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Both obesity and being underweight have been associated with increased mortality. Underweight, defined as a body mass index (BMI) ≥18.5 m 2 in adults and ≤-2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive, feeding and eating disorder and/or anorexia nervosa. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported. We previously showed that hemizygosity of a ∼4600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance. {\textcopyright} 2011 Macmillan Publishers Limited. All rights reserved.

In urban environments, roadside vegetation provides important ecosystem services. Reliable and up-to-date information on urban vegetation is therefore needed as a basis for sustainable urban design and regular tasks such as vegetation maintenance. Mobile laser scanning (MLS), i. e., the use of vehicle-mounted laser scanners, offers strong potential for capturing 3D point clouds of road environments on a large scale at a low cost. In this paper, the potential and challenges of using MLS for vegetation mapping are discussed. To lay a foundation for MLS-based inventories of roadside vegetation, a concept for the automatic detection and analysis of vegetation in MLS point clouds using deep learning is presented. The proposed workflow covers vegetation detection and classification, delineation of individual trees, and estimation of tree attributes. In a case study, an initial implementation of the workflow is tested using MLS datasets from two German cities and the results are evaluated through visual inspection. It is demonstrated that the proposed deep-learning approach is able to detect and classify vegetation in MLS point clouds of complex urban road scenes. When delineating individual trees, accurate results are obtained for solitary trees and trees with little canopy overlap, while the delineation of trees with strongly overlapping canopies needs further improvement in some cases. The results indicate that geometric tree attributes such as tree height and trunk diameter can be accurately estimated from MLS point clouds if the accuracy of the preceding processing steps is sufficiently high.