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The relationship between topography, soil factors, and distribution of ecological vegetation groups in the Nodoushan arid rangelands of Yazd province (Iran) was investigated. The present species were recorded in each vegetation group using a randomized-systematic sampling method. Plant cover and density were estimated quantitatively using the transect and quadrate methods, and the two-way indicator species analysis (TWINSPAN), after which vegetation was classified into different groups. Soil samples were taken from 0–30 cm in each quadrat. In each vegetation group, 20 environmental variables including altitude, slope, aspect, percentage of bare rock, grazing intensity, percentage of gravel, soluble ions (Na+, K+, Mg2+, Ca2+), total nitrogen, organic matter, lime, gypsum, EC, pH, and percentage of sand, silt and clay were measured. Multivariate techniques including detrended correspondence analysis (DCA) were used to analyze the collected data. The results showed that the vegetation distribution was related to elevation, slope, and soil characteristics such as texture, organic matter, gypsum, acidity, lime, and gravity percentage.

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Factors affecting the spatial variations of water quality of the Mazandaran coastal ecosystem was determined in summer 2012. For this purpose, water quality parameters (nutrients, temperature, conductivity, salinity, dissolved oxygen, pH, chlorophyll α and turbidity) were evaluated along 4 transects (Amirabad, Babolsar, Noshahr and Ramsar) in the depths of 5, 10, 20 and 50m, using multivariate analysis methods.  Based on the cluster analysis of data, the sampling sites could be classified into 5 distinct groups, including 35-50m water layer of station 50m in all transects, 0-5m layer in station 5m in all transects, Ramsar transect and the majority of surface and bottom layers of 3 transects including Noshahr, Babolsar and Amirabad. Based on discriminant analysis, 86.40% of the sampling sites were correctly classified. Factor analysis explained 87.53% of the total variance, the five principal components of which (viz. temperature, turbidity, nitrate, silica and ammonium) were considered as the most effective parameters on the spatial variation of water quality. This study suggests that the number of sampling locations can be reduced to two transects. Thermocline, transport of nutrients (specially phosphorus and ammonium) from rivers, sea floor, cage culture and the ctenophore, M. leidyi, were the most effective sources on spatial variations of water quality. Moreover, the multivariate statistical methods were found to be useful tools to recognize the spatial variations pattern along the Mazandaran coasts in summer.

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Aims: There are few data regarding the prevalence and trends of Klebsiella pneumoniae antibiotic resistance in Algeria. The present study was conducted to investigate the spatial distribution of K. pneumoniae antibiotic resistance phenotypes in time and according to specimen source.   
Materials & Methods: This retrospective study was performed between January 2011 and December 2015 at Mila Hospital, Algeria. A total of 172 K. pneumoniae were isolated from consulting and hospitalized patients, and their antimicrobial susceptibility was tested. The Principal Component Analysis (PCA) was used to study correlations among antimicrobial resistance phenotypes observed, and Factorial Correspondence Analysis (FCA) was used to study the spatial distribution of antibiotic resistance phenotypes according to specimen source.
Findings: The specimens were obtained from urine (n=89), vagina (n=39), pus (n=33), blood (n=9) and surgery (n=2). PCA showed two principals associations of resistance phenotypes gathered in two clusters. The first profile regroups amoxicillin-clavulanic acid, cefazolin and ampicillin. The second assembles cefotaxime, nalidixic acid and sulfamethoxazole-trimethoprim. In FCA, nalidixic acid was connected with urine specimens, registering maximum resistance (52.8%) compared to the other samples. Vagina specimens were associated to sulfamethoxazole-trimethoprim and colistin phenotypes registering maximum resistances with 89.7 and 76.9%, respectively. Pus manifested a near association to cefotaxime with a maximum resistance (48.5%).
Conclusion: The model developed in FCA, highlights typical associations of antibiotic resistance phenotypes to specimen source and confirms the difference in resistance profile according the source of specimen in K. pneumoniae infections.

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This study was designed to investigate the genetic diversity and relationships between yield and related traits in oily sunflower lines. 152 sunflower lines collected from different parts of the world were investigated at completely randomized design with nine replications on Urmia University in 1391 under pot conditions. 14 agro-morphological traits including plant height, stem diameter, number of leaves, leaf length, leaf width, petiole length, head diameter, 100 seed weight, head dry weight, , seed yield per plant, number of days from planting to flowering, and number of days from planting to maturity, dehulled kernel to whole kernel and harvest index were measured. Analysis of variance revealed significant differences among genotypes for all studied traits. Among the traits, the highest coefficient of phenotypic variation was observed for seed yield per plant (56.30), harvest index (44.4) and head dry weight (35.44). The results of correlation analysis showed that there is significant and positive correlation between seed yield per plant with most of the studied traits. Results of sequential path analysis revealed that the variables such as number of leaves, dehulled kernel to whole kernel, head diameter, and plant height were arranged as the first-order variables. Cluster analysis subdivided the genotypes into 4 groups. The maximum distance were observed between the genotype from groups 3 and 4 (28.30).

