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Rodríguez J, Villalobos AM, Castro-Molinare J, Jorquera H. Local and NON-LOCAL source apportionment of black carbon and combustion generated PM 2.5. Environ Pollut 2024; 346:123568. [PMID: 38382732 DOI: 10.1016/j.envpol.2024.123568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
Current methods for measuring black carbon aerosol (BC) by optical methods apportion BC to fossil fuel and wood combustion. However, these results are aggregated: local and non-local combustion sources are lumped together. The spatial apportioning of carbonaceous aerosol sources is challenging in remote or suburban areas because non-local sources may be significant. Air quality modeling would require highly accurate emission inventories and unbiased dispersion models to quantify such apportionment. We propose FUSTA (FUzzy SpatioTemporal Apportionment) methodology for analyzing aethalometer results for equivalent black carbon coming from fossil fuel (eBCff) and wood combustion (eBCwb). We applied this methodology to ambient measurements at three suburban sites around Santiago, Chile, in the winter season 2021. FUSTA results showed that local sources contributed ∼80% to eBCff and eBCwb in all sites. By using PM2.5 - eBCff and PM2.5 - eBCwb scatterplots for each fuzzy cluster (or source) found by FUSTA, the estimated lower edge lines showed distinctive slopes in each measurement site. These slopes were larger for non-local sources (aged aerosols) than for local ones (fresh emissions) and were used to apportion combustion PM2.5 in each site. In sites Colina, Melipilla and San Jose de Maipo, fossil fuel combustion contributions to PM2.5 were 26 % (15.9 μg m-3), 22 % (9.9 μg m-3), and 22 % (7.8 μg m-3), respectively. Wood burning contributions to PM2.5 were 22 % (13.4 μg m-3), 19 % (8.9 μg m-3) and 22% (7.3 μg m-3), respectively. This methodology generates a joint source apportionment of eBC and PM2.5, which is consistent with available chemical speciation data for PM2.5 in Santiago.
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Affiliation(s)
- Jessika Rodríguez
- Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile; Center for Sustainable Urban Development (CEDEUS), Los Navegantes 1963, Providencia, Santiago 7520246, Chile
| | - Ana María Villalobos
- Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile
| | - Julio Castro-Molinare
- Gestion Ambiental Consultores, General del Canto 421, piso 6, Santiago 7500588, Chile
| | - Héctor Jorquera
- Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile; Center for Sustainable Urban Development (CEDEUS), Los Navegantes 1963, Providencia, Santiago 7520246, Chile.
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2
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Yousefirizi F, Shiri I, O JH, Bloise I, Martineau P, Wilson D, Bénard F, Sehn LH, Savage KJ, Zaidi H, Uribe CF, Rahmim A. Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients. Phys Eng Sci Med 2024:10.1007/s13246-024-01408-x. [PMID: 38512435 DOI: 10.1007/s13246-024-01408-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 02/18/2024] [Indexed: 03/23/2024]
Abstract
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.
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Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Joo Hyun O
- College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | | | | | - Don Wilson
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | | | - Laurie H Sehn
- BC Cancer, Vancouver, BC, Canada
- Centre for Lymphoid Cancer, BC Cancer, Vancouver, Canada
| | - Kerry J Savage
- BC Cancer, Vancouver, BC, Canada
- Centre for Lymphoid Cancer, BC Cancer, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- University Medical Center Groningen, University of Groningens, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Vancouver, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Carlos F Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Departments of Physics and Biomedical Engineering, University of British Columbia, Vancouver, Canada
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Manoharan H, Selvarajan S, Aluvalu R, Abdelhaq M, Alsaqour R, Uddin M. Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation. PeerJ Comput Sci 2023; 9:e1709. [PMID: 38192458 PMCID: PMC10773856 DOI: 10.7717/peerj-cs.1709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/29/2023] [Indexed: 01/10/2024]
Abstract
The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.
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Affiliation(s)
- Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India
| | | | - Rajanikanth Aluvalu
- Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India
| | - Maha Abdelhaq
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Raed Alsaqour
- Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Mueen Uddin
- College of Computing and IT, University of Doha for Science and Technology, Qatar
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Jafarzade N, Kisi O, Yousefi M, Baziar M, Oskoei V, Marufi N, Mohammadi AA. Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources. Heliyon 2023; 9:e18415. [PMID: 37520981 PMCID: PMC10382293 DOI: 10.1016/j.heliyon.2023.e18415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs. Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R2) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R2 of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.
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Affiliation(s)
- Naghmeh Jafarzade
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
| | - Mahmood Yousefi
- Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Mansour Baziar
- Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
| | - Vahide Oskoei
- School of Life and Environmental Science, Deakin University, Geelong, Australia
| | - Nilufar Marufi
- Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Mohammadi
- Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran
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Wen H, Liu L, Zhang J, Hu J, Huang X. A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines. J Environ Manage 2023; 342:118177. [PMID: 37210819 DOI: 10.1016/j.jenvman.2023.118177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/03/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
Preparation of pipeline risk zoning is essential for pipeline construction and safe operation. Landslides are one of the main sources of risk to the safe operations of oil and gas pipelines in mountainous areas. This work aims to propose a quantitative assessment model of landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data along oil and gas pipelines. Using the Changshou-Fuling-Wulong-Nanchuan (CN) gas pipeline dataset, two independent assessments were carried out: landslide susceptibility assessment and pipeline vulnerability assessment. Firstly, the study combined the recursive feature elimination and particle swarm optimization-AdaBoost method (RFE-PSO-AdaBoost) to develop a landslide susceptibility mapping model. The RFE method was used to select the conditioning factors, while PSO was used to tune the hyper-parameters. Secondly, considering the angular relationship between the pipelines and landslides, and the segmentation of the pipelines using the fuzzy clustering (FC), the CRITIC method (FC-CRITIC) was combined to develop a pipeline vulnerability assessment model. Accordingly, a pipeline risk map was obtained based on pipeline vulnerability and landslide susceptibility assessment. The study results show that almost 35.3% of the slope units were in extremely high susceptibility zones, 6.68% of the pipelines were in extremely high vulnerability areas, the southern and eastern pipelines segmented in the study area were located in high risk areas and coincided well with the distribution of landslides. The proposed hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines can provide a scientific and reasonable risk classification for new planning or in service pipelines to avoid landslide-oriented risk and ensure their safe operation in mountainous areas.
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Affiliation(s)
- Haijia Wen
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education; National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas; School of Civil Engineering, Chongqing University, Chongqing 400045, China
| | - Lei Liu
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education; National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas; School of Civil Engineering, Chongqing University, Chongqing 400045, China
| | - Jialan Zhang
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education; National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas; School of Civil Engineering, Chongqing University, Chongqing 400045, China.
| | - Jiwei Hu
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education; National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas; School of Civil Engineering, Chongqing University, Chongqing 400045, China
| | - Xiaomei Huang
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education; National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas; School of Civil Engineering, Chongqing University, Chongqing 400045, China
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Wuttisarnwattana P, Auephanwiriyakul S. Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data. J Digit Imaging 2023; 36:588-602. [PMID: 36441277 PMCID: PMC10039202 DOI: 10.1007/s10278-022-00736-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/29/2022] Open
Abstract
Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel algorithm for automatically segmenting spleen substructures including white pulp and red pulp for the first time. The algorithm is designed for datasets created by a cryo-imaging system. This unique technology can effectively enable cellular tracking anywhere in the whole mouse with single-cell sensitivity. The proposed algorithm consists of four components: initial spleen mask creation, feature extraction, Supervised Patch-based Fuzzy c-Mean (spFCM) classification, and post-processing. The algorithm accurately and efficiently labeled spleen tissues in all experiment settings. The algorithm also improved the spleen segmentation throughput by 90 folds as compared to the manual segmentation. Moreover, we show that our novel spFCM algorithm outperformed traditional fast-learning classifiers as well as the U-Net deep-learning model in many aspects. Two major contributions of this paper are (1) an explainable algorithm for segmenting spleen tissues in cryo-images for the first time and (2) an spFCM algorithm as a new classifier. We also discussed that our work can be beneficial to researchers who work not only in the fields of graft-versus-host disease (GVHD) mouse models, but also in that of other immunological disease models where spleen analysis is essential. Future work building upon our research may lay the foundations for biomedical studies that utilize cryo-imaging technology.
