Sulfate transporters (SULTRs) are an essential plant transporter class responsible for the absorption and distribution of sulfur, an essential plant growth element. SULTRs are also involved in processes related to growth and development and in response to environmental stimuli. In the present study, 22 TdSULTR family members were identified and characterized in the genome of Triticum turgidum L. ssp. durum (Desf.) using available bioinformatics tools. The expression levels of candidate TdSULTR genes were investigated under salt treatments of 150 and 250 mM NaCl after several different exposure times. TdSULTRs showed diversity in terms of physiochemical properties, gene structure, and pocket sites. TdSULTRs and their orthologues were classified into the known five main plant groups of highly diverse subfamilies. In addition, it was noted that segmental duplication events could lengthen TdSULTR family members under evolutionary processes. Based on pocket site analysis, the amino acids leucine (L), valine (V), and serine (S) were most often detected in TdSULTR protein binding sites. Moreover, it was predicted that TdSULTRs have a high potential to be targeted by phosphorylation modifications. According to promoter site analysis, the plant bioregulators ABA and MeJA were predicted to affect TdSULTR expression patterns. Real-time PCR analysis revealed TdSULTR genes are differentially expressed at 150 mM NaCl but show similar expression in response to 250 mM NaCl. TdSULTR reached a maximum level of expression 72 h after the 250 mM salt treatment. Overall, we conclude that TdSULTR genes are involved in the response to salinity in durum wheat. However, additional studies of functionality are needed to determine their precise function and linked-interaction pathways.

Interactions between above- and below-ground monoculture forest plantation components are critical to tree growth and development. Within the Central Hardwoods Region (CHR), synergistic relationships between tree species and soil microbial community structure and function have received limited research attention. Soil microbes are integral to forest ecosystems as their activities intrinsically promote soil organic matter decomposition, nutrient cycling, and ecosystem functioning. Here, we examined soils from two perfectly aligned stands of black walnut (BW, Juglans nigra L.) and Northern red oak (RO, Quercus rubra L.) trees. Measurements of selected soil chemical properties, microbial community structure using ester-linked fatty acid methyl ester (EL-FAME), and soil enzyme activities (EAs) were used. Analysis of modifications within microbial communities showed a significant positive response to BW based upon soil EAs and microbial indicators, compared to RO. Seasonal comparisons predictably revealed higher microbial activities during summer. Fungi dominated the soil microbial community structure with a fungal/bacterial ratio of 2:1. Gram-positive rather than Gram-negative bacteria or actinomycetes dominated the bacterial community. The activity of the soil enzymes ß-glucosidase and arylsulfatase increased, but ß-glucosaminidase and acid phosphatase decreased. Additionally, acid phosphatase and arbuscular mycorrhizal fungi revealed strong correlations. The differences observed in biological properties, specifically microbial communities and EAs, highlight the varied responses to BW and RO soil biology and subsequent soil ecosystem functions. These results indicate that variations in microbial abundance and soil functions occur throughout the course of an entire year.

Pistachio is one of the most economically important nut crops worldwide. However, there are no reports describing the chloroplast genome of this important fruit tree. In this investigation, we assembled and characterized the complete pistachio chloroplast sequence. The Pistacia vera chloroplast genome was 160,598 bp in size, similar to other members of Anacardiaceae (149,011–172,199 bp) and exhibited the typical four section structure, including a large single copy region (88,174 bp), a small single copy region (19,330 bp), and a pair of inverted repeats regions (26,547 bp). The genome contains 121 genes comprised of 87 protein-coding genes, 30 tRNA genes and 4 rRNA genes. Thirteen intron-containing genes were identified in the genome wherein two genes had more than two introns. The genomic patterns of GC content resembled those for other Anacardiaceae. P. vera displayed the highest number of simple sequence repeats (SSRs) among the genera studied, which may be useful for molecular marker development and future population studies. Amino acid analysis revealed that Leucine is the most frequent (10.69%) amino acid in the chloroplast genome followed by Isoleucine (8.53%) and Serine (7.77%). Cysteine (1.30%) and Tryptophan (1.74%) were the least frequent amino acids. Phylogenetic analysis revealed P. vera is most like its taxonomically close relative P. weinmaniifolia, followed by Rhus chinensis; all placed taxonomically in the tribe Rhoeae. Members of Anacardiaceae were most closely related to Rhoeae, followed by members of Spondieae. The reports of this chloroplast genome will be useful for future conservation studies, genetic evaluation and breeding of P. vera, and more comprehensive phylogenetic analysis of the Pistacia species and its closely-related genera.

Linseed (Linum usitatissimum L.), a globally cultivated oilseed crop in high demand, is the focal point of our efforts aimed at improving yield production. The achievement of robust yield outcomes relies on the intricate interplay of various agronomic traits. This study, conducted over two years at a research farm in Iran, presents a comprehensive analysis evaluating diverse agronomic characteristics inherent to different linseed cultivars and hybrids. Essential parameters, including days to emergence, days to flowering, plant height, number of branches, number of capsules per plant, number of seeds per capsule, 1000-seed weight, and seed yield per plant, were examined. For predictive insights into seed yield, machine learning techniques, specifically multilayer perceptron (MLP) and multiple linear regression (MLR), were employed. The analysis of contribution percentages for each agronomic variable to linseed seed yield revealed that the number of capsules per plant emerged as the most influential factor, contributing 30.7% among the considered variables. The results indicated the superiority of MLP over MLR, with RMSE and MAPE values equaling 0.062 g/plant and 3.585%, respectively. Additionally, R² values for training, validation, and test phases exceeded 0.97. Consequently, MLP served as a merit function in the genetic algorithm (GA), targeting the identification of optimal trait levels to maximize linseed yield. The optimization outcomes demonstrated the potential achievement of a yield of 4.40 g/plant. To attain this performance, a set of agronomic characteristic values was proposed by GA, initiating a discussion on genetic modification possibilities. The findings of this study highlight the remarkable efficacy of machine learning tools, particularly neural networks, when paired with evolutionary optimization techniques such as genetic algorithms. These methodologies prove to be invaluable assets in aiding biotechnologists as they strive to enhance the genetic makeup of products for various applications, providing unwavering reliability and invaluable guidance in the pursuit of genetic modification endeavors.

The modeling and prediction of crop seed yield can be a vital improvement in the precision agriculture industry as it provides reliable assessments of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are established for predicting sesame (Sesamum indicum L.) seed yield (SSY) and incorporating agro-morphological features. Models utilized for coupled PCA-ML (Principal component analysis-Machine Learning) methods were compared with original ML models to evaluate predicted efficiency. The Gaussian process regression (GPR) and Radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions with determination coefficients or R2 values of 0.99 and 0.91, respectively. The root-mean-square error (RMSE) for the ML models fluctuated between 0 to 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. Estimation of sesame seed yield with coupled PCA-ML models improved performance accuracy. The K-fold process suggested the utilization of datasets with the lowest error rates to ensure the continued accuracy of GPR and RBF models. Sensitivity analysis revealed the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.