Publications
181 results found
Zhang S, Shi J, Li X, et al., 2023, Triplex qPCR assay for Campylobacter jejuni and Campylobacter coli monitoring in wastewater., Sci Total Environ, Vol: 892
Campylobacter spp. is one of the most frequent pathogens of bacterial gastroenteritis recorded worldwide. Campylobacter jejuni (C. jejuni) and Campylobacter coli (C. coli) are the two major disease-associated species, accounting for >95 % of infections, and thus have been selected for disease surveillance. Monitoring temporal variations in pathogen concentration and diversity excreted from community wastewater allows the early detection of outbreaks. Multiplex real-time/quantitative PCR (qPCR) enables multi-target quantification of pathogens in various types of samples including wastewater. Also, an internal amplification control (IAC) is required for each sample when adopting PCR-based methods for pathogen detection and quantification in wastewater to exclude the inhibition of the wastewater matrix. To achieve reliable quantification of C. jejuni and C. coli towards wastewater samples, this study developed and optimized a triplex qPCR assay by combining three qPCR primer-probe sets targeting Campylobacter jejuni subsp. jejuni, Campylobacter coli, and Campylobacter sputorum biovar sputorum (C. sputorum), respectively. This triplex qPCR assay not only can directly and simultaneously detect the concentration of C. jejuni and C. coli in wastewater but also can achieve the PCR inhibition control using C. sputorum primer-probe set. This is the first developed triplex qPCR assay with IAC for C. jejuni and C. coli, to be used in the wastewater-based epidemiology (WBE) applications. The optimized triplex qPCR assay enables the detection limit of the assay (ALOD100%) and wastewater (PLOD80%) as 10 gene copy/μL and 2 log10 cells/mL (2 gene copies/μL of extracted DNA), respectively. The application of this triplex qPCR to 52 real raw wastewater samples from 13 wastewater treatment plants demonstrated its potential as a high-throughput and economically viable tool for the long-term monitoring of C. jejuni and C. coli prevalence in communities and the surrounding e
Habgood-Coote D, Wilson C, Shimizu C, et al., 2023, Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature, Med, Vol: 4, Pages: 635-654.e5, ISSN: 2666-6340
BACKGROUND: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. METHODS: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children. FINDINGS: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures. CONCLUSIONS: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. FUNDING: European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.
Li F, Guo X, Bi Y, et al., 2023, Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins., Comput Biol Med, Vol: 163
The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18-20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.
Herberg J, Shah P, Voice M, et al., 2023, Relationship between molecular pathogen detection and clinical disease in febrile children across Europe: a multicentre, prospective observational study, The Lancet Regional Health. Europe, Vol: 32, Pages: 1-17, ISSN: 2666-7762
The PERFORM study aimed to understand causes of febrile childhood illness by comparing molecular pathogen detection with current clinical practice. Methods. Febrile children and controls were recruited on presentation to hospital in 9 European countries 2016-2020. Each child was assigned a standardized diagnostic category based on retrospective review of local clinical and microbiological data. Subsequently, centralised molecular tests (CMTs) for 19 respiratory and 27 blood pathogens were performed.Findings. Of 4,611 febrile children, 643 (14%) were classified as definite bacterial infection (DB), 491 (11%) as definite viral infection (DV), and 3,477 (75%) had uncertain aetiology. 1,061 controls without infection were recruited. CMTs detected blood bacteria more frequently in DB than DV cases for N.meningitidis (OR: 3.37, 95% CI: 1.92 – 5.99), S.pneumoniae (OR: 3.89, 95% CI: 2.07 – 7.59), Group A streptococcus (OR 2.73, 95% CI 1.13 – 6.09) and E.coli (OR 2.7, 95% CI 1.02 – 6.71). Respiratory viruses were more common in febrile children than controls, but only influenza A (OR 0.24, 95% CI 0.11 – 0.46), Influenza B (OR 0.12, 95% CI 0.02 – 0.37) and RSV (OR 0.16, 95% CI: 0.06 – 0.36) were less common in DB than DV cases. Of 16 blood viruses, enterovirus (OR 0.43, 95% CI 0.23 – 0.72) and EBV (OR 0.71, 95% CI 0.56 – 0.90) were detected less often in DB than DV cases. Combined local diagnostics and CMTs respectively detected blood viruses and respiratory viruses in 360 (56%) and 161 (25%) of DB cases, and virus detection ruled-out bacterial infection poorly, with predictive values of 0.64 and 0.68 respectively. Interpretation. Most febrile children cannot be conclusively defined as having bacterial or viral infection when molecular tests supplement conventional approaches. Viruses are detected in most patients with bacterial infections, and the clinical value of individual pathogen detection in determining treatment is
Zhang S, Shi J, Li X, et al., 2023, Wastewater-based epidemiology of Campylobacter spp.: A systematic review and meta-analysis of influent, effluent, and removal of wastewater treatment plants., Sci Total Environ, Vol: 903
