Establishment of diagnostic criteria for feline nonflea-induced hypersensitivity dermatitis

Hypersensitivity dermatitides (HD) are commonly seen in cats, and they are usually caused by environmental, food and/or flea allergens. Affected cats normally present with one of the following clinical reaction patterns: head and neck excoriations, usually symmetrical self-induced alopecia, eosinophilic skin lesions or miliary dermatitis. Importantly, none of these clinical presentations is considered to be pathognomonic for HD skin diseases, and the diagnosis of HD is usually based on the exclusion of other pruritic diseases and on a positive response to therapy. The objectives of this study were to propose sets of criteria for the diagnosis of nonflea-induced HD (NFHD). We recruited 501 cats with pruritus and skin lesions and compared clinical parameters between cats with NFHD (encompassing those with nonflea, nonfood HD and those with food HD), flea HD and other pruritic conditions. Using simulated annealing techniques, we established two sets of proposed criteria for the following two different clinical situations: (i) the diagnosis of NFHD in a population of pruritic cats; and (ii) the diagnosis of NFHD after exclusion of cats with flea HD. These criteria sets were associated with good sensitivity and specificity and may be useful for homogeneity of enrolment in clinical trials and to evaluate the probability of diagnosis of NFHD in clinical practice. Finally, these criteria were not useful to differentiate cats with NFHD from those with food HD.

Using Context Information to Re-architect a System

Successful software systems cope with complexity by organizing classes into packages. However, a particular organization may be neither straightforward nor obvious for a given developer. As a consequence, classes can be misplaced, leading to duplicated code and ripple effects with minor changes effecting multiple packages. We claim that contextual information is the key to rearchitecture a system. Exploiting contextual information, we propose a technique to detect misplaced classes by analyzing how client packages access the classes of a given provider package. We define locality as a measure of the degree to which classes reused by common clients appear in the same package. We then use locality to guide a simulated annealing algorithm to obtain optimal placements of classes in packages. The result is the identification of classes that are candidates for relocation. We apply the technique to three applications and validate the usefulness of our approach via developer interviews.