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The availability of allergen molecules ('components') from several protein families has advanced our understanding of immunoglobulin E (IgE)-mediated responses and enabled 'component-resolved diagnosis' (CRD). The European Academy of Allergy and Clinical Immunology (EAACI) Molecular Allergology User's Guide (MAUG) provides comprehensive information on important allergens and describes the diagnostic options using CRD. Part A of the EAACI MAUG introduces allergen molecules, families, composition of extracts, databases, and diagnostic IgE, skin, and basophil tests. Singleplex and multiplex IgE assays with components improve both sensitivity for low-abundance allergens and analytical specificity; IgE to individual allergens can yield information on clinical risks and distinguish cross-reactivity from true primary sensitization. Part B discusses the clinical and molecular aspects of IgE-mediated allergies to foods (including nuts, seeds, legumes, fruits, vegetables, cereal grains, milk, egg, meat, fish, and shellfish), inhalants (pollen, mold spores, mites, and animal dander), and Hymenoptera venom. Diagnostic algorithms and short case histories provide useful information for the clinical workup of allergic individuals targeted for CRD. Part C covers protein families containing ubiquitous, highly cross-reactive panallergens from plant (lipid transfer proteins, polcalcins, PR-10, profilins) and animal sources (lipocalins, parvalbumins, serum albumins, tropomyosins) and explains their diagnostic and clinical utility. Part D lists 100 important allergen molecules. In conclusion, IgE-mediated reactions and allergic diseases, including allergic rhinoconjunctivitis, asthma, food reactions, and insect sting reactions, are discussed from a novel molecular perspective. The EAACI MAUG documents the rapid progression of molecular allergology from basic research to its integration into clinical practice, a quantum leap in the management of allergic patients.

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This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -driven by the exposure of the loan and the loss given default -and the operational income given by the loan. Additionally, one of the major advantages of * NOTICE: this is the author's version of a work that was accepted for publication in the European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Please cite this paper as follows: Verbraken, T., Bravo, C., Weber, R. and Baesens, B. (2014) Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research. In Press. Available Online: http://www.sciencedirect.com/ science/article/pii/S0377221714003105 † Email Addresses: thomas.verbraken@kuleuven.be, crbravo@utalca.cl (corresponding), rweber@dii.uchile.cl, bart.baesens@kuleuven.be 1 using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment.