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Li D, Deng Y, Liu L, Wang J, Huang Z, Zhang X. Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining. Environ Geochem Health 2024; 46:146. [PMID: 38578375 DOI: 10.1007/s10653-024-01928-1] [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] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/20/2024] [Indexed: 04/06/2024]
Abstract
With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.
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Affiliation(s)
- De'an Li
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Yirong Deng
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China.
| | - LiLi Liu
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Jun Wang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Zaoquan Huang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Xiaolu Zhang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
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Chen K, Shi X, Zhang Z, Chen S, Ma J, Zheng T, Alfonso L. Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks. Water Sci Technol 2024; 89:1757-1770. [PMID: 38619901 DOI: 10.2166/wst.2024.087] [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] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/06/2024] [Indexed: 04/17/2024]
Abstract
The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.
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Affiliation(s)
- Kan Chen
- School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China; Suzhou Sujing Environmental Engineering Co., Ltd, 2 Weixin Road, Suzhou, Jiangsu, China
| | - Xiaofei Shi
- School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China
| | - Zhihao Zhang
- School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China
| | - Shijun Chen
- Suzhou Sujing Environmental Engineering Co., Ltd, 2 Weixin Road, Suzhou, Jiangsu, China
| | - Ji Ma
- Suzhou Sujing Environmental Engineering Co., Ltd, 2 Weixin Road, Suzhou, Jiangsu, China
| | - Tong Zheng
- School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China E-mail:
| | - Leonardo Alfonso
- IHE Delft Institute of Water Education, Westvest 7, 2611AX Delft, The Netherlands
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Oyinloye TM, Yoon WB. Artificial saliva induced structural breakdown of surimi gels with starch under continuous compressive motions. Food Res Int 2024; 182:114156. [PMID: 38519183 DOI: 10.1016/j.foodres.2024.114156] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 03/24/2024]
Abstract
Food texture perception is dynamic, influenced by food properties and oral processing. Using the Repeatable Dual Extrusion Cell (RDEC), the oral processing dynamics of surimi gel with different corn starch concentrations (0-15%) in the presence of 1 ml artificial saliva or water were studied. The force-time curve showed increased peak forces with higher corn starch concentrations, peaking significantly at 10%, then decreasing at 15%. Salivary amylase played a crucial role in gel sample degradation, especially in samples with 5% starch, with a work value depletion ratio of 0.535 for sample with 1 ml water (SGW-5) and 0.406 for sample with 1 ml saliva (SGS-5). SEM analysis confirmed the formation of a continuous starch network with reduced intermolecular spaces in SGS-5. The starch-iodine complex showed decreasing order with increasing starch concentration, and SGS-5 exhibited the highest degradation rate (61.61 ± 0.92%). Mathematical modeling revealed that initial decay rates (k1) in gel sample decreased with increasing starch concentration, and samples with starch and artificial saliva had higher initial degradation rates. These findings highlight the intricate interplay between saliva and starch in the surimi gel matrix under continuous compressive motions by RDEC apparatus, providing insights for formulating food products with tailored textures properties.
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Affiliation(s)
- Timilehin Martins Oyinloye
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea; Elder-Friendly Research Center, Agriculture and Life Science Research Institute, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea.
| | - Won Byong Yoon
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea; Elder-Friendly Research Center, Agriculture and Life Science Research Institute, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea.
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Zhang X, Ogasawara I, Konda S, Matsuo T, Uno Y, Miyakawa M, Nishizawa I, Arita K, Liu J, Nakata K. Absorption function loss due to the history of previous ankle sprain explored by unsupervised machine learning. Gait Posture 2024; 109:56-63. [PMID: 38277765 DOI: 10.1016/j.gaitpost.2024.01.021] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/28/2024]
Abstract
BACKGROUND Ankle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. PURPOSE To identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. METHODS Eighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. FINDINGS The ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. SIGNIFICANCE PCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.
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Affiliation(s)
- Xuemei Zhang
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Issei Ogasawara
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan; Department Sports Medical Biomechanics, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Shoji Konda
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan; Department Sports Medical Biomechanics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Tomoyuki Matsuo
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuki Uno
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Motoi Miyakawa
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Izumi Nishizawa
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Kazuki Arita
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jianting Liu
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Ken Nakata
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
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Li L, Momma H, Chen H, Nawrin SS, Xu Y, Inada H, Nagatomi R. Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study. Eur J Nutr 2024:10.1007/s00394-024-03342-w. [PMID: 38403812 DOI: 10.1007/s00394-024-03342-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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/30/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques. METHODS Data were obtained from Japanese male participants enrolled in a prospective cohort study between August 2008 and August 2010. A final dataset of 447 male participants was used for analysis. Dimension reduction using uniform manifold approximation and projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to evaluate the association between dietary patterns and the incidence of hypertension. RESULTS We identified four dietary patterns: 'Low-protein/fiber High-sugar,' 'Dairy/vegetable-based,' 'Meat-based,' and 'Seafood and Alcohol.' Compared with 'Seafood and Alcohol' as a reference, the protective dietary patterns for hypertension were 'Dairy/vegetable-based' (OR 0.39, 95% CI 0.19-0.80, P = 0.013) and the 'Meat-based' (OR 0.37, 95% CI 0.16-0.86, P = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding. CONCLUSION This study finds that relative to the 'Seafood and Alcohol' pattern, the 'Dairy/vegetable-based' and 'Meat-based' dietary patterns are associated with a lower risk of hypertension among men.
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Affiliation(s)
- Longfei Li
- School of Physical Education and Health, Heze University, 2269 University Road, Mudan District, Heze, 274-015, Shandong, China
- Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Haruki Momma
- Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Haili Chen
- Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Saida Salima Nawrin
- Division of Biomedical Engineering for Health & Welfare, Tohoku University Graduate School of Biomedical Engineering, 6-6-12, Aramaki Aza Aoba Aoba-ku, Sendai, Miyagi, 980-8579, Japan
| | - Yidan Xu
- Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Hitoshi Inada
- Department of Developmental Neuroscience, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8502, Japan.
| | - Ryoichi Nagatomi
- Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
- Division of Biomedical Engineering for Health & Welfare, Tohoku University Graduate School of Biomedical Engineering, 6-6-12, Aramaki Aza Aoba Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
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Zhou J, Zhou J, Feng Y, Feng L, Xiao L, Chen X, Feng Z, Yang J, Wang G. The novel subtype of major depressive disorder characterized by somatic symptoms is associated with poor treatment efficacy and prognosis: A data-driven cluster analysis of a prospective cohort in China. J Affect Disord 2024; 347:576-583. [PMID: 38065479 DOI: 10.1016/j.jad.2023.12.005] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/15/2023] [Accepted: 12/02/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND There is not yet a valid and evidence-based system to classify patients with MDD into more homogeneous subtypes based on their clinical features. This study aims to identify symptom-based subtypes of MDD and investigate whether the treatment outcomes of those subtypes would be different. METHOD The cohort was established at 12 densely populated cities of China. A total of 1487 patients were enrolled. All participants were 18-65 years old and diagnosed with MDD. Participants were followed up at baseline, weeks 4, 8, and 12, and months 4 and 6. K-means algorithm was used to cluster patients with MDD according to clinical symptoms. The network analysis was adopted to characterize and compare the symptom patterns in the clusters. We also examined the associations between the clusters and the clinical outcomes. RESULTS The optimal number of the clusters was determined to be 2. Each cluster's maximum Jaccard Co-efficient was calculated to be >0.5 (cluster1 = 0.53, cluster 2 = 0.67). The symptom "depressed mood" and some other affective symptoms were the most prominent in cluster 1. Somatic symptoms, such as weight loss and general somatic symptoms, had the greatest expected influence in cluster 2. Compared with the response rates of the patients in the "somatic cluster", those of the patients in the "affective cluster" were significantly higher (P < 0.05). CONCLUSIONS Patients with MDD might be classified into two symptom-based subtypes featured with affective symptoms or somatic symptoms. The treatment efficacy and prognosis of the subtype featured with somatic symptoms may be worse.
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Affiliation(s)
- Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jia Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lei Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Le Xiao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xu Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zizhao Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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Goldstein A, Shahar Y, Weisman Raymond M, Peleg H, Ben-Chetrit E, Ben-Yehuda A, Shalom E, Goldstein C, Shiloh SS, Almoznino G. Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study. Bioengineering (Basel) 2024; 11:97. [PMID: 38275577 PMCID: PMC10813477 DOI: 10.3390/bioengineering11010097] [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: 11/29/2023] [Revised: 12/28/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.
