Polydisperse star polymer solutions

We analyze the effect of polydispersity in the arm number on the effective interactions, structural correlations and the phase behavior of star polymers in a good solvent. The effective interaction potential between two star polymers with different arm numbers is derived using scaling theory. The resulting expression is tested against monomer-resolved molecular dynamics simulations. We find that the theoretical pair potential is in agreement with the simulation data in a much wider polydispersity range than other proposed potentials. We then use this pair potential as an input in a many-body theory to investigate polydispersity effects on the structural correlations and the phase diagram of dense star polymer solutions. In particular we find that a polydispersity of 10%, which is typical in experimental samples, does not significantly alter previous findings for the phase diagram of monodisperse solutions.Comment: 14 pages, 7 figure

Phase separation in star polymer-colloid mixtures

We examine the demixing transition in star polymer-colloid mixtures for star arm numbers f=2,6,16,32 and different star-colloid size ratios. Theoretically, we solve the thermodynamically self-consistent Rogers-Young integral equations for binary mixtures using three effective pair potentials obtained from direct molecular computer simulations. The numerical results show a spinodal instability. The demixing binodals are approximately calculated, and found to be consistent with experimental observations.Comment: 4 pages, 4 figures, submitted to PR

Agent programming in the cognitive Era

It is claimed that, in the nascent 'Cognitive Era', intelligent systems will be trained using machine learning techniques rather than programmed by software developers [10]. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments [14]. In this paper, we argue that the unique strengths of Belief-Desire-Intention (BDI) agent programming languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems

Agent programming in the cognitive era

It is claimed that, in the nascent \u2018Cognitive Era\u2019, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., \u2018AI as a service\u2019, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., \u2018AI embedded into agents\u2019 raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

Serodiagnosis of tuberculosis using nine in silico predicted B-cell epitopes peptides derived from Mycobacterium tuberculosis proteins

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Evaluation of a multi-antigen test based on B-cell epitope peptides for the serodiagnosis of pulmonary tuberculosis

SETTING: Two sample panels: 1) 20 pulmonary tuberculosis (PTB) patients and 10 healthy subjects from a country with a low incidence of TB (Italy); and 2) 47 PTB patients and 26 healthy subjects from a country with a high incidence of TB (Morocco). OBJECTIVE: To identify a combination of Mycobacterium tuberculosis peptides useful for the serodiagnosis of active PTB. METHODS: Fifty-seven B-cell epitope peptides of M. tuberculosis were evaluated by immunoenzymatic assay and the data were analysed using logistic regression analysis and the random forest method. RESULTS: The best discriminating peptide between PTB patients and healthy subjects from the sample of the low TB incidence country was the 23 amino acid peptide of the Rv3878 protein. The sensitivity and specificity were respectively 65% and 100%. The same peptide had a sensitivity and specificity of respectively 47% and 100% for the sample from the high TB incidence country. The best combination of peptides was a pool of nine peptides which had a sensitivity of 70.2% and a specificity of 100% in the high TB incidence country. CONCLUSIONS: The 9-peptide pool can be useful in identifying patients with active PTB