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Juraska M, Bai H, deCamp AC, Magaret CA, Li L, Gillespie K, Carpp LN, Giorgi EE, Ludwig J, Molitor C, Hudson A, Williamson BD, Espy N, Simpkins B, Rudnicki E, Shao D, Rossenkhan R, Edlefsen PT, Westfall DH, Deng W, Chen L, Zhao H, Bhattacharya T, Pankow A, Murrell B, Yssel A, Matten D, York T, Beaume N, Gwashu-Nyangiwe A, Ndabambi N, Thebus R, Karuna ST, Morris L, Montefiori DC, Hural JA, Cohen MS, Corey L, Rolland M, Gilbert PB, Williamson C, Mullins JI. Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features. Proc Natl Acad Sci U S A 2024; 121:e2308942121. [PMID: 38241441 PMCID: PMC10823214 DOI: 10.1073/pnas.2308942121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/13/2023] [Indexed: 01/21/2024] Open
Abstract
In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations.
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Affiliation(s)
- Michal Juraska
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Hongjun Bai
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD20910
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD20817
| | - Allan C. deCamp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Craig A. Magaret
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Li Li
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Kevin Gillespie
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Lindsay N. Carpp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Elena E. Giorgi
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - James Ludwig
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Cindy Molitor
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Aaron Hudson
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Brian D. Williamson
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA98101
| | - Nicole Espy
- Science and Technology Policy Fellowships, American Association for the Advancement of Science, Washington, DC20005
| | - Brian Simpkins
- Department of Computer Science, Pitzer College, Claremont, CA91711
| | - Erika Rudnicki
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Danica Shao
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Raabya Rossenkhan
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Paul T. Edlefsen
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Dylan H. Westfall
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA98195
| | - Wenjie Deng
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA98195
| | - Lennie Chen
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA98195
| | - Hong Zhao
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA98195
| | | | - Alec Pankow
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna171 77, Sweden
| | - Ben Murrell
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna171 77, Sweden
| | - Anna Yssel
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - David Matten
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Talita York
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Nicolas Beaume
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Asanda Gwashu-Nyangiwe
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Nonkululeko Ndabambi
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Ruwayhida Thebus
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - Shelly T. Karuna
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Lynn Morris
- HIV Virology Section, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg2192, South Africa
- Antibody Immunity Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg2000, South Africa
- Centre for the AIDS Programme of Research in South Africa, University of KwaZulu-Natal, Durban4041, South Africa
| | | | - John A. Hural
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
| | - Myron S. Cohen
- Institute of Global Health and Infectious Diseases, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Lawrence Corey
- Department of Medicine, University of Washington, Seattle, WA98195
- Department of Laboratory Medicine, University of Washington, Seattle, WA98195
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA98109
| | - Morgane Rolland
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD20910
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD20817
| | - Peter B. Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA98109
- Department of Biostatistics, University of Washington, Seattle, WA98195
- Department of Global Health, University of Washington, Seattle, WA98195
| | - Carolyn Williamson
- Institute of Infectious Disease and Molecular Medicine, and Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology, Faculty of Health Sciences, University of Cape Town and National Health Laboratory Service, Cape Town7701, South Africa
