Estimating food ingredient compositions based on mandatory product labeling

Having a specific understanding of the actual ingredient composition of products helps to calculate additional nutritional information, such as containing fatty and amino acids, minerals and vitamins, as well as to determine its environmental impacts. Unfortunately, producers rarely provide information on how much of each ingredient is in a product. Food manufacturers are, however, required to declare their products in terms of a label comprising an ingredient list (in descending order) and Big7 nutrient values. In this paper, we propose an automated approach for estimating ingredient contents in food products. First, we parse product labels to extract declared ingredients. Next, we exert mathematical formulations on the assumption that the weighted sum of Big7 ingredients as available from food compositional tables should resemble the product’s declared overall Big7 composition. We apply mathematical optimization techniques to find the best fitting ingredient composition estimate. We apply the proposed method to a dataset of 1804 food products spanning 11 product categories. We find that 76% of these products could be analyzed by our approach, and a composition within the prescribed nutrient tolerances could be calculated, using 20% of the allowed tolerances per Big7 ingredient on average. The remaining 24% of the food products could still be estimated when relaxing one or multiple nutrient tolerances. A study with known ingredient compositions shows that estimates are within a 0.9% difference of products’ actual recipes. Hence, the automated approach presented here allows for further analysis of large product quantities and provides possibilities for more intensive nutritional and ecological evaluations of food

Just pain, no gain?: Data management systems and biodiversity data

Scientists often see research data management as a burden. At first glance, it appears as yet another task with lots of effort and no apparent benefits. In fact, proper research data management provides many benefits and is important for biodiversity research projects. It is a key factor for their success and for the long-term impact of the research. Data management systems provide established informatics standards such as online access, versioning, and backup. Other features support scientists to organize, document, and verify their data according to accuracy and validity. Such system offers functionalities for data search, access control and traceability. This allows a collaborative, all-time available and trustable teamwork. Beside it, more and more funding agencies and publishers ask for accessible and reproducible data with a guarantee of long-term availability. In addition, stakeholders and the community request data access. Data management system curated data is ready to be published and cited. We believe that, in order to truly support researchers and ensure that they can reap the benefits of their efforts, data management platforms are needed that deal with the entire data lifecycle. We have developed the data management system “Biodiversity Exploratories Information System”. It acts as a platform to support researchers of the Priority Program “Biodiversity Exploratories” of the German Science Foundation (DFG). It guaranties data curation and enables data interchange and reuse since nearly 10 years. The system is also the foundation of the BEXIS2 data management platform. With our poster, we show the role of a data management system as a service to researchers and projects. We illustrate the benefits of such a system inside the whole life cycle of data seen from researcher and project perspective

The further development and validation of three new self-report measures of transdiagnostic processes in the anxiety disorders

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Towards the definition of an 'eating disorder'

This dissertation is concerned with the diagnosis and classification of clinical eating disorders such as anorexia nervosa and bulimia nervosa. There were four overarching aims. The first was to describe the clinical characteristics of patients with the neglected DSM-FV eating disorder diagnosis "eating disorder NOS" and compare them with those of patients with bulimia nervosa. It was found that the two groups were remarkably similar. The second was to examine how the classificatory problems associated with this diagnosis might be solved. Three solutions were proposed and the clinical utility of two of them examined. It was concluded that the best interim solution was to broaden the diagnostic criteria for anorexia nervosa and bulimia nervosa and re-label the remaining cases of eating disorder NOS as either binge eating disorder or as a new eating disorder diagnosis. The third aim was to derive an operational definition of what constitutes an "eating disorder" This involved developing an interview-based measure of functional impairment secondary to eating disorder features and administering it to a large sample of people exhibiting the full range of eating disorder psychopathology. Multivariate statistics identified specific severity levels on five eating disorder features that were strongly associated with the presence of a clinically significant level of impairment. These eating disorder features were: the pursuit of strict dietary rules, objective bulimic episodes, purging episodes, dissatisfaction with shape and weight, and over-concern with maintaining strict control over eating. The presence of two or more of these features above the identified thresholds was most predictive of a clinically significant level of impairment. Thus, an impairment-based, transdiagnostic, provisional operational definition of an eating disorder was derived. The fourth aim was to develop a clinically useful, easily administered measure of psychosocial impairment secondary to eating disorder features. Such an instrument was created. Studies of its psychometric properties, reliability, validity and sensitivity to change all supported its use. Certain of the research strategies used in this dissertation could be usefully applied to other psychiatric disorders

Towards the definition of an 'eating disorder'

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The strengths of r- and K-selection shape diversity-disturbance relationships.

Disturbance is a key factor shaping species abundance and diversity in plant communities. Here, we use a mechanistic model of vegetation diversity to show that different strengths of r- and K-selection result in different disturbance-diversity relationships. R- and K-selection constrain the range of viable species through the colonization-competition tradeoff, with strong r-selection favoring colonizers and strong K-selection favoring competitors, but the level of disturbance also affects the success of species. This interplay among r- and K-selection and disturbance results in different shapes of disturbance-diversity relationships, with little variation of diversity with no r- and no K-selection, a decrease in diversity with r-selection with disturbance rate, an increase in diversity with K-selection, and a peak at intermediate values with strong r- and K-selection. We conclude that different disturbance-diversity relationships found in observations may reflect different intensities of r- and K-selection within communities, which should be inferable from broader observations of community composition and their ecophysiological trait ranges