Increasing evidence suggests that degradation of biodiversity in human populated areas is a threat for the ecosystem processes that are relevant for human well-being. Fungi are a megadiverse kingdom that plays a key role in ecosystem processes and affects human well-being. How urbanization influences fungi has remained poorly understood, partially due to the methodological difficulties in comprehensively surveying fungi. Here we show that both aerial and soil fungal communities are greatly poorer in urban than in natural areas. Strikingly, a fivefold reduction in fungal DNA abundance took place in both air and soil samples already at 1 km scale when crossing the edge from natural to urban habitats. Furthermore, in the air, fungal diversity decreased with urbanization even more than in the soil. This result is counterintuitive as fungal spores are known to disperse over large distances. A large proportion of the fungi detectable in the air are specialized to natural habitats, whereas soil fungal communities comprise a large proportion of habitat generalists. The sensitivity of the aerial fungal community to anthropogenic disturbance makes this method a reliable and efficient bioindicator of ecosystem health in urban areas.

As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of [Formula: see text] in Dice similarity, specificity, and sensitivity.

Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert's manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

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Considering the variable stability of rugae points during growth, the weighted rugae superimposition method results in more promising registrations on serial models. This method prioritizes registration landmarks based on clinical criteria of choice and is suitable for analysis of other structures such as tooth movements.

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