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Affiliation(s)
- Ayelet Goldstein
- Computer Science Department, Hadassah Academic College, Jerusalem 9101001, Israel;
| | - Yuval Shahar
- Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel; (Y.S.)
| | - Michal Weisman Raymond
- Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel; (Y.S.)
| | - Hagit Peleg
- Rheumatology Unit, Hadassah Medical Center, Jerusalem 9112102, Israel
| | - Eldad Ben-Chetrit
- Rheumatology Unit, Hadassah Medical Center, Jerusalem 9112102, Israel
| | - Arie Ben-Yehuda
- Division of Internal Medicine, Hadassah Medical Center, Jerusalem 9112102, Israel
| | - Erez Shalom
- Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel; (Y.S.)
| | - Chen Goldstein
- Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean’s Office, Hadassah Medical Center, Jerusalem 91120, Israel
| | - Shmuel Shay Shiloh
- Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean’s Office, Hadassah Medical Center, Jerusalem 91120, Israel
| | - Galit Almoznino
- Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean’s Office, Hadassah Medical Center, Jerusalem 91120, Israel
- Department of Oral Medicine, Sedation & Maxillofacial Imaging, Hadassah Medical Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
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Myznikov A, Korotkov A, Zheltyakova M, Kiselev V, Masharipov R, Bursov K, Yagmurov O, Votinov M, Cherednichenko D, Didur M, Kireev M. Dark triad personality traits are associated with decreased grey matter volumes in 'social brain' structures. Front Psychol 2024; 14:1326946. [PMID: 38282838 PMCID: PMC10811166 DOI: 10.3389/fpsyg.2023.1326946] [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: 10/24/2023] [Accepted: 12/29/2023] [Indexed: 01/30/2024] Open
Abstract
Introduction Personality traits and the degree of their prominence determine various aspects of social interactions. Some of the most socially relevant traits constitute the Dark Triad - narcissism, psychopathy, and Machiavellianism - associated with antisocial behaviour, disregard for moral norms, and a tendency to manipulation. Sufficient data point at the existence of Dark Triad 'profiles' distinguished by trait prominence. Currently, neuroimaging studies have mainly concentrated on the neuroanatomy of individual dark traits, while the Dark Triad profile structure has been mostly overlooked. Methods We performed a clustering analysis of the Dirty Dozen Dark Triad questionnaire scores of 129 healthy subjects using the k-means method. The variance ratio criterion (VRC) was used to determine the optimal number of clusters for the current data. The two-sample t-test within the framework of voxel-based morphometry (VBM) was performed to test the hypothesised differences in grey matter volume (GMV) for the obtained groups. Results Clustering analysis revealed 2 groups of subjects, both with low-to-mid and mid-to-high levels of Dark Triad traits prominence. A further VBM analysis of these groups showed that a higher level of Dark Triad traits may manifest itself in decreased grey matter volumes in the areas related to emotional regulation (the dorsolateral prefrontal cortex, the cingulate cortex), as well as those included in the reward system (the ventral striatum, the orbitofrontal cortex). Discussion The obtained results shed light on the neurobiological basis underlying social interactions associated with the Dark Triad and its profiles.
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Affiliation(s)
- Artem Myznikov
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Alexander Korotkov
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Maya Zheltyakova
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Vladimir Kiselev
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Ruslan Masharipov
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Kirill Bursov
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Orazmurad Yagmurov
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Mikhail Votinov
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Denis Cherednichenko
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Michael Didur
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
| | - Maxim Kireev
- Russian Academy of Science, N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russia
- Saint Petersburg State University, Saint Petersburg, Russia
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Peixoto MC, Castro NF, Crispim Romão M, Oliveira MGJ, Ochoa I. Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets. Front Artif Intell 2023; 6:1268852. [PMID: 38162833 PMCID: PMC10755015 DOI: 10.3389/frai.2023.1268852] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.
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Affiliation(s)
- Miguel Caçador Peixoto
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Nuno Filipe Castro
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
- Departamento de Física, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Miguel Crispim Romão
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
- Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
| | - Maria Gabriela Jordão Oliveira
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Inês Ochoa
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Lisbon, Portugal
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Firouznia M, Henningsson M, Carlhäll CJ. F K-means: automatic atrial fibrosis segmentation using fractal-guided K-means clustering with Voronoi-clipping feature extraction of anatomical structures. Interface Focus 2023; 13:20230033. [PMID: 38106915 PMCID: PMC10722213 DOI: 10.1098/rsfs.2023.0033] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/10/2023] [Indexed: 12/19/2023] Open
Abstract
Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation. However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE-MRI data and achieved a Dice score of 0.75, similar to the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which uses the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D UNet method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis.
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Affiliation(s)
- Marjan Firouznia
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Markus Henningsson
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Clinical Psychology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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Lacasa M, Launois P, Prados F, Alegre J, Casas-Roma J. Unsupervised Cluster Analysis Reveals Distinct Subtypes of ME/CFS Patients Based on Peak Oxygen Consumption and SF-36 Scores. Clin Ther 2023; 45:1228-1235. [PMID: 37802746 DOI: 10.1016/j.clinthera.2023.09.007] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 08/26/2023] [Accepted: 09/09/2023] [Indexed: 10/08/2023]
Abstract
PURPOSE Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET). METHODS Two datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber's classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters. FINDINGS The results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p - value < 0.05) for classifying patients with ME/CFS. IMPLICATIONS Low oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model.
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Affiliation(s)
- Marcos Lacasa
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.
| | - Patricia Launois
- Myalgic Encephalomyelitis / Chronic Fatigue Syndrome Unit, Division of Rheumatology, Vall d'Hebron Hospital Research Institute Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ferran Prados
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain; Center for Medical Image Computing, University College London, London, United Kingdom; National Institute for Health Research Biomedical Research Centre at UCL and UCLH, London, United Kingdom; Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - José Alegre
- Myalgic Encephalomyelitis / Chronic Fatigue Syndrome Unit, Division of Rheumatology, Vall d'Hebron Hospital Research Institute Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jordi Casas-Roma
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
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Zhou D, Liu Z, Gong G, Zhang Y, Lin L, Cai K, Xu H, Cong F, Li H, Chen A. Decreased Functional and Structural Connectivity is Associated with Core Symptom Improvement in Children with Autism Spectrum Disorder After Mini-basketball Training Program. J Autism Dev Disord 2023:10.1007/s10803-023-06160-x. [PMID: 37882897 DOI: 10.1007/s10803-023-06160-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] [Accepted: 10/15/2023] [Indexed: 10/27/2023]
Abstract
Exercise intervention has been proven helpful to ameliorate core symptoms of Autism Spectrum Disorder (ASD). However, the underlying mechanisms are not fully understood. In this study, we carried out a 12-week mini-basketball training program (MBTP) on ASD children and examined the changes of brain functional and structural networks before and after exercise intervention. We applied individual-based method to construct functional network and structural morphological network, and investigated their alterations following MBTP as well as their associations with the change in core symptom. Structural MRI and resting-state functional MRI data were obtained from 58 ASD children aged 3-12 years (experiment group: n = 32, control group: n = 26). ASD children who received MBTP intervention showed several distinguishable alternations compared to the control without special intervention. These included decreased functional connectivity within the sensorimotor network (SM) and between SM and the salience network, decreased morphological connectivity strength in a cortical-cortical network centered on the left inferior temporal gyrus, and a subcortical-cortical network centered on the left caudate. Particularly, the aforementioned functional and structural changes induced by MBTP were associated with core symptoms of ASD. Our findings suggested that MBTP intervention could be an effective approach to improve core symptoms in ASD children, decrease connectivity in both structure and function networks, and may drive the brain change towards normal-like neuroanatomy.
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Affiliation(s)
- Dongyue Zhou
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou, China
| | - Guanyu Gong
- Department of Oncology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yunge Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Lin Lin
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Kelong Cai
- College of Physical Education, Yangzhou University, Yangzhou, China
| | - Huashuai Xu
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
- Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, Liaoning Province, China
| | - Huanjie Li
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.
- Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, Liaoning Province, China.
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou, China.
- Key Laboratory of Brain Disease and Integration of Sport and Health, Yangzhou University, Yangzhou, China.
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13
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Wang C, Li Y, Wang J, Dong K, Li C, Wang G, Lin X, Zhao H. Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data. Front Endocrinol (Lausanne) 2023; 14:1230921. [PMID: 37929026 PMCID: PMC10623421 DOI: 10.3389/fendo.2023.1230921] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 09/26/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters. Methods 2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters. Results Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all p<0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)]. Conclusion Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.
