Models of estimation on the content of secondary metabolites in some Hypericum species

In the present study, models for estimation of the content of main secondary metabolites, namely hypericin, pseudohypericin and hyperforin were developed for Hypericum originafolium Willd, Hypericum perfoliatum L. and Hypericum montbretii Spach. growing in Northern Turkey. Wild growing plants were harvested at vegetative, floral budding, full flowering, fresh fruiting, mature fruiting stages and dissected into stem, leaf and reproductive tissues. Actual secondary metabolite contents of plant materials were measured by High Performance Liquid Chromatography method. Multiple regression analysis with Excel 2003 computer package program was performed for each species and chemicals separately to develop multiple regression models. The produced equation for predicting the content of secondary metabolites in different tissues of the species was formulized as: SMC= [a + (b1 x S) + (b2 x L) + (b3 x RP) + (b4 x S²) + (b5 x (1/RP))] where SMC is whole plant secondary metabolite content, S is stem secondary metabolite content, L is leaf secondary metabolite content, RP is secondary metabolite content of reproductive parts and a, b1, b2, b3, b4, and b5 are coefficients?. The R2 coefficient values between predicted and observed contents of secondary metabolites were determined as 0.99 for H. originafolium, 0.95–0.98 for H. perfoliatum and 0.90–0.99 for H. montbretii. All R² values and standard errors were found to be significant at the P<0.05 level

Agronomic characteristics of Serapias vomeracea (Burm. f.) Briq. salep orchids

Although salep orchids are annual plants, each new generation emerges larger than their parents. There are no data on the developmental performance of these species that cannot be cultivated due to production difficulties. This study discusses Salep vomeracea (Burm. f.) Briq., one of the most common species in temperate climate regions. Tubers obtained from natural flora were divided into seven groups according to their size. Morphological characteristics of the seedlings developed from these tubers were determined and observed up to the harvest stage. The study was carried out in a randomized plot design with three replications and for two years. As a result of statistical analysis, all parameters were found to be significant. The canonical correlation between the first pair of canonical variables was 0.995 (p<0.01). The data obtained from the length of the planted tuber made the biggest contribution to the explanatory power of the canonical variables. Additionally, the mathematical relationship between width, height and area values of salep leaves was determined

Chemical composition of Hypericum pruinatum Boiss. and Bal. from wild populations of Northern Turkey

The present study was conducted to determine the variation in the content of several plant chemicals, namely hyperforin, hypericin, pseudohypericin, chlorogenic acid, rutin, hyperoside, isoquercetine, kaempferol, quercitrine and quercetine among five Hypericum pruinatum Boiss. & Bal. populations from Northern Turkey. The aerial parts representing a total of 30 shoots were collected at full flowering. After dried at room temperature, they were assayed for the chemicals by HPLC and the presence of isoquercetine and kaempferol in this species was reported by us for the first time. The populations varied significantly in chemical contents. Plants from Ladik population produced the highest amount of hypericin, chlorogenic acid, hyperoside, isoquercetine, quercitrine and quercetine. Hyperforin and rutin of whole shoots reached the highest level in Cankiri population. The chemical variation among the populations and plant parts was discussed as possible results of different genetic and environmental factors

Doğu Anadolu, Türkiye’de Yetişen Üç Centaurea L. Türünün in vitro Biyolojik Değerlendirilmesi ve Fitokimyasal Özellikleri

WOS: 000530167400010This research was conducted at experimental site (41 degrees 10.668' N latitude, 40 degrees 54.018'E longitude), with 65 m elevation in Turkey. in the study, 8 different genotypes of Digitalis ferrruginea subsp. ferruginea, selected in the previous years with high performance, were used. These genotypes, grown in 2016-2017, were compared in terms of plant height, panicle length, number of capsules per panicle, capsule length, seeds yield per plant and 1000 seeds weight, and a modeling was developed to estimate seed yield per plant. the mean values obtained for the investigated traits were 95.21-130.43 cm for plant height, 46.29-72.57 cm for panicle length (PL), 9.63-11.14 mm for capsule length, 99.29-146.57 units for the number of capsules per panicle, 2.00-5.26 g/plant for seed yield per plant (SYP) and 0.34-0.49 g for 1000 seeds weight (TSW). However, in terms of the traits examined, each genotype showed a wide variation within itself. Multiple regression analysis was performed for the yield-prediction model relation to the seed yield per plant using the values obtained under the present conditions. As a result of the regression analysis, an equation of SYP=(-2.54)+ (0.11xPL)-(2.18xTSW) was obtained

Relationships Between Some Agronomical Traits in Genotypes of Rusty Foxglove (Digitalis ferrruginea subsp. ferrruginea)

