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Guo Z, Wang L, Yu C. Over-expressing NadA quinolinate synthase in Escherichia coli enhances the bioelectrochemistry in microbial fuel cells. Biol Open 2023; 12:297054. [PMID: 36877035 PMCID: PMC10084859 DOI: 10.1242/bio.059554] [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: 07/23/2022] [Accepted: 01/13/2023] [Indexed: 03/07/2023] Open
Abstract
The microbial fuel cell (MFC), which converts biomass energy into electricity through microbial metabolism, is one of the important devices for generating new bioenergy. However, low power production efficiency limits the development of MFCs. One possible method to solve this problem is to genetically modify the microbial metabolism pathways to enhance the efficiency of MFCs. In this study, we over-expressed the nicotinamide adenine dinucleotide A quinolinate synthase gene (nadA) in order to increase the NADH/+ level in Escherichia coli and obtain a new electrochemically active bacteria strain. The following experiments showed an enhanced performance of the MFC, including increased peak voltage output (70.81 mV) and power density (0.29 μW/cm2), which increased by 361% and 20.83% compared to the control group, respectively. These data suggest that genetic modification of electricity producing microbes could be a potential way to improve MFC performance.
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Affiliation(s)
- Zhenyu Guo
- Department of Pharmaceutical Engineering, College of Life Sciences and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lei Wang
- Department of Pharmaceutical Engineering, College of Life Sciences and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Changyuan Yu
- Department of Pharmaceutical Engineering, College of Life Sciences and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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Zucco AG, Agius R, Svanberg R, Moestrup KS, Marandi RZ, MacPherson CR, Lundgren J, Ostrowski SR, Niemann CU. Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning. Sci Rep 2022; 12:13879. [PMID: 35974050 PMCID: PMC9380679 DOI: 10.1038/s41598-022-17953-y] [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] [Received: 12/07/2021] [Accepted: 08/03/2022] [Indexed: 01/08/2023] Open
Abstract
Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.
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Affiliation(s)
- Adrian G Zucco
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark.
| | - Rudi Agius
- Department of Hematology, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Ramtin Z Marandi
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark
| | | | - Jens Lundgren
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Carsten U Niemann
- Department of Hematology, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Du C, Ye J. Hybrid weighted aggregation operator of cubic fuzzy-consistency elements and their group decision-making model in cubic fuzzy multi-valued setting. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-211205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model.
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Affiliation(s)
- Cheng Du
- Beijing Urban Construction Group Co. Lid, Haidian District, Beijing, P. R. China
| | - Jun Ye
- School of Civil and Environmental Engineering, Ningbo University, Ningbo, P. R. China
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Multifuzzy Cubic Sets and Their Correlation Coefficients for Multicriteria Group Decision-Making. MATHEMATICAL PROBLEMS IN ENGINEERING 2021. [DOI: 10.1155/2021/5520335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The notion of multifuzzy sets (MFSs) or multi-interval-valued fuzzy sets (MIVFSs) provides a new method to represent some problems with a sequence of the different and/or same fuzzy/interval-valued fuzzy membership values of an element to the set. Then, a fuzzy cubic set (FCS) consists of a certain part (a fuzzy value) and an uncertain part (an interval-valued fuzzy value) but cannot represent hybrid information of both MFS and MIVFS. To adequately depict the opinion of several experts/decision-makers by using a union/sequence of the different and/or same fuzzy cubic values for an object assessed in group decision-making (GDM) problems, this paper proposes a multifuzzy cubic set (MFCS) notion as the conceptual extension of FCS to express the hybrid information of both MFS and MIVFS in the fuzzy setting of both uncertainty and certainty. Then, we propose three correlation coefficients of MFCSs and then introduce correlation coefficients of MFSs and MIVFSs as special cases of the three correlation coefficients of MFCSs. Further, the multicriteria GDM methods using three weighted correlation coefficients of MFCSs are developed under the environment of MFCSs, which contains the MFS and MIVFS GDM methods. Lastly, these multicriteria GDM methods are applied in an illustrative example on the selection problem of equipment suppliers; then their decision results and comparative analysis indicate that the developed GDM methods are more practicable and effective and reflect that either different correlation coefficients or different information expressions can also impact on the ranking of alternatives. Therefore, this study indicates the main contribution of the multifuzzy cubic information expression, correlation coefficients, and GDM methods in the multifuzzy setting of both uncertainty and certainty.
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Cui WH, Ye J, Fu J. Cotangent similarity measure of single-valued neutrosophic interval sets with confidence level for risk-grade evaluation of prostate cancer. Soft comput 2020. [DOI: 10.1007/s00500-020-05089-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Wang J, Shang X, Bai K, Xu Y. A new approach to cubic q-rung orthopair fuzzy multiple attribute group decision-making based on power Muirhead mean. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04807-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Similarity measure with indeterminate parameters regarding cubic hesitant neutrosophic numbers and its risk grade assessment approach for prostate cancer patients. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01653-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Cui WH, Ye J. Logarithmic similarity measure of dynamic neutrosophic cubic sets and its application in medical diagnosis. COMPUT IND 2019. [DOI: 10.1016/j.compind.2019.06.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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