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Affiliation(s)
- Cuicui Wang
- Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
- Department of Gastroenterology, The 986th Hospital of Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Yan Li
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, China
| | - Jun Wang
- Department of Gastroenterology, The 986th Hospital of Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Kunjie Dong
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Chenxiang Li
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Guiyan Wang
- School of Information Engineering, Dalian Ocean University, Dalian, China
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Hui Zhao
- Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
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Nakamura K, Ogura K, Nakano H, Ikechi D, Mochizuki M, Takahashi Y, Goto T. Explorative Clustering of the Nitrogen Balance Trajectory in Critically Ill Patients: A Preliminary post hoc Analysis of a Single-Center Prospective Observational Study. Ann Nutr Metab 2023; 79:460-468. [PMID: 37812913 PMCID: PMC10711758 DOI: 10.1159/000532126] [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: 01/18/2023] [Accepted: 07/14/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND The nitrogen balance estimates a protein net difference. However, since it has a number of limitations, it is important to consider the trajectory of the nitrogen balance in the clinical course of critically ill patients. OBJECTIVES We herein exploratively classified the nitrogen balance trajectory using a machine learning method. METHOD This is a post hoc analysis of a single-center prospective study for the patients admitted to our Emergency and Critical Center ICU. The nitrogen balance was evaluated with 24-h urine collection from ICU days 1-10 with 9 points. K-means clustering was performed to classify the nitrogen balance trajectory. We also evaluated factors associated with uncovered clusters. RESULTS Seventy-six eligible patients were included in the present study. After clustering, the nitrogen balance trajectory was classified into 4 classes. Class 1 was trajected as a negative balance over 10 days (24 patients). Class 2 had a positive conversion on day 3 or 4 (8 patients). Class 3 had a positive conversion on day 8 or 9 (28 patients). Class 4 initially had a positive balance and then converted to a negative balance (16 patients). Sepsis complication and steroid use were associated with negative nitrogen balance trajectory. Class 2 was associated with lower length of hospital stay and femoral muscle volume loss, however, frequently had frailty and sarcopenia on admission. Active nutrition therapy intention was not correlated with positive trajectory. CONCLUSIONS The nitrogen balance trajectory in critically ill patients may be classified into 4 classes for clinical practice. Among patients emergently admitted to the ICU, the positive conversion of the nitrogen balance might be delayed over 10 days.
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Affiliation(s)
- Kensuke Nakamura
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Japan
| | - Kentaro Ogura
- TXP Medical Co., Ltd., Tokyo, Japan
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hidehiko Nakano
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Japan
| | - Daisuke Ikechi
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Japan
| | - Masaki Mochizuki
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Japan
| | - Yuji Takahashi
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Japan
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Li M, Li F. Assessment of college students' mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events. PeerJ Comput Sci 2023; 9:e1586. [PMID: 37810345 PMCID: PMC10557517 DOI: 10.7717/peerj-cs.1586] [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: 07/18/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023]
Abstract
The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual's psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students' mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means-its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students' mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students' mental well-being during times of fluctuating public health dynamics.
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Affiliation(s)
- Mao Li
- Sichuan Vocational College of Health and Rehabilitation, Zigong, Sichuan, China
| | - Fanfan Li
- Student Affairs Department, Huanggang Normal University, Huanggang, Hubei, China
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Peng L, Huang L, Tian G, Wu Y, Li G, Cao J, Wang P, Li Z, Duan L. Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network. Front Microbiol 2023; 14:1244527. [PMID: 37789848 PMCID: PMC10543759 DOI: 10.3389/fmicb.2023.1244527] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/16/2023] [Indexed: 10/05/2023] Open
Abstract
Background Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. Methods We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. Results GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. Conclusion The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Liangliang Huang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yan Wu
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Guang Li
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Jianying Cao
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Peng Wang
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lian Duan
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
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Dalal S, Lilhore UK, Manoharan P, Rani U, Dahan F, Hajjej F, Keshta I, Sharma A, Simaiya S, Raahemifar K. An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering. Sensors (Basel) 2023; 23:7816. [PMID: 37765873 PMCID: PMC10537273 DOI: 10.3390/s23187816] [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] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/26/2023] [Accepted: 05/02/2023] [Indexed: 09/29/2023]
Abstract
Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.
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Affiliation(s)
- Surjeet Dalal
- Department of Computer Science and Engineering, Amity University Gurugram, Gurugram 122412, Haryana, India
| | - Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, Punjab, India
| | - Poongodi Manoharan
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 5825, Qatar
| | - Uma Rani
- Department of Computer Science and Engineering, World College of Technology & Management, Gurugram 122413, Haryana, India
| | - Fadl Dahan
- Department of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Fahima Hajjej
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 13713, Saudi Arabia
| | - Ashish Sharma
- Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India
| | - Sarita Simaiya
- Apex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali 140413, Punjab, India
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PS 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L 3G1, Canada
- Faculty of Engineering, University of Waterloo, 200 University Ave. W., Waterloo, ON N2L 3G1, Canada
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Ben Laoula EM, Elfahim O, El Midaoui M, Youssfi M, Bouattane O. Traffic violations analysis: Identifying risky areas and common violations. Heliyon 2023; 9:e19058. [PMID: 37662813 PMCID: PMC10472221 DOI: 10.1016/j.heliyon.2023.e19058] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/05/2023] Open
Abstract
Road traffic accidents caused by traffic violations are a major public health issue that results in loss of lives and economic costs. Therefore, it is important to prioritize road safety measures that reduce the incidence and severity of accidents. In this study, we suggest an incremental road safety strategy that identifies high-risk areas and common traffic violations in order to prioritize further enforcement. In fact, by analyzing data on traffic violations in different districts and comparing them to the overall average using the Kolmogorov-Smirnov (KS) test, risky areas are identified and the most common violations are detected. We performed a comparison between several types of clustering optimizations to spot clusters to be enforced in order to reduce violations. Our results indicate that some Districts have a higher risk of traffic violations than others do, and some violations (Speeding, Registration, License, Belt, Influence, Phone, etc.) are more common than others are. We also find that k-means clustering provides the best results for identifying clusters of violations records and optimizing enforcement strategies. Our findings can be adopted by law enforcement agencies to focus on high-risk areas and target the most common violations in order to optimize their resources and improve road safety.
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Affiliation(s)
- El Mehdi Ben Laoula
- 2IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
| | - Omar Elfahim
- 2IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
| | - Marouane El Midaoui
- M2S2I Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
| | - Mohamed Youssfi
- 2IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
| | - Omar Bouattane
- 2IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
- M2S2I Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco
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19
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Marino S. Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel-2 NDVI time-series images in an organic farming system. Heliyon 2023; 9:e19507. [PMID: 37809718 PMCID: PMC10558738 DOI: 10.1016/j.heliyon.2023.e19507] [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: 06/30/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 10/10/2023] Open
Abstract
The study investigates the suitability of time series Sentinel-2 NDVI-derived maps for the subfield detection of a sunflower crop cultivated in an organic farming system. The aim was to understand the spatio-temporal behaviour of subfield areas identified by the K-means algorithm from NDVI maps obtained from satellite images and the ground yield data variability to increase the efficiency of delimiting management zones in an organic farming system. Experiments were conducted on a surface of 29 ha. NDVI time series derived from Sentinel-2 images and k-means algorithm for rapidly delineating the sunflower subfield areas were used. The crop achene yields in the whole field ranged from 1.3 to 3.77 t ha-1 with a significant within-field spatial variability. The cluster analysis of hand-sampled data showed three subfields with achene yield mean values of 3.54 t ha-1 (cluster 1), 2.98 t ha-1 (cluster 2), and 2.07 t ha-1 (Cluster 3). In the cluster analysis of NDVI data, the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability. The best period for identifying subfield areas starts from the inflorescences development stage to the development of the fruit stage. Analyzing the NDVI subfield areas and yield data, it was found that cluster 1 covers an area of 42.4% of the total surface and 50% of the total achene yield; cluster 2 covers 35% of both surface and yield. Instead, the surface of cluster 3 covers 22.2% of the total surface with 15% of achene yield. K-means algorithm derived from Sentinel-2 NDVI images delineates the sunflower subfield areas. Sentinel-2 images and k-means algorithms can improve an efficient assessment of subfield areas in sunflower crops. Identifying subfield areas can lead to site-specific long-term agronomic actions for improving the sustainable intensification of agriculture in the organic farming system.
