Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

The chloroplast organelle in mesophyll cells of higher plants represents a sunlight-driven metabolic factory that eventually fuels life on our planet. Knowledge of the ultrastructure and the dynamics of this unique organelle is essential to understanding its function in an ever-changing and challenging environment. Recent technological developments promise unprecedented insights into chloroplast architecture and its functionality. The review highlights these new methodical approaches and provides structural models based on recent findings about the plasticity of the thylakoid membrane system in response to different light regimes. Furthermore, the potential role of the lipid droplets plastoglobuli is discussed. It is emphasized that detailed structural insights are necessary on different levels ranging from molecules to entire membrane systems for a holistic understanding of chloroplast function.

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.15730

Chloroplast ultrastructure in plants.

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tpj.14190

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency.

Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.

/pdf/mvapp-multivariate-analysis-application-for-streamlined-data-2cshnybl8u.pdf

MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation.

A method of determining and characterizing photosynthesis in plant parts of one or more plants includes capturing a plurality of images of the plant parts of the one or more plants with a sensor. Fluorescence of the plant parts of the one or more plants is measured such as by storing a sensor image of observed fluorescence. Light absorbed by the plant parts of the one or more plants is estimated such as by observing red and/or infrared reflectance of the plant parts. A characteristic of photosynthesis such as linear electron flow in plant parts of the one or more plants is derived using the measured fluorescence of the plant parts, the reflectance and the light absorbed by the plant parts, and/or the three-dimensional model comprising the plant parts of the one or more plants.

Methods for estimating photosynthetic characteristics in plant canopies and systems and apparatus related thereto

Motivation: Plant phenomics, the collection of large-scale plant phenotype data is growing exponentially. The resources have become essential component of modern plant science. Such complex data sets are critical for understanding the mechanisms governing energy intake and storage in plants, and this is essential for improving crop productivity. However, a major issue facing these efforts is the determination of the quality of phenotypic data. Automated methods are needed to identify and characterize alteractions caused by system errors, all of which are difficult to remove in the data collection step, and distinguish them from more interesting cases of altered biological responses. Results: As a step towards solving this problem, we have developed a coarse-to-refined model called Dynamic Filter to identify abnormalities in plant photosynthesis phenotype data by comparing light responses of photosynthesis using a simplified kinetic model of photosynthesis. Dynamic Filter employs an Expectation-Maximization process to adjust the kinetic model in coarse and refined regions to identify both abnormalities and biological outliers. The experimental results show that our algorithm can effectively identify most of the abnormalities in both the real and synthetic datasets.

/pdf/plant-photosynthesis-phenomics-data-quality-control-z4l3zwf25t.pdf

Plant photosynthesis phenomics data quality control

Motivation Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments. Results We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The results on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity. Availability and implementation Software is available at phenomics.uky.edu/PhenoCurve. Contact chen.jin@uky.edu or kramerd8@cns.msu.edu. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/phenocurve-capturing-dynamic-phenotype-environment-4itl2qun1l.pdf

PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data.

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

/pdf/genomic-selection-for-milk-production-traits-in-xinjiang-n23ttnvf.pdf

Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

Simple Summary Body conformation traits are closely related to economically important characteristics and should be considered in cattle breeding programs. A variety of body conformation traits recorded by classifiers can complicate the analysis process. Factor analysis can reduce the number of variables by combining two or more variables into a single factor, which has biological significance. The results of this study could be used by breeders to define conformation indexes and implement genetic assessments for conformation traits in dual-purpose breeds. Abstract In this study, we estimated the genetic parameters for 6 composite traits and 27 body conformation traits of 1016 dual-purpose Simmental cattle reared in northwestern China from 2010 to 2019 using a linear animal mixed model. To integrate these traits, a variety of methods were used as follows: (1) genetic parameters estimates for composite and individual body conformation traits based on the pedigree relationship matrix (A) and combined genomic-pedigree relationship matrix (H); (2) factor analysis to explore the relationships among body conformation traits; and (3) genetic parameters of factor scores estimated using A and H, and the correlations of EBVs of the factor scores and EBVs of the composite traits. Heritability estimates of the composite traits using A and H were low to medium (0.07–0.47). The 24 common latent factors explained 96.13% of the total variance. Among factors with eigenvalues ≥ 1, F1 was mainly related to body frame, muscularity, and rump; F2 was related to feet and legs; F3, F4, F5, and F6 were related to teat placement, teat size, udder size, and udder conformation; and F7 was related to body frame. Single-trait analysis of factor scores yielded heritability estimates that were low to moderate (0.008–0.43 based on A and 0.04–0.43 based on H). Spearman and Pearson correlations, derived from the best linear unbiased prediction analysis of composite traits and factor scores, showed a similar pattern. Thus, incorporating factor analysis into the morphological evaluation to simplify the assessment of body conformation traits may improve the genetics of dual-purpose Simmental cattle.

/pdf/factor-analysis-of-genetic-parameters-for-body-conformation-nadsxl87.pdf

Lei Xu

Papers

Methods for estimating photosynthetic characteristics in plant canopies and systems and apparatus related thereto

Plant photosynthesis phenomics data quality control

PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data.

Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

Factor Analysis of Genetic Parameters for Body Conformation Traits in Dual-Purpose Simmental Cattle