Terri L. Cravener

Bitter taste phenotype and body weight predict children's selection of sweet and savory foods at a palatable test-meal☆

Previous studies show that children who are sensitive to the bitter taste of 6-n-propylthiouracil (PROP) report more frequent intake of sweets and less frequent intake of meats (savory fats) relative to children who are PROP insensitive. Laboratory studies are needed to confirm these findings. In this study, seventy-nine 4- to 6-year-olds from diverse ethnicities attended four laboratory sessions, the last of which included a palatable buffet consisting of savory-fats (e.g. pizza), sweet-fats (e.g. cookies, cakes), and sweets (e.g. juices, candies). PROP phenotype was classified by two methods: 1) a common screening procedure to divide children into tasters and nontasters, and 2) a three-concentration method used to approximate PROP thresholds. Height and weight were measured and saliva was collected for genotyping TAS2R38, a bitter taste receptor related to the PROP phenotype. Data were analyzed by General Linear Model ANOVA with intake from savory fats, sweet-fats, and sweets as dependent variables and PROP status as the independent variable. BMI z-score, sex, age, and ethnicity were included as covariates. Adjusted energy intake from the food group sweets at the test-meal was greater for tasters than for nontasters. PROP status did not influence childrens adjusted intake of savory-fats, but BMI z-score did. The TAS2R38 genotype did not impact intake at the test-meal. At a palatable buffet, PROP taster children preferentially consumed more sweets than nontaster children, while heavier children consumed more savory fats. These findings may have implications for understanding differences in susceptibility to hyperphagia.

Kalman filter and an example of its use to detect changes in poultry production responses

Abstract The Kalman filter, a recursive algorithm for making short-term predictions, was written in BASIC and applied to feed consumption data of a commercial laying hen flock. The results showed that trends such as steady state, slope changes and transient responses could be monitored daily. Early detection of a change in response as differentiated from normal data variation allows the manager to make appropriate management adjustments for the flock.

Prediction of amino acid profiles in feed ingredients : Genetic algorithm calibration of artificial neural networks

Linear regression (LR) has been used to predict the amino acid (AA) profiles of feed ingredients, given proximate analysis (PA) input. Artificial neural networks (ANN) have also been trained to predict AA levels, generally with better results. Past projects have indicated that ANN more effectively identified the complex relationship between nutrients and feed ingredients than did LR. It was shown that the maximum R2 value, a measurement of the amount of variability explained by the model, was highest when a general regression neural network (GRNN) with iterative calibration (GRNNIT) was used to train the ANN. This was in comparison to LR, Ward backpropagation (WBP) or 3-layer backpropagation (3BP) architectures. The current study investigated the potential of a new, advanced method of calibration using the genetic algorithm (GA) to optimize GRNN smoothing values. Calibration of an ANN allows the neural network to generalize well and therefore provide good results on new data. A GRNN architecture (NeuroShell 2® Software) with GA calibration (GRNNGA) was used to train an ANN to predict AA levels in maize, soya bean meal (SBM), meat and bone meal, fish meal and wheat, based on proximate analysis input. Within the GRNNGA architecture, ANN were trained with either an Euclidean or City Block distance metric and a (0,1), (−1,1), (logistic) or (tanh) input scale. Predictive performance was judged on the basis of the maximum R2 value. In general, maximum R2 values were higher when the GA calibration was used in comparison to LR. For example, the highest methionine (MET) R2 value for SBM was 0.54 (LR), 0.81 (3BP), 0.87 (WBP), 0.92 (GRNNIT) and 0.98 (GRNNGA). Genetic algorithm calibration of GRNN architecture led to further improvements in ANN performance for AA level predictions in most of the cases studied. Exceptions were the TSAA level in SBM (0.94 with GRNNIT vs. 0.90 with GRNNGA) and the TRY level in maize (0.88 with GRNNIT vs. 0.61 with GRNNGA).

