• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.21
• /
• pp.9495-9498
• /
• 2014

Background: The difference in prognosis of adult and childhood acute lymphoblastic leukemia (ALL) can be attributed largely to variation in cytogenetic abnormalities with age groups. Cytogenetic analysis in acute leukemia is now routinely used to assist patient management, particularly in terms of diagnosis, disease monitoring, prognosis and risk stratification. Knowing about cytogenetic profile at the time of diagnosis is important in order to take critical decisions in management of the patients. Aim and Objectives: To determine the frequency of cytogenetic abnormalities in Pakistani adult patients with ALL in order to have insights regarding behavior of the disease. Materials and Methods: A retrospective analysis of all the cases of ALL (${\geq}15$years old) diagnosed at Aga Khan University from January 2006 to June 2014 was performed. Phenotype (B/T lineage) was confirmed in all cases by flow cytometry. Cytogenetic analysis was made for all cases using the trypsin-Giemsa banding technique. Karyotypes were interpreted using the International System for Human Cytogenetic Nomenclature (ISCN) criteria. Results: A total of 166 patients were diagnosed as ALL during the study period, of which 151 samples successfully yielded metaphase chromosomes. The male to female ratio was 3.4:1. The majority (n=120, 72.3%) had a B-cell phenotype. A normal karyotype was present in 51% (n=77) of the cases whereas 49% (n=74) had an abnormal karyotype. Of the abnormal cases, 10% showed Philadelphia chromosome; t(9;22)(q34;q11.2). Other poor prognostic cytogenetic subgroups were t(4;11)(q21;q23), hypodiploidy (35-45 chromosomes) and complex karyotype. Hyperdiploidy (47-57 chromosomes) occurred in 6.6%; all of whom were younger than 30 years. Conclusions: This study showed a relatively low prevalence of Philadelphia chromosome in Pakistani adults with ALL with an increase in frequency with age (p=0.003). The cumulative prevalence of Philadelphianegative poor cytogenetic aberrations in different age groups was not significant (p=0.6).

• Proceedings of the Korea Society of Environmental Toocicology Conference
• /
• 2002.10a
• /
• pp.156-156
• /
• 2002

The goal of this study is to develop a biomarker used in monitoring abnormal behaviors of Japanese medaka (Oryzias latipes) as a model organism caused by hazardous chemicals that are toxic and persistent in the ecosystem. A widely used insecticide, diazinon (O, O-diethyl O- (2-isopropyl-4-methyl-6-pyrimidinyl) phosphorothioate), is highly neurotoxic to fish, and it is also well known that it causes vertebral malformation and behavioral changes of fish at relatively low concentrations. The fish behaviors were observed on a real time basis using an image processing and automatic data acquisition system. The genes potentially involved in the abnormal behaviors were cloned using suppression subtractive hybridization (SSH) technique. The untreated individuals showed common behavioral characteristics. When the test fish was affected by diazinon at a concentration of 0.1 and 1 ppm, some specific patterns were observed in its behavioral activity and locomotive tracks. The typical patterns were enhanced surfacing activity, opercular movement, erratic movement, tremors and convulsions as reported previously. The number of genes up-regulated tty diazinon treatment were 97 which includes 27 of unknown genes. The number of down-regulated genes were 99 including 60 of unknown genes. These gene expression patterns will be analyzed by the artificial neural networks such as self organization map (SOM) and multilayer perceptron (MLP), revealing the role of genes responsible for the behaviors. These results may provide molecular biological and neurobehavioral bases of a biomonitoring system for diazinon using a model organism such as fish.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.89-96
• /
• 2021

In recent years, the fear of terrorism due to 'Lone Wolf' terrorism is spreading in the United States and Europe. The lone wolf terrorism, which carries out terrorism independently, without an organization behind it, threatens social security around the world. In Korea, those who have explosive national/social dissatisfaction due to damage caused by national policies, and delusional mental disorders can be classified as potential 'Lone Wolf' terrorists. In 'Lone Wolf' terrorism, unlike organized terrorism, it is difficult to identify signs of terrorism in advance, and it is not easy to identify terrorist tools and targets. Therefore, in order to minimize the damage caused by 'Lone Wolf' terrorism, it is necessary to architect an independent monitoring and tracking system for the police's quick response. In this paper, we propose to architect response system that can collect information from organizations that can identify the signs of potential 'Lone Wolf' terrorism, monitor the continuity of abnormal behavior, and determine the types of 'Lone Wolf' terrorism that can happen as continuous abnormal behaviors.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.2
• /
• pp.1-8
• /
• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Environmental Analysis Health and Toxicology
• /
• v.18 no.2
• /
• pp.155-163
• /
• 2003

