Real-World COVID-19 Vaccine Protection Rates against Infection in the Delta and Omicron Eras

The real-world vaccine protection rates(VPRs)against the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection are critical in formulating future vaccination strategies against the virus.Based on a varying coefficient stochastic epidemic model,we obtain 7 countries’real-world VPRs using daily epidemiological and vaccination data,and find that the VPRs improved with more vaccine doses.The average VPR of the full vaccination was 82%(SE:4%)and 61%(SE:3%)in the pre-Delta and Deltadominated periods,respectively.The Omicron variant reduced the average VPR of the full vaccination to 39%(SE:2%).However,the booster dose restored the VPR to 63%(SE:1%)which was significantly above the 50%threshold in the Omicron-dominated period.Scenario analyses show that the existing vaccination strategies have significantly delayed and reduced the timing and the magnitude of the infection peaks,respectively,and doubling the existing booster coverage would lead to 29%fewer confirmed cases and 17%fewer deaths in the 7 countries compared to the outcomes at the existing booster taking rates.These call for higher full vaccine and booster coverage for all countries.

High-Throughput Field Plant Phenotyping:A Self-Supervised Sequential CNN Method to Segment Overlapping Plants

High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used.The first step in this process is typically plant segmentation,which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants.However,preparing such training data is both time and labor intensive.To solve this problem,we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems.This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages.The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed.We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes.We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques.This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

High-throughput phenotyping system has become more and more popular in plant science research.The data analysis for such a system typically involves two steps:plant feature extraction through image processing and statistical analysis for the extracted features.The current approach is to perform those two steps on different platforms.We develop the package“implant”in R for both robust feature extraction and functional data analysis.For image processing,the“implant”package provides methods including thresholding,hidden Markov random field model,and morphological operations.For statistical analysis,this package can produce nonparametric curve fitting with its confidence region for plant growth.A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

KAT4IA:K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes

High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a prerequisite.Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data.Unfortunately,preparing a sufficiently large training data is both time and labor-intensive.We describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system.The KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted traits.To deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images.This approach is efficient and does not require human intervention.Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

Genome–Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes.Variations of one or several traits are often assessed separately.High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals.Here,we test the association between markers within a gene and many traits simultaneously.This genome–phenome wide association study(GPWAS)is both a multi-marker and multi-trait test.Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation.Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles.Genes linked to phenomic variation in maize using GPWAS shared molecular,population genetic,and evolutionary features with classical mutants in maize.Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes.GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes.The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.