Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, coronal plane, and sagittal plane. Three different thresholds, which are based on texture features: mean, energy and entropy, are obtained automatically. This allowed to accurately separating the MRI slice into normal and abnormal one. However, the abnormality detection contained some normal blocks assigned wrongly as abnormal and vice versa. This problem is surmounted by applying the fine-tuning mechanism. Finally, the MRI slice abnormality detection is achieved by selecting the abnormal slices along its tumour region (Region of Interest-ROI).
The research is aimed at investigating how the New York Times framed the war against ISIS in its news coverage and which news sources it adopted while reporting on this war.
The research could be classified under descriptive researches. The survey methodology has been adopted and the content analysis has been used. The research sample consists of all the news stories the New York Times have published about the war against ISIS from 10/17/2016 to 4/16/2017 according to the comprehensive sampling method. The number of news stories that were analyzed was (155) news story. The research tool was (coding scheme).
The research has reached the following conclusions:
1. In its news coverage of the war against ISIS, the New York T
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In this research various of 2,5-disubstituted 1,3,4-oxadiazole (Schiff base, oxo-thiazolidine , and other compounds) were synthesized from 2,5-di(4,4?- amino-1,3,4-oxadiazole ) which use quently synthesized from mixture of 4-amino benzoic acid and hydrazine in the presence of polyphosphorus acid. The synthesized compounds were characterized by using some Spectral data (UV, FT-IR, and 1H-NMR).
Abstract Organic compounds with pyrazole cores have a variety of uses, notably in the pharmaceutical and agrochemical sectors. The interest in creating pyrazole compounds, examining their many features, and looking for potential uses is growing. Our work has concert with synthesis of chalcones and pyrazolines, then finally pyrazoline-aniline derivatives and evaluation their anti-inflammatory, antibacterial and antifungal activities
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This study was carried out from February to October 2012 in six semi salty ponds in Gwer sub-district which is the first work in the area. A total of 32 species and 2 genera of algae where reported as the new records. Mostly the non diatoms are belonging to Cyanophyta, Chlorophyta, Euglenophyta, Cryptophyta, Chrysophyceae, while diatoms or Bacilariophyceae are belong to pennals- order.
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In this study, Bis(4,4’-diaminophenoxy)ethane (compound C1) was synthesized via the reaction of p-hydroxyaniline with 1,2-dibromoethane. Schiff bases (compounds C2–C4) were subsequently obtained by condensing compound C1 with various aromatic aldehydes. These intermediates were further reacted with different anhydrides – namely phthalic anhydride and maleic anhydride – in order to yield the final derivatives (compounds C5–C10). All obtained compounds were characterized by using infrared spectroscopy and proton nuclear magnetic resonance, as well as through an assessment of their physical properties. Antimicrobial evaluation was conducted on some of the generated compounds using two bacterial strains (Escherichia coli and Staphyloc
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Objective: This study involved the synthesis of new Schiff bases and 1,3-oxazepine derivatives from the baclofen drug and study the anticancer activities. Methods: Baclofen was initially reacted with aromatic aldehydes to create Schiff base derivatives (Ia–Ib), which were then closed in the next step using anhydrous acids to form oxazepine derivatives (IIa–IId). Results: The title compounds were synthesized successfully and identified using FT-IR, 1H NMR, and 13C NMR spectroscopy. Additionally, compound (IIc)’s (3-(4-chloro-phenyl)-4-[2-(4nitro-phenyl)-4,7-dioxo-4,7-dihydro-[1,3] oxazepin-3-yl]butyric acid) anticancer activity was assessed using MTT assay against FTC-133 (thyroid cancer) compared with WRL-68 (normal cell line). Discus
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Heterocyclic compounds are crucial for medicinal chemistry and the development of therapeutic agents like broad-spectrum antibiotics. This study devised a facile procedure to synthesize novel antimicrobial bicyclic heterocycles from 2-mercapto-3-phenylquinazolin-4(3H)-one. Advanced analytical techniques including 1 H and 13C NMR, elemental analysis, and FT-IR spectroscopy characterized the intricate chemical structures of the products. In vitro assays tested the heterocycles against aerobic and anaerobic bacterial strains using fluconazole and ciprofloxacin as antifungal and antibacterial controls. Results demonstrated the formidable broad-spectrum antibacterial and antifungal activities of the synthesized compounds, with growth inhibition
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Content-based image retrieval has been keenly developed in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval becomes one of the liveliest researches in the past few years. In a given set of objects, the retrieval of information suggests solutions to search for those in response to a particular description. The set of objects which can be considered are documents, images, videos, or sounds. This paper proposes a method to retrieve a multi-view face from a large face database according to color and texture attributes. Some of the features used for retrieval are color attributes such as the mean, the variance, and the color image's bitmap. In add
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