Mysid crustaceans as standard models for the screening and testing of endocrine-disrupting chemicals

Author Posting. © Springer, 2007. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecotoxicology 16 (2007): 205-219, doi:10.1007/s10646-006-0122-0.Investigative efforts into the potential endocrine-disrupting effects of chemicals have mainly concentrated on vertebrates, with significantly less attention paid to understanding potential endocrine disruption in the invertebrates. Given that invertebrates account for at least 95% of all known animal species and are critical to ecosystem structure and function, it remains essential to close this gap in knowledge and research. The lack of progress regarding endocrine disruption in invertebrates is still largely due to: (1) our ignorance of mode-of-action, physiological control, and hormone structure and function in invertebrates; (2) lack of a standardized invertebrate assay; (3) the irrelevance to most invertebrates of the proposed activity-based biological indicators for endocrine disruptor exposure (androgen, estrogen and thyroid); (4) limited field studies. Past and ongoing research efforts using the standard invertebrate toxicity test model, the mysid shrimp, have aimed at addressing some of these issues. The present review serves as an update to a previous publication on the use of mysid shrimp for the evaluation of endocrine disruptors (Verslycke et al., 2004a). It summarizes recent investigative efforts that have significantly advanced our understanding of invertebrate-specific endocrine toxicity, population modeling, field studies, and transgeneration standard test development using the mysid model.Supported by a Fellowship of the Belgian American Educational Foundation

Feature learning to automatically assess radiographic knee osteoarthritis severity

Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and 3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA

An integrated framework for evaluating the barriers to successful implementation of reverse logistics in the automotive industry

Reverse logistics (RL) strategy can have a positive impact on productivity, and the diminishing resources, along with the strict environmental regulations, have strengthened the need for this strategy. The purpose of this study is to develop an integrated framework for identifying: (1) the critical barriers to the successful implementation of RL in the automotive industry; (2) the importance and implementation priorities of these barriers; and (3) the causal relations among them. The proposed framework is composed of the Delphi method to identify the most relevant barriers, the best-worst method (BWM) to determine their importance, and the weighted influence non-linear gauge system (WINGS) to analyze their causal relationships. The proposed framework is applied to a case study in the automotive industry. The results indicate the economic barriers are the most important, and the knowledge barriers are the least important barriers to the successful implementation of RL in the automotive industry.N/