Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Objective: To examine the efficiency of both the Zeta and density gradient centrifugation (DGC) methods for the selection of normal chromatin sperm by TUNEL, sperm chromatin dispersion (SCD), acridine orange and chromomycin A3 (CMA3).Materials and Methods: The study was performed on 63 patients. Semen analysis was carried out according to WHO criteria. Semen samples were divided into three equal portions. One portion was used as the control, the second portion was used for the Zeta method and the third portion underwent DGC method. All portions were evaluated for sperm morphology (Diff Quick staining), protamine deficiency (CMA3) and DNA integrity (SCD, AO and TUNEL). Coefficients of correlation and student’s t test were carried out using SPSS 11.5.Results: The mean percentage of abnormal sperm, protamine deficiency and DNA fragmentation in the Zeta and DGC methods were significantly decreased as compared to the control group (p<0.005). In addition, the DGC method was higher in comparison to the Zeta method in the selection of sperm with normal morphology (p<0.005). The Zeta method was higher in comparison to the DGC method in the selection of sperm with intact DNA (p<0.005).Conclusion: Both Zeta and DGC methods were effective in the selection of sperm with better quality in terms of normal morphology, normal protamine content and DNA integrity. Therefore, we suggested that the combined Zeta and DGC method should be used for selection of normal sperm.

Background: The High Resolution Melting (HRM) method is a new scanning method for detecting unknown changes in DNA and its advantages have persuaded researchers to recruit it as a screening method. Objectives: Here, we developed a HRM method to screen R188H SNP (rs3218536) of XRCC2 and compared the results with a well known PCRRFLP technique.Materials and Methods: Genomic DNA samples from 350 healthy individuals were obtained. PCR-HRM analysis and PCR-RFLP method were performed simultaneously.Results: Three different melting profiles corresponding to three different genotypes recognized by HRM analysis. The results of PCR-RFLP showed no discrepancy. Conclusions: We concluded that the HRM technique can be used as a screening method for rapid discrimination of R188H genotypes in XRCC2 gene.

Background and Objectives: Knee joint as the largest synovial joint in the body, consists of the lateral collateral ligament, cruciate ligament, and medial and lateral meniscus. For proper management, identification of damage to any structures of the knee is necessary. In this study, snsitivity, specificity, and diagnostic accuracy of high Resolution CT scan in meniscal lesions, were assessed. Methods: In this test accuracy assessment study, 57 patients with a history of positive meniscus lesion, who referred to Ayatollah Mousavi Hospital of Zanjan city from 2015 to 2016, were investigated by magnetic resonance imaging, high Resolution computed tomography, and arthroscopy. Magnetic resonance imaging and high Resolution computed tomography data, were compared to knee arthroscopy as the diagnosis gold standard. Kappa (κ ) correlation coefficient was used to calculate the consistency of the tests. Results: Out of the 57 patients presented with meniscal lesion, 52 patients (91. 2%) were male and 5 patients (8. 8%) were female. The highest incidence of meniscal lesions was in ages from 21 to 30 years. Among the causes of meniscal lesions, exercise with prevalence of 47. 9%, was the most prevalent cause of injury. In comparison with arthroscopy, the sensitivity, specificity, and accuracy of high Resolution computed tomography were determined to be 64. 7%, 55%, and 60%, respectively, and for magnetic resonance imaging were 85. 2%, 50%, and 77. 5%, respectively. Conclusion: According to the results of the present study, specificity, sensitivity, and accuracy of high Resolution computed tomography in the diagnosis of meniscal lesions, was lower than the previous studies.

Generating an orthoimage from high-Resolution satellite images is an important undertaking for various remote sensing and photogrammetric applications. In this paper, a method is proposed that uses Artificial Neural Networks (ANN) to generate orthoimage lkonos Geo images. For orthoimage generation, a Digital Elevation Model (DEM) with a cell size of 4 m and RMS error of 0.24 m is constructed with neural networks, based on a Quad Tree (QT) structure. In order to determine object-to-image relationships, rational function models, polynomials and neural networks with back propagation learning algorithms were used. Ground Control Points (GCPs) and check points were taken from topographic maps of 1:2000, with a contour interval of 2.5 m, to evaluate the accuracy of DEM and object-to-image transformations. The method described in this paper is tested with an lkonos Geo image from a region of Bilesavar, Iran.

In this paper, a CODEC system based on super Resolution, is proposed for compression of high Resolution textual images. It employs image resizing to decrease image dimensions and consequently, to improve the compression ratio; but at the other hand, it may reduce the image quality. Therefore, the decompression unit employs super Resolution to simultaneously increase the reconstructed image dimensions and quality. In the employed interpolation-based super-Resolution method, using an efficient textual image matting algorithm, the input low-Resolution textual image is decomposed into three layers after which, each layer is enlarged using a particular method. Finally, the enlarged layers are combined to build the high Resolution reconstructed textual image. An interesting feature of the proposed method is the ability to use existing compression methods such as JPEG, JPEG2000 and SPIHT. We have employed the aforementioned compression methods in the proposed CODEC system and evaluated the compression results with respect to OCR rate, Mean Opinion Score (MOS), and PSNR measures. Considering the OCR and MOS measures, the proposed method outperformed the others but not so with respect to PSNR.

Probability density functions of the involved random variables are essential for the reliability-based design of offshore structures. The objective of present research was the derivation of probability density function (PDF) for the local joint flexibility (LJF) factor, fLJF, in two-planar tubular DK-joints commonly found in jacket-type offshore structures. A total of 162 finite element (FE) analyses were carried out on 81 FE models of DK-joints subjected to two types of axial loading. Generated FE models were validated using available experimental data, FE results, and design formulas. Based on the results of parametric FE study, a sample database was prepared for the fLJF values and density histograms were generated for respective samples based on the Freedman-Diaconis rule. Nine theoretical PDFs were fitted to the developed histograms and the maximum likelihood (ML) method was applied to evaluate the parameters of fitted PDFs. In each case, the Kolmogorov-Smirnov and chi-squared tests were used to evaluate the goodness of fit. Finally, the Inverse Gaussian model was proposed as the governing probability distribution function for the fLJF. After substituting the values of estimated parameters, two fully defined PDFs were presented for the fLJF in tubular DK-joints subjected to two types of axial loading.