In this Issue

Aim: Molluscivorous shorebirds supposedly developed their present wintering distribution after the last ice age. Currently, molluscivorous shorebirds are abundant on almost all shores of the world, except for those in the Indo-West Pacific (IWP). Long before shorebirds arrived on the scene, molluscan prey in the IWP evolved strong anti-predation traits in a prolonged evolutionary arms race with durophagous predators including brachyuran crabs. Here, we investigate whether the absence of molluscivorous shorebirds from a site in Oman can be explained by the molluscan community being too well-defended. Location: The intertidal mudflats of Barr Al Hikman, Oman. Methods: Based on samples from 282 locations across the intertidal area the standing stock of the macrozoobenthic community was investigated. By measuring anti-predation traits (burrowing depth, size and strength of armour), the fraction of molluscs available to molluscivorous shorebirds was calculated. Results: Molluscs dominated the macrozoobenthic community at Barr Al Hikman. However, less than 17% of the total molluscan biomass was available to shorebirds. Most molluscs were unavailable either because of their hard-to-crush shells, or because they lived too deeply in the sediment. Repair scars and direct observations confirmed crab predation on molluscs. Although standing stock densities of the Barr Al Hikman molluscs were of the same order of magnitude as at intertidal mudflat areas where molluscivorous shorebirds are abundant, the molluscan biomass available to shorebirds was distinctly lower at Barr Al Hikman. Main conclusions: The established strong molluscan anti-predation traits against crabs precludes molluscan exploitation by shorebirds at Barr Al Hikman. This study exemplifies that dispersal of "novel" predators is hampered in areas where native predators and prey exhibit strongly developed attack and defence mechanisms, and highlights that evolutionary arms races can have consequences for the global distribution of species

Facilitation of a tropical seagrass by a chemosymbiotic bivalve increases with environmental stress

Facilitation of foundation species is critical to the structure, function and persistence of ecosystems. Understanding the dependence of the strength of this facilitation on environmental conditions is important for informed ecosystem management and for predicting the impacts of global change. In coastal seagrass habitats, chemosymbiotic lucinid bivalves can facilitate seagrasses by decreasing potentially toxic levels of sulphide in sediment porewater. However, variation in the strength of lucinid–seagrass facilitation with environmental context has not been experimentally investigated. We tested the hypothesis that the presence of the tiger lucine Codakia orbicularis becomes more important to the growth and survival of the seagrass Thalassia testudinum under decreased light availability and increased sulphide stress. In a mesocosm experiment, we reduced average ambient-light to T. testudinum by 64% and/or increased sediment porewater sulphide concentrations by ~200% and compared growth and tissue chemistry of T. testudinum with and without C. orbicularis. We found that T. testudinum was better able to maintain growth under shading and sulphide stress when C. orbicularis was present. C. orbicularis strongly decreased sediment porewater sulphide, an effect that minimized sulphur build-up in seagrass tissue and was likely achieved through bioirrigation as well as chemoautotrophy. The relative effects of C. orbicularis on T. testudinum growth were strongest in the presence of environmental stressors. Synthesis. The strength of lucinid–seagrass facilitation increases under environmental conditions that hinder the ability of seagrass to detoxify sulphide. Our results provide evidence of a potential mechanism by which the spatiotemporal association between lucinids and seagrasses is maintained and support the incorporation of interspecific facilitation into conservation and restoration strategies for foundation species in the face of increasing anthropogenic impact and global change

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION: Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.METHODS: A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.RESULTS: Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.CONCLUSION: This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.</p

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION: Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.METHODS: A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.RESULTS: Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.CONCLUSION: This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.</p

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION: Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.METHODS: A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.RESULTS: Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.CONCLUSION: This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.</p