Campylobacter spp. is one of the four leading causes of diarrhoeal diseases worldwide, which are generally mild but can be fatal in children, the elderly, and immunosuppressed persons. The existing disease surveillance for Campylobacter infections is usually based on untimely clinical reports. Wastewater surveillance or wastewater-based epidemiology (WBE) has been developed for the early warning of disease outbreaks and the detection of the emerging new variants of human pathogens, especially after the global pandemic of COVID-19. However, the WBE monitoring of Campylobacter infections in communities is rare due to a few large data gaps. This study is a meta-analysis and systematic review of the prevalence of Campylobacter spp. in various wastewater samples, primarily the influent of wastewater treatment plants. The results showed that the overall prevalence of Campylobacter spp. was 53.26 % in influent wastewater and 52.97 % in all types of wastewater samples. The mean concentration in the influent was 3.31 ± 0.39 log10 gene copies or most probable number (MPN) per 100 mL. The detection method combining culture and PCR yielded the highest positive rate of 90.86 %, while RT-qPCR and qPCR were the two most frequently used quantification methods. In addition, the Campylobacter concentration in influent wastewater showed a seasonal fluctuation, with the highest concentration in the autumn at 3.46 ± 0.41 log10 gene copies or MPN per 100 mL. Based on the isolates of all positive samples, Campylobacter jejuni (62.34 %) was identified as the most prevalent species in wastewater, followed by Campylobacter coli (30.85 %) and Campylobacter lari (4.4 %). These findings provided significant data to further develop and optimize the wastewater surveillance of Campylobacter spp. infections. In addition, large data gaps were found in the decay of Campylobacter spp. in wastewater, indicating insufficient resea
Bader SM, Cooney JP, Sheerin D, et al., 2023, SARS-CoV-2 mouse adaptation selects virulence mutations that cause TNF-driven age-dependent severe disease with human correlates., Proc Natl Acad Sci U S A, Vol: 120
The diversity of COVID-19 disease in otherwise healthy people, from seemingly asymptomatic infection to severe life-threatening disease, is not clearly understood. We passaged a naturally occurring near-ancestral SARS-CoV-2 variant, capable of infecting wild-type mice, and identified viral genomic mutations coinciding with the acquisition of severe disease in young adult mice and lethality in aged animals. Transcriptomic analysis of lung tissues from mice with severe disease elucidated a host antiviral response dominated mainly by interferon and IL-6 pathway activation in young mice, while in aged animals, a fatal outcome was dominated by TNF and TGF-β signaling. Congruent with our pathway analysis, we showed that young TNF-deficient mice had mild disease compared to controls and aged TNF-deficient animals were more likely to survive infection. Emerging clinical correlates of disease are consistent with our preclinical studies, and our model may provide value in defining aberrant host responses that are causative of severe COVID-19.
Hall MB, Lima L, Coin LJM, et al., 2023, Drug resistance prediction for Mycobacterium tuberculosis with reference graphs., Microb Genom, Vol: 9
Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.
Zhu Y, Li F, Guo X, et al., 2023, TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters., Brief Bioinform, Vol: 24
BACKGROUND: Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essential for synthesizing the gene-encoded products by bacteria to grow and adapt to different environmental conditions. A variety of machine learning-based predictors for bacterial promoters have been developed; however, most of them were designed specifically for a particular species. To date, only a few predictors are available for identifying general bacterial promoters with limited predictive performance. RESULTS: In this study, we developed TIMER, a Siamese neural network-based approach for identifying both general and species-specific bacterial promoters. Specifically, TIMER uses DNA sequences as the input and employs three Siamese neural networks with the attention layers to train and optimize the models for a total of 13 species-specific and general bacterial promoters. Extensive 10-fold cross-validation and independent tests demonstrated that TIMER achieves a competitive performance and outperforms several existing methods on both general and species-specific promoter prediction. As an implementation of the proposed method, the web server of TIMER is publicly accessible at http://web.unimelb-bioinfortools.cloud.edu.au/TIMER/.
Chen A, Sun J, Viljoen A, et al., 2023, Genetic Mapping, Candidate Gene Identification and Marker Validation for Host Plant Resistance to the Race 4 of Fusarium oxysporum f. sp. cubense Using Musa acuminata ssp. malaccensis., Pathogens, Vol: 12, ISSN: 2076-0817
Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to Fusarium oxysporum f. sp. Cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. Malaccensis segregating populations, segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on chromosome 3 of 'DH-Pahang' reference assembly v4. Within this region, there was a cluster of pattern recognition receptors, namely leucine-rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell-wall-associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins, positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant progenies but not in the susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent 'Ma850' and a susceptible line 'Ma848', to show that the STR4 resistance co-segregated with marker '28820' at this locus. Finally, an informative SNP marker 29730 allowed the locus-specific resistance to be assessed in a collection of diploid and polyploid banana plants. Of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4-resistant, such as 'Pahang', 'SH-3362', 'SH-3217', 'Ma-ITC0250', and 'DH-Pahang/CIRAD 930'. Additional screening in the International Institute for Tropical Agriculture's collection suggests that the dominant allele is common among the elite 'Matooke' NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland
Jackson HR, Miglietta L, Habgood-Coote D, et al., 2023, Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature, Journal of the Pediatric Infectious Diseases Society, Vol: 12, Pages: 322-331, ISSN: 2048-7207
BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.