| | - James I. Mullins
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA98195
- Department of Global Health, University of Washington, Seattle, WA98195
- Department of Microbiology, University of Washington, Seattle, WA98109
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Liu Y, Caglar T, Peterson C, Kirby M. Integrating geometries of ReLU feedforward neural networks. Front Big Data 2023; 6:1274831. [PMID: 38033354 PMCID: PMC10682363 DOI: 10.3389/fdata.2023.1274831] [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: 08/08/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
This paper investigates the integration of multiple geometries present within a ReLU-based neural network. A ReLU neural network determines a piecewise affine linear continuous map, M, from an input space ℝm to an output space ℝn. The piecewise behavior corresponds to a polyhedral decomposition of ℝm. Each polyhedron in the decomposition can be labeled with a binary vector (whose length equals the number of ReLU nodes in the network) and with an affine linear function (which agrees with M when restricted to points in the polyhedron). We develop a toolbox that calculates the binary vector for a polyhedra containing a given data point with respect to a given ReLU FFNN. We utilize this binary vector to derive bounding facets for the corresponding polyhedron, extraction of "active" bits within the binary vector, enumeration of neighboring binary vectors, and visualization of the polyhedral decomposition (Python code is available at https://github.com/cglrtrgy/GoL_Toolbox). Polyhedra in the polyhedral decomposition of ℝm are neighbors if they share a facet. Binary vectors for neighboring polyhedra differ in exactly 1 bit. Using the toolbox, we analyze the Hamming distance between the binary vectors for polyhedra containing points from adversarial/nonadversarial datasets revealing distinct geometric properties. A bisection method is employed to identify sample points with a Hamming distance of 1 along the shortest Euclidean distance path, facilitating the analysis of local geometric interplay between Euclidean geometry and the polyhedral decomposition along the path. Additionally, we study the distribution of Chebyshev centers and related radii across different polyhedra, shedding light on the polyhedral shape, size, clustering, and aiding in the understanding of decision boundaries.
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Affiliation(s)
- Yajing Liu
- Department of Mathematics, Colorado State University, Fort Collins, CO, United States
| | - Turgay Caglar
- Department of Computer Science, Colorado State University, Fort Collins, CO, United States
| | - Christopher Peterson
- Department of Mathematics, Colorado State University, Fort Collins, CO, United States
| | - Michael Kirby
- Department of Mathematics, Colorado State University, Fort Collins, CO, United States
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Juyal A, Hosseini R, Novikov D, Grinshpon M, Zelikovsky A. Reconstruction of Viral Variants via Monte Carlo Clustering. J Comput Biol 2023; 30:1009-1018. [PMID: 37695837 PMCID: PMC10518690 DOI: 10.1089/cmb.2023.0154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023] Open
Abstract
Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.
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Affiliation(s)
- Akshay Juyal
- Department of Computer Science and Georgia State University, Atlanta, Georgia, USA
| | - Roya Hosseini
- Department of Computer Science and Georgia State University, Atlanta, Georgia, USA
| | - Daniel Novikov
- Department of Computer Science and Georgia State University, Atlanta, Georgia, USA
| | - Mark Grinshpon
- Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA
| | - Alex Zelikovsky
- Department of Computer Science and Georgia State University, Atlanta, Georgia, USA
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Asatryan MN, Timofeev BI, Shmyr IS, Khachatryan KR, Shcherbinin DN, Timofeeva TA, Gerasimuk ER, Agasaryan VG, Ershov IF, Shashkova TI, Kardymon OL, Ivanisenko NV, Semenenko TA, Naroditsky BS, Logunov DY, Gintsburg AL. [Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H 3N 2]. Vopr Virusol 2023; 68:252-264. [PMID: 37436416 DOI: 10.36233/0507-4088-179] [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: 06/14/2023] [Indexed: 07/13/2023]
Abstract
INTRODUCTION The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data. MATERIALS AND METHODS In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks. RESULTS Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues. CONCLUSION The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.
