mohaddeseh amiri

Atropa belladonna L. is a perennial plant belonging to the Solanaceae family. The plants of Solanaceae family are the best-known source of tropane alkaloids primary hyoscyamine and scopolamine that act on the Parasympatic nervous system. The purpose of the present study was to determine the effect of storage on the alkaloid content of A. belladonna L. leaves dried by different traditional drying methods such as shade drying, sun drying, and also hot air drying at temperature of 80 °C and oven drying at temperature of 80 °C. Alkaloid compounds were measured after 1, 15, 30, and 60 days of storage. The alkaloids were determined by high-performance liquid chromatography (HPLC) method immediately after drying and during storage. Analysis of the experimental data revealed that the amount of total alkaloid, atropine, and scopolamine was significantly different (p< 0.01) for different drying methods and storage time. The total alkaloid was significantly at the lowest level in sun-dried samples while it was at the highest level in shade-dried samples. The atropine and scopolamine contents increased significantly just after one-day storage in the samples dried by hot air drying at 80 °C. Storage caused fluctuation in the amount of alkaloids compounds. The atropine content decreased in the oven drying method at 80 °C on the first day of storage and increased after 60 days of storage. The scopolamine content increased in the oven drying method at 80 °C after 15 days of storage.

Predicting the effect of climate change on native ecosystems is one of the longstanding goals of ecologists and is essential for their conservation and management. Species distribution models (SDMs) are the most widely used tools to predict the effects of climate change on the geographical range of plants. In this study, two regression techniques (GLM and MARS) and two machine learning techniques (ANN and RF), along with environmental factors were used to predict the distribution of Onobrychis cornuta L. The species response to future climate (2050-2070) was investigated under optimistic (SSP1-2.6), pessimistic (SSP3-7.0) and very pessimistic (SSP5-8.5) emission scenarios of the IPSL-CM6A-LR climatic model from CMIP6 models. Based on results, the ensemble model and then MARS presented the most accurate prediction. ANN had the lowest prediction accuracy with a significant difference from other models (p<0.05). The sensitivity analysis revealed altitude (24.64%), maximum temperature of the warmest month (20.31%), temperature seasonality (16.57%) and diurnal range of mean temperature (16%) as the most effective variables on the distribution. According to the ensemble model, the suitable habitat occupies about 27% of the area, but its distribution will be decrease under the future climate. The SSP5-8.5 scenario will have the greatest impact on the displacement of the species distribution range. The resulting prediction maps provide valuable information for conservation strategies, including identifying suitable places for its reintroduction and cultivation in the framework of rangeland management plans.

In this study, examines oil percentage and fatty acid composition of Silybum marianum seeds were determined in habitats located in the west of Mazandaran province at different elevations and soil physiochemical properties. After identifying Silybum marianum shrubs in elevation classes of the study area, seeds were sampled using systematic random method and along a 100 m transect at 30 m intervals from three points. After the establishment of transects, in order to study soil physiochemical properties and their relationships with the quantity and quality of fatty acids and oil, three plots were placed in each site and as the number of plant samples, 18 soil samples were taken from the depth of 30 cm and around the root of the plant. Hexane solvent and Soxhlet were used to determine oil content. Identifying fatty acid compositions and Silymarin extraction were done using gas chromatography spectrometer (GC/MS) and methanol solvent in ultrasonic bath, respectively. Based on the results, the highest amount of oil and Silymarin were obtained from plains and mountainous regions, respectively. According to the results, unlike Silymarin, habitat did not have a significant effect on oil content, also the highest amount of oil and Silymarin were obtained from plains and mountainous regions, respectively. Considering the significant effect of habitat on the quality of oil, Oleic acid showed significant negative response to habitat. Soil organic matter and calcium carbonate also had negative inverse relationships with elevation. Sand, potassium, phosphorus and nitrogen indicated negative relationship with oil percentage. Elevation had positive relationship with Margaric acid and Linoleic acid, but negative relationship with Oleic acid. There was positive relationship between Palmitic acid and sand, phosphorus and nitrogen. Because the Oleic acid was predominant followed by the Linoleic acid, it can be concluded that Silybum marianum oil is in the Oleic-Linoleic oil group with high nutritional quality, thus cultivation of this oily seed could be an alternative to produce edible oil and reduce the dependence on other countries.

Understanding the effect of climate change on distribution of plant species is essential for sustainable management and conservation of rangelands, especially in arid and semi-arid ecosystems which are vulnerable to desertification, because of their sensitivity and frangibility to climate change. Due to the complexity of natural systems and phenomenon of climate change, species distribution models are used to understand the impact of climate change on potential niche of species. In this study, five modeling algorithms including artificial neural network, generalized boosting method, classification tree analysis, multivariate adaptive regression splines and maximum entropy were used to predict current and future distribution of Artemisia sieberi Besser in Central Iran. First, the ensemble model as the average predicted probability of single models’ occurrence was created. Next, pessimistic (RCP 8.5) and optimistic (RCP 2.6) scenarios of CCSM4 and NorESM1-ME climate models for the years of 2050 and 2070 were evaluated to assess the trend in spatio-temporal variations of distribution. For this purpose, layers of environmental factors including six bioclimatic and two physiographic variables were used as inputs of species distribution models. Among the environmental variables, altitude, annual precipitation, isothermality, and slope had the most impact on the habitat suitability. Modeling evaluation indicated that the generalized boosting model had better predictions of climatic habitats than other models, and ensemble model than single models. Maximum probability of species presence was determined in plains and low slope areas at altitude of 1000-2000 m and annual precipitation of 100-200 mm. Analysis of the climate change scenarios showed that, the species habitat would be decreased in 2070 more than 2050, leading to the expansion of desert areas. The results can be used for planning to combat desertification in the habitat of Artemisia sieberi, as well as its restoration and rehabilitation in the vast regions of Iran.

The utilization of acetaminophen (AC) as a homogeneous electrocatalyst in the presence of N-acetyl-L-cysteine (NAC) at the surface of carbon paste electrode (CPE) modified with multi-walled carbon nanotubes (MWCNT) has been surveyed in aqueous medium using double step potential chronoamperometry (CHA), cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods. Though, NAC itself shows a very negligible electrochemical response at bare CPE, the response could be greatly increased in the presence of acetaminophen at MWCNT paste electrode (MWCNT-CPE) which enables a sensitive electrochemical determination of the NAC. The kinetic parameters such as transfer coefficient (α), the apparent diffusion coefficient (Dapp) and the chemical reaction rate constant (kh) were calculated 0.33, 5.56×10-6 cm2 s-1 and 1.35×102 cm3 mol-1 s-1, respectively. A linear calibration range of 1.0×10-6 M-3.0×10-5 M and detection limit of 3.9×10-7 M were obtained for determination of NAC at MWCNT-CPE using DPV. The offered method is useful for the routine analysis of NAC in real sample.