Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK RP202G0230Hope Foundation for Cancer Research, UK RM60G0680Medical Research Council Confidence in Concept Award, UK MC_PC_17171MINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR1

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions : A Bayesian Belief Network Application

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development

Quantifying the Spatial Ecology of Wide-Ranging Marine Species in the Gulf of California: Implications for Marine Conservation Planning

There is growing interest in systematic establishment of marine protected area (MPA) networks and representative conservation sites. This movement toward networks of no-take zones requires that reserves are deliberately and adequately spaced for connectivity. Here, we test the network functionality of an ecoregional assessment configuration of marine conservation areas by evaluating the habitat protection and connectivity offered to wide-ranging fauna in the Gulf of California (GOC, Mexico). We first use expert opinion to identify representative species of wide-ranging fauna of the GOC. These include leopard grouper, hammerhead sharks, California brown pelicans and green sea turtles. Analyzing habitat models with both structural and functional connectivity indexes, our results indicate that the configuration includes large proportions of biologically important habitat for the four species considered (25–40%), particularly, the best quality habitats (46–57%). Our results also show that connectivity levels offered by the conservation area design for these four species may be similar to connectivity levels offered by the entire Gulf of California, thus indicating that connectivity offered by the areas may resemble natural connectivity. The selected focal species comprise different life histories among marine or marine-related vertebrates and are associated with those habitats holding the most biodiversity values (i.e. coastal habitats); our results thus suggest that the proposed configuration may function as a network for connectivity and may adequately represent the marine megafauna in the GOC, including the potential connectivity among habitat patches. This work highlights the range of approaches that can be used to quantify habitat protection and connectivity for wide-ranging marine species in marine reserve networks

Risks to Birds Traded for African Traditional Medicine: A Quantitative Assessment

Few regional or continent-wide assessments of bird use for traditional medicine have been attempted anywhere in the world. Africa has the highest known diversity of bird species used for this purpose. This study assesses the vulnerability of 354 bird species used for traditional medicine in 25 African countries, from 205 genera, 70 families, and 25 orders. The orders most represented were Passeriformes (107 species), Falconiformes (45 species), and Coraciiformes (24 species), and the families Accipitridae (37 species), Ardeidae (15 species), and Bucerotidae (12 species). The Barn owl (Tyto alba) was the most widely sold species (seven countries). The similarity of avifaunal orders traded is high (analogous to ‘‘morphospecies’’, and using Sørensen’s index), which suggests opportunities for a common understanding of cultural factors driving demand. The highest similarity was between bird orders sold in markets of Benin vs. Burkina Faso (90%), but even bird orders sold in two geographically separated countries (Benin vs. South Africa and Nigeria vs. South Africa) were 87% and 81% similar, respectively. Rabinowitz’s ‘‘7 forms of rarity’’ model, used to group species according to commonness or rarity, indicated that 24% of traded bird species are very common, locally abundant in several habitats, and occur over a large geographical area, but 10% are rare, occur in low numbers in specific habitats, and over a small geographical area. The order with the highest proportion of rare species was the Musophagiformes. An analysis of species mass (as a proxy for size) indicated that large and/or conspicuous species tend to be targeted by harvesters for the traditional medicine trade. Furthermore, based on cluster analyses for species groups of similar risk, vultures, hornbills, and other large avifauna, such as bustards, are most threatened by selective harvesting and should be prioritised for conservation action.University of the Witwatersrand SPARC Prestigious and URC Postdoctoral Fellowships; National Research Foundatio

Anuran responses to spatial patterns of agricultural landscapes in Argentina

Context: Amphibians are declining worldwide and land use change to agriculture is recognized as a leading cause. Argentina is undergoing an agriculturalization process with rapid changes in landscape structure. Objectives: We evaluated anuran response to landscape composition and configuration in two landscapes of east-central Argentina with different degrees of agriculturalization. We identified sensitive species and evaluated landscape influence on communities and individual species at two spatial scales. Methods: We compared anuran richness, frequency of occurrence, and activity between landscapes using call surveys data from 120 sampling points from 2007 to 2009. We evaluated anuran responses to landscape structure variables estimated within 250 and 500-m radius buffers using canonical correspondence analysis and multimodel inference from a set of candidate models. Results: Anuran richness was lower in the landscape with greater level of agriculturalization with reduced amount of forest cover and stream length. This pattern was driven by the lower occurrence and calling activity of seven out of the sixteen recorded species. Four species responded positively to the amount of forest cover and stream habitat. Three species responded positively to forest cohesion and negatively to rural housing. Two responded negatively to crop area and diversity of cover classes. Conclusions: Anurans within agricultural landscapes of east-central Argentina are responding to landscape structure. Responses varied depending on species and study scale. Life-history traits contribute to responses differences. Our study offers a better understanding of landscape effects on anurans and can be used for land management in other areas experiencing a similar agriculturalization process.Facultad de Ciencias ExactasCentro de Investigaciones del Medioambient