Article
Plant Sciences
Ce Liu, Xiaoxiao Liu, Yike Han, Xi'ao Wang, Yuanyuan Ding, Huanwen Meng, Zhihui Cheng
Summary: Genomic prediction was applied in cucumber breeding, demonstrating high predictive ability of GCA models for cucumber traits. Non-additive effects significantly influenced trait prediction, with a relatively higher proportion of additive-by-additive genetic variance components.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Multidisciplinary Sciences
N. Hernandez, J. Soenksen, P. Newcombe, M. Sandhu, I Barroso, C. Wallace, J. L. Asimit
Summary: Joint fine-mapping leveraging information between quantitative traits improves accuracy and resolution. Flashfm fine-maps signals for multiple traits using summary statistics, allowing for missing trait measurements and use of related individuals.
NATURE COMMUNICATIONS
(2021)
Article
Ecology
Georgios Charizanos, Haydar Demirhan
Summary: This study utilizes Bayesian modeling to predict the probability of wildfires based on environmental predictors and forest vulnerability. The results indicate that forest vulnerability is the dominant predictor of wildfire probability. The findings can be used to create a Wildfire Warning Index for targeted preventative actions in high-risk areas.
ECOLOGICAL INFORMATICS
(2023)
Article
Agriculture, Multidisciplinary
Henk J. van Lingen, Arjan Jonker, Ermias Kebreab, David Pacheco
Summary: This study found a relationship between dietary variables and animal characteristics with enteric CH4 emissions and N excretion in sheep, but did not conclusively prove a direct trade-off between the two.
AGRICULTURE ECOSYSTEMS & ENVIRONMENT
(2021)
Article
Engineering, Civil
He-Qing Mu, Ji-Hui Shen, Zi-Tong Zhao, Han-Teng Liu, Ka-Veng Yuen
Summary: This paper proposes the BASIC-UQ method, which utilizes Bayesian joint distribution to address the difficulties in generative approaches for MF prediction under varying EC. It includes three stages: introducing probabilistic model class candidates, conducting Bayesian inference on parameters and model class candidates, and deriving predictive PDF of MF conditional on incomplete information of EC. The method shows promising results in joint PDF modelling of MF and EC and predictive PDF derivation of MF using incomplete EC information.
ENGINEERING STRUCTURES
(2022)
Article
Fisheries
Fucun Wu, Ming Li, Youkang Ji, Wei Wang, Guofan Zhang
Summary: This study investigated the longitudinal growth characteristics of Pacific abalone and identified the optimal age for selection based on genetic analysis. The results showed a decreasing trend in heritability with age and high genetic correlation between contiguous ages.
Article
Spectroscopy
Kate H. Lepore, Caroline R. Ytsma, M. Darby Dyar
Summary: The accuracy of laser-induced breakdown spectroscopy (LIBS) methods for analyzing geological samples is improved when calibration standards and unknown targets are compositionally similar. Creating customized submodels can optimize calibration datasets and enhance accuracy. However, the size reduction of the training dataset due to submodel creation can negatively affect prediction accuracy. Customized LIBS standards can overcome this problem in specific applications, but extensive and robust initial databases are needed for submodel approaches to improve prediction accuracies.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2022)
Article
Statistics & Probability
Michael Guggisberg
Summary: This article presents a Bayesian approach to multiple-output quantile regression. The prior knowledge is elicited as the distance between the tau-Tukey depth contour and the Tukey median, which is the first of its kind. The consistency of the parametric model is proven and a procedure for obtaining confidence intervals is proposed. A nonparametric multiple-output regression method is also introduced.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Mathematical & Computational Biology
Haotian Zou, Kan Li, Donglin Zeng, Sheng Luo
Summary: The article introduces a framework for a multivariate joint model linking multiple correlated longitudinal outcomes to a survival outcome, using scalar-on-function regression to include voxel-based whole-brain MRI data as functional predictors. Bayesian paradigm is employed for statistical inference, and a dynamic prediction framework is developed to predict an individual's future longitudinal outcomes and risk of a survival event.
STATISTICS IN MEDICINE
(2021)
Article
Environmental Sciences
Feifei He, Hairong Zhang, Qinjuan Wan, Shu Chen, Yuqi Yang
Summary: Medium-term hydrological streamflow forecasting is of great significance for improving the utilization of hydropower energy and has been a research hotspot. This study proposes a Bayesian model average integrated prediction method that combines artificial intelligence algorithms to predict water resources utilization in advance, and the experimental results prove the high accuracy of the model.
