Ecological and health risks of soil and grape heavy metals in long-term fertilized vineyards (Chaharmahal and Bakhtiari province of Iran).

Heavy metal pollution in food-producing ecosystems resulting from long-term fertilizing and other agricultural practices is causing major concern due to its health risk potential for human and other organisms. This study was designed to investigate heavy metal concentrations (Cu, Zn, Pb, Cr and Cd) and assess their ecological risk using contamination factor (Cf), pollution load index (PLI) and ecological risk index. Health risk assessment (HRA) was also estimated by investigating non-carcinogenic and carcinogenic risks of soil via oral ingestion, dermal contact, inhalation, and health risk of grape consumption. To this aim, 38 fertilized vineyards of the region were investigated during harvesting seasons of 2016 and 2017. The results showed a same ranking order of the metals in the soil and grape that was Zn > Cu > Pb > Cr > Cd. The average concentrations of Zn and Pb in the grape samples were significantly higher than the national permissible limits. According to Cf and PLI indices, Cd had the most ecological risk compared to the other studied metals. The HRA results showed that adults and children are not exposed to non-carcinogenic risk from the studied heavy metals in the vineyard soils. But, it was a tolerable level of carcinogenic health risk only by Cr in some of the vineyards. The PLI had most significant correlation (p < 0.01) with the other studied indices. So, it can be considered as a rapid and applicable index to assess ecological and health risk of heavy metals in the region. To food-chain security and community health, some comprehensive managing plans are necessary to increase farmer's awareness about the standard amount of chemical fertilizers and pesticides. Furthermore, continuous monitoring of heavy metals in soil and grape of the vineyards should be performed.

Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran.

Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classification algorithms (LDA and SVM) were applied in a total of 16 scenarios. Results showed that the grapevine varieties were discriminated with overall accuracy of 89.88%-100% in test sets. Among the data reduction methods, the combination of ANOVA and PCA yielded higher performance as opposed to PLSR. Accordingly, optimal wavelengths in discrimination of studied grapevine varieties were located in vicinity of 695, 752, 1148, 1606 nm and 582, 687, 1154, 1927 nm at leaf and canopy levels, respectively. Optimal spectral indices were R680, WI, SGB and RATIO975_2, DattA, Greenness at leaf and canopy levels, respectively. Also, the importance of spectral regions in discriminating studied grapevine varieties was ranked as near-infrared > mid-infrared and red edge region > visible. As a general conclusion, the canopyspectral indices-ANOVA-PCA-SVM scenario discriminated the studied species most accurately.

Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques.

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis.

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants' physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350-2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

Inhibitory effects of ß-cyclodextrin-helenalin complexes on H-TERT gene expression in the T47D breast cancer cell line - results of real time quantitative PCR.

BACKGROUND: Nowadays, the encapsulation of cytotoxic chemotherapeutic agents is attracting interest as a method for drug delivery. We hypothesized that the efficiency of helenalin might be maximized by encapsulation in ß-cyclodextrin nanoparticles. Helenalin, with a hydrophobic structure obtained from flowers of Arnica chamissonis and Arnica Montana, has anti-cancer and anti-inflammatory activity but low water solubility and bioavailability. ß-Cyclodextrin (ß-CD) is a cyclic oligosaccharide comprising seven D-glucopyranoside units, linked through 1,4-glycosidic bonds. MATERIALS AND METHODS: To test our hypothesis, we prepared ß-cyclodextrin- helenalin complexes to determine their inhibitory effects on telomerase gene expression by real-time polymerase chain reaction (q-PCR) and cytotoxic effects by colorimetric cell viability (MTT) assay. RESULTS: MTT assay showed that not only ß-cyclodextrin has no cytotoxic effect on its own but also it demonstrated that ß-cyclodextrin- helenalin complexes inhibited the growth of the T47D breast cancer cell line in a time and dose-dependent manner. Our q-PCR results showed that the expression of telomerase gene was effectively reduced as the concentration of ß-cyclodextrin-helenalin complexes increased. CONCLUSIONS: ß-Cyclodextrin-helenalin complexes exerted cytotoxic effects on T47D cells through down-regulation of telomerase expression and by enhancing Helenalin uptake by cells. Therefore, ß-cyclodextrin could be superior carrier for this kind of hydrophobic agent.