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Affiliation(s)
- Patiwet Wuttisarnwattana
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50300, Thailand.
- Optimization Theory and Applications for Engineering Systems Research Group (OASYS), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, 50300, Thailand.
| | - Sansanee Auephanwiriyakul
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50300, Thailand.
- Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, 50300, Thailand.
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Rao J, He Y. Forecasting the energy intensity of industrial sector in China based on FCM-RS-SVM model. Environ Sci Pollut Res Int 2023; 30:46669-46684. [PMID: 36723837 DOI: 10.1007/s11356-023-25511-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Analysis of industrial energy intensity is greatly significant in China specifically from the perspective of sector heterogeneity due to considerably different levels of energy utilization in various industrial sub-sectors. This study proposes a new methodology to forecast energy intensity in industrial sub-sectors, considering the complexity of the socioeconomic system. This research collects the data of 36 industrial sub-sectors in China and combines fuzzy C-means clustering (FCM), rough set (RS) and support vector machine (SVM) to predict the energy intensity of industrial sub-sectors in 2030. First, this method classifies all the industrial sub-sectors according to energy intensity level and identifies the main factors that affect the energy consumption of the industrial sub-sectors. Second, the resulting classification paves the way for specifying models to forecast energy consumption. Finally, scenario analysis predicts the energy intensity of each industrial sub-sector in 2030. This exploration has the following results. (1) Energy intensity has significantly different trends in various industrial sub-sectors. For example, industrial sub-sectors with low energy intensity mainly belong to the manufacturing industry (S06-S33). In contrast, the medium- and high-energy intensity categories mainly belong to the mining industry (S01-S05) and energy extraction and supply industry (S34-S36). (2) The critical factors affecting industrial energy consumption are fixed assets, R&D investment, and labor investment. (3) By 2030, the energy intensity has a downward trend in various industrial sub-sectors in China. The scenario analysis implies that China's energy intensity would reach the current world average level under the low-speed development scenario. Also, China's energy intensity would reach the current world advanced level under the medium-speed or high-speed development scenario.
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Affiliation(s)
- Jiwen Rao
- School of Management, Guangdong University of Technology, Guangzhou, 510520, China
| | - Yong He
- School of Management, Guangdong University of Technology, Guangzhou, 510520, China.
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Jorquera H, Villalobos AM. A new methodology for source apportionment of gaseous industrial emissions. J Hazard Mater 2023; 443:130335. [PMID: 36370478 DOI: 10.1016/j.jhazmat.2022.130335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/22/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Air quality modeling (AQM) is often used to investigate gaseous pollution around industrial zones. However, this methodology requires accurate emission inventories, unbiased AQM algorithms and realistic boundary conditions. We introduce a new methodology for source apportionment of industrial gaseous emissions, which is based on a fuzzy clustering of ambient concentrations, along with a standard AQM approach. First, by applying fuzzy clustering, ambient concentration is expressed as a sum of non-negative contributions - each corresponding to a specific spatiotemporal pattern (STP); we denote this method as FUSTA (FUzzy SpatioTemporal Apportionment). Second, AQM of the major industrial emissions in the study zone generates another set of STP. By comparing both STP sets, all major source contributions resolved by FUSTA are identified, so a source apportionment is achieved. The uncertainty in FUSTA results may be estimated by comparing results for different numbers of clusters. We have applied FUSTA in an industrial zone in central Chile, obtaining the contributions from major sources of ambient SO2: a thermal power plant complex and a copper smelter, and other contributions from local and regional sources (outside the AQM domain). The methodology also identifies SO2 episodes associated to emissions from the copper smelter.
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Affiliation(s)
- Héctor Jorquera
- Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile; Centro de Desarrollo Urbano Sustentable, Santiago, Chile.
| | - Ana María Villalobos
- Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile
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Kumar S, Mallik A, Kumar A, Ser JD, Yang G. Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals. Comput Biol Med 2023; 153:106511. [PMID: 36608461 DOI: 10.1016/j.compbiomed.2022.106511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/21/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
Electrocardiogram (ECG) is a widely used technique to diagnose cardiovascular diseases. It is a non-invasive technique that represents the cyclic contraction and relaxation of heart muscles. ECG can be used to detect abnormal heart motions, heart attacks, heart diseases, or enlarged hearts by measuring the heart's electrical activity. Over the past few years, various works have been done in the field of studying and analyzing the ECG signals to detect heart diseases. In this work, we propose a deep learning and fuzzy clustering (Fuzz-ClustNet) based approach for Arrhythmia detection from ECG signals. We started by denoising the collected ECG signals to remove errors like baseline drift, power line interference, motion noise, etc. The denoised ECG signals are then segmented to have an increased focus on the ECG signals. We then perform data augmentation on the segmented images to counter the effects of the class imbalance. The augmented images are then passed through a CNN feature extractor. The extracted features are then passed to a fuzzy clustering algorithm to classify the ECG signals for their respective cardio diseases. We ran intensive simulations on two benchmarked datasets and evaluated various performance metrics. The performance of our proposed algorithm was compared with several recently proposed algorithms for heart disease detection from ECG signals. The obtained results demonstrate the efficacy of our proposed approach as compared to other contemporary algorithms.
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Affiliation(s)
- Sanjay Kumar
- Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi 110042, India.
| | - Abhishek Mallik
- Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi 110042, India.
| | - Akshi Kumar
- Department of Computing & Mathematics, Faculty of Science & Engineering, Manchester Metropolitan University, Manchester, United Kingdom.
| | - Javier Del Ser
- TECNALIA, Basque Research & Technology, Alliance (BRTA), 48160 Derio, Spain; University of the Basque Country, 48013 Bilbao, Spain.
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, United Kingdom; Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, United Kingdom.