Article
Environmental Sciences
Lu Yu, Tianyuan Zheng, Ruyu Yuan, Xilai Zheng
Summary: This study used hydrochemical characteristics and isotope analysis to determine the sources of nitrate (NO3-) in groundwater. The results showed that chemical fertilizers and natural sources were the primary contributors of NO3- in the vegetable cultivation area. The proposed PCA-APCS-MLR model offers a simpler and more convenient method compared to previous approaches.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiao-Song Tang, Han-Bing Huang, Xiong-Feng Liu, Dian-Qing Li, Yong Liu
Summary: This study proposes an efficient Bayesian method using parametric bootstrap to characterize the multivariate PDF of multiple soil parameters. The method improves efficiency by replacing the complex multi-dimensional integral with a simple arithmetic average. It is not limited by the multivariate distribution model or prior distribution of the soil parameters. Two practical examples and one numerical example demonstrate the effectiveness of the proposed method in characterizing the multivariate PDF of multiple soil parameters. Informative prior knowledge reduces the demand for multivariate site-specific test data.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Engineering, Geological
Nicolas A. Fuentes, Jorge C. Flores, Jorge E. Egger, Felipe A. Vicencio, Victor Aguilar, Sergio J. Yanez
Summary: This study conducts a comparative analysis of multiple Chilean seismic design codes for reinforced concrete buildings, and reveals that there is not significant difference in seismic response, but noticeable dependencies among parameters in different code cases.
BULLETIN OF EARTHQUAKE ENGINEERING
(2023)
Article
Automation & Control Systems
Xianshuang Yao, Yanning Shao, Siyuan Fan, Shengxian Cao
Summary: In this paper, a novel echo state network with multiple delayed outputs (MDO-ESN) is proposed for time series prediction. The delayed characteristics of output signals are studied to adaptively adjust the output equation of MDO-ESN. A sufficient condition is given to ensure the stability of MDO-ESN. Furthermore, a parameter optimization method is provided to reduce the dependence of prediction accuracy on reservoir parameters. The effectiveness of MDO-ESN is demonstrated through numerical and actual simulation examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Chunzheng Cao, Xin Liu, Shuren Cao, Jian Qing Shi
Summary: We propose a heavy-tailed process functional regression for joint classification and prediction of time-varying functional data. The model uses two independent scale mixtures of Gaussian Processes to model random effects and random errors, providing robust inferences against outliers in magnitude and shape. Classification is done based on posterior predictive probabilities of class labels, and a weighted prediction of future curve trends is offered. The proposed model is evaluated using simulated studies and real data sets.
PATTERN RECOGNITION
(2023)
Article
Biochemistry & Molecular Biology
Lingzhao Fang, Wentao Cai, Shuli Liu, Oriol Canela-Xandri, Yahui Gao, Jicai Jiang, Konrad Rawlik, Bingjie Li, Steven G. Schroeder, Benjamin D. Rosen, Cong-jun Li, Tad S. Sonstegard, Leeson J. Alexander, Curtis P. Van Tassell, Paul M. VanRaden, John B. Cole, Ying Yu, Shengli Zhang, Albert Tenesa, Li Ma, George E. Liu
Article
Agriculture, Dairy & Animal Science
Emmanuel A. Lozada-Soto, Christian Maltecca, Hanna Wackel, William Flowers, Kent Gray, Yuqing He, Yijian Huang, Jicai Jiang, Francesco Tiezzi
Summary: This study investigated the effects of sex, breed, and age on genetic variation within swine populations, focusing on recombination rates. Differences were found in recombination rates between sexes, breeds, and age groups, with females showing higher rates with increasing parity. Heritability and repeatability estimates for recombination rates were low across all populations, but genetic correlations were high and positive within breeds. GWAS results for recombination rates varied across sex/breed populations, indicating variability in recombination within purebred swine populations.
JOURNAL OF ANIMAL BREEDING AND GENETICS
(2021)
Article
Genetics & Heredity
Dzianis Prakapenka, Zuoxiang Liang, Jicai Jiang, Li Ma, Yang Da
Summary: Epistasis studies for production and fertility traits in Holstein cattle revealed that majority of epistasis effects are within chromosome regions while a small percentage involve interactions between different chromosomes. This suggests a complex genetic mechanism underlying quantitative traits in cattle.
Article
Genetics & Heredity
Botong Shen, Ellen Freebern, Jicai Jiang, Christian Maltecca, John B. Cole, George E. Liu, Li Ma
Summary: Meiotic recombination is a fundamental biological process that promotes genetic diversity and division. The study identified a quadratic trend between maternal age and recombination rate in cattle, as well as a positive correlation between environmental temperature during fetal development and recombination rate in female parents. The final model confirmed the impact of maternal age and environmental temperature on recombination rate in cattle.
FRONTIERS IN GENETICS
(2021)
Article
Genetics & Heredity
Yuqing He, Francesco Tiezzi, Jicai Jiang, Jeremy T. Howard, Yijian Huang, Kent Gray, Jung-Woo Choi, Christian Maltecca
Summary: This study investigates the use of feeding behavior and gut microbiome in predicting growth and body composition traits of finishing pigs, and finds that they provide non-redundant information for predicting porcine growth.
