Exploring the power of data mining for uncovering traditional medicinal plant knowledge: A case study in Shahrbabak, Iran.

The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants.

Multivariate analysis of the phytochemical composition and antioxidant properties in twenty-five accessions across three Achillea species.

This study explored the chemical composition, antioxidant activity, and total phenol content of aerial parts from 25 accessions of three Achillea species (Achillea wilhelmsii C. Koch, Achillea vermicularis Trin., and Achillea tenuifolia Lam.). The plants were collected from various natural habitats across Iran, encompassing regions such as Central, Western, Southern, Northern, Western, and Northwestern parts of the country. Subsequently, they were grown together under field conditions. The study revealed significant variation in essential oil yields among accessions of A. wilhelmsii, ranging from 0.01 to 0.107%, A. vermicularis with a range of 0.075 to 1.5%, and A. tenuifolia showing a variation of 0.1 to 2%. The study utilized Gas Chromatography-Mass Spectrometry (GC-MS) analysis, revealing 75, 49, and 75 compounds in the essential oils of A. wilhelmsii, A. tenuifolia, and A. vermicularis, respectively. Major components included camphor, 1,8-cineole, anethole, α-pinene, and phytol in A. wilhelmsii, 1,8-cineole, camphor, levo-carvone, and δ-terpinene in A. vermicularis, and ß-cubebene, elixene, ß-sesquiphellandrene, 1,8-cineole, camphor, and δ-terpinene in A. tenuifolia. The essential oil compositions of A. wilhelmsii and A. vermicularis were predominantly characterized by oxygenated monoterpenes, whereas that of A. tenuifolia was characterized by sesquiterpenes. Cluster analysis grouped accessions into three clusters, with A. tenuifolia forming a distinct group. Principal Component Analysis (PCA) triplot (62.21% of total variance) confirmed these results and provided insights into compound contributions. Furthermore, total phenolic content and antioxidant activity of the accessions of three species were assessed over 2 years. A. tenuifolia exhibited the highest levels in both categories, with statistically significant linear regression between antioxidant activity and total phenol content for A. tenuifolia and A. wilhelmsii. These findings emphasize significant phytochemical diversity within Achillea species, positioning them as promising natural sources of antioxidants. Further exploration and selection of specific accessions within each species are crucial for unlocking their medicinal potential and supporting cultivation and conservation efforts.

Natural variation in photosynthesis and water use efficiency of locally adapted Persian walnut populations under drought stress and recovery.

Persian walnut is a drought-sensitive species with considerable genetic variation in the photosynthesis and water use efficiency of its populations, which is largely unexplored. Here, we aimed to elucidate changes in the efficiency of photosynthesis and water content using a diverse panel of 60 walnut families which were submitted to a progressive drought for 24 days, followed by two weeks of re-watering. Severe water-withholding reduced leaf relative water content (RWC) by 20%, net photosynthetic rate (Pn) by 50%, stomatal conductance (gs) by 60%, intercellular CO2 concentration (Ci) by 30%, and transpiration rate (Tr) by 50%, but improved water use efficiency (WUE) by 25%. Severe water-withholding also inhibited photosystem II functionality as indicated by reduced quantum yield of intersystem electron transport (φEo) and transfer of electrons per reaction center (ET0/RC), also enhanced accumulation of QA (VJ) resulted in the reduction of the photosynthetic performance (PIABS) and maximal quantum yield of PSII (FV/FM); while elevated quantum yield of energy dissipation (φDo), energy fluxes for absorption (ABS/RC) and dissipated energy flux (DI0/RC) in walnut families. Cluster analysis classified families into three main groups (tolerant, moderately tolerant, and sensitive), with the tolerant group from dry climates exhibiting lesser alterations in assessed parameters than the other groups. Multivariate analysis of phenotypic data demonstrated that RWC and biophysical parameters related to the chlorophyll fluorescence such as FV/FM, φEo, φDo, PIABS, ABS/RC, ET0/RC, and DI0/RC represent fast, robust and non-destructive biomarkers for walnut performance under drought stress. Finally, phenotype-environment association analysis showed significant correlation of some photosynthetic traits with geoclimatic factors, suggesting a key role of climate and geography in the adaptation of walnut to its habitat conditions.

