Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm arises from Bcr-Abl gene translocation (called Ph chromosome) in hematopoietic stem cells (HSCs). This genetic abnormality results in constitutive activation of tyrosine kinase and subsequent uncontrol growth and multiplication of granulocytes. The cornerstone in treatment of CML are tyrosine kinase inhibitors, of which imatinib is the most effectively used. JAK2V617F mutation is an acquired single nucleotide polymorphism (SNP) occurs in JAK2 gene and is associated with many hematological malignancy other than CML. It was thought that the two genetic abnormalities (Bcr-Abl and JAK2V617F) occur mutually; however, growing body of evidences suggested the reverse. This study aimed to investigate the prevalence of JAK2V617 mutation associated with serum levels of alkaline phophatase (ALK) and lactate dehydrogenase (LDH) in Ph+ CML Iraqi patients treated with imatinib. A total of 43 Ph+ CML patients (24 males and 18 females, age range 16-80 years) who attend Iraqi National Center of Hematology for Research and Treatment/Baghdad were enrolled in this study. Each patient has been received at least six month therapy with imatinib. A consent form involving age, gender, height, weight, smoking status, residency and first family relative history of leukemia was obtained from each patient. Besides, blood samples were collected, from which the granulocytes were separated and then DNA was extracted using a ready kit. Two assays were used for detection of JAK2V617F mutation; real time polymerase chain reaction (qPCR) using specific primers and probe, and allele specific PCR (AS-PCR) using specific primers. Total white blood corpuscles (WBC) as well as serum levels of ALP and LDH were measured. qPCR assay revealed 5 patients out of 43 (11.62%) were heterozygous for the muatant allele of JAK2V617F mutation (genotype GT). The concentration of this allele ranged from 0.01% to 0.12%. None of blood sample gave positive result for AS-PCR assay. From the all risk factors, only gender had significant association with the incidence of JAK2V617F mutation (p= 0.034, OR= 0.5, 95%CI= 0.364-0.687). Average total WBC count, and serum levels of ALP and LDH were higher in JAK2V617F-positive patients (9042±1512.55, 146.05±8.028 IU/L and 204±10.85 IU/L respectively) than that of JAK2V617F-negative patients (6039±1772.239, 64.45±40.15 IU/L and 178.33±13.693 IU/L respectively) with significant differences. These results indicate that JAK2V617F mutation can occur simultaneously with Ph chromosome in CML patients, and qPCR is a highly sensitive method for the detection of this mutation. Furthermore, serum activity of APL can be used as an indicator for the presence of JAK2V617F mutation in CML patients.
Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.
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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative
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Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the
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Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a
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Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local
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This research deals with processing and Interpretation of Bouguer anomaly gravity field, using two dimensional filtering techniques to separate the residual gravity field from the Bouguer gravity map for a part of Najaf Ashraf province in Iraq. The residual anomaly processed in order to reduce noise and give a more comprehensive vision about subsurface linear structures. Results for descriptive interpretation presented as colored surfaces and contour maps in order to locate directions and extensions of linear features which may interpret as faults. A comparison among gravity residual field , 1st derivative and horizontal gradient made along a profile across the study area in order to assign the exact location of a major fault. Furthermor
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