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Affiliation(s)
- M N Asatryan
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - B I Timofeev
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - I S Shmyr
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | - D N Shcherbinin
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - T A Timofeeva
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | - V G Agasaryan
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - I F Ershov
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | | | | | | | - T A Semenenko
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - B S Naroditsky
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - D Y Logunov
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
| | - A L Gintsburg
- National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya
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Magaret C, Li L, deCamp A, Rolland M, Juraska M, Williamson B, Ludwig J, Molitor C, Benkeser D, Luedtke A, Simpkins B, Carpp L, Bai H, Deariove B, Greninger A, Roychoudhury P, Sadoff J, Gray G, Roels S, Vandebosch A, Stieh D, Le Gars M, Vingerhoets J, Grinsztejn B, Goepfert P, Truyers C, Van Dromme I, Swann E, Marovich M, Follmann D, Neuzil K, Corey L, Hyrien O, Paiva de Sousa L, Casapia M, Losso M, Little S, Gaur A, Bekker LG, Garrett N, Heng F, Sun Y, Gilbert P. Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features. Res Sq 2023:rs.3.rs-2743022. [PMID: 37398105 PMCID: PMC10312950 DOI: 10.21203/rs.3.rs-2743022/v1] [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] [Indexed: 07/04/2023]
Abstract
It is of interest to pinpoint SARS-CoV-2 sequence features defining vaccine resistance. In the ENSEMBLE randomized, placebo-controlled phase 3 trial, estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were measured from 484 vaccine and 1,067 placebo recipients who acquired COVID-19 during the trial. In Latin America, where Spike diversity was greatest, VE was significantly lower against Lambda than against Reference and against all non-Lambda variants [family-wise error rate (FWER) p < 0.05]. VE also differed by residue match vs. mismatch to the vaccine-strain residue at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20). VE significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 different antibody-epitope escape scores and by 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccine recipient sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against viruses with greatest distances. These results help map antigenic specificity of in vivo vaccine protection.
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Affiliation(s)
| | - Li Li
- Fred Hutchinson Cancer Center
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Beatriz Grinsztejn
- Evandro Chagas National Institute of Infectious Diseases-Fundacao Oswaldo Cruz
| | - Paul Goepfert
- Department of Medicine, Division of Infectious Diseases, University of Alabama at Birmingham
| | | | | | | | - Mary Marovich
- National Institute of Allergy and Infectious Diseases
| | | | | | | | | | | | | | | | - Susan Little
- Department of Medicine, University of California, San Diego, CA 92903
| | | | | | - Nigel Garrett
- Centre for the AIDS Program of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa 4041
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Serrano S, Scarpa M. Accuracy comparisons of fingerprint based song recognition approaches using very high granularity. Multimed Tools Appl 2023; 82:1-16. [PMID: 37362687 PMCID: PMC10028751 DOI: 10.1007/s11042-023-14787-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/30/2022] [Accepted: 02/05/2023] [Indexed: 06/28/2023]
Abstract
Music and song recognition is an activity of wide interest for researchers and companies due to the intrinsic challenges and the possible economical profits it can give. Despite basic algorithms about song recognition are simple in principle, it is quite difficult to obtain an efficient and robust approach able to generate an effective algorithm for identifying short piece of audio on the fly. In this paper, we compare the results obtained using a new algorithm we recently proposed against several baseline approaches in terms of accuracy when very short pieces of audio are processed. Experimental results, performed using both a subset of the MTG-Jamendo dataset and a proprietary audio corpus containing 7000 songs, show our approach outperform the others in particular for excerpts of audio shorter than 3s.
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Affiliation(s)
- Salvatore Serrano
- Department of Engineering, University of Messina, C.da Di Dio (Villaggio S. Agata), Messina, 98166 ME Italy
| | - Marco Scarpa
- Department of Engineering, University of Messina, C.da Di Dio (Villaggio S. Agata), Messina, 98166 ME Italy
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Banik B, Alam S, Chakraborty A. Comparative study between GRA and MEREC technique on an agricultural-based MCGDM problem in pentagonal neutrosophic environment. Int J Environ Sci Technol (Tehran) 2023; 20:1-16. [PMID: 36817165 PMCID: PMC9928147 DOI: 10.1007/s13762-023-04768-1] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/16/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
In this research article, an improved Multi-criteria group decision-making (MCGDM) strategy has been developed in pentagonal neutrosophic environment incorporating grey relational analysis and method on the removal effects of criteria (MEREC) techniques to address the relative advantages and disadvantages of these aspects in MCGDM. The aim of the study is to improve MCGDM technique which can capture the underlying uncertainties in robust way and can produce consistent results in a more rigorous way. Here, the conception of Hamming distance between two pentagonal neutrosophic number (PNN)s is introduced and the weighted arithmetic and geometric averaging operators in PNN arena are deployed to craft our computational technique more progressive and robust. An agriculture-based numerical problem is illustrated to demonstrate the ranking results of the alternatives by both of the techniques. After evaluating the problem by two aggregation operators, it is found that "plantation crop" is the best alternative under certain circumstances. Lastly, the sensitivity investigation is performed which reveals that with the appliance of arithmetic and geometric aggregation operators the best ranked alternative preserves its position by both of the ranking methods, which definitely exhibit the consistency and robustness of our executed methodology.