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Achom A, Das R, Pakray P. An improved Fuzzy based GWO algorithm for predicting the potential host receptor of COVID-19 infection. Comput Biol Med 2022; 151:106050. [PMID: 36334362 PMCID: PMC9404081 DOI: 10.1016/j.compbiomed.2022.106050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 08/12/2022] [Accepted: 08/20/2022] [Indexed: 12/27/2022]
Abstract
Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and has infected millions worldwide. SARS-CoV-2 spike protein uses Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) for entering and fusing the host cell membrane. However, interaction with spike protein receptors and protease processing are not the only factors determining coronaviruses' entry. Several proteases mediate the entry of SARS-CoV-2 virus into the host cell. Identifying receptor factors helps understand tropism, transmission, and pathogenesis of COVID-19 infection in humans. The paper aims to identify novel viral receptor or membrane proteins that are transcriptionally and biologically similar to ACE2 and TMPRSS2 through a fuzzy clustering technique that employs the Grey wolf optimizer (GWO) algorithm for finding the optimal cluster center. The exploratory and exploitation capability of GWO algorithm is improved by hybridizing mutation and crossover operators of the evolutionary algorithm. Also, the genetic diversity of the grey wolf population is enhanced by eliminating weak individuals from the population. The proposed clustering algorithm's effectiveness is shown by detecting novel viral receptors and membrane proteins associated with the pathogenesis of SARS-CoV-2 infection. The expression profiles of ACE2 protein and its co-receptor factor are analyzed and compared with single-cell transcriptomics profiling using the Seurat R toolkit, mass spectrometry (MS), and immunohistochemistry (IHC). Our advanced clustering method infers that cell that expresses high ACE2 level are more affected by SARS-CoV-infection. So, SARS-CoV-2 virus affects lung, intestine, testis, heart, kidney, and liver more severely than brain, bone marrow, skin, spleen, etc. We have identified 58 novel viral receptors and 816 membrane proteins, and their role in the pathogenicity mechanism of SARS-CoV-2 infection has been studied. Besides, our study confirmed that Neuropilins (NRP1), G protein-coupled receptor 78 (GPR78), C-type lectin domain family 4 member M (CLEC4M), Kringle containing transmembrane protein 1 (KREMEN1), Asialoglycoprotein receptor 1 (ASGR1), A Disintegrin and metalloprotease 17 (ADAM17), Furin, Neuregulin-1,(NRG1), Basigin or CD147 and Poliovirus receptor (PVR) are the potential co-receptors of SARS-CoV-2 virus. A significant finding is that heparin derivative glycosaminoglycans could block the replication of SARS-CoV-2 virus inside the host cytoplasm. The membrane protein N-Deacetylase/N-Sulfotransferase-2 (NDST2), Extostosin protein (EXT1, EXT2, and EXT3), Glucuronic acid epimerase (GLCE), and Xylosyltransferase I, II (XYLT1, XYLT2) could act as the therapeutic target for inhibiting the spread of SARS-CoV-2 infection. Drugs such as carboplatin and gemcitabine are effective in such situations.
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Affiliation(s)
- Amika Achom
- Department of Computer Science and Engineering, National Institute of Technology, Mizoram, Aizwal, 796001, Mizoram, India.
| | - Ranjita Das
- Department of Computer Science and Engineering, National Institute of Technology, Mizoram, Aizwal, 796001, Mizoram, India.
| | - Partha Pakray
- Department of Computer Science and Engineering, National Institute of Technology, Silchar, Silchar, 788003, Assam, India.
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11
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Huang P, Yan H, Hu Z, Liu Z, Tian Y, Dai J. Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters. Quant Imaging Med Surg 2021; 11:4731-4741. [PMID: 34888185 DOI: 10.21037/qims-20-1095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/05/2021] [Indexed: 12/25/2022]
Abstract
Background To develop a fuzzy clustering neural network to predict radiation-induced pneumonitis (RP) using four-dimensional computed tomography (4DCT) ventilation image (VI) based dosimetric parameters for thoracic cancer patients. Methods The VI were retrospectively calculated from pre-treatment 4DCT data using a deformable image registration (DIR) and an improved VI algorithm. Similar to dose-volume histogram (DVH) of intensity modulated radiotherapy (IMRT), dose-function histogram (DFH) was derived from dose distribution and VI. Then, the dose-function metrics were calculated from DFH. For comparison, the dose-volume metrics were calculated from DVH. Correspondingly, two sets of feature vectors were formed from the dose-volume metrics and the dose-function metrics, respectively. For the feature vectors of each set, they were first pre-processed by principal component analysis (PCA) to reduce feature dimensions. Then, they were grouped to few clusters determined by fuzzy c-means (FCM) algorithm. Next, the neural network was trained to correlate the dosimetric parameters with RP based on the feature vectors of each cluster. Finally, the occurrence of RP was predicted by the neural network on the test data. Results Through PCA analysis, the top 5 principal components were selected. Their contribution is more than 98%, which is adequate to represent the original feature space of input data. Based on the clustering validity indexes, the optimal number of clusters is 4 and used for subsequent fuzzy clustering of the input data. After network training, the areas under the curve (AUC) of the prediction model is 0.77 using VI-based dosimetric parameters and 0.67 using structure-based dosimetric parameters. Conclusions Compared to the structure-based dosimetric features, the VI-based dosimetric features are more relevant to lung function and presented higher prediction accuracy of RP. The fuzzy clustering neural network improved the prediction accuracy of RP compared to the conventional neural network. The combination of VI-based dose-function metrics and fuzzy clustering neural network provides an effective predictive model for assessing lung toxicity risk after radiotherapy.
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Affiliation(s)
- Peng Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhihui Hu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhiqiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Tian
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Park M, Jeong HB, Lee JH, Park T. Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes. BMC Bioinformatics 2021; 22:480. [PMID: 34607566 PMCID: PMC8489107 DOI: 10.1186/s12859-021-04395-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 09/17/2021] [Indexed: 01/11/2023] Open
Abstract
Background Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene–gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling’s T2 statistic to evaluate interaction models, but it is well known that Hotelling’s T2 statistic is highly sensitive to heavily skewed distributions and outliers. Results We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR. Conclusions Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.
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Affiliation(s)
- Mira Park
- Department of Preventive Medicine, Eulji University, Daejeon, 34824, Republic of Korea
| | - Hoe-Bin Jeong
- Department of Statistics, Korea University, Seoul, 02841, Republic of Korea
| | - Jong-Hyun Lee
- Department of Statistics, Korea University, Seoul, 02841, Republic of Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, 08826, Republic of Korea.
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13
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Della Rosa PA, Miglioli C, Caglioni M, Tiberio F, Mosser KHH, Vignotto E, Canini M, Baldoli C, Falini A, Candiani M, Cavoretto P. A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation. BMC Pregnancy Childbirth 2021; 21:306. [PMID: 33863296 PMCID: PMC8052693 DOI: 10.1186/s12884-021-03654-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/15/2021] [Indexed: 12/15/2022] Open
Abstract
Background Etiopathogenesis of preterm birth (PTB) is multifactorial, with a universe of risk factors interplaying between the mother and the environment. It is of utmost importance to identify the most informative factors in order to estimate the degree of PTB risk and trace an individualized profile. The aims of the present study were: 1) to identify all acknowledged risk factors for PTB and to select the most informative ones for defining an accurate model of risk prediction; 2) to verify predictive accuracy of the model and 3) to identify group profiles according to the degree of PTB risk based on the most informative factors. Methods The Maternal Frailty Inventory (MaFra) was created based on a systematic review of the literature including 174 identified intrauterine (IU) and extrauterine (EU) factors. A sample of 111 pregnant women previously categorized in low or high risk for PTB below 37 weeks, according to ACOG guidelines, underwent the MaFra Inventory. First, univariate logistic regression enabled p-value ordering and the Akaike Information Criterion (AIC) selected the model including the most informative MaFra factors. Second, random forest classifier verified the overall predictive accuracy of the model. Third, fuzzy c-means clustering assigned group membership based on the most informative MaFra factors. Results The most informative and parsimonious model selected through AIC included Placenta Previa, Pregnancy Induced Hypertension, Antibiotics, Cervix Length, Physical Exercise, Fetal Growth, Maternal Anxiety, Preeclampsia, Antihypertensives. The random forest classifier including only the most informative IU and EU factors achieved an overall accuracy of 81.08% and an AUC of 0.8122. The cluster analysis identified three groups of typical pregnant women, profiled on the basis of the most informative IU and EU risk factors from a lower to a higher degree of PTB risk, which paralleled time of birth delivery. Conclusions This study establishes a generalized methodology for building-up an evidence-based holistic risk assessment for PTB to be used in clinical practice. Relevant and essential factors were selected and were able to provide an accurate estimation of degree of PTB risk based on the most informative constellation of IU and EU factors. Supplementary Information The online version contains supplementary material available at (10.1186/s12884-021-03654-3).
