Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the

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Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

Endocrine factors released from the central nervous system, gastrointestinal tract, adipose tissue and other peripheral organs mediate the regulation of food intake. Although many studies have evaluated the effect of fed-to-starved transition on the expression of appetite-related genes, little is known about how the expression of appetite-regulating peptides is regulated by the macronutrient composition of the diet. The aim of the present study was to examine the effect of diet composition and nutritional status on the expression of four peptides involved in food intake control in gilthead sea bream (Sparus aurata): neuropeptide Y (NPY), ghrelin, cholecystokinin (CCK) and leptin. Quantitative real-time RT-PCR showed that high protein/low carbohydrate diets stimulated the expression of CCK and ghrelin in the intestine and leptin in the adipose tissue, while downregulation of ghrelin and NPY mRNA levels was observed in the brain. Opposite effects were found for the expression of the four genes in fish fed low protein/high carbohydrate diets or after long-term starvation. Our findings indicate that the expression pattern of appetite-regulating peptides, particularly CCK and ghrelin, is modulated by the nutritional status and diet composition in S. aurata.

The purpose of this study was to evaluate the growth, thermal stress resistance, antioxidant enzyme activities and skin colour of Labidochromis caeruleus (electric yellow cichlid) fed the diets supplemented with extracts derived from brown macroalga Sargassum boveanum, red macroalga Gracilaria persica and green macroalga Entromorpha intestinalis. One hundred and forty‐four fish were randomly distributed into 12 tanks and subjected to cold‐shock stress after 8 weeks of feeding the diets containing 1,000 mg extracts of macroalgae. Supplementation of fish diet with algal extracts led to improved growth performance (including higher final weight and weight gain) when compared to the control group, which was fed the non‐supplemented diet. The survival rate after cold‐shock stress was significantly higher in those fish fed the diets containing macroalgal extracts, especially E. intestinalis extract (75%). The superoxide dismutase activity in all dietary treatments was significantly lower than control, whereas no significant difference in the activities of catalase and lysozyme was observed among treatments. In addition, inclusion of macroalgal extracts in the diet resulted in higher a* (redness) and b* (yellowness) values compared to the control group. These results suggest that macroalgal extracts, especially E. intestinalis extract, can be used as feed additive for increasing antioxidants capacities as well as enhancing pigmentation in electric yellow cichlid.