Scleractinian coral species harbour communities of photosynthetic taxa of the genus Symbiodinium. As many as eight genetic clades (A, B, C, D, E, F, G and H) of Symbiodinium have been discovered using molecular biology. These clades may differ from each other in their physiology, and thus influence
the ecological distribution and resilience of their host corals to environmental stresses. Corals of the Persian Gulf are
normally subject to extreme environmental conditions including high salinity and seasonal variation in temperature. This study
is the first to use molecular techniques to identify the Symbiodinium of the Iranian coral reefs to the level of phylogenetic clades. Samples of eight coral species were collected at two different
depths from the eastern part of Kish Island in the northern Persian Gulf, and Larak Island in the Strait of Hormuz. Partial
28S nuclear ribosomal (nr) DNA of Symbiodinium (D1/D2 domains) were amplified by polymerase chain reaction (PCR). PCR products were analyzed using single stranded conformational
polymorphism and phylogenetic analyses of the LSU DNA sequences from a subset of the samples. The results showed that Symbiodinium populations were generally uniform among and within the populations of eight coral species studied, and there are at least
two clades of Symbiodinium from Kish and Larak islands. Clade D was detected from eight of the coral species while clade C was found in two of species
only (one species hosted two clades simultaneously). The dominance of clade D might be explained by high temperatures or the
extreme temperature variation, typical of the Persian Gulf.
Publication of this article was held up owing to technical problems. The publisher apologizes sincerely for this lengthy delay. 相似文献
In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO2, CO, NO2, O3, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)12 with NO2 model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.