Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Surface analysis of titanium dental implants with different topographies

Cylindrical dental implants made of commercially pure titanium were analysed in four different surface finishes: as-machined, Al2O3 blasted with Al2O3 particles, plasma-sprayed with titanium beads and electrolytically coated with hydroxyapatite. Scanning electron microscopy (SEM) with Energy Dispersive X-ray Analysis (EDX) revealed the topography of the surfaces and provided qualitative results of the chemical composition of the different implants. X-ray Photoelectron Spectroscopy (XPS) was used to perform chemical analysis on the surface of the implants while Laser Scanning Confocal Microscopy (LSM) produced topographic maps of the analysed surfaces. Optical Profilometry was used to quantitatively characterise the level of roughness of the surfaces. The implant that was plasma-sprayed and the hydroxyapatite coated implant showed the roughest surface, followed by the implant blasted with alumina and the as-machined implant. Some remnant contamination from the processes of blasting, coating and cleaning was detected by XPS

Coronary CT angiographic predictors of vasodilator induced myocardial ischemia: a comprehensive analysis

Cardiovascular Aspects of Radiolog

Comparative Effectiveness of CT-Derived Atherosclerotic Plaque Metrics for Predicting Myocardial Ischemia

OBJECTIVES This study sought to investigate the performance of various cardiac computed tomography (CT)-derived atherosclerotic plaque metrics for predicting provocable myocardial ischemia.BACKGROUND The association of coronary arterial diameter stenosis with myocardial ischemia is only modest, but cardiac CT provides several other, readily available atherosclerosis metrics, which may have incremental value.METHODS The study analyzed 873 nonstented coronary arteries and their myocardial perfusion territories in 356 patients (mean 62 years of age) enrolled in the CORE320 (Coronary Artery Evaluation using 320-row Multidetector Computed Tomography Angiography and Myocardial Perfusion) study. Myocardial perfusion defects in static CT perfusion imaging were graded at rest and after adenosine in 13 myocardial segments using a 4-point scale. The summed difference score was calculated by subtracting the summed rest score from the summed stress score. Reversible ischemia was defined as summed difference score >= 1. In a sensitivity analysis, results were also provided using single-photon emission computed tomography (SPECT) as the reference standard. Vessel based predictor variables included maximum percent diameter stenosis, lesion length, coronary calcium score, maximum cross-sectional calcium arc, percent atheroma volume (PAV), low-attenuation atheroma volume, positive (external) vascular remodeling, and subjective impression of "vulnerable plaque." The study used logistic regression models to assess the association of plaque metrics with myocardial ischemia.RESULTS In univariate analysis, all plaque metrics were associated with reversible ischemia. In the adjusted logistic model, only maximum percent diameter stenosis (1.26; 95% confidence interval: 1.15 to 1.38) remained an independent predictor. With SPECT as outcome variable, PAV and "vulnerable" plaque remained predictive after adjustment. In vessels with intermediate stenosis (40% to 70%), no single metric had clinically meaningful incremental value.CONCLUSIONS Various plaque metrics obtained by cardiac CT predict provocable myocardial ischemia by CT perfusion imaging through their association with maximum percent stenosis, while none had significant incremental value. With SPECT as reference standard, PAV and "vulnerable plaque" remained predictors of ischemia after adjustment but the predictive value added to stenosis assessment alone was small. (C) 2019 by the American College of Cardiology Foundation.Cardiovascular Aspects of Radiolog

Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities

Cardiovascular Aspects of Radiolog