Search Results (40)

Grafting is a prevalent horticultural technique that enhances crop yields and stress resilience; nevertheless, compatibility issues frequently constrain its efficacy. This research examined the physiological, hormonal, and transcriptional factors regulating compatibility between the litchi (Litchi chinensis Sonn.) cultivars Feizixiao (FZX) and Ziniangxi (ZNX). The anatomical and growth investigations demonstrated significant disparities between compatible (FZX as scion and ZNX as rootstock) and incompatible (ZNX as scion and FZX as rootstock) grafts, with the latter showing reduced levels of indole acetic acid (IAA). Exogenous 1-naphthalene acetic acid (NAA) application markedly improved the graft survival, shoot development, and hormonal synergy, whereas the auxin inhibitor tri-iodobenzoic acid (TIBA) diminished these parameters. The incompatible grafts showed downregulation of auxin transporter genes, including ATP-binding cassette (ABC) transporter, AUXIN1/LIKE AUX1 (AUX/LAX), and PIN-FORMED (PIN) genes, suggesting impaired vascular tissue growth. Metabolomic profiling revealed dynamic interactions between auxin, salicylic acid, and jasmonic acid, with NAA-treated grafts exhibiting enhanced levels of stress-responsive metabolites. Transcriptome sequencing identified differentially expressed genes (DEGs) linked to auxin signaling (ARF, GH3), seven additional phytohormones, secondary metabolism (terpenoids, anthocyanins, and phenylpropanoids), and ABC transporters. Gene ontology and KEGG analyses highlighted the significance of hormone interactions and the biosynthesis of secondary metabolites in successful grafting. qRT-PCR validation substantiated the veracity of the transcriptome data, emphasizing the significance of auxin transport and signaling in effective graft development. This study provides an in-depth review of the molecular and physiological factors influencing litchi grafting. These findings provide critical insights for enhancing graft success rates in agricultural operations via targeted hormonal and genetic approaches. Full article

Hydrangea (Hydrangea macrophylla) is distinguished by having sepals instead of real petals, a trait that facilitates color diversity. Floral color is largely predetermined by structural genes linked to anthocyanin production, but the genetic factors determining floral hue in this non-model plant remain unclear. Anthocyanin metabolites, transcriptome, and the CIEL*a*b* hue system were employed to elucidate the biochemical and molecular mechanisms of floral color formation in three hydrangea cultivars: ‘DB’ (deep blue), ‘LB’ (light blue), and ‘GB’ (green blue). UPLC-MS/MS identified 47 metabolites, with delphinidin, cyanidin, malvidin, petunidin, pelargonidin, and peonidin being prominent. Delphinidins were 90% of the primary component in ‘DB’. The dataset identifies 51 and 31 DEGs associated with anthocyanin, flavonoid, and chlorophyll biosynthesis, with CHS, CHI, F3H, F3′5′H, DFR, ANS, BZ1, and 3AT displaying the highest expression in ‘DB’. Notably, DFR (cluster-46471.3) exhibits high expression in ‘DB’ while being down-regulated in ‘LB’ and ‘GB’, correlating with higher anthocyanin levels in floral pigmentation. Comparative analyses of ‘LB’ vs. ‘DB’, ‘DB’ vs. ‘GB’, and ‘LB’ vs. ‘GB’ revealed 460, 490, and 444 differentially expressed TFs, respectively. WRKY, ERF, bHLH, NAC, and AP2/ERF showed the highest expression in ‘DB’, aligning with the color formation and key anthocyanin biosynthesis-related gene expression. The findings reveal the molecular mechanisms behind floral pigmentation variations and lay the groundwork for future hydrangea breeding programs. Full article

