Genetic Variants Associated with the Age of Onset Identified by Whole-Exome Sequencing in Fatal Familial Insomnia.

Fatal familial insomnia (FFI) is a rare autosomal-dominant inherited prion disease with a wide variability in age of onset. Its causes are not known. In the present study, we aimed to analyze genetic risk factors other than the prion protein gene (PRNP), in FFI patients with varying ages of onset. Whole-exome sequencing (WES) analysis was performed for twenty-five individuals with FFI (D178N-129M). Gene ontology enrichment analysis was carried out by Reactome to generate hypotheses regarding the biological processes of the identified genes. In the present study, we used a statistical approach tailored to the specifics of the data and identified nineteen potential gene variants with a potential effect on the age of onset. Evidence for potential disease modulatory risk loci was observed in two pseudogenes (NR1H5P, GNA13P1) and three protein coding genes (EXOC1L, SRSF11 and MSANTD3). These genetic variants are absent in FFI patients with early disease onset (19-40 years). The biological function of these genes and PRNP is associated with programmed cell death, caspase-mediated cleavage of cytoskeletal proteins and apoptotic cleavage of cellular proteins. In conclusions, our study provided first evidence for the involvement of genetic risk factors additional to PRNP, which may influence the onset of clinical symptoms in FFI.

Compressed computations using wavelets for hidden Markov models with continuous observations.

Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms-Forward Filtering, Backward Smoothing and Viterbi-and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generated by HMM with dominant self-transition probabilities. Here we extend the compressive computation approach to the classical frequentist HMM algorithms on continuous-valued observations, providing the first compressive approach for this problem. In a large-scale simulation study, we demonstrate empirically that in many settings compressed HMM algorithms very clearly outperform the classical algorithms with no, or only an insignificant effect, on the computed probabilities and infered state paths of maximal likelihood. This provides an efficient approach to big data computations with HMM. An open-source implementation of the method is available from https://github.com/lucabello/wavelet-hmms.

Retrospective In-Hospital Mortality Analysis of GeriatricPatients Treated in a Level 1 Trauma Center.

The aim of this study is to determine the critical time intervals and influencing covariates for in-hospital mortality in geriatric trauma and orthopedic patients. During a period of five years, we retrospectively review patients aged > 60 years who were hospitalized at the Department of Trauma, Orthopedic, and Plastic Surgery. The primary outcome is the mean time to death. Survival analysis is performed using an accelerated failure time model. A total of 5388 patients are included in the analysis. Two-thirds underwent surgery (n = 3497, 65%) and one-third were conservatively treated (n = 1891, 35%). The in-hospital mortality rate is 3.1% (n = 168; surgery, n = 112; conservative, n = 56). The mean time to death is 23.3 days (±18.8) after admission in the surgery group and 11.3 days (±12.5) in the conservative treatment group. The greatest accelerating effect on mortality is found in the intensive care unit (16.52, p < 0.001). We are able to identify a critical time interval for in-hospital mortality between days 11 and 23. The day of death on weekend days/holidays, hospitalization for conservative treatment, and treatment at the intensive care unit significantly increase the risk of in-hospital mortality. Early mobilization and a short hospitalization duration seem to be of major importance in fragile patients.