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Affiliation(s)
- Pasquale Anthony Della Rosa
- Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Cesare Miglioli
- Research Center for Statistics, University of Geneva, Boulevard du Pont-d'Arve 40, Geneva, 1205, Switzerland
| | - Martina Caglioni
- Obstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Francesca Tiberio
- Obstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Kelsey H H Mosser
- Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Edoardo Vignotto
- Research Center for Statistics, University of Geneva, Boulevard du Pont-d'Arve 40, Geneva, 1205, Switzerland
| | - Matteo Canini
- Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Cristina Baldoli
- Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Andrea Falini
- Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Massimo Candiani
- Obstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy
| | - Paolo Cavoretto
- Obstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
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Chakraborty T, Banik SK, Bhadra AK, Nandi D. Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images. Comput Methods Programs Biomed 2021; 202:105971. [PMID: 33611030 DOI: 10.1016/j.cmpb.2021.105971] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 02/01/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE The accurate segmentation of pre-treatment and post-treatment organs is always perceived as a challenging task in medical image analysis field. Especially, in those situations where the amount of data set is limited, the researchers are compelled to design unsupervised model for segmentation. In this paper, we propose a novel dynamically learned particle swarm optimization based neighborhood influenced fuzzy c-means (DLPSO-NIFCM) clustering (unsupervised learning model) for solving pre-treatment and post-treatment organs segmentation problems. The proposed segmentation technique has been successfully applied to segment the liver parts from the Computed Tomography (CT) images of abdomen and also the lung parenchyma from the lungs CT images. METHODOLOGY In the proposed method, we formulate a primary convex objective function by considering the membership value of a pixel as well as the membership of its other neighboring pixels. Then we apply a new algebraic transformation on the primary objective function to design a new and more suitable objective function without losing convexity of the primary objective function. This new objective function is compatible for hybridization with any heuristic search technique in true sense. In this work, we propose a dynamically learned PSO to obtain the initial cluster centroids from the final objective function. Finally, we use a graph-based isolation mechanism for refining the segmentation results. RESULTS AND CONCLUSION This hybrid method, along with the restructured single variable objective function of the distance, leads to accurate clustering results with relatively lesser converging time as compared to the state-of-the-art methods. The segmentation results, obtained through several experiments with real CT images, are encouraging. The numerical values of different performance metrics obtained over the same data set confirm that the proposed algorithm performs better with respect to the state-of-the-art methods. Hence, we may consider the proposed method as a promising tool for clustering and CT image segmentation in a Computer Aided Diagnostic (CAD) system.
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Affiliation(s)
- Tiyasa Chakraborty
- Department of Computer Science and Engineering, National Institute of Technology Durgapur, India.
| | - Samiran Kumar Banik
- Department of Computer Science and Engineering, National Institute of Technology Durgapur, India.
| | | | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technology Durgapur, India.
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15
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Nedyalkova M, Barazorda-Ccahuana HL, Sârbu C, Madurga S, Simeonov V. Fuzzy partitioning of clinical data for DMT2 patients. J Environ Sci Health A Tox Hazard Subst Environ Eng 2020; 55:1450-1458. [PMID: 32915103 DOI: 10.1080/10934529.2020.1809925] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/25/2020] [Accepted: 07/28/2020] [Indexed: 06/11/2023]
Abstract
The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.
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Affiliation(s)
- Miroslava Nedyalkova
- Faculty of Chemistry and Pharmacy, University of Sofia "St. Kl. Okhridski", Sofia, Bulgaria
| | | | - C Sârbu
- Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Sergio Madurga
- Materials Science and Physical Chemistry Department and IQTCUB, Universitat de Barcelona, Barcelona, Spain
| | - Vasil Simeonov
- Faculty of Chemistry and Pharmacy, University of Sofia "St. Kl. Okhridski", Sofia, Bulgaria
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16
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Cai J. Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images. J Med Syst 2019; 43:322. [PMID: 31602537 DOI: 10.1007/s10916-019-1459-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 09/11/2019] [Indexed: 01/17/2023]
Abstract
Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space is mapped into the high dimensional feature space through the kernel function. Then the linear weighted filtering image is obtained by combining the current pixel with its neighborhood pixels through the space information in CT image. Finally, the two-dimensional histogram between the clustered pixel and its neighborhood mean is introduced into the robust kernel space image fuzzy clustering, and the iterative expression of the fast robust fuzzy clustering in kernel space is obtained by using Lagrange multiplier method. The experimental results on four databases show that our proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.
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17
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Feher I, Magdas DA, Dehelean A, Sârbu C. Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach. J Food Sci Technol 2019; 56:5225-5233. [PMID: 31749469 DOI: 10.1007/s13197-019-03991-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/24/2019] [Accepted: 07/30/2019] [Indexed: 10/26/2022]
Abstract
A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).
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Affiliation(s)
- Ioana Feher
- 1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath, 400293 Cluj-Napoca, Romania
| | - Dana Alina Magdas
- 1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath, 400293 Cluj-Napoca, Romania
| | - Adriana Dehelean
- 1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath, 400293 Cluj-Napoca, Romania
| | - Costel Sârbu
- 2Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, 11 Arany János, 400028 Cluj-Napoca, Romania
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Loureiro H, Carrasquinha E, Alho I, Ferreira AR, Costa L, Carvalho AM, Vinga S. Modelling cancer outcomes of bone metastatic patients: combining survival data with N-Telopeptide of type I collagen (NTX) dynamics through joint models. BMC Med Inform Decis Mak 2019; 19:13. [PMID: 30654776 PMCID: PMC6337820 DOI: 10.1186/s12911-018-0728-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 12/21/2018] [Indexed: 02/08/2023] Open
Abstract
Background Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. However, several problems arise when dealing with this type of data, such as the high-dimensionality of the covariates space, the lack of knowledge about the function structure of the time series and the presence of missing data, facts that may hamper the accurate estimation of the JM. Methods We propose to apply JM for the analysis of bone metastatic patients and infer the association of their survival with several covariates, in particular the N-Telopeptide of Type I Collagen (NTX) dynamics. This biomarker has been identified as a relevant prognostic factor in patients with metastatic cancer, but only using static information in some specific time points. Results We extended this analysis using the full NTX time series for a larger cohort of patients with bone metastasis, and compared the results obtained by the JM and the extended Cox regression model. Imputation based on fuzzy clustering was used to deal with missing values and several functions for NTX evolution were compared, such as rational, exponential and cubic splines. Conclusions The JM obtained confirm the association between NTX values and patients’ response, attesting the importance of this time series, and additionally provide a deep understanding of the key survival covariates.
