Search

The transforming growth factor β (TGFβ) pathway plays a complex role in cancer biology, being involved in both tumour suppression as well as promotion. Overactive TGFβ signalling has been linked to multiple diseases, including cancer, pulmonary arterial hypertension, and fibrosis. One of the key meditators within this pathway is the TGFβ type I receptor, also termed activin receptor-like kinase 5 (ALK5). ALK5 expression level is a key determinant of TGFβ signalling intensity and duration, and perturbation has been linked to diseases. A validated ALK5 positron emission tomography (PET) tracer creates an opportunity, therefore, to study its role in human diseases. To develop ALK5 PET tracers, two small molecule ALK5 kinase inhibitors were selected as lead compounds, which were labelled with carbon-11 and fluorine-18, respectively. [11C]LR111 was synthesized with a yield of 17 ± 6%, a molar activity of 126 ± 79 GBq·μmol−1 and a purity of >95% (n = 44). [18F]EW-7197 was synthesized with a yield of 10 ± 5%, a molar activity of 183 ± 126 GBq·μmol−1 and a purity of >95% (n = 11). Metabolic stability was evaluated in vivo in mice, showing 39 ± 2% of intact [11C]LR111 and 21 ± 2% of intact [18F]EW-7197 in blood plasma at 45 min p.i. In vitro binding experiments were conducted in breast cancer MDA-MB-231 and lung cancer A431 cell lines. In addition, both tracers were used for PET imaging in MDA-MB-231 xenograft models. Selective uptake of [18F]EW-7197 and [11C]LR111 was observed in MDA-MB-231 cells, in the MDA-MB-231 tumour xenografts in vivo and in the autoradiograms. As [11C]LR111 and [18F]EW-7197 showed selectivity of binding to ALK5 in vivo and in vitro. Both tracers are thereby valuable tools for the detection of ALK5 activity.

The authors regret . The authors would like to apologise for any inconvenience caused.

Future extreme sea levels (ESLs) and flood risk along European coasts will be strongly impacted by global warming. Yet, comprehensive projections of ESL that include mean sea level (MSL), tides, waves, and storm surges do not exist. Here, we show changes in all components of ESLs until 2100 in view of climate change. We find that by the end of this century, the 100-year ESL along Europe's coastlines is on average projected to increase by 57 cm for Representative Concentration Pathways (RCP)4.5 and 81 cm for RCP8.5. The North Sea region is projected to face the highest increase in ESLs, amounting to nearly 1 m under RCP8.5 by 2100, followed by the Baltic Sea and Atlantic coasts of the UK and Ireland. Relative sea level rise (RSLR) is shown to be the main driver of the projected rise in ESL, with increasing dominance toward the end of the century and for the high-concentration pathway. Changes in storm surges and waves enhance the effects of RSLR along the majority of northern European coasts, locally with contributions up to 40%. In southern Europe, episodic extreme events tend to stay stable, except along the Portuguese coast and the Gulf of Cadiz where reductions in surge and wave extremes offset RSLR by 20–30%. By the end of this century, 5 million Europeans currently under threat of a 100-year ESL could be annually at risk from coastal flooding under high-end warming. The presented dataset is available through this link: http://data.jrc.ec.europa.eu/collection/LISCOAST. Plain Language Summary: Future extreme sea levels and flood risk along European coasts will be strongly impacted by global warming. Here, we show changes in all acting components, i.e., sea level rise, tides, waves, and storm surges, until 2100 in view of climate change. We find that by the end of this century the 100-year event along Europe will on average increase between 57 and 81 cm. The North Sea region is projected to face the highest increase, amounting to nearly 1 m under a high emission scenario by 2100, followed by the Baltic Sea and Atlantic coasts of the UK and Ireland. Sea level rise is the main driver of the changes, but intensified climate extremes along most of northern Europe can have significant local effects. Little changes in climate extremes are shown along southern Europe, with the exception of a projected decrease along the Portuguese coast and the Gulf of Cadiz, offseting sea level rise by 20–30%. By the end of this century, 5 million Europeans currently under threat of a 100-year coastal flood event could be annually at risk from coastal flooding under high-end warming.

