Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine

ESPEN Guideline: Clinical Nutrition in inflammatory bowel disease

Introduction: The ESPEN guideline presents a multidisciplinary focus on clinical nutrition in inflammatory bowel disease (IBD). Methodology: The guideline is based on extensive systematic review of the literature, but relies on expert opinion when objective data were lacking or inconclusive. The conclusions and 64 recommendations have been subject to full peer review and a Delphi process in which uniformly positive responses (agree or strongly agree) were required. Results: IBD is increasingly common and potential dietary factors in its aetiology are briefly reviewed. Malnutrition is highly prevalent in IBD – especially in Crohn's disease. Increased energy and protein requirements are observed in some patients. The management of malnu-trition in IBD is considered within the general context of support for malnourished patients. Treatment of iron deficiency (parenterally if necessary) is strongly recommended. Routine provision of a special diet in IBD is not however supported. Parenteral nutrition is indicated only when enteral nutrition has failed or is impossible. The recommended perioperative man-agement of patients with IBD undergoing surgery accords with general ESPEN guidance for patients having abdominal surgery. Probiotics may be helpful in UC but not Crohn's disease. Primary therapy using nutrition to treat IBD is not supported in ulcerative colitis, but is mod-erately well supported in Crohn's disease, especially in children where the adverse conse-quences of steroid therapy are proportionally greater. However, exclusion diets are generally not recommended and there is little evidence to support any particular formula feed when nutritional regimens are constructed. Conclusions: Available objective data to guide nutritional support and primary nutritional therapy in IBD are presented as 64 recommendations, of which 9 are very strong recom-mendations (grade A), 22 are strong recommendations (grade B) and 12 are based only on sparse evidence (grade 0); 21 recommendations are good practice points (GPP)

Alu Exonization Events Reveal Features Required for Precise Recognition of Exons by the Splicing Machinery

Despite decades of research, the question of how the mRNA splicing machinery precisely identifies short exonic islands within the vast intronic oceans remains to a large extent obscure. In this study, we analyzed Alu exonization events, aiming to understand the requirements for correct selection of exons. Comparison of exonizing Alus to their non-exonizing counterparts is informative because Alus in these two groups have retained high sequence similarity but are perceived differently by the splicing machinery. We identified and characterized numerous features used by the splicing machinery to discriminate between Alu exons and their non-exonizing counterparts. Of these, the most novel is secondary structure: Alu exons in general and their 5′ splice sites (5′ss) in particular are characterized by decreased stability of local secondary structures with respect to their non-exonizing counterparts. We detected numerous further differences between Alu exons and their non-exonizing counterparts, among others in terms of exon–intron architecture and strength of splicing signals, enhancers, and silencers. Support vector machine analysis revealed that these features allow a high level of discrimination (AUC = 0.91) between exonizing and non-exonizing Alus. Moreover, the computationally derived probabilities of exonization significantly correlated with the biological inclusion level of the Alu exons, and the model could also be extended to general datasets of constitutive and alternative exons. This indicates that the features detected and explored in this study provide the basis not only for precise exon selection but also for the fine-tuned regulation thereof, manifested in cases of alternative splicing