Chen R, Li F, Guo X, et al., 2023, ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species, Briefings in Bioinformatics, Vol: 24, ISSN: 1467-5463
A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms stateof-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web. unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.
Zhang S, Shi J, Sharma E, et al., 2023, In-sewer decay and partitioning of Campylobacter jejuni and Campylobacter coli and implications for their wastewater surveillance, WATER RESEARCH, Vol: 233, ISSN: 0043-1354
Davies MRR, Keller N, Brouwer S, et al., 2023, Detection of Streptococcus pyogenes M1(UK) in Australia and characterization of the mutation driving enhanced expression of superantigen SpeA, NATURE COMMUNICATIONS, Vol: 14
Chen A, Sun J, Martin G, et al., 2023, Identification of a Major QTL-Controlling Resistance to the Subtropical Race 4 of Fusarium oxysporum f. sp. cubense in Musa acuminata ssp. malaccensis, PATHOGENS, Vol: 12
Boeddha NP, Atkins L, de Groot R, et al., 2023, Correction to: Group A streptococcal disease in paediatric inpatients: a European perspective., Eur J Pediatr, Vol: 182
Blaskovich MAT, Hansford KA, Butler MS, et al., 2022, A lipoglycopeptide antibiotic for Gram-positive biofilm-related infections, SCIENCE TRANSLATIONAL MEDICINE, Vol: 14, ISSN: 1946-6234
Kumar V, Pouw RB, Autio M, et al., 2022, Variation in CFHR3 determines susceptibility to meningococcal disease by controlling factor H concentrations, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 109, Pages: 1680-1691, ISSN: 0002-9297
Hall MB, Coin LJM, 2022, Assessment of the 2021 WHO Mycobacterium tuberculosis drug resistance mutation catalogue on an independent dataset, LANCET MICROBE, Vol: 3, Pages: E645-E645
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Liu Q, Fang H, Wang X, et al., 2022, DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions, BIOINFORMATICS, Vol: 38, Pages: 4053-4061, ISSN: 1367-4803
Bainomugisa A, Lavu E, Pandey S, et al., 2022, Evolution and spread of a highly drug resistant strain of Mycobacterium tuberculosis in Papua New Guinea, BMC INFECTIOUS DISEASES, Vol: 22
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Chang JJ-Y, Gleeson J, Rawlinson D, et al., 2022, Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection, FRONTIERS IN IMMUNOLOGY, Vol: 13, ISSN: 1664-3224
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Zhang M, Jia C, Li F, et al., 2022, Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction, BRIEFINGS IN BIOINFORMATICS, Vol: 23, ISSN: 1467-5463
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Steinig E, Duchene S, Aglua I, et al., 2022, Phylodynamic Inference of Bacterial Outbreak Parameters Using Nanopore Sequencing, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 39, ISSN: 0737-4038
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Schlapbach LJ, Coin L, 2022, Understanding Detrimental Host Response to Infection-The Promise of Transcriptomics, PEDIATRIC CRITICAL CARE MEDICINE, Vol: 23, Pages: 133-135, ISSN: 1529-7535
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Ganesamoorthy D, Robertson AJ, Chen W, et al., 2022, Whole genome deep sequencing analysis of cell-free DNA in samples with low tumour content, BMC CANCER, Vol: 22
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Li F, Dong S, Leier A, et al., 2022, Positive-unlabeled learning in bioinformatics and computational biology: a brief review, BRIEFINGS IN BIOINFORMATICS, Vol: 23, ISSN: 1467-5463
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Li F, Guo X, Xiang D, et al., 2022, Computational analysis and prediction of PE_PGRS proteins using machine learning, COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, Vol: 20, Pages: 662-674, ISSN: 2001-0370
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Zhang S, Li X, Wu J, et al., 2021, Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis, WATER, Vol: 13
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De T, Goncalves A, Speed D, et al., 2021, Signatures of TSPAN8 variants associated with human metabolic regulation and diseases, iScience, Vol: 24, ISSN: 2589-0042
Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as 1H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in TSPAN8.
Parry R, Gifford RJ, Lytras S, et al., 2021, No evidence of SARS-CoV-2 reversetranscription and integration as the origin of chimeric transcripts in patient tissues, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 118, ISSN: 0027-8424
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- Citations: 15
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