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Simpson, D.

Publications and source records attributed to Simpson, D..

21 records · Page 2Linked to original sources

Estimating relative CWD susceptibility and disease progression in farmed whitetail deer with rare PRNP alleles

Chronic wasting disease is a prion disease affecting both free-ranging and farmed cervids in North America and Scandinavia. A range of cervid species have been found to be susceptible, each with variations in the gene for the normal prion protein, PRNP, reportedly influencing both disease susceptibility and progression in the respective hosts. Despite the finding of several different PRNP alleles in whitetail deer, the majority of past research has focused on two of the more common alleles identified - the 96G and 96S alleles. In the present study, we evaluate both infection status and disease stage in nearly 2100 farmed deer depopulated in the United States and Canada, including 714 CWD-positive deer and correlate our findings with PRNP genotype, including the more rare 95H, 116G, and 226K alleles. We found significant differences in either likelihood of being found infected or disease stage (and in many cases both) at the time of depopulation in all genotypes present, relative to the most common 96GG genotype. Despite high prevalence in many of the herds examined, infection was not found in several of the reported genotypes. These findings suggest that additional research is necessary to more properly define the role that these genotypes may play in managing CWD in both farmed and free-ranging whitetail deer, with consideration for factors including relative fitness levels, incubation periods, and the kinetics of shedding in animals with these rare genotypes.

microbiology↗

G2M splits mouse embryonic stem cells into naïve and formative pluripotency states

Embryonic stem cells (ESCs) express heterogeneous levels of pluripotency and developmental transcription factors (TFs) and their cell cycle is unsynchronised when grown in the presence of serum. Here, we asked whether the cell cycle and developmental heterogeneities of ESCs are coordinated by determining the state identities of G1- and G2M-enriched mouse ESCs (mESCs) at single cell resolution. We found that G2M cells were not all the same and demonstrate their split into the naive and formative (intermediate) pluripotency states marked by high or low Esrrb expression, respectively. The naive G2M sub-state resembles ground state pluripotency of the LIF/2i cultured mESCs. The naive and formative G2M sub-states exist in the pre- and post-implantation stages of the mouse embryo, respectively, verifying developmental distinction. Moreover, the G2M sub-states partially match between the mouse and human ESCs, suggesting higher similarity of transcriptional control between these species in G2M. Our findings propose a model whereby G2M separates mESCs into naive and formative pluripotency states. This concept of G2M-diverted pluripotency states provides new framework for understanding the mechanisms of pluripotency maintenance and lineage specification in vitro and in vivo, and the development of more efficient and clinically relevant reprogramming strategies.

developmental biology↗

REMAP: An online remote sensing application for land cover classification and monitoring

O_LIRecent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to make ecosystem maps from remote sensing data.\nC_LIO_LIWe have developed a free and open-access online remote sensing and environmental modelling application, REMAP (the remote ecosystem monitoring and assessment pipeline; https://remap-app.org) that enables volunteers, managers, and scientists with little or no experience in remote sensing to develop high-resolution classified maps of land cover and land use change over time.\nC_LIO_LIREMAP utilizes the geospatial data storage and analysis capacity of the Google Earth Engine, and requires only spatially resolved training data that define map classes of interest (e.g., ecosystem types). The training data, which can be uploaded or annotated interactively within REMAP, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in REMAP represent topographic (e.g. slope, elevation), spectral (Landsat Archive image composites) and climatic variables (precipitation, temperature) that can inform on the distribution of ecosystems and land cover classes.\nC_LIO_LIThe ability of REMAP to develop and export high-quality classified maps in a very short (<10 minute) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, REMAP can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem-based spatial conservation planning, mapping ecosystem loss at local scales, and supporting environmental education initiatives.\nC_LI

ecology↗