Abstract. Hassan FM, Mahdi WM, Al-Haideri HH, Kamil DW. 2022. Identification of new species record of Cyanophyceae in Diyala River, Iraq based on 16S rRNA sequence data. Biodiversitas 23: 5239-5246. The biodiversity and water quality of the Diyala River require screening water in terms of biological contamination, because it is the only water source in Diyala City and is used for many purposes. This study aimed to identify a new species record of Cynaophyceae and emphasize the importance of using molecular methods beside classic morphological approaches, particularly in the water-shrinkage-aqua system. Five different sites along Diyala River were selected for Cyanophyceae identification. Morphological examination and 16S rRNA sequence analysis was conducted, and the phylogenetic tree was constructed using Mega 6 Programme. The morphological examination of samples showed a total of 28 species corresponds to Cyanophyceae, including one species of Spirulina. In our study of 28 identified species, three new species record were identified in Diyala River. The newly recorded species were confirmed by 16S rRNA and the phylogenetic tree construction. The species are registered in the National Centre for Biotechnology Information (NCBI) with the following accession numbers: Arthrospira indica (MW854667.1), Arthrospira platensis (MW854665.1), and Limnospira fusiformis (MW854666.1). Most notably, Arthrospira platensis is not listed in the checklist of Iraqi algae. Thus, these species are considered as a new record of Iraqi algal flora. The identification of new species record in Diyala River reflexes the impact of climate change on this river, and the necessity to use 16S rRNA to identify microalgae in all ecosystems.
A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).
A new technique for embedding image data into another BMP image data is presented. The image data to be embedded is referred to as signature image, while the image into which the signature image is embedded is referred as host image. The host and the signature images are first partitioned into 8x8 blocks, discrete cosine transformed “DCT”, only significant coefficients are retained, the retained coefficients then inserted in the transformed block in a forward and backward zigzag scan direction. The result then inversely transformed and presented as a BMP image file. The peak signal-to-noise ratio (PSNR) is exploited to evaluate the objective visual quality of the host image compared with the original image.
Several specimens of the avocet, Recurvirostra avocetta L. are found infected with
Himantocestus gigantivcus sp. nov. ( Cestoda , Diploposthidae) . This cestode is related to H.
blanksoni Ukoli 1965 but easily differentiated from it in having longer and wider strobila,
larger size of testes but lesser in number, cirrus situated in the middle of mature segment
histead of anterior third and slightly posterior to the middle in gravid segment instead of the
middle , ovary and vitelline gland are larger , and the uterus has more branches.
The current study presents the cellar spiders genus Nita Huber & El-Hennawy, 2007 (Araneae, Pholcidae) as the first record for Iraq spider fauna, this genus represented by the species Nita elsaff Huber & El-Hennawy, 2007 were identified based on morphological characteristics and DNA sequence data. A short morphological description is also presented for cellar spiders listed in Iraq; including this species in addition to Artema Atlanta Walckenaer, 1837.
Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holl
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