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Wu X, Jia W. Multilayer Annotation Strategy AnnoSePS: Disentangling the Intricate Structure of Selenium-Containing Polysaccharides Based on Preferential Fragmentation Patterns. Anal Chem 2024; 96:10696-10704. [PMID: 38904260 DOI: 10.1021/acs.analchem.4c01576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Precision mapping of selenium at structural and position levels poses significant challenges in selenium-containing polysaccharide identification. Due to the absence of reference spectra, database-centric approaches are still limited in the discovery of selenium binding sites and distinction among different isomeric structures. A multilayer annotation strategy, AnnoSePS, is proposed for achieving the identification of seleno-substituent and the unbiased profiling of polysaccharides. Applying Snoop-triggered multiple reaction monitoring (Snoop-MRM) identified multidimensional monosaccharides in selenium-containing polysaccharides. Galactose, galacturonic acid, and glucose were the predominant monosaccharides with a molar ratio of 25.19, 19.45, and 11.72, respectively. Selenium present in seleno-rhamnose was found to substitute the hydroxyl group located at C-1 positions through the formation of a Se-H bond. Ions C6H9O3Se-, C6H7O3Se-, C5H5O3Se-, C4H5O2Se-, C3H5O2Se-, C2H3O2Se-, and CHOSe- were defined as the characteristic fragments of seleno-rhamnose. The agglomerative hierarchical clustering algorithm is applied to group spectra from each run based on the characteristic information. Preferential fragmentation patterns in mass spectrometry are revealed by training a probabilistic model. A list of candidate oligosaccharides is generated by step-by-step browsing through the transition pairs for all reference spectra and applying the transitions (addition, insertion, removal, and substitution) to reference structures. Combining time course analyses revealed the linkage composition of selenium-containing oligosaccharides. Glycosidic linkages were annotated based on a synthesis-driven approach. T-Galactose (16.67 ± 5.23%) and T-Galacturonic acid (11.54 ± 4.66%) were the predominant linkage residues. As the database-independent mapping strategy, AnnoSePS makes it possible to comprehensively interrogate spectral data and dissect the fine structure of selenium-containing polysaccharides.
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Affiliation(s)
- Xixuan Wu
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China
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Rajput V, Mulay P. Fact Finding Instructor-based Clustering Technique for BP Estimation using Human Speech Signals. Comput Methods Biomech Biomed Engin 2023:1-16. [PMID: 37929760 DOI: 10.1080/10255842.2023.2273203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023]
Abstract
Blood Pressure (BP) is considered an essential factor that provides information regarding cardiovascular function. Regular monitoring of the BP is required for proper healthcare maintenance that avoids the high risk of life due to high and low BP. Several methods were devised for the estimation of BP, but the estimation accuracy is still a challenging task. Hence this research introduces an efficient BP estimation technique using the Fact Finding Instructor (FFI) based clustering method by considering the speech signal of the patients. An efficient BP extraction technique is introduced using the FFI Optimization algorithm an integration of the mannerism of the fact finder that identifies the suspect who commits the criminal offense and, with the instructor with good knowledge, these make the trainee more efficient. The detection and suspect's arrest contain two phases, the fact-finding phase and the chasing phase. Initially, the speech signal is collected from the database and pre-processed for removing noise and artifacts. Then feature extraction is used for the minimization of the computation overhead that generates a feature vector. The clustering of BP is employed with the k-means clustering algorithm and the proposed FFI optimization algorithm. The FFI Optimization algorithm provides a fast convergence rate due to the fact-finding phase and provides accurate detection of the suspect's location along with that the clustering of classes of patients' BP by considering the feature of the speech signal. The clusters formed using the FFI optimization algorithm are combined with the K-means clustering, by multiplying the clusters the BP estimation is implemented on three criteria Low BP, Normal, and, High BP. Finally, the output generated by both the clustering operations is multiplied together for the estimation of the BP. The performance of the proposed method is evaluated using the metrics like Davies Bouldin score, Homogeneity score, Completeness score, Jacquard Similarity score, Silhouette score, and Dunn's Index which acquired the improvement rate of 0.98, 0.96, 0.96, 0.98, 0.95, and 0.98 for training percentage 90, respectively to the existing Teaching Learning Based Optimization(TLBO) clustering technique.