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Affiliation(s)
- Stefano Marino
- Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, Via De Sanctis, I-86100, Campobasso, Italy
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20
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Zelasky S, Martin CL, Weaver C, Baxter LK, Rappazzo KM. Identifying groups of children's social mobility opportunity for public health applications using k-means clustering. Heliyon 2023; 9:e20250. [PMID: 37810086 PMCID: PMC10560027 DOI: 10.1016/j.heliyon.2023.e20250] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 10/10/2023] Open
Abstract
Background The Opportunity Atlas project is a pioneering effort to trace social mobility and adulthood socioeconomic outcomes back to childhood residence. Half of the variation in adulthood socioeconomic outcomes was explainable by neighborhood-level socioeconomic characteristics during childhood. Clustering census tracts by Opportunity Atlas characteristics would allow for further exploration of variance in social mobility. Our objectives here are to identify and describe spatial clustering trends within Opportunity Atlas outcomes. Methods We utilized a k-means clustering machine learning approach with four outcome variables (individual income, incarceration rate, employment, and percent of residents living in a neighborhood with low levels of poverty) each given at five parental income levels (1st, 25th, 50th, 75th, and 100th percentiles of the national distribution) to create clusters of census tracts across the contiguous United States (US) and within each Environmental Protection Agency region. Results At the national level, the algorithm identified seven distinct clusters; the highest opportunity clusters occurred in the Northern Midwest and Northeast, and the lowest opportunity clusters occurred in rural areas of the Southwest and Southeast. For regional analyses, we identified between five to nine clusters within each region. PCA loadings fluctuate across parental income levels; income and low poverty neighborhood residence explain a substantial amount of variance across all variables, but there are differences in contributions across parental income levels for many components. Conclusions Using data from the Opportunity Atlas, we have taken four social mobility opportunity outcome variables each stratified at five parental income levels and created nationwide and EPA region-specific clusters that group together census tracts with similar opportunity profiles. The development of clusters that can serve as a combined index of social mobility opportunity is an important contribution of this work, and this in turn can be employed in future investigations of factors associated with children's social mobility.
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Affiliation(s)
- Sarah Zelasky
- Oak Ridge Associated Universities at the U.S. Environmental Protection Agency, Chapel Hill, NC, USA
| | - Chantel L. Martin
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA
| | - Christopher Weaver
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, Durham, NC, USA
| | - Lisa K. Baxter
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, Durham, NC, USA
| | - Kristen M. Rappazzo
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, Durham, NC, USA
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21
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Brandner C, Raynal E, Ruggeri P. Interindividual variations in associative visual learning: Exploration, description, and partition of response characteristics. Behav Res Methods 2023:10.3758/s13428-023-02208-z. [PMID: 37620746 DOI: 10.3758/s13428-023-02208-z] [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] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.
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Affiliation(s)
- Catherine Brandner
- Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland.
| | - Elsa Raynal
- Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland
| | - Paolo Ruggeri
- Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland
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22
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Huang P, Wang S, Chen J, Li W, Peng X. Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7. Sensors (Basel) 2023; 23:7112. [PMID: 37631649 PMCID: PMC10459580 DOI: 10.3390/s23167112] [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] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement defect detection model based on an improved YOLOv7 architecture. The model introduces four key enhancements: first, the incorporation of the SPPCSPC_Group grouped space pyramid pooling module to reduce the parameter load and computational complexity; second, the utilization of the K-means clustering algorithm for generating anchors, accelerating model convergence; third, the integration of the Ghost Conv module, enhancing feature extraction while minimizing the parameters and calculations; fourth, introduction of the CBAM convolution module to enrich the semantic information in the last layer of the backbone network. The experimental results demonstrate that the improved model achieved an average accuracy of 91%, and the accuracy in detecting broken plates and repaired models increased by 9% and 8%, respectively, compared to the original model. Moreover, the improved model exhibited reductions of 14.4% and 29.3% in the calculations and parameters, respectively, and a 29.1% decrease in the model size, resulting in an impressive 80 FPS (frames per second). The enhanced YOLOv7 successfully balances parameter reduction and computation while maintaining high accuracy, making it a more suitable choice for pavement defect detection compared with other algorithms.
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Affiliation(s)
| | - Shenghuai Wang
- School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China; (P.H.); (J.C.); (W.L.); (X.P.)
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23
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Hao X, Han L, Zheng D, Jin X, Li C, Huang L, Huang Z. Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China. BMC Health Serv Res 2023; 23:808. [PMID: 37507799 PMCID: PMC10375627 DOI: 10.1186/s12913-023-09803-y] [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: 02/01/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
OBJECTIVE Today, the development mode of public hospitals in China is turning from expansion to efficiency, and the management mode is turning from extensive to refined. This study aims to evaluate the efficiency of clinical departments in a Chinese class A tertiary public hospital (Hospital M) to analyze the allocation of hospital resources among these departments providing a reference for the hospital management. METHODS The hospitalization data of inpatients from 32 clinical departments of Hospital M in 2021 are extracted from the hospital information system (HIS), and a dataset containing 38,147 inpatients is got using stratified sampling. Considering the non-homogeneity of clinical departments, the 38,147 patients are clustered using the K-means algorithm based on workload-related data labels including inpatient days, intensive care workload index, nursing workload index, and operation workload index, so that the medical resource consumption of inpatients from non-homogeneous clinical departments can be transformed into the homogeneous workload of medical staff. Taking the numbers of doctors, nurses, and beds as input indicators, and the numbers of inpatients assigned to certain clusters as output indicators, an input-oriented BCC model is built named the workload-based DEA model. Meanwhile, a control DEA model with the number of inpatients and medical revenue as output indicators is built, and the outputs of the two models are compared and analyzed. RESULTS Clustering of 38,147 patients into 3 categories is of better interpretability. 14 departments reach DEA efficient in the workload-based DEA model, 10 reach DEA efficient in the control DEA model, and 8 reach DEA efficient in both models. The workload-based DEA model gives a relatively rational judge on the increase of income brought by scale expansion, and evaluates some special departments like Critical Care Medicine Dept., Geriatrics Dept. and Rehabilitation Medicine Dept. more properly, which better adapts to the functional orientation of public hospitals in China. CONCLUSION The design of evaluating the efficiency of non-homogeneous clinical departments with the workload as output proposed in this study is feasible, and provides a new idea to quantify professional medical human resources, which is of practical significance for public hospitals to optimize the layout of resources, to provide real-time guidance on manpower grouping strategies, and to estimate the expected output reasonably.
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Affiliation(s)
- Xiaoxiong Hao
- Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Lei Han
- Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Danyang Zheng
- Department of Critical Care Medicine, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Xiaozhi Jin
- Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Chenguang Li
- Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Lvshuai Huang
- Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China
| | - Zhaohui Huang
- Department of Health Service, Medical Training Base, Army Medical University, Chongqing, 400038, People's Republic of China.
- General Hospital of Central Theater Command, Postdoctoral Workstation, Wuhan, 430070, People's Republic of China.
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24
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Song S, Ren X, He J, Gao M, Wang J, Wang B. An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm. Diagnostics (Basel) 2023; 13:2454. [PMID: 37510198 PMCID: PMC10377835 DOI: 10.3390/diagnostics13142454] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/06/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Oral cancer is introduced as the uncontrolled cells' growth that causes destruction and damage to nearby tissues. This occurs when a sore or lump grows in the mouth that does not disappear. Cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard and soft palate, and lungs (throat) are types of this cancer that will be deadly if not detected and cured in the beginning stages. The present study proposes a new pipeline procedure for providing an efficient diagnosis system for oral cancer images. In this procedure, after preprocessing and segmenting the area of interest of the inputted images, the useful characteristics are achieved. Then, some number of useful features are selected, and the others are removed to simplify the method complexity. Finally, the selected features move into a support vector machine (SVM) to classify the images by selected characteristics. The feature selection and classification steps are optimized by an amended version of the competitive search optimizer. The technique is finally implemented on the Oral Cancer (Lips and Tongue) images (OCI) dataset, and its achievements are confirmed by the comparison of it with some other latest techniques, which are weight balancing, a support vector machine, a gray-level co-occurrence matrix (GLCM), the deep method, transfer learning, mobile microscopy, and quadratic discriminant analysis. The simulation results were authenticated by four indicators and indicated the suggested method's efficiency in relation to the others in diagnosing the oral cancer cases.
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Affiliation(s)
- Simin Song
- The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China
| | - Xiaojing Ren
- The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Jing He
- The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China
| | - Meng Gao
- The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China
| | - Jia'nan Wang
- The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China
| | - Bin Wang
- The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China
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25
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Tian J, Zeng Y, Ji L, Zhu H, Guo Z. Control Method of Cold and Hot Shock Test of Sensors in Medium. Sensors (Basel) 2023; 23:6536. [PMID: 37514830 PMCID: PMC10385061 DOI: 10.3390/s23146536] [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: 06/16/2023] [Revised: 07/11/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and -40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test.
