A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey

A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium.

BACKGROUND: Invasive fungal diseases (IFDs) remain important causes of morbidity and mortality. The consensus definitions of the Infectious Diseases Group of the European Organization for Research and Treatment of Cancer and the Mycoses Study Group have been of immense value to researchers who conduct clinical trials of antifungals, assess diagnostic tests, and undertake epidemiologic studies. However, their utility has not extended beyond patients with cancer or recipients of stem cell or solid organ transplants. With newer diagnostic techniques available, it was clear that an update of these definitions was essential. METHODS: To achieve this, 10 working groups looked closely at imaging, laboratory diagnosis, and special populations at risk of IFD. A final version of the manuscript was agreed upon after the groups' findings were presented at a scientific symposium and after a 3-month period for public comment. There were several rounds of discussion before a final version of the manuscript was approved. RESULTS: There is no change in the classifications of "proven," "probable," and "possible" IFD, although the definition of "probable" has been expanded and the scope of the category "possible" has been diminished. The category of proven IFD can apply to any patient, regardless of whether the patient is immunocompromised. The probable and possible categories are proposed for immunocompromised patients only, except for endemic mycoses. CONCLUSIONS: These updated definitions of IFDs should prove applicable in clinical, diagnostic, and epidemiologic research of a broader range of patients at high-risk

Global and regional brain metabolic scaling and its functional consequences

Background: Information processing in the brain requires large amounts of metabolic energy, the spatial distribution of which is highly heterogeneous reflecting complex activity patterns in the mammalian brain. Results: Here, it is found based on empirical data that, despite this heterogeneity, the volume-specific cerebral glucose metabolic rate of many different brain structures scales with brain volume with almost the same exponent around -0.15. The exception is white matter, the metabolism of which seems to scale with a standard specific exponent -1/4. The scaling exponents for the total oxygen and glucose consumptions in the brain in relation to its volume are identical and equal to $0.86\pm 0.03$, which is significantly larger than the exponents 3/4 and 2/3 suggested for whole body basal metabolism on body mass. Conclusions: These findings show explicitly that in mammals (i) volume-specific scaling exponents of the cerebral energy expenditure in different brain parts are approximately constant (except brain stem structures), and (ii) the total cerebral metabolic exponent against brain volume is greater than the much-cited Kleiber's 3/4 exponent. The neurophysiological factors that might account for the regional uniformity of the exponents and for the excessive scaling of the total brain metabolism are discussed, along with the relationship between brain metabolic scaling and computation.Comment: Brain metabolism scales with its mass well above 3/4 exponen

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

Sugary Soda Consumption and Albuminuria: Results from the National Health and Nutrition Examination Survey, 1999–2004

BACKGROUND: End-stage renal disease rates rose following widespread introduction of high fructose corn syrup in the American diet, supporting speculation that fructose harms the kidney. Sugar-sweetened soda is a primary source of fructose. We therefore hypothesized that sugary soda consumption was associated with albuminuria, a sensitive marker for kidney disease. METHODOLOGY/PRINCIPAL FINDINGS: Design was a cross-sectional analysis. Data were drawn from the National Health and Nutrition Examination Survey (NHANES), 1999-2004. The setting was a representative United States population sample. Participants included adults 20 years and older with no history of diabetes mellitus (n = 12,601); after exclusions for missing outcome and covariate information (n = 3,243), the analysis dataset consisted of 9,358 subjects. Exposure was consumption of two or more sugary soft drinks, based on 24-hour dietary recall. The main outcome measure was Albuminuria, defined by albumin to creatinine ratio cutpoints of >17 mg/g (males) and >25 mg/g (females). Logistic regression adjusted for confounders (diet soda, age, race-ethnicity, gender, poverty). Interactions between age, race-ethnicity, gender, and overweight-obesity were explored. Further analysis adjusted for potential mediators: energy intake, basal metabolic rate, obesity, hypertension, lipids, serum uric acid, smoking, energy expenditure, and glycohemoglobin. Alternative soda intake definitions and cola consumption were employed. RESULTS: Weighted albuminuria prevalence was 11%, and 17% consumed 2+ sugary soft drinks/day. The confounder-adjusted odds ratio for sugary soda was 1.40 (95% confidence interval: 1.13, 1.74). Associations were modified by gender (p = 0.008) and overweight-obesity (p = 0.014). Among women, the OR was 1.86 (95% CI: 1.37, 2.53); the OR among males was not significant. In the group with body mass under 25 kg/m(2), OR = 2.15 (95% confidence interval: 1.42, 3.25). Adjustment for potential mediators and use of alternative definitions of albuminuria and soda consumption did not appreciably change results. Diet sodas were not associated with albuminuria. CONCLUSIONS: Findings suggest that sugary soda consumption may be associated with kidney damage, although moderate consumption of 1 or fewer sodas does not appear to be harmful. Additional studies are needed to assess whether HFCS itself, overall excess intake of sugar, or unmeasured lifestyle and confounding factors are responsible