Feeding Strategies Derived from Behavioral Economics and Psychology Can Increase Vegetable Intake in Children as Part of a Home-Based Intervention: Results of a Pilot Study

BACKGROUNDnBehavioral economics and psychology have been applied to altering food choice, but most studies have not measured food intake under free-living conditions.nnnOBJECTIVESnTo test the effects of a strategy that pairs positive stimuli (ie, stickers and cartoon packaging) with vegetables and presents them as the default snack.nnnDESIGNnA randomized controlled trial was conducted with children who reported consumption of fewer than two servings of vegetables daily. Children (aged 3 to 5 years) in both control (n=12) and treatment (n=12) groups received a weeks supply of plainly packaged (ie, generic) vegetables, presented by parents as a free choice with an alternative snack (granola bar), during baseline (Week 1) and follow-up (Week 4). During Weeks 2 and 3, the control group continued to receive generic packages of vegetables presented as a free choice, but the treatment group received vegetables packaged in containers with favorite cartoon characters and stickers inside, presented by parents as the default choice. Children in the treatment group were allowed to opt out of the vegetables and request the granola bar after an imposed 5-minute wait.nnnSTATISTICAL ANALYSISnGeneral Linear Model repeated measures analysis of variance was conducted to compare vegetable and granola bar intake between control and treatment groups across the 4-week study. Both within- and between-subjects models were tested.nnnRESULTSnA time×treatment interaction on vegetable intake was significant. The treatment group increased vegetable intake from baseline to Week 2 relative to control (P<0.01), but the effects were not sustained at Week 4 when the treatment was removed. Granola bar intake decreased in the treatment group at Week 2 (P≤0.001) and Week 3 (P≤0.005) relative to baseline.nnnCONCLUSIONSnParents were able to administer feeding practices derived from behavioral economics and psychology in the home to increase childrens vegetable intake and decrease intake of a high-energy-density snack. Additional studies are needed to test the long-term sustainability of these practices.

Stochastic true digestible amino acid values

Abstract The concept of using amino acid digestibility coefficients has been reported to be valuable in formulating diets to meet the requirements of livestock. Digestibility coefficients of nutrients reflect the amounts of components of a feedstuff that become available to meet livestock requirements. Most biological factors, including feed ingredient amino acid means and their digestibility values, have variabilities associated with them. Chance constrained (stochastic) programming for feed formulation has been suggested as a method that accounts for variability. The ingredient matrix for stochastic formulation requires means and standard deviations (S.D.s) for all nutrients involved in the formulation. Several references were used to provide ingredient amino acid means and S.D.s, and true digestibility coefficient means and S.D.s. When there is variability associated with two or more variables in a calculation (e.g. nutrient mean and S.D., coefficient of true digestibility mean and S.D.), it is a nonlinear problem involving the application of the principles of probability theory for a solution. It is a mathematical oversimplification to multiply an amino acid S.D. by the coefficient of true digestibility for the calculation of the digestibility S.D. This technique would result in an under-estimation of the S.D. associated with the digestibility of the amino acid. A Microsoft Excel

Egg yolk and serum antibody titers of broiler breeder hens immunized with uricase and or urease

This study evaluated whether broiler breeder hens immunized with uricase (UC), urease (UE), or UC + UE would develop antibody (IgY) titers against these enzymes to prevent manure-N degradation and NH(3) release. Ross × Arbor Acres hens were assigned to PBS (control), UC, UE, or UC + UE injection treatments. Each group had 19 hens per treatment. On d 0, each of the enzymes or PBS was emulsified with complete Freunds adjuvant and administered intramuscularly, whereas on d 7 and 14, a booster injection of PBS or enzymes was administered as an incomplete adjuvant. Blood samples were taken on d 0, 4, 9, 12, 17, 21, and 24 for serum-specific IgY titer analysis. Eggs were collected for yolk-specific IgY titer analysis. Manure samples were taken for nutrient, pH, and NH(3) measurements. Elevated egg yolk anti-UC-IgY titers were observed from UC-immunized hens after the second immunization (P ≤ 0.0001), and they remained higher than those of the PBS- or UE-immunized hens from d 9 to 24. After the first injection, egg yolk anti-UE-IgY titers from hens immunized with UE or the combined antigen were greater than those of birds injected with PBS or UC (P ≤ 0.01). The serum anti-UC-IgY response to UC immunization was observed after the first injection (P ≤ 0.01) and on d 9 (P ≤ 0.0001), and titers remained greater than those of hens immunized with PBS or UE until d 28. The serum anti-UE-IgY titers remained low until much later compared with the anti-UC-IgY titers. Only at 24 and 28 d were anti-UE-IgY titers significantly greater in the UE-immunized hens than in hens immunized with PBS or UC. Hens immunized with UC or UE responded with both egg yolk and serum IgY titers. The combined antigens were significantly greater than the PBS control but had less effect than the individual UC or UE in both the egg yolk and serum. These findings indicate that despite measurable egg yolk and serum IgY titers, immunizing hens with UC, UE, or the combined antigens did not affect the manure nutrients or NH(3) emissions of the treated hens.