Evoked Emotions Predict Food Choice

In the current study we show that non-verbal food-evoked emotion scores significantly improve food choice prediction over merely liking scores. Previous research has shown that liking measures correlate with choice. However, liking is no strong predictor for food choice in real life environments. Therefore, the focus within recent studies shifted towards using emotion-profiling methods that successfully can discriminate between products that are equally liked. However, it is unclear how well scores from emotion-profiling methods predict actual food choice and/or consumption. To test this, we proposed to decompose emotion scores into valence and arousal scores using Principal Component Analysis (PCA) and apply Multinomial Logit Models (MLM) to estimate food choice using liking, valence, and arousal as possible predictors. For this analysis, we used an existing data set comprised of liking and food-evoked emotions scores from 123 participants, who rated 7 unlabeled breakfast drinks. Liking scores were measured using a 100-mm visual analogue scale, while food-evoked emotions were measured using 2 existing emotion-profiling methods: a verbal and a non-verbal method (EsSense Profile and PrEmo, respectively). After 7 days, participants were asked to choose 1 breakfast drink from the experiment to consume during breakfast in a simulated restaurant environment. Cross validation showed that we were able to correctly predict individualized food choice (1 out of 7 products) for over 50% of the participants. This number increased to nearly 80% when looking at the top 2 candidates. Model comparisons showed that evoked emotions better predict food choice than perceived liking alone. However, the strongest predictive strength was achieved by the combination of evoked emotions and liking. Furthermore we showed that non-verbal food-evoked emotion scores more accurately predict food choice than verbal food-evoked emotions scores.     

Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.     

A common point mutation in the tyrosine hydroxylase gene in autosomal recessive L-DOPA-responsive dystonia in the Dutch population

This report concerns one new mutation in the tyrosine hydroxylase (TH) gene in three patients originating from three unrelated Dutch families with autosomal recessive L-DOPA-responsive dystonia (DRD). In this study, all exons of the TH gene were amplified by the polymerase chain reaction and subjected to analyses by single-strand conformation polymorphism. An aberrant migration pattern was observed for exon 6 of the TH gene in all patients. Direct sequencing of the coding region of exon 6 revealed the presence of one novel missense mutation. An a698g transition resulted in the substitution of the evolutionary conserved arginine 233 by a histidine (R233H). All patients were homozygous for the mutation. This new mutation in the TH gene was confirmed by restriction enzyme analysis with the restriction enzyme HhaI. Thus, a high proportion of defective TH alleles may be R233H in The Netherlands. Received: 25 July 1997 / Accepted: 10 February 1998     

Arrangement on the Chromosome of the Known Pre-Early Genes of Bacteriophages T5 and BF23

Genetic crosses between mutants defective in the known pre-early genes of T5 and BF23 and the detection of putative N-terminal fragments have allowed the determination of the order of genes along the initially transferred 8% section of DNA of these phages.     

Aqueous peroxyl radical exposure to THP-1 cells causes glutathione loss followed by protein oxidation and cell death without increased caspase-3 activity

Protein oxidation within cells exposed to oxidative free radicals has been reported to occur in an uninhibited manner with both hydroxyl and peroxyl radicals. In contrast, THP-1 cells exposed to peroxyl radicals (ROO(*)) generated by thermo decomposition of the azo compound AAPH showed a distinct lag phase of at least 6 h, during which time no protein oxidation or cell death was observed. Glutathione appears to be the source of the lag phase as cellular levels were observed to rapidly decrease during this period. Removal of glutathione with buthionine sulfoxamine eliminated the lag phase. At the end of the lag phase there was a rapid loss of cellular MTT reducing activity and the appearance of large numbers of propidium iodide/annexin-V staining necrotic cells with only 10% of the cells appearing apoptotic (annexin-V staining only). Cytochrome c was released into the cytoplasm after 12 h of incubation but no increase in caspase-3 activity was found at any time points. We propose that the rapid loss of glutathione caused by the AAPH peroxyl radicals resulted in the loss of caspase activity and the initiation of protein oxidation. The lack of caspase-3 activity appears to have caused the cells to undergo necrosis in response to protein oxidation and other cellular damage.     

Probable interaction between S100A7 and E-FABP in the cytosol of human keratinocytes from psoriatic scales

The overexpression of E-FABP and S100A7 in lesional psoriatic skin suggests a possible link with this hyperproliferative skin disease. In order to investigate a role for the proteins in this disease, the purifications for both proteins were re-analyzed. Moreover, a specific antiserum directed against purified human S100A7 was generated. By SDS-PAGE immunoblotting we show that E-FABP and S100A7 are expressed in cultured human differentiating keratinocytes and confirm their overexpression in psoriatic scales. Gel filtration and non-denaturing PAGE revealed that S100A7 co-purified with E-FABP, indicating an association between the two proteins. Ion-exchange chromatography resulted in the dissociation of the complex. Finally, immunoprecipitations using antiserum against E-FABP revealed that S100A7 co-immunoprecipitated with E-FABP from protein extracts of psoriatic scales. These data indicate that E-FABP and S100A7 might form a complex in the cytosol of human keratinocytes.