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Affiliation(s)
- Jinming Tian
- School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, China
| | - Yue Zeng
- School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, China
| | - Linhai Ji
- School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, China
| | - Huimin Zhu
- School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, China
| | - Zu Guo
- School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, China
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26
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García-Marín NM, Marrero GA, Guerra-Neira A, Rivera-Deán A. Profiles of travelers to intermediate-high health risk areas following the reopening of borders in the COVID-19 crisis: A clustering approach. Travel Med Infect Dis 2023; 54:102607. [PMID: 37353065 PMCID: PMC10284617 DOI: 10.1016/j.tmaid.2023.102607] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/15/2023] [Accepted: 06/13/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND The reactivation of international travel in 2021 has created a new scenario in which the profile of the traveler to medium-high health risk areas may well have changed. However, few studies have analyzed this new profile since the reopening of borders in that year. METHODS We designed an ad hoc questionnaire that was administered face-to-face by our medical team during appointments with 330 travelers in the second half of 2021. Information was collected on the following topics: sociodemographic and socioeconomic status; type of travel and previous travel experience; health status and risk perception (of COVID-19 and tropical infectious diseases). Using all features simultaneously, an unsupervised machine learning approach (k-means) is implemented to characterize groups of travelers. Pairwise chi-squared tests were performed to identify key features that showed statistically significant differences between clusters. RESULTS The travelers were clustered into seven groups. We associated the clusters with different intensities of perceived risk of acquiring COVID-19 and tropical infectious diseases on the trip. The perceived risk of both diseases was low in the group "middle or lower middle class young inexperienced male tourist" but high in the group "middle or lower middle-class young with children inexperienced business traveler". CONCLUSIONS Broadening our knowledge of the profiles of travelers to intermediate-high health risk areas would help to tailor the health advice provided by practitioners to their characteristics and type of travel. In a changing health context, the k-means approach supposes a flexible statistical method that calculates travelers' profiles and can be easily adapted to process new information.
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Affiliation(s)
- Nidia M García-Marín
- International Vaccination Center at Santa Cruz de Tenerife, Spanish Ministry of Health, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain
| | - Gustavo A Marrero
- University of La Laguna, Department of Economics, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain; University of La Laguna, IUDR, Spain.
| | - Ana Guerra-Neira
- International Vaccination Center at Santa Cruz de Tenerife, Spanish Ministry of Health, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain
| | - Almudena Rivera-Deán
- International Vaccination Center at Santa Cruz de Tenerife, Spanish Ministry of Health, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain
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27
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Chen Y, Sun Y, Bie C, Wang X, He X, Song X. Hierarchical K-means clustering method for accelerated Lorentzian estimation (KALE) in chemical exchange saturation transfer-magnetic resonance imaging quantification. Quant Imaging Med Surg 2023; 13:4350-4364. [PMID: 37456289 PMCID: PMC10347364 DOI: 10.21037/qims-22-1379] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/14/2023] [Indexed: 07/18/2023]
Abstract
Background Quantification of in vivo chemical exchange saturation transfer (CEST) magnetic resonance signals is challenging due to contamination from coexisting effects, including the direct water effect and asymmetric magnetization transfer. Fitting-based analysis allows the calculation of multiple types of signals from the line shape of Z-spectra. However, the conventional voxelwise method has several drawbacks, including its long computation time and its susceptibility to image noise and Z-spectra oscillations, and it is difficult to determine the initial fitting parameters. Methods Herein, we propose a K-means clustering method for accelerated Lorentzian estimation (KALE) in CEST quantification. Briefly, voxels in CEST images are clustered into K groups according to their Z-spectra characteristics. A 'groupwise' fitting process is then performed with preset initial values, yielding a set of fitted spectra and fitted parameters for each group. With the updated initial values, each group is further clustered into subgroups, and groupwise fitting is performed again. This hierarchical K-means clustering and parameter updating process continues until the pixel number or intensity error meets the termination criteria. Voxelwise fitting could be further conducted to improve the quantification images (termed voxel-K) by utilizing the previous groupwise KALE results as the initial values (termed group-K). Results Incorporated with Lorentzian difference (LD) quantification, KALE was first optimized and evaluated on 5 healthy human brain datasets at 3 Tesla. Compared with traditional voxel-by-voxel LD quantification, the computation times of group-K and voxel-K were significantly reduced by ~85% and ~70%, respectively (P<0.001). Furthermore, the group-K images exhibited better denoising performance than traditional LD and voxel-K. KALE was further validated on six ischemic rat brains acquired at 7 Tesla, with both LD_group-K and LD_voxel-K displaying almost identical contrast maps with traditional voxelwise maps. When incorporated with the five-pool Lorentzian fitting (LF), KALE exhibited an improved contrast-to-noise ratio (CNR) for amplitude maps of each pool [P=0.003, 0.015, 0.047, and 0.047 for amide, nuclear Overhauser effect (NOE), magnetic transfer (MT) and guanidine amine, respectively] and improved fitting goodness (P=0.033). Conclusions KALE quantification provides comparable or even superior contrast maps to traditional voxelwise fitting, with significantly reduced computation time. The 'smart' and hierarchical voxel-clustering and parameter updating process of KALE may facilitate more preclinical and clinical CEST applications.
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Affiliation(s)
- Yanrong Chen
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Yaozong Sun
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Chongxue Bie
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Xiaoli Wang
- Department of Medical Imaging, Weifang Medical University, Weifang, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China
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28
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Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics (Basel) 2023; 13:diagnostics13111957. [PMID: 37296809 DOI: 10.3390/diagnostics13111957] [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: 04/01/2023] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.
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Affiliation(s)
- Khaled M Alalayah
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a P.O. Box 1152, Yemen
| | - Hany F Atlam
- Cyber Security Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
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Allmuttar AYO, Alkhafaji SKD. Using data mining techniques deep analysis and theoretical investigation of COVID-19 pandemic. Measur Sens 2023; 27:100747. [PMID: 36945699 PMCID: PMC10017173 DOI: 10.1016/j.measen.2023.100747] [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: 12/29/2022] [Revised: 01/21/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
This study uses K-Means Clustering to analyze Corona-Virus Diseases (Covid-19). Data mining in medicine has generated novel approaches to examine diseases. Coronavirus is difficult to treat because of its intricate structure, shape, and texture. Due to data mining improvements, the K-Means approach has been developed for evaluating covid-19. Observe the outbreak's evolution, including its peak, and containment measures. A basic K-Means model is used to simulate Coronavirus's prevalence in Iraq. Pandemic-prevention efforts may slow its spread. If inhibition grows to 50%, Iraq will have 500,000 patients by year's end. If precautions were halved, the number would top 1 million. If we abandon all measures, the sickness will worsen. In that case, 55% of the population may be affected by the end of the month. This number will drop after September.
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Affiliation(s)
- Atheer Y O Allmuttar
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Iraq
- Al-Ayen University, Thi-Qar, Iraq
| | - Sarmad K D Alkhafaji
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Iraq
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Niyozov S, Domaneschi M, Casas JR, Delgadillo RM. Temperature Effects Removal from Non-Stationary Bridge-Vehicle Interaction Signals for ML Damage Detection. Sensors (Basel) 2023; 23:s23115187. [PMID: 37299918 DOI: 10.3390/s23115187] [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: 04/17/2023] [Revised: 05/10/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
Bridges are vital components of transport infrastructures, and therefore, it is of utmost importance that they operate safely and reliably. This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge interaction. In detail, the current study presents an approach to temperature removal in the case of forced vibrations in the bridge using principal component analysis, with detection and localization of damage using an unsupervised machine learning algorithm. Due to the difficulty in obtaining real data on undamaged and later damaged bridges that are simultaneously influenced by traffic and temperature changes, the proposed method is validated using a numerical bridge benchmark. The vertical acceleration response is derived from a time-history analysis with a moving load under different ambient temperatures. The results show how machine learning algorithms applied to bridge damage detection appear to be a promising technique to efficiently solve the problem's complexity when both operational and environmental variability are included in the recorded data. However, the example application still shows some limitations, such as the use of a numerical bridge and not a real bridge due to the lack of vibration data under health and damage conditions, and with varying temperatures; the simple modeling of the vehicle as a moving load; and the crossing of only one vehicle present in the bridge. This will be considered in future studies.
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Affiliation(s)
- Sardorbek Niyozov
- Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Marco Domaneschi
- Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Joan R Casas
- Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
| | - Rick M Delgadillo
- Department of Civil Engineering, Universidad de Ingenieria y Tecnologia-UTEC, Jr. Medrano Silva 165, Barranco, Lima 15063, Peru
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31
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Sarris AL, Sidiropoulos E, Paraskevopoulos E, Bamidis P. Towards a Digital Twin in Human Brain: Brain Tumor Detection Using K-Means. Stud Health Technol Inform 2023; 302:1052-1056. [PMID: 37203579 DOI: 10.3233/shti230345] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Digital Twins come to revolutionize the ongoing procedures of healthcare industry, with their ability to stimulate and predict patients' diagnosis and treatment. In this paper a K-means based brain tumor detection algorithm and its 3D modelling design, both derived from MRI scans, are presented towards to the creation of the digital twin.
