A single mutation in the envelope protein modulates flavivirus antigenicity, stability, and pathogenesis

The structural flexibility or 'breathing' of the envelope (E) protein of flaviviruses allows virions to sample an ensemble of conformations at equilibrium. The molecular basis and functional consequences of virus conformational dynamics are poorly understood. Here, we identified a single mutation at residue 198 (T198F) of the West Nile virus (WNV) E protein domain I-II hinge that regulates virus breathing. The T198F mutation resulted in a ~70-fold increase in sensitivity to neutralization by a monoclonal antibody targeting a cryptic epitope in the fusion loop. Increased exposure of this otherwise poorly accessible fusion loop epitope was accompanied by reduced virus stability in solution at physiological temperatures. Introduction of a mutation at the analogous residue of dengue virus (DENV), but not Zika virus (ZIKV), E protein also increased accessibility of the cryptic fusion loop epitope and decreased virus stability in solution, suggesting that this residue modulates the structural ensembles sampled by distinct flaviviruses at equilibrium in a context dependent manner. Although the T198F mutation did not substantially impair WNV growth kinetics in vitro, studies in mice revealed attenuation of WNV T198F infection. Overall, our study provides insight into the molecular basis and the in vitro and in vivo consequences of flavivirus breathing

MEPicides: Potent antimalarial prodrugs targeting isoprenoid biosynthesis

AbstractThe emergence of Plasmodium falciparum resistant to frontline therapeutics has prompted efforts to identify and validate agents with novel mechanisms of action. MEPicides represent a new class of antimalarials that inhibit enzymes of the methylerythritol phosphate (MEP) pathway of isoprenoid biosynthesis, including the clinically validated target, deoxyxylulose phosphate reductoisomerase (Dxr). Here we describe RCB-185, a lipophilic prodrug with nanomolar activity against asexual parasites. Growth of P. falciparum treated with RCB-185 was rescued by isoprenoid precursor supplementation, and treatment substantially reduced metabolite levels downstream of the Dxr enzyme. In addition, parasites that produced higher levels of the Dxr substrate were resistant to RCB-185. Notably, environmental isolates resistant to current therapies remained sensitive to RCB-185, the compound effectively treated sexually-committed parasites, and was both safe and efficacious in malaria-infected mice. Collectively, our data demonstrate that RCB-185 potently and selectively inhibits Dxr in P. falciparum, and represents a promising lead compound for further drug development.</jats:p

A stochastic model for the fracture network in the Habanero enhanced geothermal system

GeoCat; 74874Fracture Network Modelling (FNM) plays an important role in many areas where the characterization of discontinuities in deep ground is required. Applications of the FNM include, but not limited, hydrocarbon reservoir production, mineral extraction, tunnelling, underground storage or disposal of hazardous wastes and geothermal systems. One important step in FNM is to estimate the density of fractures and geometries and properties of individual fractures such as the size and orientation. Due to the lack of data, the tortuous nature of fractures and the great uncertainty involved in practice, the only feasible approach is via a stochastic modelling. This paper describes a general optimization approach to modelling the fracture network in a geothermal reservoir, conditioned on the seismic events several kilometres beneath the surface detected during the fracture stimulation process. Two key aspects of our method are the construction of an appropriate objective function and the derivation of an efficient updating scheme, which still remain to be the two challenging issues of most global optimization techniques. In our application, the objective function consists of two important components: the minimisation of squared distances of the seismic points to the fracture model and the minimisation of number of fractures or the amount of fracturing, which corresponds to the least consumption of fracturing energy. The model updating process includes several proposals for perturbing the parameters of individual fractures and also to alter the size of the fracture network in order to get a global optimal solution. As a case study, the model is applied to Geodynamics’ Habanero reservoir in the Cooper Basin of South Australia.Seifollahi, S., Dowd, P-A and Xu,

Geostatistics in the Presence of Multivariate Complexities: Comparison of Multi-Gaussian Transforms

Published 05 April 2023. OnlinePublOne of the most challenging aspects of multivariate geostatistics is dealing with complex relationships between variables. Geostatistical co-simulation and spatial decorrelation methods, commonly used for modelling multiple variables, are ineffective in the presence of multivariate complexities. On the other hand, multi-Gaussian transforms are designed to deal with complex multivariate relationships, such as non-linearity, heteroscedasticity and geological constraints. These methods transform the variables into independent multi-Gaussian factors that can be individually simulated. This study compares the performance of the following multi-Gaussian transforms: rotation based iterative Gaussianisation, projection pursuit multivariate transform and flow transformation. Case studies with bivariate complexities are used to evaluate and compare the realisations of the transformed values. For this purpose, commonly used geostatistical validation metrics are applied, including multivariate normality tests, reproduction of bivariate relationships, and histogram and variogram validation. Based on most of the metrics, all three methods produced results of similar quality. The most obvious difference is the execution speed for forward and back transformation, for which flow transformation is much slower.Sultan Abulkhair, Peter A. Dowd, Chaoshui X