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Affiliation(s)
- Vaishali Rajput
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Vishwakarma Institute of Technology, Pune, India
| | - Preeti Mulay
- Vishwakarma Institute of Technology, Pune, India
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Yu B, Zheng Z, Dai J. K-DGHC: A hierarchical clustering method based on K-dominance granularity. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Minh Quang N, Tran Thai H, Le Thi H, Duc Cuong N, Hien NQ, Hoang D, Ngoc VTB, Ky Minh V, Van Tat P. Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer's Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors. ACS OMEGA 2023; 8:11076-11099. [PMID: 37008140 PMCID: PMC10061515 DOI: 10.1021/acsomega.2c07934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer's disease (AD) is an important current goal. The QSARKPLS, QSARANN, and QSARSVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R 2 and Q 2 values for the QSARKPLS, QSARANN, and QSARSVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC50 activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSARKPLS model using DFs, are consistent with the experimental results and those from the QSARANN and QSARSVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol-1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSARANN and QSARSVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC50 activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E HOMO and E LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.
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Affiliation(s)
- Nguyen Minh Quang
- Faculty
of Chemical Engineering, Industrial University
of Ho Chi Minh City, 12 Nguyen Van Bao, Dist. Go Vap, Ho Chi Minh 700000, Viet Nam
| | - Hoa Tran Thai
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
| | - Hoa Le Thi
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
| | - Nguyen Duc Cuong
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
- School
of Hospitality and Tourism, Hue University, 22 Lam Hoang, Hue City 530000, Viet
Nam
| | - Nguyen Quoc Hien
- Vietnam
Atomic Energy Institute, 59 Ly Thuong Kiet, Dist. Hoan Kiem, Hanoi
City 100000, Viet Nam
| | - DongQuy Hoang
- Faculty
of
Materials Science and Technology, University of Science, Vietnam National University, Ho Chi Minh 700000, Viet Nam
- Vietnam
National University, Ho Chi Minh
City 700000, Viet Nam
| | - Vu Thi Bao Ngoc
- Faculty
of Chemistry and Environment, University
of Dalat, 01 Phu Dong Thien Vuong, Dalat City 660000, Viet Nam
| | - Vo Ky Minh
- Franklin
High School, 6400 Whitelock Pkwy, Elk Grove, California 95757, United States
| | - Pham Van Tat
- Department
of Sciences and Journal Management, Hoa
Sen University, 08 Nguyen Van Trang, Dist. 01, Ho Chi Minh 700000, Viet Nam
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Liu N, Xu Z, Wu H. Decision field theory-combined multi-attribute group decision-making method for incomplete linear ordinal ranking. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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A New Inclusion Measure-based Clustering Method and Its Application to Product Classification. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Jiao L, Yang H, Liu ZG, Pan Q. Interpretable fuzzy clustering using unsupervised fuzzy decision trees. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Wang Y, Pang W, Zhou J. An improved density peak clustering algorithm guided by pseudo labels. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Integrating prospect theory with variable reference point into the conversion-based framework for linear ordinal ranking aggregation. Soft comput 2022; 26:11713-11732. [PMID: 36043119 PMCID: PMC9415265 DOI: 10.1007/s00500-022-07339-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2022] [Indexed: 11/26/2022]
Abstract
Considering that converting linear ordinal ranking (LOR) information into interval utility values can not only improve the computability of LOR information but also explore the degree of preference for different alternatives for decision-makers hidden behind LOR information, this paper proposes a conversion-based LOR aggregation method to aggregate LOR information under risk. Given that the behaviours of decision-makers are influenced by risk, this paper adopts prospect theory to depict the decision-makers’ behaviours under risk in the conversion-based aggregation process. To achieve this, the information energy for LOR is constructed firstly, and its features are analysed, which makes a basis for the conversion process. After that, the details about how to integrate the prospect theory with variable reference points into the conversion-based aggregation framework are presented. Finally, an example (exploring the financial product preferences of a group of respondents) evidences the practicality of the proposed method. Further, some analyses and discussions are conducted to verify the rationality and stability of the method.
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Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.
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Mishra G, Kar AK, Mishra AC, Mohanty SK, Panda M. SEND: A novel dissimilarity metric using ensemble properties of the feature space for clustering numerical data. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.05.059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Dinh DT, Huynh VN, Sriboonchitta S. Clustering mixed numerical and categorical data with missing values. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.04.076] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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