Sharif Digital Repository

Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results... 

Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results... 

This paper presents a two layer texture modeling method to recognize architectural distortion in mammograms. We propose a method that models a Gaussian mixture on the Curvelet coefficients and the outputs of Spiculated Lesion Filters. The Curvelet transform and the Spiculated Lesion Filters have been applied to extract textural features of mammograms in literature. However the key difference between this study and the previous ones is that in our approach, a Gaussian mixture models the textural features extracted by the Curvelet transform and the Spiculated Lesion Filters. The results of the current study are shown in the form of accuracy and the area under the receiver operating... 

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty agematched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing... 

Liver-shape analysis and quantification is still an open research subject. Quantitative assessment of the liver is of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Liver-shape classification is of clinical importance for corresponding intra-subject and inter-subject studies. In this research, we propose a novel technique for the liver-shape classification based on Spherical Harmonics (SH) coefficients. The proposed liver-shape classification algorithm consists of the following steps: (a) Preprocessing, including mesh generation and simplification, point-set matching, and surface to template alignment; (b) Liver-shape parameterization,... 

In this extended summary, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. We employ the minimum description length (MDL) criterion to reduce the dimension of the data and estimate the number of components, which makes ICA work more efficiently. We also use a simple oscillating index method to select automatically the components of interest. MDL and oscillating index criteria have not already been used in applying ICA for analyzing fMRI data. In order to investigate the advantage of using MDL and oscillating index, we perform some experiments... 

Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72 h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who... 

The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sclerosis (MS) patients and 390 healthy subjects. Single nucleotide polymorphisms (SNPs) within ANRIL (rs1333045, rs1333048, rs4977574 and rs10757278), EVI5 (rs6680578, rs10735781 and rs11810217), ACE (rs4359 and rs1799752), MALAT1 (rs619586 and rs3200401), GAS5 (rs2067079 and rs6790), H19 (rs2839698 and rs217727), NINJ2 (rs11833579 and rs3809263), GRM7 (rs6782011 and rs779867), VLA4 (rs1143676), CBLB (rs12487066) and VEGFA (rs3025039 and... 

Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD... 

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the... 

Periodontitis is a common oral disorder leading to tooth loss in both developed and developing regions of the world. This multifactorial condition is related to the abnormal activity of several molecular pathways, among them are oxytocin-related pathways. In this study, we enrolled 26 patients and 28 controls and assessed the expression of four oxytocin-related genes, namely, FOS, ITPR, RCAN1, and RGS2, in circulation and affected tissues of enrolled individuals using real-time PCR. Expression of FOS was downregulated in total periodontitis tissues compared with total control tissues [ratio of mean expression (RME) = 0.23, P-value = 0.03]. Expression of FOS was also lower in total blood... 

Nuclear factor kappa B (NF-κB) is an important regulator of immune responses and is involved in the pathoetiology of periodontitis. We have measured levels of four NF-κB-related genes, namely CEBPA, CEBPA-DT, FBXL19-AS and DILC in blood and tissue samples of patients with chronic periodontitis compared with controls. Expression of CEBPA-DT was significantly lower in blood of patients compared with controls (Ratio of mean expression (RME) = 0.13, P value = 0.01). This pattern was also seen among male subgroups (RME = 0.10, P value = 0.01). FBXL19-AS was down-regulated in venous blood of total patients compared with controls (RME = 0.03, P value < 0.001) and in patients of both sexes compared... 

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms... 

LncRNAs are involved in the modulation of several signaling pathways which have a crucial effect in the differentiation of periodontal ligament cells and the induction of cementum regeneration. Autophagy and Wnt signaling are two important pathways with a wide range of interrelationships associated with periodontitis. We chose four lncRNAs based on their potential interaction with these two pathways. We examined the expression of FOXD2-AS1, NNT-AS1, GAS8-AS1, and CCAT1 lncRNAs in tissues and blood specimens of patients with periodontitis and unaffected controls using qRT-PCR. Expression amounts of FOXD2-AS1 were lower in blood of cases compared with controls (relative expression (RE) = 0.08,... 

Objective(s): This study aims to evaluate combined proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) metabolic profiling approaches, for discriminating between mustard airway diseases (MADs) and healthy controls and for providing biochemical information on this disease. Materials and Methods: In the present study, analysis of serum samples collected from 17 MAD subjects and 12 healthy controls was performed using NMR. Of these subjects, 14 (8 patients and 6 controls) were analyzed by GC-MS. Then, their spectral profiles were subjected to principal component analysis (PCA) and orthogonal partial least squares regression discriminant... 

Background: Nonalcoholic fatty liver disease (NAFLD) is the most abundant chronic liver disorder, because racial and ethnic differences may influence prevalence and severity of NAFLD. Objectives: This metabolomic study was conducted to identify the metabolic biomarkers and determine the mechanism of progress of NAFLD in Iranian patients. Methods: Serum samples were collected from 75 participants (37 healthy controls and 38 patients with NAFLD) after an overnight fast. The metabolome of all samples were determined by nuclear magnetic resonance (NMR) and were compared by multivariate statistical analysis. Results: Totally, 19 metabolomic biomarkers were identified by NMR. Compared to healthy... 

Protein inhibitors of activated STAT (PIAS) are involved in the regulation of the JAK/STAT signaling pathway and have interactions with NF-κB, p73 and p53. These proteins regulate immune responses; therefore dysregulation in their expression leads to several immune-mediated disorders. In the present study, we examined expression of PIAS1-4 in peripheral blood of patients with acute/chronic inflammatory demyelinating polyradiculoneuropathy (AIDP/CIDP) compared with healthy subjects. We demonstrated down-regulation of all PIAS genes in both AIDP and CIDP cases compared with controls. Similarly, comparisons in gender-based groups revealed down-regulation of these gene0s in patients of each... 

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis... 

Growth arrest-specific gene 6 (GAS6) is a growth factor-like cytokine whose function is related with vitamin K. This protein interacts with receptor tyrosine kinase proteins such as Tyro3, Axl, and TAM Receptor family, therefore affecting the tumorigenic processes via different mechanisms. GAS6-antisense 1 (GAS6-AS1) is a long non-coding RNAs (lncRNAs) that is transcribed from a genomic regions nearby GAS6. This lncRNA is also implicated in the pathobiology of cancer. We intended to judge the role of GAS6 and GAS6-AS1 in the pathogenesis of breast cancer through appraisal of their expression levels in breast cancer tissues and their paired neighboring non-cancerous samples. Expression of...