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32
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Xu S, Wang X, Zhu R, Wang D. Spatio-temporal effects of regional resilience construction on carbon emissions: Evidence from 30 Chinese provinces. Sci Total Environ 2023; 887:164109. [PMID: 37182764 DOI: 10.1016/j.scitotenv.2023.164109] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/25/2023] [Accepted: 05/08/2023] [Indexed: 05/16/2023]
Abstract
In response to the threat of rapidly rising carbon emissions, a variety of measures are being implemented to achieve carbon reduction. Resilience construction offers a fresh approach to improving the regional anti-interference ability to cope with various risks, and it is worth considering its impact on carbon emissions. The objective of this study is to investigate the spatio-temporal impacts of resilience construction (RCI) on carbon intensity (CI) in 30 Chinese provinces from 2010 to 2019. The relation pattern between RCI and CI is thoroughly examined after developing a hybrid model by integrating gray correlation analysis (GRA) and coupled coordination degree (CCD). Using the GTWR model, the coefficients reveal the spatio-temporal pattern of the influence of each variable on CI. Furthermore, this study pioneeringly blends GTWR regression results with the K-Means approach to identify areas with homogeneity and heterogeneity of the pattern. Firstly, the findings indicate that there is a significant link between CI and all dimensions -economic resilience (RE), social resilience (RS), and ecological resilience (REn). The relation between REn and CI is the greatest, although it has been declining recently while relations of RS, REn, and CI have all been steadily rising. Secondly, according to the results of CCD, resilience construction and carbon reduction are progressively reaching orderly development but there are still some provinces at low levels of CCD. Thirdly, the study area is divided into four clusters, and the structure of spatial grouping tends to become stable. Moreover, we analyze each cluster's features and suggest appropriate policy measures. The findings aid in the scientific planning of the direction of resilience construction with the goal of collaborative management of carbon emissions.
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Affiliation(s)
- Shan Xu
- School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao 066004, China
| | - Xinran Wang
- School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao 066004, China.
| | - Ruiguang Zhu
- School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao 066004, China
| | - Ding Wang
- School of Civil Engineering & Mechanics, Yanshan University, Qinhuangdao 066004, China
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Faqih M, Omar MB, Ibrahim R. Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning. Sensors (Basel) 2023; 23:3863. [PMID: 37112203 PMCID: PMC10145957 DOI: 10.3390/s23083863] [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] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/27/2023] [Accepted: 04/03/2023] [Indexed: 06/19/2023]
Abstract
Dry-Low Emission (DLE) technology significantly reduces the emissions from the gas turbine process by implementing the principle of lean pre-mixed combustion. The pre-mix ensures low nitrogen oxides (NOx) and carbon monoxide (CO) production by operating at a particular range using a tight control strategy. However, sudden disturbances and improper load planning may lead to frequent tripping due to frequency deviation and combustion instability. Therefore, this paper proposed a semi-supervised technique to predict the suitable operating range as a tripping prevention strategy and a guide for efficient load planning. The prediction technique is developed by hybridizing Extreme Gradient Boosting and K-Means algorithm using actual plant data. Based on the result, the proposed model can predict the combustion temperature, nitrogen oxides, and carbon monoxide concentration with an accuracy represented by R squared value of 0.9999, 0.9309, and 0.7109, which outperforms other algorithms such as decision tree, linear regression, support vector machine, and multilayer perceptron. Further, the model can identify DLE gas turbine operation regions and determine the optimum range the turbine can safely operate while maintaining lower emission production. The typical DLE gas turbine's operating range can operate safely is found at 744.68 °C -829.64 °C. The proposed technique can be used as a preventive maintenance strategy in many applications involving tight operating range control in mitigating tripping issues. Furthermore, the findings significantly contribute to power generation fields for better control strategies to ensure the reliable operation of DLE gas turbines.
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Affiliation(s)
- Mochammad Faqih
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;
| | - Madiah Binti Omar
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;
| | - Rosdiazli Ibrahim
- Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;
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Zhang L, Huang D, Chen X, Zhu L, Xie Z, Chen X, Cui G, Zhou Y, Huang G, Shi W. Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification. J Biophotonics 2023:e202300020. [PMID: 36966458 DOI: 10.1002/jbio.202300020] [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: 01/20/2023] [Revised: 03/07/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large-eared white rabbits were acquired using a visible near-infrared hyperspectral camera, and K-means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500-622 and 700-858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo.
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Affiliation(s)
- Lechao Zhang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Danfei Huang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Xiaojing Chen
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Libin Zhu
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhonghao Xie
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Xiaoqing Chen
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guihua Cui
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Yao Zhou
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Guangzao Huang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Wen Shi
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
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Xi Y, Zhao T, Liu R, Song F, Deng J, Ai N. Assessing Sensory Attributes and Properties of Infant Formula Milk Powder Driving Consumers' Preference. Foods 2023; 12:foods12050997. [PMID: 36900514 PMCID: PMC10000600 DOI: 10.3390/foods12050997] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/18/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023] Open
Abstract
Infant formula milk powder (IFMP) is an excellent substitute for breast milk. It is known that the composition of maternal food during pregnancy and lactation and exposure level to food during infancy highly influence taste development in early infancy. However, little is known about the sensory aspects of infant formula. Herein, the sensory characteristics of 14 brands of infant formula segment 1 marketed in China were evaluated, and differences in preferences for IFMPs were determined. Descriptive sensory analysis was performed by well-trained panelists to determine the sensory characteristics of evaluated IFMPs. The brands S1 and S3 had significantly lower astringency and fishy flavor compared to the other brands. Moreover, it was found that S6, S7 and S12 had lower milk flavor scores but higher butter scores. Furthermore, internal preference mapping revealed that the attributes fatty flavor, aftertaste, saltiness, astringency, fishy flavor and sourness negatively contributed to consumer preference in all three clusters. Considering that the majority of consumers prefer milk powders rich in aroma, sweet and steamed flavors, these attributes could be considered for enhancement by the food industry.
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Affiliation(s)
- Yanmei Xi
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health, Beijing Technology & Business University, Beijing 100048, China
| | - Tong Zhao
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health, Beijing Technology & Business University, Beijing 100048, China
| | - Ruirui Liu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health, Beijing Technology & Business University, Beijing 100048, China
| | - Fuhang Song
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health, Beijing Technology & Business University, Beijing 100048, China
| | - Jianjun Deng
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Correspondence: (J.D.); (N.A.)
| | - Nasi Ai
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health, Beijing Technology & Business University, Beijing 100048, China
- Correspondence: (J.D.); (N.A.)
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36
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Tong Z, Kong Z, Jia X, Yu J, Sun T, Zhang Y. Spatial Heterogeneity and Regional Clustering of Factors Influencing Chinese Adolescents' Physical Fitness. Int J Environ Res Public Health 2023; 20:3836. [PMID: 36900845 PMCID: PMC10001620 DOI: 10.3390/ijerph20053836] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
There is often significant spatial heterogeneity in the factors influencing physical fitness in adolescents, yet less attention has been paid to this in established studies. Based on the 2018 Chinese National Student Physical Fitness Standard Test data, this study uses a multi-scale, geographically weighted regression (MGWR) model combined with a K-means clustering algorithm to construct a spatial regression model of the factors influencing adolescent physical fitness, and to investigate the degree of spatial variation in the physical fitness of Chinese adolescents from a socio-ecological perspective of health promotion. The following conclusions were drawn: the performance of the youth physical fitness regression model was significantly improved after taking spatial scale and heterogeneity into account. At the provincial scale, the non-farm output, average altitude, and precipitation of each region were strongly related to youth physical fitness, and each influencing factor generally showed a banded spatial heterogeneity pattern, which can be summarized into four types: N-S, E-W, NE-SW, and SE-NW. From the perspective of youth physical fitness, China can be divided into three regions of influence: the socio-economic-influenced region, mainly including the eastern region and some of the central provinces of China; the natural-environment-influenced region, which mainly includes the northwestern part of China and some provinces in the highland region; and the multi-factor joint-influenced region, which mainly includes the provinces in the central and northeastern regions of China. Finally, this study provides syndemic suggestions for physical fitness and health promotion for youths in each region.
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37
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Martins A, Fonseca I, Farinha JT, Reis J, Cardoso AJM. Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance. Sensors (Basel) 2023; 23:2402. [PMID: 36904607 PMCID: PMC10007291 DOI: 10.3390/s23052402] [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] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/07/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.
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Affiliation(s)
- Alexandre Martins
- EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal
- CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal
| | - Inácio Fonseca
- Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal
| | - José Torres Farinha
- Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal
- Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal
| | - João Reis
- EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal
| | - António J. Marques Cardoso
- CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal
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38
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Kurt Z, Işık Ş, Kaya Z, Anagün Y, Koca N, Çiçek S. Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma. Neural Comput Appl 2023; 35:12121-12132. [PMID: 36843903 PMCID: PMC9940669 DOI: 10.1007/s00521-023-08344-z] [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: 06/22/2022] [Accepted: 01/25/2023] [Indexed: 02/23/2023]
Abstract
When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.