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Affiliation(s)
- Hugo Loureiro
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa, 1000-029, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Eunice Carrasquinha
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa, 1000-029, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Irina Alho
- Instituto de Medicina Molecular, Av. Professor Egas Moniz, Lisboa, 1649-028, Portugal
| | - Arlindo R Ferreira
- Instituto de Medicina Molecular, Av. Professor Egas Moniz, Lisboa, 1649-028, Portugal
| | - Luís Costa
- Instituto de Medicina Molecular, Av. Professor Egas Moniz, Lisboa, 1649-028, Portugal
| | - Alexandra M Carvalho
- Instituto de Telecomunicações, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.,Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa, 1000-029, Portugal. .,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.
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Azimi S, Azhdary Moghaddam M, Hashemi Monfared SA. Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering. J Contam Hydrol 2019; 220:6-17. [PMID: 30471981 DOI: 10.1016/j.jconhyd.2018.10.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 10/28/2018] [Accepted: 10/29/2018] [Indexed: 06/09/2023]
Abstract
Drought is one of the most significant natural phenomena affecting different aspects of human life and the environment. Due to water scarcity, prediction of water quality reduction is very crucial for urban and rural communities. This study contributes by applying artificial neural network and modified fuzzy clustering techniques to estimate the drops in potential drinking water quality in the GIS environment. In this research, the probability of occurrence of adverse annual changes in the water quality of drinking water is estimated. The model was tested using real instances of the southeast aquifers, the regions of the central parts of the IRAN and especially the significant portions of the aquifers of the east area. To validate the model, the data adequacy test and the standardization of the drought index are used. The results of the lowest available water quality and the highest drought using ANNs show that the qualitative stress conditions in large part of the country's aquifers are in unfavorable conditions. Evidence from this research shows that the aquifers in these areas are expected to have severe drought stress and poor quality class status. Also, the computational results indicate that the modified clustering method increases the efficiency of the prediction model as against the previous research. The outcomes do not show a relatively favorable state of drinking water quality for some aquifers in the country. However, the conditions for quantitative changes in the depth of water, based on the predicted results of ANN, are considered critical. The generated maps demonstrate that about 64% of the study area is subjected to a severe deterioration in the quality of drinking water if the current trend continues in the exploitation of aquifers. As a result, the main finding the present study is that the probability of groundwater quality decline is significant in many aquifers in the country.
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Affiliation(s)
- S Azimi
- Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
| | - M Azhdary Moghaddam
- Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
| | - S A Hashemi Monfared
- Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
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Raza K. Fuzzy logic based approaches for gene regulatory network inference. Artif Intell Med 2018; 97:189-203. [PMID: 30573378 DOI: 10.1016/j.artmed.2018.12.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 12/10/2018] [Accepted: 12/12/2018] [Indexed: 12/26/2022]
Abstract
The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression-based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI.
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Affiliation(s)
- Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India.
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21
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Kong Y, Wu J, Yang G, Zuo Y, Chen Y, Shu H, Coatrieux JL. Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation. J Neurosci Methods 2019; 311:17-27. [PMID: 30315839 DOI: 10.1016/j.jneumeth.2018.10.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 09/13/2018] [Accepted: 10/08/2018] [Indexed: 02/02/2023]
Abstract
BACKGROUND Although supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory. NEW METHODS To address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume. RESULTS The performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods. CONCLUSIONS The proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.
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Li W, Liu S, Pei Y, He J, Wang Q. Zoning for eco-geological environment before mining in Yushenfu mining area, northern Shaanxi, China. Environ Monit Assess 2018; 190:619. [PMID: 30269263 DOI: 10.1007/s10661-018-6996-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 09/20/2018] [Indexed: 06/08/2023]
Abstract
Zoning for the eco-geological environment (EGE) aims to protect and improve the regional ecological environment. It is the basis for evaluating the ecological characteristics of a mining area prior to mining activities and has the purpose of implementing water-preserved mining according to zoning type. In this study, four EGE types were proposed following field investigation in the Yushenfu mining area: oasis type with phreatic water and bottomland in desert (OTPWBD), oasis type with surface water and valley river (OTSWVR), loess gully type with surface runoff (LGTSR), and regional deep groundwater enrichment type (RDGET). Nine EGE evaluation indices were selected: rainfall, evaporation capacity, Luohe formation thickness, surface elevation, Sara Wusu aquifer water abundance, surface lithology, topography, slope, and normalized difference vegetation index (NDVI). Remote sensing technology and geographic information systems were first used to generate the evaluation index thematic maps. Then, the weight of each evaluation index was determined based on an analytic hierarchy process (AHP). Third, the index weight was used to form an improved weighted fuzzy C s clustering algorithm, and EGE zones were assigned using the MATLAB computing platform. For comparison, the AHP was also adopted for EGE zoning and a map of zoning differences was obtained. Finally, EGE field surveys of typical mines were carried out, which verified that EGE zoning using fuzzy clustering was accurate and reasonable.
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Affiliation(s)
- Wenping Li
- School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Shiliang Liu
- School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China
| | - Yabing Pei
- Nuclear Industry Huzhou Engineering Survey Institute, Huzhou, 313000, Zhejiang, China
| | - Jianghui He
- School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China
| | - Qiqing Wang
- School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China
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Narduzzi L, Franciosi E, Carlin S, Tuohy K, Beretta A, Pedrotti F, Mattivi F. Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis: Following the volatiles fate in grass baths through passive diffusion sampling. J Chromatogr B Analyt Technol Biomed Life Sci 2018; 1096:56-65. [PMID: 30149295 DOI: 10.1016/j.jchromb.2018.07.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 06/15/2018] [Accepted: 07/10/2018] [Indexed: 10/28/2022]
Abstract
Phytothermotherapy ("grass baths") is a traditional phytotherapy for rheumatism consisting of taking baths in hot fermenting grass. Scientific studies have demonstrated its efficiency in treating several rheumatic diseases. However the efficiency and repeatability of the therapy is dependent on the wild fermentations, determining sometimes the appearance of unpleasant conditions leading to the early abandonment of the therapy. The metabolism undergoing in the grass baths is unknown and there is not an established method to evaluate and predict grass baths quality. The aim of this study is to establish a simple VOCs profiling method able to evaluate the grass baths, predicting their evolution, through the identification of marker volatiles related to the best conditions and/or the spoilage. After replicating in real scale the traditional grass baths, the volatile profiles were measured using passive diffusion samplers injected in a thermal desorption-comprehensive GC × GC-TOF-MS. The high dimensionality of the data coupled with the limited number of time points, required a rigorous method development for the analysis of the data, achieved through the development of a novel R package for variable selection in GC × GC data matrices. The further application of a fuzzy clustering approach demonstrated to be a useful tool dealing with short time series, allowing to discard un-trending volatiles and giving a clear snapshot of the main trends in the data. A broad coverage of the volatolome was provided, thus suitable to describe the main metabolic changes ongoing in the grass baths. Coupling this data with the temperature and pH, and comparing it to the data from similar processes, like silage and compost, we demonstrated that the established method can be helpful to evaluate short time series, allowing us to obtain a list of volatiles as candidate markers for the quality of the grass baths. The established method gave a list of markers applicable to real scale grass baths to predict spoilage; furthermore it provides a list of volatiles where to search for candidate markers with reported health-related effects and can be used to generate hypothesis on the mechanisms of action of the treatment.
