The Addition of Epistatic Genetic Effects Increases Genomic Prediction Accuracy for Reproduction and Production Traits in Duroc Pigs Using Genomic Models

Background: Genomic selection has been implemented in livestock genetic evaluations for years. However, currently most genomic selection models only consider the additive effects associated with SNP markers and nonadditive genetic effects have been for the most part ignored. Methods: Production traits for 26,735 to 27,647 Duroc pigs and reproductive traits for 5,338 sows were used, including off-test body weight (WT), off-test back fat (BF), off-test loin muscle depth (MS), number born alive (NBA), number born dead (NBD), and number weaned (NW). All animals were genotyped with the PorcineSNP60K Bead Chip. Variance components were estimated using a linear mixed model that includes inbreeding coefficient, additive, dominance, additive-by-additive, additive-by-dominance, dominance-by-dominance effect, and common litter environmental effect. Genomic prediction performance, including all nonadditive genetic effects, was compared with a reduced model that included only additive genetic effect. Results: Significant estimates of additive-by-additive effect variance were observed for NBA, BF, and WT (31%, 9%, and 10%, respectively). Production traits showed significant large estimates of additive-by-dominance variance (9%-23%). MS also showed large estimate of dominance-by-dominance variance (10%). Dominance effect variance estimates were low for all traits (0%-2%). Compared to the reduced model, prediction accuracies using the full model, including nonadditive effects, increased significantly by 12%, 12%, and 1% for NBA, WT, and MS, respectively. A strong dominance association signal with BF was identified near AK5.Conclusions: Sizable estimates of epistatic effects were found for the reproduction and production traits, while the dominance effect was relatively small for all traits yet significant for all production traits. Including nonadditive effects, especially epistatic effects in the genomic prediction model, significantly improved prediction accuracy for NBA, WT, and MS.

Generalized Empirical Interpolation Method With H1 Regularization: Application to Nuclear Reactor Physics

The generalized empirical interpolation method (GEIM) can be used to estimate the physical field by combining observation data acquired from the physical system itself and a reduced model of the underlying physical system. In presence of observation noise, the estimation error of the GEIM is blurred even diverged. We propose to address this issue by imposing a smooth constraint, namely, to constrain the H1 semi-norm of the reconstructed field of the reduced model. The efficiency of the approach, which we will call the H1 regularization GEIM (R-GEIM), is illustrated by numerical experiments of a typical IAEA benchmark problem in nuclear reactor physics. A theoretical analysis of the proposed R-GEIM will be presented in future works.

State relevance for model order reduction applied to a microgrid

This paper presents a systematic model order reduction (MOR) algorithm based on state relevance applied to an islanded microgrid with electronic power generation. MOR of such islanded microgrids may not benefit, a priori, from the well-established time-scale separation usually applied to conventional power systems, and a systematic MOR is still an open issue. The proposed algorithm uses a balanced realization of the linear system, where state variables may not have physical meaning, to obtain the states' energies. It then calculates the relevance of the original system states from those energy values. The newly proposed ``state-relevance coefficient'' should help to choose which states to consider in a reduced model in each study case. Detailed nonlinear simulation results show that the proposed algorithm is able to find the relevant states to include in the reduced model systematically, even in operation points near the stability limit, where ad-hoc MOR techniques are likely to fail. The performance of the algorithm is illustrated in a system with grid-forming converters in various scenarios but can be easily applied to other systems.

State relevance for model order reduction applied to a microgrid

This paper presents a systematic model order reduction (MOR) algorithm based on state relevance applied to an islanded microgrid with electronic power generation. MOR of such islanded microgrids may not benefit, a priori, from the well-established time-scale separation usually applied to conventional power systems, and a systematic MOR is still an open issue. The proposed algorithm uses a balanced realization of the linear system, where state variables may not have physical meaning, to obtain the states' energies. It then calculates the relevance of the original system states from those energy values. The newly proposed ``state-relevance coefficient'' should help to choose which states to consider in a reduced model in each study case. Detailed nonlinear simulation results show that the proposed algorithm is able to find the relevant states to include in the reduced model systematically, even in operation points near the stability limit, where ad-hoc MOR techniques are likely to fail. The performance of the algorithm is illustrated in a system with grid-forming converters in various scenarios but can be easily applied to other systems.

A Prediction Model for Pediatric Radiographic Pneumonia

BACKGROUND: Chest radiographs (CXRs) are frequently used in the diagnosis of community-acquired pneumonia (CAP). We sought to construct a predictive model for radiographic CAP based on clinical features to decrease CXR use. METHODS: We performed a single-center prospective study of patients 3 months to 18 years of age with signs of lower respiratory infection who received a CXR for suspicion of CAP. We used penalized multivariable logistic regression to develop a full model and bootstrapped backward selection models to develop a parsimonious reduced model. We evaluated model performance at different thresholds of predicted risk. RESULTS: Radiographic CAP was identified in 253 (22.2%) of 1142 patients. In multivariable analysis, increasing age, prolonged fever duration, tachypnea, and focal decreased breath sounds were positively associated with CAP. Rhinorrhea and wheezing were negatively associated with CAP. The bootstrapped reduced model retained 3 variables: age, fever duration, and decreased breath sounds. The area under the receiver operating characteristic for the reduced model was 0.80 (95% confidence interval: 0.77–0.84). Of 229 children with a predicted risk of <4%, 13 (5.7%) had radiographic CAP (sensitivity of 94.9% at a 4% risk threshold). Conversely, of 229 children with a predicted risk of >39%, 140 (61.1%) had CAP (specificity of 90% at a 39% risk threshold). CONCLUSIONS: A predictive model including age, fever duration, and decreased breath sounds has excellent discrimination for radiographic CAP. After external validation, this model may facilitate decisions around CXR or antibiotic use in CAP.