Association mapping with a diverse population of Puccinia graminis f. sp. tritici identified avirulence loci interacting with the barley Rpg1 stem rust resistance gene.

BACKGROUND: Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is an important disease of barley and wheat. A diverse sexual Pgt population from the Pacific Northwest (PNW) region of the US contains a high proportion of individuals with virulence on the barley stem rust resistance (R) gene, Rpg1. However, the evolutionary mechanisms of this virulence on Rpg1 are mysterious considering that Rpg1 had not been deployed in the region and the gene had remained remarkably durable in the Midwestern US and prairie provinces of Canada. METHODS AND RESULTS: To identify AvrRpg1 effectors, genome wide association studies (GWAS) were performed using 113 Pgt isolates collected from the PNW (n = 89 isolates) and Midwest (n = 24 isolates) regions of the US. Disease phenotype data were generated on two barley lines Morex and the Golden Promise transgenic (H228.2c) that carry the Rpg1 gene. Genotype data was generated by whole genome sequencing (WGS) of 96 isolates (PNW = 89 isolates and Midwest = 7 isolates) and RNA sequencing (RNAseq) data from 17 Midwestern isolates. Utilizing ~1.2 million SNPs generated from WGS and phenotype data (n = 96 isolates) on the transgenic line H228.2c, 53 marker trait associations (MTAs) were identified. Utilizing ~140 K common SNPs generated from combined analysis of WGS and RNAseq data, two significant MTAs were identified using the cv Morex phenotyping data. The 55 MTAs defined two distinct avirulence loci, on supercontig 2.30 and supercontig 2.11 of the Pgt reference genome of Pgt isolate CRL 75-36-700-3. The major avirulence locus designated AvrRpg1A was identified with the GWAS using both barley lines and was delimited to a 35 kb interval on supercontig 2.30 containing four candidate genes (PGTG_10878, PGTG_10884, PGTG_10885, and PGTG_10886). The minor avirulence locus designated AvrRpg1B identified with cv Morex contained a single candidate gene (PGTG_05433). AvrRpg1A haplotype analysis provided strong evidence that a dominant avirulence gene underlies the locus. CONCLUSIONS: The association analysis identified strong candidate AvrRpg1 genes. Further analysis to validate the AvrRpg1 genes will fill knowledge gaps in our understanding of rust effector biology and the evolution and mechanism/s of Pgt virulence on Rpg1.

Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer's disorder detection using EEG signals.

Alzheimer's disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer's disease more crucial since it lessens the amount of work for staff members and improves diagnostic outcomes. The major aim of this work is "to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal". Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Troops espoused Alzheimer's Disorder Detection using EEG signals (DSRNN-AGTO-ADD) is proposed in this paper. Here, Dynamic Context-Sensitive Filter (DCSF) is considered to eliminate the noise, and interference from the EEG signal. Then Adaptive and Concise Empirical Wavelet Transform (ACEWT) is utilized to separate the filtered signals from the frequency bands, and to feature extraction from the EEG signals. Signal's characteristics, like logarithmic bandwidth power, standard deviation, variance, kurtosis, mean energy, mean square, norm are combined to ACEWT method to create feature vectors and enhance diagnostic performance. After that, the extracted features are fed to Dynamically Stabilized Recurrent Neural Network (DSRNN) for task classification. Weight parameter of DSRNN is enhanced using Artificial Gorilla Troops Optimization Algorithm (AGTOA). The proposed DSRNN-AGTOA-ADD algorithm is activated in MATLAB. The metrics including accuracy, specificity, sensitivity, precision, computation time, ROC are examined for AD diagnosis. The performance of the proposed DSRNN-AGTOA-ADD approach attains 12.98%, 5.98% and 23.45% high specificity; 29.98%, 23.32% and 19.76% lower computation Time and 29.29%, 8.365%, 8.551% and 7.915% higher ROC compared with the existing methods.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.