We explore the set of preferences defined over temporal lotteries in an infinite horizon setting. We provide utility representations for all preferences that are both recursive and monotone. Our results indicate that the class of monotone recursive preferences includes Uzawa–Epstein preferences and risk‐sensitive preferences, but leaves aside several of the recursive models suggested by Epstein and Zin (1989) and Weil (1990). Our representation result is derived in great generality using Lundberg's (1982, 1985) work on functional equations.

Understanding of developmental haemostasis is critical to ensure optimal prevention, diagnosis, and treatment of haemorrhagic and thrombotic diseases in children. As coagulation test results are known to be dependent on the reagents/analysers used, it is recommended for each laboratory to define the age-dependent reference ranges by using its own technical condition. That study was carried out in seven centers to establish age-specific reference ranges using the same reagents and analyser. Plasma samples were obtained from 1437 paediatric patients from the following age groups: 15 days-4 weeks (n=36), 1-5 months (n=320), 6-12 months (n=176), 1-5 years (n=507), 6-10 years (n=132) and 11-17 years (n=262). Indication of coagulation testing was pre-operative screening for non-acute diseases in most cases. PT values were similar in the different age groups to those in adults, whereas longer aPTTs were demonstrated in the younger children. Plasma levels of all clotting factors, except for FV, were significantly decreased (p<0.0001) in the youngest children, adult values being usually reached before the end of the first year. The same applied to antithrombin, protein C/S, and plasminogen. In contrast, FVIII and VWF levels were elevated in the youngest children and returned to adult values within six months. The same applied to D-dimer levels, which were found elevated, particularly until six months of life, until puberty. These data suggest that most coagulation test results are highly dependent on age, mainly during the first year of life, and that age-specific reference ranges must be used to ensure proper evaluation of coagulation in children.

No abstract

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We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et al., 2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta and coauthors (Panetta, et al., 2003). We apply the model to a population with variability as given in Panetta and coauthors (Panetta, et al., 2003). Our optimization algorithm is a variant in the class of Monte-Carlo tree search algorithms. We do not impose periodicity constraint on our solution. We set the objective of tumor size minimization while not allowing more severe toxicity levels than the standard Maximum Tolerated Dose (MTD) regimen. The protocol we propose achieves higher efficacy in the sense that –compared to the usual MTD regimen– it divides the tumor size by approximately 7.66 after 336 days –the 95% confidence interval being [7.36–7.97]. The toxicity is similar to MTD. Overall, our protocol, obtained with a very flexible method, gives significant results for the present case of temozolomide and calls for further research mixing operational research or artificial intelligence and clinical research in oncology.