Novel benzofuroxan derivatives against multidrug-resistant Staphylococcus aureus strains: Design using Topliss` decision tree, synthesis and biological assay

The aim of this study was the design of a set of benzofuroxan derivatives as antimicrobial agents exploring the physicochemical properties of the related substituents. Topliss` decision tree approach was applied to select the substituent groups. Hierarchical cluster analysis was also performed to emphasize natural clusters and patterns. The compounds were obtained using two synthetic approaches for reducing the synthetic steps as well as improving the yield. The minimal inhibitory concentration method was employed to evaluate the activity against multidrug-resistant Staphylococcus aureus strains. The most active compound was 4-nitro-3-(trifluoromethyl)[N`-(benzofuroxan-5-yl) methylene] benzhydrazide (MIC range 12.7-11.4 mu g/mL), pointing out that the antimicrobial activity was indeed influenced by the hydrophobic and electron-withdrawing property of the substituent groups 3-CF(3) and 4-NO(2), respectively. (C) 2011 Elsevier Ltd. All rights reserved.

Incorporating demand-side aspects into regional policy: variations in the importance of private investment decision factors across regions

Most regional programs focus on the supply side of regions, emphasizing the attraction conditions offered, such as infrastructure, labor skills, tax incentives, etc. This study analyzes one aspect of the demand side, that is, how investment decisions of private firms are made by asking the question: ""Do corporations decide the same way on investments in different parts of the territory?"" The paper analyzes the investments of 373 large Brazilian firms during 1996-2004. Based on the investment decisions of these firms, the role of sales, cash-flow, external financing, and working capital is investigated through regression analysis. The regional influence is captured by explanatory variables representing regional and firm characteristics, and by interaction dummies between the region and the main investment determinants. The results indicate significant differences across regions in the importance of investment determinants. This information is important for regional development policy, because different mechanisms should be used in different regions to foster private investments.

Decision rules for egg recognition are related to functional roles and chemical cues in the queenless ant Dinoponera quadriceps

The capacity to distinguish colony members from strangers is a key component in social life. In social insects, this extends to the brood and involves discrimination of queen eggs. Chemical substances communicate colony affiliation for both adults and brood; thus, in theory, all colony members should be able to recognize fellow nestmates. In this study, we investigate the ability of Dinoponera quadriceps workers to discriminate nestmate and non-nestmate eggs based on cuticular hydrocarbon composition. We analyzed whether cuticular hydrocarbons present on the eggs provide cues of discrimination. The results show that egg recognition in D. quadriceps is related to both age and the functional role of workers. Brood care workers were able to distinguish nestmate from non-nestmate eggs, while callow and forager workers were unable to do so.

Decision-Making Deficits Linked to Real-life Social Dysfunction in Crack Cocaine-Dependent Individuals

Crack cocaine-dependent individuals (CCDI) present abnormalities in both social adjustment and decision making, but few studies have examined this association. This study investigated cognitive and social performance of 30 subjects (CCDI x controls); CCDI were abstinent for 2 weeks. We used the Social Adjustment Scale (SAS), Wisconsin Card Sorting Test (WCST), and Iowa Gambling Task (IGT). Disadvantageous choices on the IGT were associated with higher levels of social dysfunction in CCDI, suggesting the ecological validity of the IGT. Social dysfunction and decision making may be linked to the same underlying prefrontal dysfunction, but the nature of this association should be further investigated. (Am J Addict 2010;00: 1-9).

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.