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.

Background and Aim: Auditory temporal resolution skill is calculated according to the minimum required time to analyze or integrate acoustic events. This skill, which is essential for speech perception, was evaluated by gap detection tests. The aim of this study was to determine the presence or absence of any differences between the right and left ear in the gap detection test.Method: After performing auditory tests to rule out any hearing loss or auditory processing disorders, the gap detection test was administered to 40 adults.Results: Gap detection thresholds were 5.35 ms and 5.30 ms in the right and left ear, respectively, and the average correct answers percentages were 69.03% and 68.03% in the right and left ear, respectively. According to these data, results were similar for both ears.Conclusion: There is no laterality in temporal resolution tasks. However, we cannot generalize the results of the present study as being applicable to the whole society.

Let $S=K[x_1,\ldots,x_n]$ be a polynomial ring over a field $K$ and$I\subset S$ be a monomial ideal with a linearresolution. Let$\frak{m}=(x_1,\ldots,x_n)$ be the unique homogeneous maximal ideal and $I\frak{m}$ be apolymatroidal ideal. We prove that if either $I\frak{m}$ is polymatroidal with strongexchange property, or $I$ is a monomial ideal in at most 4variables, then $I$ is polymatroidal. We also show that the firsthomological shift ideal of polymatroidal ideal is againpolymatroidal.

Coreference resolution is the problem of clustering mentions in a text that refer to the same entities, and is a crucial and difficult step in every natural language processing task. Despite the efforts that have been made to solve this problem during the past, its performance still does not meet today’s application requirements. Given the importance of the verbs in sentences, in this work, we tried to incorporate three types of their information on coreference resolution problem, namely, selectional restriction of verbs on their arguments, semantic relation between verb pairs, and the truth that arguments of a verb cannot be coreferent of each other. As a needed resource for supporting our model, we generate a repository of semantic relations between verb pairs automatically using Distributional Memory (DM), a state-of-the-art framework for distributional semantics. This resource consists of pairs of verbs associated with their probable arguments, their role mapping, and significance scores based on our measures. Our proposed model for coreference resolution encodes verb’s knowledge with Markov logic network rules on top of the deterministic Stanford coreference resolution system. Experiment results show that this semantic layer can improve the recall of the Stanford system while preserves its precision and improves it slightly.