In this task, you are given an abstract of article. Your task is to generate label "True" if abstract is structured, otherwise generate "False". A structured abstract is composed of a topic sentence (or key sentence), relevant supporting sentences, and a closing (or transition) sentence. This structure is key to keeping your abstract focused on the main idea and creating a clear and concise image.

BACKGROUND Microarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assays. In this study applied to infectious diseases, data from a microarray designed for molecular serotyping of Streptococcus pneumoniae was used, identifying the presence of any one of 91 known pneumococcal serotypes from DNA extracts. This microarray incorporated oligonucleotide probes for all known capsular polysaccharide synthesis genes and required a statistical analysis of the microarray intensity data to determine which serotype, or combination of serotypes, were present within a sample based on the combination of genes detected.   
 RESULTS We propose an empirical Bayesian model for calculating the probabilities of combinations of serotypes from the microarray data. The model takes into consideration the dependencies between serotypes, induced by genes they have in common, and by homologous genes which, although not identical, are similar to each other in sequence. For serotypes which are very similar in capsular gene composition, extra probes are included on the microarray, providing additional information which is integrated into the Bayesian model. For each serotype combination with high probability, a second model, a Bayesian random effects model is applied to determine the relative abundance of each serotype.   
 CONCLUSIONS To assess the accuracy of the proposed analysis we applied our methods to experimental data from samples containing individual serotypes and samples containing combinations of serotypes with known levels of abundance. All but two of the known serotypes of S. pneumoniae that were tested as individual samples could be uniquely determined by the Bayesian model. The model also enabled the presence of combinations of serotypes within samples to be determined. Serotypes with very low abundance within a combination of serotypes can be detected (down to 2% abundance in this study). As well as detecting the presence of serotype combinations, an approximate measure of the percentage abundance of the serotypes within the combination can be obtained.
True

Choline is an essential nutrient and methyl donor required for epigenetic regulation. Here, we assessed the impact of gut microbial choline metabolism on bacterial fitness and host biology by engineering a microbial community that lacks a single choline-utilizing enzyme. Our results indicate that choline-utilizing bacteria compete with the host for this nutrient, significantly impacting plasma and hepatic levels of methyl-donor metabolites and recapitulating biochemical signatures of choline deficiency. Mice harboring high levels of choline-consuming bacteria showed increased susceptibility to metabolic disease in the context of a high-fat diet. Furthermore, bacterially induced reduction of methyl-donor availability influenced global DNA methylation patterns in both adult mice and their offspring and engendered behavioral alterations. Our results reveal an underappreciated effect of bacterial choline metabolism on host metabolism, epigenetics, and behavior. This work suggests that interpersonal differences in microbial metabolism should be considered when determining optimal nutrient intake requirements.
False

BACKGROUND The aim of this study was to examine the clustering of four major lifestyle risk factors (smoking, heavy drinking, lack of fruit and vegetables consumption, and lack of physical activity), and to examine the variation across different socio-demographic groups in the English adult population.   
 METHODS The study population was derived from the 2003 Health Survey for England (n=11,492). Clustering was examined by comparing the observed and expected prevalence of the different possible combinations. A multinomial multilevel regression model was conducted to examine the socio-demographic variation in the clustering of the four risk factors.   
 RESULTS The study found that, when using British health recommendations, a majority of the English population have multiple lifestyle risk factors at the same time. Clustering was found at both ends of the lifestyle spectrum and was more pronounced for women than for men. Overall, multiple risk factors were more prevalent among men, lower social class households, singles, and people who are economically inactive, but less prevalent among home owners and older age groups.   
 CONCLUSIONS The clustering of multiple risk factors provides support for multiple-behavior interventions as opposed to single-behavior interventions.

True

