In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and har

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

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Brucella melitensis isolates were obtained from human infections , and milk which obtained from aborted sheep at Mosul city vicinity . One isolate from each source was used in carrying out this study. Brucella liquid culture was added to sheep milk at 2.5 % for treatments . To first treatment 2 % of yoghurt starter ( Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus salivarius subsp. thermophilus ( 1: 1 ) ) . Second treatment was carried out without addition of yoghurt starter but the pH was lowered using lactic acid in pattern similar to first treatment . Third treatment was similar to the first treatment but contained buffer to alleviate the reduction in pH , which reduced to 6.1 in comparison to 4.9 of the first treatment .

The study aimed to assess Milk Thistle oi reducing the aflatoxin damage and improve the health status of local breed hens. The study was performed on 60 hens aged 35 weeks, weighing 2-2.5 kg of the body was obtained and housed on a private poultry farm in north of Baghdad (Altaji) for a period of 9 weeks from 22 October until 22 December 2020. Hens were divided to three treatments (20 hens). The first was fed a basal diet free of aflatoxin. The 2nd fed on a contaminated diet with aflatoxin (14.6 ppb) while the 3rd fed as in the 2nd with 0.5% of Milk Thistle oil /kg feed. A 5 hens from each group were slaughtered at the end of the trial for the histopathological inspection of liver, spleen and intestine tissues to evaluate the harmfu