Litchi exhibits a large number of flowers, many flowering batches, and an inconsistent ratio of male and female flowers, frequently leading to a low fruit-setting rate. Floral sexual differentiation is a crucial phase in perennial trees to ensure optimal fruit production. However, the mechanism behind floral differentiation remains unclear. The objective of the study was to identify the role of auxin in floral differentiation at the transcriptional level. The results showed that the ratio of female flowers treated with naphthalene acetic acid (NAA) was significantly lower than that of the control stage (M0/F0). The levels of endogenous auxin and auxin metabolites were measured in male and female flowers at different stages of development. It was found that the levels of IAA, IAA-Glu, IAA-Asp, and IAA-Ala were significantly higher in male flowers compared to female flowers. Next-generation sequencing and modeling were employed to perform an in-depth transcriptome analysis on all flower buds in litchi ‘Feizixiao’ cultivars (Litchi chinensis Sonn.). Plant hormones were found to exert a significant impact on the litchi flowering process and flower proliferation. Specifically, a majority of differentially expressed genes (DEGs) related to the auxin pathway were noticeably increased during male flower bud differentiation. The current findings will enhance our comprehension of the process and control mechanism of litchi floral sexual differentiation. It also offers a theoretical foundation for implementing strategies to regulate flowering and enhance fruit production in litchi cultivation. Full article

This innovative article provides a detailed description of the successful biosynthesis of zinc nanoparticles (ZnO-NPs) using an aqueous extract of Algerian Date Syrup, also known as molasses. A meticulous process was carried out to determine the optimal calcination temperature for ZnO-NPs, a crucial step in the preparation of these nanoparticles. The study was further extended by creating ZnO/rGOx nanocomposites through a hydrothermal method, varying the concentrations of reduced graphene oxide (rGO) at 5%, 10%, and 15%. The characteristics of the nanocomposites were thoroughly explored, encompassing chemical, optical, and morphological aspects, using sophisticated analysis techniques such as scanning electron microscopy (SEM), UV-visible diffuse reflectance spectroscopy (UV DRS), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). These analyses provided an in-depth understanding of the structure and properties of the nanocomposites. The centerpiece of this study is the evaluation of the photocatalytic degradation capacity of ZnO-NPs and ZnO/rGOx nanocomposites. These materials have demonstrated their ability to act as cost-effective and environmentally friendly photocatalysts for wastewater treatment. Experiments on methylene blue degradation under UV irradiation were conducted, yielding impressive results: a degradation efficiency of 86.6% was achieved in 140 min using 1 g/L of ZnO-NPs, and this rate reached 100% with the ZnO/rGO catalyst in the same time frame, highlighting its superiority as a photocatalyst. Furthermore, this study examined the variables affecting the photocatalysis experiment, including the solution’s pH and the amount of catalyst. The results revealed that the ZnO/rGO photocatalyst reached its optimal efficiency under neutral pH conditions and at a concentration of 1 g/L, providing crucial information for practical use of these materials. This enriched article highlights the promising potential of ZnO-NPs and ZnO/rGOx nanocomposites as efficient photocatalysts for methylene blue degradation, paving the way for significant environmental applications in wastewater treatment. Full article

Light is an important environmental signal that governs plant growth, development, and metabolism. Constitutive photomorphogenic 1 (COP1) is a light signaling component that plays a vital role in plant light responses. We isolated the COP1 gene (LoCOP1) from the petals of Lilium ‘Siberia’ and investigated its function. The LoCOP1 protein was found to be the most similar to Apostasia shenzhenica COP1. LoCOP1 was found to be an important factor located in the nucleus and played a negative regulatory role in floral scent production and emission using the virus-induced gene silencing (VIGS) approach. The yeast two-hybrid, β-galactosidase, and bimolecular fluorescence complementation (BiFC) assays revealed that LoCOP1 interacts with LoMYB1 and LoMYB3. Furthermore, light modified both the subcellular distribution of LoCOP1 and its interactions with LoMYB1 and MYB3 in onion cells. The findings highlighted an important regulatory mechanism in the light signaling system that governs scent emission in Lilium ‘Siberia’ by the ubiquitination and degradation of transcription factors via the proteasome pathway. Full article

