Predicting variety composite means without diallel crossing

Prediction of variety composite means was shown to be feasible without diallel crossing the parental varieties. Thus, the predicted mean for a quantitative trait of a composite is given by: Yk = a1 sigmaVj + a2sigmaTj + a3 - a4, with coefficients a1 = (n - 2k)/k²(n - 2); a2 = 2n(k - 1)/k²(n - 2); a3 = n(k - 1)/k(n - 1)(n - 2); and a4 = n²(k - 1)/k(n - 1)(n - 2); summation is for j = 1 to k, where k is the size of the composite (number of parental varieties of a particular composite) and n is the total number of parent varieties. Vj is the mean of varieties and Tj is the mean of topcrosses (pool of varieties as tester), and and are the respective average values in the whole set. Yield data from a 7 x 7 variety diallel cross were used for the variety means and for the "simulated" topcross means to illustrate the proposed procedure. The proposed prediction procedure was as effective as the prediction based on Yk = - ( -)/k, where and refer to the mean of hybrids (F1) and parental varieties, respectively, in a variety diallel cross. It was also shown in the analysis of variance that the total sum of squares due to treatments (varieties and topcrosses) can be orthogonally partitioned following the reduced model Yjj’ = mu + ½(v j + v j’) + + h j+ h j’, thus making possible an F test for varieties, average heterosis and variety heterosis. Least square estimates of these effects are also given

The control of deliberate waiting strategies in a stop-signal task

To inhibit an ongoing flow of thoughts or actions has been largely considered to be a crucial executive function, and the stop-signal paradigm makes inhibitory control measurable. Stop-signal tasks usually combine two concurrent tasks, i.e., manual responses to a primary task (go-task) are occasionally countermanded by a stimulus which signals participants to inhibit their response in that trial (stop-task). Participants are always instructed not to wait for the stop-signal, since waiting strategies cause the response times to be unstable, invalidating the data. The aim of the present study was to experimentally control the strategies of waiting deliberately for the stop-signal in a stop-task by means of an algorithm that measured the variation in the reaction times to go-stimuli on-line, and displayed a warning legend urging participants to be faster when their reaction times were more than two standard deviations of the mean. Thirty-four university students performed a stop-task with go- and stop-stimuli, both of which were delivered in the visual modality and were lateralized within the visual field. The participants were divided into two groups (group A, without the algorithm, vs group B, with the algorithm). Group B exhibited lower variability of reaction times to go-stimuli, whereas no significant between-group differences were found in any of the measures of inhibitory control, showing that the algorithm succeeded in controlling the deliberate waiting strategies. Differences between deliberate and unintentional waiting strategies, and anxiety as a probable factor responsible for individual differences in deliberate waiting behavior, are discussed.

Differential gene expression profiles of hepatocellular carcinomas associated or not with viral infection

Chronic hepatitis B (HBV) and C (HCV) virus infections are the most important factors associated with hepatocellular carcinoma (HCC), but tumor prognosis remains poor due to the lack of diagnostic biomarkers. In order to identify novel diagnostic markers and therapeutic targets, the gene expression profile associated with viral and non-viral HCC was assessed in 9 tumor samples by oligo-microarrays. The differentially expressed genes were examined using a z-score and KEGG pathway for the search of ontological biological processes. We selected a non-redundant set of 15 genes with the lowest P value for clustering samples into three groups using the non-supervised algorithm k-means. Fisher’s linear discriminant analysis was then applied in an exhaustive search of trios of genes that could be used to build classifiers for class distinction. Different transcriptional levels of genes were identified in HCC of different etiologies and from different HCC samples. When comparing HBV-HCC vs HCV-HCC, HBV-HCC/HCV-HCC vs non-viral (NV)-HCC, HBC-HCC vs NV-HCC, and HCV-HCC vs NV-HCC of the 58 non-redundant differentially expressed genes, only 6 genes (IKBKβ, CREBBP, WNT10B, PRDX6, ITGAV, and IFNAR1) were found to be associated with hepatic carcinogenesis. By combining trios, classifiers could be generated, which correctly classified 100% of the samples. This expression profiling may provide a useful tool for research into the pathophysiology of HCC. A detailed understanding of how these distinct genes are involved in molecular pathways is of fundamental importance to the development of effective HCC chemoprevention and treatment.

Isolation and recovery of glycomacropeptide from milk whey by means of thermal treatment

During enzymatic process of cheese manufacturing, rennin cleaves κ-casein releasing two fractions: para-κ-casein and glycomacropeptide (GMP), which remains soluble in milk whey. GMP is a peptide with structural particularities such as chain carbohydrates linked to specific threonine residues, to which a great variety of biological activities is attributed. Worldwide cheese production has increased generating high volumes of milk whey that could be efficiently used as an alternative source of high quality peptide or protein in foodstuff formulations. In order to evaluate isolation and recovery on whey GMP by means of thermal treatment (90 °C), 18 samples (2 L each) of sweet whey, resuspended commercial whey (positive control) and acid whey (negative control) were processed. Indirect presence of GMP was verified using chemical tests and PAGE-SDS 15%. At 90 °C treated sweet whey, 14, 20 and 41 kDa bands were observed. These bands may correspond to olygomers of GMP. Peptide recovery showed an average of 1.5 g/L (34.08%). The results indicate that industrial scale GMP production is feasible; however, further research must be carried out for the biological and nutritional evaluation of GMP's incorporation to foodstuff as a supplement.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.