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Affiliation(s)
- B. Banik
- Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103 India
| | - S. Alam
- Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103 India
| | - A. Chakraborty
- Department of Engineering Science, Academy of Technology, Adisaptagram, West Bengal 712502 India
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Barrett C, Bura A, He Q, Huang F, Reidys C. The arithmetic topology of genetic alignments. J Math Biol 2023; 86:34. [PMID: 36695949 PMCID: PMC9875784 DOI: 10.1007/s00285-023-01868-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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
We propose a novel mathematical paradigm for the study of genetic variation in sequence alignments. This framework originates from extending the notion of pairwise relations, upon which current analysis is based on, to k-ary dissimilarity. This dissimilarity naturally leads to a generalization of simplicial complexes by endowing simplices with weights, compatible with the boundary operator. We introduce the notion of k-stances and dissimilarity complex, the former encapsulating arithmetic as well as topological structure expressing these k-ary relations. We study basic mathematical properties of dissimilarity complexes and show how this approach captures watershed moments of viral dynamics in the context of SARS-CoV-2 and H1N1 flu genomic data.
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Affiliation(s)
- Christopher Barrett
- Biocomplexity Institute, University of Virginia, 994 Research Park Boulevard, Charlottesville, VA 22911 USA ,Department of Computer Science, University of Virginia, 351 McCormick Road, Charlottesville, VA 22904 USA
| | - Andrei Bura
- Biocomplexity Institute, University of Virginia, 994 Research Park Boulevard, Charlottesville, VA 22911 USA
| | - Qijun He
- Biocomplexity Institute, University of Virginia, 994 Research Park Boulevard, Charlottesville, VA 22911 USA
| | - Fenix Huang
- Biocomplexity Institute, University of Virginia, 994 Research Park Boulevard, Charlottesville, VA 22911 USA
| | - Christian Reidys
- Biocomplexity Institute, University of Virginia, 994 Research Park Boulevard, Charlottesville, VA, 22911, USA. .,Department of Mathematics, University of Virginia, 141 Cabell Drive, Charlottesville, VA, 22904, USA.
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9
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Jiang Y, Wang Y, Shen L, Adjeroh DA, Liu Z, Lin J. Identification of all-against-all protein-protein interactions based on deep hash learning. BMC Bioinformatics 2022; 23:266. [PMID: 35804303 PMCID: PMC9264577 DOI: 10.1186/s12859-022-04811-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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein-protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming. RESULTS In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship. CONCLUSIONS The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from [Formula: see text] to [Formula: see text] for performing an all-against-all PPI prediction for a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.
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Affiliation(s)
- Yue Jiang
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350108, People's Republic of China
| | - Yuxuan Wang
- No. 2 Thoracic Surgery Department Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, People's Republic of China
| | - Lin Shen
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350108, People's Republic of China
| | - Donald A Adjeroh
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, USA
| | - Zhidong Liu
- No. 2 Thoracic Surgery Department Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, People's Republic of China.
| | - Jie Lin
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350108, People's Republic of China.
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Sepesy Maučec M, Donaj G. Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences. Sensors (Basel) 2021; 21:s21206920. [PMID: 34696132 PMCID: PMC8537990 DOI: 10.3390/s21206920] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/05/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022]
Abstract
The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person’s common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person’s day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets.
