DETERMINATION OF ENERGY VALUE OF HAMBURGERS AND MEATBALLS BY DIRECT CALORIMETRY AND FOOD ANALYSIS COMPOSITION. COMPARISON WITH NUTRITION INFORMATION DISPLAYED ON PACKAGES

The nutritional composition found in the laboratory and those present on labels of manufactured foods can differ significantly. The purpose of this study was to determine the nutritional composition of hamburgers and meatballs and compare them with your labels. The food analysis was performed following the Analytical Standards Institute`s Adolfo Lutz and energy content was determined by bomb calorimetry. Regarding the energy value, all the samples had values less than informed on the label. The content of lipids of hamburgers and meatballs ( except the beef) were lower than those reported on the label. The values of protein for the meatballs and chicken hamburger had lower values than those labels. Thus, the labels may overestimate as underestimate some nutritional values, leading to population erroneous information.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

Wide Dispersal and Possible Multiple Origins of Low-Copy-Number Plasmids in Rickettsia Species Associated with Blood-Feeding Arthropods

Plasmids are mobile genetic elements of bacteria that can impart important adaptive traits, such as increased virulence or antibiotic resistance. We report the existence of plasmids in Rickettsia (Rickettsiales; Rickettsiaceae) species, including Rickettsia akari, ""Candidatus Rickettsia amblyommii,"" R. bellii, R. rhipicephali, and REIS, the rickettsial endosymbiont of Ixodes scapularis. All of the rickettsiae were isolated from humans or North and South American ticks. R. parkeri isolates from both continents did not possess plasmids. We have now demonstrated plasmids in nearly all Rickettsia species that we have surveyed from three continents, which represent three of the four major proposed phylogenetic groups associated with blood-feeding arthropods. Gel-based evidence consistent with the existence of multiple plasmids in some species was confirmed by cloning plasmids with very different sequences from each of two ""Ca. Rickettsia amblyommii"" isolates. Phylogenetic analysis of rickettsial ParA plasmid partitioning proteins indicated multiple parA gene origins and plasmid incompatibility groups, consistent with possible multiple plasmid origins. Phylogenetic analysis of potentially host-adaptive rickettsial small heat shock proteins showed that hsp2 genes were plasmid specific and that hsp1 genes, found only on plasmids of ""Ca. Rickettsia amblyommii,"" R. felis, R. monacensis, and R. peacockii, were probably acquired independently of the hsp2 genes. Plasmid copy numbers in seven Rickettsia species ranged from 2.4 to 9.2 per chromosomal equivalent, as determined by real-time quantitative PCR. Plasmids may be of significance in rickettsial evolution and epidemiology by conferring genetic plasticity and host-adaptive traits via horizontal gene transfer that counteracts the reductive genome evolution typical of obligate intracellular bacteria.