative blood-test and low-dose CT scan, as a part of a cardiac SPECT exam, between 01 January 2015 and 31 December 2017 were enrolled in this retrospective study. Hemoglobin (Hb) levels and hematocrit were derived from clinical records. CT images were visually assessed (qualitative analysis) for the detection of inter-ventricular septum sign (IVSS) and aortic rim sign (ARS) and quantitative analysis were performed. The diagnostic accuracy for detecting anemia was compared using Hb values as the standard of reference. A total of 229 patients were included (110 with anemia; 57 mild; 46 moderate; 7 severe). The AUC of IVSS and ARS were 0.830 and 0.669, respectively (p<0.0001). The quantitative analysis outperformed ARS and IVSS; (AUC of 0.893, p=0.29). The optimal anemia cut-off using Youden index was 4.5 HU. CONCLUSION Quantitative analysis derived from low-dose CT images, as a part of cardiac SPECT exams, have a diagnostic accuracy similar to that of hematocrit for the detection of anemia and may allow discriminating different anemia severities