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11
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Choi HI, Lee Y, Shin H, Lee S, Choi SS, Han CY, Kwon SH. The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods. Animals (Basel) 2021; 11:2664. [PMID: 34573628 DOI: 10.3390/ani11092664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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] [Received: 08/10/2021] [Revised: 08/25/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary In this paper, we examine whether the canine nose pattern, which is an interlocking pattern of beads and grooves on a dog’s nose, is unique to each individual animal. For this purpose, the nose images of ten beagle dogs were taken every month for the ten-month period starting from month two and ending in month eleven. Six of them are siblings born of one dam and the other four of another dam. In this longitudinal study, the canine nose patterns of these ten dogs are examined visually and by a biometric algorithm to determine whether the canine nose patterns in two images of the same dog taken at different time remain the same and whether two images of different dogs are indeed different regardless of when the images are taken. It is found that the canine nose pattern of the beagle dog is fully formed at the second month after birth, that this nose pattern stays invariant, and that the canine nose pattern is indeed unique to each animal. Our finding confirms and enhances the claims of earlier works that the canine nose pattern is indeed unique to each animal, and could be used as a unique biometric marker. Abstract The formation and invariance of the canine nose pattern is studied. Nose images of ten beagle dogs were collected for ten months from month two to month eleven. The nose patterns in these images are examined visually and by a biometric algorithm. It is found that the canine nose pattern is fully formed at the end of the second month since birth and remains invariant until the end of the eleventh month. This study also strongly indicates that the canine nose pattern can be used as a unique biometric marker for each individual dog.
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Benkeser D, Juraska M, Gilbert PB. Assessing trends in vaccine efficacy by pathogen genetic distance. J Soc Fr Statistique (2009) 2020; 161:164-175. [PMID: 33244440 PMCID: PMC7685316] [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] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to sieve analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.
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Affiliation(s)
- David Benkeser
- Department of Biostatistics and Bioinformatics, Emory University; 1518 Clifton Rd. NE; Atlanta, GA USA 30322
| | - Michal Juraska
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; 1100 Fairview Ave. N; Seattle, WA USA 98109
| | - Peter B Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; 1100 Fairview Ave. N; Seattle, WA USA 98109
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Cunha LFI, Protti F. Genome Rearrangements on Multigenomic Models: Applications of Graph Convexity Problems. J Comput Biol 2019; 26:1214-1222. [PMID: 31120333 DOI: 10.1089/cmb.2019.0091] [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] [Indexed: 11/13/2022] Open
Abstract
Genome rearrangements are events where large blocks of DNA exchange pieces during evolution. The analysis of such events is a tool for understanding evolutionary genomics, in whose context many rearrangement distances have been proposed, based on finding the minimum number of rearrangements to transform one genome into another, using some predefined operation. However, when more than two genomes are considered, we have new challenging problems. Studying such problems from a combinatorial point of view has been shown to be a useful tool to approach such problems, for example, the reconstruction of phylogenetic trees. We focus on genome rearrangement problems related to graph convexity. Such an approach is in connection with some other well-known studies on multigenomic models, for example, those based on the median and on the closest string. We propose an association between graph convexities and genome rearrangements in such a way that graph convexity problems deal with input sets of vertices and try to answer questions concerning the closure of such inputs. The concept of closure is useful for studies on genome rearrangement by suggesting mechanisms to reduce the genomic search space. Regarding the computational complexity, and considering the Hamming distance on strings, we solve the following problems: decide if a given set is convex; compute the interval and the convex hull of a given set; and determine the convexity number, interval number, and hull number of a Hamming graph. All such problems are solved for three types of convexities: geodetic, monophonic, and P3. Considering the Cayley distance on permutations, we solve the convexity number and interval determination problems for the geodetic convexity.
