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.     

Nine color eleven parameter immunophenotyping using three laser flow cytometry.

BACKGROUND: This study describes a three laser flow cytometer, reagents, and software used to simultaneously evaluate nine distinct fluorescent parameters on one cell sample. We compare the quality of data obtained with (1) full software compensation and (2) the use of partial spectral compensation of selected pairs of parameters in analog hardware, in combination with final software compensation. An application characterizing low frequency murine B cell subpopulations is given. METHODS: The fluorochromes used are: fluorescein (FITC), phycoerythrin (PE), Cy5PE and Cy7PE, excited at 488 nm by an argon laser; Texas Red (TR), allophycocyanin (APC), and Cy7APC excited at 595 nm by a pumped dye laser; and cascade blue (CB) and cascade yellow (CY) excited at 407 nm by a violet-enhanced krypton laser. Custom additions to commercial electronics and an extended optical bench allow the measurement of these nine parameters plus forward and side scatter light signals. RESULTS: We find the use of partial analog compensation reduces the variation in the background staining levels introduced by the compensation process. Novel B cell populations with frequencies below 1% are characterized. CONCLUSIONS: Nine color flow cytometry is capable of providing measurements with high information content. The choice of reagent-dye combinations and the ability to compensate in multi-parameter measurement space are crucial to obtaining satisfactory results.     

From food‐dependent statistics to metabolic parameters,a practical guide to the use of dynamic energy budget theory

The standard model of the dynamic energy budget theory for metabolic organisation has variables and parameters that can be quantified using indirect methods only. We present new methods (and software) to extract food‐independent parameter values of the energy budget from food‐dependent quantities that are easy to observe, and so facilitate the practical application of the theory to enhance predictability and extrapolation. A natural sequence of 10 steps is discussed to obtain some compound parameters first, then the primary parameters, then the composition parameters and finally the thermodynamic parameters; this sequence matches a sequence of required data of increasing complexity which is discussed in detail. Many applications do not require knowledge of all parameters, and we discuss methods to extrapolate parameters from one species to another. The conversion of mass, volume and energy measures of biomass is discussed; these conversions are not trivial because biomass can change in chemical composition in particular ways thanks to different forms of homeostasis. We solve problems like “What would be the ultimate reproduction rate and the von Bertalanffy growth rate at a specific food level, given that we have measured these statistics at abundant food?” and “What would be the maximum incubation time, given the parameters of the von Bertalanffy growth curve?”. We propose a new non‐destructive method for quantifying the chemical potential and entropy of living reserve and structure, that can potentially change our ideas on the thermodynamic properties of life. We illustrate the methods using data on daphnids and molluscs.     

Defining the role of the MHC in autoimmunity: a review and pooled analysis

The major histocompatibility complex (MHC) is one of the most extensively studied regions in the human genome because of the association of variants at this locus with autoimmune, infectious, and inflammatory diseases. However, identification of causal variants within the MHC for the majority of these diseases has remained difficult due to the great variability and extensive linkage disequilibrium (LD) that exists among alleles throughout this locus, coupled with inadequate study design whereby only a limited subset of about 20 from a total of approximately 250 genes have been studied in small cohorts of predominantly European origin. We have performed a review and pooled analysis of the past 30 years of research on the role of the MHC in six genetically complex disease traits – multiple sclerosis (MS), type 1 diabetes (T1D), systemic lupus erythematosus (SLE), ulcerative colitis (UC), Crohn's disease (CD), and rheumatoid arthritis (RA) – in order to consolidate and evaluate the current literature regarding MHC genetics in these common autoimmune and inflammatory diseases. We corroborate established MHC disease associations and identify predisposing variants that previously have not been appreciated. Furthermore, we find a number of interesting commonalities and differences across diseases that implicate both general and disease-specific pathogenetic mechanisms in autoimmunity.     

Histopathological effects of cisplatin,doxorubicin and 5-flurouracil (5-FU) on the liver of male albino rats

Cisplatin, doxorubicin and fluorouracil (5-FU), drugs belonging to different chemical classes, have been extensively used for chemotherapy of various cancers. Despite extensive investigations into their hepatotoxicity, there is very limited information on their effects on the structure and ultra-structure of liver cells in vivo. Here, we demonstrate for the first time, the effects of these three anticancer drugs on rat liver toxicity using both light and electron microscopy. Light microscopic observations revealed that higher doses of cisplatin and doxorubicin caused massive hepatotoxicity compared to 5-FU treatment, including dissolution of hepatic cords, focal inflammation and necrotic tissues. Interestingly, low doses also exhibited abnormal changes, including periportal fibrosis, degeneration of hepatic cords and increased apoptosis. These changes were confirmed at ultrastructural level, including vesiculated rough endoplasmic reticulum and atrophied mitochondria with ill-differentiated cisternae, dense collection of macrophages and lymphocytes as well as fibrocytes with collagenous fibrils manifesting early sign of fibrosis, especially in response to cisplatin and doxorubicin -treatment. Our results provide in vivo evidence, at ultrastructural level, of direct hepatotoxicity caused by cisplatin, doxorubicin and 5-FU at both light and electron microscopi. These results can guide the design of appropriate treatment regimen to reduce the hepatotoxic effects of these anticancer drugs.     

The Ras guanine nucleotide exchange factor RasGRF1 promotes matrix metalloproteinase-3 production in rheumatoid arthritis synovial tissue

Introduction  

Fibroblast-like synoviocytes (FLS) from rheumatoid arthritis (RA) patients share many similarities with transformed cancer cells, including spontaneous production of matrix metalloproteinases (MMPs). Altered or chronic activation of proto-oncogenic Ras family GTPases is thought to contribute to inflammation and joint destruction in RA, and abrogation of Ras family signaling is therapeutic in animal models of RA. Recently, expression and post-translational modification of Ras guanine nucleotide releasing factor 1 (RasGRF1) was found to contribute to spontaneous MMP production in melanoma cancer cells. Here, we examine the potential relationship between RasGRF1 expression and MMP production in RA, reactive arthritis, and inflammatory osteoarthritis synovial tissue and FLS.     

Beta-sheet folding mechanisms from perturbation energetics

Amide backbone and sidechain mutagenesis data can be used in combination with kinetic and thermodynamic measurements to understand the energetic contributions of backbone hydrogen bonding and the hydrophobic effect to the acquisition of beta-sheet structure. For example, it has been revealed that loop 1 of the WW domain forms in the transition state, consistent with the emerging theme that reverse turn formation is rate limiting in beta-sheet folding. A distinct subset of WW domain residues principally influences thermodynamic stability by forming hydrogen bonds and hydrophobic interactions that stabilize the native state. Energetic data and sequence mining reveal that only a small subset of the molecular information contained in sequences or observed in high-resolution structures is required to generate folded functional beta-sheets, consistent with evolutionary robustness.