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The genetic consequences of the subdivision of populations are regarded as significant to long‐term evolution, and research has shown that the scale and speed at which this is now occurring is critically reducing the adaptive potential of most species which inhabit human‐impacted landscapes. Here, we provide a rare and, to our knowledge, the first analysis of this process while it is happening and demonstrate a method of evaluating the effect of mitigation measures such as fauna crossings. We did this by using an extensive genetic data set collected from a koala population which was intensely monitored during the construction of linear transport infrastructure which resulted in the subdivision of their population. First, we found that both allelic richness and effective population size decreased through the process of population subdivision. Second, we predicted the extent to which genetic drift could impact genetic diversity over time and showed that after only 10 generations the resulting two subdivided populations could experience between 12% and 69% loss in genetic diversity. Lastly, using forward simulations we estimated that a minimum of eight koalas would need to disperse from each side of the subdivision per generation to maintain genetic connectivity close to zero but that 16 koalas would ensure that both genetic connectivity and diversity remained unchanged. These results have important consequences for the genetic management of species in human‐impacted landscapes by showing which genetic metrics are best to identify immediate loss in genetic diversity and how to evaluate the effectiveness of any mitigation measures. [ABSTRACT FROM AUTHOR]

Vegetation clearing has been implicated as a major contributor to biodiversity loss. It therefore stands to reason that developers should face a regulatory requirement to assess potential impacts and to avoid, mitigate and compensate for loss of vegetation wherever proposed infrastructure developments impact on vegetation considered to be habitat for threatened species. Environmental Impact Assessments (EIA) rely on accurate information to describe the distributions of threatened species within the footprint of proposed infrastructure developments, which is critical to ensuring appropriate mitigation of potentially deleterious impacts on these populations. EIA survey guidelines seek to determine species' presence accurately, while acknowledging the limits imposed by time and budget constraints. As such, the EIA guidelines may recommend: (1) stratifying the landscape based on previous knowledge of habitat variables relevant to a species; and (2) targeting survey effort to strata with high probability of occupancy. Here, we use koala Phascolarctos cinereus surveys as a case study to explore the extent to which application of EIA guidelines result in accurate occupancy estimates. We compared the presence/absence distribution across one landscape survey and three EIA surveys, and found that koala occupancy was not well predicted by koala habitat criteria widely used to inform sampling design. In the context of EIA, we provide an example of how targeting survey effort to strata with high probability of occupancy risks misrepresenting true occurrence patterns. A general issue with survey designs that rely on previous knowledge is that they self‐reinforce erroneous assumptions. Our findings stand as a warning that EIA might neither quantify the impact of proposed infrastructure developments adequately, nor inform the ensuing mitigation measures. Threatened species' protection in the face of infrastructure development will require new approaches to EIAs to ensure that providers are enabled to undertake comprehensive environmental surveys capable of detecting priority species. [ABSTRACT FROM AUTHOR]

This study reports the establishment of a bone marrow mononuclear cell (BMMC) 3D culture model and the application of this model to define sensitivity and resistance biomarkers of acute myeloid leukaemia (AML) patient bone marrow samples in response to Cytarabine (Ara-C) treatment. By mimicking physiological bone marrow microenvironment, the growth conditions were optimized by using frozen BMMCs derived from healthy donors. Healthy BMMCs are capable of differentiating into major hematopoietic lineages and various types of stromal cells in this platform. Cryopreserved BMMC samples from 49 AML patients were characterized for ex vivo growth and sensitivity to Ara-C. RNA sequencing was performed for 3D and 2D cultures to determine differential gene expression patterns. Specific genetic mutations and/or gene expression signatures associated with the ability of the ex vivo expansion and response to Ara-C were elucidated by whole-exome and RNA sequencing. Data analysis identified unique gene expression signatures and novel genetic mutations associated with sensitivity to Ara-C treatment of proliferating AML specimens and can be used as predictive therapeutic biomarkers to determine the optimal treatment regimens. Furthermore, these data demonstrate the translational value of this ex vivo platform which should be widely applicable to evaluate other therapies in AML., (© 2019 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

Objectives: Demographic crashes due to emerging diseases can contribute to population fragmentation and increase extinction risk of small populations. Ebola outbreaks in 2002-2004 are suspected to have caused a decline of more than 80% in some Western lowland gorilla (Gorilla gorilla gorilla) populations. We investigated whether demographic indicators of this event allowed for the detection of spatial fragmentation in gorilla populations., Materials and Methods: We collected demographic data from two neighbouring populations: the Lokoué population, suspected to have been affected by an Ebola outbreak (followed from 2001 to 2014), and the Romani population, of unknown demographic status before Ebola outbreaks (followed from 2005 to 2014)., Results: Ten years after the outbreak, the Lokoué population is slowly recovering and the short-term demographic indicators of a population crash were no longer detectable. The Lokoué population has not experienced any additional demographic perturbation over the past decade. The Romani population did not show any of the demographic indicators of a population crash over the past decade. Its demographic structure remained similar to that of unaffected populations., Discussion: Our results highlighted that the Ebola disease could contribute to fragmentation of gorilla populations due to the spatially heterogeneous impact of its outbreaks. The demographic structure of populations (i.e., age-sex and group structure) can be useful indicators of a possible occurrence of recent Ebola outbreaks in populations without known history, and may be more broadly used in other emerging disease/species systems. Longitudinal data are critical to our understanding of the impact of emerging diseases on wild populations and their conservation., (© 2017 Wiley Periodicals, Inc.)

There is an urgent need for pre-clinical and clinical biomarkers predictive of vaccine immunogenicity, efficacy and safety to reduce the risks and costs associated with vaccine development. Results emerging from immunoprofiling studies in non-human primates and humans demonstrate clearly that (i) type and duration of immune memory are largely determined by the magnitude and complexity of the innate immune signals and (ii) genetic signatures highly predictive of B-cell and T-cell responses can be identified for specific vaccines. For vaccines with similar composition, e.g. live attenuated viral vaccines, these signatures share common patterns. Signatures predictive of vaccine efficacy have been identified in a few experimental challenge studies. This review aims to give an overview of the current literature on immunoprofiling studies in humans and also presents some of our own data on profiling of licensed and experimental vaccines in non-human primates., (© 2011 Merck Sharp & Dohme Corp. Microbial Biotechnology © 2011 Society for Applied Microbiology and Blackwell Publishing Ltd.)