Enhanced Twitter Community Detection using Node Content and Attributes
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(2)
Publication Date
Mon Dec 28 2020
Journal Name
The Iraqi Journal Of Veterinary Medicine
Serological and Molecular Phylogenetic Detection of Coxiella burnetii in Lactating Cows, Iraq
Coxiella burnetii
cattle
Iraq
IS1111A transposase gene
ELISA
PCR
This study is carried out to investigate the prevalence of Coxiella burnetii (C. burnetii) infections in cattle using an enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) assay targeting IS1111A transposase gene. A total of 130 lactating cows were randomly selected from different areas in Wasit province, Iraq and subjected to blood and milk sampling during the period extended between November 2018 and May 2019. ELISA and PCR tests revealed that 16.15% and 10% of the animals studied were respectively positive. Significant correlations (P<0.05) were detected between the positive results and clinical data. Two positive PCR products were analyzed phylogenetically, named as C. burnetii IQ-No.5 and C. burnet
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Publication Date
Fri Mar 04 2022
Journal Name
Pakistan Journal Of Medical And Health Science
Publication Date
Tue Apr 01 2025
Journal Name
Iop Conference Series: Earth And Environmental Science
Molecular Detection and Antiserum Preparation of Begomoviruses Infecting Zucchini Squash in Iraq
Abstract<p>Begomoviruses infecting zucchini squash were investigated. Leaf samples were collected from zucchini squash growing areas in Baghdad (Jhadryaa and Yusufiyah), Babylon (Jibela and Mahmudiyah) and Diyala (Khan Bani Saad) Provinces. Samples were screened for the presence of begomoviruses using polymerase chain reaction (PCR) and Deng genus specific primers. Sixteen out of 40 samples were begomovirus positive. Sequence analysis confirmed the detection of Tomato leaf curl Palampur virus (TLCPALV) <italic>Begomovirus solanumpalampurense</italic>, Squash leaf curl virus (SLCuV) <italic>B. cucurbitapeponis</italic> and Tomato yellow leaf curl virus (TYLCV) <italic>B. </italic></p>
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Publication Date
Thu Oct 01 2020
Journal Name
Biochemical And Cellular Archives
Publication Date
Sun Jan 01 2023
Journal Name
مجلة الأطروحة العلمية المحكمة-العلوم الصرفة والتطبيقية
Detection of Lead (Pb) and
Cadmium (Cd) Concentrations
in Some Indomie Samples
Andomie product
Lead
Cadmium
heavy metals
Atomic Absorption spectrometer
Publication Date
Tue Jan 01 2019
Journal Name
Biochemical And Cellular Archives
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
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Publication Date
Sat Apr 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Detection of Mineral and Microbial Contaminants in some Types of Imported Meat
Abstract<p>The main target of the current study is to investigate the microbial content and mineral contaminants of the imported meat available in the city of Baghdad and to ensure that it is free from harmful bacteria, safe and it compliances with the Iraqi standard specifications. Some trace mineral elements such as (Iron, Copper, Lead, and Cadmium) were also estimated, where 10 brands of these meats were collected. Bacteriological tests were carried out which included (total bacterial count, <italic>Staphylococcus</italic> bacteria, <italic>Salmonella</italic> bacteria). The results showed highest number of total bacterial count 13×10<sup>5</sup> CFU/g in F8 bra</p>
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
Adverse Drug Reaction
Artificial Neural Network
Classification
Deep Learning
Latent Semantic Analysis
Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
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