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Aims: This research aimed to evaluate the spatial patterns of water quality and its controlling factors in the Mazandaran coastal ecosystem during winter using the multivariate analysis methods.
Materials and methods: Water quality parameters such as nutrients, temperature, conductivity, salinity, DO, pH, chlorophyll-a and turbidity were measured monthly in 16 stations (44 layers) along 4 transects (Amirabad, Babolsar, Noushahr and Ramsar). To evaluate the data, several multivariate statistical methods were used including discriminant function analysis, cluster and factor analysis as well as correlation test.
Findings: Results of cluster analysis showed that the sampling sites (44 layers) were classified into 4 groups. Based on discriminant analysis, 93.20% of the sampling sites correctly classified. Factor analysis extracted 4 principal components that explained 74.05% of the total variance. Based on these analyses, organic phosphorus, organic nitrogen, turbidity, chlorophyll-a and temperature were the most effective parameters on the spatial variation of water quality.
Conclusion: This study suggested that the number of sampling locations could be reduced to 3 transects including Amirabad, Babolsar and west coasts (Noushahr and Ramsar) and 2 stations (one surface layer and one deep layer). Transport of nutrients from land, sea floor and fish cage culture were the most effective factors on spatial patterns of water quality in Mazandaran coasts. Based on the results, multivariate statistical methods are also introduced as one of the useful methods for identifying the spatial pattern of water quality.

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It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features, color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloe vera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features.

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In this study, a core collection of 180 Aegilops and Triticum accessions belonging to six diploid (T. boeoticum Bioss., T. urartu Gandilyan., Ae. speltoides Tausch., Ae. tauschii Coss., Ae. caudata L. and Ae. umbellulata Zhuk.), five tetraploid (T. durum, Ae. neglecta Req. ex Bertol., Ae. cylindrica Host. and Ae. crassa Boiss) and one hexaploid (T. aestivum L.) species collected from different regions of Iran were evaluated using 20 agro-morphological characters. Statistical analysis showed significant differences among accessions. The Shannon-Weaver (Hʹ_SW) and Nei’s (Hʹ_N) genetic diversity indices disclosed intermediate to high diversity for most characters in both Aegilops and Triticum core sets. In factor analysis, the first five components justified 82.17% of the total of agro-morphological variation. Based on measured characters, the 180 accessions were separated into two major groups by cluster analysis. Furthermore, based on the 2D-plot generated using two discriminant functions, different species were separated into six groups, so that distribution of species accorded with their genome construction. Overall, our results revealed considerable levels of genetic diversity among studied Iranian Aegilops and Triticum accessions, which can open up new avenues for rethinking the connections between wild relatives to explore valuable agronomic traits for the improvement and adaptation of wheat.

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The aim of this study was to determine the differences in digit sizes from both pelvic limbs of 169 dairy cows of different genetic groups (Holstein, Jersey, Brown Swiss, and Jersolando), in lactating and non-lactating conditions, and their relation with diseases. Images were taken from the plantar view of the lateral and medial digits, and the length and width of the sole and bulb were then measured. Variables were discarded and factors for analysis were defined. Data were defined according to the highest coefficients and used to discriminate genetic groups and their association with indexes of foot diseases and productive performance. The Chi-square test showed that higher involvement occurred in lactating cows and in the right pelvic limb. Differences among all genetic groups were observed regarding the variables body weight, productive longevity, incidence of foot diseases, and generated factors. The Jersey breed was isolated from the other genetic groups because it presented lower coefficients for all variables; Holstein and Brown Swiss presented the highest morphometric measures of the digits, a higher body weight, and productive longevity, and higher rates of foot diseases. The Jersolando presented intermediate values ​​between those of the two breeds from which it originated. The susceptibility to foot diseases is associated with breed, lactational stage, body weight, and with the morphometric parameters of the digits.

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 Enzymes play a crucial role in plant-pathogen interactions and are very important to manage plant diseases. Take-all is a disease (Gaeumannomyces graminis var. tritici) affecting the crowns and roots in wheat. So far, the resistance mechanism of this disease has not been identified; therefore, this research was performed to identify the components of resistance to this disease in a number of wheat genotypes. In this study, 8 bread wheat genotypes were cultured, and the changes in “peroxidase, Polyphenol Oxidase (PPO), Phenylalanine Ammonia-Lyase (PAL), and total protein” was assessed in 0, 4, 7, 9, and 12 days after inoculation. The results showed that different genotypes of wheat had different pathogenicity reactions to the take-all disease. Based on the average disease intensity, the genotypes were divided into three groups: resistant, moderately resistant, and susceptible. The results indicated that the level of polyphenol oxidase and phenylalanine ammonia-lyase, and the total protein increased in the resistant and moderately resistant groups. Cluster analysis by K-means was performed to produce three clusters. Polyphenol oxidase activity, phenylalanine ammonia-lyase activity, and total protein content in the second (resistant) and third (moderately resistant) clusters were higher than the first cluster (susceptible). Multivariate analysis indicated that peroxidase enzyme might indirectly influence the resistance. The results have clarified the role of polyphenol oxidase enzymes and total protein in enhancing resistance to take-all disease.