Canonical discriminant analysis applied to broiler chicken performance

The mechanisms involved in the control of growth in chickens are too complex to be explained only under univariate analysis because all related traits are biologically correlated. Therefore, we evaluated broiler chicken performance under a multivariate approach, using the canonical discriminant analysis. A total of 1920 chicks from eight treatments, defined as the combination of four broiler chicken strains (Arbor Acres, AgRoss 308, Cobb 500 and RX) from both sexes, were housed in 48 pens. Average feed intake, average live weight, feed conversion and carcass, breast and leg weights were obtained for days 1 to 42. Canonical discriminant analysis was implemented by SAS((R)) CANDISC procedure and differences between treatments were obtained by the F-test (P < 0.05) over the squared Mahalanobis` distances. Multivariate performance from all treatments could be easily visualised because one graph was obtained from two first canonical variables, which explained 96.49% of total variation, using a SAS((R)) CONELIP macro. A clear distinction between sexes was found, where males were better than females. Also between strains, Arbor Acres, AgRoss 308 and Cobb 500 (commercial) were better than RX (experimental), Evaluation of broiler chicken performance was facilitated by the fact that the six original traits were reduced to only two canonical variables. Average live weight and carcass weight (first canonical variable) were the most important traits to discriminate treatments. The contrast between average feed intake and average live weight plus feed conversion (second canonical variable) were used to classify them. We suggest analysing performance data sets using canonical discriminant analysis.

A comparative study of similarity measures for manufacturing cell formation

We introduced a spectral clustering algorithm based on the bipartite graph model for the Manufacturing Cell Formation problem in [Oliveira S, Ribeiro JFF, Seok SC. A spectral clustering algorithm for manufacturing cell formation. Computers and Industrial Engineering. 2007 [submitted for publication]]. It constructs two similarity matrices; one for parts and one for machines. The algorithm executes a spectral clustering algorithm on each separately to find families of parts and cells of machines. The similarity measure in the approach utilized limited information between parts and between machines. This paper reviews several well-known similarity measures which have been used for Group Technology. Computational clustering results are compared by various performance measures. (C) 2008 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Neurodynamics of an election

Variables influencing decision-making in real settings, as in the case of voting decisions, are uncontrollable and in many times even unknown to the experimenter. In this case, the experimenter has to study the intention to decide (vote) as close as possible in time to the moment of the real decision (election day). Here, we investigated the brain activity associated with the voting intention declared 1 week before the election day of the Brazilian Firearms Control Referendum about prohibiting the commerce of firearms. Two alliances arose in the Congress to run the campaigns for YES (for the prohibition of firearm commerce) and NO (against the prohibition of firearm commerce) voting. Time constraints imposed by the necessity of studying a reasonable number (here, 32) of voters during a very short time (5 days) made the EEG the tool of choice for recording the brain activity associated with voting decision. Recent fMRI and EEG studies have shown decision-making as a process due to the enrollment of defined neuronal networks. In this work, a special EEG technique is applied to study the topology of the voting decision-making networks and is compared to the results of standard ERP procedures. The results show that voting decision-making enrolled networks in charge of calculating the benefits and risks of the decision of prohibiting or allowing firearm commerce and that the topology of such networks was vote-(i.e., YES/NO-) sensitive. (C) 2010 Elsevier B.V. All rights reserved.

A Graphical Representation Model for Telemedicine and Telehealth Center Sustainability

This study shows the creation of a graphical representation after the application of a questionnaire to evaluate the indicative factors of a sustainable telemedicine and telehealth center in Sao Paulo, Brazil. We categorized the factors into seven domain areas: institutional, functional, economic-financial, renewal, academic-scientific, partnerships, and social welfare, which were plotted into a graphical representation. The developed graph was shown to be useful when used in the same institution over a long period and complemented with secondary information from publications, archives, and administrative documents to support the numerical indicators. Its use may contribute toward monitoring the factors that define telemedicine and telehealth center sustainability. When systematically applied, it may also be useful for identifying the specific characteristics of the telemedicine and telehealth center, to support its organizational development.

Puzzling challenges in contemporary neuroscience: Insights from complexity and emergence in epileptogenic circuits

The brain is a complex system that, in the normal condition, has emergent properties like those associated with activity-dependent plasticity in learning and memory, and in pathological situations, manifests abnormal long-term phenomena like the epilepsies. Data from our laboratory and from the literature were classified qualitatively as sources of complexity and emergent properties from behavior to electrophysiological, cellular, molecular, and computational levels. We used such models as brainstem-dependent acute audiogenic seizures and forebrain-dependent kindled audiogenic seizures. Additionally we used chemical OF electrical experimental models of temporal lobe epilepsy that induce status epilepticus with behavioral, anatomical, and molecular sequelae such as spontaneous recurrent seizures and long-term plastic changes. Current Computational neuroscience tools will help the interpretation. storage, and sharing of the exponential growth of information derived from those studies. These strategies are considered solutions to deal with the complexity of brain pathologies such as the epilepsies. (C) 2008 Elsevier Inc. All rights reserved.

Reference distribution of the bioelectrical impedance vector in healthy term newborns

Bioelectrical impedance vector analysis (BIVA) is a new method that is used for the routine monitoring of the variation in body fluids and nutritional status with assumptions regarding body composition values. The aim of the present study was to determine bivariate tolerance intervals of the whole-body impedance vector and to describe phase angle (PA) values for healthy term newborns aged 7-28 d. This descriptive cross-sectional study was conducted on healthy term neonates born at a low-risk public maternity. General and anthropometric neonatal data and bioelectrical impedance data (800 mu A-50 kHz) were obtained. Bivariate vector analysis was conducted with the resistance-reactance (RXc) graph method. The BIVA software was used to construct the graphs. The study was conducted on 109 neonates (52.3% females) who were born at term, adequate for gestational age, exclusively breast-fed and aged 13 (SD 3.6) d. We constructed one standard, reference, RXc-score graph and RXc-tolerance ellipses (50, 75 and 95 %) that can be used with any analyser. Mean PA was 3.14 (SD 0.43)degrees (3.12 (SD 0.39)degrees for males and 3.17 (SD 0.48)degrees for females). Considering the overlapping of ellipses of males and females with the general distribution, a graph for newborns aged 7-28 d with the same reference tolerance ellipse was defined for boys and girls. The results differ from those reported in the literature probably, in part, due to the ethnic differences in body composition. BIVA and PA permit an assessment without the need to know body weight and the prediction error of conventional impedance formulas.