m. hashemzadeh

The aim of this study was to analyze the annual, monthly, and seasonal rainfall changes over a thirty-year period as well as before and after the emergence of declining oak. (Quercus brantii var. persica) trees (2000) in Ilam and Lorestan provinces. For this purpose, the authors used long-term data of daily rainfall in 1987–2017 period recorded by four synoptic meteorological stations; Ilam, Dehloran, Khoramabad, and Aligudarz. The Mann-Kendall non-parametric test was used to determine the rainfall trends. The decreasing trends of annual rainfall as well as mean differences in annual precipitation before and after emerging oak decline were not statistically significant at all meteorological stations. The average rainfall of four stations was 502 mm in the first decade (1987-1997), while it decreased to 422 and 371 mm in the second (1998-2007) and third decades (2008- 2017), respectively. The difference in the amount of annual rainfall before and after the emerging oak decline (442 against 401 mm) did not alter the mean rainfall event (̴7 mm). Although the thirty-year trends of seasonal rainfall were not significant, winter rainfall was decreased after emerging oak decline by 11% and spring, autumn, and summer rainfalls were increased by 5, 4, and 2%, respectively. Rainfall fluctuations in the Zagros vegetation region may act an accelerating factor for the emergence and extension of oak trees declining in the Zagros forests of Lorestan and Ilam provinces.

Generally, the photos captured by drones and satellites include both natural scenes and man-made objects. Having these two categories classified, we will be able to extract important information from aerial scenes such as the shapes and the alignments of the structures and then, create labeled aerial images accordingly. Obtaining such information is of great interest in, for example, military, urban, and environmental protection applications. However, due to a huge amount of data that is collected in form of images, it seems that manually processing of such data is impossible. Therefore, employing automatic techniques based on artificial intelligence has become more on demand. There are numerous researches on this topic from which detection of buildings, vehicles, roads, and vegetation are of more interest. In this paper, we aim to introduce a method to detect man-made objects in aerial images based on a new set of color statistical features, which can be easily extracted, together with a learning model. Experimental results on a publicly available dataset, Massachusetts dataset, have shown promising results in terms of both accuracy and processing time; the accuracy and the average processing time are 90.07% and 0.96 seconds, respectively.

In spring of 2012,two ewes of a sheep flock from west region of Alborz province with neurological disorders as circling,dullness,depression,incoordination and anorexia  were referred to veterinary school hospital of Islamic Azad University of Karaj branch.on initial examination,cantral nervous system lesions were suspected.in order to confirm the diagnosis,one of them utanaized.macroscopic findings in necropsy were leptomeningial opacity,cloudy cerebrospinal fluid and meningeal vessels congestion.many foci of yellow-brown discoloration (0.1 mm in diameter) were seen in brain stem section.microscopically,neuronal necrosis,gliosis,perivascular cuffing,neutrophilic micro abscesses and meningoencephalitis were observed.listeria monocytogenes was isolated from brain samples and CSF in bacteriological Lab.on the basis of macroscopic signs,histopathological findings and microbiological test results,meningoencephalic listeriosis was detected in these cases.

Hearing loss is the most frequent sensory disorder affecting 1 in 500 neonates with more than 50% of inherited cases. This trait is a very heterogeneous disorder and happens due to genetic or environmental causes or both. More than 46 genes may be involved in non-syndromic hearing loss. Recently, DFNB59 gene has been shown to cause deafness in some Iranian populations. The aim of this study was to determine the role of DFNB59 gene mutations causing deafness in a group of 130 deaf pupils in Fars province.

This descriptive-laboratory based study investigated the frequency of DFNB59 gene mutations using PCR-SSCP/HA strategy.

Two different DFNB59 polymorphism including 874G>A and 793C>G were found in 1 and 9 of 130 patients studied respectively. However, no DFNB59 mutation was identified.

The results of this study shows that the association of DFNB59 mutations with deafness in Fars province is very low.