Biology subjects

Gonzalez, E. M.

Publications and source records attributed to Gonzalez, E. M..

3 recordsLinked to original sources

bioRxiv2024

Quantifying Leaf Symptoms of Sorghum Charcoal Rot in Images of Field-Grown Plants 9 Using Deep Neural Networks

Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection. EfficientNet-B3 and a fully convolutional network (FCN) emerged as the top-performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet-B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models validation scores increased linearly, and their processing time decreased exponentially. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a base for drone-based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web-based application where users can easily analyze their own images. Core ideasO_LIAutomated phenotyping tools are required for the efficient detection and quantification of charcoal rot of sorghum. C_LIO_LIClassification and segmentation models can distinguish between concurrent plant stresses with similar symptoms. C_LIO_LILarger image patch sizes generally improve model performance and reduce processing time. C_LI

plant biology↗

bioRxiv2024

NeuroTri2-VISDOT: An open-access tool to harness the power of second trimester human single cell data to inform models of Mendelian neurodevelopmental disorders

Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and have led to the rapid identification of novel syndromes. For many Mendelian neurodevelopmental disorders (NDDs), there is a lack of pre-existing model systems for mechanistic work. Thus, it is critical for translational researchers to have an accessible phenotype- and genotype-informed approach for model system selection. Single-cell RNA sequencing data can be informative in such an approach, as it can indicate which cell types express a gene of interest at the highest levels across time. For Mendelian NDDs, such data for the developing human brain is especially useful. A valuable single-cell RNA sequencing dataset of the second trimester developing human brain was produced by Bhaduri et al in 2021, but access to these data can be limited by computing power and the learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we have built the web-based tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT), for exploring this single-cell dataset, and we have employed it in several different settings to demonstrate its utility for the translational research community.

genetics↗

bioRxiv2024

m6A-dependent circular RNA formation mediatestau-induced neurotoxicity

Circular RNAs (circRNAs), covalently closed RNA molecules that form due to back-splicing of RNA transcripts, have recently been implicated in Alzheimers disease and related tauopathies. circRNAs are regulated by N6-methyladenosine (m6A) RNA methylation, can serve as "sponges" for proteins and RNAs, and can be translated into protein via a cap-independent mechanism. Mechanisms underlying circRNA dysregulation in tauopathies and causal relationships between circRNA and neurodegeneration are currently unknown. In the current study, we aimed to determine whether pathogenic forms of tau drive circRNA dysregulation and whether such dysregulation causally mediates neurodegeneration. We identify circRNAs that are differentially expressed in the brain of a Drosophila model of tauopathy and in induced pluripotent stem cell (iPSC)-derived neurons carrying a tau mutation associated with autosomal dominant tauopathy. We leverage Drosophila to discover that depletion of circular forms of muscleblind (circMbl), a circRNA that is particularly abundant in brains of tau transgenic Drosophila, significantly suppresses tau neurotoxicity, suggesting that tau-induced circMbl elevation is neurotoxic. We detect a general elevation of m6A RNA methylation and circRNA methylation in tau transgenic Drosophila and find that tau-induced m6A methylation is a mechanistic driver of circMbl formation. Interestingly, we find that circRNA and m6A RNA accumulate within nuclear envelope invaginations of tau transgenic Drosophila and in iPSC-derived cerebral organoid models of tauopathy. Taken together, our studies add critical new insight into the mechanisms underlying circRNA dysregulation in tauopathy and identify m6A-modified circRNA as a causal factor contributing to neurodegeneration. These findings add to a growing literature implicating pathogenic forms of tau as drivers of altered RNA metabolism.

neuroscience↗