Mean Differential Coefficient Research Articles

Genetic Diversity and Structure of Chanterelles (Cantharellus spp.) in Hyrcanian Forest Using SSR Markers

Cantharellus is one of the most widely used ectomycorrhizal fungus in the world, which forms mycorrhizal communities with various plant species and plays a major ecological role in forest ecosystems. The present study is the first report to know the extent of genetic diversity and differentiation of Zarde-Kija mushroom populations using microsatellite markers within the internal transcribed spacer (ITS). In this study, 70 fungal samples were collected from six populations of this species in the Hyrcanian Forest (Gorgan, Neka, Sari, Noor, Chalous, and Rasht populations) and its genetic diversity and structure were measured. The number of observed alleles ranged from 5 to 7 and the expected heterozygosity ranged from 0.320 to 0.864. AMOVA analysis showed that the largest portion of the genetic variation was among individuals within the population (95%), whereas 5% was among populations. The mean coefficient of differentiation (FST) was 0.096, which indicates the medium genetic differentiation between the populations under study. The mean inbreeding value (FIS) and gene flow (NM) were 0.853, 2.509, respectively. Most loci showed deviation from Hardy–Weinberg equilibrium due to the excess of heterozygotes in all populations. The genetic landscape shape plot revealed that high pairwise genetic distances among individuals in central populations of the Hyrcanian forest. This study revealed that the effective population size of Zarde-Kija has been decreased in the Hyrcanian forest and emphasizes the need to develop an effective strategy for the conservation of this species. However, it is suggested that research on this species be repeated using stronger markers and more populations to increase the accuracy of the results.

Soil organic matter determination based on artificial olfactory system and PLSR-BPNN

Soil organic matter (SOM) is a key indicator of soil fertility. For accurate measurement of SOM, a novel method based on an artificial olfactory system (AOS) was proposed. The response curves of soil volatile organic compounds (VOCs) were measured using a metal-oxide semiconductor sensor array, and four features (including maximum value, mean differential coefficient, response area, and the transient value at the 20th second) were obtained from the curves and used to build olfactory feature space. Then, prediction models were established using the pattern recognition algorithm. To further enhance the accuracy of AOS measurement, we used Monte Carlo cross-validation (MCCV) to identify and eliminate the abnormal samples of the soil olfactory feature space. Then, the dimension reduction method of the genetic algorithm (GA)back-propagation (BP) was used to find the appropriate feature vectors, and two types of hybrid models were presented. One was the support vector machine (SVM) and group method of data handling (GMDH) combined model—SVM-GMDH. The other was a combination of partial least squares regression (PLSR) and back-propagation neural network (BPNN)—PLSR-BPNN. The forecasting performances of three single models (BPNN, PLSR, support vector regression: SVR) and two combined models (PLSR-BPNN, SVM-GMDH) were comparatively evaluated. The evaluation indices included coefficient of determination (R 2), root mean square error (RMSE), ratio of performance to deviation and relative prediction error (RPE). It was found that the predictive capabilities of all five tested models were improved after elimination of abnormal samples and feature reduction. Moreover, PLSR-BPNN performed the best in predicting SOM concentrations, with R 2 = 0.952, RMSE = 1.771, PRD = 4.291, and slight variation of RPE within 0–0.185, and thus can offer a reference for predicting SOM via AOS.

English

Twenty five (25) accessions of Lucerne (Medicago sp.) collected from Leh valley of trans-Himalayan region of Ladakh (Jammu and Kashmir) were analyzed using inter simple sequence repeats (ISSRs) and random amplified polymorphic DNA (RAPD).The results of this study revealed that the level of genetic variation in the collectedMedicago ecotypes were relatively high (P=96.54%, I=0.430, Ht=0.285). RAPD fingerprinting detected more polymorphic loci (97.96%) than ISSR fingerprinting (95.12%). Clustering of genotypes within groups was not similar when RAPD and ISSR derived dendrogram were compared, whereas the pattern of clustering of the genotypes remained more or less the same in RAPD and combined data of RAPD + ISSR. The mean coefficient of differentiation (Gst) was 0.0584 indicating 30.23% of the genetic diversity within the populations. The overall value of mean estimated number of gene flow (Nm = 8.0682) revealed large gene exchanges among populations. Analysis of molecular variance (AMOVA) indicated that the distribution of genetic diversity was 49% among populations and 51% within populations. The plant is capable of reproducing by self-sowing, thus can influence population genetic structure. The pronounced genetic variation tells us that Medicago species is a proper plant for genetic research and that there is great potential of breeding this species for improved forage varieties.   Key words: Genetic variation, ISSR, RAPD, Medicago species, population structure.