Plants produce an incredible variety of volatile organic compounds (VOCs) that assist the interactions with their environment, such as attracting pollinating insects and seed dispersers and defense against herbivores, pathogens, and parasites. Furthermore, VOCs have a significant economic impact on crop quality, as well as the beverage, food, perfume, cosmetics and pharmaceuticals industries. These VOCs are mainly classified as terpenoids, benzenoids/phenylpropanes, and fatty acid derivates. Fruits and vegetables are rich in minerals, vitamins, antioxidants, and dietary fiber, while aroma compounds play a major role in flavor and quality management of these horticultural commodities. Subtle shifts in aroma compounds can dramatically alter the flavor and texture of fruits and vegetables, altering their consumer appeal. Rapid innovations in -omics techniques have led to the isolation of genes encoding enzymes involved in the biosynthesis of several volatiles, which has aided to our comprehension of the regulatory molecular pathways involved in VOC production. The present review focuses on the significance of aroma volatiles to the flavor and aroma profile of horticultural crops and addresses the industrial applications of plant-derived volatile terpenoids, particularly in food and beverages, pharmaceuticals, cosmetics, and biofuel industries. Additionally, the methodological constraints and complexities that limit the transition from gene selection to host organisms and from laboratories to practical implementation are discussed, along with metabolic engineering’s potential for enhancing terpenoids volatile production at the industrial level. Full article

Climate change, global warming, stagnant productivity of wheat and food security concerns owing to frequent spells of drought stress (DS) have necessitated finding biologically viable drought-mitigation strategies. A trial was conducted to test two promising wheat cultivars (Ujala-16 and Zincol-16) that were subjected to pre-sowing priming treatments with different doses of ZnO nanoparticles (NPs = 40, 80, 120 and 160 ppm) under 50% and 100% field capacity (FC) conditions. The ZnO NPs were prepared with a co-precipitation method and characterized through X-ray diffraction (XRD) and with a scanning electron microscope (SEM). For comparison purposes, untreated seeds were sown as the control treatment. The response variables included botanical traits (lengths, fresh and dry wrights of root and shoot), chlorophyll (a, b and total) contents, antioxidant and proline contents and nutrients status of wheat cultivars. The results showed that DS significantly decreased all traits of wheat cultivars, while ZnO NPs, especially the 120 ppm dose, remained superior by increasing all botanical traits at 100% FC. In addition, ZnO NPs increased the chlorophyll a (1.73 mg/g FW in Ujala-16 and 1.75 mg/g FW in Zincole-16) b (0.70 mg/g FW in Ujala-16 and 0.71 mg/g FW in Zincole-16) and total chlorophyll content (2.43 mg/g FW in Ujala-16 and 2.46 mg/g FW in Zincole-16) by improving the activity of antioxidant and proline content. Moreover, plant nutrients such as Ca, Mg, Fe, N, P, K, and Zn contents were increased by ZnO NPs, especially in the Zincol-16 cultivar. To summarize, Zincol-16 remains superior to Ujala-16, while ZnO NPs (120 ppm dose under 100% FC) increases the growth and mineral contents of both wheat varieties. Thus, this combination might be recommended to wheat growers after testing further in-depth evaluation of more doses of ZnO NPs. Full article

The electrochemical treatment of canal water was investigated in a batch-wise system in the presence of stainless steel 316-grade electrodes. Three effective process parameters, including current density, reaction time, and electrode spacing, were evaluated in the range of 0.25–2.5 mA/cm², 1–10 min, and 0.5–2.5 cm, respectively. Operational variables of electrochemical disinfection are optimized in response surface methodology (RSM) using Box–Behnken design. Before electrochemical disinfection, a pretreatment process of coagulants mixing for turbidity removal was conducted. Results revealed that a 10 ppm dosage of Ferric chloride (FeCl₃.6H₂O) and alum (Al₂(SO₄)₃·16H₂O) at neutral pH is appropriate. Furthermore, the RSM analysis shows that interelectrode spacing is the most prominent factor affecting the disinfection performance, and increasing electrode spacing inversely affects the disinfection efficiency. Results revealed that 1.52 mA/cm² current density, 6.35 min reaction time, and 1.13 cm of electrode spacing are the optimum conditions, resulting in a statistically 98.08% disinfection of the total coliform. The energy required for electrochemically disinfection of water at optimum conditions was 0.256 kWh/m³. Full article

