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

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

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

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

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

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

Self-assembly and anti-amyloid cytotoxicity activity of amyloid beta peptide derivatives

The self-assembly of two derivatives of KLVFF, a fragment Abeta(16-20) of the amyloid beta (Abeta) peptide, is investigated and recovery of viability of neuroblastoma cells exposed to Abeta is observed at sub-stoichiometric peptide concentrations. Fluorescence assays show that NH2-KLVFF-CONH2 undergoes hydrophobic collapse and amyloid formation at the same critical aggregation concentration (cac). In contrast, NH2-K(Boc)LVFF-CONH2 undergoes hydrophobic collapse at a low concentration, followed by amyloid formation at a higher cac. These findings are supported by the beta-sheet features observed by FTIR. Electrospray ionization mass spectrometry indicates that NH2-K(Boc)LVFF-CONH2 forms a significant population of oligomeric species above the cac. Cryo-TEM, used together with SAXS to determine fibril dimensions, shows that the length and degree of twisting of peptide fibrils seem to be influenced by the net peptide charge. Grazing incidence X-ray scattering from thin peptide films shows features of beta-sheet ordering for both peptides, along with evidence for lamellar ordering of NH2-KLVFF-CONH2. This work provides a comprehensive picture of the aggregation properties of these two KLVFF derivatives and show their utility, in unaggregated form, in restoring the viability of neuroblastoma cells against Abeta-induced toxicity