Higher charges and regularized quantum trace identities in su(1,1) Landau-Lifshitz model

We solve the operator ordering problem for the quantum continuous integrable su(1,1) Landau-Lifshitz model, and give a prescription to obtain the quantum trace identities, and the spectrum for the higher-order local charges. We also show that this method, based on operator regularization and renormalization, which guarantees quantum integrability, as well as the construction of self-adjoint extensions, can be used as an alternative to the discretization procedure, and unlike the latter, is based only on integrable representations. (C) 2010 American Institute of Physics. [doi:10.1063/1.3509374]

Stable isotopes of bulk organic matter to trace carbon and nitrogen dynamics in an estuarine ecosystem in Babitonga Bay (Santa Catarina, Brazil)

The biogeochemical processes affecting the transport and cycling of terrestrial organic carbon in coastal and transition areas are still not fully understood One means of distinguishing between the sources of organic materials contributing to particulate organic matter (POM) in Babitonga Bay waters and sediments is by the direct measurement of delta(13)C of dissolved inorganic carbon (DIC) and delta(13)C and delta(15)N in the organic constituents. An isotopic survey was taken from samples collected in the Bay in late spring of 2004. The results indicate that the delta(13)C and delta(15)N compositions of OM varied from -21.7 parts per thousand to -26 2 parts per thousand. and from + 9 2 parts per thousand. to -0 1 parts per thousand, respectively. delta(13)C from DIC ranges from +0.04 parts per thousand to -12.7 parts per thousand The difference in the isotope compositions enables the determination of three distinct end-members terrestrial, marine and urban Moreover, the evaluation of source contribution to the particulate organic matter (POM) in the Bay, enables assessment of the anthropogenic impact. Comparing the depleted values of delta(13)C(DIC) and delta(13)C(POC) it is possible to further understand the carbon dynamic within Babitonga Bay (C) 2010 Elsevier BV All rights reserved

Variation in the Distribution of Trace Elements in Renal Cell Carcinoma

The development of cancer is a complex, multistage process during which a normal cell undergoes genetic changes that result in phenotypic alterations and in the acquisition of the ability to invade other sites. Inductively coupled plasma optical emission spectroscopy was used to estimate the contents of Al, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, P, Pb, and Zn in healthy kidney and renal cell carcinoma (RCC), and significant differences were found for all elements. Along with the progression of the malignant disease, a progressive decrease of Cd and K was observed. In fact, for Cd, the concentration in stage T4 was 263.9 times lower than in stage T1, and for K, the concentration in stage T4 was 1.73 times lower than in stage T1. Progressive accumulation was detected for P, Pb, and Zn in stage T4. For P, the concentration in stage T4 was 11.1 times higher than in stage T1; for Pb, the concentration in stage T4 was 232.7 times higher than in T1; and for Zn, the concentration in T4 was 8.452 times higher than in T1. This study highlights the marked differences in the concentrations of selected trace metals in different malignant tumor stages. These findings indicate that some trace metals may play important roles in the pathogenesis of RCC.

Collimated scanning LS-INAA for testing trace elements homogeneity in Brazilian coffee beans

The degree of homogeneity is normally assessed by the variability of the results of independent analyses of several (e.g., 15) normal-scale replicates. Large sample instrumental neutron activation analysis (LS-INAA) with a collimated Ge detector allows inspecting the degree of homogeneity of the initial batch material, using a kilogram-size sample. The test is based on the spatial distributions of induced radioactivity. Such test was applied to samples of Brazilian whole (green) coffee beans (Coffea arabica and Coffea canephora) of approximately I kg in the frame of development of a coffee reference material. Results indicated that the material do not contain significant element composition inhomogeneities between batches of approximately 30-50 g, masses typically forming the starting base of a reference material.

Compton suppression instrumental neutron activation analysis performance in determining trace- and minor-element contents in foodstuff

In 2003-2004, several food items were purchased from large commercial outlets in Coimbra, Portugal. Such items included meats (chicken, pork, beef), eggs, rice, beans and vegetables (tomato, carrot, potato, cabbage, broccoli, lettuce). Elemental analysis was carried out through INAA at the Technological and Nuclear Institute (ITN, Portugal), the Nuclear Energy Centre for Agriculture (CENA, Brazil), and the Nuclear Engineering Teaching Lab of the University of Texas at Austin (NETL, USA). At the latter two, INAA was also associated to Compton suppression. It can be concluded that by applying Compton suppression (1) the detection limits for arsenic, copper and potassium improved; (2) the counting-statistics error for molybdenum diminished; and (3) the long-lived zinc had its 1115-keV photopeak better defined. In general, the improvement sought by introducing Compton suppression in foodstuff analysis was not significant. Lettuce, cabbage and chicken (liver, stomach, heart) are the richest diets in terms of human nutrients.