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Affiliation(s)
- Luca Narduzzi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy.
| | - Elena Franciosi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy
| | - Silvia Carlin
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy
| | - Kieran Tuohy
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy
| | - Alberto Beretta
- Terme di Garniga, Via dei Bagni di Fieno 13, 38060 Garniga Terme, Italy
| | - Franco Pedrotti
- University of Camerino, Via Pontoni, 5, 62032, Camerino, Italy
| | - Fulvio Mattivi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy; University of Trento, Center Agriculture Food Environment (CAFÉ), 38010 San Michele all'Adige, TN, Italy
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Vassallo P, Bianchi CN, Paoli C, Holon F, Navone A, Bavestrello G, Cattaneo Vietti R, Morri C. A predictive approach to benthic marine habitat mapping: Efficacy and management implications. Mar Pollut Bull 2018; 131:218-232. [PMID: 29886940 DOI: 10.1016/j.marpolbul.2018.04.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 03/09/2018] [Accepted: 04/09/2018] [Indexed: 06/08/2023]
Abstract
The availability of marine habitats maps remains limited due to difficulty and cost of working at sea. Reduced light penetration in the water hampers the use of optical imagery, and acoustic methods require extensive sea-truth activities. Predictive spatial modelling may offer an alternative to produce benthic habitat maps based on complete acoustic coverage of the seafloor together with a comparatively low number of sea truths. This approach was applied to the coralligenous reefs of the Marine Protected Area of Tavolara - Punta Coda Cavallo (NE Sardinia, Italy). Fuzzy clustering, applied to a set of observations made by scuba diving and used as sea truth, allowed recognising five coralligenous habitats, all but one existing within EUNIS (European Nature Information System) types. Variable importance plots showed that the distribution of habitats was driven by distance from coast, depth, and lithotype, and allowed mapping their distribution over the MPA. Congruence between observed and predicted distributions and accuracy of the classification was high. Results allowed calculating the occurrence of the distinct coralligenous habitats in zones with different protection level. The five habitats are unequally protected since the protection regime was established when detailed marine habitat maps were not available. A SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis was performed to identify critical points and potentialities of the method. The method developed proved to be reliable and the results obtained will be useful when modulating on-going and future management actions in the studied area and in other Mediterranean MPAs to develop conservation efforts at basin scale.
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Affiliation(s)
- Paolo Vassallo
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, 16132 Genova, Italy
| | - Carlo Nike Bianchi
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, 16132 Genova, Italy
| | - Chiara Paoli
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, 16132 Genova, Italy.
| | - Florian Holon
- Andromède Océanologie, 7 Place Cassan, 34280 Carnon-Plage, France
| | - Augusto Navone
- Area Marina Protetta di Tavolara - Punta Coda Cavallo, Via San Giovanni 14, 07026 Olbia, Italy
| | - Giorgio Bavestrello
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, 16132 Genova, Italy
| | - Riccardo Cattaneo Vietti
- Dipartimento di Scienze della Vita e dell'Ambiente, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
| | - Carla Morri
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, 16132 Genova, Italy
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Juneja A, Rana B, Agrawal RK. A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI. Comput Methods Programs Biomed 2018; 155:139-152. [PMID: 29512494 DOI: 10.1016/j.cmpb.2017.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 10/21/2017] [Accepted: 12/04/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia. But, fMRI data is huge in dimension. Therefore dimension reduction is indispensable. It is additionally required to identify the discriminative brain regions. Hence, we aim to build an effective decision model that incorporates suitable dimension reduction and also identifies discriminative brain regions. METHODS We propose a three-phase dimension reduction. First phase involves spatially-constrained fuzzy clustering of 3-dimensional spatial maps (obtained from general linear model and independent component analysis). In the second phase, non-linear features are extracted from each cluster using a generalized discriminant analysis. In the third phase, a novel fuzzy rough feature selection is proposed. The features obtained after the third phase are used for learning a decision model by the help of support vector machine classifier. This complete method is implemented within leave-one-out cross-validation on two balanced datasets (respectively acquired on 1.5Tesla and 3Tesla scanners). Both these datasets are created using Function Biomedical Informatics Research Network multisite data and contain fMRI data acquired during auditory oddball task performed by age-matched schizophrenia patients and healthy subjects. A permutation test is also carried out to ensure that no bias is involved in the learning. RESULTS The results indicate that the proposed method achieves maximum classification accuracy of 97.1% and 98.0% for the two datasets respectively. The proposed method outperforms the state-of-the-art methods. The results of the permutation test show that p-values are lesser than the significance level i.e. 0.05. Therefore, the classifier has found a significant class structure and does not involve any bias. Further, discriminative brain regions are identified and are in agreement with the findings in related literature. CONCLUSION The proposed method is able to derive suitable non-linear features and the related brain regions for effective computer-aided diagnosis. The fuzzy and rough set based approaches help in handling uncertainty and ambiguity in real data.
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Affiliation(s)
- Akanksha Juneja
- School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India.
| | - Bharti Rana
- School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India
| | - R K Agrawal
- School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India
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Fontes CH, Budman H. A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine. ISA Trans 2017; 71:513-529. [PMID: 28927843 DOI: 10.1016/j.isatra.2017.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 07/04/2017] [Accepted: 09/05/2017] [Indexed: 06/07/2023]
Abstract
A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.
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Affiliation(s)
- Cristiano Hora Fontes
- Graduate Program in Industrial Engineering, Polytechnic School, Federal University of Bahia, Brazil.
| | - Hector Budman
- Department of Chemical Engineering, University of Waterloo, Canada.
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Slattery A. Validating an image segmentation program devised for staging lymphoma. Australas Phys Eng Sci Med 2017; 40:799-809. [PMID: 28971313 DOI: 10.1007/s13246-017-0587-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
Hybrid positron emission tomography-computed tomography (PET-CT) imaging systems are an important tool for assessing the progression of lymphoma. PET-CT systems offer the ability to quantitatively assess lymphocytic bone involvement throughout the body. There is no standard methodology for staging lymphoma patients using PET-CT images. Automatic image segmentation algorithms could offer medical specialists a means to evaluate bone involvement from PET-CT images in a consistent manner. To devise and validate an image segmentation program that may assist staging lymphoma by determining the degree of bone involvement based from PET-CT studies. A custom-made program was developed to segment regions-of-interest from images by utilising an enhanced fuzzy clustering technique that incorporates spatial information. The program was subsequently tested on digital and physical phantoms using four different performance metrics before being employed to extract the bony regions of clinical PET-CT images acquired from 248 patients staged for lymphoma. The algorithm was satisfactorily able to delineate regions-of-interest within all phantoms. When applied to the clinical PET-CT images, the algorithm was capable of accurately segmenting bony regions in less than half of the subjects (n = 103). The performance of the algorithm was adversely affected by the presence of oral contrast, metal implants and the poor image quality afforded by low dose CT images in general. Significant changes are necessary before the algorithm can be employed clinically in an unsupervised fashion. However, with further work performed, the algorithm could potentially prove useful for medical specialists staging lymphoma in the future.
