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Henry, G.

Publications and source records attributed to Henry, G..

3 recordsLinked to original sources

Disrupting traditions of science: Indigenous Knowledge to model species habitat use

While Indigenous Knowledge (IK) contains a wealth of information on the behaviour and habitat use of species, it is rarely included in the species-habitat models frequently used by Western species management authorities. As decisions from these authorities can limit access to species that are important culturally and for subsistence, exclusion of IK in conservation and management frameworks can negatively impact both species and Indigenous communities. In partnership with Inupiat hunters, we developed methods to statistically characterize IK of species-habitat relationships and developed models that rely solely on IK to identify species habitat use and important areas. We provide methods for different types of IK documentation and for dynamic habitat types (e.g., ice concentration). We apply the method to ringed seals (natchiq in Inupiaq) in Alaskan waters, a stock for which the designated critical habitat has been debated in part due to minimal inclusion of IK. Our work demonstrates how IK of species-habitat relationships, with the inclusion of dynamic habitat types, expands on existing mapping approaches and provides another method to identify species habitat use and important areas. The results of this work provide a straightforward and meaningful approach to include IK in species management, especially through co-management processes. "Agencies have a traditional way they do science and including Indigenous Knowledge is less traditional." - Taqulik Hepa, subsistence hunter and Director, North Slope Borough Department of Wildlife Management Statement of PositionalityThis study and the conversion and application of Indigenous Knowledge (IK) for habitat use models was initiated through discussions with the North Slope Borough Department of Wildlife Management (DWM). The DWM is an agency of the regional municipal government representing eight primarily Inupiat subsistence communities in Northern Alaska. One of the goals of the DWM is to "assure participation by Borough residents in the management of wildlife and fish... so that residents can continue to practice traditional methods of subsistence harvest of wildlife resources in perpetuity" (1). Additionally, this project was presented to the Ice Seal Committee (ISC) for review, input, and approval. The ISC is an Alaskan Native organization with representatives from five regions that cover ice-associated seal ranges and "was established to help preserve and enhance ice seal habitat; protect and enhance Alaska Native culture, traditions-particularly activities associated with the subsistence use of ice seals" (2). Both the DWM and the ISC have mandates to manage ice-associated seals considering both IK and Western scientific knowledge (1, 2), and this study was developed to meet those mandates. Inupiat hunters from Utqia[g]vik, Alaska (Figure 1) were collaborators on this project, five of whom are co-authors (B. Adams, B. Frantz, J. Gatten, Q. Harcharek, and R. Sarren), while the other hunter chose to remain anonymous for this publication. The other authors are not Indigenous: R. Gryba was a PhD candidate at the University of British Columbia, M. Auger-Methe and G. Henry are professors at the University of British Columbia, A. Von Duyke is a researcher at the DWM, and H. Huntington is an independent social scientist. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/556613v2_fig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@bf6ec5org.highwire.dtl.DTLVardef@149f2org.highwire.dtl.DTLVardef@9dc98aorg.highwire.dtl.DTLVardef@11aa42a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1.C_FLOATNO Study area around Utqia[g]vik, Alaska (shown in the yellow triangle). C_FIG Significance StatementIndigenous Knowledge (IK) is an extensive source of information of species habitat use and behavior, but is still rarely included in statistical methods used for species conservation and management. Because current conservation practices are frequently still rooted in Western practices many Indigenous organizations are looking for ways for IK to be better included and considered. We worked with Inupiat hunters to develop a new statistical approach to characterize IK and use it as a sole data source in habitat models. This work expands on mapping approaches, that are valuable, but cannot be applied to dynamic habitat types (e.g., ice concentration). This work shows how IK can be meaningfully included in modelling and be considered in current approaches for species management.

ecology↗

Characterization of Three Variants of SARS-CoV-2 in vivo Shows Host-Dependent Pathogenicity in Hamsters

Animal models are used in preclinical trials to test vaccines, antivirals, monoclonal antibodies, and immunomodulatory drug therapies against SARS-CoV-2. However, these drugs often do not produce equivalent results in human clinical trials. Here, we show how different animal models infected with some of the most clinically relevant SARS-CoV-2 variants, WA1/2020, B.1.617.2/Delta, B.1.1.529/Omicron and BA5.2/Omicron, have independent outcomes. We show that in mice, B.1.617.2 is more pathogenic, followed by WA1, while B.1.1.529 showed an absence of clinical signs. Only B.1.1.529 was able to infect C57BL/6J mice, which lack the human ACE2 receptor. B.1.1.529-infected ACE2 mice had different T cell profiles compared to infected K18-hACE2 mice, while viral shedding profiles and viral titers in lungs were similar between the ACE2 and the C57BL/6J mice. These data suggest B.1.1.529 virus adaptation to a new host and shows that asymptomatic carriers can accumulate and shed virus. Next, we show how B.1.617.2, WA1 and BA5.2/Omicron have similar viral replication kinetics, pathogenicity, and viral shedding profiles in hamsters, demonstrating that the increased pathogenicity of B.1.617.2 observed in mice is host-dependent. Overall, these findings suggest that small animal models are useful to parallel human clinical data, but the experimental design places an important role in interpreting the data. IMPORTANCEThere is a need to investigate SARS-CoV-2 variants phenotypes in different animal models due to the lack of reproducible outcomes when translating experiments to the human population. Our findings highlight the correlation of clinically relevant SARS-CoV-2 variants in animal models with human infections. Experimental design and understanding of correct animal models are essential to interpreting data to develop antivirals, vaccines, and other therapeutic compounds against COVID-19.

microbiology↗

SeqWho: Reliable, rapid determination of sequence file identity using k-mer frequencies

With the vast improvements in sequencing technologies and increased number of protocols, sequencing is finding more applications to answer complex biological problems. Thus, the amount of publicly available sequencing data has tremendously increased in repositories such as SRA, EGA, and ENCODE. With any large online database, there is a critical need to accurately document study metadata, such as the source protocol and organism. In some cases, this metadata may not be systematically verified by the hosting sites and may result in a negative influence on future studies. Here we present SeqWho, a program designed to heuristically assess the quality of sequencing files and reliably classify the organism and protocol type. This is done in an alignment-free algorithm that leverages a Random Forest classifier to learn from native biases in k-mer frequencies and repeat sequence identities between different sequencing technologies and species. Here, we show that our method can accurately and rapidly distinguish between human and mouse, nine different sequencing technologies, and both together, 98.32%, 97.86%, and 96.38% of the time in high confidence calls respectively. This demonstrates that SeqWho is a powerful method for reliably checking the identity of the sequencing files used in any pipeline and illustrates the programs ability to leverage k-mer biases.

bioinformatics↗