Minimizing the expected value of the asymmetric loss and an inequality of the variance of the loss

For some estimations and predictions, we solve minimization problems with asymmetric loss functions. Usually, we estimate the coefficient of regression for these problems. In this paper, we do not make such the estimation, but rather give a solution by correcting any predictions so that the prediction error follows a general normal distribution. In our method, we can not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss

Genetic Factors Associated with Heading Responses Revealed by Field Evaluation of 274 Barley Accessions for 20 Seasons

Heading time is a key trait in cereals affecting the maturation period for optimal grain filling before harvest. Here, we aimed to understand the factors controlling heading time in barley (Hordeum vulgare). We characterized a set of 274 barley accessions collected worldwide by planting them for 20 seasons under different environmental conditions at the same location in Kurashiki, Japan. We examined interactions among accessions, known genetic factors, and an environmental factor to determine the factors controlling heading response. Locally adapted accessions have been selected for genetic factors that stabilize heading responses appropriate for barley cultivation, and these accessions show stable heading responses even under varying environmental conditions. We identified vernalization requirement and PPD-H1 haplotype as major stabilizing mechanisms of the heading response for regional adaptation in Kurashiki

森林被覆率の非線形回帰モデリング

要旨あり時空間統計解析：新たなる分野横断的展開原著論

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses

Parental legacy and regulatory novelty in Brachypodium diurnal transcriptomes accompanying their polyploidy

Polyploidy is a widespread phenomenon in eukaryotes that can lead to phenotypic novelty and has important implications for evolution and diversification. The modification of phenotypes in polyploids relative to their diploid progenitors may be associated with altered gene expression. However, it is largely unknown how interactions between duplicated genes affect their diurnal expression in allopolyploid species. In this study, we explored parental legacy and hybrid novelty in the transcriptomes of an allopolyploid species and its diploid progenitors. We compared the diurnal transcriptomes of representative Brachypodium cytotypes, including the allotetraploid Brachypodium hybridum and its diploid progenitors Brachypodium distachyon and Brachypodium stacei. We also artificially induced an autotetraploid B. distachyon. We identified patterns of homoeolog expression bias (HEB) across Brachypodium cytotypes and time-dependent gain and loss of HEB in B. hybridum. Furthermore, we established that many genes with diurnal expression experienced HEB, while their expression patterns and peak times were correlated between homoeologs in B. hybridum relative to B. distachyon and B. stacei, suggesting diurnal synchronization of homoeolog expression in B. hybridum. Our findings provide insight into the parental legacy and hybrid novelty associated with polyploidy in Brachypodium, and highlight the evolutionary consequences of diurnal transcriptional regulation that accompanied allopolyploidy

Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity

Convergence of the Gram-Charier expansion after the normalizing Box-Cox transformation

Exponential dispersion model, exponential family, exponential tilting, power variance function, saddlepoint approximation, stable distribution,

Image classification based on Markov random field models with Jeffreys divergence

This paper considers image classification based on a Markov random field (MRF), where the random field proposed here adopts Jeffreys divergence between category-specific probability densities. The classification method based on the proposed MRF is shown to be an extension of Switzer's soothing method, which is applied in remote sensing and geospatial communities. Furthermore, the exact error rates due to the proposed and Switzer's methods are obtained under the simple setup, and several properties are derived. Our method is applied to a benchmark data set of image classification, and exhibits a good performance in comparison with conventional methods.Bayes estimate Discriminant analysis Image analysis Kullback-Leibler information