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

Effect of different exposure times on caries detection and pixel value in a wireless digital system

Objectives: The aim of this study was to assess, using the CDR Wireless®, the effect of different exposure times on caries detection and pixel intensity values. Materials and Methods: Forty teeth were x-rayed using a Schick CDR Wireless sensor at eight different exposure times - 0.06, 0.10, 0.13, 0.16, 0.20, 0.25, 0.30, and 0.32 s. Four observers evaluated the images for presence of carious lesions scoring proximal surfaces of each tooth on a 5-point scale. Scores were compared to histological sections of the teeth. Accuracy was evaluated by means of ROC curve analysis. Radiographs of an aluminum step wedge were obtained using the same eight exposure times. Pixel intensity measurements were obtained, and mean pixel values were statistically analyzed using linear regression. Results: The Az for each exposure time varied from 0.53 to 0.62. Two-way analysis of variance and Tukey test demonstrated that the exposure time of 0.25 s presented the best result and was significantly higher than 0.30 s and 0.35 s. In regard to mean pixel values, two different behaviors were observed, and the exposure time of 0.20 s presented mean pixel values in both phases. Conclusion: The performance of the exposure times from 0.06 s to 0.25 s was satisfactory for proximal caries detection, and 0.25 s is the best as indicated for this finality. Clinical Relevance: Considering that a reduction of exposure time represents a reduction of patient exposure dose, and this reduction cannot neglect image quality, the behavior of any digital system must be carefully evaluated.30566566

Efficacy of a cone beam computed tomography metal artifact reduction algorithm for the detection of peri-implant fenestrations and dehiscences

To determine whether the use of a metal artifact reduction (MAR) algorithm improves the detection of peri-implant fenestrations and dehiscences on cone beam computed tomography scans. Study Design. One hundred titanium fixtures were implanted into bovine ribs after the creation of defects simulating fenestrations and dehiscences. Images were acquired using four different protocols, namely, A2 (MAR on, voxel 0.2 mm), A3 (MAR on, voxel 0.3 mm), B2 (MAR off, voxel 0.2 mm), and B3 (MAR off, voxel 0.3 mm). For all protocols, receiver operating characteristic (ROC) curves were determined. Values for the areas under the ROC curves (Az) were subjected to analysis of variance. Results. Az values were not statistically different among protocols regardless of the defect type (P > .05). Conclusions. The MAR algorithm tested by us did not improve the diagnosis of peri-implant fenestrations and dehiscences with use of either the 0.2 mm or the 0.3 mm voxel sizes.To determine whether the use of a metal artifact reduction (MAR) algorithm improves the detection of peri-implant fenestrations and dehiscences on cone beam computed tomography scans. One hundred titanium fixtures were implanted into bovine ribs after the1215550556sem informaçãosem informaçãoWe are thankful to Neodent for providing the implants and surgical instruments for the experiments in this study. We also thank Dr. Saulo Leonardo Sousa Melo and Jeroen Van Dessel for their assistance with the manuscrip

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