Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine-and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine-and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine-and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014)(2015)(2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and −0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine-and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine-and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine-and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

Prediction of breeding values and phenotypes is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine and deep learning algorithms applied to complex traits in plants can improve prediction accuracies in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning -based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and -0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. Four explored Bayesian models gave similar accuracies, which were less than machine and deep learning-based models, and required increased computational time. Green normalized difference vegetation index best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine and deep learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

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Background: Menopause is a transitional phase marking the finale of the reproductive life of a woman. It’s associated with complex physical and psychological changes. The vasomotor symptoms are the hallmark of menopause, affecting 75% of women and among this 25% are severely affected and are the most common and long-lasting symptoms according to many studies. In Ayurvedic literature “Rajonivrutti” has very little references available. The age of Rajonivruthi is said to be around 50 years as per the classics and it is considered as a premonitory sign of Jara in women. The vasomotor symptoms like hot flushes, night sweats, irritability clearly indicate the involvement of dominant Pitta Dosha associated with Vata. Hence a drug that pacifies Pitta along with Vata, without disturbing Kapha will be ideal for treatment. Dahaprashamana Gana explained in Charaka Samhitha Sutrasthana, denotes a group of medicinal plants, which has been indicated as useful in removing Daha which is a direct manifestation of Pitta. Aim: To analyse the effect of Dahaprashamana Churna in the management of vasomotor symptoms in perimenopause and menopause. Method: A simple randomized open label controlled clinical study thirty subjects fulfilling the diagnostic criteria of Vasomotor symptoms were selected and randomly categorized to Group A and Group B by using lottery method. Result: Dahaprashamana Churna found to be effective in all subjective and objective parameters. Conclusion: Dahaprashamana Churna is more effective than Vitamin E in the management of Vasomotor symptoms in Perimenopause and Menopause