Pilus-encoding islets in S. agalactiae and its association with antibacterial resistance and serotype distribution.

Group B Streptococcus (GBS) is a versatile organism which uses multiple virulence factors which bind to the surface of epithelial cells. Pili are one of virulence factors detected in recent years. A total of 90 isolates were collected from invasive and non-invasive isolates among adults throughout 2014-2015. Isolates were serotyped at molecular level based on capsular polysaccharide (cps) serotyping and analyzed for pilus island profiles, scpB gene, and hvgA gene presence. Isolates were subjected to antimicrobial susceptibility towards penicillin, tetracycline, erythromycin, clindamycin, moxifloxacin, levofloxacin, and vancomycin by disk diffusion method and MICs for erythromycin and clindamycin were determined by broth dilution methods. Overall, 4 serotypes were identified, serotype III (68.88%), V (20%), II (10%) and Ib (1.11%) and hvgA gene was detected in 7.7% (n = 7) of the isolates; all were serotype III/ST 17. All isolates were susceptible to penicillin and vancomycin, except one isolate which showed intermediate resistance to penicillin and other complete resistance to vancomycin. Isolates were resistant to tetracycline (98%), erythromycin (25%), clindamycin (22%), moxifloxacin (8%), and levofloxacin (6%). The scpB gene was detected in all isolates, while isolates harbored at least one PI, of which the PI-1+PI-2a was the most frequent combination observed. Our data show the presence of the relation between serotype or pilus genes among clinical isolates of Streptococcus agalactiae. These data are principal to help in designing prevention and treatment strategies for GBS infections in the region.

Analysis of land suitability for urban development in Ahwaz County in southwestern Iran using fuzzy logic and analytic network process (ANP).

The ever-increasing development of cities due to population growth and migration has led to unplanned constructions and great changes in urban spatial structure, especially the physical development of cities in unsuitable places, which requires conscious guidance and fundamental organization. It is therefore necessary to identify suitable sites for future development of cities and prevent urban sprawl as one of the main concerns of urban managers and planners. In this study, to determine the suitable sites for urban development in the county of Ahwaz, the effective biophysical and socioeconomic criteria (including 27 sub-criteria) were initially determined based on literature review and interviews with certified experts. In the next step, a database of criteria and sub-criteria was prepared. Standardization of values and unification of scales in map layers were done using fuzzy logic. The criteria and sub-criteria were weighted by analytic network process (ANP) in the Super Decision software. Next, the map layers were overlaid using weighted linear combination (WLC) in the GIS software. According to the research findings, the final land suitability map was prepared with five suitability classes of very high (5.86 %), high (31.93 %), medium (38.61 %), low (17.65 %), and very low (5.95 %). Also, in terms of spatial distribution, suitable lands for urban development are mainly located in the central and southern parts of the Ahwaz County. It is expected that integration of fuzzy logic and ANP model will provide a better decision support tool compared with other models. The developed model can also be used in the land suitability analysis of other cities.

Urban development change detection based on Multi-Temporal Satellite Images as a fast tracking approach--a case study of Ahwaz County, southwestern Iran.

Rapid land-use/land-cover changes in suburbs of metropolitan cities of Iran have recently caused serious environmental damages. Detection of these changes can be a very important step in urban planning and optimal use of natural resources. Accordingly, the present study was carried out to track land-use/land-cover (LULC) changes of Ahwaz County in southwestern Iran using remote sensing techniques over a period of 26 years, from 1987 to 2013. For this, ISODATA algorithm and Maximum Likelihood were initially used for unsupervised and supervised classifications of the satellite images. The accuracy of the LULC maps was checked by the Kappa Coefficient and the Overall Accuracy methods. As the final step, the LULC changes were detected using the cross-tabulation technique. The obtained results indicated that urban and agricultural areas have been increased about 57.5 and 84.5 %, respectively, from 1987 to 2013. Further, the area of poorly vegetated regions, in the same period, has been decreased to approximately 36 %. The largest land conversion area belongs to the poorly vegetated regions, which have been declined to about 10,371 and 1,334 ha during 1987-2007 and 2007-2013, respectively. Approximately 1,670 and 382 ha of the agricultural lands have also been changed to built-up areas by about 1,670 and 382 ha during the same periods. As a result, it was found that the northwest, southwest, and south of the county were highly subjected to urban development. This would be of great importance for urban planning decision-making faced by the planners of the city in the present and future.