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Affiliation(s)
- Luís Felipe I Cunha
- PESC/COPPE-Program Systems Engineering and Computer Science, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fábio Protti
- IC/UFF-Institute of Computing, Fluminense Federal University, Niterói, Brazil
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14
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Ma XQ, Yu CC, Chen XX, Zhou L. Large-Scale Person Re-Identification Based on Deep Hash Learning. Entropy (Basel) 2019; 21:E449. [PMID: 33267163 DOI: 10.3390/e21050449] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 04/27/2019] [Accepted: 04/28/2019] [Indexed: 11/28/2022]
Abstract
Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image. Then, a hash layer with three-step calculation is incorporated in the fully connected layer of the network. The hash function is learned and mapped into a hash code through the connection between the network layers. The generation of the hash code satisfies the requirements that minimize the error of the sum of quantization loss and Softmax regression cross-entropy loss, which achieve the end-to-end generation of hash code in the network. After obtaining the hash code through the network, the distance between the pedestrian image hash code to be retrieved and the pedestrian image hash code library is calculated to implement the person re-identification. Experiments conducted on multiple standard datasets show that our deep hashing network achieves the comparable performances and outperforms other hashing methods with large margins on Rank-1 and mAP value identification rates in pedestrian re-identification. Besides, our method is predominant in the efficiency of training and retrieval in contrast to other pedestrian re-identification algorithms.
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15
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Abstract
BACKGROUND Epigenome is highly dynamic during the early stages of embryonic development. Epigenetic modifications provide the necessary regulation for lineage specification and enable the maintenance of cellular identity. Given the rapid accumulation of genome-wide epigenomic modification maps across cellular differentiation process, there is an urgent need to characterize epigenetic dynamics and reveal their impacts on differential gene regulation. METHODS We proposed DiffEM, a computational method for differential analysis of epigenetic modifications and identified highly dynamic modification sites along cellular differentiation process. We applied this approach to investigating 6 epigenetic marks of 20 kinds of human early developmental stages and tissues, including hESCs, 4 hESC-derived lineages and 15 human primary tissues. RESULTS We identified highly dynamic modification sites where different cell types exhibit distinctive modification patterns, and found that these highly dynamic sites enriched in the genes related to cellular development and differentiation. Further, to evaluate the effectiveness of our method, we correlated the dynamics scores of epigenetic modifications with the variance of gene expression, and compared the results of our method with those of the existing algorithms. The comparison results demonstrate the power of our method in evaluating the epigenetic dynamics and identifying highly dynamic regions along cell differentiation process.
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Affiliation(s)
- Xia Zhang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China.
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jihong Guan
- Department of Computer Science and Technology,Tongji University, Shanghai, China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai, China
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Bessafi M, Mihailović DT, Malinović-Milićević S, Mihailović A, Jumaux G, Bonnardot F, Fanchette Y, Chabriat JP. Spatial and Temporal Non-Linear Dynamics Analysis and Predictability of Solar Radiation Time Series for La Reunion Island (France). Entropy (Basel) 2018; 20:E946. [PMID: 33266670 DOI: 10.3390/e20120946] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 11/30/2018] [Accepted: 12/06/2018] [Indexed: 11/16/2022]
Abstract
Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural intermittency of solar irradiation is mainly triggered by atmospheric turbulent conditions, radiative transfer, optical properties of cloud and aerosol, moisture and atmospheric stability, orographic and thermal forcing, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the period 2011-2015, at 32 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use the tools of non-linear dynamics: the intermittency and chaos analysis, the largest Lyapunov exponent, Sample entropy, the Kolmogorov complexity and its derivatives (Kolmogorov complexity spectrum and its highest value), and spatial weighted Kolmogorov complexity combined with Hamming distance to assess complexity and corresponding predictability. Finally, we have clustered the Kolmogorov time (that quantifies the time span beyond which randomness significantly influences predictability) for daily cumulative solar irradiation for all stations. We show that under the record-breaking 2011-2012 La Nina event and preceding a very strong El-Nino 2015-2016 event, the predictability of daily incident solar energy over La Réunion is affected.