Article
Biotechnology & Applied Microbiology
Alexis Marceau, Yahui Gao, Ransom L. Baldwin, Cong-jun Li, Jicai Jiang, George E. Liu, Li Ma
Summary: This study identified a large number of uniquely expressed lncRNAs in rumen tissue of dairy cattle, highlighting their functional role in rumen development and weaning transition. Additionally, significant enrichment of traits related to production, reproduction, health, and body conformation in dairy cattle was observed in both pre- and post-weaning lncRNAs.
Article
Agriculture, Dairy & Animal Science
Yuqing He, Francesco Tiezzi, Jicai Jiang, Jeremy Howard, Yijian Huang, Kent Gray, Jung-Woo Choi, Christian Maltecca
Summary: This study evaluated the performance of eight different methods in estimating the diversity of gut microbiota composition and predicting the growth and body composition traits in pig breeds. The results showed that different methods had varying performance in predicting different traits and breeds, highlighting the importance of gut microbiome data in understanding complex traits in pigs with diverse genetic backgrounds.
JOURNAL OF ANIMAL SCIENCE
(2022)
Editorial Material
Genetics & Heredity
Jingyue Ellie Duan, Jicai Jiang, Yanghua He
FRONTIERS IN GENETICS
(2022)
Meeting Abstract
Agriculture, Dairy & Animal Science
Junjian Wang, Julie Hicks, Jicai Jiang, Hsiao-Ching Liu
JOURNAL OF ANIMAL SCIENCE
(2022)
Article
Agriculture, Dairy & Animal Science
Emmanuel A. Lozada-Soto, Francesco Tiezzi, Jicai Jiang, John B. Cole, Paul M. VanRaden, Christian Maltecca
Summary: Maintaining genetic diversity in dairy cattle is crucial for adaptation and fitness. This study characterized the genomic landscape of autozygosity and genetic diversity trends in 5 US dairy cattle breeds. The results showed variations in runs of homozygosity (ROH) among breeds and selection patterns for milk production, health, and reproduction. Inbreeding coefficients revealed differences in inbreeding accumulation among breeds. Furthermore, the effective population size has been decreasing historically.
JOURNAL OF DAIRY SCIENCE
(2022)
Article
Biochemistry & Molecular Biology
Zuoxiang Liang, Dzianis Prakapenka, Paul M. VanRaden, Jicai Jiang, Li Ma, Yang Da
Summary: A genome-wide association study identified new effects and confirmed effects on daughter pregnancy rate (DPR), cow conception rate (CCR), and heifer conception rate (HCR) in U.S. Holstein cows, with several effects located near known reproduction-related genes. Specific SNPs were recommended for heifer culling based on their negative effects on fertility traits. The study provided valuable insights into the genetic variants and genomic regions influencing fertility traits in Holstein cows.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biotechnology & Applied Microbiology
Yahui Gao, Alexis Marceau, Victoria Iqbal, Jose Antonio Torres-Vazquez, Mahesh Neupane, Jicai Jiang, George E. Liu, Li Ma
Summary: Our large-scale GWAS analyses identified a major QTL in the bovine MHC region for early first calving in heifers. Additional functional enrichment and TWAS analyses confirmed the MHC QTL with relevant biological evidence. Our results revealed the complex genetic basis of heifer health and fertility traits and indicated a potential connection between the immune system and reproduction in cattle.
Article
Genetics & Heredity
Alexis Marceau, Junjian Wang, Victoria Iqbal, Jicai Jiang, George E. Liu, Li Ma
Summary: This study aims to identify and analyze lncRNA transcripts in Bos taurus mammary tissue samples to understand their features and functions, particularly their connection to lactation. The findings reveal that the lncRNAs in mammary tissue have distinct characteristics and functional annotations, and are associated with both mammary tissue development and genes/proteins related to pregnancy.
Article
Biochemical Research Methods
Jian Cheng, Christian Maltecca, Paul M. VanRaden, Jeffrey R. O'Connell, Li Ma, Jicai Jiang
Summary: SLEMM is a new software tool for large-scale genomic prediction, which can improve prediction accuracy through SNP weighting. Extensive analyses on multiple datasets showed that SLEMM had the best predictive ability and computational performance compared to other genomic prediction methods. Simulation analyses also demonstrated that SLEMM had comparable accuracy to BayesR.
Meeting Abstract
Agriculture, Dairy & Animal Science
Junjian Wang, Christian Maltecca, Francesco Tiezzi, Yijian Huang, Jicai Jiang
JOURNAL OF ANIMAL SCIENCE
(2023)