Investigating Giant (Gt) Repression in the Formation of Partially Overlapping Pair-Rule Stripes

Drosophila pair-rule genes are expressed in striped patterns with a precise order of overlap between stripes of different genes. We investigated the role of Giant (Gt) in the regulation of even-skipped, hairy, runt, and fushi tarazu stripes formed in the vicinity of Gt expression domains. In gt null embryos, specific stripes of eve, h, run, and ftz are disrupted. With an ectopic expression system, we verified that stripes affected in the mutant are also repressed. Simultaneously hybridizing gt misxpressing embryos with two pair-rule gene probes, we were able to distinguish differences in the repression of pairs of stripes that overlap extensively. Together, our results showed Gt repression roles in the regulation of two groups of partially overlapping stripes and that Gt morphogen activity is part of the mechanism responsible for the differential positioning of these stripes borders. We discuss the possibility that other factors regulate Gt stripe targets as well. Developmental Dynamics 239:2989-2999, 2010. (C) 2010 Wiley-Liss, Inc.

An alternative to the Pythagorean rule? Reevaluating Problem 1 of cuneiform tablet BM 34 568

The first problem of the Seleucid mathematical cuneiform tablet BM 34 568 calculates the diagonal of a rectangle from its sides without resorting to the Pythagorean rule. For this reason, it has been a source of discussion among specialists ever since its first publication. but so far no consensus in relation to its mathematical meaning has been attained. This paper presents two new interpretations of the scribe`s procedure. based on the assumption that he was able to reduce the problem to a standard Mesopotamian question about reciprocal numbers. These new interpretations are then linked to interpretations of the Old Babylonian tablet Plimpton 322 and to the presence of Pythagorean triples in the contexts of Old Babylonian and Hellenistic mathematics. (C) 2007 Elsevier Inc. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Trace elements removal from water using modified activated carbon

This paper present the possible alternative options for the remove of trace elements from drinking water supplies in the trace. Arsenic and chromium are two of the most toxic pollutants, introduced into natural waters from a variety of sources and causing various adverse effects on living bodies. The performance of three filter bed methods was evaluated in the laboratory. Experiments were conducted to investigate the sorption of arsenic and chromium on carbon steel and removal of trace elements from drinking water with a household filtration process. The affinity of the arsenic and chromium species for Fe / Fe3C (iron / iron carbide) sites is the key factor controlling the removal of the elements. The method is based on the use of powdered block carbon, powder carbon steel and ceramic spheres in the ion-sorption columns as a cleaning process. The modified powdered block carbon is a satisfactory and economical sorbent for trace elements (arsenite and chromate) dissolved in water due to its low unit cost of about $23 and compatibility with the traditional household filtration system.

Discriminant analysis of trace elements in normal, benign and malignant breast tissues measured by total reflection X-ray fluorescence

In this work total reflection X-ray fluorescence spectrometry has been employed to determine trace element concentrations in different human breast tissues (normal, normal adjacent, benign and malignant). A multivariate discriminant analysis of observed levels was performed in order to build a predictive model and perform tissue-type classifications. A total of 83 breast tissue samples were studied. Results showed the presence of Ca, Ti, Fe, Cu and Zn in all analyzed samples. All trace elements, except Ti, were found in higher concentrations in both malignant and benign tissues, when compared to normal tissues and normal adjacent tissues. In addition, the concentration of Fe was higher in malignant tissues than in benign neoplastic tissues. An opposite behavior was observed for Ca, Cu and Zn. Results have shown that discriminant analysis was able to successfully identify differences between trace element distributions from normal and malignant tissues with an overall accuracy of 80% and 65% for independent and paired breast samples respectively, and of 87% for benign and malignant tissues. (C) 2009 Elsevier B.V. All rights reserved.

Evaluation of serum trace element, biochemical and hematological data of a healthy elderly group residing in So Paulo city, Brazil

In this study, blood serum trace elements, biochemical and hematological parameters were obtained to assess the health status of an elderly population residing in So Paulo city, SP, Brazil. Results obtained showed that more than 93% of the studied individuals presented most of the serum trace element concentrations and of the hematological and biochemical data within the reference values used in clinical laboratories. However, the percentage of elderly presenting recommended low density lipoprotein (LDL) cholesterol concentrations was low (70%). The study indicated positive correlation between the concentrations of Zn and LDL-cholesterol (p < 0.06).

Determination of trace elements in scalp hair of an elderly population by neutron activation analysis

Neutron activation analysis was applied to assess trace elements concentrations in head hair from healthy elderly people living in the Sao Paulo metropolitan area. Concentrations of As, Br, Ca, Cl, Co, Cr, Cu, Fe, K, La, Mn, Na, Sb, Se and, Zn were determined. Comparisons were made between the results obtained for dyed and non-dyed hair as well as for hair from females and males of two different age groups. The results were also compared with range values established by clinical laboratories and published data.

Determination of trace elements in human brain tissues using neutron activation analysis

Neutron activation analysis was applied to assess trace element concentrations in brain tissues from normal (n = 21) and demented individuals (n = 21) of both genders aged more than 50 years. Concentrations of the elements Br, Fe, K, Na, Rb, Se and Zn were determined. Comparisons were made between the results obtained for the hippocampus and frontal cortex tissues, as well as, those obtained in brains of normal and demented individuals. Certified reference materials, NIST 1566b Oyster Tissue and NIST 1577b Bovine Liver were analyzed for quality of the analytical results.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

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.