Rapid updating of resource knowledge with sensor information

Resource models are generally constructed from directly observed data (e.g., grades of drill cores) that have relatively high accuracy. However, the accuracy of resource models is therefore limited by the scale on which the data are collected. As mining progresses, more information becomes available on different scales from various types and sources of data (e.g., blast hole samples, sensors on drill rigs, conveyor belts and draw points). This continuous stream of production data can be used to update resource knowledge in near real-time. The ensemble Kalman filter has been successfully applied to update resource and grade control models. However, due to the Gaussianity assumption, the ensemble Kalman filter must be combined with some kind of Gaussian transformation, such as a normal score transform. Multi-Gaussian transformations can yield better results in terms of reproducing relationships between multiple grade variables. This poster presents a case study demonstrating the application of the ensemble Kalman filter and the projection pursuit multivariate transform for sequential updating of multivariate geostatistical models.Sultan Abulkhair, Peter A. Dowd, Chaoshui X

An enhanced stochastic optimization in fracture network modelling conditional on seismic events

This paper presents an approach to modelling fracture networks in hot dry rock geothermal reservoirs. A detailed understanding of the fracture network within a geothermal reservoir is critically important for assessments of reservoir potential and optimal production design. One important step in fracture network modelling is to estimate the fracture density and the fracture geometries, particularly the size and orientation of fractures. As fracture networks in these reservoirs can never be directly observed there is significant uncertainty about their true nature and the only feasible approach to modelling is a stochastic one. We propose a global optimization approach using simulated annealing which is an extension of our previous work. The fracture model consists of a number of individual fractures represented by ellipses passing through the micro-seismic points detected during the fracture stimulation process, i.e. the fracture model is conditioned on the seismic points. The distances of the seismic points from fitted fracture planes (ellipses) are, therefore, important in assessing the goodness-of-fit of the model. Our aims in the proposed approach are to formulate an appropriate objective function for the optimal fitting of a set of fracture planes to the micro-seismic data and to derive an efficient modification scheme to update the model parameters. The proposed objective function consists of three components: orthogonal projection distances of the seismic points from the nearest fitted fractures, the amount of fracturing (fitted fracture areas) and the volumes of the convex hull of the associated points of fitted fractures. The functions used in the model update scheme allow the model to achieve an acceptable fit to the points and to converge to acceptable fitted fracture sizes. These functions include two groups of proposals: one for updating fracture parameters and the other for determining the size of the fracture network. To increase the efficiency of the optimization, a spatial clustering approach, the Distance-Directional Transform, was developed to generate parameters for newly proposed fractures. A simulated dataset was used as an example to evaluate our approach and we compared the results to those derived using our previously published algorithm on a real dataset from the Habanero geothermal field in the Cooper Basin, South Australia. In a real application, such as the Habanero dataset, it is difficult to determine definitively which algorithm performs better due to the many uncertainties but the number of association points, the number of final fractures and the error are three important factors that quantify the effectiveness of our algorithm. © 2014 Elsevier Ltd.S. Seifollahi, P.A. Dowd, C. X

Sharing Longevity Risk: Why Governments Should Issue Longevity Bonds

Government-issued longevity bonds would allow longevity risk to be shared efficiently and fairly between generations. In exchange for paying a longevity risk premium, the current generation of retirees can look to future generations to hedge their systematic longevity risk. Longevity bonds will lead to a more secure pension savings market, together with a more efficient annuity market. By issuing longevity bonds, governments can aid the establishment of reliable longevity indices and key price points on the longevity risk term structure and help the emerging capital market in longevity-linked instruments to build on this term structure with liquid longevity derivatives

Prediction of photoperiodic regulators from quantitative gene circuit models

Photoperiod sensors allow physiological adaptation to the changing seasons. The external coincidence hypothesis postulates that a light-responsive regulator is modulated by a circadian rhythm. Sufficient data are available to test this quantitatively in plants, though not yet in animals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their lightsensitive proteins are thought to form an external coincidence sensor. We use 40 timeseries of molecular data to model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, the models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modelling makes this complexity explicit and may thus contribute to crop improvement