LPL, Towards a GDPR-Compliant Privacy Language: Formal Definition and Usage

International audienc

Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia

Abstract Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low

On the Runtime Enforcement of Timed Properties

International audienceRuntime enforcement refers to the theories, techniques, and tools for enforcing correct behavior of systems at runtime. We are interested in such behaviors described by specifications that feature timing constraints formalized in what is generally referred to as timed properties. This tutorial presents a gentle introduction to runtime enforcement (of timed properties). First, we present a taxonomy of the main principles and concepts involved in runtime enforcement. Then, we give a brief overview of a line of research on theoretical runtime enforcement where timed properties are described by timed automata and feature uncontrollable events. Then, we mention some tools capable of runtime enforcement, and we present the TiPEX tool dedicated to timed properties. Finally, we present some open challenges and avenues for future work. Runtime Enforcement (RE) is a discipline of computer science concerned with enforcing the expected behavior of a system at runtime. Runtime enforcement extends the traditional runtime verification [12-14, 42, 43] problem by dealing with the situations where the system deviates from its expected behavior. While runtime verification monitors are execution observers, runtime enforcers are execution modifiers. Foundations for runtime enforcement were pioneered by Schneider in [98] and by Rinard in [95] for the specific case of real-time systems. There are several tutorials and overviews on runtime enforcement for untimed systems [39, 47, 59], but none on the enforcement of timed properties (for real-time systems). In this tutorial, we focus on runtime enforcing behavior described by a timed property. Timed properties account for physical time. They allow expressing constraints on the time that should elapse between (sequences of) events, which is useful for real-time systems when specifying timing constraints between statements, their scheduling policies, the completion of tasks, etc [5, 7, 88, 101, 102]. This tutorial comprises four stages: 1. the presentation of a taxonomy of concepts and principles in RE (Sec. 1); 2. the presentation of a framework for the RE of timed properties where specifications are described by timed automata (preliminary concepts are recalled in Sec. 2, the framework is overviewed in Sec. 3, and presented in more details in Sec. 4); 3. the demonstration of the TiPEX [82] tool implementing the framework (Sec. 5); 4. the description of some avenues for future work (Sec. 6)