WOS: 000530167400010This research was conducted at experimental site (41 degrees 10.668' N latitude, 40 degrees 54.018'E longitude), with 65 m elevation in Turkey. in the study, 8 different genotypes of Digitalis ferrruginea subsp. ferruginea, selected in the previous years with high performance, were used. These genotypes, grown in 2016-2017, were compared in terms of plant height, panicle length, number of capsules per panicle, capsule length, seeds yield per plant and 1000 seeds weight, and a modeling was developed to estimate seed yield per plant. the mean values obtained for the investigated traits were 95.21-130.43 cm for plant height, 46.29-72.57 cm for panicle length (PL), 9.63-11.14 mm for capsule length, 99.29-146.57 units for the number of capsules per panicle, 2.00-5.26 g/plant for seed yield per plant (SYP) and 0.34-0.49 g for 1000 seeds weight (TSW). However, in terms of the traits examined, each genotype showed a wide variation within itself. Multiple regression analysis was performed for the yield-prediction model relation to the seed yield per plant using the values obtained under the present conditions. As a result of the regression analysis, an equation of SYP=(-2.54)+ (0.11xPL)-(2.18xTSW) was obtained

An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods

The distribution of the studies conducted between 2011-2021 in the fields of (Electrooculography) EOG and eye movements, EOG and wheelchair, EOG and eye angle, EOG and sleep state, EOG and mood estimation and EOG and game application was determined according to years, and the most cited studies were examined and presented. The study areas are listed as Eye Movement Classification, Wheelchair, Sleep state, Eye Angle, Mood State and Game Applications from the most to the least number of articles. When we examine in terms of the number of citations, they are listed as Sleeping state, Eye Movement Classification, Wheelchair, Eye Angle, Mood State and Game Applications, from the most to the least. In these studies, it has been tried to make the lives of people who have become disabled in various ways better by using the brain-computer interface with machine learning

Effect of Salt Stress and Irrigation Water on Growth and Development of Sweet Basil (Ocimum basilicum L.)

This study was conducted to assess the influence of different salinity and irrigation water treatments on the growth and development of sweet basil (Ocimum basilicum L.). Five salinity levels (0.4, 1.00, 2.50, 4.00 and 8.00 dSm-1) and three different irrigation water regimes (80, 100, 120% of full irrigation) were applied in a factorial design with three replications. Dry root weight, aerial part dry weight and aerial part/root ratio were determined and evaluated as experimental parameters at the end of growing period. Results revealed significant decreases in yields with increasing salinity levels. However, basil managed to survive high salt stress. With increasing salinity levels, decreases in growth were higher in roots than in leaves. Changes in the amount of irrigation water also significantly affected the evaluated parameters

Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region

To manage arable areas according to land resources for future generations, it is crucial to determine the quality of the soils. The main purpose of this study is to identify soil quality for cultivated lands in the semi-humid terrestrial ecosystem in the Black Sea region. Multi-criteria decision-analysis was performed in weighted linear combination approach and standard scoring function (linear-L and nonlinear-NL) integrated with GIS techniques and interpolation models It was tested to predict soil quality index (SQI) values using artificial neural network (SQIANN). The soil quality index values obtained using the linear method ranged from 0.444 to 0.751, while those obtained using the non-linear method ranged from 0.315 to 0.683. As a result, we determined the soil quality indices of cultivation areas. According to our statistical analysis, there were no statistically significant differences between the soil quality index values obtained from SQIL and SQIL-ANN while the same results were found between SQINL and SQINL-ANN. According to the cluster analysis, 98.2% similarity between SQIL and SQIL-ANN, and 99.2% between SQINL and SQINL-ANN was determined. In addition, the spatial distribution maps obtained by both the clustering analysis and the geostatistical analysis showed quite a lot of similarity between SQI values.</p

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field (652000-659000E-W and 4528000-4536000N- S) in 2019. This study's primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS (Geographical Information Systems) and deep learning (DL) technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88% was attained when classifying "class" data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered

The effects of salt and drought stress on phenolic accumulation in greenhouse-grown Hypericum pruinatum

Hypericum pruinatum is a medicinal herb containing several bioactive compounds with important pharmacological activity. In this study, we investigated the effects of the salt (0.03 - control, 1, 2.5, 4 and 8 dS m–1 of MgSO4, CaCl2 and NaCl salts) and drought stress (80, 100 and 120% of required water) on the content of phenolic compounds, namely chlorogenic acid, rutin, hyperoside, isoquercetine, quercitrine and quercetine in greenhouse grown plantlets. In general, the salt stress especially in elevating doses increased the levels of all of the compounds analysed, whereas drought stress did not cause a significant chance in chemical content of the plantlets. The present results indicated that abiotic stress factors, particularly salinity, have a marked influence on the content of phenolic constituents in H. pruinatum and it is a salt tolerant species. The results also indicated that phenolic compounds play significant physiological role in salinity tolerance