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Kim SS, Fang H, Bernstein K, Zhang Z, DiFranza J, Ziedonis D, Allison J. Acculturation, Depression, and Smoking Cessation: a trajectory pattern recognition approach. Tob Induc Dis 2017; 15:33. [PMID: 28747857 PMCID: PMC5525352 DOI: 10.1186/s12971-017-0135-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 07/06/2017] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Korean Americans are known for a high smoking prevalence within the Asian American population. This study examined the effects of acculturation and depression on Korean Americans' smoking cessation and abstinence. METHODS This is a secondary data analysis of a smoking cessation study that implemented eight weekly individualized counseling sessions of a culturally adapted cessation intervention for the treatment arm and a standard cognitive behavioral therapy for the comparison arm. Both arms also received nicotine patches for 8 weeks. A newly developed non-parametric trajectory pattern recognition model (MI-Fuzzy) was used to identify cognitive and behavioral response patterns to a smoking cessation intervention among 97 Korean American smokers (81 men and 16 women). RESULTS Three distinctive response patterns were revealed: (a) Culturally Adapted (CA), since all identified members received the culturally adapted intervention; (b) More Bicultural (MB), for having higher scores of bicultural acculturation; and (c) Less Bicultural (LB), for having lower scores of bicultural acculturation. The CA smokers were those from the treatment arm, while MB and LB groups were from the comparison arm. The LB group differed in depression from the CA and MB groups and no difference was found between the CA and MB groups. Although depression did not directly affect 12-month prolonged abstinence, the LB group was most depressed and achieved the lowest rate of abstinence (LB: 1.03%; MB: 5.15%; CA: 21.65%). CONCLUSION A culturally adaptive intervention should target Korean American smokers with a high level of depression and a low level of biculturalism to assist in their smoking cessation. TRIAL REGISTRATION NCT01091363. Registered 21 March 2010.
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Affiliation(s)
- Sun S Kim
- University of Massachusetts, Boston, Boston, MA 02125 USA
| | - Hua Fang
- University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747 USA
- Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, Dion Building, Room 317 285 Old Westport Road Dartmouth, Dartmouth, MA 02747-2300 USA
- Division of Biostatistics and Health Services Research Department of Quantitative Health Sciences, University of Massachusetts Medical School, Albert Sherman Bldg, Office: AS8-2061, 368 Plantation St. Worcester, Dartmouth, MA 01605-0002 USA
| | - Kunsook Bernstein
- Hunter College, City University of New York, New York, New York 10010 USA
| | - Zhaoyang Zhang
- University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747 USA
| | - Joseph DiFranza
- University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747 USA
| | - Douglas Ziedonis
- University of California San Diego, Deparetment of Psychiatry, 9500 Gilman Drive #0602, La Jolla, CA 92093-0602 USA
| | - Jeroan Allison
- University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747 USA
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Abstract
Missing data are common in longitudinal observational and randomized controlled trials in smart health studies. Multiple-imputation based fuzzy clustering is an emerging non-parametric soft computing method, used for either semi-supervised or unsupervised learning. Multiple imputation (MI) has been widely-used in missing data analyses, but has not yet been scrutinized for unsupervised learning methods, although they are important for explaining the heterogeneity of treatment effects. Built upon our previous work on MIfuzzy clustering, this paper introduces the MIFuzzy concepts and performance, theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non- and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of MIFuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.
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Affiliation(s)
- Hua Fang
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA 01655
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Diano M, D'Agata F, Cauda F, Costa T, Geda E, Sacco K, Duca S, Torta DM, Geminiani GC. Cerebellar Clustering and Functional Connectivity During Pain Processing. Cerebellum 2016; 15:343-56. [PMID: 26202672 DOI: 10.1007/s12311-015-0706-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The cerebellum has been traditionally considered a sensory-motor structure, but more recently has been related to other cognitive and affective functions. Previous research and meta-analytic studies suggested that it could be involved in pain processing. Our aim was to distinguish the functional networks subserved by the cerebellum during pain processing. We used functional magnetic resonance imaging (fMRI) on 12 subjects undergoing mechanical pain stimulation and resting state acquisition. For the analysis of data, we used fuzzy c-mean to cluster cerebellar activity of each participant during nociception. The mean time courses of the clusters were used as regressors in a general linear model (GLM) analysis to explore brain functional connectivity (FC) of the cerebellar clusters. We compared our results with the resting state FC of the same cluster and explored with meta-analysis the behavior profile of the FC networks. We identified three significant clusters: cluster V, involving the culmen and quadrangular lobules (vermis IV-V, hemispheres IV-V-VI); cluster VI, involving the posterior quadrangular lobule and superior semilunar lobule (hemisphere VI, crus 1, crus 2), and cluster VII, involving the inferior semilunar lobule (VIIb, crus1, crus 2). Cluster V was more connected during pain with sensory-motor areas, cluster VI with cognitive areas, and cluster VII with emotional areas. Our results indicate that during the application of mechanical punctate stimuli, the cerebellum is not only involved in sensory functions but also with areas typically associated with cognitive and affective functions. Cerebellum seems to be involved in various aspects of nociception, reflecting the multidimensionality of pain perception.
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Liu Y, Wan X. Information bottleneck based incremental fuzzy clustering for large biomedical data. J Biomed Inform 2016; 62:48-58. [PMID: 27260783 DOI: 10.1016/j.jbi.2016.05.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 04/24/2016] [Accepted: 05/30/2016] [Indexed: 10/21/2022]
Abstract
Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, often fail to produce reasonable results and may even assign all the objects to a single cluster. In this paper, we propose two incremental algorithms based on information bottleneck, Single-Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB). These two algorithms modify conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects. spFCM-IB and oFCM-IB are used to group a collection of biomedical text abstracts from Medline database. Experimental results show that clustering performances of our approaches are better than such prominent counterparts as spFCM, spHFCM, oFCM and oHFCM, in terms of accuracy.
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Affiliation(s)
- Yongli Liu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China.
| | - Xing Wan
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
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Zhang Z, Fang H. Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data. IEEE Int Conf Connect Health Appl Syst Eng Technol 2016; 2016:219-228. [PMID: 29034067 PMCID: PMC5635859 DOI: 10.1109/chase.2016.19] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Disentangling patients' behavioral variations is a critical step for better understanding an intervention's effects on individual outcomes. Missing data commonly exist in longitudinal behavioral intervention studies. Multiple imputation (MI) has been well studied for missing data analyses in the statistical field, however, has not yet been scrutinized for clustering or unsupervised learning, which are important techniques for explaining the heterogeneity of treatment effects. Built upon previous work on MI fuzzy clustering, this paper theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non-and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of multiple-imputation (MI) based fuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.
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Affiliation(s)
- Zhaoyang Zhang
- Division of Biostatistics and Health Services Research, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA 01655
| | - Hua Fang
- Division of Biostatistics and Health Services Research, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA 01655
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Zhang Z, Fang H, Wang H. Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth. J Med Syst 2016; 40:146. [PMID: 27126063 DOI: 10.1007/s10916-016-0499-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/11/2016] [Indexed: 11/27/2022]
Abstract
Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.
