Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.
Ontology highlight
ABSTRACT:
SUBMITTER: Blagrove MSC
PROVIDER: S-EPMC9467978 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.
Blagrove Marcus S C MSC Baylis Matthew M Wardeh Maya M
Nature communications 20220912 1
Similar Datasets
Project description:Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.
| S-EPMC8828720 | biostudies-literature
Project description:The four endemic human coronaviruses HCoV-229E, -NL63, -OC43, and -HKU1 contribute a considerable share of upper and lower respiratory tract infections in adults and children. While their clinical representation resembles that of many other agents of the common cold, their evolutionary histories, and host associations could provide important insights into the natural history of past human pandemics. For two of these viruses, we have strong evidence suggesting an origin in major livestock species while primordial associations for all four viruses may have existed with bats and rodents. HCoV-NL63 and -229E may originate from bat reservoirs as assumed for many other coronaviruses, but HCoV-OC43 and -HKU1 seem more likely to have speciated from rodent-associated viruses. HCoV-OC43 is thought to have emerged from ancestors in domestic animals such as cattle or swine. The bovine coronavirus has been suggested to be a possible ancestor, from which HCoV-OC43 may have emerged in the context of a pandemic recorded historically at the end of the 19th century. New data suggest that HCoV-229E may actually be transferred from dromedary camels similar to Middle East respiratory syndrome (MERS) coronavirus. This scenario provides important ecological parallels to the present prepandemic pattern of host associations of the MERS coronavirus.
| S-EPMC7112090 | biostudies-literature
Project description:This paper reviews concepts and methods for the economic valuation of nature in the context of wildlife conservation and questions them in light of alternative approaches based on deliberation. Economic valuations have been used to set priorities, consider opportunity costs, assess co-benefits of conservation, support the case for conservation in public awareness and advocacy, and drive novel schemes to change incentives. We discuss the foundational principles of mainstream economic valuation in terms of its assumptions about values, markets, and human behaviour; propose a list of valuation studies in relation to wildlife protection; and explain the methods used. We then review critiques of these approaches focusing on the narrow way in which economics conceives of values, and institutional, power, and equity concerns. Finally, we complement conventional approaches commonly used for wildlife valuation with two forms of deliberative valuation: deliberated preferences and deliberative democratic monetary valuation. These are discussed in terms of their potential to address the drawbacks of mainstream economics and to realise the potential of valuation in bridging conservation of nature for its own sake and its important contributions to human well-being.Supplementary informationThe online version contains supplementary material available at 10.1007/s10344-023-01658-2.
| S-EPMC9999082 | biostudies-literature
Project description:The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.
| S-EPMC8141697 | biostudies-literature
Project description:Recent studies have suggested that bats are the natural reservoir of a range of coronaviruses (CoVs), and that rhinolophid bats harbor viruses closely related to the severe acute respiratory syndrome (SARS) CoV, which caused an outbreak of respiratory illness in humans during 2002-2003. We examined the evolutionary relationships between bat CoVs and their hosts by using sequence data of the virus RNA-dependent RNA polymerase gene and the bat cytochrome b gene. Phylogenetic analyses showed multiple incongruent associations between the phylogenies of rhinolophid bats and their CoVs, which suggested that host shifts have occurred in the recent evolutionary history of this group. These shifts may be due to either virus biologic traits or host behavioral traits. This finding has implications for the emergence of SARS and for the potential future emergence of SARS-CoVs or related viruses.
| S-EPMC2851503 | biostudies-literature
Project description:The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.
| S-EPMC8087038 | biostudies-literature
Project description:China has about 11% of the world's total wildlife species, so strengthening China's wildlife conservation is of great significance to global biodiversity. Despite some successful cases and conservation efforts, 21.4% of China's vertebrate species are threatened by human activities. The booming wildlife trade in China has posed serious threat to wildlife in China and throughout the world, while leading to a high risk of transmission of infectious zoonotic diseases. China's wildlife conservation has faced a series of challenges, two of which are an impractical, separated management of wildlife and outdated protected species lists. Although the Wildlife Protection Law of China was revised in 2016, the issues of separated management remain, and the protected species lists are still not adequately revised. These issues have led to inefficient and overlapping management, waste of administrative resources, and serious obstacles to wildlife protection. In this article, we analyze the negative effects of current separated management of wildlife species and outdated protected species lists, and provide some suggestions for amendment of the laws and reform of wildlife management system.
| S-EPMC7430277 | biostudies-literature
Project description:We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017-2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.
| S-EPMC7754113 | biostudies-literature
Project description: Not available
| S-EPMC8520503 | biostudies-literature
Project description:Novel pathogenic coronaviruses - such as SARS-CoV and probably SARS-CoV-2 - arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.
| S-EPMC7887240 | biostudies-literature