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Affiliation(s)
- Zuhal Kurt
- Department of Computer Engineering, Atilim University, Ankara, Turkey
| | - Şahin Işık
- Department of Computer Engineering, Eskisehir Osmangazi University Meselik Campus, Eskisehir, Turkey
| | - Zeynep Kaya
- Department of Electrical and Energy, Bilecik Seyh Edebali University, Osmaneli Vocational School, Bilecik, Turkey
| | - Yıldıray Anagün
- Department of Computer Engineering, Eskisehir Osmangazi University Meselik Campus, Eskisehir, Turkey
| | - Nizameddin Koca
- Department of Internal Medicine, University of Health Sciences, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey
| | - Sümeyye Çiçek
- Department of Internal Medicine, University of Health Sciences, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey
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Steyn Y, Lawlor T, Masuda Y, Tsuruta S, Legarra A, Lourenco D, Misztal I. Nonparallel genome changes within subpopulations over time contributed to genetic diversity within the US Holstein population. J Dairy Sci 2023; 106:2551-2572. [PMID: 36797192 DOI: 10.3168/jds.2022-21914] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/03/2022] [Indexed: 02/16/2023]
Abstract
Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future.
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Affiliation(s)
- Y Steyn
- Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602.
| | - T Lawlor
- Holstein Association USA Inc., Brattleboro, VT 05302
| | - Y Masuda
- Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602
| | - S Tsuruta
- Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602
| | - A Legarra
- GenPhySE, INRA, INPT, ENVT, Université de Toulouse, Castanet-Tolosan 31520, France
| | - D Lourenco
- Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602
| | - I Misztal
- Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602
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40
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Meirmans PG. Analyzing Autopolyploid Genetic Data Using GenoDive. Methods Mol Biol 2023; 2545:261-277. [PMID: 36720818 DOI: 10.1007/978-1-0716-2561-3_14] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Analyzing autopolyploid genetic data still presents numerous challenges due to, e.g., missing dosage information of genotypes and the presence of multiple ploidy levels within species or populations, but also because the choice of software is limited when compared to what is available for diploid data. However, over the last years, the number of software programs that can deal with polyploid data is slowly increasing. The software GENODIVE is one of the most widely used programs for the analysis of polyploid genetic data, presenting a wide array of different methods. In this chapter, I outline several frequently used types of population genetic analyses and explain how these apply to polyploid data, including possible pitfalls and biases. I then explain how GENODIVE approaches these analyses and whether and how it can overcome possible biases. Specifically, I focus on analyses of genetic diversity, Hardy-Weinberg equilibrium, quantifying population differentiation, clustering, and calculation of genetic distances. GENODIVE can be downloaded freely from http://www.patrickmeirmans.com/software .
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Affiliation(s)
- Patrick G Meirmans
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The Netherlands.
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Uzcategui-Salazar M, Lillo J. Assessment of social vulnerability to groundwater pollution using K-means cluster analysis. Environ Sci Pollut Res Int 2023; 30:14975-14992. [PMID: 36161573 DOI: 10.1007/s11356-022-22810-6] [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: 04/06/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
It is possible to assess the harm that society suffers as a consequence of groundwater contamination in aquifers. Indexing methodologies are commonly applied to assess the social vulnerability to polluted aquifers. However, they assign weighting and rating values to the different factors involved, which makes them very subjective. This research aims to assess the social vulnerability to groundwater pollution taking into account three factors: the uses of groundwater resources, the exposed population, and the socio-economic losses. In order to eliminate the subjectivity of current indexing methodologies, this work uses a K-means cluster analysis for the assessment of social vulnerability. With this method, a social vulnerability map can be produced with greater objectivity. The proposed methodology is applied to an aquifer located in central Spain, an area with significant agricultural development. Low population density and unproductive zones result in low social vulnerability in most of the area. However, high social vulnerability is observed in the southern sector due to agricultural development, which leads to higher socio-economic variables and demand for groundwater resources. Similarly, high social vulnerability is observed in the northeast, mainly influenced by the groundwater use and the exposed population. These results show that social vulnerability in most of the study area is not very significant for assessing the risk of groundwater contamination, because the damage to the social, environmental, or economic sector is low. However, in the south and northeast of the study area, pesticides and fertilizers should be used with caution, as they significantly increase the risk of groundwater contamination. The K-means clustering method proved to be an objective and reliable option for assessing social vulnerability to groundwater pollution in aquifers.
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Affiliation(s)
- Marisela Uzcategui-Salazar
- International Doctoral School, Rey Juan Carlos University, 29833 Móstoles, Madrid, Spain.
- TERRA Research Group, Geological Engineering School, Los Andes University, Mérida, 5101, Venezuela.
- Department of Geomechanics, Los Andes University, 5101, Mérida, Venezuela.
| | - Javier Lillo
- Global Earth Change and Environmental Geology Research Group, Department of Biology, Geology, Physics and Inorganic Chemistry, Rey Juan Carlos University, Madrid, 29833, Móstoles, Spain
- IMDEA Water Institute, Av. Punto Com, 2, 28805 Alcalá de Henares, Madrid, Spain
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Padannayil NM, Sharma DS, Nangia S, Patro KC, Gaikwad U, Burela N. IMPT of head and neck cancer: unsupervised machine learning treatment planning strategy for reducing radiation dermatitis. Radiat Oncol 2023; 18:11. [PMID: 36639667 PMCID: PMC9840252 DOI: 10.1186/s13014-023-02201-y] [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: 08/23/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
Radiation dermatitis is a major concern in intensity modulated proton therapy (IMPT) for head and neck cancer (HNC) despite its demonstrated superiority over contemporary photon radiotherapy. In this study, dose surface histogram data extracted from forty-four patients of HNC treated with IMPT was used to predict the normal tissue complication probability (NTCP) of skin. Grades of NTCP-skin were clustered using the K-means clustering unsupervised machine learning (ML) algorithm. A new skin-sparing IMPT (IMPT-SS) planning strategy was developed with three major changes and prospectively implemented in twenty HNC patients. Across skin surfaces exposed from 10 (S10) to 70 (S70) GyRBE, the skin's NTCP demonstrated the strongest associations with S50 and S40 GyRBE (0.95 and 0.94). The increase in the NTCP of skin per unit GyRBE is 0.568 for skin exposed to 50 GyRBE as compared to 0.418 for 40 GyRBE. Three distinct clusters were formed, with 41% of patients in G1, 32% in G2, and 27% in G3. The average (± SD) generalised equivalent uniform dose for G1, G2, and G3 clusters was 26.54 ± 6.75, 38.73 ± 1.80, and 45.67 ± 2.20 GyRBE. The corresponding NTCP (%) were 4.97 ± 5.12, 48.12 ± 12.72 and 87.28 ± 7.73 respectively. In comparison to IMPT, new IMPT-SS plans significantly (P < 0.01) reduced SX GyRBE, gEUD, and associated NTCP-skin while maintaining identical dose volume indices for target and other organs at risk. The mean NTCP-skin value for IMPT-SS was 34% lower than that of IMPT. The dose to skin in patients treated prospectively for HNC was reduced by including gEUD for an acceptable radiation dermatitis determined from the local patient population using an unsupervised MLA in the spot map optimization of a new IMPT planning technique. However, the clinical finding of acute skin toxicity must also be related to the observed reduction in skin dose.
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Affiliation(s)
- Noufal Manthala Padannayil
- grid.506152.5Department of Medical Physics, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu 400053 India
| | - Dayananda Shamurailatpam Sharma
- grid.506152.5Department of Medical Physics, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu 400053 India
| | - Sapna Nangia
- grid.506152.5Department of Radiation Oncology, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu India
| | - Kartikeshwar C. Patro
- grid.506152.5Department of Medical Physics, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu 400053 India
| | - Utpal Gaikwad
- grid.506152.5Department of Radiation Oncology, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu India
| | - Nagarjuna Burela
- grid.506152.5Department of Radiation Oncology, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu India
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Li X, Zhang J, Safara F. Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm. Neural Process Lett 2023; 55:153-69. [PMID: 33814965 DOI: 10.1007/s11063-021-10491-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2021] [Indexed: 01/20/2023]
Abstract
Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.
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Yao Y, Wu S, Liu C, Zhou C, Zhu J, Chen T, Huang C, Feng S, Zhang B, Wu S, Ma F, Liu L, Zhan X. Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning. Ann Med 2023; 55:2249004. [PMID: 37611242 PMCID: PMC10448834 DOI: 10.1080/07853890.2023.2249004] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/25/2023] Open
Abstract
OBJECTIVE The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results. METHODS A total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared. RESULTS Two subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay. CONCLUSION K-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies.
