High prevalence of SARS-CoV-2 and influenza A virus (H1N1) coinfection in dead patients in Northeastern Iran.

In the last months of 2019, an outbreak of fatal respiratory disease started in Wuhan, China, and quickly spread to other parts of the world. It was named COVID-19, and to date, thousands of cases of infection and death are reported worldwide. This disease is associated with a wide range of symptoms, which makes accurate diagnosis of it difficult. During previous severe acute respiratory syndrome (SARS) pandemic in 2003, researchers found that the patients with fever, cough, or sore throat had a 5% influenza virus-positive rate. This finding made us think that the wide range of symptoms and also relatively high prevalence of death in our patients may be due to the coinfection with other viruses. Thus, we evaluated the coinfection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with other respiratory viruses in dead patients in North Khorasan. We evaluated the presence of influenza A/B virus, human metapneumovirus, bocavirus, adenovirus, respiratory syncytial virus (RSV), and parainfluenza viruses in 105 SARS-CoV-2 positive dead patients, using polymerase chain reaction (PCR) and reverse transcription PCR tests. We found coinfection with influenza virus in 22.3%, RSV, and bocavirus in 9.7%, parainfluenza viruses in 3.9%, human metapneumovirus in 2.9%, and finally adenovirus in 1.9% of SARS-CoV-2 positive dead cases. Our findings highlight a high prevalence of coinfection with influenza A virus and the monopoly of coinfection with Human metapneumovirus in children.

Ecological capability evaluation for afforestation and forest expansion using Geographic Information System (GIS) in management area of Caspian Sea.

Ecological capability evaluation is referred to as evaluation of potential capability of land and determination of its natural land use. In this paper, ecological capability of Lille Basin in Lahijan was evaluated for afforestation and forest expansion using Geographical Information Systems (GIS). For this purpose, information layers (slope, elevation, geographical directions, temperature, rainfall, humidity, soil type, and canopy cover) were prepared by digitizing the data obtained from concerned organizations. First, the layers were prepared based on coordinate systems and pixel value. Then, they were standardized using fuzzy membership functions. The evaluation criteria were prioritized with regard to expert opinion, and by using multi-criteria evaluation method (MCE) and analysis of hierarchic process (AHP), and the final weight of each criterion was determined. The weight for 8 criteria including elevation, soil type, canopy cover (%), directions, slope, temperature, humidity, rainfall were 0.330, 0.210, 0.128, 0.117, 0.076, 0.054, 0.046 and 0.210, respectively. According to these weights, elevation has the highest value and the slope is the lowest. The results suggest that ecological capability exhibits sensitivity to variations of different criteria and its distribution is directly correlated to ecological factors especially climatic and physiographical ones. The incompatibility index (0.09) also indicated that the weights were logical. Then valuation of the maps performed. Finally, based on these values, capabilities of each environmental unit were identified and potentials of the area for afforestation were determined in five capability classes.

Evaluation of deciduous broadleaf forests mountain using satellite data using neural network method near Caspian Sea in North of Iran.

During the recent decades, deciduous forests have been molested by human intervention. Easy access, abundance and diversity of valuable forest products have led to increased population density, creating new residential areas and deforestation activities. Revealing changes is one of the fundamental methods in management and assessment of natural resources. This study is evaluated changes in forests area of 2013 using satellite images. In order to mapping the forest extent condition 2013, images of the mentioned years were digitized and geo-referenced by using the ground control points and the maps of mapping organization. After selecting the best set of band using the Bhattacharya distance index, the image classification was performed by using artificial neural network algorithm. Classification by neural network method 2013 in showed that it has a high overall accuracy equal to 95.96%.

The query of suitable areas for plantation and development of Taxus baccata L species by using GIS in northern Iran.

Taxus baccata is a native species to the Caspian forests and is considered as the identification of these forests by some resources. The aim of this study was to understand the ecological characteristics of T. baccata in order to find suitable areas for its plantation in the Pone Aram preserve of Golestan province. Therefore, Multi-Criteria Evaluation (MCE) methods based on Analytical Hierarchy Process (AHP) were used. In this process at first the needed and effective natural indexes were used as 10 parameters. Then the final map of suitable areas for T. baccata plantation were produced based on Bolian and MCE models. According to the results, in Bolian method about 6218 ha of the total area of the study site (30554 ha) were estimated as suitable and semi suitable and 24336 ha were estimated as unsuitable for MCE model showed that about 2482 ha of the whole 30554 ha area of the study site is adequate for T. baccata plantation and 6181 ha is not adequate for T. baccata plantation. Regarding the results of the MCE and Bolian methods it has been concluded that for dynamic ecological parameters in delineation of suitable areas for T. baccata plantation the MCE weighting is more appropriate than Bolian. Using the results of this investigation it is possible to adequate areas for the presence of T. baccata and to execute a plan in order to facilitate the plantation of T. baccata in northern forests of Iran (watershed 88).