This study aimed to evaluate 2-(N-((2′-(2H-tetrazole-5-yl)-[1,1′-biphenyl]-4yl)-methyl)-pentanamido)-3-methyl butanoic acid-based ester derivatives as a new class of angiotensin-II receptor antagonists. For this purpose, a series of compounds were synthesized using a variety of phenols. Their chemical characterization was established by FTIR, ¹HNMR, and ¹³CNMR techniques. The biological activities including antioxidant potentials using the DPPH assay, the antihypertensive assay, the urease enzyme inhibition assay, and the antibacterial assay using agar well diffusion methods were performed. All the new compounds showed significant free radical scavenging potentials more than the parent drug while retaining antihypertensive potentials along with urease inhibition properties. However, the AV2 test compound was found to be the most potent against hypertension. Most of the synthesized analogs showed urease inhibitory actions. Molecular docking studies were performed for all the active analogs to decode the binding detail of the ligands with receptors of the enzyme’s active site. Full article

Non-arid region countries, including Canada, enjoy abundant water resources, while arid countries such as Qatar struggle to meet their water needs. However, climate change threats to water resources are similar for both climatic regions. Therefore, this article discusses water dimensions, security, and governance for these different regions, i.e., non-arid Canada and arid Qatar, that distinctly respond to their water-related challenges. Limitations of the article include lesser water-related literature availability for Qatar than for Canada. Canada’s water resources appear vulnerable to climate change as it is projected to face >0.6 °C above the global average of 1.6 °C for the 20th-century temperature. Qatar is extremely vulnerable to dust storms, and rising sea levels, with the maximum temperature approaching 50 °C during the summer, and flooding during the winter. The sustainable use of water resources needs to address social, economic, political, climate change, and environmental dimensions of water. Other than climate change impacts and high per capita consumption of water, Qatar faces challenges of a rise in population (~29 million as of now), acute shortage of freshwater from rainfall (~80 mm per annum), high evapotranspiration (~95% of the total rainfall), depletion of groundwater, and low agricultural productivity due to infertile lands and water scarcity, all leading to food insecurity. The sustainable use of water resources requires improved regulations for water governance and management. Comparisons of water sustainability issues, dimensions, security, and governance facilitate discussions to improve water governance structures for resource sustainability, food security, and climate change adaptability, and show how one country could learn from the experiences of the other. Full article

Recent advances in developmental biology have been made possible by using multi-omic studies at single cell resolution. However, progress in plants has been slowed, owing to the tremendous difficulty in protoplast isolation from most plant tissues and/or oversize protoplasts during flow cytometry purification. Surprisingly, rapid innovations in nucleus research have shed light on plant studies in single cell resolution, which necessitates high quality and efficient nucleus isolation. Herein, we present efficient nuclei isolation protocols from the leaves of ten important plants including Arabidopsis, rice, maize, tomato, soybean, banana, grape, citrus, apple, and litchi. We provide a detailed procedure for nucleus isolation, flow cytometry purification, and absolute nucleus number quantification. The nucleus isolation buffer formula of the ten plants tested was optimized, and the results indicated a high nuclei yield. Microscope observations revealed high purity after flow cytometry sorting, and the DNA and RNA quality extract from isolated nuclei were monitored by using the nuclei in cell division cycle and single nucleus RNA sequencing (snRNA-seq) studies, with detailed procedures provided. The findings indicated that nucleus yield and quality meet the requirements of snRNA-seq, cell division cycle, and likely other omic studies. The protocol outlined here makes it feasible to perform plant omic studies at single cell resolution. Full article