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Affiliation(s)
- Yuanlin Yao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Chengqian Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Sitan Feng
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Bin Zhang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Siling Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Fengzhi Ma
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Lu Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. China
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Ferreño D, Revuelta JM, Sainz-Aja JA, Wert-Carvajal C, Casado JA, Diego S, Carrascal IA, Silva J, Gutiérrez-Solana F. Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: A micro-ct ex-vivo study. Med Eng Phys 2022; 110:103919. [PMID: 36564142 DOI: 10.1016/j.medengphy.2022.103919] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/12/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022]
Abstract
This paper is aimed at identifying by means of micro-CT the microstructural differences between normal and degenerative mitral marginal chordae tendineae. The control group is composed of 21 normal chords excised from 14 normal mitral valves from heart transplant recipients. The experimental group comprises 22 degenerative fibroelastic chords obtained at surgery from 11 pathological valves after mitral repair or replacement. In the control group the superficial endothelial cells and spongiosa layer remained intact, covering the wavy core collagen. In contrast, in the experimental group the collagen fibers were arranged as straightened thick bundles in a parallel configuration. 100 cross-sections were examined by micro-CT from each chord. Each image was randomized through the K-means machine learning algorithm and then, the global and local Shannon entropies were obtained. The optimum number of clusters, K, was estimated to maximize the differences between normal and degenerative chords in global and local Shannon entropy; the p-value after a nested ANOVA test was chosen as the parameter to be minimized. Optimum results were obtained with global Shannon entropy and 2≤K≤7, providing p < 0.01; for K=3, p = 2.86·10-3. These findings open the door to novel perioperative diagnostic methods in order to avoid or reduce postoperative mitral valve regurgitation recurrences.
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Affiliation(s)
- Diego Ferreño
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain.
| | - José M Revuelta
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain; Cardiovascular Surgery. Hospital Universitario Marqués de Valdecilla, Av/Valdecilla, s/n, 39008 Santander, Spain
| | - José A Sainz-Aja
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain
| | - Carlos Wert-Carvajal
- Universidad Carlos III de Madrid. Avda. de la Universidad, 30. 28911 Madrid, Spain; University of California, San Diego. 9500 Gilman Drive, MC 0412 La Jolla, California
| | - José A Casado
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain
| | - Soraya Diego
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain
| | - Isidro A Carrascal
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain
| | - Jacobo Silva
- Hospital Universitario Central de Asturias, Av. Roma, s/n, 33011 Oviedo, Asturias, Spain
| | - Federico Gutiérrez-Solana
- LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av/Los Castros 44, 39005 Santander, Spain
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Nichols L, Taverner T, Crowe F, Richardson S, Yau C, Kiddle S, Kirk P, Barrett J, Nirantharakumar K, Griffin S, Edwards D, Marshall T. In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm. J Clin Epidemiol 2022; 152:164-175. [PMID: 36228971 PMCID: PMC7613854 DOI: 10.1016/j.jclinepi.2022.10.011] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/16/2022] [Accepted: 10/05/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND OBJECTIVES To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. METHODS We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples. RESULTS In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). CONCLUSION LCA achieved higher aRI than other clustering algorithms.
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Affiliation(s)
- Linda Nichols
- Research Fellow, Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
| | - Tom Taverner
- Research Fellow, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK
| | - Francesca Crowe
- Lecturer in Epidemiology and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK
| | - Sylvia Richardson
- Emeritus Director, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Christopher Yau
- Professor of Artificial Intelligence, Nuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Steven Kiddle
- Director, Health Data Science, AstraZeneca, 1 Francis Crick Avenue, Cambridge, Biomedical Campus, Cambridge, CB2 0AA, UK
| | - Paul Kirk
- MRC Investigator, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Jessica Barrett
- MRC Investigator, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Krishnarajah Nirantharakumar
- Professor of Public Health and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK
| | - Simon Griffin
- Professor of General Practice, Primary Care Unit, Strangeways Research Laboratory Worts Causeway Cambridge CB1 8RN, UK
| | - Duncan Edwards
- Senior Clinical Research Associate, Primary Care Unit, Primary Care Unit, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Tom Marshall
- Professor of Public Health and Primary Care, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
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Madival SD, Mishra DC, Sharma A, Kumar S, Maji AK, Budhlakoti N, Sinha D, Rai A. A Deep Clustering-based Novel Approach for Binning of Metagenomics Data. Curr Genomics 2022; 23:353-368. [PMID: 36778191 PMCID: PMC9878855 DOI: 10.2174/1389202923666220928150100] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 11/22/2022] Open
Abstract
Background One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets. Objective To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures. Methods In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used. Results In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods. Conclusion An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.
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Affiliation(s)
| | - Dwijesh Chandra Mishra
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India;,Address correspondence to this author at the Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India; E-mail:
| | - Anu Sharma
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
| | - Sanjeev Kumar
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
| | - Arpan Kumar Maji
- Division of Computer Applications, ICAR-IASRI, New Delhi- 110012, India
| | - Neeraj Budhlakoti
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
| | - Dipro Sinha
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
| | - Anil Rai
- Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
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Chen J, Zhu X, Liu H. A mutual neighbor-based clustering method and its medical applications. Comput Biol Med 2022; 150:106184. [PMID: 37859282 DOI: 10.1016/j.compbiomed.2022.106184] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 09/23/2022] [Accepted: 10/08/2022] [Indexed: 11/03/2022]
Abstract
Clustering analysis has been widely used in various real-world applications. Due to the simplicity of K-means, it has become the most popular clustering analysis technique in reality. Unfortunately, the performance of K-means heavily relies on initial centers, which should be specified in prior. Besides, it cannot effectively identify manifold clusters. In this paper, we propose a novel clustering algorithm based on representative data objects derived from mutual neighbors to identify different shaped clusters. Specifically, it first obtains mutual neighbors to estimate the density for each data object, and then identifies representative objects with high densities to represent the whole data. Moreover, a concept of path distance, deriving from a minimum spanning tree, is introduced to measure the similarities of representative objects for manifold structures. Finally, an improved K-means with initial centers and path-based distances is proposed to group the representative objects into clusters. For non-representative objects, their cluster labels are determined by neighborhood information. To verify the effectiveness of the proposed method, we conducted comparison experiments on synthetic data and further applied it to medical scenarios. The results show that our clustering method can effectively identify arbitrary-shaped clusters and disease types in comparing to the state-of-the-art clustering ones.
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Affiliation(s)
- Jun Chen
- Zhejiang Industry Polytechnic College, Shaoxing 312000, PR China.
| | - Xinzhong Zhu
- Zhejiang Normal University, Jinhua 321000, PR China.
| | - Huawen Liu
- Shaoxing University, Shaoxing 312000, PR China.
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Chen R, Li B, Jia B, Xu J, Ma L, Yang H, Wang H. Oil spill identification in X-band marine radar image using K-means and texture feature. PeerJ Comput Sci 2022; 8:e1133. [PMID: 36426254 PMCID: PMC9680884 DOI: 10.7717/peerj-cs.1133] [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: 05/27/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by "Sperry Marine" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.
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Affiliation(s)
- Rong Chen
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Bo Li
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Baozhu Jia
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
- Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Guangdong, China
| | - Jin Xu
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Long Ma
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Hongbo Yang
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Haixia Wang
- Navigation College, Dalian Martime University, Dalian, Liaoning, China
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Chen TL, Fushing H, Chou EP. Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics. Entropy (Basel) 2022; 24:1382. [PMID: 37420402 DOI: 10.3390/e24101382] [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: 08/30/2022] [Revised: 09/22/2022] [Accepted: 09/26/2022] [Indexed: 07/09/2023]
Abstract
We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs.-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics' data analysis tasks by discovering major factors underlying such Re-Co dynamics by only making use of data's categorical nature. The major factor selection protocol at the heart of Categorical Exploratory Data Analysis (CEDA) paradigm is illustrated and carried out by employing Shannon's conditional entropy (CE) and mutual information (I[Re;Co]) as the two key Information Theoretical measurements. Through the process of evaluating these two entropy-based measurements and resolving statistical tasks, we acquire several computational guidelines for carrying out the major factor selection protocol in a do-and-learn fashion. Specifically, practical guidelines are established for evaluating CE and I[Re;Co] in accordance with the criterion called [C1:confirmable]. Following the [C1:confirmable] criterion, we make no attempts on acquiring consistent estimations of these theoretical information measurements. All evaluations are carried out on a contingency table platform, upon which the practical guidelines also provide ways of lessening the effects of the curse of dimensionality. We explicitly carry out six examples of Re-Co dynamics, within each of which, several widely extended scenarios are also explored and discussed.
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Affiliation(s)
- Ting-Li Chen
- Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
| | - Hsieh Fushing
- Department of Statistics, University of California, Davis, CA 95616, USA
| | - Elizabeth P Chou
- Department of Statistics, National Chengchi University, Taipei 11605, Taiwan
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