Fluoranthene, a common polycyclicaromatic hydrocarbon (PAH), exhibits phototoxicity which may affect aquatic organisms. The eventual goal of this study is to develop a biomarker of Japanese medaka (Oryzias latipes) used in monitoring hazardous chemicals in the ecosystem. In this study we investigated neural toxicity of fluoranthene in Japanese medaka (Oryzias latipes) along with comparative analysis of corresponding behavioral response. The untreated individuals shooed normal behavioral characteristics (i. e., smooth and linear movements). The treated fish, however, showed stopping and abrupt change of orientation (100 ppb), and severely reduced locomotive activity and enhanced surfacing activity (1,000 ppb). Treatment of the medaka fish with fluoranthene caused a significant suppresson of acetycholine esterase (AChE) activities in the body portion but not in the head portion. When fish were exposed to 1,000 ppb of fluoranthene for 24 hr, the body AChE activities decreased from 126.${\pm}$31.89 (nmoles substrate hydrolyzed per min per mg protein) to 49.51${\pm}$11.99. Expressions of tyrosine hydroxylase (TH) protein in the different organs from both head and body portions were comparatively analyzed using an immunohistochemical technique. Five organs of the medaka fish showing a strong TH protein expression were the olfactory bulb, hypothalamus, optic lobe, pons and myelencephalon regions. This study provides molecular and neurobehavioral bases of a biomonitoring system for toxic chemicals using fish as a model organism.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.183-193
• /
• 2022

Smart cities are expected to provide residents with convenience via various agents such as CCTV, delivery robots, security robots, and unmanned shuttles. Environmental data collected by various agents can be used for various purposes, including advertising and security monitoring. This study suggests a surveillance map data framework for efficient and integrated multimodal data representation from multi-agents. The suggested surveillance map is a multilayered global information grid, which is integrated from the multimodal data of each agent. To confirm this, we collected surveillance map data for 4 months, and the behavior patterns of humans and vehicles, distribution changes of elevation, and temperature were analyzed. Moreover, we represent an anomaly detection algorithm based on a surveillance map for security service. A two-stage anomaly detection algorithm for unusual situations was developed. With this, abnormal situations such as unusual crowds and pedestrians, vehicle movement, unusual objects, and temperature change were detected. Because the surveillance map enables efficient and integrated processing of large multimodal data from a multi-agent, the suggested data framework can be used for various applications in the smart city.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.6
• /
• pp.850-854
• /
• 2014

Analyzing dynamic performance between pantograph and contact wire depends on mechanical and electrical conditions such as contact force, currents, aerodynamics of pantograph and tension of overhead contact wire. For the characteristic of dynamic performance between pantograph and overhead contact wire, various evaluation systems are used to measuring of the interaction of the contact line and the pantograph. Among the various methods, the contact force and percentage of arcing are intended to prove the safety and the quality of the current collection system on the train. However, these methods are only capable of measuring on the train which are installed measurement systems. Therefore in this paper, a track-side monitoring system was implemented to measure electrical characteristics from active overhead contact wire systems in order to constantly estimate current collection performance of railway operation. In addition, a method to analyze loss of contact phenomena was proposed. According to simulation results, the proposed system was capable of measuring abnormal electrical behavior of pantograph and contact wires on the track-side. The advantage of the proposed system is possible to detect loss of contact or any other electrical abnormalities of all types of trains within sections from sub to sub without the need to install any on-board equipment on trains.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.531-540
• /
• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Science of Emotion and Sensibility
• /
• v.10 no.3
• /
• pp.373-381
• /
• 2007

The purpose of this study was to extract important problem features for care of senile dementia patients. Selected cognitive ability test (Korean Mini-Mental State Examination: K-MMSE) and survey of basic & problem characteristics were conducted on 110 hospitalized senile dementia patients and 30 normal subjects. Problem features of senile dementia patients were extracted using factor analysis. The frequency difference of problem features due to the gender and dementia severities was verified using one-way ANOVA. Twenty problem features were extracted by the factor analysis. According to the gender, there are significant differences in the frequency of problem features in violent language & confabulation, collecting behavior, and repetitive behavior. According to the dementia severities, there are significant differences in the frequency of all problem features except abnormal sexual behavior and audio-visual disorder. The result of this study is expected to be used for the development of the senile dementia patients' life-care monitoring system.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.8
• /
• pp.107-118
• /
• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.