In mining engineering, resource and grade control models aim to characterize the spatial distribution generating by geostatistics methods of ore tonnage and grade in an deposit. A continuously self-updating resource model concept has recently been developed by Wambeke and others (2016) and aims to improve the raw material quality control and process efficiency of any type of mining operation. The proposed concept integrates sensor data measured with different support along the production line into the resource or grade control model and provides continuously locally more accurate estimates. Applications in underground mines include the identification of different components of the mineralogy and geochemistry. This study aims to develop an efficient updating framework based on a sequential ensemble filtering on a compositional environment (Tolosana-Delgado (2013)). The importance of respecting as well physical conservation principles has long been recognized. During the data assimilation procedure, the mass of each component should be preserved within each ensemble member through the procedure used to update the model. Different approaches to constraining ensemble based Kalman Filters have been presented as solutions of a set of regularized least squares optimization problems. Some of these have been formulated by imposing non-negativity constrains or by using transform methods such as anamorphosis, at the price of violating mass preservation. Compositional approaches supersede this problem by dealing with the positivity condition and the mass preservation implicitly through assimilating log-ratios instead of the original components. After a detailed literature review, a compositional sequential ensemble filter approach adapted to specific application in mining is presented. Method validation results are presented for a 2D case study in a fully controllable environment. After validation, a sensitivity analysis investigates the effects of different parameters and derived practical implementation aspects for an effective application. This research is part of the European Union funded "Real Time Mining" project, which aims at developing a new framework to reduce uncertainties during the block extraction process. New sensor based technology will provide georeferenced information about mineralogy and grades by taking images of the mine face, during block extraction from the muckpile, or via sensors installed on the conveyor belt about the mean composition of the ores. Based on a discrete time event simulation, the project will test the capabilities of incorporating sensor imaging information as pointwise data and of mean compositions as block data.

A key requirement for mining operations is the need to be planned according to the existence knowledge about the geometallurgical features of the mineral resource. Geostatistical models are created out of exploration data to describe such geological features as a resource and grade control model. To collect information during the mining production process about these features is becoming more popular due to sensor technology advances. In order to incorporate this new information during the mining production process to the geostatistical models, new approaches based on data assimilation theory are being developed. These are Ensemble Sequential Updating techniques. They provide a comprehensive solution for these challenges since, that allow to relate the potentially non-linear relation that exists between the resource and grade control model state variables and the observations. However, there are different challenges to face in order to understand how the information that proceed from sensors informs about the geostatistical models and how to feed updated information back to the resource model. On one hand, the state variables of such models are actually compositions, mineral and/or chemical. Classical data assimilation solutions need thus to be modified to account for mass conservation of each component, while keeping their physical relations, univariate or multi-variable (e.g., stability of mineral assemblages, total sum constraint, positivity). On the other hand, some sensors might deliver information restricted to a subcomposition. A compositional data assimilation approach supersede some of these problems by dealing with the positivity condition and the mass preservation implicitly through assimilating log-ratios instead of the original components, which naturally allow a subcompositionally coherent modification if the sensors used require it. The univariate and the compositional data assimilation approaches are tested in two different virtual assets models created as a fully controllable environment with different geometallurgical features. After validation, a sensitivity analysis investigates the effects of different parameters and derived practical implementation aspects for an effective application of compositional data assimilation within an operating mine.

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational scheme requires the implementation of the adjoint of (the tangent linear approximation of) the original model which is a tremendous programming effort, that hampers new applications of the method. Recently a new ensemble approach to variational inverse modelling using Proper Orthogonal Decomposition (POD) model reduction has been proposed that does not require the implementation of the adjoint model. Using an ensemble of forward model simulations an approximation of the covariance matrix of the model variability is determined. A limited number of leading eigenvectors of this matrix are selected to define a model sub space. By projecting the original model onto this subspace an approximate linear model is obtained. Once this reduced model is available the minimization process can be solved completely in reduced space with negligible computational costs. Schemes based on the well-known Kalman filtering algorithm are also used recently for inverse modeling. The last years a number of ensemble based algorithms have been proposed, e.g., the Ensemble Kalman filter (EnKF), the Reduced Rank Square Root filter (RRSQRT) and the Ensemble Square Root filter (ESRF). Although introduced for linear state estimation, these new algorithms 102 Ensemble methods for large scale inverse problems are able to handle nonlinear models accurately and, therefore, are very attractive for solving combined state and parameter estimation problems. It has been shown recently that the so-called symmetric version of the ESRF introduces the smallest increments and, therefore, is in most applications more accurate then the original version of this algorithm.