Iran stands out as a significant center of genetic diversity for alfalfa (Medicago sativa) worldwide, harboring diverse types of this plant. Ensuring the authenticity of alfalfa populations and varieties is crucial for farmers and seed producers, as the genetic makeup of this species directly influences forage and seed yield quality. In this study, we developed a method to identify and differentiate key Iranian cultivated alfalfa populations using microsatellite markers. We collected random samples, each containing 100 seeds, from various alfalfa accessions. Nine microsatellite loci were screened and employed to differentiate these populations based on specific allelic genotypes. Notably, the MTIC233, BI90, ACT009, TC7, MTIC183, MS30, MTIC238, and AFCA11 markers exhibited the highest differentiation ability. The genetic distance analysis revealed that 5-B and foreign accessions, as well as 29-N and foreign accessions, were the most distant from each other. Conversely, 27-G, 9-H, and 21-R exhibited the closest genetic similarity. The results revealed that, accessions 9-H, 21-R, 27-G, 25-B, 5-B, and 2-G shared a common genetic background, suggesting their close relatedness. Our proposed method allows straightforward identification of target alfalfa accessions within a short timeframe (one to two days) without the need for DNA extraction from leaves.

Nanobiotechnology is the assessment of the application of biological material and elements in nao scale. In this scale everything regardless of its nature could poses a novel characteristic, and two nano-particles with different sizes could show different behavior. In nanotechnology, with the aid of novel specificity produced, new nano-particles with higher functional capabilities are generated that could be used in labeling different biological molecules. Recent advances in the field of gold nanotechnology, resulted in the construction of specific probes with more specificity and penetrance in tissues. Gold nano-particles have found a vast application in different fields of science such as medical sciences and positive step toward diagnosis of diseases with these particles have been made. Detection of point mutations and isolation and diagnosis of target proteins in genetic diseases are example of the application of nano-particles. Moreover, using RNA interference (RNAi) technology, nanomolecules were designed that could specifically target disease associated gene products.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In damage detection the number of elements is generally more than the number of measured frequencies. Consequently, the corresponding damage detection equation is undetermined and thus has infinite solutions. Since in the damaged structures most of their elements remain healthy, the sparsest solution for the damage detection equation is mostly the actual damage. In the proposed method, the damage equation is first linearized in various ways using random finite difference increments. The sparsest solutions for created linear system of equations are derived using basis pursuit. These solutions are considered as the first population for a continuous genetic algorithm to obtain the damage solution. For investigation of the proposed method three case studies are considered. Simulation results confirm the efficiency of the proposed method compared to those found in the literature.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

Background: Cystic echinococcosis is a zoonotic infection and considered as a major economic and public health concern worldwide. This research was conducted to determine genotypic characteristics of livestock and human hydatid cyst isolates from Hamadan area, western Iran. Methods: Sampling was conducted in Hamadan industrial slaughterhouse and Beast Hospital of Hamadan City, western Iran, from 2015 to 2016. Overall, 74 livestock isolates including 69 sheep, 3 cattle and 2 goats and 9 human hydatid cysts were genotyped by PCR amplification of the rDNA ITS1 region and followed restriction fragment length polymorphism (RFLP) analysis with four restriction endonuclease enzymes, RsaI, HpaII, AluI, and TaqΙ , and sequencing. Results: The PCR amplicon size of each isolate was approximately 1 kb which was the same with that of sheep strain. According to the RFLP patterns, the isolates belonged to a single species, E. granulosus sensu stricto (G1– G3 complex). Furthermore, sequencing of representative amplicons confirmed that the RFLP-genotyped isolates corresponded to E. granulosus sensu stricto. Conclusion: E. granulosus sensu stricto is the prevailing species of E. granulosus sensu lato in the region and pointed out the importance of sheep/dog cycle in human transmission.

In this study genetic variation of Artemia urmiana and Artemia franciscana populations were assessed using five microsatellite markers including Af-B105TAIL, Af-A136, Apdq03TAIL, Apdq04TAIL and Apdq05TAIL from Artemia franciscana and Artemia parthenogenetica. DNA was extracted from 50 cysts of Artemia urmiana and Artemia franciscana populations individually by Hot Shot method. Polymerase chain reactions (PCR) were successfully conducted with all primers and then the PCR products were electrophoresed using 6% none denaturing gel and stained using silver nitrate method. Hence, all alleles were polymorphic. Average number of alleles and polymorphic information content (PIC) for Artemia urmiana and Artemia franciscana populations were 3.0, 0.54 and 2.5, 0.38, respectively. All loci in Artemia urmiana were in HWE but only the Af-A136 locus in Artemia franciscana was in HWE. The average expected heterozygosity for Artemia franciscana and Artemia urmiana were estimated as 0.6209 and 0.4531, respectively. The phylogeny dendrogram based on the Distance Matrix was drawn using UPGMA for within populations. Our findings demonstrated that microsatellite markers could be an appropriate tool for screening biodiversity in animals. Therefore, extinction of these invaluable genetic resources can be preserved using accurate breeding and management programs.