Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors

Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two‐step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two‐step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/1/risa12728_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/2/risa12728.pd

Disseminated tumour cells with highly aberrant genomes are linked to poor prognosis in operable oesophageal adenocarcinoma

Background: Chromosomal instability (CIN) has repeatedly been identified as a prognostic marker. Here we evaluated the percentage of aberrant genome per cell (PAG) as a measure of CIN in single disseminated tumour cells (DTC) isolated from patients with operable oesophageal adenocarcinoma (EAC), to assess the impact of CINhigh DTCs on prognosis. Methods: We isolated CK18(positive) DTCs from bone marrow (BM) or lymph node (LN) preparations of operable EAC patients. After whole-genome amplification, single DTCs were analysed for chromosomal gains and losses using metaphase-based comparative genomic hybridisation (mCGH). We calculated the PAG for each DTC and determined the critical threshold value that identifies high-risk patients by STEPP (Subpopulation Treatment Effect Pattern Plot) analysis in two independent EAC patient cohorts (cohort #1, n = 44; cohort # 2; n = 29). Results: The most common chromosomal alterations observed among the DTCs were typical for EAC, but the DTCs showed a varying PAG between individual patients. Generally, LNDTCs displayed a significantly higher PAG than BMDTCs. STEPP analysis revealed an increasing PAG of DTCs to be correlated with an increased risk for short survival in two independent EAC cohorts as well as in the corresponding pooled analysis. In all three data sets (cohort # 1, cohort # 2 and pooled cohort), PAG(high) DTCs conferred an independent risk for a significantly decreased survival. Conclusions: The analysis of PAG/CIN in solitary marker-positive DTCs identifies operable EAC patients with poor prognosis, indicating a more aggressive minimal residual disease