The production of wild blueberries (Vaccinium angustifolium) contributes 112.2 million dollars yearly to Canada’s revenue, which can be further increased by reducing harvest losses. A precise prediction of blueberry harvest losses is necessary to mitigate such losses. The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada (including Tracadie, Frank Webb, Small Scott, and Cooper fields) were utilized to achieve the goal. Wild blueberry losses (fruit loss on ground, leaf losses, blower losses) and yield were measured manually from randomly selected plots during mechanical harvesting. The plant height of wild blueberry, field slope, and fruit zone readings were collected from each of the plots. For the purpose of predicting ground loss as a function of fruit zone, plant height, fruit production, slope, leaf loss, and blower damage, three ML models i.e., support vector regression (SVR), linear regression (LR), and random forest (RF)—were used. Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. Tracadie, Frank Webb, Small Scott, and Cooper had R² values of 0.87, 0.91, 0.91, and 0.73, respectively. Support vector regression performed comparatively better at all the fields i.e., R² = 0.93 (Frank Webb field), R² = 0.88 (Tracadie), and R² = 0.79 (Cooper) except Small Scott field with R² = 0.07. When comparing the actual and anticipated ground loss, the SVR performed best (R² = 0.79–0.93) as compared to the other two algorithms i.e., LR (R² = 0.73 to 0.92), and RF (R² = 0.53 to 0.89) for the three fields. The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields. Full article

Malate dehydrogenase, which facilitates the reversible conversion of malate to oxaloacetate, is essential for energy balance, plant growth, and cold and salt tolerance. However, the genome-wide study of the MDH family has not yet been carried out in tomato (Solanum lycopersicum L.). In this study, 12 MDH genes were identified from the S. lycopersicum genome and renamed according to their chromosomal location. The tomato MDH genes were split into five groups based on phylogenetic analysis and the genes that clustered together showed similar lengths, and structures, and conserved motifs in the encoded proteins. From the 12 tomato MDH genes on the chromosomes, three pairs of segmental duplication events involving four genes were found. Each pair of genes had a Ka/Ks ratio < 1, indicating that the MDH gene family of tomato was purified during evolution. Gene expression analysis exhibited that tomato MDHs were differentially expressed in different tissues, at various stages of fruit development, and differentially regulated in response to abiotic stresses. Molecular docking of four highly expressed MDHs revealed their substrate and co-factor specificity in the reversible conversion process of malate to oxaloacetate. Further, co-localization of tomato MDH genes with quantitative trait loci (QTL) of salt stress-related phenotypes revealed their broader functions in salt stress tolerance. This study lays the foundation for functional analysis of MDH genes and genetic improvement in tomato. Full article

Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-validate maps. This paper aimed at evaluating the efficiency of machine learning (ML) in limiting the use of ground truth data for LULC maps. This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. The remote sensing indices that were tested include the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the normalized built-up index (NDBI), the urban index (UI), and the normalized bare land index (NBLI). Extracted vegetation indices were evaluated on three ML algorithms, namely, random forest (RF), k-nearest neighbour (K-NN), and k dimensional-tree (KD-Tree). The accuracy of these algorithms was assessed with standard statistical measures and ground truth data randomly collected in Prince Edward Island, Canada. Results showed that high kappa coefficient values were achieved by K-NN (82% and 74%), KD-Tree (80% and 78%), and RF (83% and 73%) for Sentinel-2A and Landsat-8 imagery, respectively. RF was a better classifier than K-NN and KD-Tree and had the highest overall accuracy with Sentinel-2A satellite images (92%). This approach provides the basis for limiting the collection of ground truth data and thus reduces the labour cost, time, and resources needed to collect ground truth data for LULC maps. Full article