Search

The concept of photocaging represents a promising approach to acquire spatiotemporal control over molecular bioactivity. To apply this strategy to pyridinylimidazole-based covalent JNK3 inhibitors, we used acrylamido-N-(4-((4-(4-(4-fluorophenyl)-1-methyl-2-(methylthio)-1H-imidazol-5-yl)pyridin-2-yl)amino)phenyl)benzamide (1) as a lead compound to design novel covalent inhibitors of JNK3 by modifying the amide bond moiety in the linker. The newly synthesized inhibitors demonstrated IC50 values in the low double-digit nanomolar range in a radiometric kinase assay. They were further characterized in a NanoBRETTM intracellular JNK3 assay, where covalent engagement of the target enzyme was confirmed by compound washout experiments and a loss in binding affinity for a newly generated JNK3(C154A)-NLuc mutant. The most potent compound of the series, N-(3-acrylamidophenyl)-4-((4-(4-(4-fluorophenyl)-1-methyl-2-(methylthio)-1H-imidazol-5-yl)pyridin-2-yl)amino)benzamide (13), was equipped with a photolabile protecting group leading to a nearly 10-fold decrease in intracellular JNK3 binding affinity, which was fully recovered by UV irradiation at a wavelength of 365 nm within 8 min. Our results highlight that photocaged covalent inhibitors can serve as a pharmacological tool to control JNK3 activity in live cells with light.

The title compound (S)-3-(3-((7-ethynyl-9H-pyrimido[4,5-b]indol-4-yl)amino)piperidin-1-yl)propanenitrile (2) was synthesized in five steps, starting from 4-chloro-7-iodo-9H-pyrimido[4,5-b]indole (3), and was characterized by 1H-NMR, 13C-NMR, MS and HPLC. Moreover, its structure was confirmed by single crystal X-ray diffraction. Pyrimido[4,5-b]indole 2 demonstrated an IC50 value of 2.24 µM in a NanoBRETTM TE intracellular glycogen synthase kinase-3β assay.

The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization. Validerad;2019;Nivå 2;2019-03-18 (svasva)

At the interface of geology and mineral processing, geometallurgy is a powerful tool for enhancingresource efficiency. A spatial model that represents the ore body in terms ofmineralogyand physical properties is combined with a process model that describes the concentrationprocess. The performance of a given ore in the process is computed in terms of gradeand recovery of the mineral of interest in the concentrate, but also the presence of potentialpenalty elements and energy costs. The inclusion of ore performance indicators in a blockmodel yields a geometallurgical model that considers the variations in an ore body.Progress has been made in recent years to list and study different processing options interms of data requirements and implementation costs. While providing useful data, littleadvance was made to guide decision-making and to handle uncertainty. The objective has,therefore, been to develop, choose and validate computational methods that suggest optimaldecisions in the scope of geometallurgical strategies for an iron ore and a porphyry copperdeposit.The selected approach is based on an analysis of structure and regularity fromthe ore blockdown to the mineral grains. By selecting the appropriate mathematical tool for each scale,the dimension of the data is reduced and the different scales are then taken into account inmaking decisions. Methods introduced for dimension reduction include machine learningmodels, statistical models and spectral descriptors. The decision models rely on stochasticmulti-armed bandits which are a form of reinforcement learning. The presentation of thedifferent models proceeds by zooming in from coarse scale to fine scale then taking a stepback and analyze the implications. Data that was collected during sampling campaigns andindustrial plant surveys is used to design and verify the proposedmodels.iWith regard to the dimension reduction problem, results showed the method’s ability toclassify mineral textures and identify mineral phases with more than 90 percent accuracy onthe selected data sets of optical images and incorporate different physical properties into ageometallurgical ore type classification. Decision results showed that strategies in the case ofa feed grade control or when different ore types were identified, resulted in a twofold increaseof a reward function which is either Boolean (the product fulfills quality requirements ornot), or continuous (an economic objective). The cumulative value of the reward functionmeasured the optimality of a processing strategy. Quantitative methods were introduced toevaluate ore classification as well as geometallurgical strategies.The achieved results suggest the introduction of these computationalmethods in the practiceof geometallurgy. The increased knowledge of different ore type performances and appropriatemodels lead to optimal decisions for improved resource efficiency along the ore valuechain. This is achieved by bothmaximizing profit and decreasing environmental impact, forexample by choosing processing routes that minimize energy consumption.

ISBN för värdpublikation: 978-90-8227-694-7

A geometallurgical model is the combination of a spatial model representing an ore deposit and a process model representing the comminution and concentration steps in beneficiation. The process model itself usually consists of several unit models. Each of these unit models operates at a given level of detail in material characterization - from bulk chemical elements, elements by size, bulk minerals and minerals by size to the liberation level that introduces particles as the basic entity for simulation (Paper 1). In current state-of-the-art process simulation, few unit models are defined at the particle level because these models are complex to design at a more fundamental level of detail, liberation data is hard to measure accurately and large computational power is required to process the many particles in a flow sheet. Computational cost is a consequence of the intrinsic complexity of the unit models. Mineral liberation data depends on the quality of the sampling and the polishing, the settings and stability of the instrument and the processing of the data. This study introduces new tools to simulate a population of mineral particles based on intrinsic characteristics of the feed ore. Features are extracted at the meso-textural level (drill cores) (Paper 2), put in relation to their micro-textures before breakage and after breakage (Paper 3). The result is a population of mineral particles stored in a file format compatible to import into process simulation software. The results show that the approach is relevant and can be generalized towards new characterization methods. The theory of image representation, analysis and ore texture simulation is briefly introduced and linked to 1-point, 2-point, and multiple-point methods from spatial statistics. A breakage mechanism is presented as a cellular automaton. Experimental data and examples are taken from a copper-gold deposit with a chalcopyrite flotation circuit, an iron ore deposit with a magnetic separation process. This study is covering a part of a larger research program, PREP (Primary resource efficiency by enhanced prediction). PREP

The relation between breakage mechanisms and liberation is critical in mineral processing. Recent studies underline the importance of texture in liberation. This study reviews relevant liberation models and proposes a new method for generating particles using image processing algorithms. One new texture simulation method and its relevance for liberation simulation is also introduced. PREP