Defective removal of ribonucleotides from DNA promotes systemic autoimmunity

Genome integrity is continuously challenged by the DNA damage that arises during normal cell metabolism. Biallelic mutations in the genes encoding the genome surveillance enzyme ribonuclease H2 (RNase H2) cause Aicardi-Goutières syndrome (AGS), a pediatric disorder that shares features with the autoimmune disease systemic lupus erythematosus (SLE). Here we determined that heterozygous parents of AGS patients exhibit an intermediate autoimmune phenotype and demonstrated a genetic association between rare RNASEH2 sequence variants and SLE. Evaluation of patient cells revealed that SLE- and AGS-associated mutations impair RNase H2 function and result in accumulation of ribonucleotides in genomic DNA. The ensuing chronic low level of DNA damage triggered a DNA damage response characterized by constitutive p53 phosphorylation and senescence. Patient fibroblasts exhibited constitutive upregulation of IFN-stimulated genes and an enhanced type I IFN response to the immunostimulatory nucleic acid polyinosinic:polycytidylic acid and UV light irradiation, linking RNase H2 deficiency to potentiation of innate immune signaling. Moreover, UV-induced cyclobutane pyrimidine dimer formation was markedly enhanced in ribonucleotide-containing DNA, providing a mechanism for photosensitivity in RNase H2-associated SLE. Collectively, our findings implicate RNase H2 in the pathogenesis of SLE and suggest a role of DNA damage-associated pathways in the initiation of autoimmunity

Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.Aerospace Transport & OperationsAerospace Engineerin