Is accurate perception of body image associated with appropriate weight-control behavior among adolescents of the Seychelles

BACKGROUND: We examined body image perception and its association with reported weight-control behavior among adolescents in the Seychelles.METHODS: We conducted a school-based survey of 1432 students aging 11-17 years in the Seychelles. Perception of body image was assessed using both a closed-ended question (CEQ) and Stunkard's pictorial silhouettes (SPS). Voluntary attempts to change weight were also assessed.RESULTS: A substantial proportion of the overweight students did not consider themselves as overweight (SPS: 24%, CEQ: 34%), and a substantial proportion of the normal-weight students considered themselves as too thin (SPS: 29%, CEQ: 15%). Logistic regression analysis showed that students with an accurate weight perception were more likely to have appropriate weight-control behavior.CONCLUSIONS: We found that substantial proportions of students had an inaccurate perception of their weight and that weight perception was associated with weight-control behavior. These findings point to forces that can drive the upwards overweight trends.

Body image perception and weight-related behaviour among adolescents of the Seychelles

Background: We examined one's own body image perception and its association with reported weight-related behavior among adolescents of a rapidly developing country in the African region. Methods: We conducted a school-based survey of 1432 students aged 11-17 years in the Seychelles. Weight and height were measured, and thinness, normal weight and overweight were assessed along standard criteria. A self-administered and anonymous questionnaire was administered. Perception of body image was assessed using both a closed-ended question (CEQ) and the Stunkard's pictorial silhouettes (SPS). Finally, a question assessed voluntary attempts to change weight. Results: Overall, 14.1% of the students were thin, 63.9% were normal-weight, and 22.0% were overweight or obese. There was fair agreement between actual weight status and self-perceived body image based on either CEQ or SPS. However, a substantial proportion of the overweight students did not consider themselves as overweight (SPS: 24%, CEQ: 34%) and, inversely, a substantial proportion of the normal-weight students considered themselves as too thin (SPS: 29%, CEQ: 15%). Among the overweight students, an adequate attempt to lose weight was reported more often by boys and girls who perceived themselves as overweight vs. not overweight (72-88% vs. 40-71%, p <0.05 for most comparisons). Among the normal-weight students, an inadequate attempt to gain weight was reported more often by boys and girls who perceived themselves as thin vs. not thin (27-68% vs. 11-19%, p <0.05). Girls had leaner own body ideals than boys. Conclusions: We found that substantial proportions of overweight students did not perceive themselves as overweight and/or did not want to lose weight and, inversely, that many normalweight students perceived themselves as too thin and/or wanted to gain weight: this points to forces that can drive the upwards overweight trends. Appropriate perception of one's weight was associated with adequate weight-control behavior, although not strongly, emphasizing that appropriate weight perception is only one of several factors driving adequate weight-related behavior. These findings emphasize the need to address appropriate perception of one's own weight and adequate weight-related behavior in adolescents for both individual and community weight-related interventions.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.