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.

A reference business process for ecological niche modelling

Ecological niche modelling combines species occurrence points with environmental raster layers in order to obtain models for describing the probabilistic distribution of species. The process to generate an ecological niche model is complex. It requires dealing with a large amount of data, use of different software packages for data conversion, for model generation and for different types of processing and analyses, among other functionalities. A software platform that integrates all requirements under a single and seamless interface would be very helpful for users. Furthermore, since biodiversity modelling is constantly evolving, new requirements are constantly being added in terms of functions, algorithms and data formats. This evolution must be accompanied by any software intended to be used in this area. In this scenario, a Service-Oriented Architecture (SOA) is an appropriate choice for designing such systems. According to SOA best practices and methodologies, the design of a reference business process must be performed prior to the architecture definition. The purpose is to understand the complexities of the process (business process in this context refers to the ecological niche modelling problem) and to design an architecture able to offer a comprehensive solution, called a reference architecture, that can be further detailed when implementing specific systems. This paper presents a reference business process for ecological niche modelling, as part of a major work focused on the definition of a reference architecture based on SOA concepts that will be used to evolve the openModeller software package for species modelling. The basic steps that are performed while developing a model are described, highlighting important aspects, based on the knowledge of modelling experts. In order to illustrate the steps defined for the process, an experiment was developed, modelling the distribution of Ouratea spectabilis (Mart.) Engl. (Ochnaceae) using openModeller. As a consequence of the knowledge gained with this work, many desirable improvements on the modelling software packages have been identified and are presented. Also, a discussion on the potential for large-scale experimentation in ecological niche modelling is provided, highlighting opportunities for research. The results obtained are very important for those involved in the development of modelling tools and systems, for requirement analysis and to provide insight on new features and trends for this category of systems. They can also be very helpful for beginners in modelling research, who can use the process and the experiment example as a guide to this complex activity. (c) 2008 Elsevier B.V. All rights reserved.

Family Business: how family and ownership shapes business professionalization

This article`s main purpose consists in showing how family and ownership cultures may influence the process of making a ""well-performing"" organization, based on an empirical study in family business in Brazil. The study aimed to find critical moments of company`s history and the focus was to compare critical moments with the three-dimension model of family business development proposed by Davis et al. (1996). Through facts sequence, research was organized so as to find how the process influenced company`s professionalization. The article concludes that family and its value and culture may impact on the evolution, and the first step to organize a company is to organize the family that leads the company.

Correlation of business cycles in the euro zone

In this short article we use a simple differences-in-clifferences technique to investigate whether bilateral correlation of business cycles increased more amongst members of the European Monetary Union (EMU) after the implementation of the Euro than amidst other OECD economies. We present evidence suggesting this to be the case. (C) 2008 Elsevier B.V. All rights reserved.

GREEN MANAGEMENT, CLIMATE CHANGE AND SMALL BUSINESS IN BRAZIL: IMPLICATIONS FOR TRAINING AND EDUCATION FOR SUSTAINABLE DEVELOPMENT

This paper presents the results of a study on the analysis of training needs regarding environmental (green) management and climate change topics in micro and small enterprises (MSEs) in Brazil and its implications on education for sustainable development. It reports on an e-mail survey of Brazilian small enterprises, whose results indicate that they are indeed interested in environmental management and climate change topics in an education for sustainable development context. The study indicates that proposals for courses on environmental management and climate change should follow a systemic perspective and take sustainable development into account. By applying factor analysis, it was found that the topics of interest can be grouped into thematic modules, which can be useful in the design of training courses for the top management leaders of those companies.

The food business environment and the role of China and Brazil building a ""food bridge""

Purpose - The purpose of this paper is to discuss the economic crisis of 2008/2009 and the major impacts on developing nations and food-producing countries Within this macro-environment of food chains, there is concern that food inflation might come back sooner than expected The role of China as one of the major food consumers in the future, and Brazil, as the major food producer, is described as the food bridge, and an agenda of common development of these countries suggested. Design/methodology/approach - This paper reviews literature on muses of food inflation, production shortages, and investigation of programs to solve the problem in the future, it is also based on author`s personal insights and experience of working on this field in the last 15 years, and recent discussions in forums and interviews Findings - The major factors that jointly caused food prices increase in 2007/2008 were population growth, Income distribution, urbanization, dollar devaluations, commodity funds, social programs, production shortages, and bionic`s A list of ten policies is suggested. horizontal expansion of food production, vertical expansion, reduction in transaction costs, in protectionism and other taxes, investment in logistics, technology and better coordination, contracts, new generation of fertilizers and to use the best sources of biofuels. Originality/value - Two major outputs from this paper are the ""food demand model"" that inserts in one model the trends and muses of food inflation and the solutions, and the ""food bridge concept"" that also aligns in one box the imminent major food chain cooperation between China and Brazil

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.