Alleviating the adverse effects of salinity on Roselle plants by green synthesized nanoparticles.

In the present study, an eco-friendly process was made for the rapid synthesis of silver nanoparticles using aqueous leaf extract of Hibiscus sabdariffa. The process was characterized by Fourier Transform Infrared (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-visible and X-ray diffraction (XRD). These green silver nanoparticles (NPs) were used for mitigating the adverse effects of salinity on seed germination and growth parameters in plants. Accordingly, two experiments were conducted. In the first experiment, seven concentrations of green silver NPs and nine levels of NaCl:CaCl were apptoed on seeds for germination, and their effects were evaluated. In the second experiment, three concentrations of green silver NPs and NaCl were hypothesized to affect plant growth parameters. Seed germination, plant height, leaf, and root fresh and dry weights, as well as relative water content (RWC), decreased significantly under salt stress. However, green silver NPs intervened by alleviating the adverse effects of stress. Accordingly, green silver NPs were beneficial due to (1) activation of the antioxidant system by enhancing antioxidant enzymes such as catalase (CAT), ascorbate peroxidase (APX), peroxidase (POD), and superoxide dismutase (SOD); (2) increase in the amounts of proline, soluble sugars and carbohydrates for osmoprotection; (3) improvements in flavonoid and anthocyanin contents. Real-time PCR showed that flavonoid and anthocyanin contents increased because of higher expressions in chalcone synthase (CHS), flavanone 3-hydroxylase (F3H), and anthocyanidin synthase (ANS) genes. In conclusion, green silver NPs offered an eco-friendly application for further research on agricultural development.

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models.

BACKGROUND: Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. RESULTS: Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R2 of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. CONCLUSION: Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.

Production of Haploid and Doubled Haploid Lines in Nut Crops: Persian Walnut, Almond, and Hazelnut.

This chapter deals with induction of haploidy via parthenogenesis in Persian walnut and via microspore embryogenesis in almond and hazelnut. Haploid induction through in situ parthenogenesis using pollination with irradiated pollen to stimulate the embryogenic development of the egg cell, followed by in vitro culture of the immature haploid embryos. Microspore embryogenesis allows the induction of immature pollen grains (microspores), to move away from the normal gametophytic developmental route in the direction of the sporophytic one, yielding homozygous organisms (embryos in this case). Unlike other fruit crops (such as Citrus), regeneration of entire plants has not yet been obtained in our studied nut crops; however, it gives the methodology should be used to continue the roadmap.

Combining independent de novo assemblies to optimize leaf transcriptome of Persian walnut.

Transcriptome resources can facilitate to increase yield and quality of walnuts. Finding the best transcriptome assembly has not been the subject of walnuts research as yet. This research generated 240,179,782 reads from 11 walnut leaves according to cDNA libraries. The reads provided a complete de novo transcriptome assembly. Fifteen different transcriptome assemblies were constructed from five different well-known assemblers used in scientific literature with different k-mer lengths (Bridger, BinPacker, SOAPdenovo-Trans, Trinity and SPAdes) as well as two merging approaches (EvidentialGene and Transfuse). Based on the four quality metrics of assembly, the results indicated an efficiency in the process of merging the assemblies after being generated by de novo assemblers. Finally, EvidentialGene was recognized as the best assembler for the de novo assembly of the leaf transcriptome in walnut. Among a total number of 183,191 transcripts which were generated by EvidentialGene, there were 109,413 transcripts capable of protein potential (59.72%) and 104,926 were recognized as ORFs (57.27%). In addition, 79,185 transcripts were predicted to exist with at least one hit to the Pfam database. A number of 3,931 transcription factors were identified by BLAST searching against PlnTFDB. Furthermore, 6,591 of the predicted peptide sequences contained signaling peptides, while 92,704 contained transmembrane domains. Comparison of the assembled transcripts with transcripts of the walnut and published genome assembly for the 'Chandler' cultivar using the BLAST algorithm led to identify a total number of 27,304 and 19,178 homologue transcripts, respectively. De novo transcriptomes in walnut leaves can be developed for the future studies in functional genomics and genetic studies of walnuts.

