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Constitutional thinness (CT) is characterized by a low and stable body mass index (BMI) without any hormonal abnormality. To understand the weight steadiness, energetic metabolism was evaluated. Seven CT, seven controls, and six anorexia nervosa (AN) young women were compared. CT and AN had a BMI <16.5 kg/m(2). Four criteria were evaluated: 1) energy balance including diet record, resting metabolic rate (RMR) (indirect calorimetry), total energy expenditure (TEE) (doubly labeled water), physical activity; 2) body composition (dual-energy X-ray absorptiometry); 3) biological markers (leptin, IGF-I, free T3); 4) psychological profile of eating behavior. The normality of free T3 (3.7 +/- 0.5 pmol/l), IGF-I (225 +/- 93 ng/ml), and leptin (8.3 +/- 3.4 ng/ml) confirmed the absence of undernutrition in CT. Their psychological profiles revealed a weight gain desire. TEE (kJ/day) in CT (8,382 +/- 988) was not found significantly different from that of controls (8,793 +/- 845) and AN (8,001 +/- 2,152). CT food intake (7,565 +/- 908 kJ/day) was found similar to that of controls (7,961 +/- 1,452 kJ/day) and higher than in AN (4,894 +/- 703 kJ/day), thus explaining the energy metabolism balance. Fat-free mass (FFM) (kg) was similar in CT and AN (32.5 +/- 2.9 vs. 34.1 +/- 1.9) and higher in controls (37.8 +/- 1.6). While RMR absolute values (kJ/day) were lower in CT (4,839 +/- 473) than in controls (5,576 +/- 209), RMR values adjusted for FFM were the highest in CT. TEE-to-FFM ratio was also higher in CT than in controls. Energetic metabolism balance maintains a stable low weight in CT. An increased energy expenditure-to-FFM ratio differentiates CT from controls and could account for the resistance to weight gain observed in CT.

In product line engineering, domain analysis is the process of analyzing related products to identify their common and variable features. This process is generally carried out by experts on the basis of existing product descriptions, which are expressed in a more or less structured way. Modeling and reasoning about product descriptions are error-prone and time consuming tasks. Feature models (FMs) constitute popular means to specify product commonalities and variabilities in a compact way, and to provide automated support to the domain analysis process. This paper aims at easing the transition from product descriptions expressed in a tabular format to FMs accurately representing them. This process is parameterized through a dedicated language and high-level directives (e.g., products/features scoping). We guarantee that the resulting FM represents the set of legal feature combinations supported by the considered products and has a readable tree hierarchy together with variability information. We report on our experiments based on public data and characterize the properties of the derived FMs.