2 resultados para Hybrid prediction
em BORIS: Bern Open Repository and Information System - Berna - Suiça
Resumo:
More than 375,000 BAC-end sequences (BES) of the CHORI-243 ovine BAC library have been deposited in public databases. blastn searches with these BES against HSA18 revealed 1806 unique and significant hits. We used blastn-anchored BES for an in silico prediction of gene content and chromosome assignment of comparatively mapped ovine BAC clones. Ovine BES were selected at approximately 1.3-Mb intervals of HSA18 and incorporated into a human-sheep comparative map. An ovine 5000-rad whole-genome radiation hybrid panel (USUoRH5000) was typed with 70 markers, all of which mapped to OAR23. The resulting OAR23 RH map included 43 markers derived from BES with high and unique BLAST hits to the sequence of the orthologous HSA18, nine EST-derived markers, 16 microsatellite markers taken from the ovine linkage map and two bovine microsatellite markers. Six new microsatellite markers derived from the 43 mapped BES and the two bovine microsatellite markers were linkage-mapped using the International Mapping Flock (IMF). Thirteen additional microsatellite markers were derived from other ovine BES with high and unique BLAST hits to the sequence of the orthologous HSA18 and also positioned on the ovine linkage map but not incorporated into the OAR23 RH map. This resulted in 24 markers in common and in the same order between the RH and linkage maps. Eight of the BES-derived markers were mapped using fluorescent in situ hybridization (FISH), to thereby align the RH and cytogenetic maps. Comparison of the ovine chromosome 23 RH map with the HSA18 map identified and localized three major breakpoints between HSA18 and OAR23. The positions of these breakpoints were equivalent to those previously shown for syntenic BTA24 and HSA18. This study presents evidence for the usefulness of ovine BES when constructing a high-resolution comprehensive map for a single sheep chromosome. The comparative analysis confirms and refines knowledge about chromosomal conservation and rearrangements between sheep, cattle and human. The constructed RH map demonstrates the resolution and utility of the newly constructed ovine RH panel.
An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models
Resumo:
Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.