Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development.

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (g c) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of g c of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with g c were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of g c in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of g c and have the potential to help breeders more effectively develop drought-tolerant maize for target environments

Image set for deep learning: field images of maize annotated with disease symptoms

Objectives Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. Data description This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease

PINK1 Is Necessary for Long Term Survival and Mitochondrial Function in Human Dopaminergic Neurons

Parkinson's disease (PD) is a common age-related neurodegenerative disease and it is critical to develop models which recapitulate the pathogenic process including the effect of the ageing process. Although the pathogenesis of sporadic PD is unknown, the identification of the mendelian genetic factor PINK1 has provided new mechanistic insights. In order to investigate the role of PINK1 in Parkinson's disease, we studied PINK1 loss of function in human and primary mouse neurons. Using RNAi, we created stable PINK1 knockdown in human dopaminergic neurons differentiated from foetal ventral mesencephalon stem cells, as well as in an immortalised human neuroblastoma cell line. We sought to validate our findings in primary neurons derived from a transgenic PINK1 knockout mouse. For the first time we demonstrate an age dependent neurodegenerative phenotype in human and mouse neurons. PINK1 deficiency leads to reduced long-term viability in human neurons, which die via the mitochondrial apoptosis pathway. Human neurons lacking PINK1 demonstrate features of marked oxidative stress with widespread mitochondrial dysfunction and abnormal mitochondrial morphology. We report that PINK1 plays a neuroprotective role in the mitochondria of mammalian neurons, especially against stress such as staurosporine. In addition we provide evidence that cellular compensatory mechanisms such as mitochondrial biogenesis and upregulation of lysosomal degradation pathways occur in PINK1 deficiency. The phenotypic effects of PINK1 loss-of-function described here in mammalian neurons provides mechanistic insight into the age-related degeneration of nigral dopaminergic neurons seen in PD

Scalable growth models for time‐series multispectral images

Abstract Vegetation indices (VIs) are produced as a combination of different reflectance bands that are captured by multispectral images (MSIs). These indices, such as normalized difference vegetation index (NDVI), are reported to be proxy indicators of photosynthetic activity, plant canopy biomass, and leaf area index. To determine the utility of using VI derived from MSI to model plant growth, random regression (RR) models with linear splines and different orders of Legendre polynomials were applied to data collected (years 2019 and 2020) as part of the Genome‐to‐Fields initiative. Growth curves of maize (Zea mays L.) hybrids were modeled using both NDVI and cumulative NDVI (cNDVI) phenotypes. Due to the difference in MSI recording dates, and sparse overlap in hybrids between years, all the analyses were nested within a year. Results indicate that RR models using Legendre polynomials provide a robust and scalable method for modeling growth curves using phenotypes extracted from MSI; however, RR models using linear splines showed inconsistent convergence. Growth curves estimated using NDVI and cNDVI showed low‐to‐moderate heritability (0.11–0.44) and a range of genetic correlations (−0.15 to 0.97) with grain yield. This study demonstrates the utility of MSI for modeling genetic growth trends, with the best modeling results obtained when using Legendre polynomials and cNDVI

Effects of Cold Temperature and Acclimation on Cold Tolerance and Cannabinoid Profiles of Cannabis sativa L. (Hemp)

Hemp (Cannabis sativa) is a multi-use crop garnering newfound attention from researchers and consumers. While interest has emerged, a lack of substantiated research still exists regarding effects of adverse weather events on physiological health and secondary metabolite production of hemp. The aim of this experiment was to assess cold tolerance of hemp using the cultivars ‘FINOLA’ and ‘AutoCBD’. Effects of cultivar, plant age, cold acclimation, frequency of cold treatments, and intensity of cold treatments were all considered in regard to their influence on physiological stress, biomass, and cannabinoid profile. Few effects of sequential cold treatments were noted, and they were not moderated by cold acclimation, which tended to have negative effects across many responses. This detrimental effect of cold acclimation conditions was further observed in decreased total CBD % and total THC % compared to non-acclimated plants. These findings bear consideration when assessing the unpredictability of a changing climate’s effects on the heath and cannabinoid profile of hemp

Effects of Cold Temperature and Acclimation on Cold Tolerance and Cannabinoid Profiles of <i>Cannabis sativa</i> L. (Hemp)

Hemp (Cannabis sativa) is a multi-use crop garnering newfound attention from researchers and consumers. While interest has emerged, a lack of substantiated research still exists regarding effects of adverse weather events on physiological health and secondary metabolite production of hemp. The aim of this experiment was to assess cold tolerance of hemp using the cultivars ‘FINOLA’ and ‘AutoCBD’. Effects of cultivar, plant age, cold acclimation, frequency of cold treatments, and intensity of cold treatments were all considered in regard to their influence on physiological stress, biomass, and cannabinoid profile. Few effects of sequential cold treatments were noted, and they were not moderated by cold acclimation, which tended to have negative effects across many responses. This detrimental effect of cold acclimation conditions was further observed in decreased total CBD % and total THC % compared to non-acclimated plants. These findings bear consideration when assessing the unpredictability of a changing climate’s effects on the heath and cannabinoid profile of hemp

Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease

Raw_GBS_SweetCorn_384indiv_955K_RefGen_v2.hmp

Raw genotypes from 384 sweet corn inbred lines using genotyping-by-sequencing (GBS) with 955,690 high confidence single-nucleotide polymorphism markers (SNPs) that were called using default parameters in the TASSEL 5 GBSv1 production pipeline with the ZeaGBSv2.7 Production TagsOnPhysicalMapfile in B73 RefGen_v2 coordinates. File in Hapmap format

Genome-Wide Association and Genomic Prediction Models of Tocochromanols in Fresh Sweet Corn Kernels

Sweet corn ( L.), a highly consumed fresh vegetable in the United States, varies for tocochromanol (tocopherol and tocotrienol) levels but makes only a limited contribution to daily intake of vitamin E and antioxidants. We performed a genome-wide association study of six tocochromanol compounds and 14 derivative traits across a sweet corn inbred line association panel to identify genes associated with natural variation for tocochromanols and vitamin E in fresh kernels. Concordant with prior studies in mature maize kernels, an association was detected between γ-tocopherol methyltransferase (vte4) and α-tocopherol content, along with () and () for tocotrienol variation. Additionally, two kernel starch synthesis genes, () and (), were associated with tocotrienols, with the strongest evidence for in combination with fixed, strong and alleles, accounting for the greater amount of tocotrienols in and lines. In prediction models with genome-wide markers, predictive abilities were higher for tocotrienols than tocopherols, and these models were superior to those that used marker sets targeting a priori genes involved in the biosynthesis and/or genetic control of tocochromanols. Through this quantitative genetic analysis, we have established a key step for increasing tocochromanols in fresh kernels of sweet corn for human health and nutrition