Pyramiding of qDTY1.1 and qDTY3.1 into rice mega-variety Samba Mahsuri-Sub1: physiological performance under water deficit conditions

Water deficit is a crucial factor causing huge loss to rice productivity. The present study aimed to develop a multiple stress tolerant genotype by pyramiding drought tolerant yield QTLs qDTY1.1 and qDTY3.1 into high yielding rice variety Samba Mahsuri Sub-1(SMS) through marker assisted pyramiding. To achieve this six introgression lines of SMS carrying qDTY1.1 (SAB) were crossed with DRR-50, an Essentially Derived Variety of SMS carrying qDTY3.1. The SAB lines are taller than SMS due to tight linkage between qDTY1.1 and wild type SD-1. Therefore, F2 generation of crosses were screened for recombinants between SD-1 and qDTY1.1. Phenotyping of 1530 F2 plants representing three F2 populations from 35 F1 hybrids, identified 305 dwarf plants. Three dwarf F2 plants along with three others carrying qDTY1.1 and qDTY3.1 were forwarded to F3 generation. From the six F3 (SABD) lines fourteen pyramided progenies were selected and forward to F4 generation. The six SABD F3 lines SABD-7, SABD-8, SABD-9, SABD-76, SABD-79 and SABD-80 along with parents were evaluated under moisture stress (MS) for various physiological parameters. Chlorophyll and relative water content were more, while canopy temperature and malonaldehyde (MDA) content were lesser in SABD lines compared to parents indicating tolerance under MS. Variance due to genotypes was highly significant for all the yield related traits except test weight. Based on seed morphology, agronomic characters and physiological parameters six superior lines SABD-9-3, SABD-9-2, SABD-9-6, SABD-9-7, SABD-76-2 and SABD-76-6 performing better under MS were identified, which could be released after multi-location evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01387-5.

Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification

In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner.

A surfactant-assisted high energy ball milling technique to produce colloidal nanoparticles and nanocrystalline flakes in Mn–Al alloys

A surfactant assisted-high energy ball milling technique was employed to process Mn₅₄Al₄₆ alloy in the form of colloidal nanoparticles with non-magnetic nature and high-aspect ratio nanocrystalline flakes with interesting hard magnetic properties.