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Blendowski M, Heinrich MP. Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients. Int J Comput Assist Radiol Surg 2019; 14:43-52. [PMID: 30430361 DOI: 10.1007/s11548-018-1888-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 11/06/2018] [Indexed: 01/19/2023]
Abstract
PURPOSE Deep convolutional neural networks in their various forms are currently achieving or outperforming state-of-the-art results on several medical imaging tasks. We aim to make these developments available to the so far unsolved task of accurate correspondence finding-especially with regard to image registration. METHODS We propose a two-step hybrid approach to make deep learned features accessible to a discrete optimization-based registration method. In a first step, in order to extract expressive binary local descriptors, we train a deep network architecture on a patch-based landmark retrieval problem as auxiliary task. As second step at runtime within a MRF-regularised dense displacement sampling, their binary nature enables highly efficient similarity computations, thus making them an ideal candidate to replace the so far used handcrafted local feature descriptors during the registration process. RESULTS We evaluate our approach on finding correspondences between highly non-rigidly deformed lung CT scans from different breathing states. Although the CNN-based descriptors excell at an auxiliary learning task for finding keypoint correspondences, self-similarity-based descriptors yield more accurate registration results. However, a combination of both approaches turns out to generate the most robust features for registration. CONCLUSION We present a three-dimensional framework for large lung motion estimation based on the combination of CNN-based and handcrafted descriptors efficiently employed in a discrete registration method. Achieving best results by combining learned and handcrafted features encourages further research in this direction.
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Tsyvina V, Campo DS, Sims S, Zelikovsky A, Khudyakov Y, Skums P. Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants. BMC Bioinformatics 2018; 19:360. [PMID: 30343669 PMCID: PMC6196405 DOI: 10.1186/s12859-018-2333-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Background Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. Results In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj Conclusion The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.
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Affiliation(s)
- Viachaslau Tsyvina
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.
| | - David S Campo
- Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Seth Sims
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.,Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Alex Zelikovsky
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA
| | - Yury Khudyakov
- Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Pavel Skums
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.,Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
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Mihailović DT, Bessafi M, Marković S, Arsenić I, Malinović-Milićević S, Jeanty P, Delsaut M, Chabriat JP, Drešković N, Mihailović A. Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France). Entropy (Basel) 2018; 20:E570. [PMID: 33265658 DOI: 10.3390/e20080570] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 07/28/2018] [Accepted: 07/30/2018] [Indexed: 11/16/2022]
Abstract
Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.
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20
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Abstract
As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption, which results from the uncertainty of DNA computing. DNA coding is the key step for DNA computing that could decrease the similarity of DNA sequences in DNA computing as well as correct errors from the process of image encryption and decryption. The experimental results show our method could be used to correct errors in image encryption based on DNA coding.
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Affiliation(s)
- Bin Wang
- Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian 116622, China.
| | - Yingjie Xie
- Applied Technology College, Dalian Ocean University, Dalian 116300, China.
| | - Shihua Zhou
- Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian 116622, China.
| | - Xuedong Zheng
- Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian 116622, China.
| | - Changjun Zhou
- College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China.
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21
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Heinrich MP, Blendowski M, Oktay O. TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions. Int J Comput Assist Radiol Surg 2018; 13:1311-1320. [PMID: 29850978 DOI: 10.1007/s11548-018-1797-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/21/2018] [Indexed: 10/16/2022]
Abstract
PURPOSE Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). METHODS We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. RESULTS We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. CONCLUSIONS We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.
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Affiliation(s)
- Mattias P Heinrich
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Max Blendowski
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Ozan Oktay
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
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Carriço JA, Crochemore M, Francisco AP, Pissis SP, Ribeiro-Gonçalves B, Vaz C. Fast phylogenetic inference from typing data. Algorithms Mol Biol 2018; 13:4. [PMID: 29467814 PMCID: PMC5815242 DOI: 10.1186/s13015-017-0119-7] [Citation(s) in RCA: 12] [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] [Received: 10/31/2017] [Accepted: 12/22/2017] [Indexed: 11/10/2022] Open
Abstract
Background Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.
