Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

Background: Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping are currently the largest constraints on plant breeding efforts. Datasets linking new types of high-throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision–based tools. Findings A set of maize inbreds—primarily recently off patent lines—were phenotyped using a high-throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high-density genotyping and scored for a core set of 13 phenotypes in field trials across 13 North American states in 2 years by the Genomes 2 Fields Consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released. Conclusions Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors that influence yield plasticity

Differentially Regulated Orthologs in Sorghum and the Subgenomes of Maize

Identifying interspecies changes in gene regulation, one of the two primary sources of phenotypic variation, is challenging on a genome-wide scale. The use of paired time-course data on cold-responsive gene expression in maize (Zea mays) and sorghum (Sorghum bicolor) allowed us to identify differentially regulated orthologs. While the majority of cold-responsive transcriptional regulation of conserved gene pairs is species specific, the initial transcriptional responses to cold appear to be more conserved than later responses. In maize, the promoters of genes with conserved transcriptional responses to cold tend to contain more micrococcal nuclease hypersensitive sites in their promoters, a proxy for open chromatin. Genes with conserved patterns of transcriptional regulation between the two species show lower ratios of nonsynonymous to synonymous substitutions. Genes involved in lipid metabolism, known to be involved in cold acclimation, tended to show consistent regulation in both species. Genes with species-specific cold responses did not cluster in particular pathways nor were they enriched in particular functional categories. We propose that cold-responsive transcriptional regulation in individual species may not be a reliable marker for function, while a core set of genes involved in perceiving and responding to cold stress are subject to functionally constrained cold-responsive regulation across the grass tribe Andropogoneae

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

Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

Background: Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping are currently the largest constraints on plant breeding efforts. Datasets linking new types of high-throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision–based tools. Findings A set of maize inbreds—primarily recently off patent lines—were phenotyped using a high-throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high-density genotyping and scored for a core set of 13 phenotypes in field trials across 13 North American states in 2 years by the Genomes 2 Fields Consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released. Conclusions Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors that influence yield plasticity

Impact of Mulching on Soil Moisture and Sap Flow Characteristics of Jujube Trees

The main purpose of this study was to assess the influence of grass planting and jujube branch mulching on soil moisture levels and jujube tree transpiration rates, with the ultimate goal of improving jujube tree production in rain-fed orchards. The study encompassed four treatments: jujube branch mulching (JBM), jujube branch mulching with white clover planting (JBM + WCP), white clover planting (WCP), and clean cultivation (CC). During a two-year experiment, it was observed that the JBM treatment exhibited the highest capacity for moisture conservation. Specifically, it resulted in an average increase of 2.69% (in 2013) and 2.23% (in 2014) in soil moisture content compared with the CC treatment. The application of statistical analysis revealed significant differences (p p p p p < 0.05) compared with JBM + WCP. The sap flow velocity was positively correlated with air temperature, vapor pressure deficit, wind velocity, photosynthetically active radiation, and soil temperature. Photosynthetically active radiation was identified as the main driving factor influencing jujube tree transpiration. In conclusion, the findings of this study demonstrate the effectiveness of using pruned jujube branches for coverage in rain-fed jujube orchards. This approach not only conserves mulching materials and diminishes the expenses associated with transporting pruned jujube tree branches away from the jujube orchard but also achieves multiple objectives, including increasing soil moisture, promoting jujube tree transpiration, and enhancing soil water utilization. These results have significant implications for the efficient utilization of rainwater resources in rain-fed jujube orchards and provide valuable insights for practical applications in orchard management

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

Enhance the delivery of light energy ultra-deep into turbid medium by controlling multiple scattering photons to travel in open channels.

Multiple light scattering is considered as the major limitation for deep imaging and focusing in turbid media. In this paper, we present an innovative method to overcome this limitation and enhance the delivery of light energy ultra-deep into turbid media with significant improvement in focusing. Our method is based on a wide-field reflection matrix optical coherence tomography (RM-OCT). The time-reversal decomposition of the RM is calibrated with the Tikhonov regularization parameter in order to get more accurate reversal results deep inside the scattering sample. We propose a concept named model energy matrix, which provides a direct mapping of light energy distribution inside the scattering sample. To the best of our knowledge, it is the first time that a method to measure and quantify the distribution of beam intensity inside a scattering sample is demonstrated. By employing the inversion of RM to find the matched wavefront and shaping with a phase-only spatial light modulator, we succeeded in both focusing a beam deep (~9.6 times of scattering mean free path, SMFP) inside the sample and increasing the delivery of light energy by an order of magnitude at an ultra-deep (~14.4 SMFP) position. This technique provides a powerful tool to understand the propagation of photon in a scattering medium and opens a new way to focus light inside biological tissues

Auto‐segmentation of the clinical target volume using a domain‐adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy

Abstract Purpose For postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs). Methods CT images were obtained postoperatively from 90 patients with gynaecological cancer who underwent vaginal brachytherapy, including 60 and 30 treated with applicators A and X, respectively. A basal model was devised using data from the patients treated with applicator A; next, a DANN model was constructed using these same 60 patients as well as 10 of those treated with applicator X through transfer learning techniques. The remaining 20 patients treated with applicator X comprised the validation set. The model's performance was assessed using objective metrics and manual clinical evaluation. Results The DANN model outperformed the basal model on both objective metrics and subjective evaluation (p<0.05 for all). The median DSC and 95HD values were 0.97 and 3.68 mm in the DANN model versus 0.94 and 5.61 mm in the basal model, respectively. Multi‐centre subjective evaluation by three clinicians showed that 99%, 98%, and 81% of CT slices contoured by the DANN model were acceptable versus only 73%, 77%, and 57% of those contoured by the basal model. One clinician deemed the DANN model comparable to manual delineation. Conclusion DANNs provides a realistic approach for the wide application of automatic segmentation of POVBT and can potentially be used to construct auto‐segmentation models from small datasets

New Eudesmane-Type Sesquiterpenoids from the Mangrove-Derived Endophytic Fungus Penicillium sp. J-54

Four new eudesmane-type sesquiterpenoids, penicieudesmol A–D (1–4), were isolated from the fermentation broth of the mangrove-derived endophytic fungus Penicillium sp. J-54. Their structures were determined by spectroscopic methods, the in situ dimolybdenum CD method, and modified Mosher’s method. The bioassays results showed that 2 exhibited weak cytotoxicity against K-562 cells