Variability in Floral Scent in Rewarding and Deceptive Orchids: The Signature of Pollinator-imposed Selection?

Background and Aims A comparative investigation was made of floral scent variation in the closely related, food-rewarding Anacamptis coriophora and the food-deceptive Anacamptis morio in order to identify patterns of variability of odour compounds in the two species and their role in pollinator attraction/avoidance learning. Methods Scent was collected from plants in natural populations and samples were analysed via quantitative gas chromatography and mass spectrometry. Combined gas chromatography and electroantennographic detection was used to identify compounds that are detected by the pollinators. Experimental reduction of scent variability was performed in the field with plots of A. morio plants supplemented with a uniform amount of anisaldehyde. Key Results Both orchid species emitted complex odour bouquets. In A. coriophora the two main benzenoid compounds, hydroquinone dimethyl ether (1,4-dimethoxybenzene) and anisaldehyde (methoxybenzaldehyde), triggered electrophysiological responses in olfactory neurons of honey-bee and bumble-bee workers. The scent of A. morio, however, was too weak to elicit any electrophysiological responses. The overall variation in scent was significantly lower in the rewarding A. coriophora than in the deceptive A. morio, suggesting pollinator avoidance-learning selecting for high variation in the deceptive species. A. morio flowers supplemented with non-variable scent in plot experiments, however, did not show significantly reduced pollination success. Conclusions Whereas in the rewarding A. coriophora stabilizing selection imposed by floral constancy of the pollinators may reduce scent variability, in the deceptive A. morio the emitted scent seems to be too weak to be detected by pollinators and thus its high variability may result from relaxed selection on this floral trai

Variability in Floral Scent in Rewarding and Deceptive Orchids: The Signature of Pollinator-imposed Selection?

Background and Aims A comparative investigation was made of floral scent variation in the closely related, food-rewarding Anacamptis coriophora and the food-deceptive Anacamptis morio in order to identify patterns of variability of odour compounds in the two species and their role in pollinator attraction/avoidance learning. Methods Scent was collected from plants in natural populations and samples were analysed via quantitative gas chromatography and mass spectrometry. Combined gas chromatography and electroantennographic detection was used to identify compounds that are detected by the pollinators. Experimental reduction of scent variability was performed in the field with plots of A. morio plants supplemented with a uniform amount of anisaldehyde. Key Results Both orchid species emitted complex odour bouquets. In A. coriophora the two main benzenoid compounds, hydroquinone dimethyl ether (1,4-dimethoxybenzene) and anisaldehyde (methoxybenzaldehyde), triggered electrophysiological responses in olfactory neurons of honey-bee and bumble-bee workers. The scent of A. morio, however, was too weak to elicit any electrophysiological responses. The overall variation in scent was significantly lower in the rewarding A. coriophora than in the deceptive A. morio, suggesting pollinator avoidance-learning selecting for high variation in the deceptive species. A. morio flowers supplemented with non-variable scent in plot experiments, however, did not show significantly reduced pollination success. Conclusions Whereas in the rewarding A. coriophora stabilizing selection imposed by floral constancy of the pollinators may reduce scent variability, in the deceptive A. morio the emitted scent seems to be too weak to be detected by pollinators and thus its high variability may result from relaxed selection on this floral trai

Exogenous Administration of a Recombinant Variant of TWEAK Impairs Healing after Myocardial Infarction by Aggravation of Inflammation

Background: Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) and its receptor fibroblast growth factorinducible 14 (Fn14) are upregulated after myocardial infarction (MI) in both humans and mice. They modulate inflammation and the extracellular matrix, and could therefore be important for healing and remodeling after MI. However, the function of TWEAK after MI remains poorly defined. Methods and results: Following ligation of the left coronary artery, mice were injected twice per week with a recombinant human serum albumin conjugated variant of TWEAK (HSA-Flag-TWEAK), mimicking the activity of soluble TWEAK. Treatment with HSA-Flag-TWEAK resulted in significantly increased mortality in comparison to the placebo group due to myocardial rupture. Infarct size, extracellular matrix remodeling, and apoptosis rates were not different after MI. However, HSA-Flag-TWEAK treatment increased infiltration of proinflammatory cells into the myocardium. Accordingly, depletion of neutrophils prevented cardiac ruptures without modulating all-cause mortality. Conclusion: Treatment of mice with HSA-Flag-TWEAK induces myocardial healing defects after experimental MI. This is mediated by an exaggerated neutrophil infiltration into the myocardium

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd