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  1. Article ; Online: When viruses become more virulent.

    Wertheim, Joel O

    Science (New York, N.Y.)

    2022  Volume 375, Issue 6580, Page(s) 493–494

    Abstract: Figure: see text]. ...

    Abstract [Figure: see text].
    MeSH term(s) Influenza A virus
    Language English
    Publishing date 2022-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, P.H.S. ; Comment
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.abn4887
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 27: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 77: Show issues

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  2. Article ; Online: A Glimpse Into the Origins of Genetic Diversity in the Severe Acute Respiratory Syndrome Coronavirus 2.

    Wertheim, Joel O

    Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

    2020  Volume 71, Issue 15, Page(s) 721–722

    MeSH term(s) Betacoronavirus/genetics ; COVID-19 ; Coronavirus Infections/virology ; Genetic Variation/genetics ; Humans ; Middle East Respiratory Syndrome Coronavirus/genetics ; Pandemics ; Pneumonia, Viral/virology ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-03-04
    Publishing country United States
    Document type Editorial
    ZDB-ID 1099781-7
    ISSN 1537-6591 ; 1058-4838
    ISSN (online) 1537-6591
    ISSN 1058-4838
    DOI 10.1093/cid/ciaa213
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    Zs.A 1505: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 480: Show issues

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  5. Article ; Online: Viral Evolution: Mummy Virus Challenges Presumed History of Smallpox.

    Wertheim, Joel O

    Current biology : CB

    2017  Volume 27, Issue 3, Page(s) R119–R120

    Abstract: Despite evidence of smallpox in antiquity, a new study of a 350 year-old Lithuanian child mummy suggests that the global viral genetic diversity circulating during the ... ...

    Abstract Despite evidence of smallpox in antiquity, a new study of a 350 year-old Lithuanian child mummy suggests that the global viral genetic diversity circulating during the 20
    MeSH term(s) Biological Evolution ; Child ; DNA, Viral ; Genetic Variation ; History, 17th Century ; History, 18th Century ; History, 20th Century ; Humans ; Mummies/history ; Mummies/virology ; Smallpox/history ; Smallpox/virology ; Variola virus/genetics
    Chemical Substances DNA, Viral
    Language English
    Publishing date 2017-02-06
    Publishing country England
    Document type Historical Article ; Journal Article
    ZDB-ID 1071731-6
    ISSN 1879-0445 ; 0960-9822
    ISSN (online) 1879-0445
    ISSN 0960-9822
    DOI 10.1016/j.cub.2016.12.008
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3208: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Using molecular epidemiology to trace the history of the injection-related HIV epidemic in New York City, 1985-2019.

    Torian, Lucia V / Forgione, Lisa / Wertheim, Joel O

    AIDS (London, England)

    2022  Volume 36, Issue 6, Page(s) 889–895

    Abstract: Objective: Unintentional drug poisoning and overdose deaths in New York City (NYC) increased 175% between 2010 and 2017, partly due to the transition from noninjectable opioids to heroin injection. This transition has led to concern of a resurgent HIV ... ...

    Abstract Objective: Unintentional drug poisoning and overdose deaths in New York City (NYC) increased 175% between 2010 and 2017, partly due to the transition from noninjectable opioids to heroin injection. This transition has led to concern of a resurgent HIV epidemic among persons who inject drugs (PWID) in NYC. Thus, we sought to characterize HIV transmission dynamics in PWID.
    Design: Genetic network analysis of HIV-1 public health surveillance data.
    Methods: We analyzed HIV diagnoses reported to public health surveillance to determine the trajectory of the HIV epidemic among PWID in NYC, from 1985 through 2019. Genetic distance-based clustering was performed using HIV-TRACE to reconstruct transmission patterns among PWID.
    Results: The majority of the genetic links to PWID diagnosed in the 1980s and 1990s are to other PWID. However, since 2011, there has been a continued decline in new diagnoses among PWID, and genetic links between PWID have become increasingly rare, although links to noninjecting MSM and other people reporting sexual transmission risk have become increasingly common. However, we also find evidence suggestive of a resurgence of genetic links among PWID in 2018-2019. PWID who reported male-male sexual contact were not preferentially genetically linked to PWID over the surveillance period, emphasizing their distinct risk profile from other PWID.
    Conclusion: These trends suggest a transition from parenteral to sexual transmission among PWID in NYC, suggesting that harm reduction, syringe exchange programs, and legalization of over-the-counter syringe sales in pharmacies have mitigated HIV risk by facilitating well tolerated injection among new PWID.
    MeSH term(s) Drug Users ; Gene Regulatory Networks ; HIV Infections/epidemiology ; Homosexuality, Male ; Humans ; Male ; Molecular Epidemiology ; New York City/epidemiology ; Sexual and Gender Minorities ; Substance Abuse, Intravenous/complications ; Substance Abuse, Intravenous/epidemiology
    Language English
    Publishing date 2022-02-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 639076-6
    ISSN 1473-5571 ; 0269-9370 ; 1350-2840
    ISSN (online) 1473-5571
    ISSN 0269-9370 ; 1350-2840
    DOI 10.1097/QAD.0000000000003208
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2228: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article: Optimizing ancestral trait reconstruction of large HIV Subtype C datasets through multiple-trait subsampling.

