Background: Obesity is a worldwide challenge and is closely
connected to many metabolic diseases. Two types of
adipose tissue, white adipose tissue (WAT) and brown
adipose tissue (BAT) have been identified. White fat cells
store chemical energy, brown adipocytes defend against
hypothermia, obesity and diabetes.
Objective: To localize and quantify brown adipocytes in
human subcutaneous (S) and visceral (V) adipose tissue by
histology and immunohistochemistry.
Type of the study: A cross –sectional study.
Methods: Adipose tissue was obtained from histopathology
specimens taken from ten patients, of different age, sex and
body mass index (BMI), undergoing surgery for different
pathologies

This study is designed to isolate and molecular identification of C. gattii, C. gattii is pathogenic yeast and effect immunocomposed and immunocompetent, Methods: collect 50 samples from eucalyptus leaves. The collection time was extended from November 2021 to February 2022 and then culture at SDA, Cryptococcus Differential Agar esculin agar and Eucalyptus leaves agar, Brain heart infusion agar with methyldopa and Brain heart infusion agar with methyldopa media, biochemical test including urease test, and then confirm identification by molecular identification by PCR technique sequencing and genetic analysis. The results showed that 4 swaps taken from eucalyptus leaves included cryptococcus neoformans. This study indicated that the virulenc

Five species of Lactic acid bacteriawere isolated from raw milk, yoghurt, vegetables and pickles, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus brevis, Lactobacillus casei and Lactobacillus bulgaricus isolates were identified by 16S rRNA gene. Evaluate of antimicrobial activity against all the bacterial strains Staphylococcus aureus, Salmonella spp., Pseudomonas fluorescens, Escherichia coli, Bacillus cereus and Bacillus subtilis. It showed that bacteriocin of Lactic acid bacteriamore effective than supernatant of lactic acid bacteria, the results showed that isolatemost efficient isolate belonging to Lactobacillus brevis, the diameter of the inhibition of the bacteriocin of Lactobacillus brevis were 27.7, 26.3 and 25.1

This study was aimed to one of the most prevalent causes for endodontic treatment failure is the presence of Enterococcus faecalis bacterium within teeth root canals. To achieve successful treatment, it is so important to study E. faecalis behavior. The aim of study was to investigate biofilm production and antibiotic sensitivity of E. faecalis isolated from root canals. Results showed isolation of E. feacalis (65%) of samples, identified by specific gene by PCR technique. Most isolates were sensitive to Imipenem and resistant to Erythromycin, Clindamycin, Tetracycline and Trimethoprim. Strong biofilm production was detected among 29.5% of highest antibiotic resistant isolates. The results may indicate that infected root canals with E. feac

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

In this paper a new idea was introduced which is finding a new distribution from other distributions using mixing parameters; w_i  where  0 < w_i < 1 ­and . Therefore we can get many mixture distributions with a number of parameters. In this paper I introduced the idea of a mixture Weibull distribution which is produced from mixing two Weibull distributions; the first with two parameters, the scale parameter , and the shape parameter,  and the second also has the scale parameter , and the shape parameter,  in addition to the location parameter, . These two distributions were mixed using a new parameter which is the mixing parameter w which represents the proportion

The present paper is very important for study of biodiversity of Iraq , to know how the
changes going and how the laughing dove S. senegalensis was a rare species in Iraq and now
is common and also the (baz) gosh hawk A. gentilis is common and the most famous 6rd of
pray in Iraq, till now missing from ornithologist and bird watcher to record it

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an