Gamma irradiation affects the total phenol, anthocyanin and antioxidant properties in three different persian pistachio nuts.

The effects of gamma irradiation (GR) on total phenol, anthocyanin and antioxidant activity were investigated in three different Persian pistachio nuts at doses of 0, 1, 2 and 4 kGy. The antioxidant activity, as determined by FRAP and DPPH methods, revealed a significant increase in the 1-2 kGy dose range. Total phenol content (TPC) revealed a similar pattern or increase in this range. However, when radiation was increased to 4 kGy, TPC in all genotypes decreased. A radiation dose of 1 kGy had no significant effect on anthocyanin content of Kale-Ghouchi (K) and Akbari (A) genotypes, while it significantly increased the anthocyanin content in the Ghazvini (G) genotype. In addition, increasing the radiation to 4 kGy significantly increased the anthocyanin content of K and G genotypes. To conclude, irradiation could increase the phenolic content, anthocyanin and antioxidant activity of pistachio nuts.

Phytochemical components, total phenol and mineral contents and antioxidant activity of six major medicinal plants from Rayen, Iran.

The aim of this work was to evaluate the phytochemical components, minerals, the antioxidant activity and total phenol contents of the essential oil from aerial parts of six major medicinal plants in Rayen, Iran. The plants included Ranunculus arvensis, Teucrium polium, Dracocephalum polychaetum, Kelussia odoratissima, Artemisia sieberi and Thymus kotschyanus. Total phenol content ranged from 0.03 to 0.158 mg/mL. A. sieberi showed the highest radical scavenging ability (IC50 = 94 µg/mL). The amount of minerals ranged as follows: P (0.23-29%), K (1.08-4.76%), Ca (0.78-2.35%), Mg (0.24-0.94%), Cu (8.3-15 mg/kg), Cd (0.7-1.1 mg/kg), Pb (2-11.7 mg/kg) and Fe (250-1280 mg/kg). A total of 79 compounds were identified across all plants. The main components studied in the plants were l-perillaldehyde, biosol, carvacrol, 1,8-cineol, terpinyl acetate and 1,2,3,6,7,7 a-hexahydro-5 h-inden 5-one.

Ethnopharmacological studies of indigenous medicinal plants in the south of Kerman, Iran.

ETHNOPHARMACOLOGICAL RELEVANCE: The aim of this study was to collect and document information concerning the medicinal plants used by indigenous people and traditional healers in the south of Kerman Province, Iran. MATERIALS AND METHODS: Overall, 64 informants between the ages of 30 and 89 were interviewed about the modes of application and consumption of medicinal plants in the region. Quantitative analysis was conducted that included informant consensus factor (ICF), use value (UV), relative frequency of citation (RFC) and cultural importance index (CI). RESULTS AND DISCUSSION: In the current study, a total of 115 medicinal plants in 41 families were reported in the south of the Kerman region. Apiaceae, Asteraceae and Lamiaceae (with 14 species each) were the families with the most medicinal plants. The most frequently used plant parts were leaves at 26.17% and aerial parts (23.49%). Decoction (53%), liniment (23%) and infusion (9%) were the most common preparation methods. The highest UVs were obtained from the following medicinal plants: Amygdalus eburnea Spach, Genista tinctoria L., Calotropis procera (Aiton) Dryand., Fortuynia garcinii (Burm.f.) Shuttlew. and Cerasus mahaleb (L.) Mill. The ICF results indicated that cold-flu and fever were the most common diseases (0.67) in the south of Kerman. CONCLUSION: Based on the current study, the south of Kerman has many potential medicinal plants, and these plants should be the focus of future research.