    Li, Xingguang / Trovão, Nídia S / Wertheim, Joel O / Baele, Guy / de Bernardi Schneider, Adriano

    Virus evolution

    2023  Volume 9, Issue 2, Page(s) vead069

    Abstract: Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence ... ...

    Abstract Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of human immunodeficiency virus Type 1 Subtype C using a comprehensive subsampling strategy and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that a most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by all available traits, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the trait risk group. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatio-temporal patterns of infectious diseases.
    Language English
    Publishing date 2023-11-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2818949-8
    ISSN 2057-1577
    ISSN 2057-1577
    DOI 10.1093/ve/vead069
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  8. Article ; Online: Accuracy in Near-Perfect Virus Phylogenies.

    Wertheim, Joel O / Steel, Mike / Sanderson, Michael J

    Systematic biology

    2021  Volume 71, Issue 2, Page(s) 426–438

    Abstract: Phylogenetic trees from real-world data often include short edges with very few substitutions per site, which can lead to partially resolved trees and poor accuracy. Theory indicates that the number of sites needed to accurately reconstruct a fully ... ...

    Abstract Phylogenetic trees from real-world data often include short edges with very few substitutions per site, which can lead to partially resolved trees and poor accuracy. Theory indicates that the number of sites needed to accurately reconstruct a fully resolved tree grows at a rate proportional to the inverse square of the length of the shortest edge. However, when inferred trees are partially resolved due to short edges, "accuracy" should be defined as the rate of discovering false splits (clades on a rooted tree) relative to the actual number found. Thus, accuracy can be high even if short edges are common. Specifically, in a "near-perfect" parameter space in which trees are large, the tree length $\xi$ (the sum of all edge lengths) is small, and rate variation is minimal, the expected false positive rate is less than $\xi/3$; the exact value depends on tree shape and sequence length. This expected false positive rate is far below the false negative rate for small $\xi$ and often well below 5% even when some assumptions are relaxed. We show this result analytically for maximum parsimony and explore its extension to maximum likelihood using theory and simulations. For hypothesis testing, we show that measures of split "support" that rely on bootstrap resampling consistently imply weaker support than that implied by the false positive rates in near-perfect trees. The near-perfect parameter space closely fits several empirical studies of human virus diversification during outbreaks and epidemics, including Ebolavirus, Zika virus, and SARS-CoV-2, reflecting low substitution rates relative to high transmission/sampling rates in these viruses.[Ebolavirus; epidemic; HIV; homoplasy; mumps virus; perfect phylogeny; SARS-CoV-2; virus; West Nile virus; Yule-Harding model; Zika virus.].
    MeSH term(s) COVID-19 ; Humans ; Models, Genetic ; Phylogeny ; SARS-CoV-2 ; Zika Virus ; Zika Virus Infection
    Language English
    Publishing date 2021-08-16
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1482572-7
    ISSN 1076-836X ; 1063-5157
    ISSN (online) 1076-836X
    ISSN 1063-5157
    DOI 10.1093/sysbio/syab069
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  9. Article ; Online: Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.

    Mazrouee, Sepideh / Little, Susan J / Wertheim, Joel O

    PLoS computational biology

    2021  Volume 17, Issue 9, Page(s) e1009336

    Abstract: HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This ... ...

    Abstract HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.
    MeSH term(s) Algorithms ; Cluster Analysis ; Feasibility Studies ; HIV Infections/epidemiology ; HIV Infections/transmission ; Humans ; Machine Learning ; Metadata
    Language English
    Publishing date 2021-09-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1009336
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  10. Article ; Online: Incorporating metadata in HIV transmission network reconstruction

    Sepideh Mazrouee / Susan J Little / Joel O Wertheim

    PLoS Computational Biology, Vol 17, Iss 9, p e

    A machine learning feasibility assessment.

    2021  Volume 1009336

    Abstract: HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This ... ...

    Abstract HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.
    Keywords Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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