A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. This paper proposes an algorithm based on hybrid transform (stationary wavelet transform proceeding by slantlet transform); The slantlet transform is applied to the approximation subband of the stationary wavelet transform. BlockShrink thresholding technique is applied to the hybrid transform coefficients. This technique can decide the optimal block size and thresholding for every wavelet subband by risk estimate (SURE). The proposed algorithm was executed by using MATLAB R2010aminimizing Stein’s unbiased with natural images contaminated by white Gaussian noise. Numerical results show that our algorithm competes favorably with SWT, and SLT based algorithms, and obtain up to 1.23 dB PSNR improvement.
Background: Trauma to the anterior teeth is a common injury in young children. Themaxillary incisors being the most affected. Although root fractures are rare, theydo occur and were previously and often considered hopeless and were extracted.The time between the injury and the initiation of treatment, level of the fractureline, and stage of root development are some criteria to be considered whenchoosing a treatment approach for a complicated tooth fracture. This case reportdescribes the management of a traumatized immature maxillary central incisorwith Elise class IV fracture with vertical oblique subgingival fracture of the root.Materials and method: Apexification was carried out using biodentine followed byremoval of the fracture
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After about twelve months or maybe more, some people can’t achieve pregnancy. This might be a sign of infertility as a reproductive system disease. The following study was carried out to investigate the DAZ 1 gene methylation level and its association with azoospermia in Iraqi patients. One hundred and fifty human blood samples were collected from from different regions in Baghdad governorate, including (private medicals Labs and the high institute for infertility diagnosis assisted reproductive techniques and Kamal Al- Samara'ay IVF Hospital) from both fertile and infertile men. The control group consists of 50 samples ranging from 22 to 51 years old, while the patient (infertile group) consists of 100 samples ranging between 25 and 51 y
... Show MoreBackground: Diabetes mellitus is a major health issue that is one of the leading causes of cardiovascular disease. Recent studies have found a link between uncontrolled diabetes and cardiovascular disease, with dyslipidaemia predicting glycated-hemoglobin (HbA1c), which could be a major contributor to type 2 diabetes complications and etiology.
Objectives: The objective of present study was estimate lipid profiles among control and uncontrolled type 2 diabetic patients.
Subjects and Methods: Analytical case control based study, One hundred twenty participate were included in study, 70 patients with DM as case group refer to Abuagala Center and difference follow up diabetic center and 50 non diabetic subjects taken as
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A new ligand N-(methylcarbamothioyl) acetamide (AMP) was synthesized by reaction of acetyl chloride with adenine. The ligand was characterized by FT-IR, NMR spectra and the elemental analysis. The transition metal complexes of this ligand where synthesize and characterized by UV-Visible spectra, FT-IR, magnetic suscepility, conductively measurement. The general formula [M(AMP)2Cl2], where M+2 = (Mn, Co, Ni, Cu, Zn, Cd, Hg).
The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
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Background: Cigarette smoking is an important risk factor that has a clear strong association with the prevalence and severity of chronic periodontitis (CP). Salivary biochemical parameters may be affected by both smoking and CP together. Materials and methods: Eighty systematically healthy male patients were included in this study. They were grouped based on their periodontal and smoking status. Unstimulated whole saliva (UWS) was collected from all subject. Salivary flow rate (FR) was measured during sample collection. Parameters such as salivary pH, total protein (TP), albumin (Alb), total fucose (TF), protein bound fucose (PBF) and C-reactive protein (CRP) were estimated. Results: Salivary flow rate was not altered regarding to smoking
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