Foliar-applied silicon and selenium nanoparticles modulated salinity stress through modifying yield, biochemical attribute, and fatty acid profile of Physalis alkekengi L.

Soil salinity is a major environmental problem owing to its negative impact on agricultural productivity and sustainability. Nanoparticles (NPs) have recently been highlighted for their ability to alleviate salinity stress. The current study aimed to alleviate salt stress by using silicon (Si) and selenium (Se) NPs on the growth and physiological attributes of Physalis alkekengi L. Plants were irrigated with saline water at 50, 100, and 200 mM NaCl, and Si NPs (200 mg L-1) and Se NPs (50 mg L-1) were sprayed on leaves three times in a pot experiment in 2022. Leaf chlorophyll (Chl) content, antioxidant capacity, and fatty acid (FA) profile of fruits were measured to find the effects of NPs and salinity in the plants. Salinity at 50 mM did not significantly differ from the control, but at 100-200 mM, salt stress had a substantial impact on the majority of traits. Compared with non-saline conditions, 200 mM NaCl led to decreases in shoot weight (40%), fruit weight (30%), Chl a (30%), Chl b (39%), anthocyanin (31%), ascorbic acid (16%), total phenolic content (TPC, 11%) but increases in total soluble solids (TSS, 79%), titration acidity (TA, 17%), and TSS/TA (52%) in plants without spraying the NPs. However, Si and Se NPs modulated salinity stress by increasing shoot and fruit weight, Chl content, anthocyanin, and TPC, and with decreasing TSS and TSS/TA. Salinity elevated polyunsaturated fatty acids (PUFAs) and lowered monounsaturated fatty acids (MUFAs). According to multivariate analysis, 50 mM and control were found to be in the same cluster, whereas 100 and 200 mM were shown to be in different clusters. Foliar application of Si and Se NPs at 200 and 50 mg L-1, respectively, can be recommended for mitigating salt stress at 100-200 mM NaCl in P. alkekengi L. Plants. Farmers can use the findings to increase the ability of Si and Se NPs to protect plants against salt.

A novel weighted support vector machine based on particle swarm optimization for gene selection and tumor classification.

We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.