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Affiliation(s)
- Zhaoyang Zhang
- Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Hua Fang
- Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA, 01655, USA.
| | - Honggang Wang
- Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA
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Arnedo J, Mamah D, Baranger DA, Harms MP, Barch DM, Svrakic DM, de Erausquin GA, Cloninger CR, Zwir I. Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy. Neuroimage 2015; 120:43-54. [PMID: 26151103 DOI: 10.1016/j.neuroimage.2015.06.083] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 06/01/2015] [Accepted: 06/28/2015] [Indexed: 11/24/2022] Open
Abstract
Fractional anisotropy (FA) analysis of diffusion tensor-images (DTI) has yielded inconsistent abnormalities in schizophrenia (SZ). Inconsistencies may arise from averaging heterogeneous groups of patients. Here we investigate whether SZ is a heterogeneous group of disorders distinguished by distinct patterns of FA reductions. We developed a Generalized Factorization Method (GFM) to identify biclusters (i.e., subsets of subjects associated with a subset of particular characteristics, such as low FA in specific regions). GFM appropriately assembles a collection of unsupervised techniques with Non-negative Matrix Factorization to generate biclusters, rather than averaging across all subjects and all their characteristics. DTI tract-based spatial statistics images, which output is the locally maximal FA projected onto the group white matter skeleton, were analyzed in 47 SZ and 36 healthy subjects, identifying 8 biclusters. The mean FA of the voxels of each bicluster was significantly different from those of other SZ subjects or 36 healthy controls. The eight biclusters were organized into four more general patterns of low FA in specific regions: 1) genu of corpus callosum (GCC), 2) fornix (FX)+external capsule (EC), 3) splenium of CC (SCC)+retrolenticular limb (RLIC)+posterior limb (PLIC) of the internal capsule, and 4) anterior limb of the internal capsule. These patterns were significantly associated with particular clinical features: Pattern 1 (GCC) with bizarre behavior, pattern 2 (FX+EC) with prominent delusions, and pattern 3 (SCC+RLIC+PLIC) with negative symptoms including disorganized speech. The uncovered patterns suggest that SZ is a heterogeneous group of disorders that can be distinguished by different patterns of FA reductions associated with distinct clinical features.
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Wu X, Shen J, Li Y, Lee KY. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods. ISA Trans 2014; 53:699-708. [PMID: 24559835 DOI: 10.1016/j.isatra.2013.12.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2013] [Revised: 10/28/2013] [Accepted: 12/26/2013] [Indexed: 06/03/2023]
Abstract
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
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Affiliation(s)
- Xiao Wu
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou #2, Nanjing 210096, China.
| | - Jiong Shen
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou #2, Nanjing 210096, China.
| | - Yiguo Li
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou #2, Nanjing 210096, China.
| | - Kwang Y Lee
- Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA.
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Spyridonos P, Gaitanis G, Tzaphlidou M, Bassukas ID. Spatial fuzzy c-means algorithm with adaptive fuzzy exponent selection for robust vermilion border detection in healthy and diseased lower lips. Comput Methods Programs Biomed 2014; 114:291-301. [PMID: 24661607 DOI: 10.1016/j.cmpb.2014.02.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Revised: 02/17/2014] [Accepted: 02/26/2014] [Indexed: 06/03/2023]
Abstract
INTRODUCTION Accurate lip contour identification is demanding since variations in color, form and surface texture, even in normal lips, introduce artifacts in non-adapted segmentation algorithms. Herein, a method for vermilion border detection and quantification in healthy and diseased lower lips is presented. AIM To quantify the morphological irregularities of lower lip border, to validate its discriminative power in solar cheilosis diagnosis and to provide supportive tools toward, cost effective, non invasive, disease monitoring. MATERIALS Segmentation algorithm for lower lip border was based on spatial fuzzy c-means clustering algorithm with adaptive selection of fuzzy exponent m. Lip features measuring morphological lip border deviations were estimated. The method of lip border extraction and quantitative description was evaluated in a gold standard set of 25 young volunteers without onset of lip diseases. Quantitative descriptors were evaluated in terms of correct classification rates in differentiating 30 healthy control cases from 41 patients with solar cheilosis and were further applied to quantify the therapeutic outcome after immunocryosurgery in eight patients with solar cheilosis. RESULTS Adaptive estimation of fuzzy exponent m substantially boosted the segmentation quality in gold standard cases yielding quite smooth lip contours and uniformly low values of lip irregularity features. Discriminant analysis highlighted the distance between the extracted and modeled vermilion border as a feature with excellent diagnostic accuracy (sensitivity and specificity 98% and 93% respectively). Results on patients with solar cheilosis followed up after treatment with immunocryosurgery showed that proposed quantitative lip marker was able to trace the improvement of disease after treatment. CONCLUSION Correct lip border recognition is the prerequisite for extracting essential morphological descriptors from lips with epithelial diseases like solar cheilosis. In this paper we presented an efficient method for the automatic identification and quantitative description of lower lip vermilion border morphology in health and disease using digital photography and image analysis techniques.
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Affiliation(s)
- Panagiota Spyridonos
- Department of Medical Physics, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece.
| | - Georgios Gaitanis
- Department of Skin and Venereal Diseases, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece
| | - Margaret Tzaphlidou
- Department of Medical Physics, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece.
| | - Ioannis D Bassukas
- Department of Skin and Venereal Diseases, University of Ioannina, School of Health Sciences, University Campus, 45110 Ioannina, Greece
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Torres C, Barguil S, Melgarejo M, Olarte A. Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis. Artif Intell Med 2013; 60:41-51. [PMID: 24388398 DOI: 10.1016/j.artmed.2013.11.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 11/29/2013] [Accepted: 11/30/2013] [Indexed: 11/26/2022]
Abstract
OBJECTIVE This article presents a model of a dengue and severe dengue epidemic in Colombia based on the cases reported between 1995 and 2011. METHODOLOGY We present a methodological approach that combines multiresolution analysis and fuzzy systems to represent cases of dengue and severe dengue in Colombia. The performance of this proposal was compared with that obtained by applying traditional fuzzy modeling techniques on the same data set. This comparison was obtained by two performance measures that evaluate the similarity between the original data and the approximate signal: the mean square error and the variance accounted for. Finally, the predictive ability of the proposed technique was evaluated to forecast the number of dengue and severe dengue cases in a horizon of three years (2012-2015). These estimates were validated with a data set that was not included into the training stage of the model. RESULTS The proposed technique allowed the creation of a model that adequately represented the dynamic of a dengue and severe dengue epidemic in Colombia. This technique achieves a significantly superior performance to that obtained with traditional fuzzy modeling techniques: the similarity between the original data and the approximate signal increases from 21.13% to 90.06% and from 18.90% to 76.83% in the case of dengue and severe dengue, respectively. Finally, the developed models generate plausible predictions that resemble validation data. The difference between the cumulative cases reported from January 2012 until July 2013 and those predicted by the model for the same period was 24.99% for dengue and only 4.22% for severe dengue. CONCLUSIONS The fuzzy model identification technique based on multiresolution analysis produced a proper representation of dengue and severe dengue cases for Colombia despite the complexity and uncertainty that characterize this biological system. Additionally, the obtained models generate plausible predictions that can be used by surveillance authorities to support decision-making oriented to designing and developing control strategies.
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Affiliation(s)
- Claudia Torres
- Laboratorio de Automática e Inteligencia Computacional, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40-53, Bogotá, Colombia.
| | - Samier Barguil
- Laboratorio de Automática e Inteligencia Computacional, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40-53, Bogotá, Colombia
| | - Miguel Melgarejo
- Laboratorio de Automática e Inteligencia Computacional, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40-53, Bogotá, Colombia
| | - Andrés Olarte
- Grupo de Modelamiento y Control de Sistemas Biológicos, Facultad de Ingeniería, Universidad Nacional de Colombia, Carrera 45 No. 26-85, Bogota, Colombia
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