Henbane has a high medicinal value due to the presence of hyoscyamine and scopolamine alkaloids. Improving the quality and quantity of henbane alkaloids using modern breeding methods requires evaluating the genetic diversity. The genetic diversity of henbane has been investigated using morphological, biochemical and molecular markers in several studies and the superiority of molecular markers over other markers has been proven. To this end, in 2018, the genetic diversity of 96 henbane genotypes collected from the habitats of northwest Iran was investigated using IRAP and REMAP molecular markers. For IRAP markers, out of 36 possible combinations obtained from eight LTR primers, seven combinations had a fine and scalable amplification. In the REMAP technique, the combination of 11 ISSR primers with eight LTR primers was used, and 12 combinations could be scored out of 88 possible combinations. The average amount of polymorphic information for IRAP and REMAP markers was 0. 30 and 0. 32, respectively, and the average marker index for these two markers was estimated as 2. 59 and 2. 47. Based on these criteria, REMAP marker was more efficient than IRAP in estimating the genetic diversity of henbane. In the ANALYSIS of molecular variance using IRAP and REMAP markers, intra-population variability was estimated to be higher than inter-population, which indicates the high diversity of these populations in northwestern Iran. CLUSTER ANALYSIS based on IRAP marker failed to separate species and populations, but REMAP marker was able to separate H. pusillus and H. reticulatus species to a high degree. A high shannon index in this research suggests that IRAP and REMAP retrotransposon markers resulted in a high genetic diversity within henbane populations with a high insertion in the genome of henbane populations.

In recent years, traffic accidents have been the ninth cause of fatalities in the world and the second in Iran (after heart diseases). Many individuals suffer financially from traffic accidents or lose their lives every year. Traffic accident ANALYSIS, as a powerful tool, can give valuable data regarding the cause of accidents and appropriate solutions. Such ANALYSIS would be possible at two levels; the "project" and the "network". In this paper, the ANALYSIS has been carried out at "network" (provinces) level to achieve a proper approach to increase the macro management abilities for the presentation of classified approaches appropriate to the conditions of each district. Because every province is unique, regarding driver behavior, infrastructure conditions, precautionary measures before and relief measures after an accident, classification of similar provinces for the purpose prioritization of safety measures and necessary decision making to reduce the number of fatalities in accidents deems necessary. Also, group study of similar condition provinces helps decision makers to provide similar measures for the solution of safety problems. "CLUSTER ANALYSIS" is a method used for the purpose of classification. In this study, CLUSTER ANALYSIS, using six methods, has been carried out for all provinces, on the basis of accidents data in 2004, obtained from Legal Medicine Organization (LMO). Necessary variables required for the CLUSTER ANALYSIS, were obtained through the study of accidents data epidemiology and contain eight major groups called number of fatalities, gender, age, education, occupation, place of death, user situation and final death cause (totally 64 variables). The ANALYSIS was done in 4 CLUSTERs and the results were validated with help of "Chernoff Faces Method". The results obtained reveal that some provinces maybe classified under one group, but some other provinces, like Isfahan, need special studies.

The conventional electrophoresis methods are well known techniques for protein detection and ANALYSIS of cerebrospinal fluid (CSF). Disc electrophoresis (DEP) was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid (CSF) on polyacrilamide gel. However, the advance of automation has made rapid collection of large amounts of data feasible and the development of microcomputers has made sophisticated processing even of old electrophoregrams possible. Automated ANALYSIS, data storage and sophisticate data acquisition were carried out with Gel Pro Analyzer 3.1, which is specifically structured to analyze gels and elctrophoregrams: complex band pattern matching (gel variation, dendogram ANALYSIS etc.); lane relation studies (sophisticated lane database); general gel ANALYSIS (accurate molecular size, quantitative determination of protein mixture etc.). CLUSTERing techniques have been applied for detection of intrathecal immune response. Different hierarchic CLUSTER ANALYSIS methods such as single linkage, complete linkage, unweighted pair-group average (UPMGA) were used. In addition, other CLUSTER characteristics such, distance matrix and Euclidean distance matrix were calculated. Pairing of electrophoresis methods and CLUSTER image ANALYSIS, could lead to additional diagnostic information of inflammatory conditions of the central nervous system (CNS) or dysfunction of blood-CSF barrier.

