• Asian Pacific Journal of Cancer Prevention
• /
• 제15권17호
• /
• pp.6989-6999
• /
• 2014

Colorectal cancer (CRC) is one of the major healthcare problems worldwide and its processes of genesis include a sequence of molecular pathways from adenoma to carcinoma. The discovery of microRNAs, a subset of regulatory non-coding RNAs, has added new insights into CRC diagnosis and management. Together with several causes of colorectal neoplasia, aberrant expression of oncomiRs (oncogenic and tumor suppressor miRNAs) in cancer cells was found to be indirectly result in up- or down-regulation of targeted mRNAs specific to tumor promoter or inhibitor genes. The study of miRNAs as CRC biomarkers utilizes expression profiling methods from traditional tissue samples along with newly introduced non-invasive samples of faeces and body fluids. In addition, miRNAs could be employed to predict chemo- and radio-therapy responses and be manipulated in order to alleviate CRC characteristics. The scope of this article is to provide a comprehensive review of scientific literature describing aberrantly expressed miRNAs, and consequently dysregulation of targeted mRNAs along with the potential role of miRNAs in CRC diagnosis and prognosis, as well as to summarize the recent findings on miRNA-based manipulation methods with the aim of advancing in anti-CRC therapies.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권8호
• /
• pp.551-560
• /
• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권9호
• /
• pp.3151-3168
• /
• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권11호
• /
• pp.3619-3637
• /
• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권2호
• /
• pp.45-51
• /
• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).

• 약학회지
• /
• 제55권3호
• /
• pp.267-272
• /
• 2011

Human NK cells, identified 30 years ago based on their ability to spontaneously kill tumor cells, constitute a subset of lymphocytes, which play an important role in the first line of immune defense and the effective function of these cells are enhanced by cytokines. Lung carcinoma has been one of the most commonly diagonosed cancer as well as the leading cause of cancer death in male. Here we provide the evidence that human natural killer cells has inhibitory effects on tumor growth of human lung cancer cell NCI-H460 (non-small cell lung cancer). Enriched NK cell population was obtained by 2 weeks cultivation in interleukin-2(IL-2)-containing medium. The resulting population comprised 26% CD3$^+$ cells, 9% CD3$^+$CD4$^+$ cells, 16% CD3$^+$CD8$^+$ cells, 76% CD56$^+$ cells, 6% CD3$^+$CD56$^+$ cells and 70% CD3$^-$CD56$^+$ cells. Activated NK cells at doese of 2.5, 5, and 10 million cells per mouse inhibited 2%, 12% and 45% of NCI-H460-induced tumor growth in nude mouse xenograft assays, repectively. This result suggests that NK cell-based immunotherapy may be used as an adoptive immunotherapy for lung cancer patients.

• Journal of Periodontal and Implant Science
• /
• 제23권3호
• /
• pp.454-460
• /
• 1993

Abnormalities of the T cell subsets have been detected in the immunologically mediated disease sites such as periodontal lesions which are attributable to the regulatory effect of cell differentiation and specific chemokinetic effect of various cytokines. Macrophage Inflammatory protein$(MIP)-1{\alpha}$ and gammain terferon$({\gamma}-IFN)$ serve as important immunoregulatory molecules through which growth and differentiation of specific T cell subsets are known to be negatively regulated. Murine cytolytic T cell line CTLL-2 were used to perform the [$^3H$]-thymidine incorporation test, by which we obtained more comprehensive view in regulatory actions of cytokines on the T cell subset proliferation. 1. $rMIP-{\alpha}$(200ng/ml) and $r{\gamma}-IFN$(100U/ml) appreared to suppress the proliferation rate to CTLL-2 by 74 and 86% respectively, and the suppressive action of two cytokines were synergisic. 2. Culture supernatant of anti-CD3 mAb-stimulated mouse splenocyte enhanced the proliferation rate of CTLL-2 up to 10-fold with dose-dependent manner. However, culture supernatant of unstimulated splenocyte showed only 2-fold increase in the proliferation rate. 3. CTLL-2 cell proliferation was strictly IL-2 dependent.

• 전기학회논문지
• /
• 제60권4호
• /
• pp.862-870
• /
• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 경영정보학연구
• /
• 제5권2호
• /
• pp.37-51
• /
• 2003

본 연구에서는 모바일 비즈니스를 특정 단말기 형태에 국한되지 않고, 이동성이 부과된 e-비즈니스로 정의하고, 더 포괄적인 가상공간에서의 상거래 프레임워크를 이루는 두 축으로 연결성(connectivity)과 이동성(mobility)을 도출하여 두 축의 조화(combination)로 인한 각 서비스의 내용들을 여러 사례들을 제시하여 설명한다. 기존 전자 상거래와 e-비즈니스를 설명할 때 주로 고려되었던 "연결성" 요소 이외에, 오프라인상의 "이동성" 요소를 추가한 점이 본프레임워크의 특징이라 할 수 있겠다. 이동성이 '장소(위치)'가 전제되어야 한다는 점에 착안하여 이와 관련된 기술적 요소로 위치 기반 서비스(LBS: Location-Based Service)가 새로운 e-비즈니스 모델 및 서비스 개발에 중요한 역할을 할 것으로 기대된다.

• Journal of Contemporary Eastern Asia
• /
• 제18권1호
• /
• pp.7-29
• /
• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.