Various pathological specimens (180) were collected from patients suffering
from pseudomonas aeruginosa infections from different hospitals in Baghdad from January
to May 2011; these specimens include (Blood samples,sputum,urine and wound swabs) were
tested for pseudomonas aeruginosa producing 2-Aminoacetophenone.Wounds swabs
specially taken from burns and post surgical infections producing a higher concentration of 2-Acetophenone material than from other samples were tested for this material and most of
these were isolated bases on their distinctive grape- like odor of 2-Aminoacetophenone
production usually linked with patients whose immune system compromised by disease or
trauma, its gains access to these patients tissue through burns, although the grape odor is
sometimes difficult to detect in culture media.
These methods can be utilized to assay 2-aminoacetophenone (2AA) production in a
varies media. Its synthesis occurs relatively early in the growth cycle. It has proved easy and
convenient to detect 2-aminoacetophenone (2AA) excretion by Pseudomonas aeruginosa after
24 h of incubation on blood agar plates employing thin layer chromatography of ether extracts
of the agar medium.
Abstract
This work deals with a numerical investigation to evaluate the utilization of a water pipe buried inside a roof to reduce the heat gain and minimize the transmission of heat energy inside the conditioning space in summer season. The numerical results of this paper showed that the reduction in heat gain and energy saving could be occurred with specific values of parameters, like the number of pipes per square meter, the ratio of pipe diameter to the roof thickness, and the pipe inlet water temperature. Comparing with a normal roof (without pipes), the results indicated a significant reduction in energy heat gain which is about 37.8% when the number of pipes per m
... Show MoreThis paper presents the design and analysis of composite right left hand (CRLH) electromagnetic bandgap (EBG) structure. The proposed unit cell is consistent of a dielectric substrate with dimensions of 5×5×1 mm 3 made of FR4-Epoxy with εr = 4.4 underneath of a conductive patch with dimensions of 4.4×4.4mm 2 . The unit cell is structured to perform a negative permittivity (ε) and negative permeability (µ) in different bands. The proposed unit cell is developed to 5G systems in the sub-6GHz bands. In this work, a complete analysis of the unit cell in terms of Sparameters, constitutive parameters and refraction index are evaluated using HFSS simulation package based on Finite Element Method (FEM).
Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took th
... Show MoreInterest in belowground plant growth is increasing, especially in relation to arguments that shallow‐rooted cultivars are efficient at exploiting soil phosphorus while deep‐rooted ones will access water at depth. However, methods for assessing roots in large numbers of plants are diverse and direct comparisons of methods are rare. Three methods for measuring root growth traits were evaluated for utility in discriminating rice cultivars: soil‐filled rhizotrons, hydroponics and soil‐filled pots whose bottom was sealed with a non‐woven fabric (a potential method for assessing root penetration ability). A set of 38 rice genotypes including the Oryza
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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