Synthesis of cubic diamond in the graphite-magnesium carbonate and graphite-K2Mg(CO3)(2) systems at high pressure of 9-10 GPa region

Cubic diamond was synthesized with two systems, (1) graphite with pure magnesium carbonate (magnesite) and (2) graphite with mixed potassium and magnesium carbonate at pressures and temperatures above 9.5 GPa, 1600 degrees C and 9 GPa, 1650 degrees C, respectively. At these conditions (1) the pure magnesite is solid, whereas (2) the mixed carbonate exists as a melt. In this pressure range, graphite seems to be partially transformed into hexagonal diamond. Measured carbon isotope delta(13)C values for all the materials suggest that the origin of the carbon source to form cubic diamond was the initial graphite powder, and not the carbonates

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty. In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia. A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions. Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics

Publisher Correction: Towards an ecosystem model of infectious disease

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-021-01454-8, published online 17 May 2021

Quantitative characterization of plastic deformation of single diamond crystals: A high pressure high temperature (HPHT) experimental deformation study combined with electron backscatter diffraction (EBSD)

We report the results of a high-pressure high-temperature (HPHT) experimental investigation into the deformation of diamonds using the D-DIA apparatus. Electron backscatter diffraction (EBSD) data confirm that well-defined 300–700 nm wide {111} slip lamellae are in fact deformation micro-twins with a 60° rotation around a axis. Such twins formed at high confining pressures even without any apparatus-induced differential stress; mechanical anisotropy within the cell assembly was sufficient for their formation with very little subsequent lattice bending ( slip system dominates as expected for the face-centred cubic (FCC) structure of diamond. Slip occurs on multiple {111} planes resulting in rotation around axes. Deformation microstructure characteristics depend on the orientation of the principal stress axes and finite strain but are independent of confining pressure and nitrogen content. All of the uniaxially deformed samples took on a brown colour, irrespective of their initial nitrogen characteristics. This is in contrast to the two quasi-hydrostatic experiments, which retained their original colour (colourless for nitrogen free diamond, yellow for single substitutional nitrogen, Type Ib diamond) despite the formation of {111} twin lamellae. Comparison of our experimental data with those from two natural brown diamonds from Finsch mine (South Africa) shows the same activation of the dominant slip system. However, no deformation twin lamellae are present in the natural samples. This difference may be due to the lower strain rates experienced by the natural samples investigated. Our study shows the applicability and potential of this type of analysis to the investigation of plastic deformation of diamonds under mantle conditions

Global warming will reshuffle the areas of high prevalence and richness of three genera of avian blood parasites

The importance of parasitism for host populations depends on local parasite richness and prevalence: usually host individuals face higher infection risk in areas where parasites are most diverse, and host dispersal to or from these areas may have fitness consequences. Knowing how parasites are and will be distributed in space and time (in a context of global change) is thus crucial from both an ecological and a biological conservation perspective. Nevertheless, most research articles focus just on elaborating models of parasite distribution instead of parasite diversity. We produced distribution models of the areas where haemosporidian parasites are currently highly diverse (both at community and within-host levels) and prevalent among Iberian populations of a model passerine host: the blackcap Sylvia atricapilla; and how these areas are expected to vary according to three scenarios of climate change. Based on these models, we analysed whether variation among populations in parasite richness or prevalence are expected to remain the same or change in the future, thereby reshuffling the geographic mosaic of host-parasite interactions as we observe it today. Our models predict a rearrangement of areas of high prevalence and richness of parasites in the future, with Haemoproteus and Leucocytozoon parasites (today the most diverse genera in blackcaps) losing areas of high diversity and Plasmodium parasites (the most virulent ones) gaining them. Likewise, the prevalence of multiple infections and parasite infracommunity richness would be reduced. Importantly, differences among populations in the prevalence and richness of parasites are expected to decrease in the future, creating a more homogeneous parasitic landscape. This predicts an altered geographic mosaic of hostparasite relationships, which will modify the interaction arena in which parasite virulence evolves

Assessing rotation-invariant feature classification for automated wildebeest population counts

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.CJT is supported by a Complex Systems Scholar Award from the James S. McDonnell Foundation. JGCH is supported by a Lord Kelvin Adam Smith Fellowship, funding from the British Ecological Society and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641918 AfricanBioServices. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

From serological surveys to disease burden: a modelling pipeline for Chagas disease.

In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021–2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000–648 000) in 2020 with a 1.0% (95%CrI = 0.8–1.3%) prevalence in the general population and 2400 (95%CrI = 1900–3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’

Systematic in vivo analysis of the intrinsic determinants of amyloid Beta pathogenicity.

Protein aggregation into amyloid fibrils and protofibrillar aggregates is associated with a number of the most common neurodegenerative diseases. We have established, using a computational approach, that knowledge of the primary sequences of proteins is sufficient to predict their in vitro aggregation propensities. Here we demonstrate, using rational mutagenesis of the Abeta42 peptide based on such computational predictions of aggregation propensity, the existence of a strong correlation between the propensity of Abeta42 to form protofibrils and its effect on neuronal dysfunction and degeneration in a Drosophila model of Alzheimer disease. Our findings provide a quantitative description of the molecular basis for the pathogenicity of Abeta and link directly and systematically the intrinsic properties of biomolecules, predicted in silico and confirmed in vitro, to pathogenic events taking place in a living organism