Extended Abstract Background: Salinity is one of the major abiotic stresses and the most limiting factor in agricultural production worldwide, affecting the growth, development, and final yields of crops. Rapeseed is one of the most important sources of oilseeds in the world, and its seeds contain more than 40% of oil. Moreover, the meal obtained from oil extraction has more than 35% protein, hence it currently ranks third among oil crops in the world after soybean and oil palm, making it necessary to identify the genotypes that tolerate salinity stress. The development and improvement of rapeseed cultivars with salinity tolerance and acclimation offer promising prospects for improving sustainable production in this area. Therefore, the current study aimed to investigate the responses of rapeseed genotypes to salinity stress through analyses of agronomic and biochemical traits. Methods: The genetic diversity between rapeseed lines in terms of agronomic, morphological, and physiological traits in saline soils was investigated in an experiment based on a randomized complete block design with 17 autumn rapeseed genotypes with three replicates in the research farm of East-Azarbaijan Agricultural and Natural Resources Research and Education Center. The measured traits were plant height, the number of fertile pods, number of seeds per pod, pod length, pod area, plant growth rate, 1000-seed weight (TSW), seed yield, oil content, and oil yield. The relationships between yield, yield components, and morphological traits were explored using the ANALYSIS of variance (ANOVA), comparison of averages, correlation ANALYSIS, CLUSTER ANALYSIS, and biplot to understand the relative importance of traits affecting the yield of the studied genotypes. Results: The studied genotypes were significantly different from each other in pod length, pod area, number of fertile pods, number of seeds per pod, plant growth rate, seed oil percentage, plant height, TSW, grain yield, and oil yield. However, there were no significant differences between the studied genotypes in terms of harvest index and number of actual pods to potential pods. According to the mean comparisons, genotypes 5, 11, and 15 can be introduced as salinity-tolerant lines, and genotypes 2, 4, 6, 9, and 12 can be considered salinity-sensitive lines. According to the other traits, genotype 11 produced a high pod length, number of fertile pods, oil percentage, and oil yield, genotype 5 had a high growth rate and oil percentage, and genotype 15 presented a high height and number of fertile pods. According to the CLUSTER ANALYSIS, the second and third groups contained tolerant and susceptible genotypes, respectively. The genotypes in the second group had the highest percentage of positive deviation from the overall mean for grain yield, plant height, harvest index, seed oil percentage, pod length, pod area, and number of fertile pods. Based on the biplot ANALYSIS, Karaj 8 and 14 genotypes had a strong relationship with the number of  fertile pods, number of seeds per pod, pod length, pod area, and plant growth rate. Based on the obtained results, the plant height, TSW, seed yield, and oil content traits were closely correlated with Karaj 5, 7, 11, 10, and 15 genotypes. Based on the results of correlation ANALYSIS, the correlation coefficient of seed yield was positive and significant for three traits, i.e., plant height, oil percentage, and number of fertile pods, and the highest correlation coefficient (r = 0.879) was obtained for seed yield with seed oil percentage. Positive and significant correlations were measured for the number of seeds in pods with pod length (r = 0.699), pod area (r = 0.555), number of fertile pods (r = 0.678), and number of actual pods. Therefore, genotypes characterized by longer and more abundant pods play a crucial role in improving seed quantity, a key component of grain yield in saline environments. Consequently, the size and number of pods per plant serve as indicators of high-yield potential under such conditions. Based on the results of the principal component ANALYSIS (PCA), the first and second components had the highest relative variances, accounting for 44.66% and 31.22% of the total variance, respectively. Together, these two components accounted for 75.88% of the total variance. Factor loadings showed that traits such as number of fertile pods, seed yield, oil yield, and seed oil content had the highest factor loadings in the first component. Similarly, the plant growth rate had the highest factor load in the second component among all the studied traits. CLUSTER ANALYSIS divided the genotypes into four groups, and its dendrogram showed that all the studied genotypes were divided into four separate groups based on all the measured traits. The first group comprised three Karaj 1, 13, and 16 genotypes, the fourth group (like the first group) contained three Karaj 5, 11, and 15 genotypes, and the third group had five genotypes, namely Karaj 3, 7, 8, 10, and 14. The remaining genotypes were assigned to the second group. Conclusion: The results of the present study demonstrate acceptable genetic diversity among rapeseed genotypes in terms of the evaluated traits in saline lands. This shows the importance and the possibility of using these genetic resources to achieve promising and superior genotypes in breeding programs.

CLUSTER ANALYSIS is one of the most important methods in classification in which the observations of each CLUSTER has maximum similarity in terms of some desirable variables. In general the CLUSTERing methods are divided into two parts, crisp and fuzzy. In usual CLUSTERing methods an observation is in only one CLUSTER whereas in fuzzy CLUSTERing it may fall into two or more CLUSTERs simultaneously. Yang and Ko (1996) introduced a fuzzy CLUSTERing method. Their method is an extension of the usual k-means CLUSTERing method as they assumed that the observations are fuzzy. A fuzzy regression model is used for studying the relationship between the explanatory variables and dependent variable. In some situations when some observations are dispersed and are heterogeneous, the regression model may not have a goodness of fit for data. To solve this problem Yang and Ko classified data and then based on fuzzy observations fitted a regression model to each CLUSTER. In this paper we first explain the semi-parametric regression model introduced by Hesamian et al. [2017] and then use their model to perform our CLUSTERing method for fuzzy observations. Finally, based on some suggested goodness of fit criterions. We compare our results with those of Yang and Ko.