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Anderson CS, McCall PR, Stern HA, Yang H, Topham DJ. Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation. BMC Bioinformatics 2018; 19:51. [PMID: 29433425 PMCID: PMC5809904 DOI: 10.1186/s12859-018-2042-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 01/24/2018] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists. RESULTS Here we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used. CONCLUSION This work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications.
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Affiliation(s)
- Christopher S. Anderson
- New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY USA
| | - Patrick R. McCall
- New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY USA
| | - Harry A. Stern
- Center for Integrated Research Computing, University of Rochester, Rochester, NY USA
| | - Hongmei Yang
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY USA
| | - David J. Topham
- New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY USA
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Jiang D, Shi Y, Yao D, Fan Y, Wang M, Song Z. Multimodal image registration based on binary gradient angle descriptor. Int J Comput Assist Radiol Surg 2017; 12:2157-67. [PMID: 28861704 DOI: 10.1007/s11548-017-1661-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 08/17/2017] [Indexed: 10/19/2022]
Abstract
PURPOSE Multimodal image registration plays an important role in image-guided interventions/therapy and atlas building, and it is still a challenging task due to the complex intensity variations in different modalities. METHODS The paper addresses the problem and proposes a simple, compact, fast and generally applicable modality-independent binary gradient angle descriptor (BGA) based on the rationale of gradient orientation alignment. The BGA can be easily calculated at each voxel by coding the quadrant in which a local gradient vector falls, and it has an extremely low computational complexity, requiring only three convolutions, two multiplication operations and two comparison operations. Meanwhile, the binarized encoding of the gradient orientation makes the BGA more resistant to image degradations compared with conventional gradient orientation methods. The BGA can extract similar feature descriptors for different modalities and enable the use of simple similarity measures, which makes it applicable within a wide range of optimization frameworks. RESULTS The results for pairwise multimodal and monomodal registrations between various images (T1, T2, PD, T1c, Flair) consistently show that the BGA significantly outperforms localized mutual information. The experimental results also confirm that the BGA can be a reliable alternative to the sum of absolute difference in monomodal image registration. The BGA can also achieve an accuracy of [Formula: see text], similar to that of the SSC, for the deformable registration of inhale and exhale CT scans. Specifically, for the highly challenging deformable registration of preoperative MRI and 3D intraoperative ultrasound images, the BGA achieves a similar registration accuracy of [Formula: see text] compared with state-of-the-art approaches, with a computation time of 18.3 s per case. CONCLUSIONS The BGA improves the registration performance in terms of both accuracy and time efficiency. With further acceleration, the framework has the potential for application in time-sensitive clinical environments, such as for preoperative MRI and intraoperative US image registration for image-guided intervention.
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Abstract
We present a fast method for calculation of Hamming distance vector based on a simple preprocessing of the target text. For applications on protein sequences, with alphabet of 20 symbols or more, the proposed method is an order of magnitude faster than the brute force approach while much simpler than previously published methods.
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Affiliation(s)
- Strahil Ristov
- Department of Electronics, Ruđer Bošković Institute , Zagreb, Croatia
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Abstract
Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In ). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X'X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model.
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Affiliation(s)
- By Tracy Ke
- Princeton University and Carnegie Mellon University
| | - Jiashun Jin
- Princeton University and Carnegie Mellon University
| | - Jianqing Fan
- Princeton University and Carnegie Mellon University
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Yang G, Xi X, Yin Y. Finger vein recognition based on a personalized best bit map. Sensors (Basel) 2012; 12:1738-57. [PMID: 22438735 PMCID: PMC3304137 DOI: 10.3390/s120201738] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2011] [Revised: 02/02/2012] [Accepted: 02/03/2012] [Indexed: 11/16/2022]
Abstract
Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.
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Affiliation(s)
- Gongping Yang
- School of Computer Science and Technology, Shandong University, Jinan 250101, China.
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